THE EFFECTS OF CHANGES IN ENERGY BALANCE ON …

The Pennsylvania State University

The Graduate School

The Huck Institute of the Life Sciences

THE EFFECTS OF CHANGES IN ENERGY BALANCE ON

IMMUNE REGULATION AND TUMOR PROGRESSION IN

THE 4T1.2 MAMMARY TUMOR MODEL

A Dissertation in

Integrative and Biomedical Physiology

by

William J. Turbitt, Jr.

© 2017 William J. Turbitt, Jr.

Submitted in Partial Fulfillment of the Requirements

for the Degree of

Doctor of Philosophy

August 2017

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The dissertation of William J. Turbitt, Jr. was reviewed and approved* by the following:

Connie J. Rogers Associate Professor of Nutrition and Physiology Dissertation Advisor Chair of Committee

Andrea Mastro Professor, Department of Biochemistry and Molecular Biology

K. Sandeep Prabhu Professor, Department of Veterinary and Biomedical Sciences

Vijay Kumar Assistant Professor of Nutritional Sciences

Donna H. Korzick

Professor of Integrative and Biomedical Physiology and Kinesiology Chair of the Intercollege Graduate Program in Integrative and Biomedical

*Signatures are on file in the Graduate School.

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ABSTRACT

One significant challenge in the field of breast cancer (BC) research is to

determine how to reduce and/or eliminate the mortality associated with metastatic

BC. Novel therapies, especially non-pharmacological, lifestyle-based interventions

that prevent or slow metastatic disease with less severe side effects are greatly

needed. Numerous lifestyle factors (including dietary components, body weight,

and physical activity patterns) significantly impact BC risk and survival. Emerging

population data suggests an inverse relation between physical activity and BC

incidence, as well as an important role for exercise in the prevention of cancer

recurrence and mortality. The observational nature of these studies limit the ability

to determine biological mechanisms and the extent to which exercise, as opposed

to changes in body weight, drive beneficial effects. Additionally, very little is known

about the mechanisms contributing to the relation between physical activity and

survival. Given the importance of metastases in the mortality of women with BC,

understanding the role of exercise on metastatic burden may reveal important new

targets for secondary and tertiary cancer prevention.

The aim of study one was to control for weight and examine the effects of

exercise, mild dietary restriction, or the combination of diet and exercise on the

inflammation-immune axis and tumor progression in a preclinical metastatic BC

model to determine the extent to which exercise or body weight contribute to

cancer prevention. Dietary energy restriction-induced weight control (i.e., SED+ER

mice) was effective at altering host splenic immunity and the expression of key

genes in the tumor microenvironment (TME) related to immunosuppression and

metastatic progression; however, this intervention failed to induce changes in

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primary tumor growth or spontaneous metastases. Moderate exercise in weight

stable mice (EX+ER) resulted in a similar reduction in immunosuppressive and

metastatic genes in the TME compared with the SED+ER mice; however, in

addition, EX+ER mice had the greatest reduction in splenic immunosuppressive

cells and plasma insulin-like growth factor-1 (IGF-1). The effects of moderate

exercise in weight stable mice culminated in a significant delay in primary tumor

growth and spontaneous metastases, suggesting that exercise-induced alterations

in metabolic drivers of tumorigenesis, not simply a change in body weight, underlie

the protective effects in the dual intervention group. Interestingly the exercise-

induced protective effect on the emergence of immunosuppressive factors and

reduced tumor burden was lost when mice continued to gain weight over the

course of the study, suggesting that weight gain-induced disturbances on

hormonal, inflammatory, and/or immunological function can override the exercise-

induced benefits. Collectively, study one provided a deeper understanding of the

extent to which exercise, and changes in body weight, underlies cancer protection.

Few researchers have examined the effect of energy balance interventions

on the efficacy of immunotherapeutic strategies. Two subsequent studies were

designed to investigate the response to emerging cancer therapeutics in mice

randomized to an energy balance paradigm (i.e., sedentary, ad libitum, weight gain

[WG] group vs. exercising, mild dietary restriction, weight maintenance [WM]

group) to identify potential mechanisms and provide translational support.

Study two aimed to determine if there were any additive effects of moderate

exercise in weight stable mice and the therapeutic administration of a broad-based,

allogeneic, whole tumor cell cancer vaccine (VAX). There was a significant effect

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of both WM and VAX alone on primary tumor growth; and an additive effect of

WM+VAX on primary tumor growth, lung and heart spontaneous metastases,

splenocyte count at sacrifice, the number of total splenic myeloid-derived

suppressor cells (MDSCs) and granulocytic subset of MDSCs, and plasma levels

of IGF-1. Splenic interferon gamma (IFN) secretion in response to re-stimulation

with tumor antigens was significantly elevated in response to VAX and WM;

however, there was no additive effect of WM+VAX. These results suggested that

our whole tumor cell cancer vaccine augmented the weight maintenance (via diet

and exercise) effects on primary tumor growth and spontaneous metastasis; and

suggested that vaccination may provide an immune stimulus to further promote

the protective effects of moderate exercise alone in the metastatic 4T1.2 mammary

tumor model.

Study three aimed to determine if there were any additive effects of

moderate exercise in weight stable mice and the dual therapeutic administration

of a whole tumor cell cancer vaccine and programmed cell death protein-1 (PD-1)

checkpoint blockade. We observed a cancer prevention effect of PD-1 checkpoint

blockade in WG mice on primary tumor growth and spontaneous lung metastasis.

However, moderate activity in weight stable mice, independent of PD-1 checkpoint

blockade, was effective in reducing primary tumor growth and metastatic burden.

The WM+PD-1 group displayed the lowest number of splenic MDSCs and

granulocytic MDSCs and maintained its splenic lymphoid populations. Neither the

number of tumor-infiltrating immune cells, the effector or activation status of tumor-

infiltrating CD4+ helper and CD8+ cytotoxic T cells, nor functional outcomes were

significantly different between groups. PD-1 checkpoint blockade in WG mice,

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moderate exercise in weight stable mice, and PD-1 checkpoint blockade in

moderately exercising, weight stable mice showed comparable, albeit subtle

differences, in tumor-immune crosstalk gene expression markers that drive the

expansion of immunosuppressive cell types and impact metastatic progression.

The lack of responsiveness to VAX+PD-1 checkpoint blockade in WM mice

suggests that moderate exercise in weight stable mice may be enhancing

antitumor immunity and/or reducing protumorigenic factors (i.e., similar

mechanisms mediated by VAX+PD-1 checkpoint blockade).

Results from the current studies provided insight into the extent to which

exercise in weight stable mice underlie cancer protection. Also, results provided

insight into potential mechanisms by which exercise can act via the inflammation-

immune axis to attenuate the generation of a protumorigenic and

immunosuppressive TME. These data demonstrated that preventing weight gain

through diet and exercise may be an important recommendation to maintain

prolonged antitumor effector responses and improve clinical outcomes. Results

from the current studies provided insight into potential mechanisms by which

physical activity exerts primary and secondary cancer prevention effects and

provides a biological rationale for randomized clinical trials to investigate physical

activity strategies to prevent metastatic progression in BC survivors and ultimately

improve survival outcomes.

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TABLE OF CONTENTS

LIST OF FIGURES...............................................................................................xii

LIST OF TABLES................................................................................................xiv

LIST OF ABBREVIATIONS.................................................................................xv

ACKNOWLEDGEMENTS..................................................................................xvii

CHAPTER 1: INTRODUCTION.............................................................................1

CHAPTER 2: LITERATURE REVIEW...................................................................3

2.1. Introduction................................................................................................3

2.2. Breast Cancer............................................................................................3

2.2.1. Overview of the breast.....................................................................3

2.2.2. Breast cancer..................................................................................4

2.3. The Immune System in Cancer..................................................................9

2.3.1. Overview of the immune system......................................................9

2.3.2. Innate vs. adaptive immunity..........................................................9

2.3.3. Immunosurveillance......................................................................13

2.3.4. The tumor microenvironment........................................................14

2.3.5. Protumor immunosuppressive cells..............................................15

2.3.5.1. Regulatory T cells............................................................15

2.3.5.2. Tumor-associated macrophages.....................................16

2.3.5.3. Myeloid-derived suppressor cells....................................18

2.3.6. Immunosuppressive mechanisms.................................................20

2.4. Immunotherapy........................................................................................22

2.4.1. Overview of immunotherapeutic strategies...................................22

2.4.2. Cancer vaccines...........................................................................24

2.4.2.1. Clinical studies................................................................25

2.4.2.2. Preclinical studies...........................................................27

2.4.3. Immune checkpoint blockade........................................................30

2.4.3.1. Clinical studies................................................................32


2.5. Risk Factors for Breast Cancer.................................................................33

2.5.1. Diet and breast cancer incidence and mortality.............................36

2.5.1.1. Clinical and epidemiologic studies...................................36


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2.5.2. Physical activity and breast cancer incidence and mortality...........40

2.5.2.1. Clinical and epidemiologic studies...................................40


2.5.3. Weight control and breast cancer incidence and mortality.............43

2.6. Mechanisms by which Modifiable Risk Factors Influence Cancer Progression....................................................................................................45

2.6.1. Genetic and epigenetic alterations................................................45

2.6.2. Tumor cell survival........................................................................47

2.6.3. Cellular energetics........................................................................48

2.6.4. Tumor vascularization...................................................................50

2.6.5. Inflammation.................................................................................51

2.6.6. Sex hormones...............................................................................54

2.6.7. Metabolic signaling and growth factors..........................................55

2.6.8. Myokines.......................................................................................57

2.6.9. Adipokines....................................................................................58

2.6.10. The immune response.................................................................59

2.7. Rationale for Current Research................................................................60

2.7.1. Model selection.............................................................................63

2.7.2. Activity intervention selection........................................................66

2.8. Specific Aims and Hypotheses.................................................................67

CHAPTER 3: WEIGHT MAINTENANCE ACHIEVED VIA MILD DIETARY ENERGY RESTRICTION AND MODERATE PHYSICAL ACTIVITY ON TUMOR PROGRESSION IN THE 4T1.2 MAMMARY TUMOR MODEL............................69

3.1. Abstract....................................................................................................69

3.2. Introduction..............................................................................................71

3.3. Materials and Methods.............................................................................76

3.3.1. Screening for intrinsic running behavior........................................76

3.3.2. Tumor cell line and cell culture......................................................76

3.3.3. Animal model................................................................................77

3.3.4. Metastatic burden.........................................................................78

3.3.5. Bioluminescent in vivo imaging.....................................................79

3.3.6 Splenic and tumor-infiltrating Immune cell assays..........................79

3.3.6.1. Isolation of splenic immune cells.....................................79

3.3.6.2. Splenic immune cell proliferation and cytotoxicity assays..........................................................................................80

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3.3.6.3. Splenic IFN secretion post five-day bulk culture.............81

3.3.6.4. Splenic MDSC suppression assay...................................81

3.3.6.5. Isolation of tumor-infiltrating immune cells.......................82

3.3.6.6. Flow cytometric analyses................................................82

3.3.7. Plasma mediators.........................................................................84

3.3.8. Gene expression in the tumor microenvironment .........................84

3.3.9. Statistical analyses.......................................................................85

3.4. Results.....................................................................................................86

3.4.1. Intrinsic running behavior..............................................................86

3.4.2. Body weight and wheel activity......................................................88

3.4.3. Primary tumor growth....................................................................89


3.4.5. Splenic immunity...........................................................................92

3.4.6. Plasma mediators.........................................................................97

3.4.7. The tumor microenvironment........................................................99

3.4.7.1. Tumor-infiltrating immunity..............................................99

3.4.7.2. Tumor-immune crosstalk gene array.............................101

3.4.8. High vs. low activity within the moderately exercising, weight stable mice......................................................................................................102

3.5. Discussion..............................................................................................104

CHAPTER 4: MODERATE EXERCISE IN WEIGHT STABLE MICE AND THE ADMINISTRATION OF A WHOLE TUMOR CELL CANCER VACCINE IN THE 4T1.2 MAMMARY TUMOR MODEL..................................................................121

4.1. Abstract..................................................................................................121

4.2. Introduction............................................................................................123

4.3. Materials and Methods...........................................................................126

4.3.1. Tumor cell line and cell culture....................................................126

4.3.2. Optimizing the whole tumor cell cancer vaccine..........................127

4.3.3. Optimizing splenic immune assays.............................................128

4.3.4. Animal model..............................................................................129

4.3.5. Whole tumor cell cancer vaccine.................................................130

4.3.6. Metastatic burden.......................................................................130

4.3.7. Splenic and tumor-infiltrating Immune cell assays.......................131

4.3.7.1. Isolation of splenic immune cells...................................131

4.3.7.2. Splenic CD4+ helper T cell proliferation assay...............132

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4.3.7.3. Splenic IFN secretion post five-day bulk culture….......132

4.3.7.4. Isolation of tumor-infiltrating immune cells.....................133

4.3.7.5. Flow cytometric analyses..............................................133

4.3.8. Plasma mediators.......................................................................134

4.3.9. Statistical analyses.....................................................................135

4.4. Results...................................................................................................136

4.4.1. Determination of method to induce tumor cell death prior to vaccination............................................................................................136

4.4.2. Assessment of vaccine efficacy and splenic immune cell co-culture and bulk assays....................................................................................137

4.4.3. Body weight and wheel activity....................................................140

4.4.4. Primary tumor growth..................................................................142


4.4.6. Splenic immunity.........................................................................145

4.4.7. Tumor-infiltrating immunity..........................................................150

4.4.8. Plasma mediators.......................................................................152

4.5. Discussion..............................................................................................154

CHAPTER 5: MODERATE EXERCISE IN WEIGHT STABLE MICE AND THE DUAL ADMINISTRATION OF A WHOLE TUMOR CELL CANCER VACCINE AND PD-1 CHECKPOINT BLOCKADE IN THE 4T1.2 MAMMARY TUMOR MODEL..............................................................................................................170

5.1. Abstract..................................................................................................170

5.2. Introduction............................................................................................172

5.3. Materials and Methods...........................................................................176

5.3.1. Tumor cell line and cell culture......................................................176

5.3.2. Animal model................................................................................177

5.3.3. Whole tumor cell cancer vaccine..................................................178

5.3.4. PD-1 checkpoint blockade............................................................178


5.3.6. Bioluminescent in vivo imaging.....................................................180

5.3.7. Splenic, non-draining lymph nodes, and tumor-infiltrating immune assays....................................................................................................180

5.3.7.1. Isolation of splenic and non-draining lymph node immune cells............................................................................................180

5.3.7.2. Splenic CD4+ helper T cell proliferation assay...............181

5.3.7.3. Isolation of tumor-infiltrating immune cells.....................181

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5.3.7.4. Flow cytometric analyses..............................................182

5.3.7.5. Splenic and tumor-infiltrating immune cell assays.........183

5.3.8. Gene expression in the tumor microenvironment........................184

5.3.9. Statistical analyses.....................................................................185

5.4. Results...................................................................................................186

5.4.1. PD-1 cell surface expression on splenic CD4+ helper and CD8+ cytotoxic T cells.....................................................................................186

5.4.2. Optimizing in vivo PD-1 checkpoint blockade..............................186

5.4.3. Splenic immunity in the PD- 1 pilot study.....................................189

5.4.4. CD4+ helper and CD8+ cytotoxic T cell subpopulations and effector T cell activation and exhaustion markers in the PD-1 pilot study............190

5.4.5. Splenic and tumor-infiltrating immune cell co-culture and bulk assays in the PD-1 pilot study...............................................................193

5.4.6. Body weight and wheel activity....................................................197

5.4.7. Primary tumor growth..................................................................199


5.4.9. Splenic immunity.........................................................................204

5.4.9.1. Splenic immune cells.....................................................204

5.4.9.2. Splenic immune cell co-culture and bulk assays............206

5.4.10. The tumor microenvironment..................................................209

5.4.10.1. Tumor-infiltrating immunity..........................................209

5.4.10.2. Tumor-infiltrating immune cell co-culture assay...........213

5.4.10.3. Tumor-immune crosstalk gene array...........................215

5.5. Discussion..............................................................................................216

CHAPTER 6: RESEARCH SUMMARY AND FUTURE DIRECTIONS...............230

6.1. Summary of Research............................................................................230

6.2. Future Directions....................................................................................239

6.3. Concluding Remarks..............................................................................248

REFERENCES...................................................................................................250

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LIST OF FIGURES

Figure 2.1. Immune and plasma alterations over time in the 4T1.2 mammary tumor model cancer........................................................................................................65

Figure 3.1. Screening BALB/c mice for intrinsic running activity...........................87

Figure 3.2. Study design, body weight and wheel activity.....................................89

Figure 3.3. Moderate exercise in weight stable mice reduced primary tumor growth and metastatic outcomes......................................................................................91

Figure 3.4. Moderate exercise in weight stable mice altered splenic antitumor immunity...............................................................................................................93

Figure 3.5. Moderate exercise in weight stable mice reduced splenic protumor immunosuppressive cell populations....................................................................96

Figure 3.6. Moderate exercise in weight stable mice altered plasma metabolic mediators.............................................................................................................98

Figure 3.7. Moderate exercise in weight stable mice altered gene expression in the tumor microenvironment...............................................................................101

Figure 3.8. High vs. low activity within moderately exercising, weight stable mice...................................................................................................................103

Figure 4.1. Optimizing the induction of 4T1.2luc cell senescence for the whole tumor cell cancer vaccine...................................................................................136

Figure 4.2. Optimizing the in vivo injection of a whole tumor cell cancer vaccine, assessing efficacy, and assay development in the 4T1.2 mammary tumor model.................................................................................................................139

Figure 4.3. Study design, body weight, and wheel activity..................................142

Figure 4.4. Weight maintenance, alone and in combination with a whole tumor cell cancer vaccine, reduced primary tumor growth..................................................144

Figure 4.5. The combination of weight maintenance and vaccination reduced splenomegaly and altered splenic immunity.......................................................146

Figure 4.6. The combination of weight maintenance and vaccination reduced splenic protumor immunosuppressive cell populations.......................................150

Figure 4.7. Vaccination reduced tumor-infiltrating protumor immunosuppressive cell populations in weight gain mice....................................................................152

Figure 4.8. The combination of weight maintenance and vaccination reduced plasma metabolic mediators...............................................................................153

Figure 5.1. PD-1 expression on splenic CD4+ helper and CD8+ cytotoxic T cells....................................................................................................................186

Figure 5.2. Optimizing in vivo PD-1 checkpoint blockade in the 4T1.2 mammary tumor model.......................................................................................................188

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Figure 5.3. Splenic, non-draining lymph node, and tumor-infiltrating CD4+ helper T cell populations and effector CD4+ helper T cell activation and exhaustion markers in the PD-1 pilot study...........................................................................191

Figure 5.4. Splenic, non-draining lymph node, and tumor-infiltrating CD8+ cytotoxic T cell populations and effector CD8+ cytotoxic T cell activation and exhaustion markers in the PD-1 pilot study.........................................................192

Figure 5.5. Splenic immune cell 24-hour co-culture and five-day bulk assay in the PD-1 pilot study..................................................................................................194

Figure 5.6. Tumor-infiltrating immune cell 24-hour co-culture assay in the PD-1 pilot study...........................................................................................................196

Figure 5.7. Study design, body weight, and wheel activity..................................198

Figure 5.8. PD-1 checkpoint blockade administration in weight gain mice reduced primary tumor growth. Weight maintenance independent of PD-1 checkpoint blockade reduced primary tumor growth.............................................................201

Figure 5.9. Bioluminescent in vivo IVIS imaging.................................................203

Figure 5.10. The combination of weight maintenance and PD-1 checkpoint blockade reduced protumor immunosuppressive cell populations......................205

Figure 5.11. Splenic immune cell 24-hour co-culture and five-day bulk assay..................................................................................................................208

Figure 5.12. Tumor-infiltrating CD4+ helper and CD8+ cytotoxic T cell populations and effector activation and exhaustion markers..................................................212

Figure 5.13. Tumor-infiltrating immune cell 24-hour co-culture assay................214

Figure 5.14. Moderate exercise in weight stable mice altered gene expression in the tumor microenvironment...............................................................................215

Figure 6.1. Exercise-induced effects along the cancer continuum.....................238

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LIST OF TABLES

Table 3.1. The percentage and number of splenic immune cell populations.........95

Table 3.2. The percentage and number of tumor-infiltrating immune cell populations.........................................................................................................100

Table 4.1. The percentage and number of splenic immune cell populations.......149


Table 5.1. The percentage and number of splenic immune cell populations in the PD-1 pilot study..................................................................................................189

Table 5.2. The percentage and number of splenic immune cell populations.......206


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LIST OF ABBREVIATIONS

4T1 Murine metastatic breast cancer model Akt Protein kinase B AL Ad libitium AMPK AMP-activated protein kinase ANOVA Analysis of variance APC Antigen-presenting cell Arg-1 Arginase-1 BC Breast cancer CCL CC-chemokine ligand CCR CC-chemokine receptor CD Cluster of differentiation Cox-2 Cyclooxygenase-2 CTLA-4 Cytotoxic T-lymphocyte-associated protein 4 CXCL CXC-chemokine ligand CXCR CXC-chemokine receptor DC Dendritic cell DNA Deoxyribonucleic acid ELISA Enzyme-linked immunosorbent assay EMT Endothelial to mesenchymal transformation ER Dietary energy restriction EX Exercise FBS Fetal bovine serum FoxP3 Forkhead box O3 G-CSF Granulocyte colony-stimulating factor GLUT Glucose transporter GM-CSF Granulocyte-macrophage colony-stimulating factor gMDSC Granulocytic myeloid-derived suppressor cell HER2 Human epidermal growth factor 2 IDO Indoleamine 2,3-dioxygenase

IFN Interferon gamma IGF Insulin-like growth factor IGFBP Insulin-like growth factor binding protein IgG Immunoglobulin G, isotype control IL Interleukin iMC Immature myeloid-lineage cell iNOS Nitric oxide synthase 2 IP Intraperitoneal ITAM Immunoreceptor tyrosine-based activation motifs ITIM Immunoreceptor tyrosine-based inbitory motifs I.V. Intravenous KLRG1 Killer cell lectin-like receptor 1 LUC Luciferase Ly6C Lymphocyte antigen 6C Ly6G Lymphocyte antigen 6G

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MAPK Mitogen-activated protein kinase MCP-1 Monocyte chemotactic activating protein M-CSF Macrophage colony-stimulating factor MDSC Myeloid-derived suppressor cell MHC Major histocompatibility complex mMDSC monocytic myeloid-derived suppressor cell mTOR The mechanistic target of rapamycin nDLN Non-draining lymph node NFAT Nuclear factor of activated T cell NK Natural killer cell NKCC Natural killer cell cytotoxicity NO Nitric oxide

NF-B Nuclear factor kappa B PBS Phosphate-buffered saline PD-1 Programmed cell death protein-1 PD-L1 Programmed death-ligand 1 PD-L2 Programmed death-ligand 2 PI3K Phosphoinositide 3-kinase PLC phospholipase-C

PPAR Peroxisome proliferator-activated receptor alpha RIN RNA integrity number ROS Reactive oxygen species RPMI Roswell Park Memorial Institute SED Sedentary STAT Signal Transducer and Activator of Transcription TAA Tumor-associated antigens TAM Tumor-associated macrophage TCM Central memory T cell TCR T cell receptor TEM Effector memory T cell TERT Telomerase reverse transcriptase TGFβ Transforming growth factor beta TH Helper T cell TME Tumor microenvironment TNBC Triple-negative breast cancer

TNF Tumor necrosis factor alpha Treg Regulatory T cell (CD4+CD25+FoxP3+ TH cells) VAX Vaccine VEGF Vascular endothelial growth factor VEH Vehicle WG Weight gain WM Weight maintenance

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ACKNOWLEDGEMENTS

I first want to thank my adviser and mentor, Dr. Connie Rogers, for her

support and mentorship. After completing my undergraduate degree, Dr. Rogers

hired me as a laboratory technician and has been cultivating my scientific mind

since May of 2010. She encouraged me to enter graduate school and this

education would not be possible without her patience, support, and guidance. I

want to sincerely thank her for sharing her knowledge, time, and expertise. The

professional development that I have received from Dr. Rogers has not only

shaped my scientific mind, but my overall character.

We practice team science in the Rogers’s Lab, so I also want to thank my

fellow graduate students, Sherry Xu, Shizhao Duan, Hannah Van Every, and Ester

Oh, as well as our undergraduate research assistant, Allie Hamilton. Much thanks

to our former laboratory technician, Donna Sosnoski, and former graduate

students, Dr. Huicui Meng and Dr. Shawtanwnee Collins. Your support,

encouragement, and help has made this dissertation possible and my graduate

school experience would not be the same without all of you.

I would like to thank my committee members, Dr. Andrea Mastro, Dr.

Sandeep Prabhu, and Dr. Vijay Kumar for their help, support, and suggestions

over the past few years. Dr. Mastro has provided helpful information on model-

specific questions and provided practice samples early in my graduate education

to help develop my technical skills. I am also grateful for the training I received

through the T32 Training Program in Animal Models of Inflammation

(T32AI074551).

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Over the past 11 years, I’ve called Penn State and State College home.

Numerous Penn State faculty and staff have impacted my life in meaningful ways.

Being a member of an intercollege graduate degree program, I have relied on The

Huck Institutes for the Life Sciences, The Department of Nutritional Sciences, and

The Department of Veterinary and Biomedical Sciences faculty and staff to assist

me with coursework, registration, appointments, and general paperwork. I am

grateful for this Penn State network of support.

My family has provided incredible support throughout my educational

journey and I would not have completed my graduate degree without their love and

encouragement. They calmed my anxiety and helped me maintain a healthy and

positive perspective. My fiancé, Becky, has provided tremendous balance and

support. She is patient and understanding of the demands of both graduate school

and research and I am lucky to have such a wonderful person in my life. I could

not have done it without you!

1

CHAPTER 1: INTRODUCTION

Breast cancer (BC) is the most commonly diagnosed cancer and the second

leading cause of cancer-related deaths among women in the United States (1, 2).

Approximately 60% of BCs are diagnosed at the local stage and the subsequent

5-year survival is 98.5%. Unfortunately, 5% of BCs are diagnosed at the distant

stage, meaning the cancer cells have metastasized. Metastatic disease remains

incurable and is the underlying cause of death in the majority of BC patients who

die of the disease (3). Median survival with metastatic BC is three years, and there

is no statistically significant change in median survival from metastatic BC cancer

in over 20 years. One significant challenge in the field of BC research is to

determine how to reduce and/or eliminate the mortality associated with metastatic

BC. Currently, treatments are available that slow metastatic tumor growth (e.g.

estrogen blockers, chemotherapy, radiation); however, these treatments carry

their own risks of morbidity and mortality. Thus, novel therapies, especially non-

pharmacological, lifestyle-based interventions that may prevent or slow metastatic

disease, with less severe side effects, are greatly needed.

Emerging population data suggests an inverse relation between physical

activity and BC incidence (4, 5), as well as an important role for exercise in the

prevention of cancer recurrence and mortality. The observational nature of these

studies limit the ability to determine biological mechanisms. Several exercise-

induced mechanisms are proposed, including exercise-induced improvements in

the inflammation-immune axis that favors sustained immunosurveillance

mechanisms. An additional limitation of the epidemiologic data is that it remains

2

uncertain the extent to which exercise, as opposed to changes in body weight,

account for the reduction in BC risk and improvements in survival outcomes. Well-

designed preclinical studies are needed to investigate mechanisms, as well as

determine if direct exercise effects or exercise-induced changes in body weight

drive protection.

Based on the recent success of immunotherapy in multiple solid tumors,

this personalized treatment option is under active investigation in BC, especially in

metastatic BC patients who have failed to respond to other treatment modalities.

Two immunotherapy treatment strategies are therapeutic cancer vaccines and

immune checkpoint blockade. Cancer vaccines provide tumor-associated

antigen(s) to stimulate an effector T cell response (6); whereas, immune

checkpoint blockade uses specific antibodies to maintain T cell activation by either

binding and agonizing co-stimulatory signals or binding and antagonizing co-

inhibitory signals. Cancer vaccines and checkpoint blockade are both designed to

stimulate a robust antitumor effector response to eliminate tumor cells through two

separate mechanisms. The release of tumor-derived factors and the generation of

an immunosuppressive tumor microenvironment reduces efficacy of

immunotherapy. Therefore, understanding the mechanisms by which exercise can

enhance immunosurveillance and determine if exercise-induced effects can blunt

the emergence or function of immunosuppressive cell populations could be a non-

pharmacological approach to improve therapeutic outcomes. It is of utmost

importance to delineate new, previously unidentified strategies to attenuate tumor-

induced inflammation and enhance immunotherapeutic efficacy to improve

treatment responses and clinical outcomes in BC patients.

3

CHAPTER 2: LITERATURE REVIEW

2.1. INTRODUCTION

Cancer encompasses a broad family of complex diseases that involve

abnormal and unregulated cell proliferation. A group of transformed cells often

form a neoplasm, or tumor; however, cells may be distributed diffusely. Cancer is

the second leading cause of death globally and the number of new cases is

expected to increase by approximately 70% over the next two decades (7, 8). The

cause of cancer is diverse and mutations can be driven by chemical (9) or physical

(10) carcinogenesis or exposure to cancer causing pathogens (11). Despite the

complex nature of cancer, Hanahan and Wineberg (12, 13) detail similar

characteristics that all cancers possess, allowing for unchecked proliferation and

survival. These characteristics include: sustainment in proliferative signals,

avoidance of programmed cell death, induction of angiogenesis, and activation of

invasion and metastasis. Updated hallmarks include emerging and enabling

characteristics of cancer cells, specifically cancer cell’s unique ability to drive

genomic instability, generate tumor-promoting inflammation, deregulate cellular

energetics, and ultimately avoid immune destruction (13).

2.2. BREAST CANCER

2.2.1. Overview of the breast

The breast is a secretory organ composed of different cell types,

including mammary stem cells, epithelial cells, adipocytes, vascular endothelial

cells, fibroblasts, and immune cells (14). Mammary epithelial cells are

organized into two layers, the basal myoepithelial layer and the luminal

4

epithelial layer (15). The basal myoepithelial cell layer is composed of

specialized contractile cells that provide structural support to the luminal

epithelial layer and play a role in milk ejection during lactation (16). The luminal

epithelial layer can be subdivided into ductal cells, which form a single layer of

polarized epithelial cells around the ductal lumen, or alveolar luminal cells,

which constitute the alveolar units that arise during pregnancy and lactation

(17). The breast is made up of 15-20 sections called lobes. Each lobe contains

smaller lobules formed by ductal and alveolar luminal epithelial cells. Lobules

are the functional unit of the breast and site of milk production in lactating

women (18). The development and growth of the breast is highly regulated and

involves a complex interaction of hormones (e.g., estrogen, progesterone,

androgens), cytokines, and growth factors (e.g., fibroblast growth factor,

epidermal growth factor) that bind to specific membrane receptors to induce a

cascade of intracellular events that promote cell activation, proliferation, and

survival (19). However, intrinsic mutations or extrinsic stressors can result in

the dysregulation of signaling cascades and the promotion of a cancer initiating

event (17, 20).

2.2.2. Breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer in United

States with current estimates predicting 252,710 new cases in 2017 (1). BC the

second leading cause of cancer-related deaths among women in the United

States (1, 2) with current estimates predicting that 40,610 patients (or 6.8% of

all cancer-related deaths) will succumb to the disease this calendar year.

5

Female BC is most commonly diagnosed in middle-aged and older women with

a median age at diagnosis of 62 years (1). Overall, the five-year survival for all

BC is 89.7%. Despite significant advances in detection, diagnosis, and

treatment of BC, several unresolved questions remain. Polyak, et al. (21)

postulates that progress must be made related to the prevention of BC,

enhancements in diagnostic sensitivity, a better understanding of tumor

progression and the causes of recurrence, improvements in treatment

modalities, greater access to high-quality medical care, and overcoming

therapeutic resistance.

BC is a heterogeneous disease characterized by a diverse spectrum of

molecular phenotypes that are associated with different disease presentation

events, treatment modalities, response to treatment, and clinical outcomes (21-

23). Tumors can arise from the basal myoepithelial layer or the luminal

epithelial layer of mammary epithelial cells and are associated with diverse

molecular phenotypes. Through histological and comprehensive gene

expression profiling, BC can be distinguished by up to 21 distinct histological

subtypes (24) and four different molecular subtypes (25, 26). The main

molecular subtypes, categorized based on epithelial cell of origin and the

presence or absence of hormone receptors (estrogen or progesterone) and

levels of human epidermal growth factor 2 (HER2), are luminal A, luminal B,

HER2+, and basal-like (27). The majority of BCs are luminal A and B subtypes,

which are typically positive for estrogen and progesterone hormone receptors.

Luminal B is further defined as HER2+ and tends to be a higher grade and more

6

aggressive than luminal A (28, 29). The HER2+ subtype lacks hormone

receptors, but overexpresses HER2, an epidermal growth factor receptor that

possesses oncogenic properties (i.e., stimulates an increase in proliferation,

angiogenesis, and invasiveness). Basal-like tumors lack the hormone and

HER2 receptors and are often called triple-negative BC (TNBC). Although BC

classification aids in determining treatment strategies and predicting prognosis,

a more sophisticated classification system is needed to address the

heterogeneous nature of BC and improve predictive power and prognosis (23,

28, 30, 31).

Women with operable, early, localized BC patients typically undergo

surgery; either breast-conserving surgery (32) or mastectomy, coupled with

radiotherapy, chemotherapy, endocrine therapy, and/or targeted therapy

depending on molecular subtype (33, 34). Luminal A-type tumors are

associated with the best prognosis due to the expression of hormone receptors

and favorable response rates to hormonal therapy. HER2+ tumors can be

effectively controlled with a diverse array of anti-HER2 therapies (e.g.,

Trastuzumab). Whereas, basal-like, TNBC tumors lack a molecular target,

respond poorly to standard chemotherapy (approximately a 20% response

rate), and are associated with the worst prognosis due to limited treatment

options (30). Additionally, TNBC are often detected as grade III tumors,

resulting in poor prognosis (35). A high priority for BC research is to identify

better detection methods and develop more favorable treatment options to

7

improve TNBC outcomes. The emergence of personalized medicine and

targeted immunotherapy is a promising treatment approach for TNBC.

Due to increased awareness and enhanced detection methods, many

BCs are diagnosed in early stages (36). Approximately 60% of BCs are

diagnosed at the local stage and the subsequent five-year survival is 98.9%

(1). Regional detection (i.e., the cancerous cells have spread to a regional

lymph node) accounts for approximately 30% of diagnoses with a subsequent

5-year survival of 85.2% (1). Unfortunately, 6% of BCs are diagnosed at the

distant stage, meaning the cancer cells have metastasized. Metastatic disease

remains incurable, has a 5-year survival of 26.9%, and is the underlying cause

of death in the majority of BC patients who die of the disease (3). Although

death rates are falling on average 1.8% each year over 2005-2014, likely due

to enhanced treatment strategies, one significant challenge in the field of BC

research is to determine how to reduce and/or eliminate the mortality

associated with metastatic BC (37, 38).

Researchers have been studying metastasis for over 100 years, dating

back to Stephen Paget’s 1889 ‘seed-soil’ hypothesis detailing the crosstalk

between cancer cells (i.e., the seeds) and specific organ microenvironments

(i.e., the soil) (39). Metastatic progression is a multi-step process that involves

the interaction between tumor cells, stromal cells, and tumor-infiltrating immune

cells to alter tumor angiogenesis, cell invasiveness, and cell migratory

pathways to promote the dissemination of tumor cells to distant sites (39, 40).

However, this dynamic process remains poorly understood (41). Currently,

8

treatments are available that slow metastatic progression (e.g., estrogen

blockers, chemotherapy, radiation); however, these treatments carry their own

risks of morbidity and mortality (42, 43). Median survival with metastatic BC is

three years, and there has been no statistically significant change in median

survival from metastatic BC in over 20 years (44, 45). All molecular BC

subtypes are capable of progressing to metastatic disease and the molecular

subtype can predict metastatic progression patterns (46, 47). For example,

TNBC is characterized by an increase rate of metastatic spread to the brain

and lung and a decrease in overall survival compared with patients with other

metastatic molecular subtypes. TNBCs are more often diagnosed at a later

stage and therefore associated with a more advanced disease. Thus, novel

therapies that prevent (48) or slow metastatic disease with less severe side

effects, especially within the metastatic TNBC population, are greatly needed

(45).

Based on recent advancements in the fields of immunology and

molecular biology, immunotherapy has become a promising emerging

treatment for cancer (49-54), including TNBC and advanced metastatic disease

(55-63). Immunotherapy treatments include therapeutic cancer vaccines,

monoclonal antibodies, adoptive cell therapies, and the administration of

immunostimulatory cytokines, all of which are designed to stimulate a robust

antitumor effector response to eliminate tumor cells through several techniques

(51, 64). The identification of numerous tumor-associated antigens (TAAs) (65)

and the use of tumor-infiltrating immune cells as a prognostic indicator (66-76)

9

provides compelling evidence that BC is immunogenic. Therefore, a brief

overview of the immune system is warranted.

2.3. THE IMMUNE SYSTEM IN CANCER

2.3.1. Overview of the immune system

The immune system is a complex network of cells, tissues, and organs

that protect the body from infection and identify and eliminate cancerous cells

(77). Immune cells originate from hematopoietic precursor cells within the bone

marrow microenvironment. Diverse and multifunctional immune cell arise

through an intricate series of highly regulated signaling events. Broadly, the

immune system can be classified into two arms: innate (or humoral) or adaptive

(or cell mediated) immunity.

2.3.2. Innate vs. adaptive immunity

The innate immune response to an invading pathogen is rapid and non-

specific. Innate immune cells include dendritic cells (DCs), natural killer (NK)

cells, granulocytes, myeloid-derived suppressor cells (MDSC), monocytes

(macrophage precursors), and macrophages. Collectively, under normal

conditions, innate immune cells play a pivotal role in first-line defense

mechanisms (e.g., phagocytosis) and in the communication with and activation

of the adaptive immune response to assist in pathogen clearance, targeting

infected or transformed cells for death, and tissue remodeling (77, 78). DCs are

antigen-presenting cells (APCs) and act as messengers between the innate

and adaptive immune response by processing antigen material and presenting

this information to cells of the adaptive immune system (77, 79). NK cells are

10

critical for innate immunity and can recognize stressed cells in the absence of

antibodies or major histocompatibility complex (MHC) and lyse cells directly

through the release of intracellular granules (e.g., perforin, granzymes) (80,

81). Granulocytes and macrophages are capable of phagocytosis and involved

in tissue remodeling (77, 78).

The adaptive immune system is composed of highly specialized cells

with specificity for an individual pathogen, toxin, or allergen (77). The adaptive

response occurs secondary to the innate response because cells must

encounter the antigen and clonally proliferate in sufficient numbers to mount an

attack. Additionally, a key feature of the adaptive response is its ability to

generate long-lived memory cells, which confer lasting protection (82).

Adaptive immunity is carried out by lymphocytes. Lymphocytes can be

categorized as B cells, which perform antibody-specific responses, or T cells,

which perform cell-mediated responses (83). B cells are central in antibody-

mediated responses, but also have an important role in processing and

presenting antigens and secreting cytokines to refine the immune response

(84). T cells represent a heterogeneous population that include cluster of

differentiation (CD)4+ helper T cells and CD8+ cytotoxic T cells. Broadly, CD4+

helper T cells play a major role in mediating immune responses through the

secretion of specific cytokines. Various activation signals and differentiation

pathways generate a diverse population of CD4+ helper T cell (TH) subsets with

distinct functions (classical T helper 1 [TH1], T helper 2 [TH2], T helper 9 [TH9],

T helper 17 [TH17], T helper 22 [TH22], follicular helper T cell [TFH], and

11

regulatory T cells [Tregs]) (85). Lastly, activated CD8+ cytotoxic T cells can

recognize infected, stressed, or transformed cells through recognition of

peptides presented on MHC class I molecules and induce apoptosis via the

release of cytotoxins, perforins, and granzymes or through the expression of

surface proteins (e.g., Fas ligand) (86).

The innate immune system is capable of phagocytoses and aids in the

clearance of extracellular pathogens. Both the innate system, specifically NK

cells, and the adaptive system, most notably CD8+ cytotoxic T cells, are

instrumental in the detection and clearance of intracellularly infected or

transformed cell populations (77). They do so through the detection and

interaction with MHC molecules present on target cells. Two classes of MHC

molecules exist, MHC class I and MHC class II. MHC class I molecules are

found on all nucleated cells and platelets. NK cells are primed to induce

programmed cell death; however, extracellular inhibitory receptors on NK cells

can bind to MHC class I and prohibit cell killing (80, 81). Pathogenic signaling

and tumorigenic factors can induce the downregulation of MHC class I

molecule as a mechanism to avoid immune detection. The removal of MHC

class-I mediated inhibition allows NK cells to release factors to promote the

programmed cell death of the target cell (81). Infected and transformed cells

can load and present endogenous antigens via MHC class I molecules. CD8+

cytotoxic T cells recognize their specific antigen in the context of MHC class I,

triggering programmed cell death mechanisms.

12

APCs, like DCs, can phagocytose exogenous (i.e., viral or bacterial

peptides) or endogenous (i.e., self-peptides) antigens and process them by

proteases to peptide fragments (77). Activated DCs migrate to proximal

draining lymph nodes where they can present antigens (79). The activation of

a naïve T cell occurs through the simultaneous engagement of the T cell

receptor (TCR) and co-stimulatory molecules (e.g., CD28 or inducible T cell co-

stimulator [ICOS]) on the T cell surface by MHC (CD4 in the context of MHC

class II and CD8 in the context of MHC class I) and co-stimulatory molecules

(e.g., CD80 [B7.1] and CD86 [B7.2]) on APCs (87). TCR stimulation alone

results in anergy (or T cell tolerance); whereas, co-stimulation alone has no

effect on T cell activation. TCR engagement orchestrates essential changes in

the gene expression profile to transition a naïve cell to an actively expanding

immune effector cell. Intracellular signaling involves a myriad of molecules, with

the most common being Src family kinases (e.g., Fyn, Lck), which

phosphorylate tyrosines on intracellular immunoreceptor tyrosine-based

activation motifs (ITAMs) on the intracellular domain of CD3 zeta (ζ)-chain (88).

Phosphorylation events result in downstream signaling mediated by

phospholipase-C (PLC), mitogen-activated protein kinase (MAPK), nuclear

factor kappa B (NF-B), and nuclear factor of activated T cell (NFAT), which

results in gene transcription in the nucleus of factors that promote cell survival

and proliferation (87). Co-stimulatory and cytokine signaling, especially

signaling via interleukin (IL)-2 (89), activate phosophoinositide-3 kinase (PI3K)

and its downstream target, protein kinase B (Akt), which increases expression

13

of glucose transporters on the cellular membrane and upregulates glycolytic

enzyme activity to meet the metabolic needs of the highly proliferative effector

cells (90). Cytokine signaling also activates specific transcriptional factors to

induce CD4+ helper T cell polarization (i.e., TH1, TH2, TH9, TH17, TH22, TFH, and

Tregs).

2.3.3. Immunosurveillance

The role of the immune system in controlling primary tumor growth is

well studied (91-93) and involves anti-tumor responses by NK cells and CD8+

cytotoxic T cells, which recognize transformed cells and prevent their

expansion in a process called immunosurveillance. In humans, NK cells

comprise approximately 5-15% of all circulating lymphocytes (94) and can

infiltrate a pathogen-infected site or a malignant tumor microenvironment

(TME) upon activation (95, 96). The number of NK cells within the tumor and in

the stroma near the cancer nests significantly correlates with a prolonged

survival time in patients with various types of cancer, including BC (74). Within

the TME, NK cells can detect malignant cells lacking MHC class I or via the

identification of “stress” ligands on targets through NK cell receptors (e.g.,

NKG2D), which can render tumor cells susceptible to NK cell-mediated lysis

(81). Activated NK cells eliminate targets through the release of cytotoxic

enzymes (e.g., perforins, granzymes, granulysin) and/or soluble factors

(chemokines and inflammatory cytokines [e.g., interferon-gamma (IFN)] (97-

99)) which, in turn, can recruit and/or activate other immune effectors cells.

CD8+ cytotoxic T cells recognize antigens in the context of MHC class I on the

14

surface of virally infected or malignant cells and rapidly proliferate in response

to activation (100). Activated CD8+ cytotoxic T cells lyse target cells through

perforin/granzyme and Fas/FasL mediated mechanisms, as well as the

production of IFN, an immunoregulatory and antitumor effector molecule (101,

102). IFN plays a role in promoting protective host responses against tumors

by up-regulating tumor cell MHC class I antigen processing and presentation

(100, 103). There is compelling evidence for an improved prognosis for BC

patients that have increased intratumoral CD8+ cytotoxic T cell infiltration (66-

73). Additionally, type I CD4+ helper T cells (TH1) produce cytokines (e.g., IFN)

and chemokines to recruit and activate NK cells and CD8+ cytotoxic T cells

(104) to further promote anti-tumor immunity. When immunosurveillance is

successful, immune-mediated cell death of transformed cells can result in

complete elimination or pressure the growing tumor into a state of equilibrium

(105). Accumulating clinical (106) and experimental evidence (107) strongly

suggests similar immunosurveillance mechanisms are involved with the

recognition of disseminated tumor cells. Memory T cells and NK cells in

metastatic niches (including the bone (108-111), lung (112-115), and liver (116)

microenvironment) can promote immune-mediated tumor dormancy (117).

Thus, BC progression is highly linked to immunosurveillance mechanisms.

2.3.4. The tumor microenvironment

As a transformed, cancerous cell progresses to a neoplasm, or tumor, it

generates a complex TME that is characterized by increased intrinsic

inflammatory gene transcription factors, inflammatory cytokines, and

15

proinflammatory signaling molecules (118, 119). These factors deregulate

hematopoietic pathways resulting in the expansion and accumulation of

immunosuppressive cell types of both lymphoid (e.g., Tregs) (120, 121) and

myeloid lineage (e.g., tumor-associated macrophages [TAMs] (122, 123) and

MDSCs) (124-128). The abundance of both lymphoid and myeloid

immunosuppressive cells is positively correlated with tumor burden (129-133).

Other immune cell types (e.g., regulatory B cells (134), neutrophils (135, 136))

or supporting cells within the TME (e.g., mesenchymal stromal cells (137)) are

intricately involved in the generation of a proinflammatory, immunosuppressive

TME. Crosstalk exists between these cell types to promote a feed-forward loop

favoring a proinflammatory immune evading microenvironment (131, 138-140).

The balance between anti-tumor immunosurveillance mechanisms and the

accumulation of immunosuppressive cells within the TME is critical for

regulating primary tumor growth (141) and metastatic progression (142).

2.3.5. Protumor immunosuppressive cells

Tumors can commandeer numerous cell types to promote sustained

proliferation and aid in cancer progression. The main immunosuppressive cells,

Tregs, TAMs, and MDSCs, are summarized within this literature review.

2.3.5.1. Regulatory T cells

Tregs are a subset of CD4+ helper T cells that co-express CD4,

CD25, and the gene transcription factor FoxP3 (143). Tregs can develop

in the thymus (i.e., natural Tregs), which comprise 5-10% of total

peripheral CD4+ helper T cells, or be generated in the periphery from

16

conventional CD4+ helper T cells (i.e., inducible Tregs) (144). Under

normal conditions Tregs are essential in the homeostasis and modulation

of the immune response by resolving inflammation and promoting

peripheral immune tolerance (143). Dysregulated Treg expansion and

function can play a role in autoimmune diseases, transplantation

tolerance, infectious diseases, allergic disease, and tumor immunity.

Tumor-infiltrating Tregs are observed in BC (145-148) and the Treg

subset can comprise up to 45% of total CD4+ helper T cell populations

within the TME (149). Increased intratumoral Tregs is correlated with BC

progression and is an indicator of poor prognosis (150). Recent findings

support the investigation of Treg-targeted therapies to attenuate the Treg-

induced suppression of antitumor effector responses and improve

therapeutic outcomes.

2.3.5.2. Tumor-associated macrophages

Monocytes are recruited to the TME by secreted factors (e.g.,

vascular endothelial growth factor [VEGF], macrophage colony-

stimulating factor [M-CSF]) and differentiate into macrophages. Based on

the cytokine milieu generated by tumor cells, stromal cells, and other

tumor-infiltrating immune cells, macrophages within the TME can polarize

into cells with antitumor or protumor function, and are broadly categorized

as M1 (classical) or M2 (alternative) macrophages, respectively (122). M1

macrophages inhibit tumor growth via phagocytosis and the secretion of

proinflammatory cytokines (e.g., tumor-necrosis factor alpha [TNF], IL-

17

1, IL-6, IL-12, and IFN) (135). Also, M1 cytokines can recruit both CD4+

helper and CD8+ cytotoxic T cells to the TME and promote the polarization

of CD4+ helper T cells to the antitumor, IFN-producing TH1 phenotype.

M2 macrophages play an important role in wound healing and tissue

repair through the secretion of growth factors (e.g., VEGF, epidermal

growth factor, and platelet-derived growth factor) and anti-inflammatory

cytokines (e.g., IL-10, transforming growth factor beta [TGFβ], IL-4, and

IL-13); however, in the context of cancer, these mechanisms can promote

tumor growth (122). TAMs closely resemble M2-polarized macrophages

and aid in cancer progression by promoting tumor cell proliferation,

angiogenesis, immunosuppression, and extracellular matrix turnover

(123).

TAM density within the TME is correlated with angiogenesis and

tumor cell invasion and migration (151-153). The presence of TAMs as a

prognostic indicator in BC is emerging. Two recent meta-analyses

collected data from five (154) and sixteen (155) studies and evaluated the

correlation between TAMs (detected via immunohistochemistry) and

clinical staging, overall survival, and disease-free survival in BC patients.

Results indicate that high intensity TAM expression was correlated with

poor disease-free and overall survival, and suggest that TAM infiltration

can serve as a novel prognostic factor in BC patients. Two active areas of

research are identifying the negative impact of TAM on BC therapies or

directly targeting TAMs to enhance current and emerging therapies.

18

2.3.5.3. Myeloid-derived suppressor cells

MDSCs represent a heterogeneous population of immature

myeloid-lineage cells that significantly expand in both animal models and

human BC patients and possess immunosuppressive and proangiogenic

properties, which facilitate tumor growth and promote metastases (125,

127, 156). In healthy individuals and mice, MDSCs are present in low

numbers in the circulation and regulate immune responses and promote

tissue repair. Under normal conditions, immature myeloid-lineage cells

(iMCs) quickly differentiate into mature granulocytes, macrophages, or

DCs (125, 127). However, in pathological inflammatory conditions such

as infectious diseases (157, 158), autoimmunity (159), and cancer (160,

161), this heterogeneous family of iMCs is halted in an immature state and

expands from the BM into the circulation and peripheral organs. Clinical

studies reveal that BC stage and metastatic tumor burden is positively

correlated with the level of circulating MDSCs (162) and elevated

circulating MDSCs is indicative of a poor prognosis (163, 164).

The intricate network regulating the expansion, trafficking, and

activation of MDSCs is reviewed elsewhere (124, 125, 127) and not only

involves crosstalk between MDSCs and tumor cells, but also

communication between MDSCs and other tumor-infiltrating cells (e.g.,

TAMs and Tregs) (139, 165-167). Briefly, key cytokines and soluble

factors that block myeloid maturation and promote MDSC expansion

include granulocyte-colony stimulating factor (G-CSF) (168, 169),

19

granulocyte macrophage-colony stimulating factor (GM-CSF) (170), M-

CSF (171), stem cell factor (172), VEGF (173), IL-6 (171, 174), and

prostaglandin E2 (PGE2) (175). Collectively, these factors trigger a

signaling cascade in iMCs, mainly through Janus tyrosine kinase 2

(JAK2)/signal transducer and activator of transcription 3 (STAT3),

resulting in alterations in downstream targets that regulate cell

differentiation, proliferation, survival, and apoptosis and block normal

physiological differentiation of iMCs; thus, driving the expansion of

pathological MDSCs (125).

Numerous factors are involved in MDSC trafficking to the tumor site

and/or premetastatic niche (124, 156, 176). Chemokine ligands of the C-

C (CCL2 and CCL12) (177-179) and C-X-C motif (CXCL1, CXCL5, and

CXCL12) (178, 180) are released from the TME and interact with related

chemokine receptors on MDSCs (CCR2, CXCR2, and CXCR4) (177-180)

to promote mobilization. Additional studies detail the essential roles of

integrin and selectin adhesion molecules (181) and tumor cell Fas-ligation

induced PGE2 production (182) in promoting MDSC mobilization.

Expansion-inducing factors can also induce mobilization, most notably

tumor-derived G-CSF, GM-CSF, and S100A8/A9 proteins (125, 183).

Tumor-derived S100A8/A9, in addition to STAT3-induced production of

S100A8/A9 from MDSCs, promote accumulation via an autocrine

feedback loop. S100A8/A9 bind to surface receptors for advanced

glycation end products on MDSCs and inhibit DC differentiation pathways

20

and promote the mobilization of MDSCs through NF-κB signaling (183).

Thus, MDSC recruitment to the TME represents a vicious cycle, with

tumor-infiltrating MDSCs augmenting the initial tumor-driven recruitment.

2.3.6. Immunosuppressive mechanisms

The mechanism(s) of Treg-mediated (184-186), TAM-mediated (187),

and MDSC-mediated (125, 127, 139, 167) immunosuppression of antitumor

effector cells is thoroughly reviewed and likely function through cell-surface

receptors and/or through the release of short-lived soluble mediators. In the

context of the TME, Tregs exert immunosuppressive effects by direct

interaction with cells through cell surface expression of inhibitory molecules

(e.g., cytotoxic T-lymphocyte-associated protein 4 [CTLA-4] (188)) and the

production of immunosuppressive cytokines, such as IL-10, IL-35, and TGF

(184, 185). In general, TAMs and MDSCs can mediate suppressive functions

through multiple players, such as Arg-1 and nitric oxide synthase 2 (iNOS), the

hyperproduction of NO and reactive oxygen species (ROS), induction of Tregs,

as well as deregulation of cyclooxygenase-2 (Cox-2)/PGE2, TGFβ, and IL-10

pathways.

ROS can catalyze the nitration of the T cell receptor (TCR) (189),

downregulate the ζ-chain of the TCR complex (190), and interfere with IL-2

signaling (a signal essential to T cell proliferation) (191) resulting in the

desensitization of the TCR response and T cell unresponsiveness.

Furthermore, enzyme metabolites from iNOS, specifically NO, can interfere

with T cell JAK/STAT signaling proteins required for numerous T cell functions,

21

inhibit MHC class II expression, and induce T cell apoptosis (125). These

enzymatic pathways also produce superoxide, which combines rapidly with NO

to form peroxynitrite, a powerful oxidant (192). Increased peroxynitrite induces

post-translational modifications, such as nitration of the TCR and CD8

molecules, resulting in antigen-specific T cell unresponsiveness (193).

Tumor cell and tumor-infiltrating immunosuppressive cells deplete

nutrients (such as L-arginine, L-cysteine, and L-tryptophan) from the

environment via consumption and sequestration to dysregulate APC function

and the activation and proliferation of antitumor effector cells (194, 195).

Depletion of L-arginine downregulates the ζ-chain of the TCR complex and

results in the proliferative arrest of antigen-activated T cells (194-196). Tumor

cells, TAMs, and MDSCs express Ido1, which encodes for indoleamine 2,3-

dioxygenase (IDO), the first and rate-limiting enzyme of tryptophan catabolism

through the kynurenine pathway (197). Depletion of tryptophan, an amino acid

essential for T cell activation, results in blunted T cell proliferation,

differentiation, effector functions, and viability (198). Additionally, bioactive

kynurenine pathway compounds can activate the aryl hydrocarbon receptor on

CD4+ helper T cells, promoting Treg polarization (199). Lastly, T cells are

unable to synthesize cysteine and rely on APCs to convert methionine or

cystine to cysteine and export surplus cysteine to T cells during antigen

presentation (195). However, MDSCs also import cystine for intracellular

conversion to cysteine, but lack the ability to export cysteine back into the

extracellular space; thus, limiting cystine pools for APC conversion (195). As a

22

result, APCs cannot export cysteine, which deprives T cells of an amino acid

essential for activation and proliferation. Tumor-derived S100A8/A9, in addition

to STAT3-induced production of S100A8/A9 from MDSCs, inhibit the

maturation of DCs (200). The culmination of these suppressive mechanisms

results in less functional DCs and less activated effector T cells.

2.4. IMMUNOTHERAPY

2.4.1. Overview of immunotherapeutic strategies

Cancer immunotherapy, deemed by Science magazine (49) as the 2013

“Breakthrough of the Year” and the 2016 and 2017 ASCO “Advance of the

Year” (201, 202), has demonstrated success in treating cancer, including very

advanced, metastatic diseases. Immunotherapy treatments include therapeutic

cancer vaccines, monoclonal antibodies, adoptive cell therapies, and the

administration of immunostimulatory cytokines, all of which are designed to

stimulate a robust antitumor effector response to eliminate tumor cells through

several techniques (51, 64). Therapeutic cancer vaccines provide TAAs to

stimulate an effector T cell response (6); whereas, immune checkpoint

blockade, like anti-CTLA-4 and anti-programmed cell death protein-1 (PD-1),

use specific antibodies to target inhibitory cell surface molecules to promote

sustained effector cell activation (203). Emerging immunotherapy strategies

are currently being investigated in advanced-stage cancer patients who have

failed to respond to other treatments (49-54), including TNBC and advanced

metastatic disease (55-63).

23

Despite the recent successes of immunotherapy in advanced-stage

cancer patients, less than half of patients who receive immunotherapy

experience an objective, durable response (204). Thus, there is great interest

in understanding the factors that contribute to the heterogeneity in response

rates to immunotherapy. Areas of research include the identification of clinically

meaningful biomarkers to direct treatment choices (i.e., personalized medicine)

and/or through the selection of appropriate combinatorial strategies (aimed to

enhance antigen presentation, reverse T cell dysfunction, and/or target

immune inhibitory mechanisms) without inducing adverse effects (56-58, 205).

The release of tumor-derived factors and the generation of an

immunosuppressive TME reduces immunotherapeutic efficacy. No study, to

our knowledge, has retrospectively or prospectively assessed the impact of

host factors known to modulate immune function (e.g., physical activity, body

mass index, or dietary and sleep patterns) on the efficacy of immunotherapeutic

outcomes. Therefore, understanding the mechanisms by which lifestyle factors

can enhance antitumor immune mechanisms or blunt the emergence of

immunosuppressive cell populations could be a non-pharmacological approach

to improve therapeutic outcomes. It is of utmost importance to delineate new,

previously unidentified strategies to attenuate tumor-induced inflammation and

enhance immunotherapeutic efficacy to improve treatment responses and

clinical outcomes in cancer patients.

24

2.4.2. Cancer vaccines

Cancer vaccines can be prophylactic (i.e., preventative) (206) or

therapeutic (51). Therapeutic cancer vaccines, categorized as DC or whole

tumor cell based, provide APCs, like DCs, with TAAs to stimulate a durable

antitumor effector CD8+ cytotoxic T cell response (51, 52, 207-209). Providing

tumor-derived peptides (210) or full-length recombinant tumor proteins are two

strategies for DC-based therapeutic cancer vaccines; however, the

development of in vivo tumor-derived peptide vaccinations has proven

problematic as free peptides can be rapidly cleared prior to uptake by APCs

(51). The co-administration of immune-stimulant adjuvants (e.g., IL-2 or GM-

CSF) with tumor-derived peptides to further promote the activation of either

DCs or CD8+ cytotoxic T cells has increased vaccine efficacy, generating a

more robust antitumor effector CD8+ cytotoxic T cell response (209, 211). An

alternative strategy is to generate DCs ex vivo from peripheral blood

mononuclear cells, pulse with TAAs, and inject the activated DCs back into the

host for subsequent activation of a CD8+ cytotoxic T cell response (208, 212,

213). Two limitations in DC-based vaccines is that T cell epitopes against TAAs

are largely undefined, making it difficult to screen and select tumor antigens

(214, 215), and ex vivo generation of DCs is laborious and costly (216).

Whole tumor cell based cancer vaccines represent an attractive

alternative source of TAAs (sourced from whole tumor cells, tumor cell lysates,

tumor oncolyates, apoptotic bodies, or transduced tumor cells), as this type of

vaccine typically displays some intrinsic adjuvant activity and allows DCs to

25

process and present numerous TAAs to induce a polyclonal effector CD8+

cytotoxic T cell response against numerous tumor cell subpopulations (207,

217). Through epitope spreading, or the process in which T cells respond to

peptides not present in the whole tumor cell cancer vaccine, tumor cells that

are not originally targeted through TAAs within the whole tumor cell cancer

vaccine can become targets through secondary priming (218-220). Autologous

(i.e., sourced from the same host receiving the vaccination) and allogeneic (i.e.,

sourced from a different host or tumor cell line) whole tumor cell cancer

vaccines are under preclinical (221-226) and clinical (227-229) development.

Several host-related factors (e.g., tumor-induced inflammation and/or

emergence of immunosuppressive cell types) can influence the efficacy of

whole tumor cell based cancer vaccines (208, 230), therefore combinatorial

strategies are investigating if current (e.g., chemotherapy, radiation) and

emerging (e.g., monoclonal antibodies, tyrosine kinase inhibitors) therapies

can reduce tumor-induced inflammation to enhance therapeutic efficacy (224,

230-234).

2.4.2.1. Clinical studies

Cancer vaccine therapies, both tumor-derived peptide based and

whole tumor cell based are under investigation in BC patients. The

majority of clinical trials assessing peptide-based strategies target HER2-

derived peptides alone, or in combination with additional

immunostimulatory agents (235). For example, a well-studied (236)

tumor-derived peptide-based vaccine targeting HER-2 (E75, also known

26

as NeuVAX) is now being investigated clinically. NeuVAX combined with

GM-CSF administration prolonged disease-free survival in a subset of BC

patients who express low levels of HER2 with a high risk of relapse (237).

A phase II study (NCT01570036) is underway to investigate the

combination of NeuVAX and targeted HER2 therapy in node positive (or

node-negative if estrogen and progesterone receptor negative) BC

patients who are disease-free after standard-of-care therapy. However, a

study investigating the administration of NeuVAX+GM-CSF in early-stage,

node-positive BC patients with low-to-intermediate HER2 expression was

halted due to ineffectiveness (NCT01479244). New targets including

peptides against Wilms tumor protein antigen (238) and immunization with

vaccinia virus modified to express mucin-1 and IL-2 (239) can induce

tumor regression in BC patients. The identification of tumor-specific

antigens in which to target remains an active area of research.

Clinically, whole tumor cell cancer vaccines are rarely investigated

as a monotherapy in patients with advanced stage disease. These

therapies are more typically genetically modified to secrete various

cytokines, such as GM-CSF, administered in combination with an immune

adjuvant (214, 229), or administered in combinatorial treatment strategies

coupled with chemotherapy, endocrine therapy, and/or targeted therapy

(240-242). In a phase I clinical trial, the safety of a plasmid

transfected/allogeneic HER2-positive cell-based GM-CSF-secreting

vaccine was confirmed in metastatic BC patients. The vaccine alone or in

27

sequence with low-dose chemotherapy induces HER2-specific T cell-

mediated immunity; however, survival data is forthcoming (240). A

separate Phase I trial modified MDA-MB-231 cells to express B7-1

(CD80), a costimulatory molecule required for antigen-presentation to T

cells. Vaccinated stage IV metastatic BC patients had an increase in

tumor-specific immune responses, but failed to show differences in tumor

progression (243). The administration of a mixed vaccine composed of

autologous breast tumor cells supplemented with allogeneic breast tumor

cells, 3 additional TAAs antigens combined with IL-2 and GM-CSF to BC

patients in a phase II study increases antigen-specific lymphocyte

response and improves clinical response (244). The administration of this

mixed vaccine to BC patients with depressed immunity improves ten-year

survival compared with historical data (228). Two active clinical trials are

investigating the safety and efficacy of modified autologous vaccinations

engineered to express GM-CSF in metastatic BC patients (NCT00317603

and NCT00880464). Future studies are required to identify the optimal

combinatorial or multi-antigen approach to improve survival outcomes.

2.4.2.2. Preclinical studies

Few researchers have designed preclinical studies to investigate

cancer vaccine monotherapy in murine models of mammary

carcinogenesis. Cell lysate from 4T1 encapsulated in poly(lactic-co-

glycolic acid) acid (PLGA) microparticles reduces spontaneous lung

metastasis, but has no effect on primary tumor growth (222). A placental

28

endothelial cell vaccine (ValloVAX) administered SQ for four weekly

vaccinations after tumor implantation inhibits 4T1 tumor growth, however

immune effector function was not characterized (245). Combinatorial

strategies are being investigated in both peptide-based and whole tumor

cell based vaccines. Combinatorial immunostimulatory strategies include

genetically modifying cells to express GM-CSF, CD40L, OX-40, and B7-1

or the co-administration of IL-12 in the vaccination protocol (225, 226, 234,

246). Soliman, et al. (225) vaccinated 4T1 tumor-bearing mice with

irradiated B78H1 bystander cell lines engineered to secrete GM-CSF and

CD40 ligand, a costimulatory ligand important for T cell activation.

Vaccinated mice display a reduction in tumor growth, concurrently with an

increase in the secretion of IL-2 and IFN from splenic T cells and an

increase in the number of tumor-infiltrating T cells. The anchoring of IL-12

and the B7-1 costimulatory molecules to 4TO7 cells results in significantly

lower tumor burden compared with controls after one treatment (246).

Vaccinated mice also display a reduction in tumor angiogenesis and an

increase in tumor-infiltrating CD8+ cytotoxic T cells. Data from Yan, et al.

(226) demonstrates that the administration of irradiated, transfected 4T1

cells that express VEGFR2 inhibits subsequent tumor growth and lung

metastasis. Lastly, the intranodal administration of autologous tumor-

derived autophagosome-based therapeutic vaccine (DRibbles) with an

anti-OX40 co-stimulatory antibody enhances T cell priming and antitumor

efficacy in 4T1 tumor-bearing mice (234). The findings from these results

29

indicate that anchored cytokines or the addition of co-stimulatory

molecules may improve therapeutic potency.

The investigation of combinatorial strategies that prevent the

emergence or function of immunosuppressive factors is underway. To

minimize the induction of Tregs, Monzavi-Karbassi, et al. (247) developed

a vaccine composed of cell lysate lacking lectin-reactive glycoconjugates.

The vaccination of 4T1 tumor-bearing mice reduces tumor growth and

spontaneous lung metastasis. In contrast, the crude lysate activates Tregs

and induces their suppressive function measured via a mixed suppression

assay. This suggests that minor refinements in TAAs can be performed to

weaken immune suppression and improve antitumor immune responses.

Cyclooxygenase-2 (Cox-2), a rate-limiting enzyme in the synthesis

of prostaglandins from arachidonic acid, is highly upregulated in

mammary cancers (248) and plays a key role in stimulating epithelial cell

proliferation, inhibiting apoptosis, stimulating angiogenesis, and mediating

immune suppression (248, 249). The short-term administration of

celecoxib, a selective Cox-2 inhibitor, as a monotherapy can reduce 4T1

tumor growth (250-252). Celecoxib administered in combination with a

tumor-lysate pulsed DC vaccine plus GM-CSF adjuvant therapy, results

in the greatest reduction in 4T1 tumor growth and spontaneous lung

metastasis, an increase in the secretion of IFN and IL-4 from re-

stimulated CD4+ helper T cells, and an increase in abundance of tumor-

infiltrating CD4+ helper and CD8+ cytotoxic T cells compared to

30

unvaccinated tumor-bearing controls (251). These data suggest that

inhibiting the expansion of Tregs or blocking Cox-2 via short-term

celecoxib therapy may be safely used to increase vaccine efficacy for

treating metastatic BC.

2.4.3. Immune checkpoint blockade

In normal physiology, inhibitory immune checkpoints are important to

protect against activation events targeted toward self-antigens (i.e.,

autoimmunity) and to return the adaptive response to a basal level and function

following the clearance of a foreign agent in a process called homeostatic

control (50). However, inhibitory immune checkpoints may act as a barrier to

the successful identification, activation, and eradication of malignant self-cells

by effector immune cell populations. Following T cell activation by APCs, T cells

migrate to specific sites and perform their effector function. Upon activation, T

cells upregulate inhibitory cell surface markers, including OX-40, lymphocyte-

activation gene 3 (Lag-3), T cell immunoglobulin and mucin-domain containing-

3 (TIM-3), PD-1, and an increasingly expanding list of other inhibitory molecules

(53, 203, 253). The immune checkpoint blockade approach to immunotherapy

involves using antibodies to maintain T cell activation by either binding and

agonizing co-stimulatory signals or binding and antagonizing co-inhibitory

signals (53, 57). The mechanism of PD-1 inhibition and the use of PD-1

checkpoint blockade in clinical and preclinical models are reviewed.

Upon activation, TCR-mediated calcium influx can induce Pdcd1

transcription (the gene encoding for PD-1) via NFATc1 (254). In tumor-bearing

31

hosts, proinflammatory signals (e.g., IFN) produced in the TME, signal to

tumor cells, stromal cells, and tumor infiltrating immune cells (e.g., MDSCs) to

increase the expression of programmed death-ligand 1 (PD-L1 or CD274) and

PD-L2 (CD273), which can bind to PD-1 on T cells (203). When PD-1 is

engaged by its ligand, the PD-1 intracellular domain, composed of an

immunoreceptor tyrosine-based inhibition motif (ITIM), inhibits kinases involved

in T cell activation through the recruitment of Src homology 2-containing

tyrosine phosphatase (SHP2) (53). PD-1 induced SHP2 can directly inhibit TCR

transduction by dephosphorylating and inactivating Zap70, a major integrator

of TCR-mediated signaling (254). Additionally, SHP2 can block the induction of

PI3K activity and downstream Akt signaling, resulting in an inhibition in the

ability of T cells to import glucose, rendering T cells more susceptible to

apoptosis (255, 256); thus, shutting down the antitumor response.

PD-1 is widely expressed on several cells important in antitumor

immunity, including B cells, NK cells, and CD4+ helper and CD8+ cytotoxic T

cells (257). Therefore, PD-1 checkpoint blockade has broad action on a number

of antitumor effector responses. Although there is excitement in the field and

clinical successes are reported, the response rate to PD-1 blockade is under

50%. Thus, there is a growing interest in improving response rates to immune

checkpoint blockade through the identification of the phenotype of responders

with the long-term goal of administering targeted, personalized therapy and/or

combinatorial treatment strategies (e.g., chemotherapy, cancer vaccines).

32

2.4.3.1. Clinical studies

Recent clinical BC studies target PD-1 in TNBC because it is

associated with limited treatment options (55, 258), high expression of

PD-L1 in the TME, and an increase in tumor-infiltrating immune cells (72,

259-261); thus, making immunotherapy an attractive treatment option.

Clinically, PD-L1 protein expression is detected in 20-30% of BC patients

and single-agent PD-1 blockade can result in objective and durable

clinical responses in BC patients, with responses more favorable for

patients with tumors positive for PD-L1 (257). Currently, there are close to

50 ongoing, or soon to open, clinical trials evaluating the efficacy of

immunotherapy strategies alone or in combination with current and

emerging therapies in the neoadjuvant and metastatic setting in BC

patients (55-58)


In the preclinical setting, the 4T1 series of BC cells are largely

considered poor responders to single-agent PD-1 blockade due to the

expansion of immunosuppressive MDSCs (262, 263). Data from Hirano,

et al. (264) confirm that 4T1 tumors upregulate PD-L1 in vivo and blockade

of PD-L1 (100 g) at day 7 and 10 post-tumor implantation partially inhibits

primary tumor growth. Beavis, et al. (265) demonstrates that single agent

PD-1 checkpoint blockade (200 g, administered at day 7, 11, and 15

post-tumor implantation) is not effective at reducing 4T1.2 primary tumor

growth or spontaneous lung metastasis when mice are removed from

33

study at day 18 post-tumor implantation. This lack of effect was attributed

to low levels of PD-1 cell surface expression on T cell subsets during

4T1.2 tumor progression.

Dual interventions that target the proinflammatory TME

concurrently with PD-1 or CTLA-4 checkpoint blockade result in

regression of 4T1 tumors, even when disease burden is advanced and

metastatic (266). The administration of PD-1 checkpoint blockade in

combination with therapies that target a reduction in systemic

inflammation (e.g., celecoxib [selective Cox-2 inhibition], azacitine or

entiostat [epigenetic modulating drug], and J32 [phosphoinositide 3-

kinase inhibitor]) are the most effective at driving an enhancement in

antitumor effector function, a reduction in established tumor growth, and

a reduction in spontaneous metastasis (266-270). However, no preclinical

study has tested if lifestyle-based interventions, i.e., physical activity and

the prevention of weight gain, which are low-cost and have known benefits

across the cancer continuum (271), can augment T cell responses (272,

273) and can enhance the efficacy of immunotherapeutic strategies.

2.5. RISK FACTORS FOR BREAST CANCER

Although the etiology of BC remains unclear (274), several non-modifiable

and modifiable risk factors are implicated in the risk of developing BC. While it is

nearly impossible to determine the cause of a cancer in an individual, population-

based statistics can draw broad conclusions on factors that increase or decrease

the risk of developing certain cancers. Cancer is likely caused by a combination of

34

non-modifiable and modifiable risk factors (275-280). Due to the complexity of

events that drive cancer initiation, it is unlikely that one factor is responsible for

cancer initiation. An appreciation of one’s risk factors can guide decision making

over one’s lifetime to reduce the risk of developing cancer.

Non-modifiable, or fixed, risk factors of BC include age, sex, race and

ethnicity (White > African > Asian), family history of BC, personal history of BC,

menstrual history (early menarche, late menopause), dense breast tissue,

previous ionizing radiation exposure to the chest, and genetic predisposition (e.g.,

mutations in BRCA1 or BRCA2 genes) (281-285). Increased age (greater than 45

years-old) is the largest non-modifiable risk factor for BC (1). Compared with white

women, African American women present with BC at a younger age, are less likely

to be diagnosed with local stage cancer, and have worse disease-free and overall

survival rates within each stage (1, 286, 287). The reason for race/ethnicity

differences in BC risk and prognosis is difficult to disentangle.

Modifiable risk factors that decrease the risk of BC include age at first

pregnancy (35 or younger is protective) and physical activity (4, 288-292).

Emerging research suggests that non-starchy vegetables, foods containing

carotenoids, dairy products, and diets high in calcium may lower the risk of BC;

however, additional studies are needed confirm this relation (293-297). Data from

clinical (298-300) and epidemiologic (4, 5, 301) studies support the existence of

an inverse relation between moderate-to-vigorous physical activity and both pre-

and postmenopausal BC incidence, although the evidence is stronger for

postmenopausal women (275). Modifiable risk factors that increase the risk of BC

35

include smoking, the use of hormone replacement therapy, nulliparity, age at first

pregnancy (35 or older increases risk), alcohol consumption, and physical inactivity

(302-307). Elevated weight has contrasting effects on the risk of premenopausal

and postmenopausal BC risk (308, 309); however, being overweight or obese

serves as a predictor of poor prognosis once BC is diagnosed regardless of

menopausal status (310). The combination of increased body weight and physical

inactivity are estimated to account for 30% of the BC risk in industrialized societies

(311, 312) and are highly linked to the etiology of BC. Therefore, since an increase

in body weight represents a known, modifiable risk factor for postmenopausal BC,

general weight control during adulthood may be an effective strategy for BC

prevention.

The current nutrition and physical activity guidelines proposed by the

American Cancer Society (ACS), as well as the World Cancer Research Fund

(WCRF)/American Institute for Cancer Research (AICR) recommend that

individuals maintain a healthy weight throughout life by balancing caloric intake,

engaging in physical activity, and losing weight if overweight or obese (275, 313).

Dietary recommendations include consuming a healthy diet with an emphasis on

plant foods and a reduction in processed meat and red meat. Physical activity

recommendations for cancer prevention include engaging in at least 150 minutes

of moderate intensity or 75 minutes of vigorous intensity activity each week.

Several cohort studies have evaluated adherence to the ACS and WCRF/AIC

guidelines and assessed BC prevention (314-317). Collectively, these studies

report 16% to 60% risk reductions in postmenopausal women who adhere to the

36

guidelines, with the association mainly linked to reduced body fatness and alcohol

intake rather than specific differences in dietary patterns. BC survivorship

populations often face lasting psychological, physical, and social side effects;

therefore, similar guidelines are recommended for BC survivors to improve quality

of life measurements.

2.5.1. Diet and breast cancer incidence and mortality

2.5.1.1. Clinical and epidemiologic studies

The role of diet, a modifiable risk factor in BC development, is

studied extensively; however, there is a lack of consensus on several key

dietary issues (318). The relation between dietary components (e.g.,

dietary fat intake or micronutrient content), whole-foods (e.g., fruits,

vegetables, red meat), or dietary patterns (e.g., calorie restriction) and BC

risk are currently under clinical and epidemiologic investigation.

Recent research suggests that non-starchy vegetables, foods

containing carotenoids, dairy products, and diets high in calcium may

lower the risk of BC; however, more studies are needed confirm these

relations (293-297). Conversely, it is hypothesized that high dietary fat

intake is associated with an increased risk of BC. The daily intake of

dietary fat in industrialized countries, including the United States, is

approximately 30-40% of total energy intake, much higher than the

recommended 15-30% of total energy intake (275). A high-fat diet is

associated with an increase in several inflammatory biomarkers; however,

the mechanisms underlying the link between a high-fat diet and cancer risk

37

and/or progression are poorly understood. A recent meta-analysis was

completed assessing dietary total fat and fatty acids intake from 24

independent prospective cohort studies involving over 38,000 BC patients

(319). Dietary intake information was acquired via self- or interviewer-

administered food frequency questionnaires or 24-hour recall methods.

Results indicate that the total dietary fat and specific fatty acids are not

associated with increased risk of BC. Brennan, et al. (320) performed a

meta-analysis assessing dietary fat and BC mortality from 15 prospective

cohort studies. Total fat intake was not associated with BC-specific or all-

cause death; however, women in the highest vs. the lowest category of

total fat intake had an increased risk of all-cause death in studies that

assessed diet pre-diagnosis. The association between dietary fat and BC

risk and survival are inconsistent and require further investigation (321,

322). Although there is limited or inconclusive evidence for several key

dietary factors, strong evidence links alcohol intake to BC risk (293, 303,

323). Consuming alcoholic drinks increases the risk for both pre- and

postmenopausal BC; however, the mechanisms whereby alcohol

influences BC risk remain uncertain.

Evidence from observational, randomized controlled, and bariatric

surgery studies suggest that dietary energy restriction reduces the risk of

BC in women (324-331). Dietary energy restriction in premenopausal and

postmenopausal years is most effective at reducing the risk of

postmenopausal primary BC. Studies investigating the influence of war-

38

related famine (332-336) on BC risk later in life have provided conflicting

results. Famine exposure prior to the age of ten is protective against BC

later in life; whereas, famine exposure after 18 years of age increases BC

risk later in life (337). Additionally, Michaels, et al. (338) prospectively

followed women diagnosed with anorexia nervosa prior to age 40,

reporting that subjects had nearly a 50% risk reduction in BC compared

with age-matched controls. It is well-established that prolonged voluntary

dietary energy restriction is difficult to maintain. Further complicating the

clinical dietary energy restriction field is the debate over the best approach

to restricting diet: continuous dietary energy restriction, intermittent dietary

energy restriction, or intermittent fasting (339). Despite convincing

preclinical evidence (340-344), there is a shortage of epidemiologic and

mechanistic human data demonstrating benefits of dietary energy

restriction on delaying cancer progression or improving survival outcomes

(275).


Several preclinical studies have been carried out to analyze the

effects of dietary components (both micro- and macronutrients) on primary

prevention (345-349) and a reduction in metastatic progression (350, 351)

in murine models of BC. Numerous inconsistent results exist within the

preclinical literature. Preclinical studies poorly model the complex gene-

environment-diet interaction and clinical translation is often difficult due to

supraphysiological doses (352). However, preclinical studies on diet and

39

cancer prevention provide important insight into potential mechanisms of

action and targets for future pharmacological drug development.

Dietary energy restriction has proven benefits in terms of delaying

primary (341, 342, 353-355) and metastatic (343, 356) BC progression in

mice. Preclinical studies demonstrate that moderate to severe dietary

energy restriction (20%-40% reduction in energy intake) delays primary

tumor growth in orthotopic (356-358) and carcinogen-induced (341, 359,

360) mammary tumor models. The protective effect of dietary energy

restriction on carcinogenesis is proportional to the degree of restriction

within the range of 20-40% (341, 361, 362). However, 10% dietary energy

restriction does not alter the percent of carcinogen-induced mammary

lesions, or the proportion of pre-malignant to malignant lesions as

compared with ad libitum-fed rats (341) suggesting that restriction alone

may need to exceed 10% to exert a cancer prevention effect.

Furthermore, a 30% reduction in calories resulted in a delay in both 4T1

and 67NR tumor growth compared with control feeding or alternate-day

feeding (363). Additional studies are needed to assess continuous dietary

energy restriction, intermittent dietary energy restriction, or intermittent

fasting in preclinical models. A recent meta-analysis by Lv, et al. (340)

summarized data from 44 preclinical studies (including models of

mammary, prostate, brain, pancreatic, and hepatic cancers) from 1994 to

2014 exploring the role of dietary energy restriction (20-50%) on cancer

initiation and progression. Approximately 90% of the studies showed a

40

dietary energy restriction effect on primary cancer growth. An assessment

of the 16 preclinical studies using BC models from 1994 to 2014 showed

that dietary energy restricted animals developed approximately 50% less

BC than controls, similar to a meta-analysis summarizing dietary energy

restriction in preclinical BC models from 1942 to 1994 (364). Few

preclinical studies have assessed dietary energy restriction and

metastatic progression. De Lorenzo, et al. (356) reports that a 40%

restriction in dietary intake reduces 4T1 primary tumor growth and both

spontaneous and I.V.-induced metastatic outgrowth. Collectively, the

preclinical data on dietary energy restriction and cancer prevention is

promising. Future clinical trials are needed to investigate the effectiveness

and safety of these dietary regimens in human populations.

2.5.2. Physical activity and breast cancer incidence and mortality

2.5.2.1. Clinical and epidemiologic studies

Data from clinical (298-300) and epidemiologic (4, 5, 301) studies

support the existence of an inverse relation between moderate-to-

vigorous physical activity and both pre- and postmenopausal BC

incidence, although the evidence is stronger for postmenopausal women

(275). Wu, et al. (4) conducted a recent meta-analysis using data collected

from 31 prospective studies involving over 63,000 subjects. Physical

activity was assessed using validated self-reported questionnaires and

expressed as times per week, hours per week (h/week), metabolic

equivalent of task (MET)-h/week, or energy expenditure in calories per

41

week. Dose-response analysis suggests that the risk of BC decreases by

3% for every 10 MET h/week increment in recreational activity and 5% for

every 3 h/week increment in moderate plus vigorous recreational activity.

Additionally, women who did the most activity (recreational, occupational,

or household activities characterized as greater than 2 h/week) had a 12%

lower risk of developing BC compared with the least active women. This

effect is observed when controlling for body mass index, suggesting that

exercise may be protective independent of weight status. Assessing the

dose and intensity of physical activity in subset analysis in Wu, et al. and

in the American Cancer Society Cancer Prevention study II Nutrition

cohort (365) suggests that moderate activity, such as brisk walking

provides a benefit; however, vigorous physical activity or walking greater

than seven h/week provides the greatest reduction in BC risk. Studies also

suggest that sustained activity over each stage of life (from adolescence

onward) provides the greatest risk reduction (366). The primary mode for

capturing physical activity data in retrospective studies is survey

questionnaires of self-reported physical activity. Self-reported data may

be unreliable due to inaccuracies in recall and is difficult to determine the

intensity of activity performed, therefore prospective studies are important

to continue to refine the relation between physical activity and BC

prevention (367).

Evidence from epidemiologic studies suggest that regular physical

activity decreases the risk of recurrence and improves BC-specific and

42

overall survival in BC patients (292, 368-378). Two recent meta-analyses

were completed using data from 16 cohort studies involving over 40,000

BC patients (292, 377). Results of both analyses indicate that patients

who participated in physical activity prior to a BC diagnosis had

approximately a 20% risk reduction in BC-specific mortality when

comparing the most active to the least active women. Additionally,

patients who participated in physical activity after a cancer diagnosis had

a 40-50% risk reduction in BC-specific mortality (379, 380). The beneficial

effects of physical activity on BC-specific mortality remain significant after

stratifying by body mass index or menopausal status, suggesting the

effects of physical activity on cancer survival may be independent of

metabolic or reproductive hormone status (379-381).

A similar association between baseline cardiorespiratory fitness

and reduced BC-specific mortality is demonstrated in the Aerobics Center

Longitudinal Study, in which 14,811 women were followed for 31 years

(382). It is unknown if the protective effect of exercise on cancer outcomes

(primary or secondary prevention) is due, in part, to the prevention of

weight gain (i.e., obesity prevention) or to a direct effect of exercise on

cancer risk and progression. Also, it remains unclear if the physical activity

improvements in BC survival are due to a reduction in metastatic disease

occurrence and/or progression (373-375, 378).

43


Numerous preclinical studies demonstrate that exercise, achieved

via access to motorized treadmill or voluntary activity wheels, reduces

primary tumor growth in orthotopic (383-386), carcinogen-induced (387-

393), and transgenic (394-396) mammary tumor models. For example,

exercise reduces 4T1 primary tumor growth when the intervention is

started concomitantly with the injection of 4T1 tumor cells into the

mammary gland (385). Few preclinical studies exist studying the effect of

exercise on metastatic progression. Interestingly, exercise has no effect

on primary tumor growth when MDA-MB-231 cells are subcutaneously

(397) or orthotopically (398) injected into athymic mice. These data

suggest that the beneficial effects of exercise on primary tumor growth

may be mediated via an immune-dependent mechanism.

2.5.3. Weight and breast cancer incidence and mortality

Strong evidence exists for an association between obesity and

postmenopausal BC development (275, 293, 308, 309). Conversely,

substantial clinical (308, 325, 399-406) and epidemiologic (407) evidence

exists suggesting that weight control is associated with decreased risk of

primary BC (309, 408, 409). A meta-analysis was completed using data from

seven studies involving 4,570 women with no history or low use of hormone-

replacement therapy and an adult weight gain range from 0-35 kg. Results

indicate that every 5 kg increase in adult weight gain is associated with

approximately an 11% increase in the risk of postmenopausal BC (407). The

44

study found no association between adult weight gain and premenopausal BC

risk. The observational nature of retrospective studies makes it difficult to

determine if the protective effects of weight control are attributable to certain

lifestyle interventions (e.g., diet or physical activity). Studies in overweight and

obese postmenopausal women are beginning to separate the effects of diet

alone, aerobic exercise alone, and the combination of diet and exercise on

inflammatory, metabolic, and sex hormone mediators (330, 410-413). Data

from these studies show that sustained weight loss can be achieved via

moderate dietary energy restriction alone (restriction to achieve a 10% weight

loss); however, diet plus aerobic exercise results in the greatest reduction in

chronic inflammation (e.g., reduced C-reactive protein, IL-6, and leptin) in

overweight and obese postmenopausal women. The reduction in chronic

inflammation may help explain how weight control achieved through diet and

exercise reduces the risk of postmenopausal BC.

Being overweight or obese serves as a predictor of poor prognosis once

BC is diagnosed regardless of menopausal status (310). Weight gain often

occurs in BC patients undergoing treatments; however, few studies have been

done to investigate the effects of weight control in BC survivorship populations.

The limited studies report generally consistent findings that weight control can

reduce mortality and improve quality of life measurements in BC survivors (414-

417). However, inconsistencies in the literature exist with Makari-Judson, et al.

(416) suggesting that weight gain after a BC diagnosis does not impact BC

prognosis, but does have a negative impact on quality of life measurements.

45

Future clinical studies are needed to assess changes in body composition and

BC outcomes.

2.6. MECHANISMS BY WHICH MODIFIABLE RISK FACTORS INFLUENCE CANCER PROGRESSION

The extent to which dietary energy restriction or physical activity, as

opposed to changes in body weight, protect against primary tumor growth and

metastatic progression remains unclear in epidemiologic, clinical, and preclinical

studies. One common hypothesis explaining the cancer protective effect of diet

and physical activity on BC risk is a mediation of the effect through body weight

(366). The observational nature of the epidemiologic studies limit the ability to

determine biological mechanisms and the extent to which diet or exercise, as

opposed to changes in body weight, account for the reduction in BC risk and

improvements in survival outcomes. Diet and exercise can directly impact

oncogenic mutation rates and the proliferative capacity of malignant cells.

Additionally, diet and exercise may mediate beneficial changes in several

physiological systems, including skeletal muscle, bone marrow, adipose tissue,

and liver, and impact tumor progression through changes in adiposity, sex

hormone levels, metabolic signals (e.g., insulin and IGF-1), inflammatory status,

and/or the immune response. It is likely that the cumulative action of these

mechanisms contributes to the effects of diet and exercise on primary cancer

prevention and improvement in survival (298, 299, 418, 419).

2.6.1. Genetic and epigenetic alterations

Genetic and epigenetic alterations can induce changes in oncogenic

gene expression resulting in an expression profile that promotes tumor

46

initiation, progression, and recurrence (420, 421). Epigenetic modulation is a

stable and heritable molecular alteration in the gene expression profile of a cell

during somatic cell division without changing the DNA sequence. These

changes include histone variants, posttranslational modifications of amino

acids on the amino-terminal tail of histones, and covalent modifications of DNA

bases (422). A recent meta-analysis reports an association between gene-

specific DNA methylation and prognosis, as well as distinct DNA methylation

levels within specific BC subtypes (423). Lifestyle-based interventions (e.g.,

diet and exercise) could induce alterations in genetic and epigenetic events,

resulting in an expression profile that prohibits or reduces the development,

progression, and/or recurrence of BC (424), yet few clinical or preclinical

studies have examined these relationships. Physical activity-induced

improvements in survival (425) and obesity-induced impairments in prognosis

(426) after a BC diagnosis may be mediated through changes in methylation

profiles of BC-related genes, with physical activity favoring the expression of

genes involved in tumor suppression and a decrease in the expression levels

of oncogenes. Zeng, et al. (427) reports that a six-month moderate-intensity

aerobic exercise program in BC survivors improves overall survival and results

in a reduction in the hypermethylation of L3BTL1, a tumor suppressor gene,

suggesting that aerobic exercise may maintain proper transcription and

translation of important tumor suppressing molecules. Calorie restriction in the

preclinical MMTV-HER2/neu murine model of BC reduces primary tumor

growth, spontaneous metastasis, and hinders aberrant epigenetic alterations

47

in genes related to BC prognosis, including estrogen receptor alpha and beta,

(428). Future studies are needed to identify specific BC-related genes of

interest and the extent to which diet and exercise can modulate epigenetic

control.

2.6.2. Tumor cell survival

Cancer cells sustain proliferative capacity through numerous

mechanisms, including an evasion in growth suppressor signaling, the

upregulation of cell cycle control proteins (e.g., cyclins, cyclin-dependent

kinases), and a resistance in apoptotic-induced cell death (13). The

dysregulation in cell cycle progression has broad influences in cancer cell

biology and induces alterations in cell metabolism, tumor-induced secretory

factors, and metastatic potential that favor cell survival. Sustained proliferative

capacity can be induced via intrinsic and autocrine signaling cascades or

through the dysregulation of cells within the TME to provide additional growth

factor signaling (13).

Experimental animal models suggest that dietary energy restriction can

reduce cancer cell proliferation by altering the expression of cell cycle proteins

(decrease cyclins, increase levels of cyclin-dependent kinase inhibitors, and

decrease cyclin-dependent kinases) (275) and altering the phosphorylation

status of tumor suppressor genes. Thompson, et al. (429) reports that dietary

energy restriction can reduce retinoblastoma protein phosphorylation, a tumor

suppressor that is dysfunctional in several cancers. Hypophosphorylation of

retinoblastoma protein maintains tumor suppressor activity; thus, promoting the

48

inhibition of cell cycle progression. In addition, dietary energy restriction can

promote the maintenance of cellular proapoptotic machinery through the

induction of B-cell lymphoma 2 (Bcl-2) family of proteins (429). A review of

preclinical in vivo studies investigating the effects of aerobic exercise on cancer

initiation, progression, and metastasis by Ashcraft, et al. (430) suggests that

physical activity can also modulate cell cycle proteins and the phosphorylation

status of tumor suppressor genes to reduce proliferation and/or favor apoptosis

in cancer cells. Future studies are required to determine the timing of diet- and

exercise-induced alterations in cell proliferation and apoptotic signaling

cascades across the cancer continuum.

2.6.3. Cellular energetics

It is well established that metabolic rewiring is orchestrated by

oncogenes; however, emerging data suggest that dysregulated metabolism

can play a primary role in oncogenetic and epigenetic events that promote

cancer (431). The hyperproliferation associated with uncontrolled tumor growth

requires an adjustment in cellular metabolism not only for fuel needs, but to

provide biosynthetic intermediates necessary for assembling new cells (13). To

meet the macromolecule demands, cancer cells reprogram glucose energetics

from oxidative phosphorylation to oxidative glycolysis, even in conditions where

oxygen is present, in a process termed aerobic glycolysis (i.e., the Warburg

effect) (432). This shift is achieved through the upregulation of glucose

transporters (GLUT), notably GLUT1, and glycolytic enzymes (13). However,

49

this shift results in substrate inflexibility and a reliance on an influx of glucose

carbons through glycolysis (433).

It is therefore feasible that lifestyle-based interventions (e.g., dietary

energy restriction (433, 434) and physical activity (435)) could induce

alterations in glucose availability, resulting in a reduction in glucose to cancer

cells; thus, limiting their ability to replicate or rendering them susceptible to cell

death. Specifically, dietary energy restriction may have an anticancer effect

through the activation of peroxisome proliferator-activated receptor (PPAR),

which simulates lipid oxidation and inhibits glycolysis (434). Physical activity

can directly control whole-body metabolism through changes in glucose, lipid,

and amino acid metabolism. An exercise-induced activation of AMP-activated

protein kinase (AMPK), which activates energy-producing pathways and

inhibits biosynthetic and anabolic pathways, could help to control cancer cell

growth (435). An eight-week motorized wheel-running intervention increased

AMPK signaling and reduced Akt and the mechanistic target of rapamycin

(mTOR) signaling in the methylnitrosurea-induced mammary carcinomas

model in rats, suggesting a shift in anabolic and protein synthesis pathways

(436-438). Lastly, weight maintenance achieved through diet and exercise in

BC survivors may reduce the rate of recurrence and improve BC-specific

mortality via an inhibition in the dysregulation of cancer cell metabolism (439).

Future studies are needed to investigate the pathways by which diet and

exercise impact the metabolism of cancer cells, as well as the physiological

50

regulation of substrate availability of glucose, lactate, glutamine, and additional

amino acids, and if these mechanisms can alter cancer progression.

2.6.4. Tumor vascularization

Solid tumors are highly metabolic and acquiesce tissue repair

mechanisms to increase tumor vasculature; thus, increasing nutrient

availability. Tumors and supporting cells (e.g., mesenchymal cells, MDSCs,

TAMs) (131, 440, 441) can secrete proangiogenic factors (e.g., VEGF) to

promote TME vascularization, and thus increase nutrient availability. However,

tumor vascularization is highly unorganized. Tumor vascularization is

correlated to the level of hypoxia within the TME. It is commonly accepted that

angiogenesis and the expression of angiogenic factors, such as VEGF, is

associated with the increased risk of metastasis and poor patient outcome

(442). Emerging studies suggest that antiangiogenic therapy can normalize

vasculature and hypoxia in the TME and improve the delivery of therapy (443).

Few clinical studies have assessed the effects of diet and exercise on tumor

vascularization or their combination with antiangiogenic therapy.

Emerging preclinical data suggests that diet and exercise can alter

tumor vasculature. Betof, et al. reports that an exercise-induced reduction in

primary tumor growth is accompanied by an increase in the density of apoptotic

cells, and microvessel density and maturity in the tumor (385). These data

suggest that exercise may alter the TME resulting in a reduction in tumor mass

and tumor hypoxia (385). Running wheel activity induces a reduction in

intratumoral VEGF and is associated with a reduction in primary tumor growth

51

in the estrogen-dependent MC4L2 murine BC model (444). Long-term exercise

exposure increases VEGF expression resulting in enhanced tumor

vascularization and a reduction in tumor burden, multiplicity, and histological

grade in the methylnitrosurea-induced mammary carcinoma (445). Lastly,

dietary energy restriction reduces blood vessel density in pre-malignant and

malignant breast pathologies in rats (429). Thus, exercise and dietary energy

restriction likely affect tumor vascularization; however, the relationships are

likely complex.

2.6.5. Inflammation

It is well established that chronic inflammation can drive tumorigenesis

(446). However, as cancerous cells progress to a tumor mass, intrinsic

inflammatory gene transcription factors (e.g., NF-B, STAT3, HIF1),

inflammatory cytokines (e.g., IL-1, IL-6, IL-23, TNF), and proinflammatory

chemokine molecules (e.g., CXCR4, CXCL12) generate a highly inflammatory,

non-resolving TME that promotes cell proliferation, survival, and invasiveness

(13, 447-449). Also, tumor-secreted factor orchestrate extrinsic changes within

the originating tissue (e.g., induction of cancer-associated-fibroblasts (450))

and promote the expansion and accumulation of immunosuppressive tumor-

infiltrating cells (e.g., MDSCs, TAMs, Tregs (131)) to generate a feed-forward

proinflammatory milieu. The mechanisms by which tumor intrinsic inflammatory

signals promote the hallmarks of cancer (e.g., sustained cell proliferation,

oncogenic and epigenetic mutations, alterations in cellular energetics, inhibition

in apoptosis) is thoroughly reviewed (13, 447-449).

52

The clinical investigation of lifestyle-based interventions (e.g., dietary

energy restriction and physical activity) in cancer patients typically characterize

the systemic inflammatory milieu (e.g., plasma or serum C-reactive protein, IL-

6, or TNF), rather than directly assessing tumor-induced gene expression

levels or secretion of proinflammatory factors within the TME. Recent advances

in microarray technology have made the analysis of tumor biopsies more

practical; however, deciphering between tumor-derived, stromal-derived, or

tumor-infiltrating immune-cell derived factors remains a logistical problem.

Preclinical studies analyzing the TME show an exercise-induced reduction in

proinflammatory signals (e.g., IL-6, TNF) within the TME (444). Diet- and

exercise-induced pathways that reduce proliferative signal or inhibit the

dysregulation of tumor cell metabolism could also play a role in the inhibition of

tumor-secreted proinflammatory mediators. However, future studies are

needed to determine diet- and exercise-induced effects directly on intrinsic

inflammatory signaling cascades within tumor cells.

Acute inflammation is essential to initiate the immune response to a

harmful stimulus. However, chronic systemic inflammation can dysregulate

several pathways that promote tumor initiation and escape (449, 451). Several

studies are investigating the use of inflammatory biomarkers (e.g., C-reactive

protein, IL-6, TNF) as predictive and prognostic indicators in BC (452-457).

Elevated serum C-reactive protein, an acute phase protein and nonspecific

marker of systemic inflammation often used in the clinical assessment of

inflammatory status, as well as serum IL-6, is associated with BC stage and an

53

indicator of poor prognosis. The relation between diet and exercise,

inflammatory mediators, and BC is emerging in the clinical and epidemiologic

literature. Plasma TNF and IL-6 is unchanged in response to a six-month

aerobic exercise trial in BC survivors. Subset analysis reveals a significant

reduction in plasma IL-6 in women who reached 80% of the intervention goal

compared with those who did not (458). Studies in tumor-free overweight and

obese postmenopausal women show that sustained weight loss is achieved via

moderate dietary energy restriction alone (restriction to achieve a 10% weight

loss). Diet plus aerobic exercise results in the greatest reduction in chronic

inflammation (e.g., reduced plasma C-reactive protein and IL-6) (330, 410-

413); however, the relation to postmenopausal BC risk is forthcoming. The

reduction in chronic inflammation may help explain how weight control

achieved through diet and exercise reduces the risk of postmenopausal BC.

Numerous preclinical studies suggest that proinflammatory cytokines

can facilitate tumor growth and metastatic progression by directly impairing

cells within the TME (mesenchymal cells, tumor-infiltrating immune cells),

resulting in an increase in tumor vasculature and tumor cell invasiveness (394,

449, 459). Treadmill running in the C3(1)SV40Tag transgenic BC model (394)

and voluntary wheel activity in the methylnitrosurea-induced mammary

carcinoma model in rats (460) reduces plasma IL-6 and is associated with a

reduction in primary tumor incidence. Both voluntary wheel activity and dietary

energy restriction (15% reduction) interventions reduce plasma IL-6 and TNF

and reduce methylnitrosurea-induced mammary carcinoma incidence and

54

multiplicity (342). Therefore, targeting systemic cytokines and inflammatory

signals may be a potential therapeutic target to enhance current and emerging

therapies (461).

2.6.6. Sex hormones

Sex hormones, or steroids, are important in sexual development and

other bodily functions. A complex relation exists between estrogen levels,

adiposity, and menopausal status (462). Prior to menopause, estrogens are

produced mainly in the ovaries of women and elevated adiposity results in the

dysregulation of ovary-specific production of estrogen. After menopause,

adipose tissue is the main source of estrogen and generally, elevated body

weight means higher circulating estrogen levels. Epidemiologic studies show a

strong association between elevated circulating estrogen levels and

postmenopausal BC risk (308, 309, 463). However, being overweight or obese


menopausal status (310). In established BC, elevated plasma estrogens

correlate with gene expression of estrogen-dependent genes with the

expression varying across the menstrual cycle of premenopausal women

(463). The role of diet, exercise, and weight control on estrogen levels and BC

risk (291, 305, 306, 309, 339, 366, 419, 464, 465) and progression (312, 466,

467) is thoroughly reviewed and likely dependent on BC subtype classification.

For example, Ibrahim, et al. (373) reports that post-diagnosis physical activity

reduces BC deaths and all-cause mortality among patients with estrogen

receptor-positive tumors; whereas, women with estrogen receptor-negative

55

disease show no gain. Therefore, the beneficial effects of diet, exercise, and

weight control on BC risk and progression is intricately linked to menopausal

status and BC subtype.

2.6.7. Metabolic signaling and growth factors

Numerous researchers have reported the role of inflammatory metabolic

mediators (e.g., insulin, IGF-1, IGFBP-3) and growth factors (e.g., G-CSF, GM-

CSF) in tumor growth (360). The effects of IGF-1 on tumor progression are

discussed.

IGF-1 is a peptide hormone involved in modulating cell growth and

survival by stimulating proliferation (468). In circulation, the action of IGF-1 is

regulated by a group of six IGF-binding proteins (IGFBPs) (469, 470). IGFBP-

3 is the primary binding protein and can exhibit IGF-1 independent actions,

such as promoting growth inhibition and apoptosis (471). Elevated circulating

concentrations of IGF-1 are firmly established as a risk factor for the

development of BC, especially estrogen positive tumors (472-477). A recent

meta-analysis was completed using data from 17 prospective studies

quantifying pre-diagnosis circulating IGF-1 concentration and BC risk involving

4,790 BC patients (475). Results indicate that patients with the highest IGF-1

concentration had a 28% increase in BC risk compared with the lowest fifth.

This association was not altered when adjusting for IGFBP3 and did not vary

by menopausal status; however, it does seem to be confined to estrogen-

receptor-positive tumors (475).

56

The role of IGF-1 in BC survivorship populations is emerging (478-481).

Since IGF-1 is responsive to changes in energy balance, researchers are

investigating the role of physical activity as a non-pharmacological intervention

to reduce IGF-1 levels in BC survivors (433, 482, 483). A recent meta-analysis

was completed using data from five randomized controlled trials involving 235

BC survivors (484). Aerobic exercise results in a significant reduction in

circulating IGF-1; however, the effect on BC recurrence and survival has yet to

be established.

Pre-clinical studies have focused on the impact of IGF-1 in cancer cell

proliferation, migration, and metastatic potential using in vitro and in vivo

models to identify the signaling pathways involved in these processes (485). It

is well established that dietary energy restriction prevents mammary

tumorigenesis in rodents and IGF-1 may play a key role in the reduction in

tumor growth. IGF-1 signaling can activate PI3K/Akt and Raf-1/MEK/ERK

pathways and downstream nuclear factors in cancerous cells to promote

proliferation and inhibit apoptosis (486-488). Thus, a dietary energy restriction-

induced reduction in IGF-1 could result in blunted tumor proliferation.

Emerging evidence suggests that IGF-1 may play a role in the epithelial-

mesenchymal transition (EMT) process (343, 489). A number of studies show

a strong correlation between EMT and high invasive and metastatic behavior

of BC (490). Results from a recent study suggest that IGF-1 levels may regulate

luminal tumor growth by modulating genes related to EMT and chemokine

57

signaling (358). Thus, alterations in energy balance achieved via dietary energy

restriction and exercise are likely linked to the EMT process.

Additionally, the IGF family can also dysregulate immune

compartments. For example, IGF-1 can activate Raf-1/MEK/ERK and decrease

ERK1/2 phosphorylation and p38 dephosphorylation pathway in DCs resulting

in a delay in maturation (or an induction in a tolerogenic DC phenotype) (491,

492). Reduced antigen uptake and presentation abilities in DCs will activate

fewer antigen-specific CD8+ cytotoxic T cells (491, 492). IGF signaling in DCs

can also induce the secretion of IL-6, TNF, and IL-10, generating an

immunosuppressive phenotype that promotes tumor escape (491, 492).

2.6.8. Myokines

Skeletal muscle is now recognized as an endocrine organ that can

secrete peptides in response to exercise-induced skeletal muscle contraction

(435) to promote an anti-inflammatory milieu (e.g., IL-6, IL-10, IL-Ira) and

regulate physiological processes in distant organs (493). An exercise-induced

shift in circulating concentrations of pro- and anti-inflammatory cytokines may

have direct effects on tumor proliferation (494), or an indirect effect through the

control of systemic levels of hormones (299) or altered immune responses

(e.g., acute mobilization of NK and T cells, enhancements in NK cytotoxicity)

(112, 495, 496). Regular physical activity may prevent cancer initiation and

improve survival via exercise-induced changes in skeletal muscle-derived

cytokines that reduce proinflammatory signaling.

58

2.6.9. Adipokines

Adipose tissue is now recognized as a major endocrine organ capable

of secreting numerous cytokines, or adipokines (497). Over 40 adipokines have

been identified; however, leptin and adiponectin are the most abundant in

human serum. Circulating leptin is positively correlated with adiposity; whereas,

circulating adiponectin is generally lower in obese compared with lean

individuals (497). Leptin has broad effects on the immune system, cytokine

production, angiogenesis, and carcinogenesis (498). Adiponectin has direct

anti-diabetic, anti-atherogenic, and anti-inflammatory properties. Data on the

association of adipokines and BC risk and prognosis are mixed (499); however,

recent meta-analyses support a link. A recent meta-analysis reports that the

concentration of plasma leptin levels increase with disease severity in BC

patients (500). Recent meta-analyses summarizing both prospective cohort

and case-control studies report an inverse relation between adiponectin levels

and BC risk (501-503). Leptin and adiponectin are responsive to changes in

energy balance and are being investigated as modulating factors mediating the

effects of diet and exercise on cancer progression (339). Numerous preclinical

studies report that dietary energy restriction and exercise can induce systemic

changes in adipokine levels and alter primary tumor growth or metastatic

progression (342, 356, 392, 504). Further studies are needed to characterize

the link between adipokines and BC risk and progression.

59

2.6.10. The immune response

Immune cells have multiple flavors of cell surface receptors and can

respond to numerous signaling molecules (e.g., inflammatory, sex hormone,

metabolic, adipokine, myokine) that direct and refine the immune response

(505). Immune cells are diverse and play a critical role in tumorigenesis

(immunosurveillance reviewed in section 2.3.3. and immunosuppression

reviewed in 2.3.5). Cancer related inflammation subverts antitumor immunity

and promotes the emergence of immunosuppressive cell types. Therefore,

weight gain-induced perturbations or diet and/or exercise-induced changes in

inflammatory tone, sex hormones, metabolic signaling, adipokines, or

myokines can not only directly affect tumor initiation and progression, but can

also impact the immune response to cancer (506-508). Changes in the

inflammation-immune axis may underlie the relation between physical activity

and BC risk and progression. Identifying the specific molecules and

mechanisms driving an exercise-induced benefit in the immune response to

cancer is an active area of research (435, 509-511).

Physical training can improve quality of life in BC survivors (414-417).

However, clinical data is emerging that physical training, both aerobic (512-

515) and resistance (516, 517), can also increase immunological anti-cancer

activity. Schmidt, et al. (518) recently reviewed relevant clinical trials and meta-

analyses studying the effects of physical activity on the immune system of BC

patients and summarized that exercise-induced benefits are likely mediated via

an increase in the number and cytotoxicity of monocytes, natural killer cells,

60

and cytokines. Exercise-induced changes in immune compartments and its

potential influence in cancer therapies is emerging (519).

Preclinical studies suggest exercise-induced benefits in T cell (273, 496)

and NK cell (520, 521) compartments. Numerous preclinical studies

demonstrate that activity (running wheel, treadmill, swimming) can improve

immune effector responses and increase the number of tumor-infiltrating

immune cells concurrently with a delay in primary tumor growth (342, 394, 520-

523) and a reduction in metastasis (524). Few studies have investigated

exercise-induced alterations in immunosuppressive cell types. Goh, et al.

(395), reports that running wheel activity decreases intratumoral expression of

Ccl22, a cytokine associated with Treg recruitment. Immunosuppressive cells

can promote tumor survival and interfere with current and emerging therapies,

therefore, additional studies on exercise-induced changes in

immunosuppressive cell expansion and/or function are needed.

Severe caloric restriction or starvation impairs immunity (525, 526).

However, preclinical studies suggest that fasting-mimicking diets increase

circulating CD8+ cytotoxic T cells and reduce Treg cells, resulting in

improvements in immunosurveillance mechanisms (527, 528). Dietary energy

restriction changes in immune compartments and its potential influence in

cancer therapies is emerging (529).

2.7. RATIONALE FOR CURRENT RESEARCH

Metastatic disease remains incurable and a significant challenge in the field

of BC research is to determine how to reduce and/or eliminate the mortality

61

associated with metastatic BC (37, 38). Currently, treatments are available that

slow metastatic progression (e.g., estrogen blockers, chemotherapy, radiation);

however, these treatments carry their own risks of morbidity and mortality (42, 43).

TNBC is characterized by an increase rate of metastatic spread to the brain and

lung and a decrease in overall survival compared with other metastatic molecular

subtypes. TNBCs are often diagnosed at a later stage and therefore associated

with a more advanced disease. Thus, novel therapies that may prevent (48) or

slow metastatic disease with less severe side effects, especially within the

metastatic TNBC population, are greatly needed (45).

Emerging population data suggests an inverse relation between physical

activity and BC incidence, as well as an important role for exercise in the

prevention of cancer recurrence and mortality. The extent to which dietary energy

restriction or physical activity, as opposed to changes in body weight, protect

against primary tumor growth and metastatic progression remains unclear in

epidemiologic, clinical, and preclinical studies. One common hypothesis explaining

the cancer protective effect of diet and physical activity on BC risk is a mediation

of the effect through body weight (366). The observational nature of the

epidemiologic studies limit the ability to determine biological mechanisms and the

extent to which diet or exercise, as opposed to changes in body weight, account

for the reduction in BC risk and improvements in survival outcomes. Thus, well-

designed preclinical studies are needed to investigate mechanisms, as well as

determine if direct exercise effects or exercise-induced changes in body weight

underlie protection.

62

Based on recent advancements in the fields of immunology and molecular

biology, immunotherapy has become a promising emerging treatment for BC,

especially metastatic disease (62, 63). Immunotherapy treatments include

therapeutic cancer vaccines, monoclonal antibodies, adoptive cell therapies, and

the administration of immunostimulatory cytokines, all of which are designed to

stimulate a robust antitumor effector response to eliminate tumor cells through

several techniques (51, 64). Therapeutic cancer vaccines provide TAAs to

stimulate an effector T cell response (6); whereas, immune checkpoint blockade,

like anti-PD-1, use specific antibodies to target inhibitory cell surface molecules to

promote sustained effector cell activation (203). An active area of research

attempts to identify ways in which to increase immunotherapy efficacy through the

identification of clinically meaningful biomarkers to direct treatment choices (i.e.,

personalized medicine) and/or through the selection of appropriate combinatorial

strategies (aimed to enhance antigen presentation, reverse T cell dysfunction,

and/or target immune inhibitory mechanisms) without inducing adverse effects.

The number of possible combinatorial treatment strategies grows exponentially. A

deeper understanding of the mechanisms by which diet, exercise, or weight control

can modulate the systemic host milieu or the TME resulting in a more favorable

inflammation-immune axis would not only provide more support for public health

interventions, but also identify novel mechanisms to target with future

pharmacological therapies. Therefore, testing if lifestyle-based interventions, i.e.,

physical activity and the prevention of weight gain, which are low-cost, have known

benefits across the cancer continuum (271), and can augment T cell responses

63

(272, 273), can enhance the efficacy of immunotherapeutic strategies represents

an attractive option.

2.7.1. Model selection

Several murine models of human BC exist and can be broadly

categorized as xenograft, inducible (chemically or virally), or genetically

engineered. The advantages and disadvantages of these model systems is

extensively reviewed (530, 531). Injectable tumors are inherently variable (e.g.,

induction rates, number of tumors induced, and metastatic progression) (532),

which is further complicated by inconsistencies in the literature in terms of the

number of cells injected or the method of injection (e.g., subcutaneous,

orthotopic). Nonetheless, transplantable mouse models remain a valuable

approach because an intact tumor-host environment is maintained, allowing for

the evaluation of therapies that require an immune response (532).

The most widely studied syngeneic murine mammary model is the

biomarker receptor triple-negative 4T1 series, consisting of several genetically

related cell lines (533). The 4T1.2 variant has a basal-like phenotype (534) and

shows characteristics of advanced human stage IV BC. 4T1.2 cells

spontaneously metastasize to several organ systems when implanted

orthotopically, including lung and bone (168, 535, 536), two common sites of

metastasis in human BC patients (38). 4T1.2 cells lack expression of estrogen

receptor, progesterone receptor, and HER2. Thus, 4T1.2 cells represent an

aggressive preclinical stage IV basal-like TNBC model (536). The 4T1 (537,

538) and 4T1.2 (figure 2.1) mammary tumor model can release endocrine (e.g.,

G-CSF and GM-CSF) and proinflammatory (e.g., IL-6) signals that dysregulate

64

hematopoiesis, resulting in the expansion and accumulation of iMCs, like

MDSCs, into the blood, spleen (e.g., splenomegaly), TME, and metastatic

niches. Additionally, 4T1 and 4T1.2 tumor growth is correlated with an increase

in immunosuppressive Tregs (539), which can further promote tumor

progression. As 4T1.2 tumors advance, there is a progressive decrease in

lymphoid-lineage cells and an increase in myeloid-lineage cells within the tumor

and secondary lymphoid organs. This dysregulated immune phenotype can

generate an immunosuppressive environment that dampens antitumor effector

functions; thus, promoting tumor survival and escape.

Historically, it is hypothesized that the 4T1 series is weakly immunogenic

and poor responders to individual immune stimulatory therapies such as

adenovirus expression of B7.1 or IL-12 (540) or cellular vaccines consisting of

MHC class II, B7.1, or the staphylococcal enterotoxin B superantigen (541,

542) due to the emergence of the highly immunosuppressive effects of MDSC

and Treg populations. Recent studies show that the immune system can

recognize the 4T1 mammary tumor, but only through the combination of MDSC

(126, 262, 266, 543-546) or Treg (539, 547) inhibition plus immune stimulatory

treatments. Thus, studying if mild dietary energy restriction and moderate

exercise can blunt tumor-induced inflammation, reduce the expansion of

immunosuppressive cell types, and sustain antitumor immunity resulting in a

reduction in primary tumor growth and metastatic disease in a clinically

relevant, immunocompetent, orthotopic, mammary tumor model is highly

innovative and readily translatable.

65

66

Figure 2.1. Immune and plasma alterations over time in the 4T1.2 mammary tumor

model. The 4T1.2 mammary tumor model is characterized by splenomegaly (A, B) with

splenocyte cell counts increasing proportionately with tumor volume. Splenomegaly is

characterized by a decrease in the percent of (C) CD3+ T cells, (D) CD4+ helper T cells,

and (E) CD8+ cytotoxic T cells and an increase in the percent of (F) Gr-1+CD11b+ MDSCs,

(G) Gr-1loCD11b+ mMDSCs, and (H) Gr-1hiCD11b+ gMDSCs as tumor progression occurs.

Tumor weight at sacrifice is correlated with (I) splenic Gr-1+CD11b+ MDSCs (Spearmen

correlation, ρ=0.849, p<0.001), (J) plasma G-CSF (Spearmen correlation, ρ=0.459,

p<0.001), and (K) plasma IL-6 (Spearmen correlation, ρ=0.499, p<0.001).

2.7.2. Activity intervention selection

Based on the association between physical activity and a reduction in

BC incidence, reduction in recurrence, and improvements in survival in

observational studies, my dissertation studies were designed to model women

who remain weight stable throughout adulthood via two lifestyle-based

strategies, a modest reduction in calories (10% of caloric intake) and moderate

physical activity to test the extent to which exercise as opposed to changes in

body weight protect against tumor growth and metastasis. Rodent models of

activity can utilize forced (i.e., treadmill running or swimming) or voluntary (i.e.,

wheel running) intervention paradigms with the advantages and disadvantages

of each intervention thoroughly reviewed (430, 548). Briefly, forced exercise

interventions can quantify intensity and duration, but are associated with an

elevated stress response (i.e., sympathetic nervous system and/or

hypothalamic-pituitary-adrenal axis activation). Conversely, mice can acclimate

to voluntary running wheel activity over time; thus, reducing stress-related

changes in the adrenal hormone corticosterone. To reduce stress-related

detrimental effects on the immune response and tumorigenesis, voluntary

wheel running was selected.

67

2.8. SPECIFIC AIMS AND HYPOTHESES

Overarching hypothesis: Moderate exercise achieved via access to voluntary

activity wheels will alter the inflammation-immune axis resulting in a reduction in

primary tumor growth and spontaneous metastasis; and an enhancement in

therapeutic efficacy in a stage IV metastatic BC model.

Specific aim 1. Determine the extent to which exercise, as opposed to

changes in body weight, protects against primary tumor growth and metastatic

progression in a stage IV metastatic BC model.

Hypothesis 1.1. Changes in body weight via mild dietary energy

restriction alone or moderate exercise in mice gaining weight over the

course of the study will reduce primary tumor growth and metastatic

burden in 4T1.2 tumor-bearing mice.

Hypothesis 1.2. Moderate exercise in weight stable mice (achieved via

the combination of mild dietary energy restriction [10% dietary energy

restriction based on control food intake] and access to an activity wheel)

will prevent the emergence of immunosuppressive factors, concurrently

with the greatest reduction in primary tumor growth and spontaneous

metastases in 4T1.2 tumor-bearing mice.

Hypothesis 1.3. Moderate exercise in weight stable mice will reduce

proinflammatory tumor-immune gene transcription factors within the TME.

Specific aim 2. Determine if moderate exercise in weight stable mice can

enhance the efficacy of a broad-based, allogeneic whole tumor cell cancer

vaccine in a stage IV metastatic BC model.

68

Hypothesis 2.1. Moderate exercise in weight stable mice will enhance

the efficacy of the broad-based immunogenic stimulus resulting in the

greatest reduction in primary tumor growth and spontaneous metastases,

as well as enhancement in antitumor immune effector function in 4T1.2

tumor-bearing mice.

Specific aim 3. Determine if moderate exercise in weight stable mice can

enhance the efficacy of the dual administration of a broad-based, allogeneic

whole tumor cell cancer vaccine and PD-1 immune checkpoint blockade in a

stage IV metastatic BC model.

Hypothesis 3.1. The beneficial effects of moderate exercise in weight

stable mice are working through an enhancement in antitumor immune

responses.

Hypothesis 3.2. Moderate exercise in weight stable mice will prevent the

emergence of immunosuppressive factors.

Hypothesis 3.3. Antagonizing the co-inhibitory PD-1 immune checkpoint

via antibody blockade will allow the immune system to completely

eradicate the tumor and prevent metastases in moderately exercising,

weight stable mice.

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CHAPTER 3: WEIGHT MAINTENANCE ACHIEVED VIA MILD DIETARY ENERGY RESTRICTION AND MODERATE PHYSICAL ACTIVITY ON TUMOR PROGRESSION IN THE 4T1.2 MAMMARY TUMOR MODEL*

3.1. ABSTRACT

Regular physical activity and the prevention of weight gain significantly

decreases breast cancer (BC) incidence and improves BC-specific and overall

survival in BC patients. Possible mechanisms in which physical activity- and diet-

induced weight control reduce the risk for primary BC include changes in

metabolic, inflammatory, and immune mediators; however, the mechanisms

underlying the improvement in survival are not well understood. How physical

activity and diet-induced weight control improve survival is of utmost importance

because metastatic disease remains incurable and is the underlying cause of

death in the majority of BC patients who die of the disease. Thus, the discovery of

any lifestyle intervention(s) that could prevent or delay metastatic disease could

be transformative in terms of cancer therapy and survivorship. The goals of the

current study were to determine if: 1. The protective effect of diet and exercise on

primary tumor growth could prevent metastases; 2. This protective effect is due

largely to a reduction in calories or to a direct effect of exercise on tumor outcomes

and metastasis; and 3. A reduction in calories, exercise, or the combination alter

the tumor microenvironment (TME) and reduce the expansion of

protumor immunosuppressive cells in tumor-bearing mice. Female BALB/c mice

*Manuscript in preparation for submission to Cancer Prevention Research: Turbitt, W.J. (co-first

author), Xu, Y., Sosnoski, D.M., Collins, S., Meng, H., Mastro, A.M., Rogers, C.J. (2017). Exercise

in weight stable mice prevents mammary tumor growth and metastases by altering the tumor

microenvironment and reducing protumorigenic factors.

70

(n=15-20/group) were randomized to sedentary (SED) or activity wheel (EX) cages

and fed ad libitum (AL) or 90% of control food intake (10% reduction in dietary

energy intake compared with ad libitum-fed controls) to study the effects of EX

alone, ER alone, and the combination of EX+ER on tumor and metastatic

outcomes. Importantly, the ad libitum groups (SED+AL and EX+AL) and dietary

energy restriction groups (SED+ER and EX+ER) were weight matched to allow the

investigation of the effects of moderate exercise in weight gain vs. weight

maintenance groups. After eight-weeks on the interventions, all mice were

orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into the fourth

mammary fat pad and continued on their respective interventions. Mice were

sacrificed at day 35 post tumor implantation. Weight maintenance mice, both

SED+ER and EX+ER groups, weighed significantly less than weight gain mice,

both SED+AL and EX+AL groups, throughout the study (p<0.001). Primary tumor

growth (time x treatment, p<0.001) and tumor weight at sacrifice (p=0.021), as well

as lung (p=0.054) and femur (p=0.018) spontaneous metastasis, were significantly

different between groups, with EX+ER mice displaying the lowest tumor volume,

tumor weight, and metastatic burden. Moderate exercise in weight stable mice

reduced splenomegaly (p=0.002) and the abundance of splenic myeloid-derived

suppressor cells (MDSCs; p=0.003) and MDSC subsets, reduced the

concentration of plasma insulin-like growth factor-1 (IGF-1; p=0.020), and altered

chemokine, proinflammatory, immunostimulatory, and immunosuppressive gene

expression in the TME. To our knowledge, this is the first study to: 1. Control for

body weight and mechanistically tease apart the contribution of diet vs. exercise

71

on mammary tumor growth and metastatic progression; and 2. Show a cancer

protective effect of moderate exercise in weight stable mice on metastatic

progression to lung and bone, common sites of metastases in BC patients,

concurrently with a shift in inflammatory status and immune responses in a highly

aggressive, stage IV BC model.

3.2. INTRODUCTION



Approximately 60% of BCs are diagnosed at the local stage (i.e., cancer cells are

confined to the breast tissue or auxiliary lymph node) with a subsequent 5-year

survival rate of 98.8%. However, 6% of BCs are diagnosed at the distant stage

(i.e., cancer cells have spread, or metastasized, outside of the breast tissue) with

a subsequent 5-year survival rate of 26.9% (1, 2). Therefore, reducing the

occurrence of metastatic disease could be transformative in terms of cancer

therapy and survivorship.

Although the etiology of BC remains unclear (274), several non-modifiable

and modifiable risk factors are implicated in the risk of developing BC. Non-

modifiable risk factors include age, sex, race, ethnicity, family history, personal

history, menstrual history, and genetics (e.g., BRCA1 or BRCA2). Modifiable risk

factors that increase the risk of BC include smoking, the use of hormone

replacement therapy, nulliparity, age at first pregnancy (35 or older increases risk),

alcohol consumption, and physical inactivity; whereas, factors that decrease the

risk include age at first pregnancy (35 or younger is protective) and physical activity

72

(275-280). Elevated weight has contrasting effects on the risk of premenopausal

and postmenopausal BC risk (308, 309); however, being overweight or obese


menopausal status (310). The combination of increased body weight and physical

inactivity are estimated to account for 30% of the BC risk in industrialized societies

(311, 312) and are highly linked to the etiology of BC. Therefore, since an increase

in body weight represents a known, modifiable risk factor for postmenopausal BC

risk, general weight control during adulthood may be an effective strategy for BC

prevention. In fact, the current nutrition and physical activity guidelines proposed

by the American Cancer Society recommend that individuals maintain a healthy

weight throughout life by balancing caloric intake, engaging in physical activity, and

losing weight if overweight or obese (313). Similar guidelines are recommended

for survivorship populations.

Data from epidemiologic studies suggest that regular physical activity

significantly decreases BC incidence (4, 5) and improves BC-specific and overall

survival in BC patients (375, 380, 549). This effect is observed when controlling for

body mass index, suggesting that exercise may be protective independent of

weight status. However, the observational nature of these studies limit the ability

to determine biological mechanisms (e.g., improvements in inflammation-immune

axis) and the extent to which exercise, as opposed to changes in body weight,

account for the reduction in BC risk and improvements in survival outcomes. Also,

it remains unclear if the physical activity improvements in BC survival are due to a

reduction in metastatic disease occurrence and/or progression (373-375, 378).

73

Therefore, well-designed preclinical studies controlling for weight and examining

the effects of exercise, mild dietary energy restriction, or the combination of diet

and exercise on the inflammation-immune axis and tumor progression are

essential to determine the extent to which exercise or body weight contribute to

protective benefits.

Several broad categories of host factors are identified as possible mediators

underlying the relation between exercise and BC risk and progression because

they are modulated by changes in energy balance and are implicated in the

process of carcinogenesis (299, 360, 366, 549-553). These include reproductive

hormones (412, 464, 554-556), metabolic mediators (557-559), and adipokine and

inflammatory mediators (413, 557, 560, 561). Emerging evidence suggests that

exercise in weight stable animals may also modulate antitumor immune

responses. In non-tumor-bearing, weight stable mice, moderate exercise

(achieved via access to voluntary activity wheels) significantly enhances antigen-

specific T cell responses and natural killer (NK) cell activity (562), two cell types

important for mediating antitumor immunity. Thus, moderate physical activity may

also reduce recurrence and increase survival in BC patients by preserving or

restoring mechanisms (e.g., metabolic and inflammatory responses) that promote

immunosurveillance.

The tumor microenvironment (TME) is characterized by dysregulated

energy metabolism and by the production of intrinsic inflammatory gene

transcription factors, inflammatory cytokines, and proinflammatory signaling

molecules (118, 119). In addition to the transformed hyperproliferative cell mass,

74

the primary TME consists of components that can be broadly classified into three

groups (563): a) non-cellular components, b) cells of mesenchymal origin, and c)

cells of hematopoietic origin. Non-cellular components, like the extracellular

matrix, provide structural and functional support to the expanding tumor mass.

Cells of mesenchymal origin, like fibroblasts, adipocytes, or endothelial cells, can

respond to tumor-derived factors to promote tumor vascularization, metastatic

dissemination, and immune suppression (564). Cells of hematopoietic origin (e.g.,

myeloid- and lymphoid-lineage immune cells) have disparate function (565). The

role of the immune system in controlling primary tumor growth is well studied (91-

93) and involves antitumor responses by NK cells and CD8+ cytotoxic T cells.

These cells can recognize transformed cells and prevent their expansion through

the release of cytotoxic enzymes (perforin 1, granzyme A and B, granulysin) and/or

soluble factors (chemokines and inflammatory cytokines [e.g., interferon (IFN)]

(97-99)). Additionally, type I CD4+ helper T cells (TH1) produce cytokines (e.g.,

interleukin (IL)-2 and IFN) and chemokines to recruit and activate NK cells and

CD8+ cytotoxic T cells (104) to further promote antitumor immunity. However,

immunosuppressive cells of both lymphoid (e.g., Tregs (120)) and myeloid (e.g.,

tumor-associated macrophages [TAMs] (122, 123) and myeloid-derived

suppressor cells [MDSCs] (124-128)) lineage are recruited to the TME by

proinflammatory tumor-derived factors and their abundance is positively correlated

with tumor burden (129-133). Immunosuppressive cells reduce antitumor immune

mechanisms through the depletion of nutrients in the TME, inhibitory cell-surface

receptors, and/or through the release of short-lived soluble mediators. The balance

75

between antitumor immunosurveillance mechanisms and the accumulation of

tumor-driven immune suppressive cells is critical for regulating primary tumor

growth (141) and metastatic progression (142). The complexity of the TME has

become more appreciated in the past two decades (12, 13); however, the effect of

changes in energy balance on the TME remains understudied.

The extent to which exercise, as opposed to changes in body weight,

protect against primary tumor growth and metastatic progression remains unclear.

Therefore, the goal of the current study was to determine if exercise or changes in

body weight via mild dietary energy restriction drive protection in an orthotopic

model of state IV metastatic BC. A 2x2 factorial design was used to test energy

balance interventions: Female BALB/c mice were randomized to sedentary (SED)

or activity wheel (EX) cages and fed ad libitum (AL) or 90% of control food intake

(10% dietary energy restriction, ER) in a 2x2 factorial design to investigate the

effects of EX alone, ER alone, and the combination of EX+ER on tumor,

metastatic, and immune outcomes. Since metastatic disease remains incurable

and is the underlying cause of death in the majority of BC patients who die of the

disease (3), the discovery of any lifestyle intervention(s) that could prevent or delay

metastatic disease could be transformative in terms of cancer therapy and

survivorship. Additionally, a deeper understanding of the mechanisms by which

exercise can modulate systemic host milieu or the TME resulting in a more

favorable inflammation-immune axis would not only provide more support for

public health interventions, but also identify novel mechanisms to target with future

pharmacological therapies.

76

3.3. MATERIALS AND METHODS

3.3.1. Screening for intrinsic running behavior

Acclimated mice were screened for voluntary running behavior by

placing mice into individual cages fitted with a running wheel apparatus (Starr

Life Sciences Corporation; Oakmont, PA) for four days as described previously

(562). Briefly, wheel revolutions of individual mice were recorded and analyzed

using Vital View software (Starr Life Sciences Corporation). Wheel revolutions

were converted to total kilometers run over the four-day test period and

histogram of distance run was generated. Mice that ran above the 25th

percentile of kilometers during the four-day test period were randomized to one

of the four intervention groups.

3.3.2. Tumor cell line and cell culture

The 4T1.2 cell line is a murine metastatic BC line derived from a

spontaneously arising mammary tumor in a BALB/cfC3H mouse (566). When

implanted orthotopically, the 4T1.2 cell line mimics the metastatic progression

of human BC with a tendency to metastasize to lung and bone (567). 4T1.2

cells stably expressing luciferase (4T1.2luc) were provided by Dr. Robin

Anderson (Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia)

and maintained in Dulbecco’s modified Eagle’s medium (Life Technologies;

Grand Island, NY) containing 10% fetal bovine serum (Gemini Bio-Products,

Sacramento, CA), 2 mM glutamine (Mediatech; Manassas, VA), 1X

nonessential amino acid (Mediatech), and 8 μg/ml puromycin (Mediatech).

77

3.3.3. Animal model

Six-week-old female BALB/c mice were obtained from Jackson

Laboratory (Bar Harbor, Maine). Mice selected for their intrinsic running

potential were randomized to sedentary (SED) or activity wheel (EX) cages and

fed ad libitum (AL) or 90% of control food intake (10% dietary energy restriction,

ER) in a 2x2 factorial design to investigate the effects of EX alone, ER alone,

and the combination of EX+ER on tumor and metastatic outcomes (Fig. 3.2A).

After eight-weeks on the interventions, all mice were orthotopically injected with

5x104 luciferase-transfected 4T1.2 cells into the fourth mammary fat pad and

continued on their respective energy balance intervention. Primary tumor

growth was measured 2-3x/week via caliper. Mice were sacrificed at day 35

post tumor implantation. An additional study randomized 38-week old, female

BALB/c mice (n=5-6/group) to single-housed running wheel cages or standard

cages and followed the same energy balance protocol as above to investigate

the energy balance effects in middle-aged, adult tumor-bearing mice. All mice

were fed AIN-76A diet (Research Diets, New Brunswick, NJ). Movement was

not monitored in mice that did not have access to running wheels. Food intake,

body weight, and tumor size (v=(short2×long)/2) were monitored as previously

reported (562, 568), and mice were observed daily for signs of ill health. All

mice were housed at the Pennsylvania State University and maintained on a

12-hour light/dark cycle with free access to water. The Institutional Animal Care

and Use Committee of the Pennsylvania State University approved all animal

experiments.

78

3.3.4. Metastatic burden

Lung, femur, liver, kidney, heart, tibia, brain, and spine were collected,

flash frozen in liquid nitrogen, and stored at -80°C. Tissues were homogenized

and genomic DNA was isolated using DNeasy Blood and Tissue Kit (Qiagen;

Valencia, CA), per manufacturer’s instructions. DNA was quantified using a

Nanodrop ND-1000 spectrophotometer (Nanodrop Products; Wilmington, DE).

Metastatic tumor burden of the tissues was quantified using the Taqman™

system performed on a Step-One Plus (Life Technologies) real time PCR

instrument to quantify luciferase. Assay sequences for luciferase were

CAGCTGCACAAAGCCATGAA (forward primer),

CTGAGGTAATGTCCACCTCGATATG (reverse primer) and

TACGCCCTGGTGCCCGGC (probe with 5’ FAM reporter and 3’ BHQ

quencher). The reference gene used for normalization was mouse telomerase

reverse transcriptase (TERT) and was assayed using the Taqman Copy

Number Reference Assay TERT (Life Technologies). The standard curve

included 5-10-fold serial dilutions (200 ng to 20 pg) of DNA extracted from

cultured 4T1.2luc cells. The standard curve and 200 ng of the tissue DNA

samples were run in duplicate for luciferase and TERT on the Step-One Plus

using the quantitative data analysis option and standard cycling parameters.

Luciferase data was normalized to the quantitative values for TERT in each

sample to correct for fluctuations in DNA amount, quality, and reaction

efficiency.

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3.3.5. Bioluminescent in vivo imaging

Mice were weighed and injected I.P. with XenoLight D-Luciferin-K+ Salt

Bioluminescent Substrate (150 mg luciferin/kg body weight) (Perkin Elmer;

Waltham, MA) 10 minutes prior to imaging. Anesthetized mice were imaged for

a two-minute exposure in an IVIS 50 Imaging System (Perkin Elmer) and

analyzed with Igor Pro - Scientific Imaging Analysis Software (Wavemetrics;

Lake Oswego, OR).

3.3.6. Splenic and tumor-infiltrating immune cell assays

3.3.6.1. Isolation of splenic immune cells.

Spleens were harvested via gross dissection and splenocytes were

prepared from individual mice by mechanical dispersion, as previously

described (345). Briefly, spleens were mechanically disrupted with a

syringe plunger and passed through a 70 μm nylon mesh strainer (BD

Biosciences; Bedford, MA), erythrocytes were lysed with ACK lysing

buffer (Lonza; Basel, Switzerland), and remaining cells washed twice in

complete medium (RPMI 1640 (Mediatech) supplemented with 10% FBS

(Gemini Bio-Products), 0.1 mM non-essential amino acids (Mediatech), 1

mM sodium pyruvate (Mediatech), 2 mM glutamine (Mediatech), 10 mM

HEPES (Mediatech), 100 U/mL penicillin streptomycin (Mediatech). Cell

counts and viability were determined via trypan blue exclusion

(Mediatech).

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3.3.6.2. Splenic immune cell proliferation and cytotoxicity assays

Splenic CD4+ helper T cells were isolated via Dynabeads

Untouched Mouse CD4 Cells Kit following manufacturer’s instructions

(Life Technologies). Splenic CD4+ helper T cells (1x105) plus 1.0 μg/ml

unlabeled anti-CD28 (BD Biosciences) were incubated in flat-bottomed,

96-well plates (Greiner Bio-One; Monroe, NC) in the presence of

increasing concentrations of anti-CD3 antibody (BD Biosciences) for 72

hours (562). Proliferation data was analyzed by tritiated (H3) thymidine

(Perkin Elmer) incorporation and quantified on a Microbeta plate reader

(Perkin Elmer). Each assay was performed in triplicate. CD4+ helper T cell

supernatant was collected following 48-hour stimulation with 0.5 μg/ml

anti-CD3 and 1.0 μg/ml anti-CD28 and stored at -80°C. IL-2 and IFN were

quantified using Legend Max ELISA kits (Biolegend; San Diego, CA).

Natural killer cell cytotoxicity (NKCC) was assessed in a standard

4-hour chromium release assay, as previously described (569), using

100:1, 50:1, 25:1, 12.5:1, and 6.25:1 effector: target ratios. NKCC

experiments were performed in triplicate using 51Cr-labeled (Perkin Elmer)

YAC-1 target cells in log phase of growth. Gamma emission was detected

using Packard Cobra II Auto-Gamma, model E5002 (Perkin Elmer). Yac-

1 targets were plated alone (spontaneous release) or with 1% Triton-X

(EMD Millipore) to induce total release and percent lysis was calculated

by: [(observed-spontaneous) / (total-spontaneous)] * 100.

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3.3.6.3 Splenic IFN secretion post five-day bulk culture

Splenic immune cells, depleted of Gr-1+ cells via magnetic bead

isolation (Miltenyi, San Diego, CA), were co-cultured for five days with

1x106 irradiated 4T1.2luc cells to generate tumor antigen-specific T cells.

Splenic effector cells were isolated and plated (0.5x106/well) in 24 well

plates (Greiner Bio-One). Supernatants were harvested (48 hours post-

incubation) and stored at -80°C. IFN was measured using Legend Max

ELISA kits (Biolegend), per manufacturer instructions. Cytokines were

quantified with an Epoch Microplate Spectrophotometer (Biotek;

Winooski, VT). Each assay was performed in duplicate.

3.3.6.4. Splenic MDSC suppression assay

Granulocytic and monocytic MDSC subsets were isolated from a

single cell suspension of splenocytes per manufacturer’s instructions

(MSDC Isolation kit; Miltenyi). Post-isolation MDSC subsets were counted

and viability determined via trypan blue exclusion. C57BL/6 T cells were

isolated via negative selection using the Dynabeads® UntouchedTM

Mouse T Cell Kit (Life Technologies). BALB/c APCs were isolated using

Dynabeads® Mouse Pan T (Thy1.2) Kit (Life Technologies). After

isolation, T cells and APCs were counted and viability was determined via

trypan blue exclusion. Isolated splenic T cells (1x105) from C57BL/6

animals were co-cultured in a mixed lymphocyte reaction with irradiated

(2000 rads) APCs (5x105) isolated from 4T1.2luc tumor-bearing BALB/c

mice and either (Gr-1HiCD11b+ granulocytic or Gr-1LoCD11b+ monocytic

82

MDSCs (5x104)). Cells were incubated in flat-bottomed, 96-well plates

and were pulsed with 1 μCi per well of tritiated thymidine (H3) at 72 hours.

Proliferation was assessed by tritiated thymidine incorporation at 96 hours

(Perkin Elmer) on a microbeta plate reader (Perkin Elmer). Each assay

was performed in triplicate.

3.3.6.5. Isolation of tumor-infiltrating immune cells

Primary tumors were harvested during dissection, weighed, minced

into fine pieces (<10mg), and incubated with 0.03 mg/ml Liberase (Roche;

Indianapolis, IN) and 12.5 U/ml DNase I (Sigma-Aldrich; St. Louis, MI) for

45 minutes at 37°C on an orbital shaker. Following the digestion,

remaining pieces were mechanically disrupted with a syringe plunger,

passed through a 70 μm nylon mesh strainer (BD Biosciences), layered

over Lympholyte-M cell separation media (Cedarlane; Burlington, NC),

and centrifuged at room temperature for 20 minutes at 1200g. Isolated

cells were washed twice in cold PBS (Mediatech) and cell counts and

viability of tumor immune infiltrates were determined via trypan blue

exclusion.

3.3.6.6. Flow cytometric analyses

Single cell suspensions of splenocytes, splenic effector cells (after

five-day bulk culture with irradiated tumor cells), and tumor-infiltrating

immune cells were washed twice in PBS containing 0.01% bovine serum

albumin (flow buffer) at 4°C. Cells were incubated with Fc block

(Biolegend) and 1x106 cells were stained with saturating concentrations

83

of conjugated antibodies for 30 min at 4°C, as previously described (345).

Fluorescently conjugated antibodies for flow cytometry included rat -

mouse CD19 (1D3), hamster -mouse CD3 (145-2C11), rat -mouse

CD4 (RM4-5), rat -mouse CD8 (53-6.7), rat -mouse CD25 (PC61.5),

mouse -mouse NK1.1 (PK136), mouse -mouse I-Ab (AF6-120.1),

hamster -mouse CD11c (HL3), rat -mouse CD11b (M1/70), rat -

mouse F4/80 (T45-2342), rat -mouse Ly6G and Ly6C [Gr-1] (RB6-8C5),

rat -mouse Ly6C (AL-21), and rat -mouse Ly6G (1A8). Antibodies were

obtained from BD Biosciences, Biolegend, and eBioscience; San Diego,

CA. Following incubation with the conjugated antibodies, cells were

washed twice in flow buffer and fixed in 1% paraformaldehyde (BD

Biosciences) in flow buffer for flow cytometric analysis. Additionally,

splenic regulatory T cells (Tregs) were quantified using the Mouse

Regulatory T Cell Staining Kit #1 (eBioscience) per manufacturer’s

instructions. Briefly, following the extracellular staining for CD4 and CD25,

cells were permeabilized for 30 minutes, washed in a

fixation/permeablization buffer, and intracellularly stained for rat -mouse

FoxP3 (FJK-16s) for 30 minutes. Following incubation with the FoxP3

antibody, cells were washed twice in fixation/permeabilization buffer and

read within 12 hours. Cells were gated on forward vs. side scatter, and a

total of 50,000 events for extracellular staining of lymphoid and myeloid

populations and a total of 100,000 events for intracellular staining for Treg

populations were analyzed. Flow cytometric analyses were performed on

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a Beckman Coulter FC500 (Beckman Coulter; Indianapolis, IN) or a BD

LSR-Fortessa (BD Bioscience) flow cytometer. Flow cytometric analyses

were plotted and analyzed using Flow Jo software (Tree Star; Ashland,

OR).

3.3.7. Plasma mediators

Fasting blood was collected at sacrifice (day 35) via submandibular

bleed in microtainer tubes (BD Biosciences), centrifuged, and plasma was

stored at -80°C. Granulocyte colony-stimulating factor (G-CSF), Interleukin-6

(IL-6), Interleukin-1 alpha (IL-1), monocyte chemoattractant protein-1 (MCP-

1), leptin, and adiponectin were measured using a Milliplex MAP Multiplex or

Singleplex Assay (EMD Millipore; Billerica, MA) and quantified on a Bio-plex

200 system (Bio-Rad; Hercules, CA) using Luminex-200 software (Luminex;

Austin, TX), per manufacturer’s instructions. Insulin-like growth factor-1 (IGF-

1) and insulin-like growth factor binding protein-3 (IGFBP-3) were measured

using R&D Systems Quantikine ELISA kits (R&D Systems; Minneapolis, MN)

and quantified with an Epoch Microplate Spectrophotometer (Biotek; Winooski,

VT). Each assay was performed in duplicate.

3.3.8. Gene expression in the tumor microenvironment

At sacrifice, tumor samples were incubated overnight in RNAlater

(Qiagen) followed by RNA isolation from individual mice using Qiashredder

columns followed by RNeasy Mini Plus Kit (Qiagen). RNA quality was assessed

via Bioanalyzer analysis (Agilent Technologies; Santa Clara, CA) with samples

requiring an RNA Integrity Number (RIN) above 7 to pass quality control testing

85

(570, 571). RNA sample was retrotranscribed using RT2 First Strand Kit

(Qiagen) according to manufacturer’s instructions and samples were loaded

onto qPCR plates (Mouse Cancer Inflammation & Immunity Crosstalk PCR

Array, Cat. # PAMM-181Z, Qiagen) and cycled according to the following

conditions: 95°C for 10 minutes; 95°C for 15 seconds; 60°C for 1 minutes for

40 cycles on a StepOnePlus (Applied Biosystems; Foster City, CA). Qiagen’s

online Data Analysis Center was used to normalize raw CT values to the Actb

housekeeping gene and fold change (2(-Ct)) was calculated as previously

described (572).

3.3.9. Statistical analyses

Tumor weight, metastatic burden, the distribution of cells in the spleen,

splenic bulk culture cells, and tumor-infiltrating immune cells, cytokine

secretion, and plasma mediators were assessed for normality and equal

variances; and either parametric or nonparametric analyses were used based

on sample distribution to detect differences between treatment groups.

Metastatic burden, cytokine secretion, and metabolic mediators were skewed;

thus, the data were transformed (log or square root) prior to statistical analysis.

Differences in tumor weight, metastatic burden, the distribution of cells in the

spleen, splenic bulk culture cells, and tumor-infiltrating immune cells, cytokine

secretion, and plasma mediators were assessed between groups via a one-

way analysis of variance (ANOVA), followed by a Bonferroni correction for

multiple comparisons where appropriate, or Kruskal-Wallis test, depending on

normality and variance. Additionally, if groups were collapsed based on body

86

weight (i.e., WG: [SED+AL and EX+AL] vs. WM: [SED+ER and EX+ER]),

significance was assessed between groups via a Student’s t-test or Mann-

Whitney test, depending on normality and variance. Body weight, primary tumor

volume, CD4+ helper T cell proliferation, and NKCC were examined using a

two-way ANOVA, followed by a Bonferroni correction for multiple comparisons

where appropriate. All data are presented as the mean plus or minus the

standard deviation of the mean. All analyses were conducted using GraphPad

Prism 5 software (GraphPad Software; La Jolla, CA) and statistical significance

was accepted at the p≤0.05 level.

3.4. RESULTS

3.4.1. Intrinsic running behavior

Following the four-day screening, mice were ordered from low to high

intrinsic running behavior and broken into quartiles (Fig. 3.1A). The top 75% of

intrinsic runners (average km/day > 3.5 km/day) were randomized to energy

balance interventions (sedentary or activity wheel cages).

87

Figure 3.1. Screening BALB/c mice for intrinsic running activity. (A) Acclimated mice

were screened for voluntary running behavior by being placed into individual cages fitted

with a mouse running wheel apparatus for 4 days. Wheel revolutions of individual mice

were recorded and analyzed using Vital View software (Starr Life Sciences Corporation)

and the top 75% were randomized to energy balance interventions (sedentary or activity

wheel cages). (B-E) Wheel revolutions (collected over 30 minute bins) for four

representative mice from each quartile over a four-day period.

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3.4.2. Body weight and wheel activity

The experimental design is displayed in Fig. 3.2A. Briefly, female

BALB/c mice were randomized to sedentary (SED) or activity wheel (EX) cages

and fed ad libitum (AL) or 90% of control food intake (10% dietary energy

restriction, ER) in a 2x2 factorial design to investigate the effects of EX alone

(in mice weight matched to the sedentary ad libitum group), ER alone (in mice

weight matched to EX+ER group), and the combination of EX+ER on tumor

and metastatic outcomes. After eight-weeks on the interventions, all mice were

orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into the

fourth mammary fat pad and continued on their respective energy balance

intervention. Mice were sacrificed at day 35 post tumor implantation. Mice that

maintained body weight, i.e., the SED+ER and EX+ER groups, weighed

significantly less than mice that gained weight, i.e., SED+AL or EX+AL groups,

over the course of the study (Fig. 3.2B; n=15-20/group; 2-way ANOVA, time x

treatment, F(39,897)=12.76, p<0.001). Wheel activity was maintained for 13

weeks over the course of the study, i.e., prior to and after tumor injection (Fig.

3.2C; n=40; average distance run per day=6.2±2.8 km).

89

Figure 3.2. Study design, body weight, and wheel activity. (A) Experimental design.

(B) Both the SED+ER and EX+ER groups (mice that maintained weight), weighed

significantly less than mice in the SED+AL or EX+AL groups that gained weight over the

course of the study (n=15-20/group; 2-way ANOVA, time x treatment, F(39,897)=12.76,

p<0.001). (C) Wheel activity was maintained for 13 weeks over the course of the study,

i.e., prior to and after tumor injection (n=40; average distance run per day=6.2 ± 2.8 km).

3.4.3. Primary tumor growth

The middle-aged, adult mice displayed similar tumor growth, metastatic

outcomes, and immune profiles compared with the 14-week mice (data not

shown); therefore, these groups were combined for subsequent data analysis.

Primary tumor growth was significantly reduced in EX+ER mice compared with

90

control (SED+AL) and single intervention (EX+AL and SED+ER) groups (Fig.

3.3A; n=15-20/group; 2-way ANOVA, time x treatment, F(18,414)=3.33, p<0.001)

compared with control (SED+AL) and single intervention (EX+AL or SED+ER)

groups. Tumor weight at sacrifice was reduced in EX+ER mice compared with

SED+AL mice (Fig. 3.3B; n=15-20/group; 1-way ANOVA, F(3,68)=3.492,

p=0.021).


Lung (Fig. 3.3C; 1-way ANOVA, F(3,58)=2.72, p=0.054), femur (Fig. 3.3D;

Kruskal-Wallis, KW=10.06, p=0.018), and liver (Fig. 3.3E; Kruskal-Wallis,

KW=7.21, p=0.065) spontaneous metastasis were significantly different

between groups with EX+ER mice displaying the lowest metastatic burden.

Kidney (Fig. 3.3F; Kruskal-Wallis, KW=6.26, p=0.100), heart (Fig. 3.3G;

Kruskal-Wallis, KW=5.97, p=0.113), and tibia (Fig. 3.3H; Kruskal-Wallis,

KW=1.58, p=0.664) spontaneous metastasis were not significantly different

between groups. Representative IVIS images (Fig. 3.3I). Samples were

collapsed into weight gain (composed of SED+AL and EX+AL mice) and or

weight maintenance (composed of SED+ER and EX+ER) to further investigate

metastatic burden. Brain (Fig. 3.3J; Mann-Whitney test, p=0.293) and spine

(Fig. 3.3K; Mann-Whitney test, p=0.275) spontaneous metastasis were not

significantly different between WG (i.e., SED+AL and EX+AL) and WM (i.e.,

SED+ER and EX+ER) groups.

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Figure 3.3. Moderate exercise in weight stable mice reduced primary tumor growth

and metastatic outcomes. (A) Primary tumor growth (measured 2-3x/week via caliper)

was significantly reduced in EX+ER mice compared with control (SED+AL) and single

intervention (EX+AL and SED+ER) groups (n=15-20/group; 2-way ANOVA, time x

treatment, F(18,414)=3.33, p<0.001) compared with control (SED+AL) and single

intervention (EX+AL or SED+ER) groups. (B) Tumor weight at sacrifice was reduced in

EX+ER mice compared with SED+AL mice (n=15-20/group; 1-way ANOVA, F(3,68)=3.49,

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p=0.021). (C-J) At sacrifice, organs (n=15-20/group) were flash frozen, homogenized, and

DNA was extracted. Quantitative PCR was used to detect luciferase expression as a

marker of metastatic burden reported as ng of luciferase DNA normalized to mouse TERT

DNA in 200 ng of sample. (C) Lung (1-way ANOVA, F(3,58)=2.72, p=0.054), (D) femur

(Kruskal-Wallis, KW=10.06, p=0.018), and (E) liver (Kruskal-Wallis, KW=7.21, p=0.065)

spontaneous metastasis were significantly different between groups with EX+ER mice

displaying the lowest metastatic burden. (F) Kidney (Kruskal-Wallis, KW=6.26, p=0.100),

(G) heart (Kruskal-Wallis, KW=5.97, p=0.113), and (H) tibia (Kruskal-Wallis, KW=1.58,

p=0.664) spontaneous metastasis were not significantly different between groups. (I)

Representative IVIS images. (J) Brain (Mann-Whitney test, p=0.293) and (K) spine (Mann-

Whitney test, p=0.275) spontaneous metastasis were not significantly different between

WG (i.e., SED+AL and EX+AL) and WM (i.e., SED+ER and EX+ER) groups. Significantly

different from SED+AL (*) and SED+ER (‡).

3.4.5. Splenic immunity

Splenocyte count was significantly reduced in EX+ER mice compared

with both SED+AL and EX+AL mice (Fig. 3.4A; 1-way ANOVA, F(3,72)=5.60,

p=0.002). CD4+ helper T cells from EX+ER mice proliferated more than CD4+

helper T cells from EX+AL (Fig. 3.4B; n=15-20/group; 2-way ANOVA,

F(3,315)=2.99, p=0.038); however, IL-2 (Fig. 3.4C; Student’s t-test, p=0.754) and

IFN (Fig. 3.4D; Student’s t-test, p=0.354) secretion were not significantly

different between WG (i.e., SED+AL and EX+AL) and WM (i.e., SED+ER and

EX+ER) groups. NK cell cytotoxicity was not significantly different between WG

and WM groups (Fig. 3.4E; n=7-8/group; 2-way ANOVA, F(1,65)=0.06, p=0.812).

No differences in the percentage of CD3+ T cells, CD3+CD4+ helper T cells,

CD3+CD8+ cytotoxic T cells, or NK1.1+ natural killer cell populations within the

bulk culture were observed between groups (Fig. 3.4F). No differences were

observed in the splenic effector cells secretion of IFN in response to re-

stimulation with tumor antigens (Fig. 3.4G; n=5-11/group, Kruskal-Wallis,

KW=0.33, p=0.955).

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94

Figure 3.4. Moderate exercise in weight stable mice altered splenic antitumor

immunity. Splenocytes (n=15-20/group) were prepared into a single cell suspension and

counted. (A) Splenocyte count was significantly reduced in EX+ER mice compared with

both SED+AL and EX+AL mice (1-way ANOVA, F(3,72)=5.60, p=0.002). (B-D) Isolated

splenic CD4+ helper T cells (0.1x106/well) were stimulated with increasing concentrations

of anti-CD3 antibody and 1 μg/ml anti-CD28 antibody, and proliferation was quantified via

[H]3 uptake. Data were converted to a stimulation index (counts per minute of stimulated

wells divided by counts per minute of unstimulated [media only] wells). CD4+ helper T cells

from EX+ER mice proliferated more than CD4+ helper T cells from EX+AL (n=15-20/group;

2-way ANOVA, F(3,315)=2.99, p=0.038). (C, D) CD4+ helper T cell supernatant was

collected following 48-hour stimulation with 0.5 μg/ml anti-CD3 and 1.0 μg/ml anti-CD28

and IL-2 and IFN were quantified using an ELISA for the WG (i.e., SED+AL and EX+AL)

vs. WM (i.e., SED+ER and EX+ER) groups. (C) IL-2 (Student’s t-test, p=0.754) and (D)

IFN (Student’s t-test, p=0.354) secretion were not significantly different between WG and

WM groups. (E) NK cell cytotoxicity was not significantly different between WG and WM

groups measured via a standard 4-hour 51Cr-release assay (n=7-8/group; 2-way ANOVA,

F(1,65)=0.06, p=0.812). (F, G) Isolated splenocytes were depleted of Gr-1+ cells and pulsed

with irradiated 4T1.2luc cells for five days to generate an antigen-specific T cell response

to tumor antigens. (F) Post bulk culture, cells were stained with anti-CD3, -CD4, -CD8,

and -NK1.1 and characterized by flow cytometry (n=5-11/group). No differences in the

percentage of CD3+ T cells, CD3+CD4+ helper T cells, CD3+CD8+ cytotoxic T cells, or

NK1.1+ natural killer cell populations within the bulk culture were observed between

groups. (G) No differences were observed in the effector cells secretion of IFN in

response to re-stimulation with tumor antigens (n=5-11/group, Kruskal-Wallis, KW=0.33,

p=0.955). Significantly different from SED+AL (*) and EX+AL (†).

The percentage of splenic CD3+ T cells (p=0.027) and CD3+CD4+ helper

T cells (p=0.002) was significantly different between groups with the EX+ER

group displaying the highest percentage (Table 3.1). The number of splenic

CD19+ B cells (p=0.007) was significantly different between groups with the

EX+ER group displaying the lowest number compared with both SED+AL and

EX+AL mice (Table 3.1). Splenic CD4+CD25+FoxP3+ Tregs were not

significantly different between groups (Fig. 3.5A; n=8-9/group; 1-way ANOVA,

F(3,34)=1.76, p=0.176); however group differences in splenic Gr-1+CD11b+

MDSCs (Fig. 3.5B; n=15-20/group, 1-way ANOVA, F(3,72)=5.06, p=0.003),

splenic CD11b+Ly6ChiLy6G- monocytic MDSCs (Fig. 3.5C; n=15-20/group,

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Kruskal-Wallis, KW=11.50, p=0.009), and splenic CD11b+Ly6CloLy6G+

granulocytic MDSCs (Fig. 3.5D; n=15-20/group, 1-way ANOVA, F(3,72)=4.38,

p=0.007) emerged with EX+ER mice displaying the lowest number compared

with either SED+AL or EX+AL groups. No group differences were observed in

MDSC suppressive capacity (Fig. 3.5E).

Table 3.1. The percentage and number of splenic immune cell populations. The

percentage of (A) splenic CD3+ T cells (p=0.027) and CD3+CD4+ helper T cells (p=0.002)

was significantly different between groups with the EX+ER group displaying the highest

percentage. (B) Splenocyte count (p=0.002) and the number of splenic CD19+ B cells

(p=0.007) were significantly different between groups with the EX+ER group displaying

the lowest number compared with both SED+AL and EX+AL mice. Significantly different

from SED+AL (*) and EX+AL (†).

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97

Figure 3.5. Moderate exercise in weight stable mice reduced splenic protumor

immunosuppressive cell populations. Splenocytes were stained with anti-CD4, -CD25,

and -FoxP3 antibodies to quantify regulatory T cell (Treg) populations and anti-Gr-1, -

CD11b, -Ly6G, and -Ly6C antibodies to quantify myeloid-derived suppressor cells

(MDSCs) and MDSC subsets by flow cytometry. (A) Splenic CD4+CD25+FoxP3+ Tregs

were not significantly different between groups (n=8-9/group; 1-way ANOVA, F(3,34)=1.76,

p=0.176); however group differences in (B) splenic Gr-1+CD11b+ MDSCs (n=15-20/group,

1-way ANOVA, F(3,72)=5.06, p=0.003), (C) splenic CD11b+Ly6ChiLy6G- monocytic MDSCs

(n=15-20/group, Kruskal-Wallis, KW=11.50, p=0.009), and (D) splenic

CD11b+Ly6CloLy6G+ granulocytic MDSCs (n=15-20/group, 1-way ANOVA, F(3,72)=4.38,

p=0.007) emerged with EX+ER mice displaying the lowest number compared with either

SED+AL or EX+AL groups. Dashed line represents non-tumor bearing control. (E) The

proliferative capacity of T cells with and without MDSC subsets, shown as a percentage

of T cell proliferation in the presence of APC alone, i.e., in the absence of MDSC subsets,

is displayed. Granulocytic and monocytic MDSCs and antigen-presenting cells were

isolated from tumor-bearing BALB/c mice and T cells were isolated from control C57BL/6

mice. No group differences were observed in MDSC suppressive capacity. Significantly

different from SED+AL (*) and EX+AL (†).


Plasma G-CSF (Fig. 3.6A; n=8-19/group; 1-way ANOVA, F(3,56)=2.37,

p=0.081), IL-6 (Fig. 3.6B; n=8-20/group; Kruskal-Wallis, KW=5.60, p=0.167),

and IGFBP-3 (Fig. 3.6C; n=5-7/group; Kruskal-Wallis, KW=0.73, p=0.866) were

not significantly different between groups. Plasma IGF-1 was significantly

different between groups (Fig. 3.6D; n=5-7/group; Kruskal-Wallis, KW=9.80,

p=0.020) with EX+ER mice displaying the lowest IGF-1 concentration

compared with SED+AL mice. Plasma IL-1 (Fig. 3.6E; Student’s t-test,

p=0.069), MCP-1 (Fig. 3.6F; Mann-Whitney, p=0.109), leptin (Fig. 3.6G;

Student’s t-test, p=0.859), and adiponectin (Fig. 3.6H; Student’s t-test,

p=0.160) were not significantly different between WG (i.e., SED+AL and

EX+AL) and WM (i.e., SED+ER and EX+ER) groups.

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99

Figure 3.6. Moderate exercise in weight stable mice altered plasma mediators.

Fasting blood was collected at sacrifice, centrifuged, and plasma was stored at -80°C.

Milliplex Multiplex Assays quantified on Luminex-200 software were used to measure (A)

G-CSF, (B) IL-6, (E) IL-1, (F) MCP-1, (G) leptin, and (H) adiponectin. R&D Systems

Quantikine ELISAs were used to measure metabolic markers for (C) IGFBP-3 and (D)

IGF-1. Plasma (A) G-CSF (n=8-19/group; 1-way ANOVA, F(3,56)=2.37, p=0.081), (B) IL-6

(n=8-20/group; Kruskal-Wallis, KW=5.60, p=0.167), and (C) IGFBP-3 (n=5-7/group;

Kruskal-Wallis, KW=0.73, p=0.866) were not significantly different between groups. (D)

Plasma IGF-1 was significantly different between groups (n=5-7/group; Kruskal-Wallis,

KW=9.80, p=0.020) with EX+ER mice displaying the lowest IGF-1 concentration

compared with SED+AL mice. Analysis of the plasma profile (n=11-12/group) in WG (i.e.,

SED+AL and EX+AL) vs. WM (i.e., SED+ER and EX+ER) groups for (E) IL-1 (Student’s

t-test, p=0.069), (F) MCP-1 (Mann-Whitney, p=0.109), (G) leptin (Student’s t-test,

p=0.859), and (H) adiponectin (Student’s t-test, p=0.160) were not significantly different

between groups. Significantly different from SED+AL (*).


3.4.7.1. Tumor-infiltrating immunity

The percentage (Table 3.2A) and number (Table 3.2B) of tumor-

infiltrating immune cell populations were not significantly different between

WG (i.e., SED+AL and EX+AL) and WM (i.e., SED+ER and EX+ER)

groups.

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Table 3.2. The percentage and number of tumor-infiltrating immune cell

populations. The (A) percentage and (B) number of tumor-infiltrating immune cell

populations were not significantly different between WG (i.e., SED+AL and EX+AL) and

WM (i.e., SED+ER and EX+ER) groups.

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3.4.7.2. Tumor-immune crosstalk gene array

Figure 3.7. Moderate exercise in weight stable mice altered gene expression in the tumor microenvironment. Tumor homogenates were assayed using Qiagen RT2 Profiler™ Mouse Cancer Inflammation & Immunity Crosstalk PCR Array. (A) Heat map displaying genes altered >2-fold. (B) Genes that were significantly altered (>2.5-fold) compared with SED+AL control.

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3.4.8. High vs. low activity within the moderately exercising, weight stable

mice

Mice running an average of 5.8-12.6 km/day (EX+ER HIGH) weighed

significantly less than mice running an average of 2.5-5.7 km/day (EX+ER

LOW) over the course of the study (Fig. 3.9A; n=17/group; 2-way ANOVA, time

x treatment, F(13,416)=2.81, p=0.001). Mice in the EX+ER HIGH group averaged

more km/day than the EX+ER LOW group over the course of the study (Fig.

3.9B; 2-way ANOVA, F(1,384)=42.33, p<0.001). Primary tumor growth was

significantly reduced in EX+ER HIGH mice compared with EX+ER LOW mice

(Fig. 3.9C; 2-way ANOVA, F(1,224)=3.32, p=0.078); however, this failed to reach

statistical significance. The Bonferroni correction for multiple comparisons was

significant for the day 28 time point. Tumor weight at sacrifice (Fig. 3.9D;

Student’s t-test, p=0.261) and lung (Fig. 3.9E; Student’s t-test, p=0.108) and

femur (Fig. 3.9F; Mann-Whitney, p=0.732) spontaneous metastasis were not

significantly different between groups. Total splenocyte counts were

significantly different between groups (Fig. 3.9G; Student’s t-test, p=0.012) with

EX+ER HIGH mice displaying a significant reduction compared with EX+ER

LOW mice. Gr-1+CD11b+ MDSCs were significantly different between groups

(Fig. 3.9H; Student’s t-test, p=0.033) with EX+ER HIGH mice displaying a

reduction compared with EX+ER LOW. CD11b+Ly6CloLy6G+ granulocytic

MDSCs (Fig. 3.9I; Student’s t-test, p=0.062) were not significantly different

between groups. Splenic effector cells from EX+ER HIGH mice harvested after

a five-day bulk secreted significantly more IFN in response to re-stimulation

with tumor antigens as compared with effector cells harvested from EX+ER

LOW mice (Fig. 3.9J; n=9/group, Student’s t-test, p<0.001).

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Figure 3.8. High vs. low activity within moderately exercising, weight stable mice.

(A) Mice running an average of 5.8-12.6 km/day (EX+ER HIGH) weighed significantly less

than mice running an average of 2.5-5.7 km/day (EX+ER LOW) over the course of the

study (n=17/group; 2-way ANOVA, time x treatment, F(13,416)=2.81, p=0.001). (B) Mice in

the EX+ER HIGH group averaged more km/day than the EX+ER LOW group over the

course of the study (2-way ANOVA, F(1,384)=42.33, p<0.001). (C) Primary tumor growth

(measured 2-3x/week via caliper) was reduced in EX+ER HIGH mice compared with

EX+ER LOW mice (2-way ANOVA, F(1,224)=3.32, p=0.078); however, this failed to reach

statistical significance. (D) Tumor weight at sacrifice (Student’s t-test, p=0.261) and (E)

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lung (Student’s t-test, p=0.108) and (F) femur (Mann-Whitney, p=0.732) spontaneous

metastasis were not significantly different between groups. Splenocytes (n=17/group)

were prepared into a single cell suspension and counted. (G) Splenocyte counts were

significantly different between groups (Student’s t-test, p=0.012) with EX+ER HIGH mice

displaying a significant reduction compared with EX+ER LOW mice. (H) Splenocytes were

stained with anti-Gr-1 and -CD11b to quantify myeloid-derived suppressor cells (MDSCs).

Gr-1+CD11b+ MDSCs were significantly different between groups (Student’s t-test,

p=0.033) with EX+ER HIGH mice displaying a reduction compared with EX+ER LOW.

Dashed line represents non-tumor bearing control. (I) Splenic CD11b+Ly6CloLy6G+

granulocytic MDSCs (Student’s t-test, p=0.062) were not significantly different between

groups. (J) Isolated splenocytes were depleted of Gr-1+ cells and pulsed with irradiated

4T1.2luc cells for 5 days to generate an antigen-specific T cell response to tumor antigens.

Effector cells harvested from EX+ER HIGH mice secreted significantly more IFN in

response to re-stimulation with tumor antigens as compared to effector cells harvested

from EX+ER LOW mice (n=9/group, Student’s t-test, p<0.001). Dashed line represents

non-tumor bearing control. Significantly different than EX+ER LOW (*).

3.5. DISCUSSION

The extent to which exercise, as opposed to changes in body weight,

protect against primary tumor growth and metastatic progression remains unclear

in epidemiologic, clinical, and preclinical studies. The current study was designed

to determine if exercise or changes in body weight via mild dietary restriction drive

protection in an orthotopic model of state IV metastatic BC. We demonstrated that

moderate exercise in weight stable mice has protective effects on primary tumor

and spontaneous metastasis in the lungs and bone, common sites of metastases

in women, in a highly aggressive stage IV mammary tumor model. Concurrent with

the protective effect of exercise on primary tumor growth and metastatic burden,

tumor-bearing mice that exercised and maintained a stable body weight had

reduced splenomegaly and splenic immunosuppressive cell types, enhanced

CD4+ helper T cell proliferation, reduced concentration of plasma insulin-like

growth factor-1 (IGF-1), and altered chemokine, proinflammatory,

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immunostimulatory, and immunosuppressive gene expression in the tumor

microenvironment (TME). Single interventions (i.e., dietary energy restriction-

induced weight control or exercise in mice gaining weight over the course of the

study) failed to show beneficial effects on primary tumor growth or metastatic

burden. Moderate exercise in weight stable mice may delay primary tumor growth

and metastatic progression in part, due to changes in the TME and through

maintaining an antitumor immune response over a protumor immunosuppressive

immune response.

Substantial clinical (308, 325, 399-406) and observational epidemiological

(292, 407) evidence exists suggesting that weight control is associated with a

decrease in the risk of primary BC (309, 408, 409). Studies investigating weight

control often use an intervention paradigm that combines mild dietary energy

restriction with physical activity (as part of the current American Cancer Society

Guidelines on Nutrition and Physical Activity for Cancer Prevention (573)) making

it difficult to uncouple these interventions and evaluate if the protective effects are

due entirely to weight maintenance, or if the individual effects of dietary energy

restriction and/or physical activity drive protection. Studies in overweight and

obese postmenopausal women are beginning to separate the effects of diet alone,

aerobic exercise alone, and the combination of diet and exercise on inflammatory,

metabolic, and sex hormone mediators (330, 410-413). Data from these studies

show that sustained weight loss is achieved via moderate dietary energy restriction

alone (restriction to achieve a 10% weight loss); however, diet plus aerobic

exercise results in the greatest reduction in chronic inflammation (e.g., reduced C-

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reactive protein, IL-6, leptin) in overweight and obese postmenopausal women.

The reduction in chronic inflammation may help explain how weight control

achieved through diet and exercise reduces the risk of postmenopausal BC.

Evidence from observational, randomized controlled, and bariatric surgery

studies suggest that dietary energy restriction reduces the risk of BC in women

(308, 324-327, 402, 574, 575). Specifically, dietary energy restriction in

premenopausal and postmenopausal years is most effective at reducing the risk

of postmenopausal primary BC. Dietary energy restriction has proven benefits in

terms of delaying cancer progression in mice. Preclinical studies demonstrate that

moderate to severe dietary energy restriction (20%-40% reduction in energy

intake) delays primary tumor growth in orthotopic (357, 358) and carcinogen-

induced (341, 359, 360) mammary tumor models. The protective effect of dietary

energy restriction on carcinogenesis is proportional to the degree of restriction

within the range of 20-40% (341, 361, 362). However, 10% dietary energy

restriction does not alter the percent of carcinogen-induced mammary lesions, or

the proportion of pre-malignant to malignant lesions compared with ad libitum-fed

rats (341) suggesting that restriction alone may need to exceed 10% to exert a

cancer prevention effect. In contrast, 10% dietary energy restriction is beneficial in

slowing weight gain in several rodent models (300, 341) since caged ad libitum-

fed animals are actually overfed (576, 577). Thus, a 10% dietary energy restriction

was chosen to prevent weight gain rather than induce a cancer prevention effect

in the current study.

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Regular physical activity is an effective intervention for the prevention of

both pre and postmenopausal primary BC (301). Clinical studies (including

randomized controlled trials) suggest that physical activity as part of daily life, and

purposeful aerobic exercise may mediate beneficial changes in a number of

physiological systems which may contribute to the primary cancer prevention effect

of exercise (298-300). Numerous preclinical studies also demonstrate that

exercise, achieved via access to motorized treadmill or voluntary activity wheels,

reduces primary tumor growth in orthotopic (383-385), carcinogen-induced (387-

392), and transgenic (394, 395) mammary tumor models. In the 4T1 model

(parental cell line of the 4T1.2 cells), exercise reduces primary tumor growth when

the intervention is started concomitantly with the injection of 4T1 tumor cells into

the mammary gland (385). Betof, et al. (385) reports that exercise induces a

reduction in primary tumor growth and is accompanied by an increase in the

density of apoptotic cells, and microvessel density and maturity in the tumor. These

data suggest that exercise may alter the TME resulting in a reduction in tumor

mass and tumor hypoxia (385). Thus, our current finding that moderately active,

weight stable mice had reduced primary tumor growth in the highly metastatic

orthotopic 4T1.2 model is consistent with data collected in several preclinical

models. Interestingly, exercise has no effect on primary tumor growth when MDA-

MB-231 cells are subcutaneously (397) or orthotopically (398) injected into athymic

mice. These data suggest that the beneficial effects of exercise on primary tumor

growth may be mediated via an immune-dependent mechanism.

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Few researchers have designed studies to investigate the effects of weight

control in BC survivorship populations. The limited studies report generally

consistent findings that weight control can reduce mortality and improve quality of

life measurements in BC survivors (375, 414-417, 578). The role of dietary energy

restriction on BC recurrence and BC-specific and overall survival in BC patients is

understudied. The relation between weight maintenance (achieved via diet alone,

exercise alone, or the combination of diet and exercise) and metastatic

progression is poorly understood in preclinical studies. In two preclinical studies,

mice were given an intravenous (I.V.) injection of mammary tumor cells and

randomized into voluntary exercise or sedentary control groups (112, 113). In both

studies, exercise following tumor cell injection did not alter the development of lung

metastases. However, because tumor cells were injected I.V., many of the

biological parameters that comprise the metastatic process were bypassed. Male

mice orthotopically implanted with murine prostate cancer cells and randomized to

sedentary or exercise groups have similar primary tumor growth rates, but exercise

causes an increase in tumor vascularization concurrently with a reduction in the

expression of prometastatic genes (386). Moderate to severe caloric restriction

(25-40% reduction in calories) in 4T1 tumor-bearing mice reduces the total number

of lung metastases that originate both spontaneously from the primary tumor and

experimentally from I.V. injection of tumor cells (579). In the current study, we

demonstrated that exercising, weight stable mice significantly reduced

spontaneous metastasis in the lung and bone using a clinically relevant, murine

metastatic model. These findings suggest a novel mechanism by which moderate

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exercise in a weight stable host may contribute to the reduction in mortality in BC

patients.

Evidence from epidemiological studies indicates that physical activity

significantly decreases the risk of recurrence and improves BC-specific and overall

survival in BC patients (298, 380). Two recent meta-analyses were completed

using data from 16 cohort studies involving over 40,000 BC patients (379, 380).

Physical activity was assessed using validated self-reported questionnaires and

expressed as times per week, hours per week (h/week), metabolic equivalent of

task (MET)-h/week, or energy expenditure in calories per week. Results of both

analyses indicate that patients who participated in any amount of physical activity

prior to a BC diagnosis had approximately a 20% risk reduction in BC-specific

mortality when comparing the most active to the least active women. Additionally,

patients who participated in physical activity after a cancer diagnosis had a 40-

50% risk reduction in BC-specific mortality (379, 380). The beneficial effects of

physical activity on BC-specific mortality remained significant after stratifying by

body mass index or menopausal status, suggesting the effects of physical activity

on cancer survival may be independent of metabolic or reproductive hormone

status (379-381). A similar association between baseline cardiorespiratory fitness

and reduced BC-specific mortality has also been demonstrated in the Aerobics

Center Longitudinal Study, in which 14,811 women were followed for 31 years

(382). It remains unclear if the protective effect of exercise on cancer outcomes

(primary or secondary prevention) is due, in part, to the prevention of weight gain

(i.e., obesity prevention) or to a direct effect of exercise on cancer risk and

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progression. The leading cause of BC-specific mortality is due to metastatic

disease (580). Thus, physical activity may be reducing BC recurrence and

improving survival by delaying or preventing metastases.

Both dietary energy restriction groups (i.e., SED+ER and EX+ER) weighed

significantly less than ad libitum-fed mice (i.e., SED+AL or EX+AL) over the course

of the study. Importantly, body weights were not significantly different between

weight gain (i.e., SED+AL and EX+AL) or weight maintenance (i.e., SED+ER and

EX+ER) groups, allowing us to evaluate if the protective effects on tumor growth

is due entirely to weight maintenance, or if the individual effects of dietary energy

restriction and/or physical activity drive protection. SED+AL control mice gained

weight over the course of the study (12% increase from baseline) and displayed

growth curves comparable to 4T1.2 growth curves in previously published reports

(168, 536). Exercise alone mice (EX+AL) gained weight over the course of the

study comparable to the SED+AL control. No protective effect of exercise was

observed on primary tumor growth or metastatic burden in mice that gained weight

over the course of the study, suggesting that weight gain-induced disturbances in

inflammatory, hormonal, or immunological function can override the exercise-

induced benefits. Dietary energy restriction alone (SED+ER) resulted in sustained

weight control (10% weight loss from baseline) over the course of the study;

however, failed to drive beneficial effects on primary tumor growth. The diet and

exercise group (EX+ER) maintained weight comparable to the SED+ER group

suggesting that even with the additional energy expenditure of the activity wheel,

the EX+ER mice remained in energy balance. EX+ER resulted in sustained weight

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control and a protective effect on primary tumor growth and lung, femur, and liver

spontaneous metastasis, as well as a decrease in metastatic flux measured

through bioluminescent imaging. Spontaneous metastasis in the kidney, heart, and

tibia was not significantly different between all groups and spontaneous metastasis

in the brain and spine was not-significantly different when collapsing to WG vs.

WM groups. The effects of diet and exercise on the dissemination and embedding

of circulating tumor cells into metastatic niches may be site specific. To our

knowledge, this is the first study to show a cancer protective effect of moderate

exercise in weight stable mice on spontaneous metastases to lung and bone,

common sites of metastases in BC patients, in a highly aggressive stage IV BC

model.

Mild dietary energy restriction and moderate physical activity could impact

several host factors implicated in primary tumor growth and metastatic progression

(298, 299, 418) including immune, metabolic, and inflammatory mediators. The

4T1.2 transplantable mouse model has an intact tumor-host environment, allowing

for the evaluation of our interventions on the tumor-immune microenvironment

(532). The 4T1 (537, 538) and 4T1.2 (figure 2.4) mammary tumor model can

release endocrine signals (e.g., G-CSF) that dysregulate hematopoiesis, resulting

in the expansion and accumulation of immature, myeloid-lineage cells into the

blood, spleen (e.g., splenomegaly), TME, and metastatic niches. As tumors

advance, there is a progressive decrease in lymphoid-lineage cells and an

increase in myeloid-lineage cells within the tumor and secondary lymphoid organs.

This dysregulated immune phenotype can generate an immunosuppressive

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environment that dampens antitumor effector functions, which promotes tumor

escape and survival. Therefore, interventions that target this shift to inhibit the

expansion of immunosuppressive cells could help to maintain an antitumor

immune response and aid in the elimination of transformed cells.

The combination of diet and exercise in weight stable mice had the lowest

concentration of plasma G-CSF and a significant reduction in the onset of

splenomegaly compared with SED+AL and EX+AL groups. EX+ER maintained

splenic lymphoid-lineage populations (splenic CD3+ T cells, CD3+CD4+ helper T

cells, and CD19+ B cells) while reducing the emergence of myeloid-lineage

populations (splenic Gr-1+CD11b+ MDSCs, CD11b+Ly6ChiLy6G- monocytic

MDSCs, and CD11b+Ly6CloLy6G+ granulocytic MDSCs). MDSCs, well known

suppressors of antitumor immunity which contribute to tumor progression and

metastases (142), were highly correlated with tumor volume. EX+ER may not alter

the immunosuppressive capacity of MDSCs on a per cell basis. However, EX+ER-

induced changes in myelopoiesis and/or the recruitment and trafficking of MDSCs

to secondary sites could result in a reduction in the number of these suppressive

cells and could help explain the protective effect on tumor progression and

metastases.

In a previous study using non-tumor-bearing mice, we demonstrate that

moderate, voluntary wheel activity in weight stable mice enhances antigen-specific

CD4+ T helper cell responses (562). In the current study, we report that these

findings are consistent in the 4T1.2 mammary tumor model. Moderate exercise in

weight stable mice enhanced splenic CD4+ helper T cell proliferation. However,

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neither IL-2 and IFN secretion from CD4+ helper T cells at 48 hours in response

to 0.5 g/ml anti-CD3, nor natural killer cell cytotoxicity measured via a standard

4-hour 51Cr-release assay, was significantly different when collapsing to WG vs.

WM groups. Previous studies report exercise-induced enhancements in NK cell

function (521); however, this may be model or time dependent. The terminal (day

35) evaluation of NK cell function may be too late in tumor progression to observe

exercise-induced enhancements. Additionally, the percentage of splenic immune

cells post bulk culture or splenic IFN secretion in response to re-stimulation with

tumor antigens was not significantly different between the four energy balance

interventions. The 4T1 series is weakly immunogenic (535); therefore, without

providing an immunogenic stimulus in vivo, our assay may not be sensitive enough

to detect differences in antigen-specific immune responses. However, the

population shifts and functional outcomes in splenic immunity could play a role in

maintaining immunosurveillance mechanisms that are critical in preventing the

metastatic process via the recognition of disseminating tumor cells and promotion

of immune-mediated dormancy (117). Thus, the combination of diet and exercise

in weight stable mice may directly impact primary tumor growth and metastases

via the augmentation of antitumor immunity and the reduction in

immunosuppressive cells.

Moderate exercise in weight stable mice may protect against detrimental

alterations in inflammatory and metabolic mediators which, in turn, could impact

tumor growth and metastases. Numerous researchers have reported the role of

inflammatory mediators (e.g., IL-1, IL-6, MCP-1), adipokines (e.g., leptin,

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adiponectin), and metabolic mediators (e.g., insulin, IGF-1, IGFBP-3) in tumor

growth (360). Cytokines and chemokines play a critical role in cancer-related

inflammation, regulating both host and malignant cells in the TME (581). Physical

activity reduces inflammatory mediators associated with obesity and other chronic

disease (493, 582). In the current study, we found no differences in plasma

inflammatory cytokine levels (G-CSF, IL-6, IL-1, MCP-1) between groups. These

results suggest that inflammatory mediators may not be impactful at this terminal

time point. Moderate exercise in weight stable mice may alter the inflammatory

milieu during early tumor growth or initiation of metastasis. Once tumor burden

reaches a critical mass, these differences may be less apparent. Future studies

are planned to examine the effects induced by moderate exercise at both earlier

time points and when tumor volumes are comparable between groups. Moderate

exercise in weight stable mice could affect reproductive hormone levels, which in

turn could impact BC development and progression. All mice in the current study

have intact ovaries, therefore the protective effects observed in EX+ER mice could

be mediated by alterations in estrogen levels. Future studies are required to

evaluate if the effect of moderate exercise in weight stable mice in the 4T1.2 model

is associated with estrogen status.

A reduction in primary tumor growth was observed in EX+ER mice

compared with weight-matched SED+ER mice. Since body weight between groups

were identical, moderate exercise-induced changes in skeletal muscle-derived

cytokines, or myokines (435, 583, 584), could drive protection in EX+ER mice.

Skeletal muscle is now recognized as an endocrine organ that can secrete

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peptides in response to exercise-induced skeletal muscle contraction (435) to

promote an anti-inflammatory milieu (e.g., IL-6, IL-10, IL-Ira) and regulate

physiological processes in distant organs (493). An exercise-induced shift in

circulating concentrations of pro- and anti-inflammatory cytokines may have direct

effects on tumor proliferation (494), or an indirect effect through the control of

systemic levels of hormones (299) or altered immune responses (e.g., acute

mobilization of NK and T cells, enhancements in NK cytotoxicity) (112, 495, 496).

Regular physical activity may prevent cancer initiation and improve survival via

exercise-induced changes in skeletal muscle-derived cytokines that reduce

proinflammatory signaling. Interestingly, in the current study, the beneficial effects

of myokines may be negated when mice participating in moderate exercise also

gain weight. Although the weight gain mice (i.e., SED+AL and EX+AL) did not

become classically obese over the course of the study, perhaps the positive energy

balance produced an inflammatory microenvironment that overshadowed the anti-

inflammatory effects induced by exercise. Future studies performing experimental

blockade of exercise-induced myokines are needed to elucidate proposed

metabolic effects on the control of tumor growth or enhancements in immune

response.

IGF-1 is a peptide hormone involved in modulating cell growth and survival

by stimulating proliferation (468). In circulation, the action of IGF-1 is regulated by

a group of six IGF-binding proteins (IGFBPs) (469, 470). IGFBP-3 is the primary

binding protein and can exhibit IGF-1 independent actions, such as promoting

growth inhibition and apoptosis (471). Elevated circulating concentrations of IGF-

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1 are firmly established as a risk factor for the development of BC, especially

estrogen positive tumors (472-477). A recent meta-analysis was completed using

data from 17 prospective studies quantifying pre-diagnosis circulating IGF-1

concentration and BC risk involving 4,790 BC patients (475). Results indicate that

patients with the highest IGF-1 concentration had a 28% increased BC risk

compared with the lowest fifth. This association was not altered when adjusting for

IGFBP3 and did not vary by menopausal status; however, it does seem to be

confined to estrogen-receptor-positive tumors (475). The role of IGF-1 in BC

survivorship populations is emerging (478-481). Since IGF-1 is responsive to

changes in energy balance, researchers are investigating the role of physical

activity as a non-pharmacological intervention to reduce IGF-1 levels in BC

survivors (433, 482, 483). A recent meta-analysis was completed using data from

five randomized controlled trials involving 235 BC survivors (484). Aerobic exercise

resulted in a significant reduction in circulating IGF-1; however, the effect on BC

recurrence and survival has yet to be established. Pre-clinical studies have

focused on the impact of IGF-1 in cancer cell proliferation, migration, and

metastasis using in vitro and in vivo models to identify the signaling pathways

involved in these processes (485). It is well established that dietary energy

restriction prevents mammary tumorigenesis in rodents, and IGF-1 may play a key

role in tumor reduction via activation of the Akt/mTOR pathway following dietary

energy restriction (488). Emerging evidence suggests that IGF-1 may play a role

in the epithelial-mesenchymal transition (EMT) process (343, 489). A number of

studies show a strong correlation between EMT and high invasive and metastatic

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behavior of BC (490). Results from a recent study suggest that IGF-1 levels may

regulate tumor growth by modulating genes related to EMT and chemokine

signaling (358). Thus, alterations in energy balance achieved via mild dietary

energy restriction and moderate physical activity are likely linked to the EMT

process. In the current study, the reduction in primary tumor growth and metastasis

is associated with decreased levels of plasma IGF-1, which may explain the cancer

prevention effect of moderate exercise in weight stable mice in the 4T1.2 model.

Moderate exercise in weight stable mice could alter the TME. The total

number of isolated tumor-infiltrating immune cells in the combined WM group

(12.26.3x106) was half of the WG group (24.320.8x106); however, no significant

differences were observed in the percentage or number of tumor-infiltrating

immune cell subsets between groups. We next investigated the effects of

moderate exercise in weight stable mice on gene expression in the TME. A

downward shift in the fold regulation of tumor-immune crosstalk genes was

observed in the single intervention (EX+AL, SED+ER) and dual intervention

(EX+ER) groups. All treatment groups experienced a downward shift in genes

(Ccl5 [Rantes] (585, 586), Cxcl1 (124, 156, 176-178), Ccl20 [Mip-3] (587), and

Ccl22 [Mdc] (588)) important for chemotaxis and recruitment of

immunosuppressive cells, like MDSCs and Tregs, to the TME. This finding is

consistent with the observed reduction in splenic MDSC subsets in the SED+ER

and EX+ER groups. Also, all treatment groups experienced a downward shift in

genes (Cxcl9 [Mig] and Cxcl10 [Inp10] (589), Ccr7 (590, 591), and Cxcr3 (592))

important for promoting angiogenesis, tumor cell invasion, and metastasis. A step-

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wise decrease (EX+AL SED+ER EX+ER) was observed in the fold regulation

of ido1 (Ido). Ido is expressed by tumor cells, supporting cells within the TME, and

infiltrating cells, like MDSCs, and encodes for indoleamine 2,3-dioxygenase (IDO),

the first and rate-limiting enzyme of tryptophan catabolism through the kynurenine

pathway (197). Depletion of tryptophan, an amino acid essential for T cell

activation, results in blunted T cell proliferation, differentiation, effector functions,

and viability (198). Future studies are needed to assess exercise-induced effects

on the IDO pathway.

The weight maintenance groups failed to separate in the tumor gene array,

therefore, since the two groups weigh the same with the only difference being

sedentary vs. exercise, we performed a subset analysis of SED+ER and EX+ER

groups, using SED+ER mice as the reference control to investigate the effects of

exercise in weight stable mice. The EX+ER mice displayed a decrease in fold-

regulation in Csf3 and Nfkb and an increase in fold-regulation in Il2. The effects of

moderate exercise in weight stable mice may be mediated through a reduction in

inflammatory gene expression markers (i.e., reduced genes encoding for G-CSF

and NF-B) that drive the generation of an immunosuppressive TME. Furthermore,

moderate exercise in weight stable mice may upregulate gene expression of

markers that directly enhance the function of antitumor effector populations (i.e.,

gene encoding for IL-2) within the TME. Future studies are needed to investigate

the individual effects of each gene on immune function and tumor growth; however,

the current study provides essential insight into potential shifts that can occur in

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immunosuppressive markers in response to moderate exercise in weight stable

mice.

Lastly, characterizing the dose, duration, frequency, and type of exercise

needed to drive cancer prevention is key to move the field of exercise oncology

forward (551, 593, 594). Preclinical investigation of these important questions is

difficult, but important to help shed light on potential mechanisms. For example,

access to voluntary activity wheels for 60 days prior to tumor implantation reduces

primary tumor growth in a dose-dependent manner (384), i.e., mice with higher

activity before tumor cell injection have a decrease in tumor mass measured at the

time of sacrifice. Subset analysis in the current study sheds light on the average

quantity of activity required in the EX+ER group to drive beneficial changes on

tumor growth in the 4T1.2 mammary tumor model. Mice running greater than 5.8

km/day displayed better weight control over the course of the study with a

reduction in tumor growth compared with mice who ran less than 5.8 km/day. The

EX+ER HIGH runners had reduced splenomegaly with less splenic MDSCs and

enhanced splenic IFN secretion in response to re-stimulation with tumor antigens.

This suggests that mice in the EX+ER HIGH group may have an inflammation-

immune axis which is better able to reduce tumor progression through maintained

immunosurveillance mechanisms. As the field of exercise oncology matures, it is

essential for investigators in the fields of exercise physiology, immunology, and

cancer biology to address the dose, duration, frequency, and type of exercise

needed to achieve a cancer prevention effect.

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Data from the current study provides critical insight into the extent to which

exercise, and not just changes in body weight, underlie cancer protection. Although

dietary energy restriction-induced weight control was effective at altering host

splenic immunity and the expression of key genes in the TME related to

immunosuppression and metastatic progression, this intervention failed to induce

changes in primary tumor growth or spontaneous metastases. Interestingly the

exercise-induced effects were lost when mice continued to gain weight over the

course of the study, suggesting that weight gain-induced disturbances in

inflammatory, hormonal, or immunological function can override the exercise-

induced benefits. Moderate exercise in weight stable mice not only resulted in a

reduction in primary tumor growth, but prevented spontaneous metastasis,

suggesting that moderate exercise in a weight stable host may reduce BC-specific

mortality by delaying metastatic occurrence or progression. This delay may be the

result of lowered metabolic drivers of tumorigenesis (i.e., prevention of the toxic,

immunosuppressive TME) or an enhancement in immunosurveillance

mechanisms that prevent tumor cell invasion and metastatic spread. Results from

the current study provide insight into potential mechanisms by which physical

activity exerts primary and secondary cancer prevention effects and provides a

biological rationale for future randomized controlled trials of exercise and the

prevention of weight gain to prevent metastatic progression in BC survivors and

ultimately improve survival outcomes.

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CHAPTER 4: MODERATE EXERCISE IN WEIGHT STABLE MICE AND THE ADMINISTRATION OF A WHOLE TUMOR CELL CANCER VACCINE IN THE 4T1.2 MAMMARY TUMOR MODEL*

4.1. ABSTRACT

Regular, moderate exercise can promote weight control and reduce both

the incidence and improve survival of breast cancer (BC). Numerous biological

mechanism(s) are proposed to explain these beneficial clinical effects. However,

little work has been done to examine the effect of moderate exercise in weight

stable hosts on emerging immunomodulatory or immunotherapeutic strategies.

We previously demonstrated (chapter three) a cancer protective effect (both

primary tumor and metastatic burden) of moderate exercise in weight stable 4T1.2

tumor-bearing mice. Our diet and exercise intervention reduced splenomegaly and

the abundance of splenic myeloid-derived suppressor cells (MDSCs) and MDSC

subsets and altered chemokine, proinflammatory, immunostimulatory, and

immunosuppressive gene expression in the tumor microenvironment (TME). Thus,

the goal of the current study was to further characterize the prevention of weight

gain through mild dietary energy restriction and moderate physical activity on

tumor progression and immune responses, and determine if there were any

additive effects of moderate exercise in weight stable mice and the therapeutic

administration of an allogeneic, whole tumor cell cancer vaccine on the

aforementioned outcomes. Female BALB/c mice were randomized into sedentary,

*Manuscript in preparation for submission to Cancer Immunotherapy and Vaccines: Turbitt, W.J., Collins, S., Meng, H., Mastro, A.M., Rogers, C.J. (2017). Moderate exercise in weight stable mice, alone and in combination with a whole tumor cell cancer vaccine, reduces mammary tumor growth and enhances antitumor immunity.

122

ad libitum-fed weight gain (WG) control or exercising, mildly calorie restricted (90%

of control food intake) weight maintenance (WM) groups (n=20-24/group). Mice

were provided access to standard cages (WG groups) or activity wheel cages (WM

groups) for eight weeks prior to the injection of 5x104 luciferase-transfected 4T1.2

tumor cells into the fourth mammary fat pad. Mice were further randomized into

vehicle control (n=11-12/group) or vaccination (n=9-12/group) groups and

administered PBS vehicle (VEH) control or 1x106 irradiated 4T1.2luc cells (VAX) at

day 7, 14, 21, and 28 post-tumor injection. Mice continued on their respective

energy balance intervention until sacrifice at day 35 post-tumor implantation. WM

mice, with or without VAX, weighed significantly less than WG mice throughout the

study (p<0.001). There was a significant effect of both WM and VAX alone on

primary tumor growth (p<0.001); and an additive effect of WM+VAX on primary

tumor growth, lung (p=0.003) and heart (p=0.049) spontaneous metastases,

splenocyte count at sacrifice (p=0.066), the number of total splenic MDSCs

(p=0.057) and the granulocytic subset of MDSCs (p=0.029), and plasma levels of

insulin-like growth factor 1 (IGF-1) (p=0.0121). Splenic interferon gamma (IFN)

secretion in response to re-stimulation with tumor antigens was significantly

elevated in response to VAX (p<0.001) and WM (p=0.017); however, there was no

additive effect of WM+VAX. These results demonstrate that moderate exercise in

weight stable mice in combination with a therapeutic, allogeneic whole tumor cell

cancer vaccine is highly effective at delaying primary tumor growth and

metastases, reducing splenomegaly and immunosuppressive cell populations, and

reducing the concentration of plasma IGF-1 in the metastatic 4T1.2 mammary

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tumor model. Preclinical models continue to investigate pharmacological

combinatorial strategies to improve cancer vaccine efficacy. However, this is the

first study to demonstrate that efficacy of a whole tumor cell cancer vaccine can

be enhanced through an exercise intervention.

4.2. INTRODUCTION

Breast cancer (BC), especially the diagnosis of metastatic disease, is a

global health concern (1, 2). BC is a heterogeneous disease characterized by a

diverse spectrum of molecular phenotypes that are associated with different

treatment modalities and clinical outcomes (21-23). Localized BC treatment

typically involves breast-conserving surgery or mastectomy and is often coupled

with radiotherapy, chemotherapy, endocrine therapy, and/or targeted therapy (33,

34). Metastatic BC treatment is most often radiation and/or chemotherapy (34);

however, metastatic disease remains incurable and is the underlying cause of

death in the majority of BC patients who die of the disease (3). Based on recent

advancements in the fields of immunology and molecular biology, immunotherapy

has become a promising emerging treatment for BC, especially metastatic disease

(62, 63). Immunotherapy treatments include therapeutic cancer vaccines,

monoclonal antibodies, adoptive cell therapies, and the administration of

immunostimulatory cytokines, all of which are designed to activate or restore

immune function and eliminate tumor cells in some manner (51). Due to the lack

of effective treatment options, emerging immunomodulatory and immunotherapy

strategies are being investigated in advanced-stage cancer patients.

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Cancer vaccines can be prophylactic (i.e., preventative) (206) or therapeutic

(51). Therapeutic cancer vaccines, categorized as dendritic-cell (DC) or whole

tumor cell based, provide antigen-presenting cells (APCs), like DCs, with tumor-

associated antigens (TAAs) to stimulate a durable antitumor effector CD8+

cytotoxic T cell response (51, 52, 207-209). Briefly, DCs phagocytose exogenous

antigens and process them by proteases to peptide fragments (77). Activated DCs

migrate to proximal draining lymph nodes where they can present antigens and

activate CD8+ cytotoxic T cells in the context of class I major histocompatibility

complex (79). Providing tumor-derived peptides (210) or full-length recombinant

tumor proteins are two strategies for DC-based therapeutic cancer vaccines;

however, the development of in vivo tumor-derived peptide vaccinations has

proven problematic as free peptides can be rapidly cleared prior to uptake by

antigen-presenting cells (51). The co-administration of immune-stimulant

adjuvants (e.g., IL-2 or GM-CSF) with tumor-derived peptides to further promote

the activation of either DCs or CD8+ cytotoxic T cells has increased vaccine

efficacy, generating a more robust antitumor effector CD8+ cytotoxic T cell

response (209, 211). An alternative strategy is to generate DCs ex vivo from

peripheral blood mononuclear cells, pulse with TAAs, and inject the activated DCs

back into the host for subsequent activation of a CD8+ cytotoxic T cell response

(208, 212, 213). Two limitations in DC-based vaccines is that T cell epitopes

against TAAs are largely undefined, making it difficult to screen and select tumor

antigens (214, 215) and the ex vivo generation of DCs is laborious and costly (216).

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Whole tumor cell based cancer vaccines represent an attractive alternative

source of TAAs (sourced from whole tumor cells, tumor cell lysates, tumor

oncolyates, apoptotic bodies, or transduced tumor cells), as this type of vaccine

typically displays some intrinsic adjuvant activity and allows DCs to process and

present numerous TAAs to induce a polyclonal effector CD8+ cytotoxic T cell

response against numerous tumor cell populations (207, 217). Through epitope

spreading, or the process by which T cells respond to peptides not present in the

whole tumor cell cancer vaccine, tumor cells that are not originally targeted through

TAAs within the whole tumor cell cancer vaccine can become targets through

secondary priming (218-220). Whole tumor cell cancer vaccines are effective in

murine models, but clinical translation has proven difficult (595). It is often difficult

for autologous cancer vaccines (i.e., sourced from the same host receiving the

vaccination) to obtain tumor cells in sufficient quantities in a timely fashion, expand

in vitro, and administer to patients. Vaccine preparation is costly and requires

standardization and regulatory oversight. Allogenic cancer vaccines (i.e., sourced

from a different host or tumor cell line) can bypass cell limitations; however, may

contain TAAs not present in the patient’s tumor and therefore may rely heavily on

cross-priming. Limitations notwithstanding, both autologous and allogeneic whole

tumor cell cancer vaccines are under preclinical (221-226) and clinical (227-229)

development. Several host-related factors (e.g., tumor-induced inflammation

and/or emergence of immunosuppressive cell types) can influence the efficacy of

whole tumor cell based cancer vaccines (208, 230), therefore combinatorial

strategies are investigating if current (e.g., chemotherapy, radiation) and emerging

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(e.g., monoclonal antibodies, tyrosine kinase inhibitors) therapies can reduce

tumor-induced inflammation to enhance therapeutic efficacy (224, 230-234).

However, no preclinical or clinical study has examined the effects of non-

pharmacological, lifestyle-based interventions on the efficacy of whole tumor cell

based cancer vaccines.

Regular physical activity and the prevention of weight gain is associated

with a decrease in the risk of primary BC (292, 308, 309, 325, 399-409) and a

reduction in mortality and improvements in quality of life in BC survivors (375, 414-

417, 578). Data from our previous findings (chapter three) show that moderately

exercising, weight stable mice display a reduction in 4T1.2luc tumor growth and

metastatic progression, concurrently with a reduction in the expansion of

immunosuppressive cell types (e.g., MDSCs), sustained lymphoid-lineage immune

phenotype, and a reduction in proinflammatory and immunosuppressive gene

expression in the TME. Thus, we hypothesized that the reduction in tumor-induced

inflammation and immunosuppression observed with moderate exercise in weight

stable mice could enhance whole tumor cell cancer vaccine efficacy. Therefore,

the goal of the current study was to determine if moderate exercise in weight stable

mice could improve the efficacy of an allogeneic, whole tumor cell cancer vaccine

in a clinically relevant, orthotopic model of stage IV metastatic BC.





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Anderson and maintained in Dulbecco’s modified Eagle’s medium (Life

Technologies) containing 10% fetal bovine serum (Gemini Bio-Products), 2 mM

glutamine (Mediatech), 1X nonessential amino acid (Mediatech), and 8 μg/ml

puromycin (Mediatech).

4.3.2. Optimizing the whole tumor cell cancer vaccine

To prevent proliferation and to induce cell death 4T1.2luc cells were

either irradiated in a cesium irradiator (10,000, 15,000, or 20,000 rad), exposed

to three freeze/thaw cycles, or cultured with (50, 100, or 200 ng/ml) mitomycin

(Enzo Life Science; Farmingdale, NY). After exposure to irradiation, repeated

freeze/thaw cycles, or mitomycin, 4T1.2luc cells were plated in a serial dilution

of cells starting at 0.1x106 4T1.2luc cells/well in a 96-well plate (Corning) for a

72-hour proliferation assay. Untreated 4T1.2luc cells were plated as a positive

control. After 52 hours, cells were pulsed with tritiated thymidine (Perkin

Elmer) and proliferation was quantified via [H]3 uptake and quantified on a

Microbeta plate reader (Perkin Elmer). Each assay was performed in triplicate.

To demonstrate the immunogenicity of the 4T1.2luc cells, a cohort of

mice (n=4; 14-week old) were administered 1x106 irradiated 4T1.2luc cells

(VAX) I.P. at day -28, -21, -14, and -7 pre-tumor injection followed by the

orthotopic injection with 5x104 luciferase-transfected 4T1.2 cells into the fourth

mammary fat pad. Primary tumor growth was measured 2-3x/week via caliper

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and all mice were removed from study by day 140 post tumor implantation. To

determine the most effective route of vaccination, BALB/c mice (14-week old)

were orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into

the fourth mammary fat pad. After injection, mice were randomized into

intraperitoneal (I.P.) or subcutaneous (SQ) vaccination groups (n=2/group) and

administered 1x106 irradiated 4T1.2luc cells (VAX) at day 7, 14, 21, and 28 post

tumor injection. Mice were sacrificed at day 35 post tumor implantation. A third

cohort of mice (n=8; 14-week old) were orthotopically injected with 5x104

luciferase-transfected 4T1.2 cells into the fourth mammary fat pad and

administered 1x106 irradiated 4T1.2luc cells (VAX) I.P. at day 7, 14, 21, and 28

post tumor injection or no irradiated 4T1.2luc cells as a control. Primary tumor

growth was measured 2-3x/week via caliper and mice were removed from

study at 35 days post tumor implantation.

4.3.3. Optimizing splenic immune assays

Isolated splenocytes were depleted of Gr-1+ cells and pulsed with

irradiated 4T1.2luc cells for one round (five days) or two rounds (after five days,

bulk culture cells were washed and re-stimulated with irradiated 4T1.2luc cells

and fresh BALB/c antigen-presenting cells for an additional five days) of bulk

culture to generate an antigen-specific T cell response to tumor antigens. Post

bulk culture, 0.5x106 effector cells were isolated, washed, and plated in 24 well

plates with a final volume of 0.5 or 1 ml of complete media. After a 24-hour

period without irradiated 4T1.2luc stimulation, cell supernatant was collected

and interferon gamma (IFN) was quantified via Legend Max ELISA kits

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(Biolegend). Splenic effector cells (0.5x106) were plated alone or with irradiated

4T1.2luc or Panc.02 cells (0.1x106), with or without fresh BALB/c antigen-

presenting cells (2.5x106), in 0.5 ml complete media. After a 24-hour period

without irradiated 4T1.2luc stimulation, cell supernatants were collected and

IFN was quantified via Legend Max ELISA kits (Biolegend). Splenic effector

cells (0.5x106) were plated alone and removed from antigen stimulation for 24

or 48 hours prior to cell supernatant collection. IFN was quantified via Legend

Max ELISA kits (Biolegend).

4.3.4. Animal model

Six-week-old female BALB/c mice were obtained from Jackson

Laboratory and screened for their intrinsic running potential. Intrinsic runners

(14-week-old) were randomized to sedentary, weight gain (WG; n=24) or

exercising, weight maintenance (WM; n=20) groups for a total of 13 weeks (Fig.

4.3A). All mice were fed AIN-76A diet (Research Diets); however, the WM mice

were fed 90% of caloric intake of WG mice to remain in energy balance (prevent

weight gain) over the course of the study. After eight-weeks on study, all mice

were orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into

the fourth mammary fat pad and continued on their intervention for 35 days.

After injection, mice were further randomized into vehicle control (n=11-

12/group) or vaccination (n=9-12/group) groups and administered PBS (VEH)

or 1x106 irradiated 4T1.2luc cells (VAX) at day 7, 14, 21, and 28 post tumor

injection. Mice were sacrificed at day 35 post tumor implantation. Movement

was not monitored in mice that did not have access to running wheels. Food

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intake, body weight, and tumor size (v=(short2×long)/2) were monitored as

previously reported (562, 568), and mice were observed daily for signs of ill

health. All mice were housed at the Pennsylvania State University and

maintained on a 12-hour light/dark cycle with free access to water. The

Institutional Animal Care and Use Committee of the Pennsylvania State

University approved all animal experiments.

4.3.5. Whole tumor cell cancer vaccine

Trypsin/EDTA (0.25%/2.21 mM) in HBSS (Corning) was added to

culture flasks to harvest 4T1.2luc cells. 4T1.2luc cells were washed twice in

4T1.2luc media and washed twice in PBS. 4T1.2luc cells were counted, adjusted

to 1.0x106/100 l, and irradiated for 15,000 rad in a cesium irradiator. Irradiated

4T1.2luc cells (VAX; 1.0x106/100 l) or PBS control (VEH; 100 l) were injected

I.P. at day 7, 14, 21, and 28 post tumor implantation.


The lung, heart, kidney, liver, and femur were collected, flash frozen in

liquid nitrogen, and stored at -80°C. Tissues were homogenized and genomic

DNA was isolated using DNeasy Blood and Tissue Kit (Qiagen), per

manufacturer’s instructions. DNA was quantified using a Nanodrop ND-1000

spectrophotometer (Nanodrop Products). Metastatic tumor burden of the

tissues was quantified using the Taqman™ system performed on a Step-One

Plus (Life Technologies) real time PCR instrument to quantify luciferase. Assay

sequences for luciferase were CAGCTGCACAAAGCCATGAA (forward

primer), CTGAGGTAATGTCCACCTCGATATG (reverse primer) and

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efficiency.

4.3.7. Splenic and tumor-infiltrating immune cell assays

4.3.7.1. Isolation of splenic immune cells

Spleens were harvested via gross dissection and splenocytes were

prepared from individual mice by mechanical dispersion, as previously

described (345). Briefly, spleens were mechanically disrupted with a

syringe plunger and passed through a 70 μm nylon mesh strainer (BD

Biosciences), erythrocytes were lysed with ACK lysing buffer (Lonza), and

remaining cells washed twice in complete medium (RPMI 1640

(Mediatech) supplemented with 10% FBS (Gemini Bio-Products), 0.1 mM

non-essential amino acids (Mediatech), 1 mM sodium pyruvate

(Mediatech), 2 mM glutamine (Mediatech), 10 mM HEPES (Mediatech),

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100 U/mL penicillin streptomycin (Mediatech). Cell counts and viability

were determined via trypan blue exclusion (Mediatech).

4.3.7.2. Splenic CD4+ helper T cell proliferation assay





96-well plates (Greiner Bio-One) in the presence of increasing

concentrations of anti-CD3 antibody (BD Biosciences) for 72 hours (562).

Proliferation data was analyzed by tritiated (H3) thymidine (Perkin Elmer)

incorporation and quantified on a Microbeta plate reader (Perkin Elmer).

Each assay was performed in triplicate. CD4+ helper T cell supernatant

was collected following 48-hour stimulation with 0.5 μg/ml anti-CD3 and

1.0 μg/ml anti-CD28 and stored at -80°C. IL-2 was quantified using

Legend Max ELISA kits (Biolegend) for the WG vs. WM groups.

4.3.7.3. Splenic IFN secretion post five-day bulk culture

Splenic immune cells, depleted of Gr-1+ cells via magnetic bead

isolation (Miltenyi), were co-cultured for five days with 1x106 irradiated

4T1.2luc cells to generate tumor antigen-specific T cells. Splenic effector

cells were isolated and plated (0.5x106/well) in 24 well plates (Greiner Bio-

One). Supernatants were harvested (48 hours post-incubation) and stored

at -80°C. IFN was measured using Legend Max ELISA kits (Biolegend),

per manufacturer instructions. Cytokines were quantified with an Epoch

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Microplate Spectrophotometer (Biotek; Winooski, VT). Each assay was

performed in duplicate.



into fine pieces (<10 mg), and incubated with 0.03 mg/ml Liberase

(Roche) and 12.5 U/ml DNase I (Sigma-Aldrich) for 45 minutes at 37°C

on an orbital shaker. Following the digestion, remaining pieces were

mechanically disrupted with a syringe plunger, passed through a 70 μm

nylon mesh strainer (BD Biosciences), layered over Lympholyte-M cell

separation media (Cedarlane), and centrifuged at room temperature for

20 minutes at 1200g. Isolated cells were washed twice in cold PBS

(Mediatech) and cell counts and viability of tumor immune infiltrates were

determined via trypan blue exclusion.



five-day bulk culture with irradiated tumor cells), and tumor-infiltrating

immune cells were washed twice in PBS containing 0.01% bovine serum

albumin (flow buffer) at 4°C. Cells were incubated with Fc block

(Biolegend) and 1x106 cells were stained with saturating concentrations

of conjugated antibodies for 30 min at 4°C, as previously described (345).

Fluorescently conjugated antibodies for flow cytometry included rat -

mouse CD19 (1D3), hamster -mouse CD3 (145-2C11), rat -mouse

CD4 (RM4-5), rat -mouse CD8 (53-6.7), mouse -mouse NK1.1

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(PK136), mouse -mouse I-Ab (AF6-120.1), hamster -mouse CD11c

(HL3), rat -mouse CD11b (M1/70), rat -mouse F4/80 (T45-2342), rat -

mouse Ly6G and Ly6C [Gr-1] (RB6-8C5), rat -mouse Ly6C (AL-21), and

rat -mouse Ly6G (1A8). Antibodies were obtained from BD Biosciences,

Biolegend, and eBioscience. Following incubation with the conjugated

antibodies, cells were washed twice in flow buffer and fixed in 1%

paraformaldehyde (BD Biosciences) in flow buffer for flow cytometric

analysis. Lymphoid and myeloid cells were gated on forward vs. side

scatter, and a total of 50,000 events were analyzed. Flow cytometric

analyses were performed on a Beckman Coulter FC500 (Beckman

Coulter; Indianapolis, IN) or a BD LSR-Fortessa (BD Bioscience) flow

cytometer. Flow cytometric analyses were plotted and analyzed using

Flow Jo software (Tree Star; Ashland, OR).


Fasting blood was collected at sacrifice (day 30-35) via mandibular

bleed in microtainer tubes (BD Biosciences), centrifuged, and plasma was

stored at -80°C. Insulin, leptin, adiponectin, Interleukin-1 alpha (IL-1),

granulocyte-colony stimulating factor (G-CSF), and Interleukin-6 (IL-6) were

measured using a Milliplex MAP Multiplex or Singleplex Assay (EMD Millipore)

and quantified on a Bio-plex 200 system (Bio-Rad) using Luminex-200 software

(Luminex), per manufacturer’s instructions. Insulin-like growth factor-1 (IGF-1)

and insulin-like growth factor binding protein-3 (IGFBP-3) were measured using

135

R&D Systems Quantikine ELISA kits (R&D Systems; Minneapolis, MN). Each

assay was performed in duplicate.


Tumor weight, metastatic burden, the distribution of cells in the spleen,

splenic bulk culture cells, and tumor-infiltrating immune cells, cytokine

secretion, and plasma mediators were assessed for normality and equal

variances; and either parametric or nonparametric analyses were used based

on sample distribution to detect differences between treatment groups.



Differences in tumor weight, metastatic burden, the distribution of cells in the

spleen, splenic bulk culture cells, and tumor-infiltrating immune cells, cytokine

secretion, and plasma mediators were assessed between groups via a one-

way analysis of variance (ANOVA), followed by a Bonferroni correction for

multiple comparisons where appropriate, or Kruskal-Wallis test, depending on

normality and variance. Body weight, primary tumor volume, and CD4+ helper

T cell proliferation were examined using a two-way ANOVA, followed by a

Bonferroni correction for multiple comparisons where appropriate. All data are

presented as the mean plus or minus the standard deviation of the mean. All

analyses were conducted using GraphPad Prism 5 software (GraphPad

Software; La Jolla, CA) and statistical significance was accepted at the p≤0.05

level.

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4.4. RESULTS

4.4.1. Determination of method to induce tumor cell death prior to vaccination

4T1.2luc cells were counted and plated in increasing concentrations of

cells per well in a 96-well plate for a 72-hour proliferation assay. After 52 hours,

cells were pulsed with tritiated thymidine and proliferation was quantified via

[H]3 uptake. 4T1.2luc cells cultured in 4T1.2luc media served as the positive

control (Fig. 4.1A). Prior to plating, cells were irradiated with 10,000, 15,000,

or 20,000 rad in a cesium irradiator (Fig. 4.1B), exposed to three freeze/thaw

cycles (Fig. 4.1C), or incubated with 50, 100, or 200 ng/ml mitomycin (Fig.

4.1D). All treatments significantly reduced the proliferative capacity of 4T1.2luc

cells vs. control.

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Figure 4.1. Optimizing the induction of 4T1.2luc cell senescence for the whole tumor cell cancer vaccine. 4T1.2luc cells were harvested, counted, and plated in increasing concentrations of cells per well in a 96-well plate for a 72-hour proliferation assay. After 52 hours, cells were pulsed with tritiated thymidine and proliferation was quantified via [H]3 uptake. (A) 4T1.2luc cells cultured in 4T1.2luc media as the positive control. Prior to plating, cells were (B) irradiated with 10,000, 15,000, or 20,000 rad in a cesium irradiator, (C) exposed to three freeze/thaw cycles, or (D) incubated with 50, 100, or 200 ng/ml mitomycin. All treatments significantly reduced the proliferative capacity of 4T1.2 luc cells vs. control.

4.4.2. Assessment of vaccine efficacy and splenic immune cell co-culture and bulk assays

To determine the immunogenicity of 4T1.2luc cells, mice (n=4) were

administered 1x106 irradiated 4T1.2luc cells (VAX) I.P. at day -28, -21, -14, and

-7 pre-tumor injection. Mice were orthotopically injected with 5x104 luciferase-

transfected 4T1.2 cells into the fourth mammary fat pad. Three of four mice had

a failed tumor take, whereas the fourth mouse had delayed onset of tumor

growth (Fig. 4.2A). All mice were removed from study by day 140 post tumor

injection.

To determine the route of administration of the whole tumor cell cancer

vaccine, BALB/c mice were orthotopically injected with 5x104 luciferase-

transfected 4T1.2 cells into the fourth mammary fat pad. After injection, mice

were randomized into intraperitoneal (I.P.) or subcutaneous (SQ) vaccination

(n=2/group) and administered 1x106 irradiated 4T1.2luc cells (VAX) at day 7,

14, 21, and 28 post tumor injection. Mice were sacrificed at day 35 post tumor

implantation. Tumor growth curves were not significantly different between I.P.

or SQ vaccination injection methods (Fig. 4.2B; 2-way ANOVA, F(1,22)=0.05,

p=0.848).

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To assess the magnitude of the effect of the therapeutic whole tumor

cell cancer vaccine in this model, BALB/c mice were orthotopically injected with


administered PBS vehicle control (n=8) or 1x106 irradiated 4T1.2luc cells (VAX;

n=8) I.P. at day 7, 14, 21, and 28 post tumor injection. Tumor growth curves

were significantly reduced in all the of mice vaccinated with the whole tumor

cell cancer vaccine compared with unvaccinated tumor-bearing mice (Fig.

4.2C; 2-way ANOVA, time x treatment, F(11,165)=10.35, p<0.001).

IFN secretion from effector cells plated in 1.0 ml of complete media,

independent of one vs. two rounds of bulk culture, fell below detection (Fig.

4.2D). IFN secretion from effector cells plated in 0.5 ml of complete media

were not significantly different between one vs. two rounds of bulk culture (Fig.

4.2D; n=4/group; Mann-Whitney, p=0.343). IFN secretion from splenic effector

cells post five-day bulk was not significantly different when cultured alone or

co-cultured with 4T1.2luc or Panc.02 cells with or without fresh antigen-

presenting cells (Fig. 4.2E; n=4-20/treatment; Kruskal-Wallis=4.13, p=0.389).

IFN secretion was not significantly different between splenic effector cells

(0.5x106) cultured alone for 24 or 48 hours post-bulk culture (Fig. 4.2F;

n=16/group; Student’s t-test, p=0.757).

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140

Figure 4.2. Optimizing the in vivo injection of a whole tumor cell cancer vaccine, assessing efficacy, and assay development in the 4T1.2 mammary tumor model. (A) BALB/c mice (n=4) were administered 1x106 irradiated 4T1.2luc cells (VAX) I.P. at day -28, -21, -14, and -7 prior to tumor injection. Mice were orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into the fourth mammary fat pad at day 0. Three of four mice had a failed tumor take; whereas, the fourth mouse had delayed onset of tumor growth. All mice were removed from study by day 140 post tumor injection. (B,C) BALB/c mice were orthotopically injected with 5x104 luciferase-transfected 4T1.2 cells into the fourth mammary fat pad. (B) After injection, mice were randomized into intraperitoneal (I.P.) or subcutaneous (SQ) vaccination (n=2/group) and administered 1x106 irradiated 4T1.2luc cells (VAX) at day 7, 14, 21, and 28 post tumor injection. Mice were sacrificed at day 35 post tumor implantation. Tumor growth curves were not significantly different between I.P. or SQ vaccination injection methods (2-way ANOVA, F(1,22)=0.05, p=0.848). (C) After injection, mice were administered PBS vehicle control (n=8) or 1x106 irradiated 4T1.2luc cells (VAX; n=8) I.P. at day 7, 14, 21, and 28 post tumor injection. Tumor growth curves for VAX mice were significantly reduced compared with unvaccinated mice (2-way ANOVA, time x treatment, F(11,165)=10.35, p<0.001). (D-F) Splenic bulk culture assay protocol development. (D) Isolated splenocytes were depleted of Gr-1+ cells and pulsed with irradiated 4T1.2luc cells for one round (five days) or two rounds (after five days, bulk cells were washed and re-stimulated with irradiated 4T1.2luc cells and fresh BALB/c antigen-presenting cells were added for an additional five days) to generate an antigen-specific T cell response to tumor antigens. Post bulk culture, 0.5x106 effector cells were isolated, washed, and plated in 24 well plates with irradiated 4T1.2luc cells at final volume of 0.5 or 1 ml of complete media. After 24 hours, cell supernatants were collected and

IFN was quantified via ELISA. IFN secretion from effector cells plated in 1.0 ml of media,

independent of one vs. two rounds of bulk culture, fell below detection. IFN secretion from

effector cells plated in 0.5 ml of media did not differ between one vs. two rounds of bulk culture (n=4/group; Mann-Whitney, p=0.343). (E) To test if effector cells post-bulk culture required additional co-stimulation, 0.5x106 effectors were plated alone or with 0.1x106 irradiated 4T1.2luc or Panc.02 cells, with or without fresh BALB/c antigen-presenting cells

(2.5x106) in 0.5 ml for 24 hours. IFN secretion from effector cells was not significantly

different between various plating conditions (n=4-20/treatment; Kruskal-Wallis=4.13, p=0.389). (F) Post-bulk effector cells (0.5x106) were removed from antigen stimulation for

24 and 48 hours prior to cell supernatant collection. IFN secretion was not significantly

different between time points (n=16/group; Student’s t-test, p=0.757).



BALB/c mice (14-week-old) were randomized to sedentary, weight gain (WG;

n=24) or exercising, weight maintenance (WM; n=20) for a total of 13 weeks.

After eight-weeks on study, all mice were orthotopically injected with 5x104

luciferase-transfected 4T1.2 cells into the fourth mammary fat pad and

continued on their intervention for 35 days. After injection, mice were further

141

randomized into vehicle control (n=11-12/group) or vaccination (n=9-12/group)

groups and administered PBS (VEH) or 1x106 irradiated 4T1.2luc cells (VAX) at

day 7, 14, 21, and 28 post tumor injection. Mice were sacrificed at day 35 post

tumor implantation. WM mice weighed significantly less than WG mice,

independent of vehicle or vaccination, throughout the course of the study (Fig.

4.3B; 2-way ANOVA, F(3,325)=41.94, p<0.001). Wheel activity was maintained

over the course of the study, i.e., prior to and after tumor injection. Activity was

not affected by the administration of the whole tumor cell cancer vaccine (Fig.

4.3C; n=20; average distance run per day=6.7±2.7 km).

142

Figure 4.3. Study design, body weight, and wheel activity. (A) Experimental design. (B) WM mice weighed significantly less than WG mice, independent of vehicle or vaccination, throughout the course of the study (2-way ANOVA, F(3,325)=41.94, p<0.001). (C) Wheel activity was maintained over the course of the study, i.e., prior to and after tumor injection. Activity was not affected by the administration of the whole tumor cell cancer vaccine (n=20; average distance run per day=6.7 ± 2.7 km).


Primary tumor growth was significantly reduced in the WM+VEH and

WG+VAX groups; however the combination of WM+VAX resulted in the

greatest reduction in primary tumor growth compared with WG+VEH control

(Fig. 4.4A; n=9-12/group; 2-way ANOVA, time x treatment, F(18,414)=3.33,

143

p<0.001). Tumor weight at sacrifice was not significantly different between

groups (Fig. 4.4B; Kruskal-Wallis, KW=5.78, p=0.123).


Lung (Fig. 4.4C; 1-way ANOVA, F(3,39)=3.44, p=0.003) and heart (Fig.

4.4D; Kruskal-Wallis, KW=7.85, p=0.049) metastatic burden were significantly

different between groups with WM+VAX mice displaying the lowest metastatic

burden compared with WG+VEH mice. Kidney (Fig. 4.4E; Kruskal-Wallis,

KW=6.34, p=0.096), liver (Fig. 4.4F; Kruskal-Wallis, KW=0.88, p=0.829), and

femur (Fig. 4.4G; Kruskal-Wallis, KW=4.84, p=0.184) metastatic burden were

not significantly different between groups.

144

145

Figure 4.4. Weight maintenance, alone and in combination with a whole tumor cell cancer vaccine, reduced primary tumor growth. (A) Primary tumor growth (measured 2-3x/week via caliper) was significantly reduced in the WM+VEH and WG+VAX groups; however, the combination of WM+VAX resulted in the greatest reduction in primary tumor growth compared with WG+VEH control (n=9-12/group; 2-way ANOVA, time x treatment, F(18,414)=3.33, p<0.001). (B) Tumor weight at sacrifice was not significantly different between groups (Kruskal-Wallis, KW=5.78, p=0.123). (C-G) At sacrifice, organs (n=9-12/group) were flash frozen, homogenized, and DNA was extracted. Quantitative PCR was used to detect luciferase expression as a marker of metastatic burden reported as ng of luciferase DNA normalized to mouse TERT DNA in 200 ng of sample. (C) Lung (1-way ANOVA, F(3,39)=3.44, p=0.003) and (D) heart (Kruskal-Wallis, KW=7.85, p=0.049) metastatic burden were significantly different between groups with WM+VAX mice displaying the lowest metastatic burden compared with WG+VEH mice. (E) Kidney (Kruskal-Wallis, KW=6.34, p=0.096), (F) liver (Kruskal-Wallis, KW=0.88, p=0.829), and (H) femur (Kruskal-Wallis, KW=4.84, p=0.184) metastatic burden were not significantly different between groups. Significantly different from WG+VEH (*) and WM+VEH (‡).


Splenocyte counts were significantly different between groups (Fig.

4.5A; n=9-12/group; 1-way ANOVA, F(3,43)=2.60, p=0.066) with WM+VAX mice

displaying a significant reduction compared with WG+VEH mice. CD4+ helper

T cells from WM+VEH mice proliferated more than CD4+ helper T cells from

WG+VAX and WM+VAX mice (Fig. 4.5B; n=7-11/group; 2-way ANOVA, time x

treatment, F(15,145)=4.71, p<0.001); however, IL-2 secretion (Fig. 4.5C; 1-way

ANOVA, F(3,32)=0.32, p=0.814) was not significantly different between groups.

The percentages of CD3+ T cells and CD3+CD8+ cytotoxic T cells post

five-day bulk were not significantly different between groups (Fig. 4.5D; n=4-

5/group). The percentage of CD3+CD4+ helper T cells was significantly

elevated in WM+VEH mice (Fig. 4.5D; p=0.028) compared with WG+VAX and

the percentage of NK1.1+ natural killer cells was significantly reduced in the

WG+VAX (Fig. 4.5D; p=0.004) group compared with both VEH controls. IFN

secretion from splenic effector cells post five-day bulk was significantly

elevated (Fig. 4.5E; n=4-5/group; Kruskal-Wallis, KW=12.07, p=0.007) in

response to VAX (p<0.001) and WM (p=0.017) compared with WG+VEH

control; however, there was no additive effect of WM+VAX.

146

147

Figure 4.5. The combination of weight maintenance and vaccination reduced splenomegaly and altered splenic immunity. (A) Splenocyte counts were significantly different between groups (1-way ANOVA, F(3,43)=2.60, p=0.066) with WM+VAX mice displaying a significant reduction compared with WG+VEH mice. (B, C) Isolated splenic CD4+ helper T cells (0.1x106/well) were stimulated with increasing concentrations of anti-CD3 antibody and 1 μg/ml anti-CD28 antibody, and proliferation was quantified via [H]3 uptake. Data were converted to a stimulation index (counts per minute of stimulated wells divided by counts per minute of unstimulated [media only] wells). CD4+ helper T cells from WM+VEH mice proliferated more than CD4+ helper T cells from WG+VAX and WM+VAX mice (n=7-11/group; 2-way ANOVA, time x treatment, F(15,145)=4.71, p<0.001). (C) CD4+ helper T cell supernatant was collected following 48-hour stimulation with 0.5 μg/ml anti-CD3 and IL-2 secretion was quantified using an ELISA. IL-2 secretion was not significantly different between groups (1-way ANOVA, F(3,32)=0.32, p=0.814). (D, E) Isolated splenocytes were depleted of Gr-1+ cells and pulsed with irradiated 4T1.2luc cells for five days to generate an antigen-specific T cell response to tumor antigens. (D) Post bulk culture, cells were stained with anti-CD3, -CD4, -CD8, and -NK1.1 and characterized by flow cytometry (n=4-5/group). The percentages of CD3+ T cells and CD3+CD8+ cytotoxic T cells were not significantly different between groups. The percentage of CD3+CD4+ helper T cells was significantly elevated in WM+VEH mice (p=0.028) compared with WG+VAX and the percentage of NK1.1+ natural killer cells was significantly reduced in the

WG+VAX (p=0.004) group compared with both VEH controls. (E) Bulk effector cell IFN secretion in response to re-stimulation with tumor antigens was significantly elevated (n=4-5/group; Kruskal-Wallis, KW=12.07, p=0.007) in response to VAX (p<0.001) and WM (p=0.017) compared with WG+VEH control; however, there was no additive effect of WM+VAX. Significantly different from WG+VEH (*), WG+VAX (†), and WM+VEH (‡).

The percentage of splenic CD3+ T cells (p=0.001) and CD3+CD4+ helper

T cells (p=0.001) was significantly different between groups with the

vaccination groups (WG+VAX and WM+VAX) displaying the highest

percentage compared with vehicle control (WG+VEH and WM+VEH) groups

(Table 4.1). The percentage of splenic I-Ab+CD11b+ macrophages (p=0.001)

was significantly different between groups with the vaccination groups

(WG+VAX and WM+VAX) displaying a lower percent compared with the

WG+VEH group (Table 4.1). The number of splenic CD19+ B cells (p=0.032)

was significantly different between groups with the WM+VAX group displaying

the lowest number compared with the WG+VEH group (Table 4.1). The number

of splenic I-Ab+CD11b+ macrophages (p=0.011) was significantly different

148

between groups with the WM+VAX group displaying the lowest number

compared with the WG+VEH group (Table 4.1). Splenic Gr-1+CD11b+ MDSCs

(Fig. 4.6A; Kruskal-Wallis, KW=7.54, p=0.057) was reduced in WM+VAX mice

compared with WG+VEH; however, this failed to reach statistical significance.

The number of splenic CD11b+Ly6ChiLy6G- monocytic MDSCs (Fig. 4.6B; 1-

way ANOVA, F(3,42)=1.62, p=0.200) was not significantly different between

groups; however, the number of splenic CD11b+Ly6CloLy6G+ granulocytic

MDSCs was significantly different between groups (Fig. 4.6C; Kruskal-Wallis,

KW=9.06, p=0.029) with WM+VAX mice displaying the lowest number

compared with WG+VEH mice.

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Table 4.1. The percentage and number of splenic immune cell populations. (A) The percentage of splenic CD3+ T cells (p=0.001) and CD3+CD4+ helper T cells (p=0.001) was significantly different between groups with the vaccination groups (WG+VAX and WM+VAX) displaying the highest percentage compared with vehicle control groups (WG+VEH and WM+VEH). The percentage of splenic I-Ab+CD11b+ macrophages (p=0.001) was significantly different between groups with the vaccination groups (WG+VAX and WM+VAX) displaying a lower percent compared with the WG+VEH group. (B) Splenocyte count (p=0.066) and the number of splenic CD19+ B cells (p=0.032) were significantly different between groups with the WM+VAX group displaying the lowest number compared with the WG+VEH group. The number of splenic I-Ab+CD11b+ macrophages (p=0.011) was significantly different between groups with the WM+VAX group displaying the lowest number compared with the WG+VEH group. Significantly different from WG+VEH (*), WG+VAX (†), and WM+VEH (‡).

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Figure 4.6. The combination of weight maintenance and vaccination reduced splenic protumor immunosuppressive cell populations. Splenocytes (n=9-12/group) were stained with anti-Gr-1, -CD11b, -Ly6G, and -Ly6C antibodies to quantify myeloid-derived suppressor cells (MDSCs) and MDSC subsets by flow cytometry. (A) Splenic Gr-1+CD11b+ MDSCs (Kruskal-Wallis, KW=7.54, p=0.057) was reduced in WM+VAX mice compared with WG+VEH; however, this failed to reach statistical significance. (B) The number of splenic CD11b+Ly6ChiLy6G- monocytic MDSCs (1-way ANOVA, F(3,42)=1.62, p=0.200) was not significantly different between groups; however, (C) the number of splenic CD11b+Ly6CloLy6G+ granulocytic MDSCs was significantly different between groups (Kruskal-Wallis, KW=9.06, p=0.029) with WM+VAX mice displaying the lowest number compared with WG+VEH mice. Dashed line represents non-tumor bearing control. Significantly different from WG+VEH (*).

4.4.7. Tumor-infiltrating immunity

When examining the distribution of tumor-infiltrating immune cells as a

percentage of total cells (Table 4A), on the percentage of tumor-infiltrating

CD19+ B cells (Table 4.2; p=0.031) was significantly different between groups

with the WG+VAX group displaying a higher percentage of cells compared with

WG+VEH control. However, when examining the number of tumor-infiltrating

immune cells, differences in several populations were detected. Total tumor-

infiltrating immune cell counts were significantly different between groups

(Table 4.2; p=0.042). Tumor-infiltrating Gr-1+CD11b+ MDSCs (Fig. 4.7A; 1-way

ANOVA, F(3,38)=0.06, p=0.570) and CD11b+Ly6ChiLy6G- monocytic MDSCs

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y6C

hi L

y6G

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Cs (

x10

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VEH VAX VEH VAX0

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20

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WG WM

CD

11b

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(Fig. 4.7B; Kruskal-Wallis, KW=4.37, p=0.224) were not significantly different

between groups. Tumor-infiltrating CD11b+Ly6CloLy6G+ granulocytic MDSCs

were significantly different between groups (Fig. 4.7C; Kruskal-Wallis,

KW=10.73, p=0.013) with WG+VAX mice displaying the lowest number

compared with WG+VEH mice.

Table 4.2. The percentage and number of tumor-infiltrating immune cell populations. (A) The percentage of tumor-infiltrating CD19+ B cells (p=0.031) was significantly different between groups with the WG+VAX group displaying a higher percentage of cells compared with WG+VEH control. (B) Tumor-infiltrating immune cell counts were significantly different between groups (p=0.042). Significantly different from WG+VEH (*).

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Figure 4.7. Vaccination reduced tumor-infiltrating protumor immunosuppressive cell populations in weight gain mice. Whole tumors were homogenized, washed, and layered over a Ficoll gradient. Tumor-infiltrating immune cells were isolated and stained for flow cytometry. (A) Tumor-infiltrating Gr-1+CD11b+ MDSCs (1-way ANOVA, F(3,38)=0.06, p=0.570) and (B) CD11b+Ly6ChiLy6G- monocytic MDSCs (Kruskal-Wallis, KW=4.37, p=0.224) were not significantly different between groups. (C) Tumor-infiltrating CD11b+Ly6CloLy6G+ granulocytic MDSCs were significantly different between groups (Kruskal-Wallis, KW=10.73, p=0.013) with WG+VAX mice displaying the lowest number compared with WG+VEH mice. Significantly different from WG+VEH (*).


Plasma insulin (Fig. 4.8A; 1-way ANOVA, F(3,36)=0.59, p=0.629), leptin

(Fig. 4.8B; Kruskal-Wallis, KW=1.66, p=0.645), adiponectin (Fig. 4.8C; Kruskal-

Wallis, KW=7.63, p=0.054), IL-1 (Fig. 4.8D; 1-way ANOVA, F(3,43)=1.72,

p=0.179), IL-6 (Fig. 4.8F; Kruskal-Wallis, KW=6.55, p=0.088), or IGFBP-3 (Fig.

4.8G; 1-way ANOVA, F(3,41)=2.15, p=0.110) were not significantly different

between groups. Plasma G-CSF (Fig. 4.8E; Kruskal-Wallis, KW=8.90,

p=0.031) and IGF-1 (Fig. 4.8H; 1-way ANOVA, F(3,43)=4.13, p=0.012) were

significantly different between groups with WM+VAX mice displaying the lowest

IGF-1 concentration compared WG+VEH mice.

CA B

Gr-

1+C

D11b

+ M

DS

Cs (

x10

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153

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Figure 4.8. The combination of weight maintenance and vaccination reduced plasma metabolic mediators. Fasting blood was collected at sacrifice (n=9-12/group), centrifuged, and plasma was stored at -80°C. Milliplex Multiplex Assays quantified on Luminex-200 software were used to measure (A) insulin, (B) leptin, (C), adiponectin, (D)

IL-1, (E) G-CSF, and (F) IL-6. R&D Systems Quantikine ELISAs were used to measure

metabolic markers for (G) IGFBP-3 and (H) IGF-1. Plasma (A) insulin (1-way ANOVA, F(3,36)=0.59, p=0.629), (B) leptin (Kruskal-Wallis, KW=1.66, p=0.645), (C) adiponectin

(Kruskal-Wallis, KW=7.63, p=0.054), (D) IL-1 (1-way ANOVA, F(3,43)=1.72, p=0.179), (F)

IL-6 (Kruskal-Wallis, KW=6.55, p=0.088), or (G) IGFBP-3 (1-way ANOVA, F(3,41)=2.15, p=0.110) were not significantly different between groups. Plasma (E) G-CSF (Kruskal-Wallis, KW=8.90, p=0.031) and (H) IGF-1 (1-way ANOVA, F(3,43)=4.13, p=0.012) were significantly different between groups with WM+VAX mice displaying the lowest IGF-1 concentration compared WG+VEH mice. Significantly different from WG+VEH (*).

4.5. DISCUSSION

In previous work we demonstrated that moderate exercise in weight stable

non-tumor bearing mice augments antigen-specific T cell response to vaccination

and in response to a viral-vector based vaccine in a pancreatic cancer model (272,

273). Furthermore, we have demonstrated (chapter three) that the prevention of

weight gain through exercise and mild dietary restriction can reduce primary tumor

growth and metastatic burden, reduce immunosuppressive cells, augment T cell

responses, and alter the tumor microenvironment in the 4T1.2 mammary tumor

model. Thus, the goal of the current study was to determine if diet and exercise

could enhance the efficacy of a whole tumor cell cancer vaccine via one or more

of the aforementioned mechanisms.

First, the preparation of the whole tumor cell cancer vaccine must prevent

proliferation and/or induce tumor cell death, yet do so in a manner that allows the

processing and presentation of tumor antigens to APCs (e.g., DCs) to stimulate an

antitumor effector T cell response. Tumor cells can be prepared by inducing

apoptosis via ultraviolet B (UVB) ray-irradiation (non-ionizing) or gamma ()-

irradiation (ionizing) or necrosis to generate a cell lysate via repeat cycles of

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freezing and thawing (214, 596). Both apoptotic and necrotic-based strategies can

generate mature DCs and elicit effective priming and antitumor efficacy in vivo;

however, the best method has long been debated (597-603). DC activation via

apoptosis-induced death could involve the detection of apoptotic bodies, exposure

to cell surface markers (e.g., phosphatyidylserine or calreticulin), or releasing

factors (e.g., GM-CSF) (600). DC activation via necrosis-induced death could

involve the recognition of heat-shock proteins or degraded RNA or DNA (600). We

tested both -irradiation and repeat cycles of freeze-thaw, as well as the DNA

replication inhibitor, mitomycin, on their ability to inhibit 4T1.2luc cell proliferation in

vitro. All three methods reduced the proliferative capacity of 4T1.2 luc cells

measured via a 72-hour proliferation assay. We chose -irradiation because it is

reported to effectively penetrate cancer cells and specifically target nucleic acids,

while conserving surface antigen proteins; thus, maintaining immunogenicity

(602). Mitomycin is rarely used clinically and cell lysate protocols often require

more extensive vaccine preparation. Additionally, -irradiation of 4T1.2luc target

cells for re-stimulation assays can be performed, exposing splenic or tumor-

infiltrating immune cells to similar antigens experienced in vivo.

Second, we tested if the whole tumor cell cancer vaccine could be

prophylactic (preventative) by treating sedentary, ad libitum-fed BALB/c mice with

the -irradiated whole tumor cell cancer vaccine I.P. at day -28, -21, -14, and -7

prior to tumor implantation with 4T1.2luc cells. One mouse had delayed onset of

tumor and was removed from study at day 125. Three of the four mice had

impalpable tumors, representing a 75% survival. All mice were removed from study

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at day 140 post-tumor implantation. The fact that prophylactic vaccination of mice

protected against 4T1.2luc tumor growth was unexpected. The 4T1 parent series is

reported to be poorly immunogenic and unresponsive to prophylactic vaccination

with irradiated 4T1 cells alone, but protection was observed when coupling

irradiated cells with adjuvant therapy or antibody blockade of immunosuppressive

factors (223, 604, 605). Perhaps the 4T1 and 4T1.2 cells, while similar, have

differences in cell surface tumor antigens or releasing factors post-irradiation that

could generate memory cells and drive the prophylactic response observed when

administering irradiated 4T1.2luc cells alone.

Third, with the knowledge that the 4T1.2 model possesses immunogenic

properties, two in vivo studies were performed to determine injection method

(intraperitoneal [I.P.] vs. subcutaneous [SQ]), as well as to assess therapeutic

efficacy and optimize in vitro recall assays. No differences were observed when

the -irradiated therapeutic whole tumor cell cancer vaccine was injected I.P. vs.

SQ in sedentary, ad libitum-fed BALB/c 4T1.2luc tumor-bearing mice at day 7, 14,

21, and 28 post tumor implantation. Therefore, we decided to test both the efficacy

of the vaccine and optimize in vitro assays in a cohort of sedentary, ad libitum-fed

BALB/c 4T1.2luc tumor-bearing mice receiving the -irradiated therapeutic whole

tumor cell cancer vaccine I.P. at day 7, 14, 21, and 28 post tumor implantation. All

mice displayed a reduction in tumor growth compared with unvaccinated 4T1.2luc

tumor-bearing mice.

Recall assays were performed to test the number of rounds needed to

generate effector cells via bulk culture, as well as the volume in which to plate

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effector cells post-bulk culture in which to optimally detect IFN secretion. No

differences were observed between one round vs. two rounds of irradiated 4T1.2luc

re-stimulation; however, splenic effector cells had to be plated at 0.5x106 cells/well

in 0.5 ml for the detection of IFN secretion post 24-hour rest. To test the specificity

of in vitro re-stimulation with irradiated 4T1.2luc cells, five-day bulk cells were co-

cultured with irradiated Panc.02 cells for 24-hours. IFN secretion was not

statistically different between co-culture with 4T1.2luc cells or Panc.02 vs. resting

the cells alone or if fresh BALB/c antigen-presenting cells were added to co-

culture; therefore, we decided to plate splenic effector cells post-bulk alone for

future IFN secretion assays. We further tested if splenic effector cells post-bulk

secreted more IFN after a 24- or 48-hour rest and found no differences in these

times. The 48-hour rest was selected for practical reasons.

Powered with this information, we designed a series of studies to investigate

the effects of moderate exercise in weight stable mice on the efficacy of a

therapeutic, whole tumor cell cancer vaccine (-irradiated and injected I.P.)

administered at day 7, 14, 21, and 28 post tumor implantation in the 4T1.2 model.

The current study was modeled to examine the effects of moderate exercise in

weight stable hosts alone and in combination with a whole tumor cell cancer

vaccine on primary tumor growth, metastatic progression, and immune responses.

As described in chapter three, we observed a cancer prevention effect of moderate

exercise in weight stable mice on primary tumor growth and metastatic burden. In

addition, a vaccination effect was observed on primary tumor growth in weight gain

mice. However, the whole tumor cell cancer vaccine enhanced the weight

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maintenance (via diet and exercise) effects on primary tumor growth, spontaneous

metastasis, splenic immunosuppressive cell types, and plasma IGF-1, and

suggests that vaccination may provide an immune stimulus to further promote the

protective effects of moderate exercise alone.

Cancer vaccine therapies, both tumor-derived peptide based and whole

tumor cell based are under investigation in BC patients. The majority of clinical

trials assessing peptide-based strategies target HER2-derived peptides alone, or

in combination with additional immunostimulatory agents (235). For example, a

well-studied (236) tumor-derived peptide-based vaccine targeting HER-2 (E75,

also known as NeuVAX) is now being investigated clinically. NeuVAX combined

with GM-CSF administration, prolongs disease-free survival in a subset of BC

patients who expressed low levels of HER2 with a high risk of relapse (237). A

phase II study (NCT01570036) is underway to investigate the combination of

NeuVAX and targeted HER2 therapy in node positive (or node-negative if estrogen

and progesterone receptor negative) BC patients who are disease-free after

standard-of-care therapy. However, a study investigating the administration of

NeuVAX+GM-CSF in early-stage, node-positive BC patients with low-to-

intermediate HER2 expression was halted due to ineffectiveness (NCT01479244).

New targets including peptides against Wilms tumor protein antigen (238) and

immunization with vaccinia virus modified to express mucin-1 and IL-2 (239) can

induce tumor regression in BC patients. The identification of tumor-specific

antigens in which to target remains an active area of research.

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Clinically, whole tumor cell cancer vaccines are rarely investigated as a

monotherapy in patients with advanced stage disease. These therapies are more

typically genetically modified to secrete various cytokines, such as GM-CSF,

administered in combination with an immune adjuvant (214, 229), or administered

in combinatorial treatment strategies coupled with chemotherapy, endocrine

therapy, and/or targeted therapy (240-242). In a phase I clinical trial, the safety of

a plasmid transfected/allogeneic HER2-positive cell-based GM-CSF-secreting

vaccine was confirmed in metastatic BC patients. The vaccine alone or in

sequence with low-dose chemotherapy induces HER2-specific T cell-mediated

immunity; however, survival data was forthcoming (240). A separate Phase I trial

modified MDA-MB-231 cells to express B7-1 (CD80), a costimulatory molecule

required for antigen-presentation to T cells. Vaccinated stage IV metastatic BC

patients had an increase in tumor-specific immune responses, but failed to show

differences in tumor progression (243). The administration of a mixed vaccine

composed of autologous breast tumor cells supplemented with allogeneic breast

tumor cells, three additional TAAs antigens combined with IL-2 and GM-CSF to

BC patients in a phase II study increases antigen-specific lymphocyte response

and an improves clinical response (244). The administration of this mixed vaccine

to BC patients with depressed immunity improves ten-year survival compared with

historical data (228). Two active clinical trials are being carried out to investigate

the safety and efficacy of modified autologous vaccinations engineered to express

GM-CSF in metastatic BC patients (NCT00317603 and NCT00880464). Future

studies are required to identify the optimal combinatorial or multi-antigen approach

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to improve survival outcomes. However, to date, no study has investigated

clinically if lifestyle-based intervention strategies can enhance peptide- or whole

tumor cell-based vaccine strategies. Thus, our work may provide the rationale for

collecting data in patients on body weight and physical activity patterns to

determine if these factors impact vaccine efficacy in ongoing trials.

Few researchers have designed preclinical studies to investigate cancer

vaccine monotherapy in murine models of mammary carcinogenesis. Cell lysate

from 4T1 encapsulated in poly(lactic-co-glycolic acid) acid (PLGA) microparticles

reduces spontaneous lung metastasis; however, there was no effect on primary

tumor growth (222). A placental endothelial cell vaccine (ValloVAX) administered

SQ for four weekly vaccinations after tumor implantation inhibits 4T1 tumor growth;

however, immune effector function was not characterized (245). Combinatorial

strategies are being investigated in both peptide-based and whole tumor cell based

vaccines. Combinatorial immunostimulatory strategies include genetically

modifying cells to express GM-CSF, CD40L, OX-40, and B7-1 or the co-

administration of IL-12 in the vaccination protocol (225, 226, 234, 246). Soliman,

et al. (225) vaccinated 4T1 tumor-bearing mice with irradiated B78H1 bystander

cell lines engineered to secrete granulocyte macrophage-colony stimulating factor

and CD40 ligand, a costimulatory ligand important for T cell activation. Vaccinated

mice display a reduction in tumor growth, concurrently with an increase in the

secretion of IL-2 and IFN from splenic T cells and an increase in tumor-infiltrating

T cells. The anchoring of IL-12 and the B7-1 costimulatory molecules to 4TO7 cells

results in significantly lower tumor burden compared with controls after one

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treatment (246). Vaccinated mice also display a reduction in tumor angiogenesis

and an increase in tumor-infiltrating CD8+ cytotoxic T cells. Data from Yan, et al.

(226) demonstrates that the administration of irradiated, transfected 4T1 cells that

express VEGFR2 inhibits subsequent tumor growth and lung metastasis. Lastly,

the intranodal administration of autologous tumor-derived autophagosome-based

therapeutic vaccine (DRibbles) with an anti-OX40 co-stimulatory antibody

enhances T cell priming and antitumor efficacy in 4T1 tumor-bearing mice (234).

The findings from these results indicate that anchored cytokines or the addition of

co-stimulatory molecules may improve therapeutic potency. Moderate exercise in

weight stable mice may create a systemic environment more conducive to a

sustained antitumor immune response. Exercise training warrants further

investigation as an immunostimulatory strategy to enhance vaccine efficacy.

The success of cancer vaccines is hampered by tumor-induced

immunosuppression (e.g., soluble tumor-derived immunosuppressive factors and

recruitment of immunosuppressive cells, including Tregs and MDSCs). Therefore,

the investigation of combinatorial strategies that prevent the emergence or function

of immunosuppressive factors is underway. To minimize the induction of Tregs,

Monzavi-Karbassi, et al. (247) developed a vaccine composed of cell lysate lacking

lectin-reactive glycoconjugates; and the vaccination of 4T1 tumor-bearing mice

reduced tumor growth and spontaneous lung metastasis. In contrast, the crude

lysate activated Tregs and induced their suppressive function measured via a

mixed suppression assay. This suggests that minor refinements in TAAs can be

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performed to weaken immune suppression and improve antitumor immune

responses.

Cyclooxygenase-2 (Cox-2), a rate-limiting enzyme in the synthesis of

prostaglandins from arachidonic acid, is highly upregulated in mammary cancers

(248) and plays a key role in stimulating epithelial cell proliferation, inhibiting

apoptosis, stimulating angiogenesis, and mediating immune suppression (248,

249). The short-term administration of celecoxib, a selective Cox-2 inhibitor, as a

monotherapy can reduce 4T1 tumor growth (250-252). Celecoxib administered in

combination with a tumor-lysate pulsed DC vaccine plus GM-CSF adjuvant

therapy, results in the greatest reduction in 4T1 tumor growth and spontaneous

lung metastasis, an increase in the secretion of IFN and IL-4 from re-stimulated

CD4+ helper T cells, and an increase in abundance of tumor-infiltrating CD4+

helper and CD8+ cytotoxic T cells (251). These data suggest that inhibiting the

expansion of Tregs or blocking Cox-2 via short-term celecoxib therapy may be

safely used to increase vaccine efficacy for treating metastatic BC. Through a

reduction in immunosuppressive factors within the TME and secondary lymphoid

organs, moderate exercise in weight stable mice may blunt immunosuppressive

cell-induced dysregulation of APCs; thus, promoting better antigen uptake and

presentation by DCs, better priming of antitumor T cells, or a sustained T cell

activation.

A key advantage of the administration of whole tumor cell cancer vaccines

compared with tumor-derived peptides or full-length recombinant tumor proteins is

that it provides numerous TAAs in which to stimulate an antitumor response; thus,

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alleviating the need to identify and purify TAAs. Whole tumor antigen vaccines can

use cell lysate or whole tumor cells. Strome, et al. (606) demonstrate that DCs

primed with irradiated tumor cells in vitro reduces tumor outgrowth in two in vivo

tumor models (E.G7 thymoma and SCCVII squamous cell carcinoma models);

however, DCs primed with freeze-thaw tumor lysate is not effective at controlling

tumor outgrowth and inhibits CD8+ cytotoxic T cell response in vitro. Several

limitations to irradiation-induced killing for whole tumor cell cancer vaccines exist.

Upon irradiation, cells can transfer phosphatidylserine, an immunosuppressive

phospholipid typically found on the inner leaflet, to the outer leaflet.

Phosphatyidylserine can induce the secretion of immunosuppressive factors from

DCs (223, 229, 607). Additionally, irradiated cells are poorly immunogenic and

retain the ability to secrete immunosuppressive factors, like VEGF (229, 608, 609).

The release of tumor-derived immunosuppressive factors may compromise the

ability of DCs to activate T cells. Although the irradiated whole tumor cell cancer

vaccine used in the current study may have had a degree of DC- and/or tumor-

derived immunosuppressive factors present, we still observed a reduction in tumor

growth in vaccinated mice. The observed reduction in tumor growth suggests that

the stimulatory capacity to generate a robust adaptive immune response

outweighed the presence of DC- and/or tumor-derived immunosuppressive

signals. Additionally, the 4T1.2 cells used for the whole tumor cell cancer vaccine

were tagged with luciferase. The ability of luciferase to drive an immunogenic

response against transfected cells injected in vivo is a matter of debate (610-612).

However, regardless of what tumor antigen(s) are driving the effects in our model,

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the whole tumor cell cancer vaccine was effective at reducing primary tumor

growth in both weight gain and weight maintenance groups. More importantly, the

whole tumor cell cancer vaccine enhanced the efficacy of moderate exercise in

weight stable mice resulting in the greatest reduction in primary tumor growth and

metastatic burden.

Moderate exercise in weight stable mice may be enhancing several host

factors resulting in enhanced whole tumor cell cancer vaccine efficacy. Whole

tumor cell cancer vaccines can activate both CD4+ helper and CD8+ cytotoxic T

cell responses (214). First, the CD4+ helper T cell proliferation of unvaccinated

mice mirrored our results (chapter three) with CD4+ helper T cells from WM mice

proliferating at a higher capacity than WG mice. We did observe an unexplained

reduction in CD4+ helper T cell proliferative capacity in vaccinated mice; however,

IL-2 cytokine secretion was not significantly different between all four intervention

groups. Second, our bulk assays were depleted of Gr-1+ cells, but they contained

both CD4+ helper and CD8+ cytotoxic T cells, as well as NK cells. Bulk effector cell

IFN secretion in response to re-stimulation with tumor antigens was significantly

elevated in response to VAX and WM compared with WG+VEH control; however,

there was no additive effect of WM+VAX. This result suggests that WM and VAX-

induced effects on IFN secretion mechanisms may overlap. The secretion of IFN

as an antitumor cytokine is only one mechanism in which effector cells can target

transformed cells (613). The killing of transformed cells can occur through direct

cytotoxicity via the granzyme/perforin pathway and death receptor pathway

(Fas/Fas Ligand, TNF-related Apoptosis-Inducing Ligand [TRAIL]), as well as the

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release of immunomodulatory cytokines (e.g., TNF, IL-10,) and chemokines (e.g.,

CCL5) (82). The contribution of CD4+ helper vs. CD8+ cytotoxic T cells, as well as

the mechanisms by which effector cells induce cell death to enhance the vaccine-

dependent reduction in primary tumor growth and metastatic burden, are under

current investigation within our laboratory.

Similar to results reported in chapter three, we observed a reduction in

plasma G-CSF and splenomegaly in the WM+VAX group. The reduction in

splenomegaly was composed of less splenic MDSCs and MDSC subsets. The

mechanism(s) of MDSC-mediated immunosuppression of T cells is reviewed

elsewhere (125, 127, 139, 167); and likely functions through cell-surface receptors

and/or through the release of short-lived soluble mediators. In general, activated

MDSCs can mediate suppressive functions through multiple players, such as

Arginase-1 (Arg-1) and nitric oxide synthase (iNOS), the hyper-production of nitric

oxide (NO) and reactive oxygen species (ROS), induction of Tregs, as well as

deregulation of Cox-2/PGE2, transforming growth factor beta (TGFβ), and IL-10

pathways. MDSCs can downregulate the ζ-chain of the TCR complex (190), and

interfere with IL-2 signaling (a signal essential to T cell proliferation) (191) resulting

in the desensitization of the TCR response and T cell unresponsiveness. Also,

MDSCs can deplete essential nutrients (such as L-arginine, L-cysteine, and L-

tryptophan) from the environment via consumption and sequestration to

dysregulate APC function and inhibit T cell activation and proliferation (194, 195).

Tumor-derived S100A8/A9, in addition to STAT3-induced production of

S100A8/A9 from MDSCs, inhibit the maturation of DCs (200). The culmination of

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these suppressive mechanisms results in less functional DCs and less activated T

cells. Previous results (chapter three) indicated that the suppressive capacity of

MDSCs were not significantly different between WG and WM groups. However,

less MDSCs in the spleen and TME of WM mice could contribute to a less

immunosuppressive phenotype resulting in better priming of CD8+ cytotoxic T cells

by DCs in proximal draining lymph nodes. Additionally, MDSCs play a role in

driving metastases through the promotion of angiogenesis and tumor cell invasion

(176). Therefore, the reduction in MDSCs in moderately exercising, weight stable

mice, may in part, explain the enhanced efficacy of our whole tumor cell cancer

vaccine and reduction in metastatic burden.

Moderate exercise in weight stable mice may be altering the TME. The

number of tumor-infiltrating immune cells was significantly different between

groups, with the number reflective of differences in end tumor weight. A vaccine

effect in weight gain mice was observed with this group displaying the lowest

number of tumor-infiltrating granulocytic MDSCs. Data presented in chapter three

demonstrated that moderate exercise in weight stable mice downregulated several

chemokines and chemokine receptor gene expression markers within the TME that

may impact vaccine efficacy. Moderate exercise in weight stable mice

downregulated the expression of genes (Ccl5 [Rantes] (585, 586), Cxcl1 (124, 156,

176-178), Ccl20 [Mip-3] (587), and Ccl22 [Mdc] (588)) important for chemotaxis

and recruitment of immunosuppressive cells, like MDSCs and Tregs, to the TME

and downregulated the expression of genes (Cxcl9 [Mig] and Cxcl10 [Inp10] (589),

Ccr7 (590, 591), and Cxcr3 (592)) important for promoting angiogenesis, tumor

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cell invasion, and metastasis. The downregulation of these genes correlate with

the observed reduction in the expansion of immunosuppressive cell types in the

spleen and TME, as well as the reduction of metastatic burden, in our dual

intervention group. Moderate exercise in weight stable mice also downregulated

the immunosuppressive gene expression marker, Ido1 (Ido). Ido is expressed by

tumor cells, supporting cells within the TME, and infiltrating cells, like MDSCs, and

encodes for indoleamine 2,3-dioxygenase (IDO), the first and rate-limiting enzyme

of tryptophan catabolism through the kynurenine pathway (197). Depletion of

tryptophan, an amino acid essential for T cell activation, results in blunted T cell

proliferation, differentiation, effector functions, and viability (198). Data from Muller,

et al. (614) indicates that IDO inhibition augments the response to chemotherapy

in the MMTV-Neu murine BC model. No IDO inhibitor is approved by the FDA;

however, with numerous preclinical and phase I and II clinical trials, researchers

are investigating IDO inhibition in combination with cancer vaccines,

chemotherapy agents, and emerging immune checkpoint blockade (615). The

effects of moderate exercise in weight stable mice may be mediated through a

reduction in inflammatory and immunosuppressive gene expression markers

within the TME that ultimately promotes a more robust antitumor effector CD8+

cytotoxic T cell response post whole tumor cell cancer vaccine. Future studies are

needed to investigate the individual effects of each gene on whole tumor cell

cancer vaccine efficacy. However, the current study provides essential insight into

potential shifts that can occur in immunosuppressive markers in response to

moderate exercise in weight stable mice.

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In the current study, moderate exercise in weight stable mice combined with

the whole tumor cell cancer vaccine failed to alter many plasma mediators (insulin,

leptin, adiponectin, IL-1, IL-6, and IGFBP-3); however, WM+VAX resulted in the

lowest concentration of plasma IGF-1, with this group also demonstrating the

lowest tumor progression and metastatic burden. IGF-1 signaling can activate

PI3K/Akt and Raf-1/MEK/ERK pathways and downstream nuclear factors in

cancerous cells to promote proliferation and inhibit apoptosis (486, 487).

Additionally, the IGF family can activate Raf-1/MEK/ERK and decrease ERK1/2

phosphorylation and p38 dephosphorylation pathway in DCs resulting in a delay in

maturation (or an induction of a tolerogenic DC phenotype) (491, 492). Reduced

antigen uptake and presentation abilities in DCs will activate fewer antigen-specific

CD8+ cytotoxic T cells (491, 492). IGF signaling in DCs can also induce the

secretion of IL-6, TNF, and IL-10, generating an immunosuppressive phenotype

promoting tumor escape (491, 492). Clinical and epidemiologic studies show that

elevated IGF-1 is a risk factor for the development of BC, especially estrogen

positive tumors (472-477). However, few studies are evaluating the role of elevated

IGF-1 on cancer vaccine efficacy. The enhanced efficacy in our whole tumor cell

cancer vaccine may be explained, in part, through the ability of moderate exercise

in weight stable mice to reduce the concentration of plasma IGF-1. A reduction in

IGF-1 may result in DCs more capable of activating antigen-specific CD8+ cytotoxic

T cells. Future studies are needed to explore this link, perhaps through the

blockade of IGF-1 action via an IGF-1 receptor inhibitor (NVP-AEW541) (491) or

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monoclonal anti-IGF1 receptor antibody in weight gain mice receiving the whole

tumor cell cancer vaccine.

In conclusion, moderate exercise in weight stable mice combined with a

therapeutic whole tumor cell cancer vaccine reduced splenomegaly and the

abundance of MDSCs and MDSC subsets, reduced the concentration of plasma

IGF-1, and ultimately reduced tumor growth and metastatic burden in 4T1.2luc

tumor-bearing mice. Preclinical and clinical combinatorial studies are underway to

investigate strategies in which to enhance cancer vaccines. However, this is the

first study, to our knowledge, that shows a non-pharmacological, lifestyle-based

intervention strategy can blunt tumor-induced inflammation and

immunosuppression, maintain lymphoid-lineage cells, and enhance the efficacy of

a whole tumor cell cancer vaccine. These data suggest that moderate exercise

with the goal of mild weight loss and weight stability may be an important lifestyle

recommendation for patients undergoing cancer vaccine-based treatment

strategies. Results from the current study provides a biological rationale for future

randomized controlled trials testing if exercise strategies can enhance the efficacy

of emerging cancer vaccines.

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CHAPTER 5: MODERATE EXERCISE IN WEIGHT STABLE MICE AND THE DUAL ADMINISTRATION OF A WHOLE TUMOR CELL CANCER VACCINE AND PD-1 CHECKPOINT BLOCKADE IN THE 4T1.2 MAMMARY TUMOR MODEL*

5.1. ABSTRACT

Immunotherapy has become a promising treatment strategy for breast

cancer (BC), especially metastatic disease. An emerging immunotherapeutic

strategy is membrane programmed cell death protein-1 (PD-1) checkpoint

blockade, which selectively targets PD-1 on T cells to promote a sustained,

antitumor effector response. Although there is excitement in the field and

successes are reported in the clinical literature, the response rate to PD-1

blockade is under 50%. Thus, there is a growing interest in improving response

rates to immune checkpoint blockade through the identification of the phenotype

of responders with the long-term goal of administering targeted, personalized

therapy and/or combinatorial treatment strategies (e.g., chemotherapy, cancer

vaccines). Moderate exercise can drive changes in metabolic, inflammatory, and

immune mediators that may improve response rates of immunotherapies and

improve clinical outcomes. However, few researchers have designed clinical or

preclinical studies to examine the effect of exercise and weight control on the

efficacy of immune checkpoint blockade. Thus, the goal of the current study was

to determine if preventing weight gain through diet (10% reduction in calories) and

exercise (voluntary running wheel activity) will improve the response to the dual

*Manuscript in preparation for submission to Cancer Immunology, Immunotherapy: Turbitt, W.J., Xu, Y., Hamilton, A.A., Mastro, A.M., Rogers, C.J. (2017). Moderate exercise in weight stable mice reduces 4T1.2 tumor burden to the same extent as PD-1 checkpoint blockade in sedentary, weight gain controls.

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administration of a whole tumor cell cancer vaccine and PD-1 checkpoint blockade.

Female BALB/c mice were randomized to sedentary, ad libitum weight gain (WG)

control or exercising, mildly restricted (90% of control food intake) weight

maintenance (WM) groups (n=32/group) and provided access to a standard cage

or activity wheel cage for eight weeks prior to the injection of 5x104 luciferase-

transfected 4T1.2 cells into the fourth mammary fat pad. Mice were randomized

into vehicle or vaccination groups, and administered PBS vehicle (VEH) control or

1x106 irradiated 4T1.2luc cells (VAX) at day 7 post-tumor injection. Mice were

further randomized (n=8/group) to receive isotype control (IgG) or PD-1 checkpoint

blockade (10 mg/kg/mouse) at day 9 and 12 post-tumor injection. Mice continued

on their respective energy balance intervention until sacrifice at day 35 post-tumor

implantation. All WM groups, regardless of immunotherapy intervention, weighed

significantly less than WG groups over the course of the study (p<0.001). We

observed a cancer prevention effect of PD-1 checkpoint blockade in WG mice on

primary tumor growth and spontaneous lung metastasis. However, moderate

activity in weight stable mice, independent of PD-1 checkpoint blockade, was

effective in reducing primary tumor growth and metastatic burden. The WM+PD-1

group displayed the lowest number of splenic myeloid-derived suppressor cells

(MDSCs; p=0.047) and granulocytic MDSCs (p=0.017) and maintained its splenic

lymphoid populations. The number of tumor-infiltrating immune cells or the effector

or activation status of tumor-infiltrating CD4+ helper and CD8+ cytotoxic T cells was

not significantly different between groups, nor were functional immune outcomes.

PD-1 checkpoint blockade in WG mice, moderate exercise in weight stable mice,

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and PD-1 checkpoint blockade in moderately exercising, weight stable mice had

comparable, albeit subtle differences, in tumor-immune crosstalk gene expression

markers that drive the expansion of immunosuppressive cell types and impact

metastatic progression. The lack of responsiveness to VAX+PD-1 checkpoint

blockade in WM mice suggests that moderate exercise in weight stable mice may

be enhancing antitumor immunity and/or reducing protumorigenic factors (i.e.,

similar mechanisms mediated by VAX+PD-1 checkpoint blockade). These data

demonstrate that preventing weight gain through diet and exercise may be an

important recommendation to maintain prolonged antitumor effector responses

and improve clinical outcomes.

5.2. INTRODUCTION



Metastatic disease remains incurable and is the underlying cause of death in the

majority of BC patients who die of the disease (3). Cancer immunotherapy,

deemed by Science magazine (49) as the 2013 “Breakthrough of the Year” and

the 2016 and 2017 ASCO “Advance of the Year” (201, 202), has demonstrated

success in treating cancer, including very advanced, metastatic diseases.

Immunotherapy treatments include therapeutic cancer vaccines, monoclonal

antibodies, adoptive cell therapies, and the administration of immunostimulatory

cytokines, all of which are designed to stimulate a robust antitumor effector

response to eliminate tumor cells through several techniques (51, 64). Therapeutic

cancer vaccines provide tumor-associated antigen(s) (TAAs) to stimulate an

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effector T cell response (6); whereas, immune checkpoint blockade, like anti-

cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and anti-programmed cell

death protein-1 (PD-1), use specific antibodies to target inhibitory cell surface

molecules to promote sustained effector cell activation (203). Studies are

underway to investigate emerging immunotherapy strategies in advanced-stage

cancer patients who have failed to respond to other treatments. Recent reviews

have summarized clinical trials investigating checkpoint blockade in BC patients

(55-58). Recent clinical BC studies target PD-1 in TNBC because it is associated

with limited treatment options (55, 258), high expression of PD-L1 in the TME, and

increased tumor-infiltrating immune cells (72, 259-261), making immunotherapy

an attractive treatment option. Despite the recent successes of immunotherapy in

advanced-stage cancer patients, less than half of patients who receive

immunotherapy experience an objective, durable response (204). Thus, there is

great interest in understanding the factors that contribute to the heterogeneity in

response rates to immunotherapy. Areas of research include the identification of

clinically meaningful biomarkers to direct treatment choices (i.e., personalized

medicine) and/or through the selection of appropriate combinatorial strategies

(aimed to enhance antigen presentation, reverse T cell dysfunction, and/or target

immune inhibitory mechanisms) without inducing adverse effects (56-58, 205). No

study, to our knowledge, has retrospectively or prospectively assessed the impact

of host factors known to modulate immune function (e.g., physical activity, body

mass index, or dietary and sleep patterns) on the efficacy of immunotherapeutic

outcomes.

174

In normal physiology, inhibitory immune checkpoints are important to

protect against activation events targeted toward self-antigens (i.e., autoimmunity)

and to return the adaptive response to a basal level and function following the

clearance of a foreign agent in a process called homeostatic control (50). However,

inhibitory immune checkpoints may act as a barrier to the successful identification,

activation, and eradication of malignant self-cells by effector immune cell

populations. Following T cell activation by APCs, T cells migrate to specific sites

and perform their effector function. Upon activation, T cells upregulate inhibitory

cell surface markers, like PD-1 (53, 616). In tumor-bearing hosts, proinflammatory

signals (e.g., interferon [IFN] gamma []) produced in the TME, signal to tumor

cells, stromal cells, and tumor infiltrating immune cells (e.g., myeloid-derived

suppressor cells [MDSCs]) to increase the expression of programmed death-

ligand 1 (PD-L1 or CD274) and PD-L2 (CD273), which can bind to PD-1 on T cells

and shut down the antitumor response by inducing apoptosis (203). The immune

checkpoint blockade approach to immunotherapy involves using antibodies to

maintain T cell activation by either binding and agonizing co-stimulatory signals or

binding and antagonizing co-inhibitory signals (53, 57). PD-1 is widely expressed

on several cells important in antitumor immunity, including B cells, natural killer

(NK) cells, and CD4+ helper and CD8+ cytotoxic T cells, making this an emerging

target for cancer therapy (257).

Clinically, PD-L1 protein expression is detected in 20-30% of BC patients

and single-agent PD-1 blockade can result in objective and durable clinical

responses in BC patients, with responses more favorable for patients with tumors

175

positive for PD-L1 (257). Currently, there are close to 50 ongoing, or soon to open,

clinical trials evaluating the efficacy of immunotherapy strategies alone or in

combination with current and emerging therapies in the neoadjuvant and

metastatic setting in BC patients (56-58). In the preclinical setting, the 4T1 series

of BC cells are largely considered poor responders to single-agent PD-1 blockade

due to the expansion of immunosuppressive MDSCs (262, 263). Dual interventions

that target the proinflammatory TME concurrently with PD-1 or CTLA-4 checkpoint

blockade result in regression of 4T1 tumors, even when disease burden is

advanced and metastatic (266). However, no study has examined if moderate

exercise in weight stable mice can drive a reduction in systemic inflammation and

blunt the expansion of immunosuppressive cell populations to promote

responsiveness to PD-1 checkpoint blockade in the 4T1 BC series.

Experimental results from previous findings (chapter three) show that

moderately exercising, weight stable mice display a reduction in 4T1.2 luc tumor

growth and metastatic progression, concurrently with a reduction in the expansion

of immunosuppressive cell types (e.g., MDSCs), sustained lymphoid-lineage

immune phenotype, and a reduction in proinflammatory and immunosuppressive

gene expression in the TME. Data presented in chapter four detailed our findings

that a broad-based immunogenic whole tumor cell cancer vaccine enhanced the

cancer protective effects of moderate exercise in weight stable 4T1.2 tumor-

bearing mice. Sanchez, et al. (57) reviewed the current BC immunotherapy

literature and notes that while DC-based cancer vaccines are under clinical

investigation in BC patients, no trial to date has investigated the combination of a

176

cancer vaccine and checkpoint blockade. Therefore, the combination of cancer

vaccines and immune checkpoint blockade are two strategies to harness the

immune system to treat cancer and their combination warrant further investigation.

Thus, the goal of the current study was to determine if moderate exercise in weight

stable mice could enhance targeted PD-1 checkpoint blockade alone, or in

combination with the same whole tumor cell cancer vaccine administered in

chapter four in our clinically relevant, orthotopic model of stage IV metastatic BC.

It is of utmost importance to delineate new, previously unidentified combinatorial

treatment strategies to attenuate tumor-induced inflammation and enhance

immunotherapeutic efficacy to improve treatment responses and clinical outcomes

in metastatic cancer patients.








Anderson and maintained in Dulbecco’s modified Eagle’s medium (Life

Technologies) containing 10% fetal bovine serum (Gemini Bio-Products), 2 mM

glutamine (Mediatech), 1X nonessential amino acid (Mediatech), and 8 μg/ml

puromycin (Mediatech).

177

5.3.2. Animal model

Six-week old female BALB/c mice were obtained from Jackson

Laboratory and screened for their intrinsic running potential. Female BALB/c

mice (14-week old) were orthotopically injected with 5x104 luciferase-

transfected 4T1.2 cells into the fourth mammary fat pad and randomized (n=4-

5/group) to receive one vs. two rounds of immunotherapy. Each round of

immunotherapy consisted of PBS vehicle control (VEH) or 1x106 irradiated

4T1.2luc cells vaccination (VAX), followed by two PD-1 checkpoint blockade

injections (10 mg/kg/mouse). Mice were sacrificed at day 35 post-tumor

implantation.

Intrinsic runners (14-week old) were randomized to sedentary, weight

gain (WG; n=36) or exercising, weight maintenance (WM; n=36) groups for a

total of 13 weeks. All mice were fed AIN-76A diet (Research Diets); however,

the WM mice were fed 90% of caloric intake of WG mice to remain in energy

balance (prevent weight gain) over the course of the study. After eight-weeks

on study, all mice were orthotopically injected with 5x104 luciferase-transfected

4T1.2 cells into the fourth mammary fat pad and continued on their intervention

for 35 days. After injection, WG and WM mice were randomized into vehicle

control (VEH) or vaccination (VAX) groups and administered PBS or 1x106

irradiated 4T1.2luc cells at day 7 post-tumor injection. Mice were further

randomized (n=7-11/group) to receive isotype control (IgG) or PD-1 checkpoint

blockade (10 mg/kg/mouse) at day 9 and 12 post-tumor implantation. Mice

were sacrificed at day 35 post-tumor implantation. A cohort of sedentary, ad

178

libitum-fed 4T1.2luc tumor-bearing mice receiving the VEH and IgG control were

sacrificed at day 24 post-tumor implantation to assess PD-1 expression on T

cells. Movement was not monitored in mice that did not have access to running

wheels. Food intake, body weight, and tumor size (v=(short2×long)/2) were

monitored as previously reported (562, 568), and mice were observed daily for

signs of ill health. All mice were housed at the Pennsylvania State University

and maintained on a 12-hour light/dark cycle with free access to water. The

Institutional Animal Care and Use Committee of the Pennsylvania State

University approved all animal experiments.

5.3.3. Whole tumor cell cancer vaccine

Trypsin/EDTA (0.25%/2.21 mM) in HBSS (Corning) was added to

culture flasks to harvest 4T1.2luc cells. 4T1.2luc cells were washed twice in

4T1.2luc media and washed twice in PBS. 4T1.2luc cells were counted, adjusted

to 1.0x106/100 l, and irradiated for 15,000 rad in a cesium irradiator. Irradiated

4T1.2luc cells (VAX; 1.0x106/100 l) or PBS control (VEH; 100 l) were injected

I.P. at day 7 post-tumor implantation.

5.3.4. PD-1 checkpoint blockade

Mice were weighed and the stock concentration of in vivo depletion

antibody was diluted to 10 mg/kg/mouse. Mice received an I.P. administration

of rat IgG2a isotype control or rat -mouse PD-1 [CD279] (RMP1-14; Bio X

Cell; West Lebanon, NH) at day 9 and 12 post-tumor implantation.

179


The lung and femur were collected, flash frozen in liquid nitrogen, and

stored at -80°C. Tissues were homogenized and genomic DNA was isolated

using DNeasy Blood and Tissue Kit (Qiagen), per manufacturer’s instructions.

DNA was quantified using a Nanodrop ND-1000 spectrophotometer (Nanodrop

Products). Metastatic tumor burden of the tissues was quantified using the

Taqman™ system performed on a Step-One Plus (Life Technologies) real time

PCR instrument to quantify luciferase. Assay sequences for luciferase were

CAGCTGCACAAAGCCATGAA (forward primer),

CTGAGGTAATGTCCACCTCGATATG (reverse primer) and











efficiency.

180

5.3.6. Bioluminescent in vivo imaging

Mice were weighed and injected I.P. with XenoLight D-Luciferin-K+ Salt

Bioluminescent Substrate (150 mg luciferin/kg body weight) (Perkin Elmer;

Waltham, MA) 10 minutes prior to imaging. Anesthetized mice were imaged for

a two-minute exposure in an IVIS 50 Imaging System (Perkin Elmer) and

analyzed with Igor Pro - Scientific Imaging Analysis Software (Wavemetrics;

Lake Oswego, OR).

5.3.7. Splenic, non-draining lymph node, and tumor-infiltrating immune cell assays

5.3.7.1. Isolation of splenic and non-draining lymph node immune cells

Spleens and non-draining lymph nodes (nDLN) were harvested via

gross dissection and cells were prepared from individual mice by

mechanical dispersion, as previously described (345). Briefly, spleens and

nDLNs were mechanically disrupted with a syringe plunger and passed

through a 70 μm nylon mesh strainer (BD Biosciences), erythrocytes were

lysed with ACK lysing buffer (Lonza), and remaining cells washed twice in

complete medium (RPMI 1640 (Mediatech) supplemented with 10% FBS

(Gemini Bio-Products), 0.1 mM non-essential amino acids (Mediatech), 1

mM sodium pyruvate (Mediatech), 2 mM glutamine (Mediatech), 10 mM

HEPES (Mediatech), 100 U/mL penicillin streptomycin (Mediatech). Cell

counts and viability were determined via trypan blue exclusion

(Mediatech).

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5.3.7.2. Splenic CD4+ helper T cell proliferation assay





96-well plates (Greiner Bio-One) in the presence of increasing

concentrations of anti-CD3 antibody (BD Biosciences) for 72 hours (562).

Proliferation data was analyzed by tritiated (H3) thymidine (Perkin Elmer)

incorporation and quantified on a Microbeta plate reader (Perkin Elmer).

Each assay was performed in triplicate.



into fine pieces (<10 mg), and incubated with 0.03 mg/ml Liberase

(Roche) and 12.5 U/ml DNase I (Sigma-Aldrich) for 45 minutes at 37°C

on an orbital shaker. Following the digestion, remaining pieces were

mechanically disrupted with a syringe plunger, passed through a 70 μm

nylon mesh strainer (BD Biosciences), layered over Lympholyte-M cell

separation media (Cedarlane), and centrifuged at room temperature for

20 minutes at 1200g. Isolated cells were washed twice in cold PBS

(Mediatech) and cell counts and viability of tumor immune infiltrates were

determined via trypan blue exclusion.

182



24-hour co-culture and five-day bulk culture with irradiated tumor cells),

nDLN cells, tumor-infiltrating immune cells, and tumor-infiltrating effector

cell (after 24-hour co-culture with irradiated tumor cells) were washed

twice in PBS containing 0.01% bovine serum albumin (flow buffer) at 4°C.

Cells were incubated with Fc block (Biolegend) and 1x106 cells were

stained with saturating concentrations of conjugated antibodies for 30 min

at 4°C, as previously described (345). Fluorescently conjugated

antibodies for flow cytometry included rat -mouse CD19 (1D3), hamster

-mouse CD3 (145-2C11), rat -mouse CD4 (RM4-5), rat -mouse CD8

(53-6.7), mouse -mouse NK1.1 (PK136), mouse -mouse I-Ab (AF6-

120.1), hamster -mouse CD11c (HL3), rat -mouse CD11b (M1/70), rat

-mouse F4/80 (T45-2342), rat -mouse Ly6G and Ly6C [Gr-1] (RB6-

8C5), rat -mouse Ly6C (AL-21), rat -mouse Ly6G (1A8). To differentiate

between naïve, central memory (TCM), effector memory (TEM), and effector

T cell populations, splenic, nDLN, and tumor-infiltrating immune cells were

stained with rat -mouse CD4 (RM4-5), rat -mouse CD8 (53-6.7), rat -

mouse CD44 (IM7), rat -mouse CD62L (MEL-14), rat -mouse CD69

(H1.2F3), rat -mouse IL-6R [CD126] (D7715A7), rat -mouse IL-7R

[CD127] (SB/199), rat -mouse CCR7 [CD197] (4B12), hamster -mouse

PD-1 [CD279] (J43), and hamster -mouse KLRG1 (2F1). Following

incubation with the conjugated antibodies, cells were washed twice in flow

183

buffer and fixed in 1% paraformaldehyde (BD Biosciences) in flow buffer

for flow cytometric analysis. Additionally, following the extracellular

staining for CD3, CD4, CD8, and NK1.1, splenic cells, splenic effector

cells, and tumor-infiltrating effector cells were permeabilized for 30

minutes, washed in a fixation/permeablization buffer, and intracellularly

stained for rat -mouse IFN (XMG1.2) for 30 minutes. Following

incubation with the IFN antibody, cells were washed twice in

fixation/permeabilization buffer and read within 12 hours. Lymphoid and

myeloid cells were gated on forward vs. side scatter, and a total of 50,000

events were analyzed. Flow cytometric analyses were performed on a BD

LSR-Fortessa (BD Bioscience) flow cytometer. Gating for naïve, TCM, TEM,

and effector T cell populations were based on expression of multiple

markers using previously published studies (617-620) as a guide. Flow

cytometric analyses were plotted and analyzed using Flow Jo software

(Tree Star; Ashland, OR).

5.3.7.5. Splenic and tumor-infiltrating immune cell assays

Splenocytes were depleted of Gr-1+ cells and pulsed with irradiated

4T1.2luc cells for a 24-hour co-culture or a five-day bulk culture to generate

an antigen-specific T cell response to tumor antigens. Tumor-infiltrating

cells were pulsed with irradiated 4T1.2luc cells for a 24-hour co-culture to

measure an antigen-specific T cell response to tumor antigens. Splenic

effector cell supernatant was collected after the 24-hour co-culture and

after a 48-hour rest following the five-day bulk culture and stored at -80°C.

184

Tumor-infiltrating cell supernatant was collected after the 24-hour co-

culture and stored at -80°C. IFN was measured using Legend Max ELISA

kits (Biolegend), per manufacturer instructions. Cytokines were quantified

with an Epoch Microplate Spectrophotometer (Biotek; Winooski, VT).

Each assay was performed in duplicate.

4T1.2luc target cells were harvested via trypsin, washed in 4T1.2luc

media, and incubated at 37°C with Violet Proliferation Dye 450 (VPD450;

BD Biosciences) for 15 minutes. Post 24-hour co-culture, splenic and

tumor-infiltrating effector cells were co-cultured with VPD450-labeled

4T1.2luc target cells. After 24 hours, cells were harvested, washed in flow

buffer, permeabilized (BD Biosciences), and intracellularly stained with

rabbit -mouse active caspase-3 (CPP32) to quantify the ability of effector

cells to induce caspase-3 in 4T1.2luc target cells, as previously described

(621). 4T1.2luc target cells were cultured alone (negative controls), or with

1.0 mM camptothethecin (Sigma-Aldrich) to induce caspase-3 expression

(positive, spontaneous control) and the percent caspase-3 induction was

calculated by: [(observed-spontaneous) / (total-spontaneous)] * 100.

5.3.8. Gene expression in the tumor microenvironment

At sacrifice, tumor samples were incubated overnight in RNAlater

(Qiagen) followed by RNA isolation from individual mice using Qiashredder

columns followed by RNeasy Mini Plus Kit (Qiagen). RNA quality was assessed

via Bioanalyzer analysis (Agilent Technologies; Santa Clara, CA) with samples

requiring an RNA Integrity Number (RIN) above 7 to pass quality control testing

185

(570, 571). RNA sample was retrotranscribed using RT2 First Strand Kit

(Qiagen) according to manufacturer’s instructions and samples were loaded

onto qPCR plates (Mouse Cancer Inflammation & Immunity Crosstalk PCR

Array, Cat. # PAMM-181Z, Qiagen) and cycled according to the following

conditions: 95°C for 10 minutes; 95°C for 15 seconds; 60°C for 1 minutes for

40 cycles on a StepOnePlus (Applied Biosystems; Foster City, CA). Qiagen’s

online Data Analysis Center was used to normalize raw CT values to the

housekeeping genes and fold change (2^(- Delta Delta CT)) was calculated as

previously described (572).


Tumor weight, metastatic burden, metastatic flux, the distribution of cells

in the spleen, splenic bulk culture cells, and tumor-infiltrating immune cells,

cytokine secretion, and caspase induction were assessed for normality and

equal variances; and either parametric or nonparametric analyses were used

based on sample distribution to detect differences between treatment groups.



Differences in tumor weight, metastatic burden, metastatic flux, the distribution

of cells in the spleen, splenic bulk culture cells, and tumor-infiltrating immune

cells, cytokine secretion, and caspase induction were assessed between two

groups via a Student’s t-test or Mann-Whitney test depending on sample

distribution or between greater than two groups via a one-way analysis of

variance (ANOVA), followed by a Bonferroni correction for multiple

186

comparisons where appropriate, or Kruskal-Wallis test, depending on normality

and variance. Body weight, primary tumor volume, and CD4+ helper T cell

proliferation were examined using a two-way ANOVA, followed by a Bonferroni

correction for multiple comparisons where appropriate. All data are presented

as the mean plus or minus the standard deviation of the mean. All analyses

were conducted using GraphPad Prism 5 software (GraphPad Software; La

Jolla, CA) and statistical significance was accepted at the p≤0.05 level.

5.4. RESULTS

5.4.1. PD-1 cell surface expression on splenic CD4+ helper and CD8+ cytotoxic T cells

Day 35 splenic CD4+ helper (Fig. 5.1A; p<0.001) and CD8 + cytotoxic

(Fig. 5.1B; p<0.001) T cells expressed a lower percentage of PD-1 than day 24

cells.

Figure 5.1. PD-1 cell surface expression on splenic CD4+ helper and CD8+ cytotoxic T cells. Splenic T cells were stained for anti-CD4, -CD8, and -PD-1 at day 24 and day 35 post-tumor implantation. Day 35 splenic (A) CD4+ helper (p<0.001) and (B) CD8 + cytotoxic (p<0.001) T cells expressed a lower percentage of PD-1 than day 24 cells. Significantly different from day 24 (*).

187

5.4.2. Optimizing in vivo PD-1 checkpoint blockade

The experimental design for the PD-1 pilot study is displayed in Fig.

5.2A. Briefly, female BALB/c mice (14-week old) were orthotopically injected

with 5x104 luciferase-transfected 4T1.2 cells into the fourth mammary fat pad

and randomized (n=4-5/group) to receive one vs. two rounds of

immunotherapy. Each round of immunotherapy consisted of PBS vehicle

control (VEH) or 1x106 irradiated 4T1.2luc cells vaccination (VAX), followed by

two PD-1 checkpoint blockade injections (10 mg/kg/mouse). Mice were

sacrificed at day 35 post-tumor implantation. Body weights were not

significantly different between immunotherapy groups throughout the course of

the study (Fig. 5.2B; n=4-5/group; 2-way ANOVA, F(3,90)=2.84, p=0.073).

Primary tumor growth was not significantly different between immunotherapy

groups (Fig. 5.2C; 2-way ANOVA, F(3,195)=0.16, p=0.920). No differences

emerged between VEH or VAX groups, therefore, the vaccination intervention

was collapsed to investigate one round (consisting of two PD-1 checkpoint

blockade injections, [x2]) vs. two rounds (consisting of four rounds of PD-1

checkpoint blockade injections, [x4]) of PD-1 checkpoint blockade. Primary

tumor growth was not significantly different between one vs. two rounds of PD-

1 checkpoint blockade (Fig. 5.2D; n=9-10/group; 2-way ANOVA, F(1,221)=0.01,

p=0.951). Tumor weight at sacrifice was not significantly different between

groups (Fig. 5.2E; Student’s t-test, p=0.811).

188

Figure 5.2. Optimizing in vivo PD-1 checkpoint blockade in the 4T1.2 mammary tumor model. (A) Experimental design. (B) Body weights were not significantly different between immunotherapy groups throughout the course of the study (2-way ANOVA, F(3,90)=2.84, p=0.073). (C) Primary tumor growth (measured 2-3x/week via caliper) was not significantly different between immunotherapy groups (2-way ANOVA, F(3,195)=0.16, p=0.920). (D) No differences emerged between VEH or VAX groups, therefore, the vaccination intervention was collapsed to investigate one round (consisting of two PD-1 checkpoint blockade injections, [x2]) vs. two rounds (consisting of four PD-1 checkpoint blockade injections, [x4]) of PD-1 checkpoint blockade. Primary tumor growth was not significantly different between one vs. two rounds of PD-1 checkpoint blockade (2-way ANOVA, F(1,221)=0.01, p=0.951). (E) Tumor weight at sacrifice was not significantly different between groups (Student’s t-test, p=0.811).

189

5.4.3. Splenic immunity in the PD-1 pilot study

The percentage and number of splenic immune cell populations were

not significantly different between groups (Table 5.1).

Table 5.1. The percentage and number of splenic immune cell populations in the PD-1 pilot study. The (A) percentage and (B) number of splenic immune cell populations were not significantly different between groups.

190

5.4.4. CD4+ helper and CD8+ cytotoxic T cell subpopulations and effector T cell activation and exhaustion markers in the PD-1 pilot study

CD44-CD62L+CCR7+ naïve, CD44+CD62L+CCR7+ central memory

(TCM), CD44+CD62L-CCR7- effector memory (TEM), and CD44-CD62L-CCR7-

effector CD4+ helper T cell populations were not significantly different between

groups in the spleen (Fig. 5.3A), non-draining lymph-node (nDLN; Fig. 5.3C),

or tumor-infiltrating immune cell (Fig. 5.3E) compartments. The percent of

CD44-CD62L-CCR7- effector CD4+ helper T cells expressing CD69, IL-6R, IL-

7R, KLRG1, or PD-1 on their cell surface were not significantly different

between groups in the spleen (Fig. 5.3B), nDLN (Fig 5.3D), or tumor-infiltrating

immune cell (Fig. 5.3F) compartment.

CD44-CD62L+CCR7+ naïve, CD44+CD62L+CCR7+ TCM, CD44+CD62L-

CCR7- TEM, and CD44-CD62L-CCR7- effector CD8+ cytotoxic T cell populations

were not significantly different between groups in the spleen (Fig. 5.4A), nDLN

(Fig. 5.4C), or tumor-infiltrating immune cell (Fig. 5.4E) compartments. The

percent of CD44-CD62L-CCR7- effector CD8+ cytotoxic T cells expressing

CD69, IL-6R, IL-7R, or PD-1 on their cell surface were not significantly

different between groups in the spleen (Fig. 5.4B), nDLN (Fig 5.4D), or tumor-

infiltrating immune cell (Fig. 5.4F) compartment. The percent of CD44-CD62L-

CCR7- effector CD8+ cytotoxic T cells expressing KLRG1 was not significantly

different in the spleen (Fig. 5.4B) or nDLN (Fig. 5.4D); however, mice receiving

(x4) PD-1 checkpoint blockade displayed a significant reduction in the cell

surface expression of KLRG1 on effector CD8+ cytotoxic T cells in the tumor-

infiltrating compartment (Fig. 5.4F; p=0.032).

191

Figure 5.3. Splenic, non-draining lymph node, and tumor-infiltrating CD4+ helper T cell populations and effector CD4+ T cell activation and exhaustion markers in the PD-1 pilot study. CD44-CD62L+CCR7+ naïve, CD44+CD62L+CCR7+ central memory (TCM), CD44+CD62L-CCR7- effector memory (TEM), or CD44-CD62L-CCR7- effector CD4+ helper T cell populations were not significantly different between groups in the (A) spleen, (C) non-draining lymph-node (nDLN), or (E) tumor-infiltrating immune cell compartments. Effector CD4+ helper T cell populations were stained for markers of activation and

exhaustion. The percent of effector CD4+ helper T cells expressing CD69, IL-6R, IL-7R, KLRG1, or PD-1 was not significantly different between groups in the (B) spleen, (D) nDLN, or (F) tumor-infiltrating immune cell compartment.

192

Figure 5.4. Splenic, non-draining lymph node, and tumor-infiltrating CD8+ helper T cell populations and effector CD8+ T cell activation and exhaustion markers in the PD-1 pilot study. CD44-CD62L+CCR7+ naïve, CD44+CD62L+CCR7+ central memory (TCM), CD44+CD62L-CCR7- effector memory (TEM), or CD44-CD62L-CCR7- effector CD8+ cytotoxic T cell populations were not significantly different between groups in the (A) spleen, (C) non-draining lymph-node (nDLN), or (E) tumor-infiltrating immune cell compartments. Effector CD8+ cytotoxic T cell populations were stained for markers of activation and exhaustion. The percent of effector CD8+ cytotoxic T cells expressing

CD69, IL-6R, IL-7R, or PD-1 was not significantly different between groups in the (B) spleen, (D) nDLN, or (F) tumor-infiltrating immune cell compartment. The percent of effector CD8+ cytotoxic T cells expressing KLRG1 was not significantly different in the (B) spleen or (D) nDLN; however, mice receiving (x4) PD-1 checkpoint blockade displayed a significant reduction in KLRG1 in the (F) tumor-infiltrating compartment (p=0.032). Significantly different from (x2) PD-1 checkpoint blockade (*).

193

5.4.5. Splenic and tumor-infiltrating immune cell co-culture and bulk assays in the PD-1 pilot study

The percentage of CD3+ T cells, CD3+CD4+ helper T cells, CD3+CD8+

cytotoxic T cells, and NK1.1+ natural killer cells, and the intracellular expression

of IFN in helper T cells, cytotoxic T cells, and natural killer cells was not

significantly different between groups receiving one vs. two rounds of

vaccination + PD-1 blockade following a 24-hour co-culture (Fig. 5.5A) or a five-

day bulk culture (Fig. 5.5D) with irradiated 4T1.2luc cells. IFN secretion

following a 24-hour co-culture was below detection (Fig. 5.5B). The percent

caspase induction in 4T1.2luc target cells co-cultured with 24-hour splenic

effector cells was not significantly different between groups (Fig. 5.5C; 6-

8/group; Mann-Whitney, p=0.284). Splenic bulk effector cell IFN secretion in

response to re-stimulation with tumor antigens was not significantly different

between groups (Fig. 5.5E; n=9-10/group; Student’s t-test, p=0.338). The

percent caspase induction in 4T1.2luc target cells co-cultured with five-day

splenic bulk effector cells was not significantly different between groups (n=9-

10/group; Mann-Whitney, p=0.497).

194

195

Figure 5.5. Splenic immune cell 24-hour co-culture and five-day bulk assay in the PD-1 pilot study. Isolated splenocytes were depleted of Gr-1+ cells and pulsed with irradiated 4T1.2luc cells for a 24-hour co-culture or a five-day bulk culture to generate an antigen-specific T cell response to tumor antigens. Post culture, cells were extracellularly

stained with anti-CD3, -CD4, -CD8, and -NK1.1 and intracellularly stained with anti-IFN

and characterized by flow cytometry (n=9-10/group). The percentage of CD3+ T cells, CD3+CD4+ helper T cells, CD3+CD8+ cytotoxic T cells, or NK1.1+ natural killer cells, or the

intracellular expression of IFN in helper T cells, cytotoxic T cells, or natural killer cells was

not significantly different between groups after a (A) 24-hour co-culture or (D) five-day

bulk culture. (B) 24-hour co-culture cell IFN secretion was below detection. (C) Post 24-hour co-culture, cells were washed and co-cultured with VPD450-labeled 4T1.2luc target cells. After 24 hours, cells were intracellularly stained for caspase expression and analyzed by flow cytometry. The percent caspase induction in 4T1.2luc target cells was not significantly different between groups (6-8/group; Mann-Whitney, p=0.284). (E) Bulk

effector cell IFN secretion in response to re-stimulation with tumor antigens was not

significantly different between groups (n=9-10/group; Student’s t-test, p=0.338). (F) The percent caspase induction in VPD450-labeled 4T1.2luc target cells co-cultured for 24 hours with bulk effector cells was not significantly different between groups (n=9-10/group; Mann-Whitney, p=0.497).

The percentage of tumor-infiltrating CD3+ T cells, CD3+CD4+ helper T

cells, CD3+CD8+ cytotoxic T cells, and NK1.1+ natural killer cells, and the

intracellular expression of IFN in helper T cells, cytotoxic T cells, and natural

killer cells were not significantly different between groups after a 24-hour co-

culture (Fig. 5.6A). 24-hour co-culture cell IFN secretion was not significantly

different between groups (Fig. 5.6B; n=6-7/group; Mann-Whitney, p=0.099).

The percent caspase induction in 4T1.2luc target cells co-cultured with 24-hour

tumor-infiltrating effector cells was not significantly different between groups

(Fig. 5.6C; n=4/group; Mann-Whitney, p=0.686).

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Figure 5.6. Tumor-infiltrating immune cell 24-hour co-culture assay in the PD-1 pilot study. Isolated tumor-infiltrating immune cells were pulsed with irradiated 4T1.2luc cells for a 24-hour co-culture to measure an antigen-specific T cell response to tumor antigens.

Post culture, cells were extracellularly stained with anti-CD3, -CD4, -CD8, and -NK1.1 and intracellularly stained with anti-IFN and characterized by flow cytometry (n=6-7/group). (A) The percentage of tumor-infiltrating CD3+ T cells, CD3+CD4+ helper T cells,

CD3+CD8+ cytotoxic T cells, or NK1.1+ natural killer cells, or the intracellular expression of IFN in helper T cells, cytotoxic T cells, or

natural killer cells was not significantly different between groups after a 24-hour co-culture. (B) 24-hour co-culture cell IFN secretion

was not significantly different between groups (n=6-7/group; Mann-Whitney, p=0.099). (C) Post 24-hour co-culture, cells were washed and co-cultured with VPD450-labeled 4T1.2luc target cells. After 24 hours, cells were intracellularly stained for caspase expression and analyzed by flow cytometry. The percent caspase induction in 4T1.2luc target cells was not significantly different between groups (n=4/group; Mann-Whitney, p=0.686).

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BALB/c mice (14-week old) were randomized to sedentary, weight gain (WG)

or exercising, weight maintenance (WM) groups (n=36/group) for a total of 13

weeks. After eight-weeks on study, all mice were orthotopically injected with


continued on their intervention for 35 days. After injection, WG and WM mice

were randomized into vaccination or vehicle control groups and administered

PBS vehicle (VEH) control or 1x106 irradiated 4T1.2luc cells (VAX) at day 7 post-

tumor injection. Mice were further randomized (n=7-11/group) to receive

isotype control (IgG) or PD-1 checkpoint blockade (10 mg/kg/mouse) at day 9

and 12 post-tumor implantation. Mice were sacrificed at day 35 post-tumor

implantation WM mice weighed significantly less than WG mice, independent

of immunotherapy, throughout the course of the study (Fig. 5.7B; n=7-11/group;

2-way ANOVA, F(3,741)=27.32, p<0.001). Wheel activity was maintained over

the course of the study, i.e., prior to and after tumor injection. Activity was not

affected by the administration of the whole tumor cell cancer vaccine or PD-1

checkpoint blockade (Fig. 5.7C; n=36; average distance run per day=5.3±2.5

km).

198

Figure 5.7. Study design, body weight, and wheel activity. (A) Experimental design. (B) WM mice weighed significantly less than WG mice, independent of immunotherapy, throughout the course of the study (2-way ANOVA, F(3,741)=27.32, p<0.001). (C) Wheel activity was maintained over the course of the study, i.e., prior to and after tumor injection. Activity was not affected by the administration of the whole tumor cell cancer vaccine or PD-1 checkpoint blockade. (n=36; average distance run per day=5.3±2.5 km).

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Weight gain (WG) groups: Primary tumor growth was significantly

reduced in the WG+VEH+PD-1 and WG+VAX+PD-1 groups compared with

both WG+VEH+IgG and WG+VAX+IgG (Fig. 5.8A; n=7-11/group; 2-way

ANOVA, time x treatment, F(36,360)=1.75, p=0.006). Tumor weight at sacrifice

was not significantly different between WG groups (Fig. 5.8B; 1-way ANOVA,

F(3,33)=2.10, p=0.121).

Weight maintenance (WM) groups: Primary tumor growth was not

significantly different between WM groups (Fig. 5.8E; n=7-11/group; 2-way

ANOVA, F(3,360)=0.37, p=0.777). Tumor weight at sacrifice was not significantly

different between WM groups (Fig. 5.8F; 1-way ANOVA, F(3,33)=0.36, p=0.782).

Collapsing the VEH and VAX groups within weight interventions to

analyze PD-1 checkpoint blockade effects (i.e., WG+IgG vs. WG+PD-1 vs.

WM+IgG vs. WM+PD1): Primary tumor growth was significantly lower with PD-

1 checkpoint blockade in mice in the WG group (Fig. 5.8I; n=14-20/group; 2-

way ANOVA, time x treatment, F(36,768)=6.38, p<0.001). WM groups had the

lowest tumor volume independent of PD-1 checkpoint blockade (p<0.001).

Tumor weight at sacrifice was reduced in both WM groups compared with

WG+IgG (Fig. 5.8J; 1-way ANOVA, F(3,67)=6.00, p=0.001).


WG groups: Lung (Fig. 5.8C; n=7-11/group; 1-way ANOVA, F(3,33)=2.10,

p=0.121) and femur (Fig. 5.8D; Kruskal-Wallis, KW=1.24, p=0.745) metastasis

were not significantly different between WG groups.

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WM groups: Lung (Fig. 5.8G; n=7-11/group; Kruskal-Wallis, KW=3.15,

p=0.369) and femur (Fig. 5.8H; Kruskal-Wallis, KW=0.76, p=0.860) metastasis

were not significantly different between WM groups.

Collapsing the vehicle and vaccination groups within weight

interventions to analyze PD-1 checkpoint blockade effects (WG+IgG vs.

WG+PD-1 vs. WM+IgG vs. WM+PD1): Spontaneous lung metastasis was

lower with PD-1 checkpoint blockade in mice in the WG (Fig. 5.8K; n=14-

30/group; 1-way ANOVA, F(3,61)=4.37, p=0.008), but not WM groups. Femur

metastasis (Fig. 5.8L; 1-way ANOVA, F(3,52)=0.96, p=0.418) was not

significantly different between groups. Representative IVIS images at day 34

post-tumor implantation (Fig. 5.9A). Whole-body metastatic flux (photons/sec)

was not significantly different between groups at day 34 post-tumor

implantation (Fig. 5.9B; 1-way ANOVA, F(3,56)=0.11, p=0.956).

201

202

Figure 5.8. PD-1 checkpoint blockade administration in weight gain mice reduced primary tumor growth. Weight maintenance independent of PD-1 checkpoint blockade reduced primary tumor growth. (A-D) WG groups. (A) Primary tumor growth (measured 2-3x/week via caliper) was significantly reduced in the WG+VEH+PD-1 and WG+VAX+PD-1 groups compared with both WG+VEH+IgG and WG+VAX+IgG (n=7-11/group; 2-way ANOVA, time x treatment, F(36,360)=1.75, p=0.006). (B) Tumor weight at sacrifice was not significantly different between WG groups (1-way ANOVA, F(3,33)=2.10, p=0.121). (C, D) At sacrifice, organs (n=7-11/group) were flash frozen, homogenized, and DNA was extracted. Quantitative PCR was used to detect luciferase expression as a marker of metastatic burden reported as ng of luciferase DNA normalized to mouse TERT DNA in 200 ng of sample (arbitrary units). (C) Lung (1-way ANOVA, F(3,33)=2.10, p=0.121) and (D) femur (Kruskal-Wallis, KW=1.24, p=0.745) metastasis were not significantly different between groups. (E-H) WM groups. (E) Primary tumor growth was not significantly different between WM groups (n=7-11/group; 2-way ANOVA, F(3,360)=0.37, p=0.777). (F) Tumor weight at sacrifice was not significantly different between WM groups (1-way ANOVA, F(3,33)=0.36, p=0.782). (G) Lung (Kruskal-Wallis, KW=3.15, p=0.369) and (D) femur (Kruskal-Wallis, KW=0.76, p=0.860) metastasis were not significantly different between groups. (I-L) Vehicle and vaccination groups were collapsed to investigate PD-1 checkpoint blockade effects in both WG and WM groups. (I) Primary tumor growth was significantly lower with PD-1 checkpoint blockade in mice in the WG group (n=14-20/group; 2-way ANOVA, time x treatment, F(36,768)=6.38, p<0.001). WM groups had the lowest tumor volume independent of PD-1 checkpoint blockade (p<0.001). (J) Tumor weight at sacrifice was reduced in both WM groups compared with WG+IgG (1-way ANOVA, F(3,67)=6.00, p=0.001). (K) Spontaneous lung metastasis was lower with PD-1 checkpoint blockade in mice in the WG (1-way ANOVA, F(3,61)=4.37, p=0.008), but not WM groups. (L) Femur metastasis (1-way ANOVA, F(3,52)=0.96, p=0.418) was not significantly different between groups. Significantly different from WG+VEH+IgG (*), WG+VAX+IgG (†), and WG+IgG (‡).

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Figure 5.9. Bioluminescent in vivo imaging. (A) Representative IVIS images at day 34 post-tumor implantation. (B) Metastatic flux was calculated by applying a whole-body gate and subtracting out the primary tumor gate. Whole-body metastatic flux was not significantly different between groups at day 34 post-tumor implantation (n=14-20/group; 1-way ANOVA, F(3,56)=0.11, p=0.956).

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5.4.9.1. Splenic immune cells

Splenocyte counts were not significantly different between groups

(Fig. 5.10A; n=14-20/group; Kruskal-Wallis, KW=7.70, p=0.053). CD4+

helper T cell proliferation was significantly different between groups (Fig.

5.10B; n=11-16/group; 2-way ANOVA, time x treatment, F(15,270)=1.81,

p=0.033) with CD4+ helper T cells from WM mice proliferating more than

CD4+ helper T cells from WG. The percentage of splenic CD3+CD4+

helper T cells was significantly different between groups (Table 5.2;

p=0.048). The number of splenic immune cell populations was not

significantly different between groups (Table 5.2). The number of splenic

Gr-1+CD11b+ MDSCs was significantly different between groups (Fig.

5.10C; n=14-20/group; Kruskal-Wallis, KW=7.96, p=0.047) with WM+PD-

1 mice displaying the lowest number compared with WG+IgG mice. The

number of splenic CD11b+Ly6ChiLy6G- monocytic MDSCs (Fig. 5.10D; 1-

way ANOVA, F(3,67)=2.28, p=0.088) was not significantly different between

groups; however, the number of splenic CD11b+Ly6CloLy6G+ granulocytic

MDSCs was significantly different between groups (Fig. 5.10E; 1-way

ANOVA, F(3,67)=3.65, p=0.017) with WM+PD-1 mice displaying the lowest

number compared with WG+VEH mice.

205

Figure 5.10. The combination of weight maintenance and PD-1 checkpoint blockade reduced protumor immunosuppressive cell populations. Splenocytes (n=14-20/group) were prepared into a single cell suspension and counted. (A) Splenocyte counts were not significantly different between groups (n=14-20/group; Kruskal-Wallis, KW=7.70, p=0.053). (B) Isolated splenic CD4+ helper T cells (0.1x106/well) were stimulated with increasing concentrations of anti-CD3 antibody and 1 μg/ml anti-CD28 antibody, and proliferation was quantified via [H]3 uptake. Data were converted to a stimulation index (counts per minute of stimulated wells divided by counts per minute of unstimulated [media only] wells). CD4+ helper T cell proliferation was significantly different between groups (n=11-16/group; 2-way ANOVA, time x treatment, F(15,270)=1.81, p=0.033). Splenocytes (n=14-20/group) were stained with anti-Gr-1, -CD11b, -Ly6G, and -Ly6C antibodies to quantify myeloid-derived suppressor cells (MDSCs) and MDSC subsets by flow cytometry. (C) The number of splenic Gr-1+CD11b+ MDSCs was significantly different between groups (n=14-20/group; Kruskal-Wallis, KW=7.96, p=0.047) with WM+PD-1 mice displaying the lowest number compared with WG+IgG mice. The number of (D) splenic CD11b+Ly6ChiLy6G- monocytic MDSCs (n=14-20/group; 1-way ANOVA, F(3,67)=2.28, p=0.088) was not significantly different between groups; however, (E) the number of splenic CD11b+Ly6CloLy6G+ granulocytic MDSCs was significantly different between groups (n=14-20/group; 1-way ANOVA, F(3,67)=3.65, p=0.017) with WM+PD-1 mice displaying the lowest number compared with WG+VEH mice. Dashed line represents non-tumor bearing control. Significantly different from WG+VEH+IgG (*).

206

Table 5.2. The percentage and number of splenic immune cell populations. (A) The percentage of splenic CD3+CD4+ helper T cells (p=0.048) was significantly different between groups. (B) The number of splenic immune cell populations was not significantly different between groups.

5.4.9.2. Splenic immune cell co-culture and bulk assays

The percentage of CD3+ T cells, CD3+CD4+ helper T cells,

CD3+CD8+ cytotoxic T cells, and NK1.1+ natural killer cells, and the

intracellular expression of IFN in helper T cells, cytotoxic T cells, and

natural killer cells was not significantly different between groups in naïve

splenocytes (Fig. 5.11A), after a 24-hour co-culture (Fig. 5.11B), or a five-

207

day bulk culture (Fig. 5.11E) with irradiated 4T1.2luc cells. 24-hour co-

culture cell IFN secretion was below detection (Fig. 5.11C). The percent

caspase induction in 4T1.2luc target cells co-cultured with 24-hour splenic

effector cells was not significantly different between groups (Fig. 5.11D;

n=14-16/group; 1-way ANOVA, F(3,60)=1.31, p=0.281). Splenic bulk

effector cell IFN secretion in response to re-stimulation with tumor

antigens was not significantly different between groups (n=14-20/group;

Kruskal-Wallis, KW=1.97, p=0.578).

208

209

Figure 5.11. Splenic immune cell co-culture and bulk assays. Splenocytes (n=14-20/group) were prepared into a single cell suspension and counted. (A) Naïve splenocytes, (B) Gr-1 depleted splenocytes co-cultured for 24-hours with irradiated 4T1.2luc, and (E) Gr-1 depleted splenocytes bulk cultured for five-days with irradiated 4T1.2luc were extracellularly stained with anti-CD3, -CD4, -CD8, and -NK1.1 and

intracellularly stained with anti-IFN and characterized by flow cytometry. No differences

in cell populations or intracellular IFN staining was observed. (C) 24-hour co-culture cell

IFN secretion was below detection. (D) Post 24-hour co-culture, cells were washed and co-cultured with VPD450-labeled 4T1.2luc target cells. After 24 hours, cells were intracellularly stained for caspase expression and analyzed by flow cytometry. The percent caspase induction in 4T1.2luc target cells was not significantly different between groups (n=14-16/group; 1-way ANOVA, F(3,60)=1.31, p=0.281). (F) Splenic bulk effector cell IFN

secretion in response to re-stimulation with tumor antigens was not significantly different between groups (n=14-20/group; Kruskal-Wallis, KW=1.97, p=0.578).


5.4.10.1. Tumor-infiltrating immunity

The percentage and number of tumor-infiltrating immune cells were

not significantly different between groups (n=6-12/group; Table 5.3).

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Table 5.3. The percentage and number of tumor-infiltrating immune cell populations. The (A) percentage and (B) number of tumor-infiltrating immune cell populations were not significantly different between groups.

The percentage of CD44-CD62L+CCR7+ naïve,

CD44+CD62L+CCR7+ TCM, CD44+CD62L-CCR7- TEM, and CD44-CD62L-

CCR7- effector CD4+ helper T cell populations was not significantly

different between groups (Fig. 5.12A; n=4-6/group). The percent of tumor-

infiltrating CD44-CD62L-CCR7- effector CD4+ helper T cells expressing

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CD69, IL-6R, IL-7R, KLRG1, or PD-1 on their cell surface was not

significantly different between groups (Fig. 5.12B).

The percentage of CD44-CD62L+CCR7+ naïve,

CD44+CD62L+CCR7+ TCM, CD44+CD62L-CCR7- TEM, or CD44-CD62L-

CCR7- effector CD8+ cytotoxic T cell populations was not significantly

different between groups (Fig. 5.12C; n=4-6/group). The percent of tumor-

infiltrating CD44-CD62L-CCR7- effector CD8+ cytotoxic T cells expressing

CD69, IL-6R, IL-7R, KLRG1, or PD-1 on their cell surface was not

significantly different between groups (Fig. 5.12D).

212

Figure 5.12. Tumor-infiltrating CD4+ and CD8+ T cell populations and effector activation and exhaustion markers. The percentage of tumor-infiltrating CD44-CD62L+CCR7+ naïve, CD44+CD62L+CCR7+ central memory (TCM), CD44+CD62L-CCR7- effector memory (TEM), or CD44-CD62L-CCR7- effector (A) CD4+ helper and (C) CD8+ cytotoxic T cell populations was not significantly different between groups. Effector CD4+ helper and CD8+ cytotoxic T cell populations were stained for markers of activation and exhaustion.

The percentage of effector (B) CD4+ helper and (D) CD8+ cytotoxic T cells expressing CD69, IL-6R, IL-7R, or PD-1 on their cell

surface was not significantly different between groups.

213

5.4.10.2. Tumor-infiltrating immune cell co-culture assay

The percentage of tumor-infiltrating CD3+ T cells, CD3+CD4+ helper

T cells, CD3+CD8+ cytotoxic T cells, and NK1.1+ natural killer cells, and

the intracellular expression of IFN in helper T cells, cytotoxic T cells, or

natural killer cells was not significantly different between groups after a

24-hour co-culture (Fig. 5.13A). 24-hour co-culture cell IFN secretion was

not significantly different between groups (Fig. 5.13B; n=5-8/group;

Kruskal-Wallis, KW=0.72, p=0.868). The percent caspase induction in

4T1.2luc target cells co-cultured with 24-hour tumor-infiltrating effector

cells was not significantly different between groups (n=6-8/group; Kruskal-

Wallis, KW=1.35, p=0.717).

214

Figure 5.13. Tumor-infiltrating immune cell 24-hour co-culture assay. Isolated tumor-infiltrating immune cells were pulsed with irradiated 4T1.2luc cells for a 24-hour co-culture to measure an antigen-specific T cell response to tumor antigens. Post culture, cells

were extracellularly stained with anti-CD3, -CD4, -CD8, and -NK1.1 and intracellularly stained with anti-IFN and characterized by flow

cytometry (n=6-8/group). (A) The percentage of tumor-infiltrating CD3+ T cells, CD3+CD4+ helper T cells, CD3+CD8+ cytotoxic T cells,

or NK1.1+ natural killer cells, or the intracellular expression of IFN in helper T cells, cytotoxic T cells, or natural killer cells was not

significantly different between groups after a 24-hour co-culture. (B) 24-hour co-culture cell IFN secretion was not significantly different between groups (Kruskal-Wallis, KW=0.72, p=0.868). (C) Post 24-hour co-culture, cells were washed and co-cultured with VPD450-labeled 4T1.2luc target cells. After 24 hours, cells were intracellularly stained for caspase expression and analyzed by flow cytometry. The percent caspase induction in 4T1.2luc target cells was not significantly different between groups (n=6-8/group; Kruskal-Wallis, KW=1.35, p=0.717).

215

5.4.10.3. Tumor-immune crosstalk gene array

Figure 5.14. Moderate exercise in weight stable mice, alone and in combination with

PD-1 checkpoint blockade, altered gene expression in the tumor microenvironment.

Tumor homogenates were assayed using Qiagen RT2 Profiler™ Mouse Cancer

Inflammation & Immunity Crosstalk PCR Array. (A) Heat map displaying genes altered >2-

fold. (B) Genes that were significantly altered (>2.5-fold) compared with WG+IgG control.

216

5.5. DISCUSSION

Previous work in our lab demonstrates that moderate exercise in weight

stable, non-tumor bearing mice augments antigen-specific T cell response to

vaccination and in response to a viral-vector based vaccine in a pancreatic cancer

model (272, 273). Furthermore, we have demonstrated (chapter three) that the

prevention of weight gain through exercise and mild dietary restriction can reduce

primary tumor growth and metastatic burden, reduce immunosuppressive cells,

augment T cell responses, and alter the tumor microenvironment in the 4T1.2

mammary tumor model. Also, we have demonstrated (chapter four) that moderate

exercise in weight stable mice in combination with the administration of an

immunogenic whole tumor cell cancer vaccine significantly reduced 4T1.2 primary

tumor growth and metastatic burden compared with exercise/weight maintenance

alone. To our knowledge, no study has investigated if lifestyle-based intervention

strategies can improve the efficacy of immune checkpoint blockade. Thus, since

numerous host factors are likely to impact immunotherapeutic efficacy, we chose

to examine if an exercise and weight maintenance intervention could blunt tumor-

induced inflammation and immunosuppression resulting in an enhancement in the

dual administration of a whole tumor cell cancer vaccine and PD-1 checkpoint

blockade. Several methodological concerns were assessed in a pilot study prior to

investigating the effects of moderate exercise in weight stable mice on the efficacy

of the dual administration of a whole tumor cell cancer and PD-1 checkpoint

blockade.

217

The role of PD-1 in tumor progression and the response to antibody

blockade in the 4T1 series of murine BC models is emerging. Data from Hirano, et

al. (264) confirm that 4T1 tumors upregulate PD-L1 in vivo and blockade of PD-L1

(100 g) alone at day 7 and 10 post-tumor implantation partially inhibits primary

tumor growth. Beavis, et al. (265) demonstrates that single agent PD-1 checkpoint

blockade (200 g, administered at day 7, 11, and 15 post-tumor implantation) is

not effective at reducing 4T1.2 primary tumor growth or spontaneous lung

metastasis when mice are removed from study at day 18 post-tumor implantation.

This lack of effect was attributed to low levels of PD-1 cell surface expression on

T cell subsets during 4T1.2 tumor progression. The exact timing of the upregulation

of PD-1 on the cell surface of T cells is dependent on numerous intrinsic factors

(53). Therefore, we quantified the level of PD-1 surface expression on splenic T

cells from untreated (i.e., no VAX or PD-1) 4T1.2luc tumor-bearing mice at day 24

and 35 post-tumor implantation. Day 24 splenic CD4+ helper and CD8 + cytotoxic

T cells expressed a higher percentage of PD-1 than day 35 T cells. Since PD-1

cell surface expression was measureable and elevated early in 4T1.2 tumor

progression, this supported the inclusion of PD-1 checkpoint blockade as a viable

treatment option in the 4T1.2 model.

An additional concern that is currently under clinical investigation is the

optimal dosage and schedule of immunological checkpoint blockade

administration (253, 622). Preclinical models use vastly different dosages and

schedules of PD-1 checkpoint blockade when administered alone, or in

combination with additional therapies (266-270). For example, the literature is

218

inconsistent if single agent PD-1 checkpoint blockade is effective at reducing

established tumor growth in the 4T1 or 4T1.2 model (265, 270). Methodological

differences in timing (prior to or after palpable tumor), number of administrations

(one-four), or dosage of anti-PD-1 (100-250 g) may drive the disparate findings.

However, the majority of articles report that the administration of PD-1 checkpoint

blockade in combination with therapies that target a reduction in systemic

inflammation (e.g., celecoxib [selective Cox-2 inhibition], azacitine or entiostat

[epigenetic modulating drug], and J32 [phosphoinositide 3-kinase inhibitor]) are the

most effective at driving an enhancement in antitumor effector function, a reduction

in established tumor growth, and a reduction in spontaneous metastasis (266-270).

The number of possible combinatorial treatment strategies grows exponentially.

Therefore, testing if lifestyle-based interventions, i.e., physical activity and the

prevention of weight gain, which are low-cost and have known benefits across the

cancer continuum (271), can augment T cell responses (272, 273) and can

enhance the efficacy of immunotherapeutic strategies represents an attractive

option.

Experimental results from previous findings (chapter three and four) show

that moderate exercise in weight stable mice can reduce the expansion of

immunosuppressive cells and enhance a broad-based immunogenic whole tumor

cell cancer vaccine. We wanted to build on this work using an emerging, targeted

immunotherapy, specifically PD-1 checkpoint blockade. Instead of selecting a

pharmacological inhibitor of systemic inflammation and immunosuppressive cells,

we decided to test if a diet and exercise intervention, coupled with the whole tumor

219

cell cancer vaccine as an immunogenic stimulus, could improve PD-1 checkpoint

blockade in 4T1.2 tumor-bearing mice. However, prior to the dual administration

of a whole tumor cell cancer vaccine and PD-1 checkpoint blockade in moderately

exercising, weight stable mice, we tested the timing and rounds of treatment in an

in vivo pilot study. Sedentary, ad libitum-fed BALB/c mice were orthotopically

injected with luciferase-transfected 4T1.2 cells and received one vs. two rounds of

immunotherapy (consisting of one or two rounds of a PBS vehicle (VEH) control or

1x106 irradiated 4T1.2luc vaccine (VAX), followed by two I.P. injections of PD-1

checkpoint blockade (10 mg/kg/mouse)). Since the diet and exercise plus PD-1

checkpoint blockade study was designed to evaluate two body weight phenotypes,

we administered 10 mg/kg/mouse of anti-PD-1 to adjust the dosage of anti-PD-1

antibody based on body weight, similar to Kim, et al. (266). Additionally, 10 mg/kg

is the dosage currently under clinical investigation in a phase I KEYNOTE-12 trial

investigating anti-PD-1 (pembrolizumab) treatment in women with PD-L1-positive

TNBC (623). Primary tumor growth was not significantly different between the four

treatment groups and no differences were observed between VEH or VAX within

one vs. two rounds of treatment. Therefore, the VAX intervention was collapsed to

investigate two (x2) vs. four (x4) PD-1 checkpoint blockade injections on primary

tumor growth and immune cell populations in various immune compartments, as

well as functional immune outcomes. Primary tumor growth was not significantly

different between two vs. four administrations of PD-1 checkpoint blockade.

Splenic, non-draining lymph node (nDLN), and tumor-infiltrating immune

populations were not significantly different between groups. No differences were

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observed in naïve, memory, or effector status of CD4+ helper or CD8+ cytotoxic T

cell populations in the spleen, nDLN, or tumor-infiltrating immune cell

compartments. The 4T1.2 model is associated with rapid in vivo tumor growth;

therefore, the limited time and lack of a true dormancy period may prohibit the

investigation of memory T cell populations. CD4+ helper or CD8+ cytotoxic T cell

populations were stained for an activation marker (CD69), a marker of antigen-

experience (KLRG1), markers with memory-forming potential (IL-6R, IL-7R),

and an exhaustion marker (PD-1). The percent of tumor-infiltrating effector CD4+

helper or effector CD8+ cytotoxic T cell populations expressing CD69, IL-6R, IL-

7R, or PD-1 was not significantly different between groups. Mice receiving (x2)

PD-1 checkpoint blockade displayed a significant increase in the percent of tumor-

infiltrating CD8+ cytotoxic T cells expressing KLRG1, a marker that is upregulated

as cells undergo terminal differentiation and can promote the transition to cell

senescense (624). However, this difference did not drive changes in tumor growth.

Lastly, re-stimulation assays (24-hour co-culture and five-day bulk culture)

assessed the tumor-killing capacity of antitumor effector cells through the

characterization of IFN intracellular production in splenic and tumor-infiltrating

lymphoid effector cells and IFN secretion post-re-stimulation, as well as direct

characterization of the cytolytic activity of lymphoid effector cells via the

quantification of cleaved-caspase-3 in 4T1.2luc target cells. Antitumor effector cells

can induce apoptosis in targeted cells by releasing perforin and granzyme A and

B, which cleaves caspases (e.g., caspase-3) and other downstream mediators of

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apoptosis (625). Re-stimulation assay outcomes were not significantly different

between groups.

Powered with this information, we selected one round of immunotherapy

consisting of one PBS vehicle (VEH) control or 1x106 irradiated 4T1.2luc (VAX) at

day 7, followed by two I.P. injections of isotype control (IgG) or PD-1 checkpoint

blockade (10 mg/kg/mouse) at day 9 and 12 post-tumor implantation for use in

combination with our diet and exercise intervention. The study was designed to

investigate the effects of moderate exercise in weight stable mice on the dual

administration of a whole tumor cell cancer vaccine and PD-1 checkpoint blockade

in the 4T1.2 model. The pilot study failed to demonstrate an additive effect of whole

tumor cell cancer vaccine with PD-1 checkpoint blockade on the prevention of

tumor growth. The VAX intervention was included in the current study because of

potential additive effects that may occur in moderately exercising, weight stable

mice receiving the dual administration of VAX+PD-1. However, like the pilot study,

the administration of one round of VAX or VEH were not significantly different

within each energy balance (WG vs. WM) group, therefore VEH and VAX groups

were collapsed to investigate PD-1 checkpoint blockade effects in both WG and

WM groups (i.e., WG+IgG vs. WG+PD-1 vs. WM+IgG vs. WM+PD-1). We

observed a protective effect of PD-1 checkpoint blockade in WG mice on primary

tumor growth and spontaneous lung metastasis. Moderate activity in weight stable

mice, independent of PD-1 checkpoint blockade, was effective at reducing primary

tumor growth and metastatic burden on par with the effectiveness of PD-1

checkpoint blockade in WG mice. The lack of vaccine effect on tumor growth in the

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current study may be due to the reduction from four to one round of whole tumor

cell vaccine administration. Since we were combining vaccination with PD-1

checkpoint blockade, we decided to reduce the number of vaccinations to one

round to allow the observation of PD-1 checkpoint blockade-specific effects. In the

collapsed groups, we did observe a cancer prevention effect of PD-1 checkpoint

blockade in WG mice on primary tumor growth and spontaneous lung metastasis.

However, moderate exercise in weight stable mice, independent of PD-1

checkpoint blockade, was effective at reducing primary tumor growth and

metastatic burden to the same extent as PD-1 checkpoint blockade in WG mice.

The cancer protective effect observed in the WM groups could reflect the reduction

in splenomegaly and immunosuppressive cell types (e.g., MDSCs), similar to

observations in chapter three and four, and/or a shift in tumor-immune crosstalk

gene expression markers (e.g., Il13 (626) and Ccl22 (588)) important in tumor

invasiveness and the expansion and recruitment of immunosuppressive cell types

to the TME. These mechanisms were discussed in chapter three and four and are

likely still relevant as potential mechanisms underlying the cancer prevention effect

of exercise in weight stable mice.

When a response to PD-1 checkpoint blockade occurs, it likely reflects

some level of prior T cell-mediated antitumor response. Thus, the contrasting

findings of PD-1 checkpoint blockade responsiveness between our WG and WM

mice provides insight into potential mechanisms in which moderate exercise in

weight stable mice may be altering host physiology to enhance antitumor immunity.

These mechanisms may include: 1. An enhancement in the activation or function

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of effector immune cells; 2. A reduction in the expression of PD-1 on T cells and

non-T lymphocytes or a reduction in the PD-1 inhibitory signaling cascade; and/or

3. A reduction in the expression of ligands for PD-1 on tumor cells and tumor-

infiltrating myeloid cells.

First, moderate exercise in weight stable mice may directly or indirectly

enhance effector immune cell function resulting in improved antitumor immunity.

The activation of a naïve T cell occurs through the simultaneous engagement of

the T cell receptor (TCR) and co-stimulatory molecules (e.g., CD28 or inducible T

cell co-stimulator [ICOS]) on the T cell surface by major histocompatibility

complexes (MHC) (CD4 in the context of MHC class II and CD8 in the context of

MHC class I) and co-stimulatory molecules (e.g., CD80 [B7.1] and CD86 [B7.2])

on APCs (87). TCR stimulation alone results in anergy (or T cell tolerance);

whereas, co-stimulation alone has no effect on T cell activation. TCR engagement

orchestrates essential changes in the gene expression profile to transition a naïve

cell to an actively expanding immune effector cell. Intracellular signaling involves

a myriad of molecules, with the most common being Src family kinases (e.g., Fyn,

Lck) which phosphorylate tyrosines on intracellular immunoreceptor tyrosine-

based activation motifs (ITAMs) on the intracellular domain of CD3 ζ-chain (88).

Phosphorylation events result in downstream signaling mediated by

phospholipase-C (PLC), mitogen-activated protein kinase (MAPK), nuclear factor

kappa B (NF-B), and nuclear factor of activated T cell (NFAT) which results in

gene transcription in the nucleus of factors that promote cell survival and

proliferation (87). Co-stimulatory and cytokine signaling, especially signaling via

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IL-2 (89), activate phosophoinositide-3 kinase (PI3K) and its downstream target

protein kinase B (Akt), which increases expression of glucose transporters on the

cellular membrane and upregulates glycolytic enzyme activity to meet the

metabolic needs of the highly proliferative effector cells (90). Cytokine signaling

also activates specific transcriptional factors to induce CD4+ helper T cell

polarization (defined subsets include TH1, TH2, TH9, TH17, TH22, and Tregs) to

refine the T cell response. Moderate exercise in weight stable mice could result in

an enhancement in the ability of APCs to present antigens to T lymphocytes,

improve TCR or co-stimulatory signaling mechanisms, or promote or sustain a

cytokine signaling milieu to favor a robust antitumor response. For example,

cytokine imbalance is one mechanism responsible for immune dysregulation within

the TME and tumor-draining lymph node (627). Tumor-derived cytokines and/or

cytokines produced by tumor-infiltrating immunosuppressive cells, like IL-10 and

TGF, can condition tumor-antigen specific T cells toward a less efficacious TH2

or Treg phenotype (627). Signaling from the TME can blunt lymphocyte production

of IL-2 and IFN (628-630); however, data from preclinical studies show an

increase in lymphocyte secretion of IL-2 and IFN in exercising mice compared

with sedentary controls (273, 522). Exercise-induced signaling via AMPK/PI3K can

increase glucose uptake and fatty acid oxidation, as well as augment mitochondrial

size, number, and enzymatic activity, in skeletal muscle (631). It is conceivable

that these effects can also occur in immune compartments important in

immunosurveillance to bypass PD-1 induced dysregulation of immunometabolism

in effector cells and ultimately enhance the eradication of transformed and

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malignant cells (632-634). In the current study, we observed a significant

difference in splenic CD4+ helper T cell proliferation, with WM mice having an

increased proliferative capacity. Both splenic and tumor-infiltrating immune cell

recall assays failed to show significant differences in intracellular IFN staining in

antitumor immune subsets or IFN secretion post re-stimulation. However, at day

35 post tumor implantation, we observed an increase in the fold regulation of Il2

and Il12b in the tumor-immune crosstalk gene array. These genes encode for

cytokines important in the activation of NK and T cells and promote the production

of IFN (635-637). Thus, the splenic and TIL bulk culture experiments may not

adequately capture what is happening in the TME. In addition, these

aforementioned functional outcomes were performed at day 35 post-tumor

implantation, but tumor growth curves began to separate at day 14 post-tumor

implantation. We may have missed the critical window to capture differences in

effector responses in the TME. Once tumor burden reaches a critical mass, the

tumor-induced cytokine imbalance could drive immune dysregulation and mask

earlier enhancements in antitumor responses. Future studies are needed to

examine the effects induced by moderate exercise at both earlier time points in

tumor growth (e.g. when the tumor growth curves start to diverge) and when tumor

volumes are comparable between groups

Second, moderate exercise in weight stable mice may result in a reduction

in the upregulation of PD-1 transcription and/or expression of PD-1 on the cell

surface, or a reduction in the inhibitory cascade induced by PD-1 in antitumor

effector cells. Upon activation, TCR-mediated calcium influx can induce Pdcd1

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transcription (the gene encoding for PD-1) via NFATc1 (254). When PD-1 is

engaged by its ligand, the PD-1 intracellular domain, composed of an

immunoreceptor tyrosine-based inhibition motif (ITIM), inhibits kinases involved in

T cell activation through the recruitment of Src homology 2-containing tyrosine

phosphatase (SHP2) (53). PD-1 induced SHP2 can directly inhibit TCR

transduction by dephosphorylating and inactivating Zap70, a major integrator of

TCR-mediated signaling (254), or by blocking the induction of PI3K activity and

downstream Akt signaling, resulting in an inhibition in the ability of T cells to import

glucose, rendering T cells more susceptible to apoptosis (255, 256). No studies to

date have assessed if exercise can alter the transcription of Pdcd1 or the

intracellular inhibitory signaling cascade induced by PD-1 ligation in antitumor

immune cells.

Additional control of PD-1-induced inhibition can occur via cytokine

signaling. For example, IL-2 signaling via the IL-2R activates Akt via signal

transducer and activator of transcription (STAT) 5 to upregulate energetic

mechanisms; thus, bypassing the PD-1-induced blockade of nutrient uptake (e.g.,

glucose uptake) and utilization within antitumor immune cells (638). Therefore, an

exercise-induced increase in IL-2 can not only directly enhance T cell activation,

but may override PD-1-induced inhibition, resulting in a sustained antitumor

response. In the current study, we saw no difference in the cell surface expression

of PD-1 on tumor-infiltrating effector CD4+ helper or CD8+ cytotoxic T cells at day

35 post-tumor implantation. The timing of PD-1 induction on immune effector cells,

as well as the best time to administer PD-1 checkpoint blockade, remains an active

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area of research (622, 639). Therefore, the lack of differences we observed in the

current study may reflect the time point at which cells were characterized, i.e.,

there may be differences in PD-1 expression among our WG and WM groups

earlier in tumor growth. Furthermore, PD-1 expression is not limited to T

lymphocytes. Tregs highly express PD-1 and PD-1 signaling can promote Treg

proliferation and immunosuppression (640). PD-1 is broadly expressed on non-T

lymphocyte cells, including NK cells and B cells; however, little is known about

energy balance and upregulation of PD-1 on these cells (53). PD-1 checkpoint

blockade may also enhance antitumor immunity by diminishing the number and/or

suppressive function of intratumoral Tregs or through an enhancement in NK

cytotoxicity or antibody production on PD-1+ B cells (53, 640). Future studies are

needed to investigate exercise-induced effects on the PD-1 response in T cells,

NK cells, and B cells.

Third, moderate exercise in weight stable mice could result in a reduction in

the expression of ligands for PD-1 on tumor cells and tumor-infiltrating myeloid

cells. The induction of PD-1 ligands can occur via innate mechanisms inherent to

transformed, malignant cells. Many cancer types, including TNBCs, are

characterized by constitutive oncogenic signaling and increased PD-L1 expression

(261, 641). Additionally, tumor-induced inflammation, as well as signaling from an

early phase antitumor immune response, can result in an adaptive induction of PD-

L1 on tumor cells and tumor-infiltrating myeloid cells (53, 642). The intricacies of

this system are best characterized by the duality of IFN signaling within the TME

(643). IFN is a potent antitumor cytokine that can promote tumor surveillance,

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immune activation, and antitumor activity (613, 644). However, a negative

feedback loop exists in which IFN can promote immunosuppression of PD-1+ T

cells via the induction of PD-L1 expression on tumor and myeloid cells (645).

Exercise could attenuate both the innate and adaptive induction of PD-L1 on tumor

cells early in tumor development; thus reducing PD-1:PD-L1 interactions and

sustaining a robust antitumor immune response. The current study did not observe

differences in PD-1 expression on tumor-infiltrating effector CD4+ helper or CD8+

cytotoxic T cells. Future studies need to assess PD-1 expression over time, as well

as PD-L1 induction in the TME. Exercise-induced enhancements in antitumor

immunity could result in a more effective or timely apoptotic- or necrotic-induction

of cell death in tumor cells, reducing the ability of tumor cells to upregulate PD-L1-

induced suppression mechanisms. However, the assessment of splenic and

tumor-infiltrating antitumor immunity via re-stimulation assays (24-hour co-culture

and five-day bulk culture), including IFN intracellular production and IFN

secretion post-re-stimulation, as well as direct characterization of the cytolytic

activity of lymphoid effector cells via the quantification of cleaved-caspase-3 in

4T1.2luc target cells, were not significantly different between groups. The lack of

effect could be due to in vitro assay limitations, the terminal timing of this endpoint,

and/or exercise induces enhanced antitumor immune killing of target cells via an

alternative mechanism. The mechanisms in which antitumor effector cells can kill

transformed and malignant cells is extensively reviewed (95, 646-649). Future

studies are needed to assess possible exercise-induced mechanisms on the

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induction of PD-1 ligands in the TME and the timing and alternative mechanisms

of immune-mediated antitumor responses.

In conclusion, the contrasting findings of PD-1 checkpoint blockade

responsiveness between our WG and WM mice provides extensive clues by which

exercise in weight stable mice may be altering host physiology to enhance

antitumor immunity. The observed response to PD-1 checkpoint blockade in WG

mice suggests that some level of prior T cell-mediated antitumor response

occurred in the WG mice. The lack of PD-1 checkpoint blockade effect in WM mice,

in combination with the comparable tumor outcomes to WG+PD-1 mice, suggests

that the T cell-mediated antitumor response was maintained in the WM mice via

an exercise-induced reduction in tumor-mediated immunosuppressive

mechanisms and/or a direct enhancement in antitumor effector mechanisms.

These data demonstrate that moderate exercise and weight control may be an

important intervention to maintain prolonged antitumor effector responses and

improve clinical outcomes.

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CHAPTER 6: RESEARCH SUMMARY AND FUTURE DIRECTIONS

6.1. SUMMARY OF RESEARCH

My dissertation studies were designed to model women who remain weight

stable throughout adulthood via two lifestyle-based strategies, a modest reduction

in calories (10% of caloric intake) and moderate physical activity, to investigate

these energy balance interventions using a clinically relevant murine metastatic

BC model. The goal of these studies was to determine 1) the extent to which

exercise, as opposed to changes in body weight, protect against primary tumor

growth and metastatic progression and 2) if moderate exercise in weight stable

mice improves responses to two immunotherapeutic interventions, whole tumor

cell cancer vaccine and PD-1 checkpoint blockade.

The study presented in chapter three was designed to examine the effects

of exercise, mild dietary restriction, or the combination of diet and exercise on

tumor progression and the inflammation-immune axis in a preclinical metastatic

BC model to determine the extent to which exercise or body weight contribute to a

cancer prevention effect. Although the prevention of weight gain via dietary energy

restriction (i.e., SED+ER mice) was effective at altering host splenic immunity and

the expression of key genes in the tumor microenvironment (TME) related to

immunosuppression and metastatic progression, this intervention failed to induce

changes in primary tumor growth or spontaneous metastases.

Body weight in the moderately exercising, weight stable mice (i.e., EX+ER)

was matched to SED+ER mice, allowing the comparison of exercise-induced vs.

body weight-induced effects on tumor growth and immune endpoints. Moderate

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exercise in weight stable mice resulted in a reduction in immunosuppressive and

metastatic genes in the TME similar to the SED+ER mice; however, EX+ER mice

had the greatest reduction in splenic immunosuppressive cells, including a

reduction in MDSCs and MDSC subsets, as well as a reduction in plasma insulin-

like growth factor 1 (IGF-1). The effects of moderate exercise in weight stable mice

culminated in a significant delay in primary tumor growth and spontaneous

metastases, suggesting that exercise-induced alterations, not simply a change in

body weight, underlie the protective effects of the combined intervention on tumor

growth. The exercise-induced effects could include the reduction in hormonal,

inflammatory, metabolic and/or proliferative signals and/or the prevention of the

acquisition of a toxic, immunosuppressive TME. These events may promote

sustained immunosurveillance mechanisms that prevent tumor cell invasion and

metastatic spread.

Metastatic progression is a multi-step process that involves the interaction

between tumor cells, stromal cells, and tumor-infiltrating immune cells to alter

tumor angiogenesis, cell invasion, and cell migration pathways to promote the

dissemination of tumor cells to distant sites. The reduction in spontaneous

metastases suggests that moderate exercise in a weight stable host may impact

this multi-step process, perhaps through a reduction in MDSCs or plasma IGF-1.

Emerging data suggests MDSCs and IGF-1 play a role in metastatic progression

by modulating genes related to epithelial-mesenchymal transition (EMT) and

chemokine signaling, as well as altering the vasculature of the TME. MDSCs can

further promote metastases by infiltrating distant sites and generating a niche more

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favorable to the seeding of disseminated cancer cells. The specific mechanism by

which exercise in weight stable mice reduces tumor growth and metastatic

progression remains unclear and warrants further investigation. However,

experimental results (chapter three) suggest a shift in tumor-induced inflammation

and immunosuppression, as well as a reduction in plasma IGF-1, resulting in a

sustained antitumor response.

Interestingly, the exercise-induced protective effect on tumor growth and

reduction in immunosuppressive factors (both immunosuppressive cell types [e.g.,

MDSCs] and reduced expression levels of immunosuppressive genes in the TME)

was lost when exercising mice continued to gain weight over the course of the

study via ad libitum feeding. Over the course of the 13-week study, ad libitum-fed

mice progressed to an overweight phenotype. The current findings suggest that

weight gain-induced disturbances in hormonal, inflammatory, and/or

immunological function can override the exercise-induced benefits on tumor

growth and metastatic progression observed in weight stable mice. Weight gain-

induced disturbances likely occur in a continuum. This further emphasizes the

importance of the current American Cancer Society guidelines to maintain a

healthy weight throughout life, or lose weight and/or maintain weight if overweight

or obese by balancing caloric intake and engaging in physical activity to reduce

the risk of developing breast cancer. Additionally, the “metabolically healthy obese”

phenotype may still result in disturbances in the inflammation-immune axis that

reduce immunosurveillance mechanisms. Therefore, weight maintenance may be

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the safest public health recommendation to prevent breast cancer until more is

known about weight maintenance vs. weight gain in this population.

Study one provides a deeper understanding of the extent to which exercise,

and not solely changes in body weight, underlie cancer protection. It provides

further evidence that exercise can act via the inflammation-immune axis to

attenuate the generation of a protumorigenic and immunosuppressive TME.

Results from the current study provide insight into potential mechanisms by which

exercise in weight stable hosts exerts primary and secondary cancer prevention

effects. Furthermore, these observations in a preclinical model provide a biological

rationale for future randomized controlled trials of exercise and the prevention of

weight gain to prevent metastatic progression in BC survivors and ultimately

improve survival outcomes.

To investigate potential mechanisms and determine if moderate exercise in

weight stable mice can improve therapeutic responses, we combined two different

emerging cancer therapies with our lifestyle-based, diet and exercise intervention.

An allogeneic, whole tumor cell cancer vaccine is a broad-based immunogenic

stimulus that provides a wide array of TAAs and drives a polyclonal T cell

response. This response involves antigen uptake, processing, and presentation

from APCs to T cells to induce antitumor effector cell function. Conversely,

targeted PD-1 checkpoint blockade reactivates the T cell-mediated antitumor

response by antagonizing the co-inhibitory PD-1 immune checkpoint via antibody

blockade. Upon T cell activation, the PD-1 inhibitory molecule is upregulated on

the T cell surface and plays a role in downregulating the antitumor response by

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inducing apoptosis. PD-1 is widely expressed on several cell types important in

antitumor immunity, including B cells, NK cells, and CD4+ helper and CD8+

cytotoxic T cells, making this an emerging target for cancer therapy. The efficacy

of both therapies can be compromised by tumor-secreted proinflammatory factors

that promote the expansion and accumulation of immunosuppressive immune cell

types within the TME and peripheral organs. Study one (chapter three)

demonstrated that the prevention of weight gain through mild dietary energy

restriction and moderate exercise can reduce primary tumor growth and metastatic

burden, reduce immunosuppressive cells, augment T cell responses, and alter the

TME in the 4T1.2 mammary tumor model. Therefore, we wanted to determine if

moderate exercise in weight stable mice could enhance the efficacy of the whole

tumor cell cancer vaccine or PD-1 checkpoint blockade. Numerous researchers

are investigating pharmacological blockade of immunosuppressive mechanisms;

however, the studies presented in chapter four and five are among the first to show

that exercise can impact the inflammation-immune axis resulting in a better

response to immunotherapy. In chapter four, we observed an additive effect of

moderate exercise in weight stable mice and the administration of the whole tumor

cell cancer vaccine. However, in chapter five, we saw no additive effect of our

lifestyle intervention and PD-1 checkpoint blockade. The disparate responses of

therapies to moderate exercise provide insight into potential mechanisms by which

exercise confers protection.

Whole tumor cell cancer vaccines utilize numerous immune mechanisms to

induce a robust antitumor response and exercise-induced changes in hormonal,

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metabolic, and inflammatory mediators could impact one or multiple mechanisms.

The whole tumor cell cancer vaccine provided APCs, like DCs, with a broad array

of TAAs. The DCs phagocytose exogenous antigens, process them by proteases

to peptide fragments, migrate to proximal draining lymph nodes, present antigens,

and activate CD8+ cytotoxic T cells in the context of class I major histocompatibility

complex. Activated T cells migrate to the TME and induce targeted killing of

cancerous cells. The exercise-induced reduction in immunosuppressive cells and

inflammatory TME, as well as the reduction in plasma IGF-1, could enhance

antigen presentation, T cell activation, and/or effector function. An exercise-

induced enhancement in antitumor cytotoxicity could improve vaccine efficacy via

epitope spreading, or the process in which T cells respond to tumor-associated

peptides not present in the original vaccine. These results indicated that the whole

tumor cell cancer vaccine can augment the weight maintenance (via diet and

exercise) effects on primary tumor growth and spontaneous metastasis and

suggest that vaccination may provide an immune stimulus to further promote the

protective effects of moderate exercise alone.

PD-1 checkpoint blockade is a more targeted approach that antagonizes

co-inhibitory signals on immune cells that induce inhibition of immune responses,

which in turn promotes prolonged tumor-fighting capabilities. We observed a

protective effect of PD-1 checkpoint blockade in WG mice on primary tumor growth

and spontaneous lung metastasis, suggesting that PD-1 checkpoint blockade was

effective at reactivating the immune system and/or preventing effector cell

inhibition in WG mice. It was hypothesized that PD-1 checkpoint blockade would

236

allow the immune system to completely eradicate the tumor and prevent

spontaneous metastases in moderately exercising, weight stable mice. However,

moderate exercise in weight stable mice, independent of PD-1 checkpoint

blockade, was effective at reducing primary tumor growth and metastatic burden

with the same effectiveness as PD-1 checkpoint blockade in WG mice. The lack

of PD-1 checkpoint blockade effect in WM mice suggests that moderate exercise

in weight stable mice is generating a systemic environment that favors prolonged

activation of antitumor effector cells (i.e., PD-1 checkpoint blockade can only be

effective if it can bind to PD-1 on antitumor cells).

The protective effect on tumor growth observed in the WM groups could be

a consequence of the reduction in splenomegaly and immunosuppressive cell

types (e.g., MDSCs), similar to observations reported in chapter three and four,

and/or a shift in tumor-immune crosstalk gene expression markers important in

tumor invasiveness and the expansion and recruitment of immunosuppressive cell

types to the TME. This exercise-induced shift in inflammatory status could delay

the upregulation of the PD-1 checkpoint on antitumor cells, reduce the inhibitory

signaling by PD-1 ligation, and/or reduce PD-L1 expression on tumor and

supporting cells within the TME, resulting in sustained antitumor immune cells.

These data demonstrate that moderate exercise and weight control may be an

important recommendation to maintain prolonged antitumor effector responses

and improve clinical outcomes.

Exercise effects are pleiotropic and likely involve complex and multifaceted

interactions between numerous physiological systems. Although the current study

237

does not definitively prove what specific host systems are involved, it does suggest

that exercise, and not merely the prevention of weight gain, is necessary for cancer

prevention. These studies demonstrate that moderate exercise in a weight stable

host can prevent spontaneous metastases, a substantial finding considering the

leading cause of BC-specific mortality is due to metastatic disease. Results from

the current study provide insight into potential mechanisms (Fig. 6.1) by which

exercise acts over the cancer continuum and provides a biological rationale for

clinical studies of exercise strategies to prevent metastatic progression in BC

survivors and ultimately improve survival outcomes. Lastly, the results provide

evidence that emerging immunotherapy interventions could be coupled with

lifestyle-based intervention strategies to improve clinical outcomes.

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1. Immunosuppressive cell secreted factors(e.g., G-CSF, IL-6, ROS, NO, VEGF, TNF a, IL-1β)

2. Suppressive enzymes (e.g., IDO, Arg-1)

3. Immunosuppressive cell types

Tumor-secreted factors(e.g. G-CSF, IL-6, PGE2, VEGF,

TNFa, IL-1β, S100A8/A9)

Decrease mutational rate in normal breast

CD8+

T cellNK

cell

CCLs and CXCLs(e.g., Ccl5, Cxcl1,

Ccl20, Ccl22)

TAMsMDSCs

Treg polarization

A

Enhance immunosurveillance mechanismsB

Reduce the generation of the proinflammatory tumor microenvironmentC

HSC

iMC

Reduce immunosuppressive mechanismsD

Reduce metastatic progressionE

CD4+ T cell

239

Figure 6.1. Exercise-induced effects along the cancer continuum. (A) In a normal

breast cell, exercise-induced effects could regulate the cell cycle, increase inflammatory

resolving capacity, decrease the rate of intrinsic oncogenic mutations, and/or alter

epigenetic modulation favoring tumor suppressor gene expression, culminating to a

reduction in cancer cell initiation events. (B) Exercise-induced effects could directly or

indirectly improve immunosurveillance mechanisms resulting in the elimination of

transformed cells. (C) In early tumor progression, exercise-induced effects could reduce

the secretion of tumor-derived suppressive factors and delay the generation of the

proinflammatory tumor microenvironment. Exercise could reduce the expression of

immune inhibitory ligands on tumor and tumor-infiltrating immune cells or reduce the

expression of inhibitory immune checkpoints on antitumor effector cells. (D) Exercise-

induced effects could reduce the emergence of immunosuppressive cell populations (e.g.,

myeloid-derived suppressor cells [MDSCs] and/or tumor-associated macrophages

[TAMs]) through a reduction in the expansion of immature myeloid-lineage cells (iMC) from

hematopoietic stem cell (HSC) progenitors, decrease Treg polarization, and decrease the

recruitment of immunosuppressive cells through a reduction in chemokine signaling (e.g.,

Ccl5, Cxcl1). Furthermore, exercise-induced effects could blunt immunosuppressive

mechanisms (e.g., release of suppressive factors, suppressive enzymes). (E) Lastly,

exercise-induced effects could alter tumor angiogenesis, epithelial-mesenchymal

transition, cell invasiveness, the ability of circulating tumor cells to extravasate into

secondary sites, and/or the metastatic niche.

6.2. FUTURE DIRECTIONS

As exercise oncology matures, it is essential for investigators in the fields

of exercise physiology, immunology, and cancer biology to understand the

mechanisms by which exercise induces beneficial changes in the inflammation-

immune response and harness these mechanisms to enhance therapeutic

efficacy. Numerous questions remain unanswered.

Study one (chapter three) provides a deeper understanding of the extent to

which exercise, and not only changes in body weight, contributes to cancer

prevention. Exercise induces pleiotropic effects on numerous physiological

systems. Goh, et al. (509) hypothesizes that exercise can affect the immune

system by 1) altering inflammatory tone and tumor-immune crosstalk, or 2) through

the release of muscle-derived cytokines, called myokines. Both mechanisms can

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result in a host environment more suitable to prolonged immunosurveillance and

tumor clearance. Identifying the specific molecules and mechanisms driving the

exercise-induced benefit in cancer prevention is an active area of research (435,

509-511). In the current study, body weight in moderately exercising, weight stable

mice (i.e., EX+ER) was matched to SED+ER mice, allowing the comparison of

exercise-induced vs. body weight-induced effects. Additionally, the exercise-

induced protective effect on the emergence of immunosuppressive factors and

reduced tumor burden was lost when mice continued to gain weight over the

course of the study. This result suggests that weight gain-induced disturbances on

hormonal, inflammatory, and/or immunological function can override the exercise-

induced benefits. Therefore, contrasting the EX+AL and EX+ER groups could shed

light onto how moderate weight gain negates exercise-induced benefits.

A more thorough exploration of muscle-derived myokines (e.g., leukemia-

inhibitory factor, IL‐6, IL‐7, brain-derived neurotrophic factor, IGF‐1, fibroblast

growth factor-2, follistatin-related protein 1) could shed light on potential pathways

by which exercise (i.e., contraction of skeletal muscle) induces cancer prevention

benefits (510). Taking mice off study at different times after tumor implantation

could provide insight into myokine signaling on the immune response over time,

as well as detail the deleterious effects of progressive tumor growth and/or ad

libitum-feeding on myokine-immune crosstalk. Additionally, follow-up studies could

selectively knock out or inhibit select myokines and observe if exercise-induced

benefits are lost.

241

In the current study, mice were sacrificed at day 35 post-tumor implantation.

Several plasma (e.g., G-CSF, IL-6, IL-1, MCP-1) and immune outcomes (e.g.,

NKCC) were not significantly different between groups, which may suggest that

these mediators are not be impactful at this terminal time point. However, primary

tumor growth curves diverged at day 14 post-tumor implantation. Moderate

exercise in weight stable mice may alter the inflammatory milieu during early tumor

growth or initiation of metastasis. Once tumor burden reaches a critical mass,

these differences may be less apparent. Ongoing studies in the Rogers lab are

planned to examine the effects induced by moderate exercise at both earlier time

points and when tumor volumes are comparable between groups.

The weight maintenance-induced reduction in immunosuppressive cells

(MDSCs, Tregs) and tumor-immune gene expression important in the expansion

of immunosuppressive cells warrants further investigation. Clinical studies reveal

that BC stage and metastatic tumor burden is positively correlated with the

percentage of circulating MDSCs (162) and intratumoral Tregs (650); and elevated

levels of immunosuppressive cell types are indicative of a poor prognosis (118,

119, 588). Additionally, both MDSCs and Tregs play a role in promoting metastatic

progression and establishing prometastatic niches within peripheral organs (91,

120, 176). For example, an exercise-induced reduction in lung-infiltrating MDSCs

may result in a microenvironment less favorable for the embedding of

disseminating tumor cells. The expansion of immunosuppressive cells, driven by

the release of tumor-derived factors, is independent of BC biomarker classification

(i.e. estrogen receptors, progesterone receptors, and/or the human epidermal

242

growth factor receptor 2 (HER2)). Therefore, regardless of biomarker-based

treatment, novel therapies targeting MDSCs or Tregs are an attractive strategy to

overcome the immunosuppression associated with the TME and enhance

conventional therapy and novel immunotherapy to prevent or slow metastatic

disease and improve clinical outcomes.

Immunosuppressive cells utilize numerous, complex, and often redundant

mechanisms to promote tumor progression. For example, various pharmacological

strategies are being investigated to reduce the expansion and accumulation of

MDSCs, inhibit their suppressive functions, or promote their differentiation into

non-suppressive cell types. Markowitz, et al. (651) has recently reviewed clinical

studies targeting MDSCs in BC patients; however, no researchers are investigating

if lifestyle-based interventions could reduce MDSC expansion or inhibitory

function. A new appreciation for the role of diet-induced obesity on the emergence

of MDSC populations and Tregs is reported in preclinical models (652, 653).

However, few researchers have designed studies to investigate if exercise-

induced changes in the inflammatory milieu can reduce the expansion and

accumulation of MDSCs and Tregs in the TME or peripheral organs. Subsets of

MDSCs, with varying suppressive function, are identified based on Gr-1 (Ly6G or

Ly6C) and CD11b expression (125). Granulocytic MDSCs, defined as Gr-

1hiCD11b+ (or CD11b+Ly6CloLy6G+) are terminally differentiated; whereas,

monocytic MDSCs, defined as Gr-1loCD11b+ (or CD11b+Ly6ChiLy6G-), can further

differentiate into macrophages or dendritic cells. Tregs can develop in the thymus

(i.e., natural Tregs) or be generated in the periphery from conventional CD4+

243

helper T cells (inducible Tregs) (144). It remains unexplored if exercise has subset-

specific effects on MDSC subpopulation expansion or function or alters the

generation or function of natural Treg or inducible Treg populations. Improving

cancer immunotherapy through the development of MDSC- or Treg-targeted

strategies is a high priority and may be the next breakthrough for clinical cancer

treatment.

The weight maintenance-induced reduction in tumor-immune gene

expression related to immunosuppressive pathways (e.g., Ido1) is interesting and

deserves additional investigation. Moderate exercise in weight stable mice may

not only reduce the expansion and accumulation of immunosuppressive cell types,

but also blunt their ability to suppress by directly altering the gene expression of

inhibitory enzymes. For example, Ido1 is expressed by tumor cells, supporting cells

within the TME, and infiltrating cells, like MDSCs, and encodes for indoleamine

2,3-dioxygenase (IDO). IDO is the first and rate-limiting enzyme of tryptophan

catabolism through the kynurenine pathway (197). Depletion of tryptophan, an

amino acid essential for T cell activation, results in blunted T cell proliferation,

differentiation, effector functions, and viability in several preclinical models (198).

No IDO inhibitor is approved by the FDA; however, IDO inhibition in combination

with cancer vaccines, chemotherapy agents, and emerging immune checkpoint

blockade is under investigation in preclinical and phase I and II clinical trials (199,

615). To date, no group is investigating if lifestyle interventions, such as moderate

exercise, can reduce IDO-mediated inhibition to promote a more robust antitumor

effector CD8+ cytotoxic T cell response. Furthermore, weight maintenance altered

244

tumor-immune expression of several genes related to immunosuppressive cell

expansion and metastatic progression. Future studies are needed to investigate

the individual effects of each chemokine gene (through knockout or small molecule

inhibitors) on subsequent expansion of immunosuppressive cells, antitumor

immune responses, and tumor progression.

It is well established that tumor-induced or immunosuppressive cell-induced

dysregulation of metabolic pathways in antitumor effector cells can promote tumor

escape. Exercise-induced enhancement of immunometabolism could bypass the

induction of T cell anergy or apoptosis, resulting in a sustained antitumor response.

Exercise-induced enhancements in skeletal muscle metabolism via IL-6 and other

acute phase myokines are well studied (631) and involve signaling via AMPK/PI3K

to increase glucose uptake and fatty acid oxidation, as well as augment

mitochondrial size, number, and enzymatic activity in myocytes. It is conceivable

that these effects can also occur in immune compartments important in

immunosurveillance and maintain antitumor activity. Future studies are needed to

assess exercise-induced effects on energy uptake (e.g., glucose transporters) and

metabolic pathways in antitumor immune cells and to determine if metabolic

alterations associated with weight change impact these mechanisms.

Metastatic progression is a dynamic process and remains poorly

understood (40). In the current experiments, it remains unknown if the exercise-

induced reduction in spontaneous metastasis is a consequence of reduced primary

tumor growth or a direct inhibition in metastatic progression. Exercise-induced

effects could alter tumor angiogenesis, epithelial-mesenchymal transition (EMT),

245

cell invasiveness, the ability of circulating tumor cells to extravasate into secondary

sites, and/or the metastatic niche. Additionally, an exercise-induced enhancement

in immunosurveillance (e.g., greater clearance of primary tumor cells, circulating

tumor cells, or cells within a metastatic lesion) could result in a reduction in

metastasis. Lastly, exercise could improve mortality outcomes associated with BC

metastases by altering the inflammation-immune axis or directly enhancing

immunosurveillance mechanisms to maintain pressure on disseminated tumor

cells to favor a dormant, non-proliferative state. Future studies, perhaps resecting

the primary 4T1.2 tumor in the current model to allow for greater metastatic

outgrowth or utilizing a dormancy model, are needed to elucidate the exercise-

induced protective effect on metastasis. To identify what immune compartment is

mediating the exercise-induced effects on immunosurveillance, immune cell

depletion experiments (e.g., CD8+ cytotoxic T cells or NK cells) are required.

The current model applied exercise for eight weeks prior to the

administration of luciferase-transfected 4T1.2 cells into the fourth mammary fat

pad. Chronic exercise exposure could generate a host with better inflammation

resolving capacity and a better functional reserve to combat the tumor insult.

However, studying exercise in the therapeutic window remains clinically relevant.

The aggressiveness of the 4T1.2 model may prohibit the study of exercise in the

therapeutic window. Future studies could assess if exercise could result in a

reduction in primary tumor growth and spontaneous metastasis if administered

once the 4T1.2 tumor was palpable or utilize transgenic models with a longer tumor

latency period.

246

Characterizing the dose, duration, frequency, and type of exercise needed

to drive cancer prevention is key to move the field of exercise oncology forward.

Subset analysis in the current study sheds light on the average quantity of activity

required in the EX+ER group (greater than 5.8 km/day) to drive beneficial changes

on tumor growth in the 4T1.2 mammary tumor model. Using speed to estimate the

percent of maximal oxygen consumption (%VO2max) or relative intensity of

exercise, we determined, based on a regression analysis reported by Fernando,

et al. (654), that mice were exercising at 60-70% of VO2max, which is indicative of

moderate exercise training. Studies implementing an exercise routine via treadmill

or automated running wheel cages are needed to further assess duration and

intensity of aerobic exercise on both immune and tumor outcomes. As the field of

exercise oncology matures, it is essential for investigators in the fields of exercise

physiology, immunology, and cancer biology to address the dose, duration,

frequency, and type of exercise needed to achieve a cancer prevention effect.

It is well accepted that participating in moderate physical activity during and

after active treatment can improve quality of life measurements, including

alleviating fatigue and maintaining physical functioning, emotional, and/or social

wellbeing (655, 656). Exercise-induced benefits on the response to therapy are

studied to a lesser extent. The observational data, combined with preclinical

studies, suggest that exercise-induced effects can also enhance the inflammation-

immune axis. Despite the recent successes of immunotherapy in advanced-stage

cancer patients, less than half of patients who receive immunotherapy experience

an objective, durable response. An active area of research attempts to identify

247

ways to increase immunotherapy efficacy through the identification of clinically

meaningful biomarkers to direct treatment choices (i.e., personalized medicine)

and/or through the selection of appropriate combinatorial strategies (aimed to

enhance antigen presentation, reverse T cell dysfunction, and/or target immune

inhibitory mechanisms) without inducing adverse effects. The number of possible

combinatorial treatment strategies grows exponentially, therefore, testing if

lifestyle-based interventions, like physical activity, which is low-cost and has known

benefits across the cancer continuum, can enhance immunotherapy represents an

attractive option. For example, immune checkpoint blockade relies on the

reactivation of an immune response that targets the cancer and therapeutic

efficacy is correlated with the presence of tumor-infiltrating immune cell

populations. Immunotherapy typically fails in patients with tumors lacking a pre-

existing immune response. No study, to our knowledge, has retrospectively or

prospectively assessed the impact of lifestyle (e.g., physical activity, weight

control) on the efficacy of immunotherapeutic outcomes. Future studies are

needed to determine if exercise-induced changes in the inflammation-immune axis

could promote immune-infiltration of the TME and convert non-responders into

responders. It remains unknown if this requires exercise over the course of one’s

lifetime, or if exercise could be started in an adjuvant setting. For example, if

exercise could be applied in the adjuvant setting, could immunotherapy be delayed

(e.g., by two or three weeks) allowing time for patients to be administered a

supervised exercise routine to increase functional reserve and create a host

environment more responsive to the therapy?

248

An emerging area of research is investigating the role of the gastrointestinal

microbiome on cancer development and response to therapy (657), with estrogen

metabolism, diet, immune modulation, and inflammatory milieu acting as potential

mediators. Monda et al. (658) reports that exercise can enhance the number of

beneficial microbial species, enrich the microflora diversity, and improve the

development of commensal bacteria. Although the exact mechanisms by which

the microbiome interacts with BC remain to be discovered, exercise-induced

changes in the inflammation-immune axis could be mediated through changes

within the gut microbiome.

6.3. CONCLUDING REMARKS

Collectively, study one provides a deeper understanding of the extent to

which exercise, and not changes in body weight, underlie cancer protection. Study

one demonstrated that moderate exercise in weight stable mice resulted in a

reduction in splenomegaly and the accumulation of splenic immunosuppressive

cell types, as well as a reduction in immunosuppressive and metastatic genes in

the TME. The efficacy of emerging cancer therapies can be compromised by

tumor-secreted proinflammatory factors that promote the expansion and

accumulation of immunosuppressive immune cell types within the TME and

peripheral organs. Therefore, we investigated if moderate exercise in weight stable

mice can blunt the acquisition of a toxic, immunosuppressive TME and improve

the therapeutic responses of two emerging immunotherapeutic strategies. We

observed (chapter four) an additive effect of moderate exercise in weight stable

mice and the administration of the whole tumor cell cancer vaccine; however, we

saw no additive effect of our lifestyle intervention and PD-1 checkpoint blockade

249

(chapter five). The disparate response of therapies to moderate exercise provide

insight into potential mechanisms by which exercise confers protection and

suggests an exercise-induced maintenance in immunosurveillance mechanisms.

Lastly, we demonstrated that exercising, weight stable mice significantly reduced

spontaneous metastasis, which suggest a novel mechanism by which moderate

exercise in a weight stable host may contribute to the reduction in mortality in BC

patients.

250

REFERENCES

1. “SEER Stat Fact Sheets: Breast,” National Cancer Institute, accessed April 30, 2017 [Available from: https://seer.cancer.gov/statfacts/html/breast.html.

2. American Cancer Society. Cancer Facts & Figures 2017. Atlanta: American Cancer Society; 2017.

3. Redig AJ, McAllister SS. Breast cancer as a systemic disease: a view of metastasis. J Intern Med. 2013;274(2):113-26.

4. Wu Y, Zhang D, Kang S. Physical activity and risk of breast cancer: a meta-analysis of prospective studies. Breast Cancer Res Treat. 2013;137(3):869-82.

5. Monninkhof EM, Elias SG, Vlems FA, van der Tweel I, Schuit AJ, Voskuil DW, et al. Physical activity and breast cancer: a systematic review. Epidemiology (Cambridge, Mass). 2007;18(1):137-57.

6. Schlom J. Therapeutic cancer vaccines: current status and moving forward. J Natl Cancer Inst. 2012;104(8):599-613.

7. Cancer Fact Sheet http://www.who.int/mediacentre/factsheets/fs297/en/: World Health Organization, February 2017; [

8. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86.

9. Loeb LA, Harris CC. Advances in Chemical Carcinogenesis: A Historical Review and Prospective. Cancer research. 2008;68(17):6863-72.

10. Soffritti M MF, Maltoni C. Physical Carcinogens. In: Kufe DW PR, Weichselbaum RR, et al., editor. Holland-Frei Cancer Medicine 6th edition. BC Decker; 2003. Chapter 21. ON: Hamilton; 2003.

11. Dalton-Griffin L, Kellam P. Infectious causes of cancer and their detection. Journal of biology. 2009;8(7):67.

12. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57-70. 13. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell.

2011;144(5):646-74. 14. Muschler J, Streuli CH. Cell–Matrix Interactions in Mammary Gland Development and

Breast Cancer. Cold Spring Harb Perspect Biol. 2010;2(10):a003202. 15. Deugnier MA, Teuliere J, Faraldo MM, Thiery JP, Glukhova MA. The importance of

being a myoepithelial cell. Breast Cancer Res. 2002;4(6):224-30. 16. Brisken C, O’Malley B. Hormone action in the mammary gland. Cold Spring Harb

Perspect Biol. 2010;2. 17. Visvader JE. Keeping abreast of the mammary epithelial hierarchy and breast

tumorigenesis. Genes & development. 2009;23(22):2563-77. 18. Javed A, Lteif A. Development of the Human Breast. Seminars in Plastic Surgery.

2013;27(1):5-12. 19. Macias H, Hinck L. Mammary Gland Development. Wiley interdisciplinary reviews

Developmental biology. 2012;1(4):533-57. 20. Place AE, Jin Huh S, Polyak K. The microenvironment in breast cancer progression:

biology and implications for treatment. Breast Cancer Research : BCR. 2011;13(6):227-. 21. Polyak K. Breast cancer: origins and evolution. J Clin Invest. 2007;117(11):3155-63. 22. Rivenbark AG, O’Connor SM, Coleman WB. Molecular and Cellular Heterogeneity in

Breast Cancer: Challenges for Personalized Medicine. Am J Pathol. 2013;183(4):1113-24.

23. Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):412-24.

251

24. Dieci MV, Orvieto E, Dominici M, Conte P, Guarneri V. Rare breast cancer subtypes: histological, molecular, and clinical peculiarities. Oncologist. 2014;19(8):805-13.

25. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869-74.

26. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70.

27. Anderson WF, Rosenberg PS, Prat A, Perou CM, Sherman ME. How many etiological subtypes of breast cancer: two, three, four, or more? J Natl Cancer Inst. 2014;106(8).

28. Blows FM, Driver KE, Schmidt MK, Broeks A, Van Leeuwen FE, Wesseling J, et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 2010;7(5):e1000279.

29. Parise CA, Caggiano V. Breast cancer survival defined by the ER/PR/HER2 subtypes and a surrogate classification according to tumor grade and immunohistochemical biomarkers. Journal of cancer epidemiology. 2014;2014.

30. Prat A, Pineda E, Adamo B, Galvan P, Fernandez A, Gaba L, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast (Edinburgh, Scotland). 2015;24 Suppl 2:S26-35.

31. Viale G. The current state of breast cancer classification. Ann Oncol. 2012;23(suppl 10):x207-x10.

32. Rezai M, Kraemer S, Kimmig R, Kern P. Breast conservative surgery and local recurrence. Breast (Edinburgh, Scotland). 2015;24 Suppl 2:S100-7.

33. Leclerc A-F, Jerusalem G, Devos M, Crielaard J-M, Maquet D. Multidisciplinary management of breast cancer. Archives of Public Health. 2016;74:50.

34. Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, et al. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016;66(4):271-89.

35. Dent R, Hanna WM, Trudeau M, Rawlinson E, Sun P, Narod SA. Pattern of metastatic spread in triple-negative breast cancer. Breast Cancer Res Treat. 2009;115(2):423-8.

36. Schneble EJ, Graham LJ, Shupe MP, Flynt FL, Banks KP, Kirkpatrick AD, et al. Current approaches and challenges in early detection of breast cancer recurrence. Journal of Cancer. 2014;5(4):281-90.

37. Eccles SA, Aboagye EO, Ali S, Anderson AS, Armes J, Berditchevski F, et al. Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res. 2013;15(5):R92.

38. Eckhardt BL, Francis PA, Parker BS, Anderson RL. Strategies for the discovery and development of therapies for metastatic breast cancer. Nat Rev Drug Discov. 2012;11(6):479-97.

39. Fidler IJ. The pathogenesis of cancer metastasis: the 'seed and soil' hypothesis revisited. Nat Rev Cancer. 2003;3(6):453-8.

40. Chambers AF, Werb Z. Invasion and metastasis--recent advances and future challenges. Journal of molecular medicine (Berlin, Germany). 2015;93(4):361-8.

41. Ribelles N, Santonja A, Pajares B, Llacer C, Alba E. The seed and soil hypothesis revisited: current state of knowledge of inherited genes on prognosis in breast cancer. Cancer Treat Rev. 2014;40(2):293-9.

42. Rugo HS, O'Shaughnessy JA, Perez EA. Current treatment options for metastatic breast cancer: what now? Clinical advances in hematology & oncology : H&O. 2011;9(11 Suppl 25):1-16.

43. Roche H, Vahdat LT. Treatment of metastatic breast cancer: second line and beyond. Ann Oncol. 2011;22(5):1000-10.

252

44. Shachar SS. Assessing Treatment Response in Metastatic Breast Cancer. American Journal of Hematology/Oncology®. 2016;12(6).

45. Cardoso F, Harbeck N, Barrios CH, Bergh J, Cortes J, El Saghir N, et al. Research needs in breast cancer. Ann Oncol. 2017;28(2):208-17.

46. Gong Y, Liu YR, Ji P, Hu X, Shao ZM. Impact of molecular subtypes on metastatic breast cancer patients: a SEER population-based study. Sci Rep. 2017;7:45411.

47. Kennecke H, Yerushalmi R, Woods R, Cheang MC, Voduc D, Speers CH, et al. Metastatic behavior of breast cancer subtypes. J Clin Oncol. 2010;28(20):3271-7.

48. Zimmer AS, Steeg PS. Meaningful prevention of breast cancer metastasis: candidate therapeutics, preclinical validation, and clinical trial concerns. Journal of molecular medicine (Berlin, Germany). 2015;93(1):13-29.

49. Couzin-Frankel J. Cancer Immunotherapy. Science. 2013;342(6165):1432-3. 50. Korman AJ, Peggs KS, Allison JP. Checkpoint blockade in cancer immunotherapy. Adv

Immunol. 2006;90:297-339. 51. Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature.

2011;480(7378):480-9. 52. Palucka K, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer.

2012;12(4):265-77. 53. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev

Cancer. 2012;12(4):252-64. 54. Trapani JA, Darcy PK. Immunotherapy of cancer. Australian family physician.

2017;46(4):194-9. 55. Hartkopf AD, Taran FA, Wallwiener M, Walter CB, Kramer B, Grischke EM, et al. PD-1

and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer. Breast care (Basel, Switzerland). 2016;11(6):385-90.

56. Pusztai L, Karn T, Safonov A, Abu-Khalaf MM, Bianchini G. New Strategies in Breast Cancer: Immunotherapy. Clin Cancer Res. 2016;22(9):2105-10.

57. Sanchez K, Page D, McArthur HL. Immunotherapy in breast cancer: An overview of modern checkpoint blockade strategies and vaccines. Current problems in cancer. 2016;40(2-4):151-62.

58. Yu LY, Tang J, Zhang CM, Zeng WJ, Yan H, Li MP, et al. New Immunotherapy Strategies in Breast Cancer. International journal of environmental research and public health. 2017;14(1).

59. Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13(11):674-90.

60. Thakur A, Lum LG, Al-Kadhimi Z, Colvin GA, Cummings FJ, Legare RD, et al. Targeted T cell Therapy in Stage IV Breast Cancer: A Phase I Clinical Trial. Clinical Cancer Research. 2015:clincanres.2280.014.

61. Zeichner SB, Terawaki H, Gogineni K. A Review of Systemic Treatment in Metastatic Triple-Negative Breast Cancer. Breast cancer : basic and clinical research. 2016;10:25-36.

62. Parish CR. Cancer immunotherapy: The past, the present and the future[ast]. Immunol Cell Biol. 2003;81(2):106-13.

63. Spellman A, Tang SC. Immunotherapy for breast cancer: past, present, and future. Cancer Metastasis Rev. 2016;35(4):525-46.

64. Jiang X. Harnessing the immune system for the treatment of breast cancer. J Zhejiang Univ Sci B. 2014;15(1):1-15.

65. Emens LA. Breast cancer immunobiology driving immunotherapy: vaccines and immune checkpoint blockade. Expert review of anticancer therapy. 2012;12(12):1597-611.

253

66. Ali HR, Provenzano E, Dawson SJ, Blows FM, Liu B, Shah M, et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann Oncol. 2014;25(8):1536-43.

67. Ibrahim EM, Al-Foheidi ME, Al-Mansour MM, Kazkaz GA. The prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancer: a meta-analysis. Breast Cancer Res Treat. 2014;148(3):467-76.

68. Liu F, Lang R, Zhao J, Zhang X, Pringle GA, Fan Y, et al. CD8(+) cytotoxic T cell and FOXP3(+) regulatory T cell infiltration in relation to breast cancer survival and molecular subtypes. Breast Cancer Res Treat. 2011;130(2):645-55.

69. Liu S, Foulkes WD, Leung S, Gao D, Lau S, Kos Z, et al. Prognostic significance of FOXP3+ tumor-infiltrating lymphocytes in breast cancer depends on estrogen receptor and human epidermal growth factor receptor-2 expression status and concurrent cytotoxic T-cell infiltration. Breast Cancer Res. 2014;16(5):432.

70. Liu S, Lachapelle J, Leung S, Gao D, Foulkes WD, Nielsen TO. CD8+ lymphocyte infiltration is an independent favorable prognostic indicator in basal-like breast cancer. Breast Cancer Research. 2012;14(2):R48.

71. Mahmoud SM, Paish EC, Powe DG, Macmillan RD, Grainge MJ, Lee AH, et al. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J Clin Oncol. 2011;29(15):1949-55.

72. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol. 2015;26(2):259-71.

73. Yoshimoto M, Sakamoto G, Ohashi Y. Time dependency of the influence of prognostic factors on relapse in breast cancer. Cancer. 1993;72(10):2993-3001.

74. Park MH, Lee JS, Yoon JH. High expression of CX3CL1 by tumor cells correlates with a good prognosis and increased tumor-infiltrating CD8+ T cells, natural killer cells, and dendritic cells in breast carcinoma. Journal of surgical oncology. 2012;106(4):386-92.

75. Dushyanthen S, Beavis PA, Savas P, Teo ZL, Zhou C, Mansour M, et al. Relevance of tumor-infiltrating lymphocytes in breast cancer. BMC Medicine. 2015;13:202.

76. García-Teijido P, Cabal ML, Fernández IP, Pérez YF. Tumor-Infiltrating Lymphocytes in Triple Negative Breast Cancer: The Future of Immune Targeting. Clinical Medicine Insights Oncology. 2016;10(Suppl 1):31-9.

77. Chaplin DD. Overview of the Immune Response. The Journal of allergy and clinical immunology. 2010;125(2 Suppl 2):S3-23.

78. Kees T, Egeblad M. Innate immune cells in breast cancer–from villains to heroes? Journal of mammary gland biology and neoplasia. 2011;16(3):189.

79. Whitton JL. Antigen presentation: Springer Science & Business Media; 2013. 80. Trinchieri G. Biology of natural killer cells. Adv Immunol. 1989;47:187-376. 81. Vivier E, Tomasello E, Baratin M, Walzer T, Ugolini S. Functions of natural killer cells.

Nat Immunol. 2008;9(5):503-10. 82. Murphy K, Weaver C. Janeway's immunobiology: Garland Science; 2016. 83. Alberts B, Johnson A, Lewis J, Raff M. The adaptive immune system. Garland Science;

2002. 84. LeBien TW, Tedder TF. B lymphocytes: how they develop and function. Blood.

2008;112(5):1570-80. 85. Luckheeram RV, Zhou R, Verma AD, Xia B. CD4(+)T Cells: Differentiation and

Functions. Clinical and Developmental Immunology. 2012;2012:925135. 86. Barry M, Bleackley RC. Cytotoxic T lymphocytes: all roads lead to death. Nat Rev

Immunol. 2002;2(6):401-9. 87. Smith-Garvin JE, Koretzky GA, Jordan MS. T Cell Activation. Annu Rev Immunol.

2009;27:591-619.

254

88. Abram CL, Lowell CA. The expanding role for ITAM-based signaling pathways in immune cells. Science's STKE : signal transduction knowledge environment. 2007;2007(377):re2.

89. Boyman O, Sprent J. The role of interleukin-2 during homeostasis and activation of the immune system. Nat Rev Immunol. 2012;12(3):180-90.

90. Fox CJ, Hammerman PS, Thompson CB. Fuel feeds function: energy metabolism and the T-cell response. Nat Rev Immunol. 2005;5(11):844-52.

91. DeNardo DG, Johansson M, Coussens LM. Immune cells as mediators of solid tumor metastasis. Cancer Metastasis Rev. 2008;27(1):11-8.

92. Dunn GP, Old LJ, Schreiber RD. The Immunobiology of Cancer Immunosurveillance and Immunoediting. Immunity. 2004;21(2):137-48.

93. Swann JB, Smyth MJ. Immune surveillance of tumors. Journal of Clinical Investigation. 2007;117(5):1137-46.

94. Fietta P, Delsante G. Focus on human natural killer cells. Rivista di biologia. 2009;102(2):219-35.

95. Smyth MJ, Hayakawa Y, Takeda K, Yagita H. New aspects of natural-killer-cell surveillance and therapy of cancer. Nat Rev Cancer. 2002;2(11):850-61.

96. Waldhauer I, Steinle A. NK cells and cancer immunosurveillance. Oncogene. 2008;27(45):5932-43.

97. Mamessier E, Sylvain A, Thibult ML, Houvenaeghel G, Jacquemier J, Castellano R, et al. Human breast cancer cells enhance self tolerance by promoting evasion from NK cell antitumor immunity. J Clin Invest. 2011;121(9):3609-22.

98. Mocikat R, Braumuller H, Gumy A, Egeter O, Ziegler H, Reusch U, et al. Natural killer cells activated by MHC class I(low) targets prime dendritic cells to induce protective CD8 T cell responses. Immunity. 2003;19(4):561-9.

99. Wallace ME, Smyth MJ. The role of natural killer cells in tumor control--effectors and regulators of adaptive immunity. Springer Semin Immunopathol. 2005;27(1):49-64.

100.Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ. Natural innate and adaptive immunity to cancer. Annu Rev Immunol. 2011;29:235-71.

101.Chavez-Galan L, Arenas-Del Angel MC, Zenteno E, Chavez R, Lascurain R. Cell death mechanisms induced by cytotoxic lymphocytes. Cell Mol Immunol. 2009;6(1):15-25.

102.Knutson KL, Disis ML. Augmenting T helper cell immunity in cancer. Curr Drug Targets Immune Endocr Metabol Disord. 2005;5(4):365-71.

103.Shankaran V, Ikeda H, Bruce AT, White JM, Swanson PE, Old LJ, et al. IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature. 2001;410(6832):1107-11.

104.Kim HJ, Cantor H. CD4 T-cell subsets and tumor immunity: the helpful and the not-so-helpful. Cancer Immunol Res. 2014;2(2):91-8.

105.Dunn GP, Old LJ, Schreiber RD. The three Es of cancer immunoediting. Annu Rev Immunol. 2004;22:329-60.

106.Feuerer M, Rocha M, Bai L, Umansky V, Solomayer EF, Bastert G, et al. Enrichment of memory T cells and other profound immunological changes in the bone marrow from untreated breast cancer patients. International journal of cancer Journal international du cancer. 2001;92(1):96-105.

107.Green TL, Cruse JM, Lewis RE, Craft BS. Circulating tumor cells (CTCs) from metastatic breast cancer patients linked to decreased immune function and response to treatment. Experimental and molecular pathology. 2013;95(2):174-9.

108.Muller M, Gounari F, Prifti S, Hacker HJ, Schirrmacher V, Khazaie K. EblacZ tumor dormancy in bone marrow and lymph nodes: active control of proliferating tumor cells by CD8+ immune T cells. Cancer Res. 1998;58(23):5439-46.

255

109.Mahnke YD, Schwendemann J, Beckhove P, Schirrmacher V. Maintenance of long-term tumour-specific T-cell memory by residual dormant tumour cells. Immunology. 2005;115(3):325-36.

110.Zhang K, Kim S, Cremasco V, Hirbe AC, Collins L, Piwnica-Worms D, et al. CD8+ T cells regulate bone tumor burden independent of osteoclast resorption. Cancer Res. 2011;71(14):4799-808.

111.Wood AH, Zhang X, Farber DL, Strome SE. CD8+ memory T lymphocytes from bone marrow--immune function and therapeutic potential. Crit Rev Immunol. 2007;27(6):527-37.

112.MacNeil B, Hoffman-Goetz L. Effect of exercise on natural cytotoxicity and pulmonary tumor metastases in mice. Med Sci Sports Exerc. 1993;25(8):922-8.

113.MacNeil B, Hoffman-Goetz L. Exercise training and tumour metastasis in mice: influence of time of exercise onset. Anticancer Res. 1993;13(6a):2085-8.

114.DeNardo DG, Barreto JB, Andreu P, Vasquez L, Tawfik D, Kolhatkar N, et al. CD4(+) T cells regulate pulmonary metastasis of mammary carcinomas by enhancing protumor properties of macrophages. Cancer cell. 2009;16(2):91-102.

115.Romero I, Garrido C, Algarra I, Collado A, Garrido F, Garcia-Lora AM. T lymphocytes restrain spontaneous metastases in permanent dormancy. Cancer Res. 2014;74(7):1958-68.

116.Schirrmacher V. T-cell immunity in the induction and maintenance of a tumour dormant state. Semin Cancer Biol. 2001;11(4):285-95.

117.Slaney CY, Rautela J, Parker BS. The emerging role of immunosurveillance in dictating metastatic spread in breast cancer. Cancer Res. 2013;73(19):5852-7.

118.Diaz-Montero CM, Finke J, Montero AJ. Myeloid-derived suppressor cells in cancer: therapeutic, predictive, and prognostic implications. Semin Oncol. 2014;41(2):174-84.

119.Montero AJ, Diaz-Montero CM, Kyriakopoulos CE, Bronte V, Mandruzzato S. Myeloid-derived Suppressor Cells in Cancer Patients: A Clinical Perspective. Journal of Immunotherapy. 2012;35(2):107-15.

120.Halvorsen EC, Mahmoud SM, Bennewith KL. Emerging roles of regulatory T cells in tumour progression and metastasis. Cancer Metastasis Rev. 2014;33(4):1025-41.

121.Kim H-J, Cantor H. CD4 T-cell Subsets and Tumor Immunity: The Helpful and the Not-so-Helpful. Cancer Immunol Res. 2014;2(2):91-8.

122.Chanmee T, Ontong P, Konno K, Itano N. Tumor-associated macrophages as major players in the tumor microenvironment. Cancers (Basel). 2014;6(3):1670-90.

123.Solinas G, Germano G, Mantovani A, Allavena P. Tumor-associated macrophages (TAM) as major players of the cancer-related inflammation. J Leukoc Biol. 2009;86(5):1065-73.

124.Condamine T, Gabrilovich DI. Molecular mechanisms regulating myeloid-derived suppressor cell differentiation and function. Trends Immunol. 2011;32(1):19-25.

125.Gabrilovich DI, Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol. 2009;9(3):162-74.

126.Le HK, Graham L, Cha E, Morales JK, Manjili MH, Bear HD. Gemcitabine directly inhibits myeloid derived suppressor cells in BALB/c mice bearing 4T1 mammary carcinoma and augments expansion of T cells from tumor-bearing mice. Int Immunopharmacol. 2009;9(7-8):900-9.

127.Ostrand-Rosenberg S. Myeloid-derived suppressor cells: more mechanisms for inhibiting antitumor immunity. Cancer Immunology, Immunotherapy. 2010;59(10):1593-600.

128.Price JE, Polyzos A, Zhang RD, Daniels LM. Tumorigenicity and metastasis of human breast carcinoma cell lines in nude mice. Cancer Research. 1990;50(3):717-21.

129.Manjili MH. Phenotypic Plasticity of MDSC in Cancers. Immunol Invest. 2012;41(6/7):711-21.

256

130.Youn J-I, Gabrilovich DI. The biology of myeloid-derived suppressor cells: The blessing and the curse of morphological and functional heterogeneity. European Journal of Immunology. 2010;40(11):2969-75.

131.Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21(3):309-22.

132.Kim IS, Zhang XH. One microenvironment does not fit all: heterogeneity beyond cancer cells. Cancer Metastasis Rev. 2016;35(4):601-29.

133.Lindau D, Gielen P, Kroesen M, Wesseling P, Adema GJ. The immunosuppressive tumour network: myeloid-derived suppressor cells, regulatory T cells and natural killer T cells. Immunology. 2013;138(2):105-15.

134.Zhang Y, Gallastegui N, Rosenblatt JD. Regulatory B cells in anti-tumor immunity. Int Immunol. 2015;27(10):521-30.

135.Kim J, Bae JS. Tumor-Associated Macrophages and Neutrophils in Tumor Microenvironment. Mediators of inflammation. 2016;2016:6058147.

136.Zhang X, Zhang W, Yuan X, Fu M, Qian H, Xu W. Neutrophils in cancer development and progression: Roles, mechanisms, and implications (Review). Int J Oncol. 2016;49(3):857-67.

137.Poggi A, Giuliani M. Mesenchymal Stromal Cells Can Regulate the Immune Response in the Tumor Microenvironment. Vaccines (Basel). 2016;4(4).

138.Chen J, Ye Y, Liu P, Yu W, Wei F, Li H, et al. Suppression of T cells by myeloid-derived suppressor cells in cancer. Human immunology. 2017;78(2):113-9.

139.Gabrilovich DI, Ostrand-Rosenberg S, Bronte V. Coordinated regulation of myeloid cells by tumours. Nat Rev Immunol. 2012;12(4):253-68.

140.Noman MZ, Janji B, Abdou A, Hasmim M, Terry S, Tan TZ, et al. The immune checkpoint ligand PD-L1 is upregulated in EMT-activated human breast cancer cells by a mechanism involving ZEB-1 and miR-200. Oncoimmunology. 2017;6(1):e1263412.

141.Gabrilovich DI, Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nature reviews Immunology. 2009;9(3):162-74.

142.Condamine T, Ramachandran I, Youn JI, Gabrilovich DI. Regulation of tumor metastasis by myeloid-derived suppressor cells. Annual review of medicine. 2015;66:97-110.

143.Dasgupta A, Saxena R. Regulatory T cells: a review. The National medical journal of India. 2012;25(6):341-51.

144.Adeegbe DO, Nishikawa H. Natural and Induced T Regulatory Cells in Cancer. Frontiers in Immunology. 2013;4:190.

145.Chaput N, Darrasse-Jeze G, Bergot AS, Cordier C, Ngo-Abdalla S, Klatzmann D, et al. Regulatory T cells prevent CD8 T cell maturation by inhibiting CD4 Th cells at tumor sites. Journal of immunology. 2007;179(8):4969-78.

146.Sakaguchi S, Sakaguchi N, Shimizu J, Yamazaki S, Sakihama T, Itoh M, et al. Immunologic tolerance maintained by CD25+ CD4+ regulatory T cells: their common role in controlling autoimmunity, tumor immunity, and transplantation tolerance. Immunological reviews. 2001;182:18-32.

147.Yamaguchi T, Sakaguchi S. Regulatory T cells in immune surveillance and treatment of cancer. Seminars in cancer biology. 2006;16(2):115-23.

148.Williams BJ, Bhatia S, Adams LK, Boling S, Carroll JL, Li XL, et al. Dendritic cell based PSMA immunotherapy for prostate cancer using a CD40-targeted adenovirus vector. PloS one. 2012;7(10):e46981.

149.Leong PP, Mohammad R, Ibrahim N, Ithnin H, Abdullah M, Davis WC, et al. Phenotyping of lymphocytes expressing regulatory and effector markers in infiltrating ductal carcinoma of the breast. Immunology letters. 2006;102(2):229-36.

257

150.Xu L, Xu W, Qiu S, Xiong S. Enrichment of CCR6+Foxp3+ regulatory T cells in the tumor mass correlates with impaired CD8+ T cell function and poor prognosis of breast cancer. Clinical immunology. 2010;135(3):466-75.

151.Valkovic T, Lucin K, Krstulja M, Dobi-Babic R, Jonjic N. Expression of monocyte chemotactic protein-1 in human invasive ductal breast cancer. Pathology, research and practice. 1998;194(5):335-40.

152.Ueno T, Toi M, Saji H, Muta M, Bando H, Kuroi K, et al. Significance of macrophage chemoattractant protein-1 in macrophage recruitment, angiogenesis, and survival in human breast cancer. Clin Cancer Res. 2000;6(8):3282-9.

153.Fujimoto H, Sangai T, Ishii G, Ikehara A, Nagashima T, Miyazaki M, et al. Stromal MCP-1 in mammary tumors induces tumor-associated macrophage infiltration and contributes to tumor progression. Int J Cancer. 2009;125(6):1276-84.

154.Zhang QW, Liu L, Gong CY, Shi HS, Zeng YH, Wang XZ, et al. Prognostic significance of tumor-associated macrophages in solid tumor: a meta-analysis of the literature. PLoS One. 2012;7(12):e50946.

155.Zhao X, Qu J, Sun Y, Wang J, Liu X, Wang F, et al. Prognostic significance of tumor-associated macrophages in breast cancer: a meta-analysis of the literature. Oncotarget. 2017;8(18):30576-86.

156.Talmadge JE, Gabrilovich DI. History of myeloid-derived suppressor cells. Nature Reviews Cancer. 2013;13(10):739-52.

157.Goh C, Narayanan S, Hahn YS. Myeloid-derived suppressor cells: the dark knight or the joker in viral infections? Immunol Rev. 2013;255(1):210-21.

158.Lai D, Qin C, Shu Q. Myeloid-derived suppressor cells in sepsis. Biomed Res Int. 2014;2014:598654.

159.Cripps JG, Gorham JD. MDSC in autoimmunity. Int Immunopharmacol. 2011;11(7):789-93.

160.Filipazzi P, Huber V, Rivoltini L. Phenotype, function and clinical implications of myeloid-derived suppressor cells in cancer patients. Cancer Immunol Immunother. 2012;61(2):255-63.

161.Nagaraj S, Gabrilovich DI. Myeloid-Derived Suppressor Cells in Human Cancer. The Cancer Journal. 2010;16(4):348-53.

162.Diaz-Montero CM, Salem ML, Nishimura MI, Garrett-Mayer E, Cole DJ, Montero AJ. Increased circulating myeloid-derived suppressor cells correlate with clinical cancer stage, metastatic tumor burden, and doxorubicin-cyclophosphamide chemotherapy. Cancer Immunol Immunother. 2009;58(1):49-59.

163.Waight JD, Netherby C, Hensen ML, Miller A, Hu Q, Liu S, et al. Myeloid-derived suppressor cell development is regulated by a STAT/IRF-8 axis. Journal of Clinical Investigation. 2013;123(10):4464-78.

164.Solito S, Falisi E, Diaz-Montero CM, Doni A, Pinton L, Rosato A, et al. A human promyelocytic-like population is responsible for the immune suppression mediated by myeloid-derived suppressor cells. Blood. 2011;118(8):2254-65.

165.Beury DW, Parker KH, Nyandjo M, Sinha P, Carter KA, Ostrand-Rosenberg S. Cross-talk among myeloid-derived suppressor cells, macrophages, and tumor cells impacts the inflammatory milieu of solid tumors. J Leukoc Biol. 2014.

166.Serafini P, Mgebroff S, Noonan K, Borrello I. Myeloid-derived suppressor cells promote cross-tolerance in B-cell lymphoma by expanding regulatory T cells. Cancer Research. 2008;68(13):5439-49.

167.Sinha P, Clements VK, Bunt SK, Albelda SM, Ostrand-Rosenberg S. Cross-Talk between Myeloid-Derived Suppressor Cells and Macrophages Subverts Tumor Immunity toward a Type 2 Response. The Journal of Immunology. 2007;179(2):977-83.

258

168.Cao Y, Slaney CY, Bidwell BN, Parker BS, Johnstone CN, Rautela J, et al. BMP4 inhibits breast cancer metastasis by blocking myeloid-derived suppressor cell activity. Cancer Research. 2014;74(18):5091-102.

169.Waight JD, Hu Q, Miller A, Liu S, Abrams SI. Tumor-derived G-CSF facilitates neoplastic growth through a granulocytic myeloid-derived suppressor cell-dependent mechanism. PloS One. 2011;6(11):e27690.

170.Serafini P, Carbley R, Noonan KA, Tan G, Bronte V, Borrello I. High-dose granulocyte-macrophage colony-stimulating factor-producing vaccines impair the immune response through the recruitment of myeloid suppressor cells. Cancer Research. 2004;64(17):6337-43.

171.Bunt SK, Yang L, Sinha P, Clements VK, Leips J, Ostrand-Rosenberg S. Reduced inflammation in the tumor microenvironment delays the accumulation of myeloid-derived suppressor cells and limits tumor progression. Cancer research. 2007;67(20):10019-26.

172.Pan P-Y, Wang GX, Yin B, Ozao J, Ku T, Divino CM, et al. Reversion of immune tolerance in advanced malignancy: modulation of myeloid-derived suppressor cell development by blockade of stem-cell factor function. Blood. 2008;111(1):219-28.

173.Gabrilovich D, Ishida T, Oyama T, Ran S, Kravtsov V, Nadaf S, et al. Vascular Endothelial Growth Factor Inhibits the Development of Dendritic Cells and Dramatically Affects the Differentiation of Multiple Hematopoietic Lineages In Vivo. Blood. 1998;92(11):4150-66.

174.Oh K, Lee O-Y, Shon SY, Nam O, Ryu PM, Seo MW, et al. A mutual activation loop between breast cancer cells and myeloid-derived suppressor cells facilitates spontaneous metastasis through IL-6 trans-signaling in a murine model. Breast Cancer Res. 2013;15(5):R79.

175.Sinha P, Clements VK, Fulton AM, Ostrand-Rosenberg S. Prostaglandin E2 Promotes Tumor Progression by Inducing Myeloid-Derived Suppressor Cells. Cancer Research. 2007;67(9):4507-13.

176.Condamine T, Ramachandran I, Youn J-I, Gabrilovich DI. Regulation of tumor metastasis by myeloid-derived suppressor cells. Annu Rev Med. 2015;66:97-110.

177.Huang B, Lei Z, Zhao J, Gong W, Liu J, Chen Z, et al. CCL2/CCR2 pathway mediates recruitment of myeloid suppressor cells to cancers. Cancer Letters. 2007;252(1):86-92.

178.Yang L, Huang J, Ren X, Gorska AE, Chytil A, Aakre M, et al. Abrogation of TGFβ Signaling in Mammary Carcinomas Recruits Gr-1+CD11b+ Myeloid Cells that Promote Metastasis. Cancer Cell. 2008;13(1):23-35.

179.Sawanobori Y, Ueha S, Kurachi M, Shimaoka T, Talmadge JE, Abe J, et al. Chemokine-mediated rapid turnover of myeloid-derived suppressor cells in tumor-bearing mice. Blood. 2008;111(12):5457-66.

180.Lévesque J-P, Hendy J, Takamatsu Y, Simmons PJ, Bendall LJ. Disruption of the CXCR4/CXCL12 chemotactic interaction during hematopoietic stem cell mobilization induced by GCSF or cyclophosphamide. J Clin Invest. 2003;111(2):187-96.

181.Jin H, Su J, Garmy-Susini B, Kleeman J, Varner J. Integrin alpha4beta1 promotes monocyte trafficking and angiogenesis in tumors. Cancer Research. 2006;66(4):2146-52.

182.Zhang Y, Liu Q, Zhang M, Yu Y, Liu X, Cao X. Fas signal promotes lung cancer growth by recruiting myeloid-derived suppressor cells via cancer cell-derived PGE2. J Immunol. 2009;182(6):3801-8.

183.Sinha P, Okoro C, Foell D, Freeze HH, Ostrand-Rosenberg S, Srikrishna G. Proinflammatory S100 proteins regulate the accumulation of myeloid-derived suppressor cells. J Immunol. 2008;181(7):4666-75.

184.Curiel TJ. Tregs and rethinking cancer immunotherapy. The Journal of clinical investigation. 2007;117(5):1167-74.

259

185.Vignali DA, Collison LW, Workman CJ. How regulatory T cells work. Nature reviews Immunology. 2008;8(7):523-32.

186.Murphy DB. T cell mediated immunosuppression. Curr Opin Immunol. 1993;5(3):411-7. 187.Noy R, Pollard JW. Tumor-associated macrophages: from mechanisms to therapy.

Immunity. 2014;41(1):49-61. 188.Wing K, Onishi Y, Prieto-Martin P, Yamaguchi T, Miyara M, Fehervari Z, et al. CTLA-4

control over Foxp3+ regulatory T cell function. Science. 2008;322(5899):271-5. 189.Nagaraj S, Gupta K, Pisarev V, Kinarsky L, Sherman S, Kang L, et al. Altered

recognition of antigen is a mechanism of CD8+ T cell tolerance in cancer. Nat Med. 2007;13(7):828-35.

190.Schmielau J, Finn OJ. Activated granulocytes and granulocyte-derived hydrogen peroxide are the underlying mechanism of suppression of t-cell function in advanced cancer patients. Cancer Research. 2001;61(12):4756-60.

191.Mazzoni A, Bronte V, Visintin A, Spitzer JH, Apolloni E, Serafini P, et al. Myeloid suppressor lines inhibit T cell responses by an NO-dependent mechanism. J Immunol. 2002;168(2):689-95.

192.Talmadge JE. Pathways Mediating the Expansion and Immunosuppressive Activity of Myeloid-Derived Suppressor Cells and Their Relevance to Cancer Therapy. Clinical Cancer Research. 2007;13(18):5243-8.

193.Corzo CA, Cotter MJ, Cheng P, Cheng F, Kusmartsev S, Sotomayor E, et al. Mechanism regulating reactive oxygen species in tumor-induced myeloid-derived suppressor cells. J Immunol. 2009;182(9):5693-701.

194.Rodríguez PC, Ochoa AC. Arginine regulation by myeloid derived suppressor cells and tolerance in cancer: mechanisms and therapeutic perspectives. Immunol Rev. 2008;222:180-91.

195.Srivastava MK, Sinha P, Clements VK, Rodriguez P, Ostrand-Rosenberg S. Myeloid-derived suppressor cells inhibit T-cell activation by depleting cystine and cysteine. Cancer Res. 2010;70(1):68-77.

196.Rodriguez PC, Quiceno DG, Ochoa AC. L-arginine availability regulates T-lymphocyte cell-cycle progression. Blood. 2007;109(4):1568-73.

197.Timosenko E, Hadjinicolaou AV, Cerundolo V. Modulation of cancer-specific immune responses by amino acid degrading enzymes. Immunotherapy. 2017;9(1):83-97.

198.Mellor A. Indoleamine 2,3 dioxygenase and regulation of T cell immunity. Biochem Biophys Res Commun. 2005;338(1):20-4.

199.Moon YW, Hajjar J, Hwu P, Naing A. Targeting the indoleamine 2,3-dioxygenase pathway in cancer. J Immunother Cancer. 2015;3:51.

200.Cheng P, Corzo CA, Luetteke N, Yu B, Nagaraj S, Bui MM, et al. Inhibition of dendritic cell differentiation and accumulation of myeloid-derived suppressor cells in cancer is regulated by S100A9 protein. J Exp Med. 2008;205(10):2235-49.

201.Burstein HJ, Krilov L, Aragon-Ching JB, Baxter NN, Chiorean EG, Chow WA, et al. Clinical Cancer Advances 2017: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology. Journal of Clinical Oncology. 2017;35(12):1341-67.

202.Dizon DS, Krilov L, Cohen E, Gangadhar T, Ganz PA, Hensing TA, et al. Clinical Cancer Advances 2016: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology. Journal of Clinical Oncology. 2016;34(9):987-1011.

203.Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348(6230):56-61.

204.Postow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. Journal of Clinical Oncology. 2015;33(17):1974-82.

260

205.Emens LA. Re-purposing cancer therapeutics for breast cancer immunotherapy. Cancer Immunol Immunother. 2012;61(8):1299-305.

206.Lazzeroni M, Serrano D. Potential use of vaccines in the primary prevention of breast cancer in high-risk patients. Breast care (Basel, Switzerland). 2012;7(4):281-7.

207.Keenan BP, Jaffee EM. Whole cell vaccines--past progress and future strategies. Semin Oncol. 2012;39(3):276-86.

208.Le DT, Pardoll DM, Jaffee EM. Cellular Vaccine Approaches. Cancer J. 2010;16(4):304-10.

209.Pizzurro GA, Barrio MM. Dendritic Cell-Based Vaccine Efficacy: Aiming for Hot Spots. Frontiers in Immunology. 2015;6:91.

210.Harao M, Mittendorf EA, Radvanyi LG. Peptide-based vaccination and induction of CD8+ T-cell responses against tumor antigens in breast cancer. BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy. 2015;29(1):15-30.

211.Macri C, Dumont C, Johnston APR, Mintern JD. Targeting dendritic cells: a promising strategy to improve vaccine effectiveness. Clinical & Translational Immunology. 2016;5(3):e66.

212.Datta J, Terhune JH, Lowenfeld L, Cintolo JA, Xu S, Roses RE, et al. Optimizing Dendritic Cell-Based Approaches for Cancer Immunotherapy. The Yale Journal of Biology and Medicine. 2014;87(4):491-518.

213.Janikashvili N, Larmonier N, Katsanis E. Personalized dendritic cell-based tumor immunotherapy. Immunotherapy. 2010;2(1):57-.

214.Chiang CL, Coukos G, Kandalaft LE. Whole Tumor Antigen Vaccines: Where Are We? Vaccines (Basel). 2015;3(2):344-72.

215.Segal NH, Parsons DW, Peggs KS, Velculescu V, Kinzler KW, Vogelstein B, et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 2008;68(3):889-92.

216.Cintolo JA, Datta J, Mathew SJ, Czerniecki BJ. Dendritic cell-based vaccines: barriers and opportunities. Future oncology (London, England). 2012;8(10):1273-99.

217.Copier J, Dalgleish A. Overview of tumor cell-based vaccines. International reviews of immunology. 2006;25(5-6):297-319.

218.Butterfield LH, Ribas A, Dissette VB, Amarnani SN, Vu HT, Oseguera D, et al. Determinant spreading associated with clinical response in dendritic cell-based immunotherapy for malignant melanoma. Clin Cancer Res. 2003;9(3):998-1008.

219.Corbiere V, Chapiro J, Stroobant V, Ma W, Lurquin C, Lethe B, et al. Antigen spreading contributes to MAGE vaccination-induced regression of melanoma metastases. Cancer Res. 2011;71(4):1253-62.

220.Ribas A, Timmerman JM, Butterfield LH, Economou JS. Determinant spreading and tumor responses after peptide-based cancer immunotherapy. Trends Immunol. 2003;24(2):58-61.

221.Convit J, Montesinos H, Oviedo H, Romero G, Maccarone B, Essenfeld E, et al. Autologous tumor lysate/Bacillus Calmette–Guérin immunotherapy as an adjuvant to conventional breast cancer therapy. Clinical & Translational Oncology. 2015;17:884-7.

222.Gross BP, Wongrakpanich A, Francis MB, Salem AK, Norian LA. A Therapeutic Microparticle-Based Tumor Lysate Vaccine Reduces Spontaneous Metastases in Murine Breast Cancer. The AAPS Journal. 2014;16(6):1194-203.

223.Huang X, Ye D, Thorpe PE. Enhancing the potency of a whole-cell breast cancer vaccine in mice with an antibody-IL-2 immunocytokine that targets exposed phosphatidylserine. Vaccine. 2011;29(29–30):4785-93.

224.Nanni P, Nicoletti G, De Giovanni C, Landuzzi L, Di Carlo E, Cavallo F, et al. Combined allogeneic tumor cell vaccination and systemic interleukin 12 prevents mammary carcinogenesis in HER-2/neu transgenic mice. J Exp Med. 2001;194(9):1195-205.

261

225.Soliman H, Mediavilla-Varela M, Antonia SJ. A GM-CSF and CD40L bystander vaccine is effective in a murine breast cancer model. Breast Cancer : Targets and Therapy. 2015;7:389-97.

226.Yan HX, Cheng P, Wei HY, Shen GB, Fu LX, Ni J, et al. Active immunotherapy for mouse breast cancer with irradiated whole-cell vaccine expressing VEGFR2. Oncol Rep. 2013;29(4):1510-6.

227.Dillman RO, Beutel LD, De Leon C, Nayak SK. Short-term tumor cell lines from breast cancer for use as autologous tumor cell vaccines in the treatment of breast cancer. Cancer Biotherapy and Radiopharmaceuticals. 2001;16(3):205-11.

228.Elliott RL, Head JF. Adjuvant breast cancer vaccine improves disease specific survival of breast cancer patients with depressed lymphocyte immunity. Surgical oncology. 2013;22(3):172-7.

229.Srivatsan S, Patel JM, Bozeman EN, Imasuen IE, He S, Daniels D, et al. Allogeneic tumor cell vaccines: The promise and limitations in clinical trials. Human vaccines & immunotherapeutics. 2014;10(1):52-63.

230.Nguyen T, Urban J, Kalinski P. Therapeutic cancer vaccines and combination immunotherapies involving vaccination. Immunotargets Ther. 2014;3:135-50.

231.De Giovanni C, Nicoletti G, Landuzzi L, Astolfi A, Croci S, Comes A, et al. Immunoprevention of HER-2/neu transgenic mammary carcinoma through an interleukin 12-engineered allogeneic cell vaccine. Cancer Res. 2004;64(11):4001-9.

232.Machiels JP, Reilly RT, Emens LA, Ercolini AM, Lei RY, Weintraub D, et al. Cyclophosphamide, doxorubicin, and paclitaxel enhance the antitumor immune response of granulocyte/macrophage-colony stimulating factor-secreting whole-cell vaccines in HER-2/neu tolerized mice. Cancer Res. 2001;61(9):3689-97.

233.Prell RA, Gearin L, Simmons A, Vanroey M, Jooss K. The anti-tumor efficacy of a GM-CSF-secreting tumor cell vaccine is not inhibited by docetaxel administration. Cancer Immunol Immunother. 2006;55(10):1285-93.

234.Yu G, Li Y, Cui Z, Morris NP, Weinberg AD, Fox BA, et al. Combinational Immunotherapy with Allo-DRibble Vaccines and Anti-OX40 Co-Stimulation Leads to Generation of Cross-Reactive Effector T Cells and Tumor Regression. Sci Rep. 2016;6:37558.

235.Aranda F, Vacchelli E, Eggermont A, Galon J, Sautes-Fridman C, Tartour E, et al. Trial Watch: Peptide vaccines in cancer therapy. Oncoimmunology. 2013;2(12):e26621.

236.Clifton GT, Gall V, Peoples GE, Mittendorf EA. Clinical Development of the E75 Vaccine in Breast Cancer. Breast care (Basel, Switzerland). 2016;11(2):116-21.

237.Mittendorf EA, Clifton GT, Holmes JP, Clive KS, Patil R, Benavides LC, et al. Clinical trial results of the HER-2/neu (E75) vaccine to prevent breast cancer recurrence in high-risk patients: from US Military Cancer Institute Clinical Trials Group Study I-01 and I-02. Cancer. 2012;118(10):2594-602.

238.Oka Y, Tsuboi A, Taguchi T, Osaki T, Kyo T, Nakajima H, et al. Induction of WT1 (Wilms' tumor gene)-specific cytotoxic T lymphocytes by WT1 peptide vaccine and the resultant cancer regression. Proc Natl Acad Sci U S A. 2004;101(38):13885-90.

239.Scholl S, Squiban P, Bizouarne N, Baudin M, Acres B, Von Mensdorff-Pouilly S, et al. Metastatic Breast Tumour Regression Following Treatment by a Gene-Modified Vaccinia Virus Expressing MUC1 and IL-2. Journal of biomedicine & biotechnology. 2003;2003(3):194-201.

240.Emens LA, Asquith JM, Leatherman JM, Kobrin BJ, Petrik S, Laiko M, et al. Timed sequential treatment with cyclophosphamide, doxorubicin, and an allogeneic granulocyte-macrophage colony-stimulating factor-secreting breast tumor vaccine: a chemotherapy dose-ranging factorial study of safety and immune activation. J Clin Oncol. 2009;27(35):5911-8.

262

241.Emens LA, Jaffee EM. Leveraging the activity of tumor vaccines with cytotoxic chemotherapy. Cancer Res. 2005;65(18):8059-64.

242.Jelovac D, Emens LA. HER2-directed therapy for metastatic breast cancer. Oncology (Williston Park). 2013;27(3):166-75.

243.Dols A, Smith JW, 2nd, Meijer SL, Fox BA, Hu HM, Walker E, et al. Vaccination of women with metastatic breast cancer, using a costimulatory gene (CD80)-modified, HLA-A2-matched, allogeneic, breast cancer cell line: clinical and immunological results. Hum Gene Ther. 2003;14(11):1117-23.

244.Jiang XP, Yang DC, Elliott RL, Head JF. Vaccination with a mixed vaccine of autogenous and allogeneic breast cancer cells and tumor associated antigens CA15-3, CEA and CA125--results in immune and clinical responses in breast cancer patients. Cancer biotherapy & radiopharmaceuticals. 2000;15(5):495-505.

245.Ichim TE, Li S, Ma H, Yurova YV, Szymanski JS, Patel AN, et al. Induction of tumor inhibitory anti-angiogenic response through immunization with interferon Gamma primed placental endothelial cells: ValloVax. Journal of translational medicine. 2015;13:90.

246.Bozeman EN, Cimino-Mathews A, Machiah DK, Patel JM, Krishnamoorthy A, Tien L, et al. Expression of membrane anchored cytokines and B7-1 alters tumor microenvironment and induces protective antitumor immunity in a murine breast cancer model. Vaccine. 2013;31(20):2449-56.

247.Monzavi-Karbassi B, Jousheghany F, Kieber-Emmons T. Tumor-associated Glycans and Tregs in Immunogenicity of an Autologous Cell-based Vaccine. Immunol Invest. 2016:1-13.

248.Singh B, Lucci A. Role of cyclooxygenase-2 in breast cancer. The Journal of surgical research. 2002;108(1):173-9.

249.Agrawal A, Fentiman IS. NSAIDs and breast cancer: a possible prevention and treatment strategy. International journal of clinical practice. 2008;62(3):444-9.

250.Arun B, Goss P. The role of COX-2 inhibition in breast cancer treatment and prevention. Semin Oncol. 2004;31(2 Suppl 7):22-9.

251.Hahn T, Alvarez I, Kobie JJ, Ramanathapuram L, Dial S, Fulton A, et al. Short-term dietary administration of celecoxib enhances the efficacy of tumor lysate-pulsed dendritic cell vaccines in treating murine breast cancer. Int J Cancer. 2006;118(9):2220-31.

252.Zhang B, Ma X, Li Z, Gao X, Wang F, Liu L, et al. Celecoxib enhances the efficacy of 15-hydroxyprostaglandin dehydrogenase gene therapy in treating murine breast cancer. J Cancer Res Clin Oncol. 2013;139(5):797-807.

253.Postow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. J Clin Oncol. 2015;33(17):1974-82.

254.Okazaki T, Chikuma S, Iwai Y, Fagarasan S, Honjo T. A rheostat for immune responses: the unique properties of PD-1 and their advantages for clinical application. Nat Immunol. 2013;14(12):1212-8.

255.Parry RV, Chemnitz JM, Frauwirth KA, Lanfranco AR, Braunstein I, Kobayashi SV, et al. CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms. Molecular and cellular biology. 2005;25(21):9543-53.

256.Riley JL. PD-1 signaling in primary T cells. Immunol Rev. 2009;229(1):114-25. 257.Emens LA, Kok M, Ojalvo LS. Targeting the programmed cell death-1 pathway in breast

and ovarian cancer. Curr Opin Obstet Gynecol. 2016;28(2):142-7. 258.Gibson J. Anti-PD-L1 for metastatic triple-negative breast cancer. Lancet Oncol.

2015;16(6):e264. 259.Chawla A, Philips AV, Alatrash G, Mittendorf E. Immune checkpoints: A therapeutic

target in triple negative breast cancer. Oncoimmunology. 2014;3(3):e28325. 260.Loi S, Michiels S, Salgado R, Sirtaine N, Jose V, Fumagalli D, et al. Tumor infiltrating

lymphocytes are prognostic in triple negative breast cancer and predictive for

263

trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann Oncol. 2014;25(8):1544-50.

261.Mittendorf EA, Philips AV, Meric-Bernstam F, Qiao N, Wu Y, Harrington S, et al. PD-L1 expression in triple-negative breast cancer. Cancer Immunol Res. 2014;2(4):361-70.

262.Sagiv-Barfi I, Kohrt HEK, Czerwinski DK, Ng PP, Chang BY, Levy R. Therapeutic antitumor immunity by checkpoint blockade is enhanced by ibrutinib, an inhibitor of both BTK and ITK. PNAS. 2015;112(9):E966-E72.

263.Dolcetti L, Peranzoni E, Ugel S, Marigo I, Fernandez Gomez A, Mesa C, et al. Hierarchy of immunosuppressive strength among myeloid-derived suppressor cell subsets is determined by GM-CSF. European Journal of Immunology. 2010;40(1):22-35.

264.Hirano F, Kaneko K, Tamura H, Dong H, Wang S, Ichikawa M, et al. Blockade of B7-H1 and PD-1 by monoclonal antibodies potentiates cancer therapeutic immunity. Cancer Res. 2005;65(3):1089-96.

265.Beavis PA, Milenkovski N, Henderson MA, John LB, Allard B, Loi S, et al. Adenosine Receptor 2A Blockade Increases the Efficacy of Anti-PD-1 through Enhanced Antitumor T-cell Responses. Cancer Immunol Res. 2015;3(5):506-17.

266.Kim K, Skora AD, Li Z, Liu Q, Tam AJ, Blosser RL, et al. Eradication of metastatic mouse cancers resistant to immune checkpoint blockade by suppression of myeloid-derived cells. PNAS. 2014;111(32):11774-9.

267.Allard B, Pommey S, Smyth MJ, Stagg J. Targeting CD73 enhances the antitumor activity of anti-PD-1 and anti-CTLA-4 mAbs. Clin Cancer Res. 2013;19(20):5626-35.

268.Li Y, Fang M, Zhang J, Wang J, Song Y, Shi J, et al. Hydrogel dual delivered celecoxib and anti-PD-1 synergistically improve antitumor immunity. Oncoimmunology. 2016;5(2):e1074374.

269.Mittal D, Young A, Stannard K, Yong M, Teng MW, Allard B, et al. Antimetastatic effects of blocking PD-1 and the adenosine A2A receptor. Cancer Res. 2014;74(14):3652-8.

270.Sharabi AB, Nirschl CJ, Kochel CM, Nirschl TR, Francica BJ, Velarde E, et al. Stereotactic Radiation Therapy Augments Antigen-Specific PD-1-Mediated Antitumor Immune Responses via Cross-Presentation of Tumor Antigen. Cancer Immunol Res. 2015;3(4):345-55.

271.Ballard-Barbash R, George SM, Alfano CM, Schmitz K. Physical activity across the cancer continuum. Oncology (Williston Park). 2013;27(6):589, 92.

272.Rogers CJ, Berrigan D, Zaharoff DA, Hance KW, Patel AC, Perkins SN, et al. Energy restriction and exercise differentially enhance components of systemic and mucosal immunity in mice. J Nutr. 2008;138(1):115-22.

273.Rogers CJ, Zaharoff DA, Hance KW, Perkins SN, Hursting SD, Schlom J, et al. Exercise enhances vaccine-induced antigen-specific T cell responses. Vaccine. 2008;26(42):5407-15.

274.Mansfield CM. A review of the etiology of breast cancer. Journal of the National Medical Association. 1993;85(3):217-21.

275.World Cancer Research Fund / American Institute for Cancer Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective. Washington DC: AICR, 2007.

276.Fentiman IS. Fixed and modifiable risk factors for breast cancer. International journal of clinical practice. 2001;55(8):527-30.

277.Howell A, Anderson AS, Clarke RB, Duffy SW, Evans DG, Garcia-Closas M, et al. Risk determination and prevention of breast cancer. Breast Cancer Res. 2014;16(5):446.

278.Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, Milne RL, et al. Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States. JAMA oncology. 2016;2(10):1295-302.

264

279.Rojas K, Stuckey A. Breast Cancer Epidemiology and Risk Factors. Clinical obstetrics and gynecology. 2016;59(4):651-72.

280.Tamimi RM, Spiegelman D, Smith-Warner SA, Wang M, Pazaris M, Willett WC, et al. Population Attributable Risk of Modifiable and Nonmodifiable Breast Cancer Risk Factors in Postmenopausal Breast Cancer. American journal of epidemiology. 2016;184(12):884-93.

281.Walsh MF, Nathanson KL, Couch FJ, Offit K. Genomic Biomarkers for Breast Cancer Risk. Adv Exp Med Biol. 2016;882:1-32.

282.Collaborative Group on Hormonal Factors in Breast C. Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies. The Lancet Oncology. 2012;13(11):1141-51.

283.Golubicic I, Borojevic N, Pavlovic T. Risk factors for breast cancer: is ionizing radiation among them? Journal of BUON : official journal of the Balkan Union of Oncology. 2008;13(4):487-94.

284.Green VL. Mammographic Breast Density and Breast Cancer Risk: Implications of the Breast Density Legislation for Health Care Practitioners. Clinical obstetrics and gynecology. 2016;59(2):419-38.

285.Kapil U, Bhadoria AS, Sareen N, Singh P, Dwivedi SN. Reproductive factors and risk of breast cancer: A Review. Indian journal of cancer. 2014;51(4):571-6.

286.Danforth DN, Jr. Disparities in breast cancer outcomes between Caucasian and African American women: a model for describing the relationship of biological and nonbiological factors. Breast Cancer Res. 2013;15(3):208.

287.Curtis E, Quale C, Haggstrom D, Smith-Bindman R. Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer. 2008;112(1):171-80.

288.Meier-Abt F, Bentires-Alj M. How pregnancy at early age protects against breast cancer. Trends Mol Med. 2014;20.

289.Bianchini F, Kaaks R, Vainio H. Weight control and physical activity in cancer prevention. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2002;3(1):5-8.

290.Friedenreich CM, Orenstein MR. Physical Activity and Cancer Prevention: Etiologic Evidence and Biological Mechanisms. J Nutr. 2002;132(11):3456S-64S.

291.Loprinzi PD, Cardinal BJ, Smit E, Winters-Stone KM. Physical activity and breast cancer risk. Journal of Exercise Science & Fitness. 2012;10(1):1-7.

292.Zhong S, Jiang T, Ma T, Zhang X, Tang J, Chen W, et al. Association between physical activity and mortality in breast cancer: a meta-analysis of cohort studies. Eur J Epidemiol. 2014;29(6):391-404.

293.World Cancer Research Fund International/American Institute for Cancer Research. Continuous Update Project Report: Diet, Nutrition, Physical Activity and Breast Cancer. 2017. Available at: wcrf.org/breast-cancer–2017.

294.Albuquerque RC, Baltar VT, Marchioni DM. Breast cancer and dietary patterns: a systematic review. Nutr Rev. 2014;72.

295.Aune D, Chan DS, Vieira AR, Rosenblatt DA, Vieira R, Greenwood DC, et al. Fruits, vegetables and breast cancer risk: a systematic review and meta-analysis of prospective studies. Breast Cancer Res Treat. 2012;134.

296.Jung S, Spiegelman D, Baglietto L, Bernstein L, Boggs DA, van den Brandt PA, et al. Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J Natl Cancer Inst. 2013;105.

265

297.Norat T, Aune D, Chan D, Romaguera D. Fruits and vegetables: updating the epidemiologic evidence for the WCRF/AICR lifestyle recommendations for cancer prevention. Cancer Treat Res. 2014;159.

298.Ballard-Barbash R, Friedenreich CM, Courneya KS, Siddiqi SM, McTiernan A, Alfano CM. Physical activity, biomarkers, and disease outcomes in cancer survivors: a systematic review. Journal of the National Cancer Institute. 2012;104(11):815-40.

299.McTiernan A. Mechanisms linking physical activity with cancer. Nat Rev Cancer. 2008;8(3):205-11.

300.Zhu Z, Jiang W, Zacher JH, Neil ES, McGinley JN, Thompson HJ. Effects of energy restriction and wheel running on mammary carcinogenesis and host systemic factors in a rat model. Cancer Prev Res (Phila). 2012;5(3):414-22.

301.American Institute for Cancer Research., World Cancer Research Fund. Food, nutrition, physical activity and the prevention of cancer : a global perspective : a project of World Cancer Research Fund International. Washington, D.C.: American Institute for Cancer Research; 2007. xxv, 517 p. p.

302.Horn J, Vatten LJ. Reproductive and hormonal risk factors of breast cancer: a historical perspective. International journal of women's health. 2017;9:265-72.

303.Chen WY, Rosner B, Hankinson SE, Colditz GA, Willett WC. Moderate alcohol consumption during adult life, drinking patterns, and breast cancer risk. JAMA. 2011;306.

304.Hormones E, Group BCC, Key TJ, Appleby PN, Reeves GK, Roddam AW, et al. Circulating sex hormones and breast cancer risk factors in post-menopausal women: reanalysis of 13 studies. Br J Cancer. 2011;105.

305.Hormones E, Group BCC, Key TJ, Appleby PN, Reeves GK, Travis RC, et al. Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol. 2013;14.

306.Kaaks R, Lukanova A. Effects of weight control and physical activity in cancer prevention: role of endogenous hormone metabolism. Ann N Y Acad Sci. 2002;963:268-81.

307.Kaaks R, Tikk K, Sookthai D, Schock H, Johnson T, Tjønneland A, et al. Premenopausal serum sex hormone levels in relation to breast cancer risk, overall and by hormone receptor status - results from the EPIC cohort. Int J Cancer. 2014;134.

308.Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE. Adult weight change and risk of postmenopausal breast cancer. JAMA. 2006;296(2):193-201.

309.Friedenreich CM. Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev. 2001;10(1):15-32.

310.Kroenke CH, Chen WY, Rosner B, Holmes MD. Weight, weight gain, and survival after breast cancer diagnosis. J Clin Oncol. 2005;23(7):1370-8.

311.Organization WH. Obesity: preventing and managing the global epidemic: World Health Organization; 2000.

312.Saxton JM. Diet, physical activity and energy balance and their impact on breast and prostate cancers. Nutr Res Rev. 2006;19(2):197-215.

313.Kushi LH, Doyle C, McCullough M, Rock CL, Demark-Wahnefried W, Bandera EV, et al. American Cancer Society guidelines on nutrition and physical activity for cancer prevention. CA: A Cancer Journal for Clinicians. 2012;62(1):30-67.

314.Hastert TA, Beresford SA, Patterson RE, Kristal AR, White E. Adherence to WCRF/AICR cancer prevention recommendations and risk of postmenopausal breast cancer. Cancer Epidemiology and Prevention Biomarkers. 2013.

315.McKenzie F, Ferrari P, Freisling H, Chajes V, Rinaldi S, de Batlle J, et al. Healthy lifestyle and risk of breast cancer among postmenopausal women in the European

266

Prospective Investigation into Cancer and Nutrition cohort study. Int J Cancer. 2015;136(11):2640-8.

316.Thomson CA, McCullough ML, Wertheim BC, Chlebowski RT, Martinez ME, Stefanick ML, et al. Nutrition and physical activity cancer prevention guidelines, cancer risk, and mortality in the women's health initiative. Cancer Prev Res (Phila). 2014;7(1):42-53.

317.Makarem N, Lin Y, Bandera EV, Jacques PF, Parekh N. Concordance with World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines for cancer prevention and obesity-related cancer risk in the Framingham Offspring cohort (1991-2008). Cancer Causes Control. 2015;26(2):277-86.

318.Vera-Ramirez L, Ramirez-Tortosa MC, Sanchez-Rovira P, Ramirez-Tortosa CL, Granados-Principal S, Lorente JA, et al. Impact of diet on breast cancer risk: a review of experimental and observational studies. Critical reviews in food science and nutrition. 2013;53(1):49-75.

319.Cao Y, Hou L, Wang W. Dietary total fat and fatty acids intake, serum fatty acids and risk of breast cancer: A meta-analysis of prospective cohort studies. Int J Cancer. 2016;138(8):1894-904.

320.Brennan SF, Woodside JV, Lunny PM, Cardwell CR, Cantwell MM. Dietary fat and breast cancer mortality: A systematic review and meta-analysis. Critical reviews in food science and nutrition. 2017;57(10):1999-2008.

321.Tabung FK, Steck SE, Liese AD, Zhang J, Ma Y, Johnson KC, et al. Patterns of change over time and history of the inflammatory potential of diet and risk of breast cancer among postmenopausal women. Breast Cancer Res Treat. 2016;159(1):139-49.

322.Mourouti N, Kontogianni MD, Papavagelis C, Panagiotakos DB. Diet and breast cancer: a systematic review. International journal of food sciences and nutrition. 2015;66(1):1-42.

323.Ferrari P, Licaj I, Muller DC, Kragh Andersen P, Johansson M, Boeing H, et al. Lifetime alcohol use and overall and cause-specific mortality in the European Prospective Investigation into Cancer and nutrition (EPIC) study. BMJ Open. 2014;4.

324.Christou NV, Lieberman M, Sampalis F, Sampalis JS. Bariatric surgery reduces cancer risk in morbidly obese patients. Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery. 2008;4(6):691-5.

325.Harvie M, Howell A, Vierkant RA, Kumar N, Cerhan JR, Kelemen LE, et al. Association of gain and loss of weight before and after menopause with risk of postmenopausal breast cancer in the Iowa women's health study. Cancer Epidemiol Biomarkers Prev. 2005;14(3):656-61.

326.Ostlund MP, Lu Y, Lagergren J. Risk of obesity-related cancer after obesity surgery in a population-based cohort study. Annals of surgery. 2010;252(6):972-6.

327.Zhang FF, John EM, Knight JA, Kaur M, Daly M, Buys S, et al. Total energy intake and breast cancer risk in sisters: the Breast Cancer Family Registry. Breast Cancer Res Treat. 2013;137(2):541-51.

328.Sue LY, Schairer C, Ma X, Williams C, Chang SC, Miller AB, et al. Energy intake and risk of postmenopausal breast cancer: an expanded analysis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort. Cancer Epidemiol Biomarkers Prev. 2009;18(11):2842-50.

329.Silvera SA, Jain M, Howe GR, Miller AB, Rohan TE. Energy balance and breast cancer risk: a prospective cohort study. Breast Cancer Res Treat. 2006;97(1):97-106.

330.Imayama I, Ulrich CM, Alfano CM, Wang C, Xiao L, Wener MH, et al. Effects of a caloric restriction weight loss diet and exercise on inflammatory biomarkers in overweight/obese postmenopausal women: a randomized controlled trial. Cancer Res. 2012;72(9):2314-26.

267

331.Howell A, Chapman M, Harvie M. Energy restriction for breast cancer prevention. Recent Results Cancer Res. 2009;181:97-111.

332.Alimujiang A, Mo M, Liu Y, Huang NS, Liu G, Xu W, et al. The association between China's Great famine and risk of breast cancer according to hormone receptor status: a hospital-based study. Breast Cancer Res Treat. 2016;160(2):361-9.

333.Vin-Raviv N, Barchana M, Linn S, Keinan-Boker L. Severe caloric restriction in young women during World War II and subsequent breast cancer risk. International journal of clinical practice. 2012;66(10):948-58.

334.Elias SG, Peeters PH, Grobbee DE, van Noord PA. The 1944-1945 Dutch famine and subsequent overall cancer incidence. Cancer Epidemiol Biomarkers Prev. 2005;14(8):1981-5.

335.Keinan-Boker L, Vin-Raviv N, Liphshitz I, Linn S, Barchana M. Cancer incidence in Israeli Jewish survivors of World War II. J Natl Cancer Inst. 2009;101(21):1489-500.

336.Koupil I, Plavinskaja S, Parfenova N, Shestov DB, Danziger PD, Vagero D. Cancer mortality in women and men who survived the siege of Leningrad (1941-1944). Int J Cancer. 2009;124(6):1416-21.

337.Uauy R, Solomons N. Diet, nutrition, and the life-course approach to cancer prevention. J Nutr. 2005;135(12 Suppl):2934s-45s.

338.Karamanis G, Skalkidou A, Tsakonas G, Brandt L, Ekbom A, Ekselius L, et al. Cancer incidence and mortality patterns in women with anorexia nervosa. Int J Cancer. 2014;134(7):1751-7.

339.Harvie M, Howell A. Energy balance adiposity and breast cancer - energy restriction strategies for breast cancer prevention. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2006;7(1):33-47.

340.Lv M, Zhu X, Wang H, Wang F, Guan W. Roles of Caloric Restriction, Ketogenic Diet and Intermittent Fasting during Initiation, Progression and Metastasis of Cancer in Animal Models: A Systematic Review and Meta-Analysis. PLoS ONE. 2014;9(12):e115147.

341.Zhu Z, Haegele AD, Thompson HJ. Effect of caloric restriction on pre-malignant and malignant stages of mammary carcinogenesis. Carcinogenesis. 1997;18(5):1007-12.

342.Zhu Z, Jiang W, Zacher JH, Neil ES, McGinley JN, Thompson HJ. Effects of energy restriction and wheel running on mammary carcinogenesis and host systemic factors in a rat model. Cancer Prevention Research (Philadelphia, Pa). 2012;5(3):414-22.

343.Dunlap SM, Chiao LJ, Nogueira L, Usary J, Perou CM, Varticovski L, et al. Dietary Energy Balance Modulates Epithelial-to-Mesenchymal Transition and Tumor Progression in Murine Claudin-Low and Basal-like Mammary Tumor Models. Cancer Prev Res. 2012;5(7):930-42.

344.Harvie M, Howell A. Energy restriction and the prevention of breast cancer. Proc Nutr Soc. 2012;71.

345.Turbitt WJ, Black AJ, Collins SD, Meng H, Xu H, Washington S, et al. Fish Oil Enhances T Cell Function and Tumor Infiltration and Is Correlated With a Cancer Prevention Effect in HER-2/neu But Not PyMT Transgenic Mice. Nutr Cancer. 2015;67(6):965-75.

346.Krishnan AV, Swami S, Feldman D. Equivalent anticancer activities of dietary vitamin D and calcitriol in an animal model of breast cancer: importance of mammary CYP27B1 for treatment and prevention. J Steroid Biochem Mol Biol. 2013;136:289-95.

347.Hardman WE, Ion G. Suppression of implanted MDA-MB 231 human breast cancer growth in nude mice by dietary walnut. Nutr Cancer. 2008;60(5):666-74.

348.Birt DF, Hendrich S, Wang W. Dietary agents in cancer prevention: flavonoids and isoflavonoids. Pharmacology & therapeutics. 2001;90(2-3):157-77.

349.Welsh J. Vitamin D and breast cancer: insights from animal models. Am J Clin Nutr. 2004;80(6 Suppl):1721s-4s.

268

350.Mandal CC, Ghosh-Choudhury T, Yoneda T, Choudhury GG, Ghosh-Choudhury N. Fish oil prevents breast cancer cell metastasis to bone. Biochem Biophys Res Commun. 2010;402(4):602-7.

351.Kanaya N, Adams L, Takasaki A, Chen S. Whole blueberry powder inhibits metastasis of triple negative breast cancer in a xenograft mouse model through modulation of inflammatory cytokines. Nutr Cancer. 2014;66(2):242-8.

352.Thompson A, Brennan K, Cox A, Gee J, Harcourt D, Harris A, et al. Evaluation of the current knowledge limitations in breast cancer research: a gap analysis. Breast Cancer Res. 2008;10(2):R26.

353.Cohen LA, Choi KW, Wang CX. Influence of dietary fat, caloric restriction, and voluntary exercise on N-nitrosomethylurea-induced mammary tumorigenesis in rats. Cancer Res. 1988;48(15):4276-83.

354.Harvey AE, Lashinger LM, Otto G, Nunez NP, Hursting SD. Decreased systemic IGF-1 in response to calorie restriction modulates murine tumor cell growth, nuclear factor-kappaB activation, and inflammation-related gene expression. Mol Carcinog. 2013;52(12):997-1006.

355.Zhu Z, Jiang W, McGinley J, Wolfe P, Thompson HJ. Effects of dietary energy repletion and IGF-1 infusion on the inhibition of mammary carcinogenesis by dietary energy restriction. Mol Carcinog. 2005;42(3):170-6.

356.De Lorenzo MS, Baljinnyam E, Vatner DE, Abarzúa P, Vatner SF, Rabson AB. Caloric restriction reduces growth of mammary tumors and metastases. Carcinogenesis. 2011;32(9):1381-7.

357.Dunlap SM, Chiao LJ, Nogueira L, Usary J, Perou CM, Varticovski L, et al. Dietary energy balance modulates epithelial-to-mesenchymal transition and tumor progression in murine claudin-low and basal-like mammary tumor models. Cancer prevention research. 2012;5(7):930-42.

358.Ford NA, Nunez NP, Holcomb VB, Hursting SD. IGF1 dependence of dietary energy balance effects on murine Met1 mammary tumor progression, epithelial-to-mesenchymal transition, and chemokine expression. Endocrine-related cancer. 2013;20(1):39-51.

359.Zhu Z, Jiang W, McGinley J, Wolfe P, Thompson HJ. Effects of dietary energy repletion and IGF-1 infusion on the inhibition of mammary carcinogenesis by dietary energy restriction. Mol Carcinog. 2005;42(3):170-6.

360.Thompson HJ, Jiang W, Zhu Z. Candidate mechanisms accounting for effects of physical activity on breast carcinogenesis. IUBMB life. 2009;61(9):895-901.

361.Ip C. Quantitative assessment of fat and calorie as risk factors in mammary carcinogenesis in an experimental model. Progress in clinical and biological research. 1990;346:107-17.

362.Welsch CW. Dietary fat, calories, and mammary gland tumorigenesis. Advances in experimental medicine and biology. 1992;322:203-22.

363.Saleh AD, Simone BA, Palazzo J, Savage JE, Sano Y, Dan T, et al. Caloric restriction augments radiation efficacy in breast cancer. Cell Cycle. 2013;12(12):1955-63.

364.Dirx MJ, Zeegers MP, Dagnelie PC, van den Bogaard T, van den Brandt PA. Energy restriction and the risk of spontaneous mammary tumors in mice: a meta-analysis. Int J Cancer. 2003;106(5):766-70.

365.Hildebrand JS, Gapstur SM, Campbell PT, Gaudet MM, Patel AV. Recreational physical activity and leisure-time sitting in relation to postmenopausal breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2013;22(10):1906-12.

366.Lynch BM, Neilson HK, Friedenreich CM. Physical activity and breast cancer prevention. Recent Results Cancer Res. 2011;186:13-42.

269

367.Carlson SA, Fulton JE, Schoenborn CA, Loustalot F. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans. American journal of preventive medicine. 2010;39(4):305-13.

368.Ammitzboll G, Sogaard K, Karlsen RV, Tjonneland A, Johansen C, Frederiksen K, et al. Physical activity and survival in breast cancer. European journal of cancer. 2016;66:67-74.

369.Borch KB, Braaten T, Lund E, Weiderpass E. Physical activity before and after breast cancer diagnosis and survival - the Norwegian women and cancer cohort study. BMC Cancer. 2015;15:967.

370.Bradshaw PT, Ibrahim JG, Khankari N, Cleveland RJ, Abrahamson PE, Stevens J, et al. Post-diagnosis physical activity and survival after breast cancer diagnosis: the Long Island Breast Cancer Study. Breast Cancer Res Treat. 2014;145(3):735-42.

371.Friedenreich CM, Gregory J, Kopciuk KA, Mackey JR, Courneya KS. Prospective cohort study of lifetime physical activity and breast cancer survival. International Journal of Cancer. 2009;124(8):1954-62.

372.Holmes MD, Chen WY, Feskanich D, Kroenke CH, Colditz GA. Physical activity and survival after breast cancer diagnosis. JAMA. 2005;293(20):2479-86.

373.Ibrahim EM, Al-Homaidh A. Physical activity and survival after breast cancer diagnosis: meta-analysis of published studies. Medical oncology (Northwood, London, England). 2011;28(3):753-65.

374.Kim J, Choi WJ, Jeong SH. The effects of physical activity on breast cancer survivors after diagnosis. Journal of cancer prevention. 2013;18(3):193-200.

375.Lahart IM, Metsios GS, Nevill AM, Carmichael AR. Physical activity, risk of death and recurrence in breast cancer survivors: A systematic review and meta-analysis of epidemiological studies. Acta oncologica (Stockholm, Sweden). 2015;54(5):635-54.

376.Peel JB, Sui X, Adams SA, Hébert JR, Hardin JW, Blair SN. A prospective study of cardiorespiratory fitness and breast cancer mortality. Medicine and Science in Sports and Exercise. 2009;41(4):742-8.

377.Schmid D, Leitzmann MF. Association between physical activity and mortality among breast cancer and colorectal cancer survivors: a systematic review and meta-analysis. Ann Oncol. 2014;25(7):1293-311.

378.Sternfeld B, Weltzien E, Quesenberry CP, Jr., Castillo AL, Kwan M, Slattery ML, et al. Physical activity and risk of recurrence and mortality in breast cancer survivors: findings from the LACE study. Cancer Epidemiol Biomarkers Prev. 2009;18(1):87-95.

379.Schmid D, Leitzmann MF. Association between physical activity and mortality among breast cancer and colorectal cancer survivors: a systematic review and meta-analysis. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO. 2014;25(7):1293-311.

380.Zhong S, Jiang T, Ma T, Zhang X, Tang J, Chen W, et al. Association between physical activity and mortality in breast cancer: a meta-analysis of cohort studies. Eur J Epidemiol. 2014;29(6):391-404.

381.Friedenreich CM, Gregory J, Kopciuk KA, Mackey JR, Courneya KS. Prospective cohort study of lifetime physical activity and breast cancer survival. International journal of cancer Journal international du cancer. 2009;124(8):1954-62.

382.Peel JB, Sui X, Adams SA, Hebert JR, Hardin JW, Blair SN. A prospective study of cardiorespiratory fitness and breast cancer mortality. Medicine and science in sports and exercise. 2009;41(4):742-8.

383.Shalamzari SA, Agha-Alinejad H, Alizadeh S, Shahbazi S, Khatib ZK, Kazemi A, et al. The effect of exercise training on the level of tissue IL-6 and vascular endothelial growth factor in breast cancer bearing mice. Iran J Basic Med Sci. 2014;17(4):231-58.

270

384.Goh J, Endicott E, Ladiges WC. Pre-tumor exercise decreases breast cancer in old mice in a distance-dependent manner. American journal of cancer research. 2014;4(4):378-84.

385.Betof AS, Lascola CD, Weitzel D, Landon C, Scarbrough PM, Devi GR, et al. Modulation of murine breast tumor vascularity, hypoxia and chemotherapeutic response by exercise. Journal of the National Cancer Institute. 2015;107(5).

386.Jones LW, Antonelli J, Masko EM, Broadwater G, Lascola CD, Fels D, et al. Exercise modulation of the host-tumor interaction in an orthotopic model of murine prostate cancer. J Appl Physiol (1985). 2012;113(2):263-72.

387.Westerlind KC, McCarty HL, Schultheiss PC, Story R, Reed AH, Baier ML, et al. Moderate exercise training slows mammary tumour growth in adolescent rats. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation. 2003;12(4):281-7.

388.Thompson HJ, Westerlind KC, Snedden J, Briggs S, Singh M. Exercise intensity dependent inhibition of 1-methyl-1-nitrosourea induced mammary carcinogenesis in female F-344 rats. Carcinogenesis. 1995;16(8):1783-6.

389.Thompson HJ, Westerlind KC, Snedden JR, Briggs S, Singh M. Inhibition of mammary carcinogenesis by treadmill exercise. Journal of the National Cancer Institute. 1995;87(6):453-5.

390.Zhu Z, Jiang W, Sells JL, Neil ES, McGinley JN, Thompson HJ. Effect of nonmotorized wheel running on mammary carcinogenesis: circulating biomarkers, cellular processes, and molecular mechanisms in rats. Cancer Epidemiol Biomarkers Prev. 2008;17(8):1920-9.

391.Zhu Z, Jiang W, McGinley JN, Thompson HJ. Energetics and mammary carcinogenesis: effects of moderate-intensity running and energy intake on cellular processes and molecular mechanisms in rats. Journal of applied physiology (Bethesda, Md : 1985). 2009;106(3):911-8.

392.Thompson HJ, Wolfe P, McTiernan A, Jiang W, Zhu Z. Wheel running-induced changes in plasma biomarkers and carcinogenic response in the 1-methyl-1-nitrosourea-induced rat model for breast cancer. Cancer Prev Res (Phila). 2010;3(11):1484-92.

393.Gillette CA, Zhu Z, Westerlind KC, Melby CL, Wolfe P, Thompson HJ. Energy availability and mammary carcinogenesis: effects of calorie restriction and exercise. Carcinogenesis. 1997;18(6):1183-8.

394.Murphy EA, Davis JM, Barrilleaux TL, McClellan JL, Steiner JL, Carmichael MD, et al. Benefits of exercise training on breast cancer progression and inflammation in C3(1)SV40Tag mice. Cytokine. 2011;55(2):274-9.

395.Goh J, Tsai J, Bammler TK, Farin FM, Endicott E, Ladiges WC. Exercise training in transgenic mice is associated with attenuation of early breast cancer growth in a dose-dependent manner. PloS one. 2013;8(11):e80123.

396.Colbert LH, Westerlind KC, Perkins SN, Haines DC, Berrigan D, Donehower LA, et al. Exercise effects on tumorigenesis in a p53-deficient mouse model of breast cancer. Medicine and Science in Sports and Exercise. 2009;41(8):1597-605.

397.Fairey AS, Courneya KS, Field CJ, Bell GJ, Jones LW, Mackey JR. Randomized controlled trial of exercise and blood immune function in postmenopausal breast cancer survivors. Journal of applied physiology (Bethesda, Md : 1985). 2005;98(4):1534-40.

398.Jones LW, Viglianti BL, Tashjian JA, Kothadia SM, Keir ST, Freedland SJ, et al. Effect of aerobic exercise on tumor physiology in an animal model of human breast cancer. J Appl Physiol (1985). 2010;108(2):343-8.

399.Cordina-Duverger E, Truong T, Anger A, Sanchez M, Arveux P, Kerbrat P, et al. Weight and weight changes throughout life and postmenopausal breast cancer risk: a case-control study in France. BMC Cancer. 2016;16(1):761.

271

400.Kawai M, Minami Y, Kuriyama S, Kakizaki M, Kakugawa Y, Nishino Y, et al. Adiposity, adult weight change and breast cancer risk in postmenopausal Japanese women: the Miyagi Cohort Study. Br J Cancer. 2010;103(9):1443-7.

401.Rosner B, Eliassen AH, Toriola AT, Chen WY, Hankinson SE, Willett WC, et al. Weight and weight changes in early adulthood and later breast cancer risk. Int J Cancer. 2017;140(9):2003-14.

402.Trentham-Dietz A, Newcomb PA, Egan KM, Titus-Ernstoff L, Baron JA, Storer BE, et al. Weight change and risk of postmenopausal breast cancer (United States). Cancer Causes Control. 2000;11(6):533-42.

403.van den Brandt PA, Dirx MJ, Ronckers CM, van den Hoogen P, Goldbohm RA. Height, weight weight change, and postmenopausal breast cancer risk: The Netherlands Cohort Study. Cancer Causes Control. 1997;8(1):39-47.

404.Wang X, Li L, Gao J, Liu J, Guo M, Liu L, et al. The Association Between Body Size and Breast Cancer in Han Women in Northern and Eastern China. Oncologist. 2016.

405.White KK, Park SY, Kolonel LN, Henderson BE, Wilkens LR. Body size and breast cancer risk: the Multiethnic Cohort. Int J Cancer. 2012;131(5):E705-16.

406.Ziegler RG, Hoover RN, Nomura AMY, West DW, Wu AH, Pike MC, et al. Relative Weight, Weight Change, Height, and Breast Cancer Risk in Asian-American Women. J Natl Cancer Inst. 1996;88(10):650-60.

407.Keum N, Greenwood DC, Lee DH, Kim R, Aune D, Ju W, et al. Adult weight gain and adiposity-related cancers: a dose-response meta-analysis of prospective observational studies. J Natl Cancer Inst. 2015;107(2).

408.Howell A, Anderson AS, Clarke RB, Duffy SW, Evans DG, Garcia-Closas M, et al. Risk determination and prevention of breast cancer. Breast Cancer Research. 2014;16(5):446.

409.Playdon MC, Matthews SB, Thompson HJ. Weight change patterns and breast cancer risk: a brief review and analysis. Critical reviews in eukaryotic gene expression. 2013;23(2):159-69.

410.Abbenhardt C, McTiernan A, Alfano CM, Wener MH, Campbell KL, Duggan C, et al. Effects of individual and combined dietary weight loss and exercise interventions in postmenopausal women on adiponectin and leptin levels. J Intern Med. 2013;274(2):163-75.

411.van Gemert WA, May AM, Schuit AJ, Oosterhof BY, Peeters PH, Monninkhof EM. Effect of Weight Loss with or without Exercise on Inflammatory Markers and Adipokines in Postmenopausal Women: The SHAPE-2 Trial, A Randomized Controlled Trial. Cancer Epidemiol Biomarkers Prev. 2016;25(5):799-806.

412.van Gemert WA, Schuit AJ, van der Palen J, May AM, Iestra JA, Wittink H, et al. Effect of weight loss, with or without exercise, on body composition and sex hormones in postmenopausal women: the SHAPE-2 trial. Breast Cancer Res. 2015;17:120.

413.You T, Berman DM, Ryan AS, Nicklas BJ. Effects of hypocaloric diet and exercise training on inflammation and adipocyte lipolysis in obese postmenopausal women. The Journal of clinical endocrinology and metabolism. 2004;89(4):1739-46.

414.Bradshaw PT, Ibrahim JG, Stevens J, Cleveland R, Abrahamson PE, Satia JA, et al. Postdiagnosis change in bodyweight and survival after breast cancer diagnosis. Epidemiology (Cambridge, Mass). 2012;23(2):320-7.

415.Caan BJ, Kwan ML, Shu XO, Pierce JP, Patterson RE, Nechuta SJ, et al. Weight Change and Survival after Breast Cancer in the After Breast Cancer Pooling Project. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(8):1260-71.

272

416.Makari-Judson G, Braun B, Jerry DJ, Mertens WC. Weight gain following breast cancer diagnosis: Implication and proposed mechanisms. World J Clin Oncol. 2014;5(3):272-82.

417.Thompson HJ, Sedlacek SM, Playdon MC, Wolfe P, McGinley JN, Paul D, et al. Weight loss interventions for breast cancer survivors: impact of dietary pattern. PLoS One. 2015;10(5):e0127366.

418.Thompson HJ, Zhu Z, Jiang W. Weight control and breast cancer prevention: are the effects of reduced energy intake equivalent to those of increased energy expenditure? J Nutr. 2004;134(12 Suppl):3407S-11S.

419.Friedenreich CM. Physical activity and breast cancer: review of the epidemiologic evidence and biologic mechanisms. Recent Results Cancer Res. 2011;188:125-39.

420.Jovanovic J, Ronneberg JA, Tost J, Kristensen V. The epigenetics of breast cancer. Molecular oncology. 2010;4(3):242-54.

421.Lo P-K, Sukumar S. Epigenomics and breast cancer. Pharmacogenomics. 2008;9(12):1879-902.

422.Dupont C, Armant DR, Brenner CA. Epigenetics: Definition, Mechanisms and Clinical Perspective. Seminars in reproductive medicine. 2009;27(5):351-7.

423.Gyorffy B, Bottai G, Fleischer T, Munkacsy G, Budczies J, Paladini L, et al. Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes. Int J Cancer. 2016;138(1):87-97.

424.Ferrini K, Ghelfi F, Mannucci R, Titta L. Lifestyle, nutrition and breast cancer: facts and presumptions for consideration. ecancermedicalscience. 2015;9:557.

425.McCullough LE, Chen J, Cho YH, Khankari NK, Bradshaw PT, White AJ, et al. Modification of the association between recreational physical activity and survival after breast cancer by promoter methylation in breast cancer-related genes. Breast Cancer Res. 2017;19(1):19.

426.McCullough LE, Chen J, Cho YH, Khankari NK, Bradshaw PT, White AJ, et al. DNA methylation modifies the association between obesity and survival after breast cancer diagnosis. Breast Cancer Res Treat. 2016;156(1):183-94.

427.Zeng H, Irwin ML, Lu L, Risch H, Mayne S, Mu L, et al. Physical activity and breast cancer survival: an epigenetic link through reduced methylation of a tumor suppressor gene L3MBTL1. Breast Cancer Res Treat. 2012;133(1):127-35.

428.Rossi EL, Dunlap SM, Bowers LW, Khatib SA, Doerstling SS, Smith LA, et al. Energy Balance Modulation Impacts Epigenetic Reprogramming, ERalpha and ERbeta Expression, and Mammary Tumor Development in MMTV-neu Transgenic Mice. Cancer Res. 2017;77(9):2500-11.

429.Thompson HJ, Zhu Z, Jiang W. Dietary energy restriction in breast cancer prevention. Journal of mammary gland biology and neoplasia. 2003;8(1):133-42.

430.Ashcraft KA, Peace RM, Betof AS, Dewhirst MW, Jones LW. Efficacy and Mechanisms of Aerobic Exercise on Cancer Initiation, Progression, and Metastasis: A Critical Systematic Review of In Vivo Preclinical Data. Cancer Res. 2016;76(14):4032-50.

431.Hirschey MD, DeBerardinis RJ, Diehl AM, Drew JE, Frezza C, Green MF, et al. Dysregulated metabolism contributes to oncogenesis. Semin Cancer Biol. 2015;35 Suppl:S129-50.

432.Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell. 2012;21(3):297-308.

433.Hursting SD, Lavigne JA, Berrigan D, Perkins SN, Barrett JC. Calorie restriction, aging, and cancer prevention: mechanisms of action and applicability to humans. Annu Rev Med. 2003;54:131-52.

434.Kopeina GS, Senichkin VV, Zhivotovsky B. Caloric restriction - A promising anti-cancer approach: From molecular mechanisms to clinical trials. Biochimica et biophysica acta. 2017;1867(1):29-41.

273

435.Pedersen L, Christensen JF, Hojman P. Effects of exercise on tumor physiology and metabolism. Cancer J. 2015;21(2):111-6.

436.Zhu Z, Jiang W, Sells JL, Neil ES, McGinley JN, Thompson HJ. Effect of nonmotorized wheel running on mammary carcinogenesis: circulating biomarkers, cellular processes, and molecular mechanisms in rats. Cancer Epidemiol Biomarkers Prev. 2008;17(8):1920-9.

437.Jiang W, Zhu Z, Thompson HJ. Effects of limiting energy availability via diet and physical activity on mammalian target of rapamycin-related signaling in rat mammary carcinomas. Carcinogenesis. 2013;34(2):378-87.

438.Jiang W, Zhu Z, Thompson HJ. Effects of physical activity and restricted energy intake on chemically induced mammary carcinogenesis. Cancer Prev Res (Phila). 2009;2(4):338-44.

439.Tonorezos ES, Jones LW. Energy balance and metabolism after cancer treatment. Semin Oncol. 2013;40(6):745-56.

440.Bruno A, Pagani A, Pulze L, Albini A, Dallaglio K, Noonan DM, et al. Orchestration of angiogenesis by immune cells. Front Oncol. 2014;4:131.

441.Lin EY, Pollard JW. Tumor-associated macrophages press the angiogenic switch in breast cancer. Cancer Res. 2007;67(11):5064-6.

442.Schneider BP, Miller KD. Angiogenesis of breast cancer. Journal of Clinical Oncology. 2005;23(8):1782-90.

443.Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science. 2005;307(5706):58-62.

444.Shalamzari SA, Agha-Alinejad H, Alizadeh S, Shahbazi S, Khatib ZK, Kazemi A, et al. The effect of exercise training on the level of tissue IL-6 and vascular endothelial growth factor in breast cancer bearing mice. Iran J Basic Med Sci. 2014;17(4):231-58.

445.Faustino-Rocha AI, Silva A, Gabriel J, Gil da Costa RM, Moutinho M, Oliveira PA, et al. Long-term exercise training as a modulator of mammary cancer vascularization. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2016;81:273-80.

446.Multhoff G, Molls M, Radons J. Chronic Inflammation in Cancer Development. Frontiers in Immunology. 2011;2:98.

447.Balkwill FR, Capasso M, Hagemann T. The tumor microenvironment at a glance. J Cell Sci. 2012;125(Pt 23):5591-6.

448.Balkwill FR, Mantovani A. Cancer-related inflammation: common themes and therapeutic opportunities. Seminars in Cancer Biology. 2012;22(1):33-40.

449.Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436-44.

450.Luo H, Tu G, Liu Z, Liu M. Cancer-associated fibroblasts: A multifaceted driver of breast cancer progression. Cancer Letters. 2015;361(2):155-63.

451.Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860-7. 452.Ahmed OI, Adel AM, Diab DR, Gobran NS. Prognostic value of serum level of

interleukin-6 and interleukin-8 in metastatic breast cancer patients. The Egyptian journal of immunology. 2006;13(2):61-8.

453.Bachelot T, Ray-Coquard I, Menetrier-Caux C, Rastkha M, Duc A, Blay JY. Prognostic value of serum levels of interleukin 6 and of serum and plasma levels of vascular endothelial growth factor in hormone-refractory metastatic breast cancer patients. Br J Cancer. 2003;88(11):1721-6.

454.Guo Y-Z, Pan L, Du C-J, Ren D-Q, Xie X-M. Association between C-reactive protein and risk of cancer: a meta-analysis of prospective cohort studies. Asian Pacific Journal of Cancer Prevention. 2013;14(1):243-8.

274

455.Pierce BL, Ballard-Barbash R, Bernstein L, Baumgartner RN, Neuhouser ML, Wener MH, et al. Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients. J Clin Oncol. 2009;27(21):3437-44.

456.Salgado R, Junius S, Benoy I, Van Dam P, Vermeulen P, Van Marck E, et al. Circulating interleukin-6 predicts survival in patients with metastatic breast cancer. Int J Cancer. 2003;103(5):642-6.

457.Stoenescu A, Gerlinger C, Solomayer EF, Scholz C, Juhasz-Böss I, Radosa J. Serum C-reactive protein as potential independent prognostic factor for breast cancer. Gynaecology. 2014:28.

458.Jones SB, Thomas GA, Hesselsweet SD, Alvarez-Reeves M, Yu H, Irwin ML. Effect of exercise on markers of inflammation in breast cancer survivors: the Yale exercise and survivorship study. Cancer Prev Res. 2013;6(2):109-18.

459.Steiner JL, Davis JM, McClellan J, Guglielmotti A, Murphy EA. Effects of the MCP-1 synthesis inhibitor bindarit on tumorigenesis and inflammatory markers in the C3(1)/SV40Tag mouse model of breast cancer. Cytokine. 2014;66(1):60-8.

460.Thompson HJ, Wolfe P, McTiernan A, Jiang W, Zhu Z. Wheel Running–Induced Changes in Plasma Biomarkers and Carcinogenic Response in the 1-Methyl-1-Nitrosourea–Induced Rat Model for Breast Cancer. Cancer Prev Res. 2010;3(11):1484-92.

461.E Goldberg J, L Schwertfeger K. Proinflammatory cytokines in breast cancer: mechanisms of action and potential targets for therapeutics. Current drug targets. 2010;11(9):1133-46.

462.Rose DP, Vona-Davis L. Interaction between menopausal status and obesity in affecting breast cancer risk. Maturitas. 2010;66(1):33-8.

463.Folkerd E, Dowsett M. Sex hormones and breast cancer risk and prognosis. Breast (Edinburgh, Scotland). 2013;22 Suppl 2:S38-43.

464.Campbell KL, Foster-Schubert KE, Alfano CM, Wang CC, Wang CY, Duggan CR, et al. Reduced-calorie dietary weight loss, exercise, and sex hormones in postmenopausal women: randomized controlled trial. J Clin Oncol. 2012;30(19):2314-26.

465.Fair AM, Montgomery K. Energy balance, physical activity, and cancer risk. Methods in molecular biology (Clifton, NJ). 2009;472:57-88.

466.Buckland G, Travier N, Agudo A. The role of diet, weight control and physical activity in breast cancer survivors. Breast Cancer Management. 2014;3(6):495-503.

467.Irwin ML, Varma K, Alvarez-Reeves M, Cadmus L, Wiley A, Chung GG, et al. Randomized controlled trial of aerobic exercise on insulin and insulin-like growth factors in breast cancer survivors: the Yale Exercise and Survivorship study. Cancer Epidemiol Biomarkers Prev. 2009;18(1):306-13.

468.Baserga R, Peruzzi F, Reiss K. The IGF-1 receptor in cancer biology. Int J Cancer. 2003;107(6):873-7.

469.Jogie-Brahim S, Feldman D, Oh Y. Unraveling insulin-like growth factor binding protein-3 actions in human disease. Endocrine reviews. 2009;30(5):417-37.

470.Jones JI, Clemmons DR. Insulin-like growth factors and their binding proteins: biological actions. Endocrine reviews. 1995;16(1):3-34.

471.Ali O, Cohen P, Lee KW. Epidemiology and biology of insulin-like growth factor binding protein-3 (IGFBP-3) as an anti-cancer molecule. Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme. 2003;35(11-12):726-33.

472.Bowers LW, Rossi EL, O'Flanagan CH, deGraffenried LA, Hursting SD. The Role of the Insulin/IGF System in Cancer: Lessons Learned from Clinical Trials and the Energy Balance-Cancer Link. Frontiers in endocrinology. 2015;6:77.

275

473.Pollak MN. Endocrine effects of IGF-I on normal and transformed breast epithelial cells: potential relevance to strategies for breast cancer treatment and prevention. Breast cancer research and treatment. 1998;47(3):209-17.

474.Baglietto L, English DR, Hopper JL, Morris HA, Tilley WD, Giles GG. Circulating insulin-like growth factor-I and binding protein-3 and the risk of breast cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2007;16(4):763-8.

475.Key TJ, Appleby PN, Reeves GK, Roddam AW. Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies. Lancet Oncol. 2010;11(6):530-42.

476.Renehan AG, Zwahlen M, Minder C, O'Dwyer ST, Shalet SM, Egger M. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet. 2004;363(9418):1346-53.

477.Shi R, Yu H, McLarty J, Glass J. IGF-I and breast cancer: a meta-analysis. Int J Cancer. 2004;111(3):418-23.

478.Brenner DR, Neilson HK, Courneya KS, Friedenreich CM. Physical Activity After Breast Cancer: Effect on Survival and Patient-Reported Outcomes. Curr Breast Cancer Rep. 2014;6(3):193-204.

479.Coughlin SS, Smith SA. The Insulin-like Growth Factor Axis, Adipokines, Physical Activity, and Obesity in Relation to Breast Cancer Incidence and Recurrence. Cancer and clinical oncology. 2015;4(2):24-31.

480.Fairey AS, Courneya KS, Field CJ, Bell GJ, Jones LW, Mackey JR. Effects of exercise training on fasting insulin, insulin resistance, insulin-like growth factors, and insulin-like growth factor binding proteins in postmenopausal breast cancer survivors: a randomized controlled trial. Cancer Epidemiol Biomarkers Prev. 2003;12(8):721-7.

481.Vadgama JV, Wu Y, Datta G, Khan H, Chillar R. Plasma insulin-like growth factor-I and serum IGF-binding protein 3 can be associated with the progression of breast cancer, and predict the risk of recurrence and the probability of survival in African-American and Hispanic women. Oncology. 1999;57(4):330-40.

482.Gems D, Partridge L. Insulin/IGF signalling and ageing: seeing the bigger picture. Current opinion in genetics & development. 2001;11(3):287-92.

483.Hursting SD, Lashinger LM, Wheatley KW, Rogers CJ, Colbert LH, Nunez NP, et al. Reducing the weight of cancer: mechanistic targets for breaking the obesity-carcinogenesis link. Best practice & research Clinical endocrinology & metabolism. 2008;22(4):659-69.

484.Meneses-Echavez JF, Jimenez EG, Rio-Valle JS, Correa-Bautista JE, Izquierdo M, Ramirez-Velez R. The insulin-like growth factor system is modulated by exercise in breast cancer survivors: a systematic review and meta-analysis. BMC Cancer. 2016;16(1):682.

485.Christopoulos PF, Msaouel P, Koutsilieris M. The role of the insulin-like growth factor-1 system in breast cancer. Molecular cancer. 2015;14:43.

486.Wilker E, Lu J, Rho O, Carbajal S, Beltran L, DiGiovanni J. Role of PI3K/Akt signaling in insulin-like growth factor-1 (IGF-1) skin tumor promotion. Mol Carcinog. 2005;44(2):137-45.

487.Zhan M, Han ZC. Phosphatidylinositide 3-kinase/AKT in radiation responses. Histology and histopathology. 2004;19(3):915-23.

488.Dogan S, Johannsen AC, Grande JP, Cleary MP. Effects of intermittent and chronic calorie restriction on mammalian target of rapamycin (mTOR) and IGF-I signaling pathways in mammary fat pad tissues and mammary tumors. Nutrition and cancer. 2011;63(3):389-401.

276

489.Ford NA, Nunez NP, Holcomb VB, Hursting SD. IGF1 dependence of dietary energy balance effects on murine Met1 mammary tumor progression, epithelial-to-mesenchymal transition, and chemokine expression. Endocr Relat Cancer. 2013;20(1):39-51.

490.Zielinska HA, Bahl A, Holly JM, Perks CM. Epithelial-to-mesenchymal transition in breast cancer: a role for insulin-like growth factor I and insulin-like growth factor-binding protein 3? Breast cancer. 2015;7:9-19.

491.Huang CT, Chang MC, Chen YL, Chen TC, Chen CA, Cheng WF. Insulin-like growth factors inhibit dendritic cell-mediated anti-tumor immunity through regulating ERK1/2 phosphorylation and p38 dephosphorylation. Cancer Lett. 2015;359(1):117-26.

492.Ullrich E, Menard C, Flament C, Terme M, Mignot G, Bonmort M, et al. Dendritic cells and innate defense against tumor cells. Cytokine Growth Factor Rev. 2008;19(1):79-92.

493.Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8(8):457-65.

494.Hojman P, Dethlefsen C, Brandt C, Hansen J, Pedersen L, Pedersen BK. Exercise-induced muscle-derived cytokines inhibit mammary cancer cell growth. American journal of physiology Endocrinology and metabolism. 2011;301(3):E504-10.

495.Gleeson M, Bishop NC. The T cell and NK cell immune response to exercise. Annals of transplantation. 2005;10(4):43-8.

496.Rogers CJ, Berrigan D, Zaharoff DA, Hance KW, Patel AC, Perkins SN, et al. Energy restriction and exercise differentially enhance components of systemic and mucosal immunity in mice. J Nutr. 2008;138(1):115-22.

497.Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. The Journal of clinical endocrinology and metabolism. 2004;89(6):2548-56.

498.Hursting SD, Berger NA. Energy balance, host-related factors, and cancer progression. J Clin Oncol. 2010;28(26):4058-65.

499.Matthews SB, Thompson HJ. The Obesity-Breast Cancer Conundrum: An Analysis of the Issues. International Journal of Molecular Sciences. 2016;17(6):989.

500.Niu J, Jiang L, Guo W, Shao L, Liu Y, Wang L. The Association between Leptin Level and Breast Cancer: A Meta-Analysis. PLoS ONE. 2013;8(6):e67349.

501.Liu LY, Wang M, Ma ZB, Yu LX, Zhang Q, Gao DZ, et al. The role of adiponectin in breast cancer: a meta-analysis. PLoS One. 2013;8(8):e73183.

502.Macis D, Guerrieri-Gonzaga A, Gandini S. Circulating adiponectin and breast cancer risk: a systematic review and meta-analysis. International journal of epidemiology. 2014;43(4):1226-36.

503.Ye J, Jia J, Dong S, Zhang C, Yu S, Li L, et al. Circulating adiponectin levels and the risk of breast cancer: a meta-analysis. Eur J Cancer Prev. 2014;23(3):158-65.

504.Chen Y, Ling L, Su G, Han M, Fan X, Xun P, et al. Effect of Intermittent versus Chronic Calorie Restriction on Tumor Incidence: A Systematic Review and Meta-Analysis of Animal Studies. Sci Rep. 2016;6:33739.

505.Straub RH. Interaction of the endocrine system with inflammation: a function of energy and volume regulation. Arthritis Research & Therapy. 2014;16(1):203-.

506.Nieman DC. Nutrition, exercise, and immune system function. Clinics in sports medicine. 1999;18(3):537-48.

507.Simpson RJ, Kunz H, Agha N, Graff R. Exercise and the Regulation of Immune Functions. Progress in molecular biology and translational science. 2015;135:355-80.

508.Walsh NP, Gleeson M, Shephard RJ, Gleeson M, Woods JA, Bishop NC, et al. Position statement. Part one: Immune function and exercise. Exerc Immunol Rev. 2011;17:6-63.

509.Goh J, Niksirat N, Campbell KL. Exercise training and immune crosstalk in breast cancer microenvironment: exploring the paradigms of exercise-induced immune modulation and exercise-induced myokines. Am J Transl Res. 2014;6(5):422-38.

277

510.Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8(8):457-65.

511.Terra R, Silva SAGd, Pinto VS, Dutra PML. Effect of exercise on immune system: response, adaptation and cell signaling. Revista Brasileira de Medicina do Esporte. 2012;18(3):208-14.

512.Campbell PT, Wener MH, Sorensen B, Wood B, Chen-Levy Z, Potter JD, et al. Effect of exercise on in vitro immune function: a 12-month randomized, controlled trial among postmenopausal women. Journal of Applied Physiology. 2008;104(6):1648-55.

513.Fairey AS, Courneya KS, Field CJ, Bell GJ, Jones LW, Mackey JR. Randomized controlled trial of exercise and blood immune function in postmenopausal breast cancer survivors. J Appl Physiol. 2005;98(4):1534-40.

514.Nieman D, Cook V, Henson D, Suttles J, Rejeski W, Ribisl P, et al. Moderate exercise training and natural killer cell cytotoxic activity in breast cancer patients. International journal of sports medicine. 1995;16(05):334-7.

515.Saxton JM, Scott EJ, Daley AJ, Woodroofe MN, Mutrie N, Crank H, et al. Effects of an exercise and hypocaloric healthy eating intervention on indices of psychological health status, hypothalamic-pituitary-adrenal axis regulation and immune function after early-stage breast cancer: a randomised controlled trial. Breast Cancer Research. 2014;16(2):R39.

516.Fairman C, Hyde P, Focht B. Resistance training interventions across the cancer control continuum: a systematic review of the implementation of resistance training principles. British journal of sports medicine. 2016:bjsports-2016-096537.

517.Hagstrom AD, Marshall PW, Lonsdale C, Papalia S, Cheema BS, Toben C, et al. The effect of resistance training on markers of immune function and inflammation in previously sedentary women recovering from breast cancer: a randomized controlled trial. Breast Cancer Res Treat. 2016;155(3):471-82.

518.Schmidt T, Hermes A, Weisser B. Physical Training Influences the Immune System of Breast Cancer Patients. Deutsche Zeitschrift für Sportmedizin. 2017;68(3).

519.Bigley AB, Simpson RJ. NK cells and exercise: implications for cancer immunotherapy and survivorship. Discov Med. 2015;19(107):433-45.

520.MacNeil B, Hoffman-Goetz L. Effect of exercise on natural cytotoxicity and pulmonary tumor metastases in mice. Medicine and Science in Sports and Exercise. 1993;25(8):922-8.

521.Pedersen L, Idorn M, Olofsson GH, Lauenborg B, Nookaew I, Hansen RH, et al. Voluntary Running Suppresses Tumor Growth through Epinephrine- and IL-6-Dependent NK Cell Mobilization and Redistribution. Cell metabolism. 2016;23(3):554-62.

522.Abdalla DR, Murta EF, Michelin MA. The influence of physical activity on the profile of immune response cells and cytokine synthesis in mice with experimental breast tumors induced by 7,12-dimethylbenzanthracene. Eur J Cancer Prev. 2013;22(3):251-8.

523.Woods JA, Davis JM, Kohut ML, Ghaffar A, Mayer EP, Pate RR. Effects of exercise on the immune response to cancer. Medicine and Science in Sports and Exercise. 1994;26(9):1109-15.

524.Hoffman-Goetz L, May KM, Arumugam Y. Exercise training and mouse mammary tumour metastasis. Anticancer Res. 1994;14(6B):2627-31.

525.Christadoss P, Talal N, Lindstrom J, Fernandes G. Suppression of cellular and humoral immunity to T-dependent antigens by calorie restriction. Cellular Immunology. 1984;88(1):1-8.

526.Taylor AK, Cao W, Vora KP, Cruz JDL, Shieh W-J, Zaki SR, et al. Protein energy malnutrition decreases immunity and increases susceptibility to influenza infection in mice. The Journal of infectious diseases. 2013;207(3):501-10.

278

527.Di Biase S, Lee C, Brandhorst S, Manes B, Buono R, Cheng C-W, et al. Fasting-mimicking diet reduces HO-1 to promote T cell-mediated tumor cytotoxicity. Cancer Cell. 2016;30(1):136-46.

528.Pietrocola F, Pol J, Vacchelli E, Rao S, Enot DP, Baracco EE, et al. Caloric restriction mimetics enhance anticancer immunosurveillance. Cancer Cell. 2016;30(1):147-60.

529.O’Flanagan CH, Smith LA, McDonell SB, Hursting SD. When less may be more: calorie restriction and response to cancer therapy. BMC Medicine. 2017;15:106.

530.Fantozzi A, Christofori G. Mouse models of breast cancer metastasis. Breast Cancer Res. 2006;8(4):212.

531.Vargo-Gogola T, Rosen JM. Modelling breast cancer: one size does not fit all. Nat Rev Cancer. 2007;7(9):659-72.

532.Talmadge JE, Singh RK, Fidler IJ, Raz A. Murine Models to Evaluate Novel and Conventional Therapeutic Strategies for Cancer. Am J Pathol. 2007;170(3):793-804.

533.Dexter DL, Kowalski HM, Blazar BA, Fligiel Z, Vogel R, Heppner GH. Heterogeneity of tumor cells from a single mouse mammary tumor. Cancer Research. 1978;38(10):3174-81.

534.Johnstone CN, Smith YE, Cao Y, Burrows AD, Cross RS, Ling X, et al. Functional and molecular characterisation of EO771.LMB tumours, a new C57BL/6-mouse-derived model of spontaneously metastatic mammary cancer. Disease models & mechanisms. 2015;8(3):237-51.

535.Aslakson CJ, Miller FR. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Research. 1992;52(6):1399-405.

536.Eckhardt BL, Parker BS, van Laar RK, Restall CM, Natoli AL, Tavaria MD, et al. Genomic analysis of a spontaneous model of breast cancer metastasis to bone reveals a role for the extracellular matrix. Mol Cancer Res. 2005;3(1):1-13.

537.DuPre SA, Hunter KW, Jr. Murine mammary carcinoma 4T1 induces a leukemoid reaction with splenomegaly: association with tumor-derived growth factors. Exp Mol Pathol. 2007;82(1):12-24.

538.DuPre SA, Redelman D, Hunter KW, Jr. The mouse mammary carcinoma 4T1: characterization of the cellular landscape of primary tumours and metastatic tumour foci. International journal of experimental pathology. 2007;88(5):351-60.

539.Chen L, Huang TG, Meseck M, Mandeli J, Fallon J, Woo SL. Rejection of metastatic 4T1 breast cancer by attenuation of Treg cells in combination with immune stimulation. Molecular therapy : the journal of the American Society of Gene Therapy. 2007;15(12):2194-202.

540.Lohr F, Hu K, Haroon Z, Samulski TV, Huang Q, Beaty J, et al. Combination treatment of murine tumors by adenovirus-mediated local B7/IL12 immunotherapy and radiotherapy. Molecular therapy : the journal of the American Society of Gene Therapy. 2000;2(3):195-203.

541.Pulaski BA, Clements VK, Pipeling MR, Ostrand-Rosenberg S. Immunotherapy with vaccines combining MHC class II/CD80+ tumor cells with interleukin-12 reduces established metastatic disease and stimulates immune effectors and monokine induced by interferon gamma. Cancer Immunol Immunother. 2000;49(1):34-45.

542.Pulaski BA, Terman DS, Khan S, Muller E, Ostrand-Rosenberg S. Cooperativity of Staphylococcal aureus enterotoxin B superantigen, major histocompatibility complex class II, and CD80 for immunotherapy of advanced spontaneous metastases in a clinically relevant postoperative mouse breast cancer model. Cancer Res. 2000;60(10):2710-5.

543.Blidner AG, Salatino M, Mascanfroni ID, Diament MJ, Joffé EBdK, Jasnis MA, et al. Differential Response of Myeloid-Derived Suppressor Cells to the Nonsteroidal Anti-

279

Inflammatory Agent Indomethacin in Tumor-Associated and Tumor-Free Microenvironments. The Journal of Immunology. 2015:1401144.

544.Farsaci B, Donahue RN, Coplin MA, Grenga I, Lepone LM, Molinolo AA, et al. Immune consequences of decreasing tumor vasculature with antiangiogenic tyrosine kinase inhibitors in combination with therapeutic vaccines. Cancer Immunol Res. 2014;2(11):1090-102.

545.Liu L, Ye TH, Han YP, Song H, Zhang YK, Xia Y, et al. Reductions in myeloid-derived suppressor cells and lung metastases using AZD4547 treatment of a metastatic murine breast tumor model. Cell Physiol Biochem. 2014;33(3):633-45.

546.Zhang B, Ma X, Li Z, Gao X, Wang F, Liu L, et al. Celecoxib enhances the efficacy of 15-hydroxyprostaglandin dehydrogenase gene therapy in treating murine breast cancer. J Cancer Res Clin Oncol. 2013;139(5):797-807.

547.Marabelle A, Kohrt H, Sagiv-Barfi I, Ajami B, Axtell RC, Zhou G, et al. Depleting tumor-specific Tregs at a single site eradicates disseminated tumors. J Clin Invest. 2013;123(6):2447-63.

548.Thompson HJ. Pre-clinical investigations of physical activity and cancer: a brief review and analysis. Carcinogenesis. 2006;27(10):1946-9.

549.Ballard-Barbash R, Friedenreich CM, Courneya KS, Siddiqi SM, McTiernan A, Alfano CM. Physical activity, biomarkers, and disease outcomes in cancer survivors: a systematic review. J Natl Cancer Inst. 2012;104(11):815-40.

550.Neilson HK, Friedenreich CM, Brockton NT, Millikan RC. Physical activity and postmenopausal breast cancer: proposed biologic mechanisms and areas for future research. Cancer Epidemiol Biomarkers Prev. 2009;18(1):11-27.

551.Rogers CJ, Colbert LH, Greiner JW, Perkins SN, Hursting SD. Physical activity and cancer prevention : pathways and targets for intervention. Sports medicine (Auckland, NZ). 2008;38(4):271-96.

552.Thomas RJ, Kenfield SA, Jimenez A. Exercise-induced biochemical changes and their potential influence on cancer: a scientific review. British journal of sports medicine. 2017;51(8):640-4.

553.Thompson HJ, Zhu Z, Jiang W. Weight Control and Breast Cancer Prevention: Are the Effects of Reduced Energy Intake Equivalent to Those of Increased Energy Expenditure? J Nutr. 2004;134(12):3407S-11S.

554.McTiernan A, Tworoger SS, Rajan KB, Yasui Y, Sorenson B, Ulrich CM, et al. Effect of exercise on serum androgens in postmenopausal women: a 12-month randomized clinical trial. Cancer Epidemiol Biomarkers Prev. 2004;13(7):1099-105.

555.McTiernan A, Tworoger SS, Ulrich CM, Yasui Y, Irwin ML, Rajan KB, et al. Effect of exercise on serum estrogens in postmenopausal women: a 12-month randomized clinical trial. Cancer Res. 2004;64(8):2923-8.

556.McTiernan A, Wu L, Chen C, Chlebowski R, Mossavar-Rahmani Y, Modugno F, et al. Relation of BMI and physical activity to sex hormones in postmenopausal women. Obesity (Silver Spring, Md). 2006;14(9):1662-77.

557.Frank LL, Sorensen BE, Yasui Y, Tworoger SS, Schwartz RS, Ulrich CM, et al. Effects of exercise on metabolic risk variables in overweight postmenopausal women: a randomized clinical trial. Obesity research. 2005;13(3):615-25.

558.Ryan AS. Insulin resistance with aging: effects of diet and exercise. Sports medicine (Auckland, NZ). 2000;30(5):327-46.

559.Warburton DE, Katzmarzyk PT, Rhodes RE, Shephard RJ. Evidence-informed physical activity guidelines for Canadian adults. Canadian journal of public health = Revue canadienne de sante publique. 2007;98 Suppl 2:S16-68.

560.Balducci S, Zanuso S, Nicolucci A, Fernando F, Cavallo S, Cardelli P, et al. Anti-inflammatory effect of exercise training in subjects with type 2 diabetes and the

280

metabolic syndrome is dependent on exercise modalities and independent of weight loss. Nutrition, metabolism, and cardiovascular diseases : NMCD. 2010;20(8):608-17.

561.Campbell PT, Campbell KL, Wener MH, Wood BL, Potter JD, McTiernan A, et al. A yearlong exercise intervention decreases CRP among obese postmenopausal women. Med Sci Sports Exerc. 2009;41(8):1533-9.

562.Rogers CJ, Zaharoff DA, Hance KW, Perkins SN, Hursting SD, Schlom J, et al. Exercise enhances vaccine-induced antigen-specific T cell responses. Vaccine. 2008;26(42):5407-15.

563.Pattabiraman DR, Weinberg RA. Tackling the cancer stem cells [mdash] what challenges do they pose? Nat Rev Drug Discov. 2014;13(7):497-512.

564.Guan J, Chen JIE. Mesenchymal stem cells in the tumor microenvironment. Biomedical Reports. 2013;1(4):517-21.

565.Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14(10):1014-22.

566.Aslakson CJ, Miller FR. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer research. 1992;52(6):1399-405.

567.Lelekakis M, Moseley JM, Martin TJ, Hards D, Williams E, Ho P, et al. A novel orthotopic model of breast cancer metastasis to bone. Clin Exp Metastasis. 1999;17(2):163-70.

568.Chen YC, Prabhu KS, Das A, Mastro AM. Dietary selenium supplementation modifies breast tumor growth and metastasis. International journal of cancer Journal international du cancer. 2013;133(9):2054-64.

569.Trinchieri G. Biology of natural killer cells. Adv Immunol. 1989;47:187-376. 570.Braly L, Brohawn, P., Higgins, P., Albright, C.A., Boland, J.F. Advancing the quality

control methodology to assess isolated total RNA and generated fragmented cRNA. Application Note, Agilent Technologies; Palo Alto, CA 2003.

571.Lightfoot S. Quantitation comparison of total RNA using the Agilent 2100 Bioanalyzer, ribogreen analysis and UV spectrometry: Application Note, Agilent Technologies; Palo Alto, CA. 2002.

572.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (San Diego, Calif). 2001;25(4):402-8.

573.Kushi LH, Doyle C, McCullough M, Rock CL, Demark-Wahnefried W, Bandera EV, et al. American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity. CA Cancer J Clin. 2012;62(1):30-67.

574.Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, et al. Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006;295(6):629-42.

575.Teras LR, Goodman M, Patel AV, Diver WR, Flanders WD, Feigelson HS. Weight loss and postmenopausal breast cancer in a prospective cohort of overweight and obese US women. Cancer Causes Control. 2011;22(4):573-9.

576.Pariza MW. Calorie Restriction, ad Libitum Feeding, and Cancer. Exp Biol Med (Maywood). 1986;183(2):293-8.

577.Pariza MW, Boutwell RK. Historical perspective: calories and energy expenditure in carcinogenesis. Am J Clin Nutr. 1987;45(1):151-6.

578.Rock CL, Demark-Wahnefried W. Nutrition and survival after the diagnosis of breast cancer: a review of the evidence. J Clin Oncol. 2002;20(15):3302-16.

579.De Lorenzo MS, Baljinnyam E, Vatner DE, Abarzua P, Vatner SF, Rabson AB. Caloric restriction reduces growth of mammary tumors and metastases. Carcinogenesis. 2011;32(9):1381-7.

281

580.Redig AJ, McAllister SS. Breast cancer as a systemic disease: a view of metastasis. J Intern Med. 2013;274(2):113-26.

581.Candido J, Hagemann T. Cancer-related inflammation. Journal of clinical immunology. 2013;33 Suppl 1:S79-84.

582.Walsh NP, Gleeson M, Shephard RJ, Gleeson M, Woods JA, Bishop NC, et al. Position statement. Part one: Immune function and exercise. Exerc Immunol Rev. 2011;17:6-63.

583.Mathur N, Pedersen BK. Exercise as a mean to control low-grade systemic inflammation. Mediators of inflammation. 2008;2008:109502.

584.Pedersen BK. Exercise-induced myokines and their role in chronic diseases. Brain Behav Immun. 2011;25(5):811-6.

585.Lv D, Zhang Y, Kim H-J, Zhang L, Ma X. CCL5 as a potential immunotherapeutic target in triple-negative breast cancer. Cell Mol Immunol. 2013;10(4):303-10.

586.Velasco-Velázquez M, Xolalpa W, Pestell RG. The potential to target CCL5/CCR5 in breast cancer. Expert Opin Ther Targets. 2014;18(11):1265-75.

587.Osuala KO, Sloane BF. Many Roles of CCL20: Emphasis on Breast Cancer. Postdoc journal : a journal of postdoctoral research and postdoctoral affairs. 2014;2(3):7-16.

588.Li YQ, Liu FF, Zhang XM, Guo XJ, Ren MJ, Fu L. Tumor secretion of CCL22 activates intratumoral Treg infiltration and is independent prognostic predictor of breast cancer. PLoS One. 2013;8(10):e76379.

589.Bronger H, Singer J, Windmuller C, Reuning U, Zech D, Delbridge C, et al. CXCL9 and CXCL10 predict survival and are regulated by cyclooxygenase inhibition in advanced serous ovarian cancer. Br J Cancer. 2016;115(5):553-63.

590.Cunningham HD, Shannon LA, Calloway PA, Fassold BC, Dunwiddie I, Vielhauer G, et al. Expression of the C-C Chemokine Receptor 7 Mediates Metastasis of Breast Cancer to the Lymph Nodes in Mice. Transl Oncol. 2010;3(6):354-61.

591.Tutunea-Fatan E, Majumder M, Xin X, Lala PK. The role of CCL21/CCR7 chemokine axis in breast cancer-induced lymphangiogenesis. Mol Cancer. 2015;14:35.

592.Zhu G, Yan HH, Pang Y, Jian J, Achyut BR, Liang X, et al. CXCR3 as a molecular target in breast cancer metastasis: inhibition of tumor cell migration and promotion of host anti-tumor immunity. Oncotarget. 2015;6(41):43408-19.

593.Brown JC, Winters-Stone K, Lee A, Schmitz KH. Cancer, Physical Activity, and Exercise. Comprehensive Physiology. 2012;2(4):2775-809.

594.Koelwyn GJ, Wennerberg E, Demaria S, Jones LW. Exercise in Regulation of Inflammation-Immune Axis Function in Cancer Initiation and Progression. Oncology (Williston Park, NY). 2015;29(12):214800.

595.Parmiani G, Pilla L, Maccalli C, Russo V. Autologous Versus Allogeneic Cell-Based Vaccines? The Cancer Journal. 2011;17(5):331-6.

596.Skitzki JJ, Repasky EA, Evans SS. Hyperthermia as an immunotherapy strategy for cancer. Current opinion in investigational drugs (London, England : 2000). 2009;10(6):550-8.

597.Basu S, Binder RJ, Suto R, Anderson KM, Srivastava PK. Necrotic but not apoptotic cell death releases heat shock proteins, which deliver a partial maturation signal to dendritic cells and activate the NF-kappa B pathway. Int Immunol. 2000;12(11):1539-46.

598.Bhardwaj N. Processing and presentation of antigens by dendritic cells: implications for vaccines. Trends Mol Med. 2001;7(9):388-94.

599.Chen Z, Moyana T, Saxena A, Warrington R, Jia Z, Xiang J. Efficient antitumor immunity derived from maturation of dendritic cells that had phagocytosed apoptotic/necrotic tumor cells. Int J Cancer. 2001;93(4):539-48.

600.Chiang CL-L, Coukos G, Kandalaft LE. Whole Tumor Antigen Vaccines: Where Are We? Vaccines. 2015;3(2):344-72.

282

601.Kotera Y, Shimizu K, Mule JJ. Comparative analysis of necrotic and apoptotic tumor cells as a source of antigen(s) in dendritic cell-based immunization. Cancer Res. 2001;61(22):8105-9.

602.Seo HS. Application of radiation technology in vaccines development. Clinical and Experimental Vaccine Research. 2015;4(2):145-58.

603.Scheffer SR, Nave H, Korangy F, Schlote K, Pabst R, Jaffee EM, et al. Apoptotic, but not necrotic, tumor cell vaccines induce a potent immune response in vivo. Int J Cancer. 2003;103(2):205-11.

604.Takeda K, Yamaguchi N, Akiba H, Kojima Y, Hayakawa Y, Tanner JE, et al. Induction of tumor-specific T cell immunity by anti-DR5 antibody therapy. J Exp Med. 2004;199(4):437-48.

605.Vo JL, Yang L, Kurtz SL, Smith SG, Koppolu BP, Ravindranathan S, et al. Neoadjuvant immunotherapy with chitosan and interleukin-12 to control breast cancer metastasis. Oncoimmunology. 2014;3(12):e968001.

606.Strome SE, Voss S, Wilcox R, Wakefield TL, Tamada K, Flies D, et al. Strategies for antigen loading of dendritic cells to enhance the antitumor immune response. Cancer Res. 2002;62(6):1884-9.

607.Huynh ML, Fadok VA, Henson PM. Phosphatidylserine-dependent ingestion of apoptotic cells promotes TGF-beta1 secretion and the resolution of inflammation. J Clin Invest. 2002;109(1):41-50.

608.Klopp AH, Spaeth EL, Dembinski JL, Woodward WA, Munshi A, Meyn RE, et al. Tumor irradiation increases the recruitment of circulating mesenchymal stem cells into the tumor microenvironment. Cancer Res. 2007;67(24):11687-95.

609.Satoh E, Naganuma H, Sasaki A, Nagasaka M, Ogata H, Nukui H. Effect of irradiation on transforming growth factor-beta secretion by malignant glioma cells. J Neurooncol. 1997;33(3):195-200.

610.Limberis MP, Bell CL, Wilson JM. Identification of the murine firefly luciferase-specific CD8 T-cell epitopes. Gene therapy. 2009;16(3):441-7.

611.Murakami T, Cardones AR, Finkelstein SE, Restifo NP, Klaunberg BA, Nestle FO, et al. Immune evasion by murine melanoma mediated through CC chemokine receptor-10. J Exp Med. 2003;198(9):1337-47.

612.Tiffen JC, Bailey CG, Ng C, Rasko JEJ, Holst J. Luciferase expression and bioluminescence does not affect tumor cell growth in vitro or in vivo. Mol Cancer. 2010;9:299-.

613.Gajewski TF, Corrales L. New perspectives on type I IFNs in cancer. Cytokine Growth Factor Rev. 2015;26(2):175-8.

614.Muller AJ, DuHadaway JB, Donover PS, Sutanto-Ward E, Prendergast GC. Inhibition of indoleamine 2,3-dioxygenase, an immunoregulatory target of the cancer suppression gene Bin1, potentiates cancer chemotherapy. Nat Med. 2005;11(3):312-9.

615.Zhai L, Spranger S, Binder DC, Gritsina G, Lauing KL, Giles FJ, et al. Molecular Pathways: Targeting IDO1 and Other Tryptophan Dioxygenases for Cancer Immunotherapy. Clin Cancer Res. 2015;21(24):5427-33.

616.Philips GK, Atkins M. Therapeutic uses of anti-PD-1 and anti-PD-L1 antibodies. Int Immunol. 2015;27(1):39-46.

617.Ahlers JD, Belyakov IM. Memories that last forever: strategies for optimizing vaccine T-cell memory. Blood. 2010;115(9):1678-89.

618.Bjorkdahl O, Barber KA, Brett SJ, Daly MG, Plumpton C, Elshourbagy NA, et al. Characterization of CC-chemokine receptor 7 expression on murine T cells in lymphoid tissues. Immunology. 2003;110(2):170-9.

619.Henao-Tamayo MI, Ordway DJ, Irwin SM, Shang S, Shanley C, Orme IM. Phenotypic Definition of Effector and Memory T-Lymphocyte Subsets in Mice Chronically Infected

283

with Mycobacterium tuberculosis. Clinical and Vaccine Immunology : CVI. 2010;17(4):618-25.

620.Pennock ND, White JT, Cross EW, Cheney EE, Tamburini BA, Kedl RM. T cell responses: naive to memory and everything in between. Advances in physiology education. 2013;37(4):273-83.

621.He L, Hakimi J, Salha D, Miron I, Dunn P, Radvanyi L. A sensitive flow cytometry-based cytotoxic T-lymphocyte assay through detection of cleaved caspase 3 in target cells. Journal of Immunological Methods. 2005;304(1–2):43-59.

622.Beyranvand Nejad E, Welters MJ, Arens R, van der Burg SH. The importance of correctly timing cancer immunotherapy. Expert Opin Biol Ther. 2017;17(1):87-103.

623.Nanda R, Chow LQ, Dees EC, Berger R, Gupta S, Geva R, et al. Pembrolizumab in Patients With Advanced Triple-Negative Breast Cancer: Phase Ib KEYNOTE-012 Study. J Clin Oncol. 2016;34(21):2460-7.

624.Gattinoni L, Klebanoff CA, Restifo NP. Paths to stemness: building the ultimate antitumour T cell. Nat Rev Cancer. 2012;12(10):671-84.

625.Lieberman J. The ABCs of granule-mediated cytotoxicity: new weapons in the arsenal. Nat Rev Immunol. 2003;3(5):361-70.

626.Fujisawa T, Joshi BH, Puri RK. IL-13 regulates cancer invasion and metastasis through IL-13Ralpha2 via ERK/AP-1 pathway in mouse model of human ovarian cancer. Int J Cancer. 2012;131(2):344-56.

627.Whiteside TL. Immune suppression in cancer: effects on immune cells, mechanisms and future therapeutic intervention. Semin Cancer Biol. 2006;16(1):3-15.

628.Lopez CB, Rao TD, Feiner H, Shapiro R, Marks JR, Frey AB. Repression of interleukin-2 mRNA translation in primary human breast carcinoma tumor-infiltrating lymphocytes. Cell Immunol. 1998;190(2):141-55.

629.Rabinowich H, Suminami Y, Reichert TE, Crowley-Nowick P, Bell M, Edwards R, et al. Expression of cytokine genes or proteins and signaling molecules in lymphocytes associated with human ovarian carcinoma. Int J Cancer. 1996;68(3):276-84.

630.Vitolo D, Zerbe T, Kanbour A, Dahl C, Herberman RB, Whiteside TL. Expression of mRNA for cytokines in tumor-infiltrating mononuclear cells in ovarian adenocarcinoma and invasive breast cancer. Int J Cancer. 1992;51(4):573-80.

631.Egan B, Zierath JR. Exercise metabolism and the molecular regulation of skeletal muscle adaptation. Cell metabolism. 2013;17(2):162-84.

632.Maciver NJ, Jacobs SR, Wieman HL, Wofford JA, Coloff JL, Rathmell JC. Glucose metabolism in lymphocytes is a regulated process with significant effects on immune cell function and survival. J Leukoc Biol. 2008;84(4):949-57.

633.Ma EH, Poffenberger MC, Wong AH, Jones RG. The role of AMPK in T cell metabolism and function. Curr Opin Immunol. 2017;46:45-52.

634.Gerriets VA, Rathmell JC. Metabolic pathways in T cell fate and function. Trends Immunol. 2012;33(4):168-73.

635.Sim GC, Radvanyi L. The IL-2 cytokine family in cancer immunotherapy. Cytokine Growth Factor Rev. 2014;25(4):377-90.

636.Tugues S, Burkhard SH, Ohs I, Vrohlings M, Nussbaum K, vom Berg J, et al. New insights into IL-12-mediated tumor suppression. Cell Death and Differentiation. 2015;22(2):237-46.

637.Yuzhalin AE, Kutikhin AG. Interleukin-12: clinical usage and molecular markers of cancer susceptibility. Growth factors (Chur, Switzerland). 2012;30(3):176-91.

638.Lockyer HM, Tran E, Nelson BH. STAT5 is essential for Akt/p70S6 kinase activity during IL-2-induced lymphocyte proliferation. J Immunol. 2007;179(8):5301-8.

639.Santarpia M, Karachaliou N. Tumor immune microenvironment characterization and response to anti-PD-1 therapy. Cancer Biology & Medicine. 2015;12(2):74-8.

284

640.Francisco LM, Salinas VH, Brown KE, Vanguri VK, Freeman GJ, Kuchroo VK, et al. PD-L1 regulates the development, maintenance, and function of induced regulatory T cells. J Exp Med. 2009;206(13):3015-29.

641.Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(5):275-87.

642.Taube J, Anders R, Xu H, Sharma R, McMiller T, Klein A, et al. B7-H1 expression co-localizes with inflammatory infiltrates in benign and malignant melanocytic lesions: Implications for immunotherapy. Sci Transl Med. 2012.

643.Minn AJ. Interferons and the Immunogenic Effects of Cancer Therapy. Trends Immunol. 2015;36(11):725-37.

644.Fuertes MB, Woo SR, Burnett B, Fu YX, Gajewski TF. Type I interferon response and innate immune sensing of cancer. Trends Immunol. 2013;34(2):67-73.

645.Wilke CM, Wei S, Wang L, Kryczek I, Kao J, Zou W. Dual biological effects of the cytokines interleukin-10 and interferon-gamma. Cancer Immunol Immunother. 2011;60(11):1529-41.

646.Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol. 2002;3(11):991-8.

647.Martinez-Lostao L, Anel A, Pardo J. How Do Cytotoxic Lymphocytes Kill Cancer Cells? Clin Cancer Res. 2015;21(22):5047-56.

648.Scott AM, Wolchok JD, Old LJ. Antibody therapy of cancer. Nat Rev Cancer. 2012;12(4):278-87.

649.Smyth MJ, Cretney E, Takeda K, Wiltrout RH, Sedger LM, Kayagaki N, et al. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) contributes to interferon gamma-dependent natural killer cell protection from tumor metastasis. J Exp Med. 2001;193(6):661-70.

650.Facciabene A, Motz GT, Coukos G. T-regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Research. 2012;72(9):2162-71.

651.Markowitz J, Wesolowski R, Papenfuss T, Brooks TR, Carson WE, 3rd. Myeloid-derived suppressor cells in breast cancer. Breast Cancer Res Treat. 2013;140(1):13-21.

652.Okwan-Duodu D, Umpierrez GE, Brawley OW, Diaz R. Obesity-driven inflammation and cancer risk: role of myeloid derived suppressor cells and alternately activated macrophages. Am J Cancer Res. 2013;3(1):21-33.

653.Hale M, Itani F, Buchta CM, Wald G, Bing M, Norian LA. Obesity Triggers Enhanced MDSC Accumulation in Murine Renal Tumors via Elevated Local Production of CCL2. PLoS ONE. 2015;10(3):e0118784.

654.Fernando P, Bonen A, Hoffman-Goetz L. Predicting submaximal oxygen consumption during treadmill running in mice. Canadian journal of physiology and pharmacology. 1993;71(10-11):854-7.

655.Lipsett A, Barrett S, Haruna F, Mustian K, O'Donovan A. The impact of exercise during adjuvant radiotherapy for breast cancer on fatigue and quality of life: A systematic review and meta-analysis. Breast (Edinburgh, Scotland). 2017;32:144-55.

656.Van Dijck S, Nelissen P, Verbelen H, Tjalma W, Gebruers N. The effects of physical self-management on quality of life in breast cancer patients: A systematic review. Breast (Edinburgh, Scotland). 2016;28:20-8.

657.Yang J, Tan Q, Fu Q, Zhou Y, Hu Y, Tang S, et al. Gastrointestinal microbiome and breast cancer: correlations, mechanisms and potential clinical implications. Breast cancer (Tokyo, Japan). 2017;24(2):220-8.

658.Monda V, Villano I, Messina A, Valenzano A, Esposito T, Moscatelli F, et al. Exercise Modifies the Gut Microbiota with Positive Health Effects. Oxidative medicine and cellular longevity. 2017;2017:3831972.

Vita William J. Turbitt, Jr.

EDUCATION:

2017: Doctor of Philosophy in Integrative and Biomedical Physiology The Pennsylvania State University. Advisor: Dr. Connie J. Rogers Dissertation title: The effects of changes in energy balance on immune regulation and tumor progression in the 4T1.2 mammary tumor model

2010: Bachelor of Science in Biobehavioral Health, minors in Neuroscience and Biology The Pennsylvania State University.

TEACHING EXPERIENCE:

Spring 2017 The Graduate School Teaching Certificate, The Pennsylvania State University

Spring 2014 Teaching assistant for Biology 473: Physiology Laboratory,

Fall 2013 Teaching assistant for Biology 142: Laboratory in Mammalian Physiology, The Pennsylvania State University

AWARDS:

2017 The Alumni Association Dissertation Award, The Pennsylvania State University

2016 The Huck Institutes Graduate Travel Award, The Pennsylvania State University

2016 The American Institute for Cancer Research Conference Scholarship Award, The American Institute for Cancer Research, Washington, DC

2015 The American Association of Immunologists Young Investigator Award at the Translational Research Cancer Centers Consortium Annual Meeting

2014-2016 T32 Training Program in Animal Models of Inflammation Awardee (T32AI074551), The Pennsylvania State University

2014 Huck Dissertation Research Award, The Huck Institute of the Life Sciences, The Pennsylvania State University

PRESENTATIONS:

1. Exercise, alone and in combination with a whole tumor cell vaccine reduces mammary tumor cell growth and enhances antitumor immunity. American Association for Cancer Research Annual Meeting. Philadelphia, PA. April 2015. 2. Activity-induced weight maintenance in combination with a whole tumor cell vaccine delays mammary tumor growth and metastases, and reduces plasma IGF-1. The Translational Research Cancer Centers Consortium Annual Meeting, Seven Springs, PA, February 2016.

POSTER PRESENTATIONS:

1. Diet and exercise-induced weight maintenance may be preventing mammary tumor growth and metastatic burden by enhancing antitumor immunity and/or reducing protumorigenic factors. The American Association for Cancer Research Annual Meeting, Washington, D.C., April 2017.

2. Weight maintenance alters the tumor microenvironment and delays murine mammary tumor growth and metastases. The American Institute for Cancer Research Annual Meeting, North Bethesda, MD, November 2016.

3. The dual administration of immunotherapy is enhanced by activity-induced weight maintenance in the 4T1.2 mammary tumor model. The Society for Immunotherapy of Cancer Annual Meeting, National Harbor, MD, November 2016.

PUBLICATIONS:

1. Turbitt, W.J. (co-first author), Xu, Y., Sosnoski, D.M., Collins, S., Meng, H., Mastro, A.M., Rogers, C.J. (2017). Exercise in weight stable mice prevents mammary tumor growth and metastases by altering the tumor microenvironment and reducing protumorigenic factors. Cancer Prevention Research (in preparation).

2. Collins, S., Turbitt, W.J. (co-first author), Rogers, C.J. (2017). Obesity accelerates pancreatic tumor growth and increases the accumulation of Gr-1+CD11b+ myeloid-derived suppressor cells. PLOS One (in preparation).

3. Turbitt, W.J., Black, A.J., Collins, S., Meng, H., Xu, H., Washington, S., Aliaga, C., El-Bayoumy, K., Manni, A., Rogers, C.J. (2015). Fish oil differentially alters T cell function and tumor growth and induces the emergence of Gr1+/CD11b+ cells in two models of mammary carcinogenesis. Nutrition and Cancer; 67(6), 965-975.

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THE EFFECTS OF CHANGES IN ENERGY BALANCE ON …