Hedgehog signalling and tumour-initiating cells as radioresistance

factors in esophageal adenocarcinoma

by

Jennifer Teichman

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Medical Biophysics

University of Toronto

© Copyright by Jennifer Teichman (2012)

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Hedgehog signalling and tumour-initiating cells as radioresistance factors in esophageal adenocarcinoma

Jennifer Teichman Master of Science, 2012 Medical Biophysics, University of Toronto

Abstract

Clinical management of esophageal adenocarcinoma (EAC) relies on radiation therapy, yet

radioresistance is a pervasive challenge in this disease. The mechanisms of EAC radioresistance

remain largely unknown due to a paucity of validated preclinical models. The present studies

report on the development of seven primary xenograft models established from patient

tumours. These models are used to interrogate the range of radiosensitivities and mechanisms

of radioresistance in EAC tumours. We found that radiation enriches the tumour-initiating cell

population in two xenograft lines tested. Furthermore, three tested xenograft lines respond to

irradiation by upregulating Hedgehog transcripts, a pathway involved in stem cell maintenance

and proliferation. Upregulation occurs in autocrine and paracrine patterns simultaneously,

suggesting that Hedgehog signalling may have a complex role in the radioresponse of EAC

tumours. These findings suggest that inhibiting stem cell pathways in combination with

radiotherapy may have an important role in the clinical management of EAC.

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Special Acknowledgements This work was co-supervised by Dr. Geoffrey Liu and Dr. Laurie Ailles. Thank you to Dr. Robert Bristow for his insight and guidance as a supervisory committee member. Thank you to Dr. Richard Hill, Dr. Helen MacKay and Dr. Naz Chaudary for their input on the Hedgehog component of this project. Thank you to Dangxiao Cheng for his help with qRT-PCR, to Joerg Schwock for his histologic evaluation of tissue sections, and to Zhuo Chen, Lorin Dodbiba, Andrew Fleet and Henry Thai for their daily assistance in the laboratory. This work would not have been possible without the collaboration of the University Health Network Tissue Bank and the generosity of the patients who donated their tissues to medical research. This work was supported by an Ontario Graduate Scholarship.

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Table of Contents

Ch.1: Introduction and Background............................................................................................ 1 1.1 Epidemiology of esophageal adenocarcinoma (EAC)........................................................ 1 1.2 Etiology of EAC.................................................................................................................. 2 1.3 Clinical management of EAC............................................................................................. 5 1.4 General mechanisms of radioresistance........................................................................... 7 1.5 Mechanisms of radioresistance in EAC............................................................................. 12 1.6 The primary human xenograft as a clinically-reflective model of EAC............................. 13 1.7 The tumour bed effect...................................................................................................... 16 1.8 Cancer stem cell theory.................................................................................................... 18 1.9 TICs and radioresistance................................................................................................... 20 1.10 TICs and EAC................................................................................................................... 23 1.11 Hedgehog signalling pathway......................................................................................... 25 1.12 Hedgehog pathway and EAC.......................................................................................... 29 1.13 Hedgehog pathway and radiation.................................................................................. 32 1.14 Aims and hypotheses..................................................................................................... 35 Ch.2: Primary xenografts and models of radiosensitivity and resistance (Aim 1).................. 38 2.1 Aim 1 Methods................................................................................................................. 38 2.1.1 Patient samples......................................................................................................... 38 2.1.2. Development of xenograft model............................................................................ 39 2.1.3 Precision irradiation: Identification of appropriate radiation doses......................... 40 2.1.4 Assessment of tumour bed effect............................................................................. 40 2.1.5 Xenograft growth delay............................................................................................. 42 2.1.6 Statistical analysis...................................................................................................... 42 2.2 Aim 1 Results..................................................................................................................... 45 2.2.1 A tumour bed effect, if present, is negligible at low radiation doses in our

xenograft model................................................................................................................. 45 2.2.2 Precision irradiation delays xenograft tumour growth............................................. 47 2.2.3 No passage effect on radiation growth delay was detected in our xenograft

models................................................................................................................................ 48 2.2.4 Specific growth delay cannot quantitatively distinguish between xenograft

tumour lines ....................................................................................................................... 49 2.3 Aim 1 Discussion................................................................................................................ 50 Ch. 3: Enrichment of tumourigenic and clonogenic cells through radiotherapy (Aim 2)......... 56 3.1 Methods............................................................................................................................ 56 3.1.1 Limiting dilution assay............................................................................................... 56 3.1.2 Clonogenic assay........................................................................................................ 59 3.2 Aim 2 Results..................................................................................................................... 60 3.2.1 Radiation may enrich the TIC fraction prior to repopulation in some EAC tumours. 60 3.2.2 The ability of radiation to enrich the clonogenic fraction was not demonstrated............. 65 3.3 Aim 2 Discussion.............................................................................................................. 66 Ch. 4: The Hedgehog pathway in response to irradiation......................................................... 69 4.1 Aim 3 Methods.................................................................................................................. 69 4.1.1 PCR primer design..................................................................................................... 69

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4.1.2 Housekeeping gene selection.................................................................................... 70 4.1.3 Hedgehog gene expression........................................................................................ 71 4.1.4 5E1 validation and toxicity study............................................................................... 73 4.1.5 In vivo Hh inhibition in a xenograft model................................................................. 74 4.1.6 Statistical analysis...................................................................................................... 75 4.2 Aim 3 Results..................................................................................................................... 75 4.2.1 ACTB, HPRT1, HSP90AB1 and YWHAZ are appropriate housekeeping genes for EAC

radiation studies................................................................................................................. 75 4.2.2 Hedgehog expression in EAC xenografts displays a predominantly epithelial-to-

mesenchymal paracrine mechanism (Aim 3a)................................................................... 80 4.2.3 Radiation upregulates both autocrine and paracrine Hh signalling in some EAC

tumours (Aim 3b)............................................................................................................... 83 4.2.4 Single dose 5E1 inhibits stromal Hh activation for up to one week.......................... 89 4.2.5 The ability of 5E1 to radiosensitize EAC xenografts was not demonstrated and

warrants further study....................................................................................................... 89 4.3 Aim 3 Discussion............................................................................................................... 90 4.3.1 Hedgehog signalling follows a predominantly paracrine signalling mechanism in EAC.............................................................................................................. 90 4.3.2 Hedgehog is involved in the radiation response of EAC tumours............................. 91 Ch. 5: Limitations, alternatives and future directions............................................................... 96 Ch. 6: Conclusion......................................................................................................................... 103 Appendix A: Xenograft growth curves for all seven models........................................................ 106 Appendix B: Sample flow cytometric plots of H2K depletion for limiting dilution and

clonogenic assays............................................................................................................... 109 References................................................................................................................................... 110

List of Tables Table 1: Risk factors and level of evidence for ESCC and EAC..................................................... 4 Table 2: Comparison of experimental models............................................................................ 15 Table 3: Primary patient specimens established as xenograft lines........................................... 38 Table 4: Effect of radiation on tumour growth........................................................................... 47 Table 5: Growth delay and specific growth delay by tumour line and passage.......................... 48 Table 6: Linear regression analysis for the effect of passage on SGD......................................... 49 Table 7: Average SGD by line...................................................................................................... 49 Table 8: Clonogenic assays.......................................................................................................... 65 Table 9: PCR primers designed for Hedgehog gene expression analysis with internal controls 70 Table 10: Housekeeping gene radiation stability score............................................................... 78 Table 11: Histologic quantification of percent tumour epithelium in xenograft samples.......... 80

List of Figures Figure 1: Disease progression and molecular alterations in EAC................................................ 5 Figure 2: The Hedgehog pathway............................................................................................... 27 Figure 3: Hh signaling promotes cell survival.............................................................................. 28

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Figure 4: Relevant xenograft tumour volume points used for LDAs and clonogenic assays....... 36 Figure 5: Tumour bed effect schematic...................................................................................... 41 Figure 6: There is no significant TBE at low radiation doses in Line 3 passage 3........................ 45 Figure 7: Linear mixed effect model on TBE experiment............................................................ 46 Figure 8: Two representative samples of growth delay derivation using the mixed effect Model.......................................................................................................................................... 48 Figure 9: Specific growth delay by tumour line and passage...................................................... 48 Figure 10: Average SGD for each line.......................................................................................... 49 Figure 11: Spectrum of radiosensitivity among xenograft lines.................................................. 53 Figure 12: Relevant xenograft tumour volume points used for LDAs and clonogenic assays..... 57 Figure 13: Xenograft limiting dilution assay................................................................................ 59 Figure 14: Limiting dilution assay Line 3 passage 4..................................................................... 62 Figure 15: Limiting dilution assay Line 4 passage 5..................................................................... 63 Figure 16: Limiting dilution assay Line 2 passage 10................................................................... 64 Figure 17: Clonogenic assay Line 3.............................................................................................. 66 Figure 18: 5E1 validation............................................................................................................. 74 Figure 19: Housekeeping gene radiation stability in 3 xenograft lines........................................ 77 Figure 20: Selection of best housekeeping gene combination using radiation stability in three tumour lines................................................................................................................................ 79 Figure 21: Localization of Hh transcripts in epithelium versus stroma of untreated tumours from six xenograft lines........................................................................................................................ 82 Figure 22: Autocrine and paracrine Hh signaling displayed in one-colour heat maps................ 83 Figure 23: Gene expression changes in Line 8 passage 4............................................................ 86 Figure 24: Bar graphs of gene expression changes in Line 8 passage 4...................................... 86 Figure 25: Gene expression changes in Line 6 passage 4............................................................ 87 Figure 26: Bar graphs of gene expression change in Line 6 passage 4........................................ 87 Figure 27: Gene expression changes in Line 7 passage 5............................................................ 88 Figure 28: Bar graphs of gene expression changes in Line 7 passage 5...................................... 88 Figure 29: 5E1 failed to radiosensitize xenograft tumours from Line 7 passage 6..................... 90

List of Appendices:

Appendix A: Xenograft growth curves for all seven models....................................................... 106 Appendix B: Sample flow cytometric plots of H2K depletion for limiting dilution and clonogenic assays........................................................................................................................ 109

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Glossary of Acronyms AUP- animal use protocol BE- Barrett’s esophagus BE-3- validated esophageal adenocarcinoma cell line CDK- Cyclin-dependent kinase CRT- chemoradiotherapy CSC- cancer stem cell DDSP- DNA damage associated secretory program DHH- Desert hedgehog DISP- Dispatched DNA-PKcs- DNA protein kinase catalytic subunit DSFM- defined serum-free media EAC- esophageal adenocarcinoma ESCC- esophageal squamous cell carcinoma GD- growth delay GEJ- gastroesophageal junction GERD- gastroesophageal reflux disease GLI1-3- Glioma associated oncogene 1-3 Gy- gray HKG- housekeeping gene IHH- Indian hedgehog Lgr5- Leucine-rich-repeat-containing G-protein-coupled receptor 5 NOD/SCID- non-obese diabetic severe combined immunodeficient OE33- validated esophageal adenocarcinoma cell line PTCH1/2- Patched 1/2 qRT- PCR- quantitative reverse transcription real-time polymerase chain reaction ROS- Reactive oxygen species RCT- randomized control trial RT- radiation therapy SGD- specific growth delay SHH- Sonic hedgehog SMO- Smoothened TBE- tumour bed effect TCD50- tumour control dose 50; radiation dose required to control growth in 50% of tumours TD50- cell dose at which 50% injections give rise to tumours TIC- tumour-initiating cell

1

Chapter 1: Introduction and Background

1.1 Epidemiology of esophageal adenocarcinoma (EAC) (All aims)

Esophageal cancer is a deadly malignancy with the eighth highest incidence of all cancers and

the sixth highest mortality rate globally.1,2 The disease is comprised of two main

histopathological types with distinctly different disease mechanisms and epidemiological

patterns: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC).

ESCC develops throughout the esophagus, while EAC is found predominantly in the distal third

or at the gastroesophageal junction. ESCC predominantly affects populations in the developing

world, particularly in the region from northern Iran to north central China, appropriately

termed the esophageal cancer belt. Because its risk factors include frequent consumption of

alcohol, tobacco, hot tea, low fruit and vegetable intake and malnutrition, ESCC is associated

with populations of a lower socioeconomic status. In addition, ESCC has a male to female

incidence ratio of 2-3:1. Conversely, esophageal adenocarcinoma (EAC) occurs mostly in the

developed world, particularly among Caucasian males. With risk factors including obesity and

gastroesophageal reflux disease (GERD), EAC is associated with populations of a higher

socioeconomic class, and its male to female ratio is closer to 7:1.3 A pooled analysis found a

strong correlation between smoking and EAC,2 however, unlike in ESCC studies, no significant

association between EAC and alcohol consumption has been found. The prevailing risk factors

for each histological type, and the associated levels of evidence are summarized in Table 1.

While the incidence of ESCC has been decreasing in the western world – due in part to the

declining prevalence of smoking4 – the incidence of EAC has increased by more than 600% over

2

the last three decades.4–7 In fact, the incidence of EAC has grown faster than any other tumour

type in the United States,8 outpacing the next closest cancer by almost three times.9 This

appears to be a real trend rather than overdiagnosis and reclassification of the tumour.5 The

increasing incidence of obesity and GERD in developed countries may be partly responsible for

the increasing incidence of EAC, although this hypothesis is controversial. A recent study

suggests that the increasing incidence of EAC preceded the rise in obesity prevalence by a

decade,10 and a disease simulation model found that increasing obesity may only account for a

small percentage (6.5%) of the rise in EAC incidence.11 In light of these concerning trends, the

project described here has focused exclusively on EAC.

1.2. Etiology of EAC (All aims)

It is largely accepted that EAC develops through a metaplasia-dysplasia-carcinoma sequence

that most commonly begins with reflux-induced Barrett’s esophagus (BE). Metaplastic and

dysplastic epithelia frequently present side-by-side in pathologic specimens, and endoscopically

surveyed patients have been observed to progress from metaplasia to low-grade dysplasia to

high-grade dysplasia and finally to invasive carcinoma.12 Acid reflux at the gastroesophageal

junction is associated with decreased lower esophageal sphincter pressure. Chronic exposure to

bile acids results in a change in the lining of the distal esophagus from normal stratified

squamous mucosa to a more injury-resistant mucin-secreting mucosa that may contain goblet

cells— histologically defined as specialized intestinal metaplasia13,14— and endoscopically

diagnosed as BE. It has been proposed that metaplasia results from changes in the

differentiation pattern of stem cells residing in the basal layer of the esophagus.15–18 Others

3

have proposed that differentiated squamous cells can convert directly into columnar cells

through a process termed transdifferentiation.19,20

Inflammation may play a critical role in the progression from metaplasia to dysplasia and

ultimately to adenocarcinoma, particularly through the production of reactive oxygen species

(ROS) that damage DNA, proteins and lipids. GERD can cause reflux esophagitis, and inflamed

Barrett’s metaplasia expresses the pro-inflammatory cytokines IL-1B, IL-8, and NF-κB, a

transcription factor involved in regulating pro-inflammatory genes.21,22 Animal models of reflux

esophagitis, BE and EAC have shown elevated levels of ROS,23 and biopsies of inflamed

esophageal squamous and Barrett’s mucosae show higher levels of ROS and lipid peroxidation

than uninflamed control tissues.24 Finally, in vitro studies of transformed and primary BE and

EAC cells demonstrate that exposure to low pH induces higher levels of ROS and DNA double

strand breaks.25 Figure 1 illustrates the disease progression from GERD to EAC.

4

Subtype Risk factor Reference

(PMID) Study type Level of risk (OR, RR or HR)

ESCC

Alcohol consumption

19828467 Prospective cohort study ≥ 30g ethanol/day: RR 4.61 (95% CI 2.24,

9.50)

21430021 Meta-analysis of 8 cohort and

case-control studies RR 3.36 (95% CI, 1.66–6.78)

21190191 Meta-analysis of 40 case-

control and 13 cohort studies

Light drinking: RR 1.31 (95% CI 1.10–1.57) Moderate drinking: RR 2.27 (95% CI 1.89–

2.72) Heavy drinking: RR 4.89 (95% CI 3.84–6.23)

Tobacco use 22131340 Meta-analysis of 4 cohort and

9 case-control studies

Ever vs. never smokers: RR 3.01 (95% CI 2.30-3.94). Current vs. never smokers: RR 3.73 (95%CI 2.16-6.43). Former vs. never smokers: RR 2.21 (95%CI 1.60-3.06)

Hot tea consumption

11058886 Meta-analysis of 5 hospital-based case-control studies

OR 4.14 (95%CI 2.24-7.67)

Diet (fruit and

vegetables) 18537156 Population-based case-control

No significant increase in risk after adjustment for other food groups

EAC

Obesity

16702363 Meta-analysis of 3 cohort and

case-control studies Males: OR 2.4 (95% CI 1.9-3.2)

Females: OR 2.1 (95%CI 1.4-3.2)

16061918 Meta-analysis of 7 population-

based case-control studies OR 2.78 (95% CI 1.850, 4.164)

18268119 Nested case-control study using abdominal obesity

rather than BMI

BMI-adjusted OR 4.78 (95% CI 1.14-20.11). Note: no association found with

ESCC

GERD

17461453 Population-based case-control OR 3.48 (95% CI 2.25-5.41)

20955441 Meta-analysis of 5

retrospective case-control studies

Weekly symptoms: OR 4.92 (95% CI 3.90, 6.22)

Daily symptoms: OR 7.40 (95% CI 4.94, 11.1)

10080844 Population-based case-control OR 7.7 (9% CI 5.3, 11.4)

Barrett’s esophagus

21995385

Population-based cohort

Without dysplasia: 1.0 case per 1000 person-years

With low-grade dysplasia: 5.1 cases per 1000 person-years

Tobacco use 20716718 Meta-analysis of 10

population-based case-control studies and 2 cohort studies

OR 1.96 (95% CI 1.64, 2.34)

Dietary fruit, vegetables

and antioxidants

17461453 Population-based case-control High fruit intake: OR 0.50 (95% CI 0.30-

0.86)

17581269 Meta-analysis of 1 cohort and

9 case-control Vitamin C: OR 0.49 (95%CI 0.39-0.62)

β-carotene: OR 0.46 (95%CI 0.36-0.59)

18537156 Population-based case-control

Vegetable intake: OR 0.86 (95%CI 0.75, 0.99)

Non-citrus fruit: OR 0.73 (95%CI 0.59, 0.90)

Table 1: Risk factors and level of evidence for esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). RR= relative risk; OR= odds ratio; HR= hazard ratio

5

Figure 1: Disease progression and molecular alterations in EAC. Adapted from [26]. 26

1.3. Clinical management of EAC (All aims)

Since the early 1990s, treatment regimens for esophageal cancer have incorporated concurrent

chemoradiotherapy (CRT) or chemotherapy and surgical resection when possible. For locally-

advanced unresectable disease, the standard approach includes 50Gy of radiation therapy (RT)

plus 4 courses of combined cisplatin and 5-FU, with the first two courses given concurrently

with RT.27 In a phase III randomized controlled trial, patients treated with this regimen had a

median survival of 12.5 months, compared to 8.9 months in patients treated with radiation

alone. The two-year survival rate in the former group was 38% compared to 10% in the latter

group (P<0.001).28

When surgical resection is possible, treatment modalities include surgery alone, neoadjuvant

chemotherapy or CRT, and adjuvant CRT.27 With surgery alone, median survival ranges

between 13 and 19 months and five-year survival rates are between 15% and 24%.29 At least

nine randomized controlled trials (RCT) have compared neoadjuvant CRT to surgery alone, with

6

mixed results. However, two meta-analyses have shown a significant advantage to neoadjuvant

treatment. The first showed a significant reduction in three-year mortality after neoadjuvant

CRT compared with surgery alone (odds ratio 0.53, P=0.03), as well as more frequent down

staging of the tumour (odds ratio 0.43, P=0.001).30 The second meta-analysis demonstrated

improved three-year survival with neoadjuvant CRT compared to surgery alone (odds ratio 0.45,

P=0.005), but only when RCTs using concurrent rather than sequential CRT were included in the

analysis.31

Neoadjuvant CRT followed by surgery is the current standard of care for patients with locally

advanced resectable esophageal cancer, but only 20-25% of patients achieve a complete

pathologic response.32,33 Five-year survival rates remain at or below 20%, due to disease

recurrence and metastasis after therapy.34 In a phase III dose escalation study of esophageal

cancer, a higher radiation dose did not increase the two-year survival rate or local regional

control rate, but was associated with higher normal tissue toxicity and higher mortality.35 The

anatomical location of the esophagus further complicates attempts to increase radiation doses.

Major blood vessels, airways, the heart and lungs are all in close proximity to the esophagus.

Nearly all patients experience treatment related morbidities while few benefit. Thus,

radioresistance is a pervasive problem in esophageal cancer and is a major contributor to

treatment failure and patient suffering.

1.4. General mechanisms of radioresistance (All aims)

7

Many factors contribute to cellular radiosensitivity. In the context of solid tumours, some of

these factors are intrinsic to the cells themselves, such as cell proliferation rate, insensitivity to

apoptosis induction and the efficiency of DNA repair. Other radioresistance elements derive

from benign elements in the tumour microenvironment, including tissue hypoxia, cytokine

secretion and the inflammatory response to radiation. Analyses of epigenetic responses to

genotoxic stress have identified several hundred factors derived from the tumour

microenvironment, a highly-conserved secretory phenomenon termed the DNA Damage

associated Secretory Program (DDSP). The DDSP includes pro-inflammatory cytokines such as

Interleukin (IL)-6 and IL-8, extracellular matrix-altering proteases, angiogenic and growth

factors with documented roles in promoting tumour growth and invasion.36 In the 1950s,

Revesz and colleagues demonstrated that co-injection of lethally irradiated and non-irradiated

tumour cells enhanced tumour growth,37 an effect that was later attributed to factors secreted

from the irradiated cells.38 More recently, growth factors secreted by senescent prostate

fibroblasts were shown to promote prostate epithelial cell proliferation.39

Tumour cell adhesion to benign components of the microenvironment—including stromal cells,

fibronectin, collagens and laminins—may also contribute to the radiation response. Adhesion of

multiple myeloma cells to bone marrow constituents promotes therapeutic resistance through

the redistribution of anti-apoptotic proteins CASP8 and FADD-like apoptosis regulator (FLIP)

from the cytoplasm to the cell membrane, proteosomal degradation of the pro-apoptotic

protein BIM, and upregulation of the cyclin-dependent kinase inhibitor p27.40,41 Thus,

interactions between tumour cells and the microenvironment, whether through physical

contact or secreted factors, influence the radiation response.

8

Several clinical studies, particularly in head and neck cancer have demonstrated unequivocally

that tumour hypoxia has a negative impact on outcome after radiotherapy.42,43 Hypoxia can

regulate radioresistance both directly – through the deprivation of reactive oxygen species—

and indirectly by inducing gene expression changes, post-translational modifications, and by

controlling mRNA translation. Radiation-induced ionizations produce DNA radicals (DNA) that

are oxidized in aerobic conditions. Oxidized DNA (DNA-OO) results in irreversible strand

breaks. Thus, radiation will produce substantially fewer DNA strand breaks and consequently

less cell death in hypoxic compared to normoxic regions within a tumour.

A deeper understanding of indirect hypoxia-mediated radioresistance developed with the

demonstration that hypoxia stimulates angiogenesis and that hypoxia-inducible factor 1 (HIF-1)

is the major transcriptional regulator of this relationship.44 Around the same time, clinical and

preclinical evidence implicated HIF-1 in radiation resistance. Expression of HIF-1 in

oropharyngeal cancer patients was associated with failure to achieve complete remission after

radiation therapy, and HIF-1α null mouse fibroblasts were more radiosensitive than their wild-

type counterparts.45,46 The first link between HIF-1 and radioresistance was provided by Moeller

et al, who showed that irradiation-induced nuclearization of HIF-1 resulted in increased levels

of vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF). These

growth factors prevent radiation-induced endothelial cell death, a critical factor in the radiation

response.47 Zhang et al subsequently demonstrated that hypoxia promotes radioresistance

among tumour cells by upregulation of mouse double minute-2 (Mdm2) and consequent

suppression of p53 in two cell lines.48

9

Instrinsic cellular factors are equally important in the radiation response. It is well established

that radiation causes both single- and double-strand DNA breaks (dsbs), although the latter is

considered the lethal event. MRE11 senses DNA-dsbs and activates ataxia telangiectasia

mutated (ATM), DNA-dependent protein kinase catalytic subunit (DNA-PKcs), and ataxia

telangiectasia and Rad3 related (ATR) kinase activity.49 These initial activations result in the

phosphorylation and activation of proteins involved in cell cycle arrest and DNA repair,

including p53, BRCA1 and RAD9. DNA repair subsequently occurs through two pathways:

homologous recombination (HR) and non-homologous end joining (NHEJ). HR requires an

undamaged DNA template, and is therefore most efficient in the S and G2 phases of the cell

cycle, when a sister chromatid is present. Repair occurs in several steps. First, the damaged

DNA is processed by 5’ to 3’ nucleolytic resection to create single-stranded 3’ overhangs. RAD51

is recruited to the single-stranded DNA and creates a nucleoprotein filament that searches for a

homologous DNA sequence. The single-stranded DNA invades the template strand, and DNA

polymerase extends the broken sequence from the 3’ end. The same process occurs on the

second 3’ overhang, creating two crossed DNA strands that are resolved to produce two

complete double-stranded molecules.50,51 Unlike HR, NHEJ does not use a complementary

strand to repair DNA, and is therefore more prone to error. Nevertheless, it is critical to cell

survival during the G1 phase of the cell cycle. NHEJ is initiated by Ku proteins that bind to

broken DNA strands. The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) forms a

complex with the Ku proteins to initiate repair of the damaged DNA.52 Severe combined

immunodeficient mice harbour a DNA-PKcs mutation that renders them hypersensitive to

radiation (discussed further in section 1.6). Thus, intrinsic cellular factors such as the amount of

10

initial DNA damage, activation of cell cycle checkpoints and the efficiency of DNA damage repair

may contribute to radiosensitivity.

In a study of 19 tumour cell lines, Chavaudra et al highlighted the role of chromatin architecture

in the induction of DNA damage after radiation. Measuring both chromatin condensation—

which mediates initial DNA damage by shielding DNA strands—and residual double strand

breaks after irradiation, the authors distinguished four groups among their panel of cell lines:

the most resistant were repair-proficient and sustained chromatin condensation after

irradiation. Cell lines that exhibited either condensed chromatin and repair deficiency or

relaxed chromatin and repair proficiency displayed intermediate radiation sensitivity, while the

most radiosensitive lines were repair deficient and had relaxed chromatin.53 Thus, differential

DNA repair efficiencies can account for variations in radiosensitivity among different tumour

cell types.

Variations in DNA damage-independent apoptosis signalling may also affect radiosensitivity. For

example, cellular stress can activate acid sphingomyelinase (asmase), leading to ceramide

release and initiation of apoptosis signalling. Ceramide-induced apoptosis depends on the cell’s

ability to rearrange lipid rafts in the membrane to form macrodomains. Cells deficient in

sphingomyelinases, as well as cells with impaired lipid raft coalescence are more resistant to

radiation than their respective counterparts.54,55

Functionality of cell cycle checkpoints, mediated largely by p53 and p21, as well as the cell cycle

phase, can also influence radiosensitivity. For example, inhibition of serine/threonine-protein

11

kinase CHK1, a checkpoint regulator at the G2-M boundary radiosensitizes p53-deficient human

cells.56 Furthermore, stabilization of p21 by RNPC1 enhances EAC cell radioresistance (discussed

in Section 1.5).57 However, the contributions of p53 and p21 status to radiosensitivity are

complex; both p53 loss of function and p53 gain of function mutations have been associated

with increased radioresistance. Furthermore, some studies demonstrate increased

radioresistance upon p53 loss while others have shown either no effect or increased

radiosensitivity.58,59 Some of this variability may be cell type- and cell context-specific.

Early studies in Chinese hamster cells showed that cells respond differently to radiation

depending on their position in the cell cycle at the time of treatment. Cells are most

radiosensitive in the G2-M phase, less sensitive in the G1 phase, and least sensitive during the

latter part of the S phase due to differential degrees of chromosomal damage and repair

efficiencies in each phase.60,52 However, the relative radiosensitivities of cells in each phase of

the cell cycle varies between cell types and is dependent on the molecular profile of the cells.

For example, BRCA2 is involved in DNA repair by homologous recombination. Mice with a

truncated BRCA2 display a proliferative defect that can be restored with genetic ablation of

p53. This permits the interrogation of BRCA2-mediated cellular radiosensitivity in proliferating

cells. Reasoning that a mouse model with a truncated BRCA2 should increase cellular

radiosensitivity, Tutt et al found that the BRCA2 mutation has little effect on cells irradiated in

quiescence but radiosensitized proliferating S and G2 cells in p53-/- mice.61 Thus, the interplay of

molecular profile and cell cycle distribution is a critical determinant of intrinsic cellular

radiosensitivity, particularly for proteins with cell-cycle specific functions like BRCA2.

12

1.5. Mechanisms of radioresistance in EAC (All aims)

The mechanisms of resistance in EAC are poorly understood, due partly to a paucity of

radioresistance models in this disease. Furthermore, the recent discovery that three commonly

used esophageal adenocarcinoma cell lines are in fact derived from other cancers has

undermined a significant amount of previously reported data.62 Through chronic exposure of a

validated EAC cell line to ionizing radiation, Lynam-Lennon et al developed an isogenic model of

radioresistance, and demonstrated that radioresistant cells had an enriched capacity to repair

damaged DNA, compared to parental cells.63 However, while useful for probing the precise

molecular responses to radiation, such isogenic models deliberately discount the clinically-

observed heterogeneity in tumour radiosensitivity. Hötte et al found that RNPC1, which

stabilizes p21 and is upregulated in therapeutically-resistant patient tumours, enhanced

radioresistance in three EAC cell lines through p21 stabilization and resultant G0/G1 cell cycle

accumulation.57 However, the reported G0/G1 accumulation was modest (5%), and did not

entirely account for the observed radioresistance. In addition, the authors probed a single time

point (72 hours) after radiation; it is likely that radioresistance is a dynamic phenomenon

requiring more frequent observations. TGF-β may also be an endogenous radiation-induced

resistance factor in EAC. Kim et al found that TGF-β was upregulated by irradiated OE33 cells (a

validated EAC cell line), and could confer radioresistance to otherwise radiosensitive cells in

vitro.64 Taken together, these studies suggest that multiple mechanisms may be responsible for

the radioresistance of EAC cell lines, but further investigation using numerous primary patient

samples is necessary in order to replicate the clinically-observed heterogeneity in EAC

radioresistance. Furthermore, studies that approach radioresistance through characteristics of

13

distinct cell populations would provide valuable information missing from in vitro models that

look at bulk changes in the whole cell population.

Each aim of this project addresses these resource and knowledge gaps (see section 1.14 for

Aims and Hypotheses). Aim 1 evaluates the utility of a primary xenograft model for studying

EAC radioresistance. Aim 2 asks whether a distinct population of radioresistant stem- or

progenitor-like cells exist in these primary EAC xenografts, and Aim 3 explores the role of

Hedgehog signalling in conferring radioresistance to EAC tumours.

1.6. The primary human xenograft as a clinically-reflective model of EAC (Aim 1)

Cancer cell lines have historically been the standard for probing tumour biology. However, it is

becoming increasingly clear that long-term culturing of genetically-modified human cells

produces tissue cultures that can bear little resemblance to the original patient tumour. These

changes arise both through in vitro selection for clones that better adapt to tissue culture

plastic, and through genetic mutations acquired from the culturing conditions themselves. For

example, Lee et al demonstrated that glioblastoma stem cells isolated from cell cultures

showed marked histologic, transcriptomic, and genomic differences compared to their

counterparts derived from primary human tumours. Furthermore, these differences were most

pronounced after ten passages, suggesting that long-term culturing in serum produces cell line

models that differ significantly from the original disease.65 Other studies have shown that

mutation of p53 or silencing of the gene encoding the DNA repair protein MGMT occur

frequently in culture.66,67

14

Xenograft models are a valuable alternative to cell culture models. While significantly more

costly and labour-intensive, these in vivo models provide tumour cells with a

microenvironment—including stroma, vasculature and a support matrix—that better reflects

the true tumour environment in a patient. Xenograft models can be derived from cell lines or

from primary patient tumours, however selection pressures during in vitro culturing result in

xenograft tumours with more a homogenous, undifferentiated histology.68 Conversely,

xenografts derived from primary patient tissue appear to retain the morphological and

molecular markers of the original tumours, even after serial passaging.69 Compared to

experimental models using primary tissue directly from the patient, xenograft models permit

the expansion of minute fragments of patient samples. This allows valuable patient specimens

to be studied more extensively, by multiple investigators, and over longer periods of time than

would be possible with only the original tissue.

Xenograft assays can be further distinguished by implantation site. Implantation of tumour

fragments into the subcutaneous space is a relatively straightforward surgical technique.

Tumours established by this method are easy to monitor and appear to maintain the original

tumour biology.69 Inherent weaknesses in this model include the following: involvement of

mouse stroma derived from an organ system distinct from that of the original tumour; poor

engraftment rates due to a comparatively sparse blood supply; a lack of natural metastasis; a

lack of an immune response to tumour formation or therapeutic intervention; and an inability

to study prophylactic therapies since tumours are intentionally implanted.

15

There is evidence to suggest that orthotopic implantation (onto the same organ that gave rise

to the original tumour) provides a tumour microenvironment that more accurately reflects that

of the primary tumour, better supports spontaneous metastasis, and produces higher

engraftment rates.70 However, tumour progression (or regression) is more difficult to assess

orthotopically. Furthermore, orthotopic implantation on certain organs and tissues (such as the

esophagus) is not feasible given the size of the animal organ, among other factors. The

strengths and weaknesses of subcutaneous and orthotopic in vivo tumour models, as well as a

traditional cell culture model are summarized in Table 2.

Subcutaneous xenograft Orthotopic xenograft Cell culture

Easy surgical procedure Need surgical expertise No surgical skill required

Comparatively inexpensive More expensive Most inexpensive

Labour and time economic Labour and time intensive Labour and time economic

Easy to monitor tumour burden and progression

More difficult to monitor tumour burden and progression

Can study single cells

Gene expression is not organ-specific

Organ-specific gene-expression

Lack of tumour microenvironment

Lack of spontaneous metastasis Spontaneous metastasis Risk of contamination from other cultures

Cannot study immune response Cannot study immune response

Lack of cell heterogeneity due to selection by culture conditions

Cannot study prophylactic therapy Cannot study prophylactic therapy

Tendency for genetic mutations to arise in long term culture

Table 2: Comparison of experimental models. Columns 1 and 2 adapted from [68]. 71

To address the need for robust models of EAC, our laboratory has developed primary human

xenograft models using esophagectomy specimens from patients with histologically-confirmed

EAC. This in vivo model provides the materials to study multiple aspects of EAC tumour biology,

most notably radiation resistance and Sonic hedgehog signalling. We have chosen to use a

subcutaneous implantation procedure since orthotopic implantation would result in weight loss

and a reduced animal life span due to obstruction from the growing tumour.

16

We have used non-obese diabetic severe combined immune-deficient (NOD/SCID) mice for this

model. The Prkdcscid mutation in SCID mice is a loss of function mutation in the DNA protein

kinase catalytic subunit (DNA-PKcs). Since the DNA-PKcs repairs DNA double-strand breaks

during V(D)J recombination, SCID mice lack T and B lymphocytes.72 When this mutation is

transferred onto an inbred NOD background, the resultant NOD/SCID strain has no Pre-B or B

cells, non-functional T cells, impaired NK cells, defective macrophages, and low serum

immunoglobulins.73 In essence, NOD/SCID mice have no adaptive immunity and a reduced

innate immunity, making implanted human tumours more likely to engraft. Current work in our

laboratory has focused on characterizing this xenograft model. Preliminary data confirms that

engrafted tumours reflect the original patient tumour both histologically and genomically, even

after 13 passages (unpublished data, personal communication L. Dodbiba).

1.7. The tumour bed effect (Aim 1)

The germ line Prkdcscid mutation and consequent DNA double strand break repair deficiency

renders SCID mice substantially more sensitive to radiation-induced cell death.74 Radiation

damage to the endothelium and connective tissue composing the tumour stroma reduces the

growth rate of subsequently implanted tumours, a phenomenon known as the “tumour bed

effect” (TBE).75 The TBE has proven useful for studies seeking to either mimic tumour

recurrence and metastasis in humans, or to investigate stromal radiosensitivity.76,77 However, it

remains unclear whether a TBE can influence measurements of intrinsic tumour radiosensitivity

when tumours are irradiated in vivo.

17

The degree of influence of the TBE on tumour radioresponse is a controversial topic. In 1993,

Budach et al demonstrated that the doses required for local control in 50% of tumours (TCD50)

transplanted in SCID mice were not significantly different from the matched TCD50 values of

tumours grown in wild-type mice.78 Since then, studies using various measures of

radiosensitivity (growth delay and TCD50) and experimental animal models have challenged

these results. Several reports from Garcia-Barros et al suggest that acid spingomyelinase

(asmase)-mediated endothelial cell damage is a significant determinant of tumour

radioresponse.79,80 In contrast, the group around Leo Gerweck maintains that intrinsic tumour

cell radioresistance is the dominant factor in the overall radiation response. They demonstrated

that the ratio of the radiation-induced growth delays of DNA-PKcs-/- and DNA-PKcs+/+ tumour

cell lines grown in nude mice was equal to the ratio of their intrinsic radiosensitivities measured

by clonogenic survival.81 Taken together, the relative contributions of intrinsic tumour

radiosensitivity and stromal radiosensitivity to the overall radiation response are not clear.

These studies, as well as subsequent investigations will be discussed further in Section 2.3.

A TBE, if present in our xenograft model, would have significant implications for our study. First,

it would confound results on the intrinsic radiosensitivity of patient-derived xenografts, as

measured by specific growth delay. Second, it would undermine our ability to enrich for

radioresistant tumour-initiating cells, since a compromised tumour bed would affect cell

survival in all tumour cells, regardless of tumour-initiating capacity. We therefore sought to

determine the magnitude of the TBE in NOD/SCID mice, as a proof of principle that this strain is

an appropriate model for xenograft irradiation and TIC studies.

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1.8. Cancer Stem Cell Theory (Aim 2)

The cancer stem cell (CSC) model offers a unique perspective for modelling EAC radioresistance.

According to the traditional clonal evolution model of cancer development, tumours arise as

cells stochastically accumulate mutations that confer growth and survival advantages over

other clones. Each mutation creates further chromosomal instability, predisposing the tissue to

malignant transformation.82,83 By this view, each cell in a tumour contributes equally to tumour

propagation through a common capacity to proliferate, metastasize and to seed a new tumour

in an immunodeficient mouse. In contrast, the CSC model proposes a hierarchical organization

of tumour cells among which only a small population is necessary and sufficient to regenerate

growth in vivo. These cells are distinguishable by phenotype, possessing properties similar to

those of stem cells, including the ability to self-renew, to proliferate extensively and to produce

progeny that differentiate into multiple lineages.84 The first evidence for the presence of CSCs

came from work by Bonnet and Dick, which demonstrated that only a small fraction of acute

myeloid leukemia cells could recapitulate the cancer in immunocompromised mice.85 Cancer

stem-like cells have since been identified in solid tumours including breast 86, brain 87, gastric 88,

hematopoietic 89, pancreatic 90, colon 91, bladder 92, head and neck 93, and lung 94 cancers.

Importantly, CSC theory views these cells as both the drivers of tumorigenesis and the

propagators after chemoradiotherapy, due to their intrinsic chemo- and radioresistance.95 It

should be noted that the clonal evolution and CSC theories are not mutually exclusive; with

accumulating evidence that non-CSCs can spontaneously convert to CSCs,96–98 a hybrid of the

two theories may more accurately reflect tumour biology.

19

Considering the controversy in terminology within the cancer stem cell field, and given the

functional tumourigenic assay used for this project, the term “tumour-initiating cell” (TIC) will

hereafter be used in lieu of “cancer stem cell.” Some exceptions will appear throughout the

text, since it remains unclear whether Hedgehog signalling—while a distinctly stem cell

pathway—is restricted to the TIC niche.

TICs are typically identified using a combination of flow cytometric analysis of cell surface

markers, in vivo tumourigenicity assays and in some cancers, in vitro or ex vivo sphere-forming

assays. While staining for cell surface markers provides information about cell phenotype, it

may over- or underestimate the fraction of cells that possess the biological function in question.

Thus, the hallmark demonstration of stemness is an enhanced ability of a cell type to grow

tumours in immune-compromised mice, and to continue doing so with serial passaging. This

would demonstrate both an ability to give rise to transit-amplifying and differentiated cells, and

to self-renew.

A limiting dilution assay is used to measure the frequency of cells possessing these properties in

a mixed population of cells. Suspensions of cells at discrete dilutions are injected

subcutaneously into mice, and the animals are monitored for tumour formation. At the limiting

dose—that is, the dose at which you have one TIC per injection volume—the probability of

injecting zero, one or greater than one TIC per mouse follows the Poisson distribution. In this

model, the probability of injecting zero cells is equal to the probability of injecting one cell, and

each of these outcomes will occur in 37% of injections at the limiting dose. However, when a

tumour forms, the observer cannot be sure whether this positive signal arose from a single TIC

20

or from more than one TIC. Thus, the fraction of injections at each dose that do not give rise to

tumours is scored, since this is probabilistically equivalent to the fraction of injections that gave

rise to a tumour from a single cell. The dose at which this negative signal occurs 37% of the time

is calculated, thus providing the TIC frequency.

In cancer biology, a clonogenic cell is defined as a neoplastic cell with the capacity to produce a

proliferating colony of descendents (generally >50 cells), and is therefore considered capable of

regrowing a tumour if left intact after treatment.52 A clonogenic assay measures the fraction of

these progenitors in a cell population. Since the stem cell compartment is likely contained

within the larger progenitor population, a clonogenic assay can be used as an indirect measure

of stem cell frequency. Dilute concentrations of cells are plated at an appropriate density such

that, after several days in culture, the observer can be sure each colony arose from a single

clonogenic cell. The clonogenic frequency is calculated as the (# of colonies per well)/(# of cell

seeded per well) x 100. Both of these assays are used to address the question stated in Aim 2—

that is, whether irradiation enriches for TICs and/or clonogenic cells in EAC tumours.

1.9. TICs and radioresistance (Aim 2)

That TICs might represent both the radioresistant and tumourigenic cell population poses

serious challenges to the clinical management of cancer, particularly since novel drug

development is usually judged by macroscopic tumour volume endpoints rather than

eradication of TICs.99 Numerous studies have demonstrated that a higher proportion of TICs

correlates with higher radioresistance.100 In a seminal demonstration of radioresistance in the

tumourigenic cell population, Hill and Milas showed that in a panel of murine tumours, TD50

21

values inversely correlated with TCD50. That is, the tumours with a higher tumourigenic

capacity required the highest doses of radiation in order to control tumour growth.101 Thus, the

number of TICs per tumour is an important determinant of tumour control after irradiation.

The advent of surface marker-based methods of cell sorting has facilitated deeper exploration

of TIC radioresistance mechanisms. Findings in the field are controversial and at times

contradictory, indicating a need for further investigation. Glioblastoma stem cells (GSCs),

characterized by expression of CD133, are less sensitive to radiation-induced apoptosis

compared to CD133- cells, and are enriched after irradiation both in culture and in mice. CD133+

cells preferentially activated checkpoint proteins in response to radiation-induced DNA

damage, and were radiosensitized by inhibition of Chk1 and Chk2 checkpoint kinases. In fact,

these cells had higher baseline levels of phosphorylated Rad17, suggesting that GSCs are

primed to respond to DNA damage. Additionally, CD133+ cells had an enriched capacity to

repair damaged DNA, measured by comet assay and phosphorylated histone 2AX nuclear

foci.102 However, the authors of this study did not report the absolute number of double strand

breaks, and did not perform clonogenic assays on the same cells used in the double strand

break analysis.

Nevertheless, in two follow-up papers the same group provided a potential mechanism for

augmented checkpoint activation in GSCs. The neuronal adhesion molecule L1CAM is

overexpressed in GSCs and increases the expression of NBS1, a critical component of the MRN

complex. Thus, L1CAM may bolster radioresistance by upregulating MRN-ATM-CHK2 signalling

in the DNA damage response of GSCs. RNA interference against L1CAM in gliomasphere

22

cultures and in vivo models suggested that this mechanism of radioresistance was restricted to

CD133+ GSCs.103,104

Building on the established principle that radiation-induced cell death is mediated by free

radicals, Diehn et al demonstrated higher levels of antioxidants and hence lower levels of

reactive oxygen species (ROS) in CD44+/CD24-/lowLin- breast TICs compared to non-TICs. In

particular, genes involved in the synthesis of the cellular reducing agent glutathione were

significantly over-expressed in a subset of TICs compared to non-TICs. Their second, albeit

controversial finding of fewer radiation-induced DNA strand breaks in breast TICs suggests that

enhanced ROS defences in TICs prevent extensive DNA damage after irradiation.105 Similar

results were obtained in a separate study by Phillips et al, which showed that while ROS levels

increased in both monolayer and mammosphere cell cultures, a smaller increase was seen in

the latter. In addition, the radiation-induced H2AX foci seen in monolayer cultures was absent

in mammosphere cultures.106 However, a recent report by Karimi-Busheri et al demonstrated

that breast TICs utilize a H2AX-independent pathway for double-strand break repair, suggesting

that the significance of this mechanism in breast TIC radioresistance had been overstated. In

addition to validating previous observations of lower ROS levels and more rapid single strand

break repair, this group also showed that downregulation of the senescence pathway through

increased telomerase activity contributed to breast TIC radioresistance.107

Taken together, these studies illustrate that TICs possess intrinsic radioresistance mechanisms

distinct from non-TICs. These mechanisms likely involve altered ROS levels and altered DNA

damage signalling with consequences for cell death pathways. However, the precise

23

mechanisms, as well as their relative contributions to overall radioresistance require further

investigations.

1.10. TICs and EAC (All aims)

To date, no study has identified bona fide TICs in esophageal cancer, and hence the

radioresistance mechanisms of this putative cell population are unknown. Once again, a lack of

validated EAC models is partly responsible, however the methods used to identify this rare cell

population may be more at fault: recent studies have produced conflicting results on whether

EAC tumours express common TIC surface markers.108,109 Thus, a surface marker approach to

identifying and isolating TICs may not be appropriate in this cancer. Aims 1 and 2 address this

methodological deficiency with a procedure for identifying tumourigenic and clonogenic cells

harvested directly from validated in vivo models of EAC.

Citing similarities in carcinogenesis among gastrointestinal cancers, particularly in the role of

chronic inflammation, several groups have used intestinal stem cell markers to track the

pathologic progression from BE to EAC. In human esophageal specimens, Musashi-1 shows

progressively increasing levels of expression from normal squamous epithelium to Barrett’s

metaplasia and dysplasia, with highest expression levels in early stage adenocarcinoma.110 In

other human specimens, the putative gastrointestinal stem cell marker DCAMKL-1 shows

increasing expression from BE to EAC, with minimal expression in normal squamous esophageal

mucosa.111 The most evidence, however, comes from studies of the intestinal stem cell marker

leucine-rich-repeat-containing G-protein-coupled receptor 5 (Lgr5).112 Lgr5 is expressed in

colon, ovarian and hepatocellular carcinoma,113,114 and is expressed in tumour spheres derived

24

from colon cancer,115 highlighting its potential as a cancer stem cell marker. Two recent

publications reported the ubiquitous expression of Lgr5 in patient samples of BE and EAC. While

the studies contradicted each other on the relative intensity of Lgr5 staining in EAC compared

to BE, both distinguished between high and low expression of Lgr5, and both correlated high

Lgr5 expression with worse patient survival.116,117 Building on these observations, Quante and

colleagues used a transgenic mouse model that over-expressed interleukin-1β to model human

esophagitis, BE and EAC. Their evidence suggested that inflammation may recruit Lgr5+ gastric

cardia progenitor cells into the squamous mouse esophagus, suggesting that BE and EAC arise

from gastric progenitor cells.118

A stem cell model of EAC development is not a recent concept. Early investigators proposed a

gastric or gastric cardia progenitor cell of origin in BE, 119,120 while other recent investigations

point to progenitors in the esophagus,15,121–123 the esophageal submucosal glands,124,125 and the

gastroesophageal junction (GEJ).126 The authors of this last report modeled Barrett’s metaplasia

using p63-deficient mice, which are unable to develop stratified squamous epithelia and quickly

develop a Barrett’s-like metaplasia. The origin of this metaplasia was traced to residual Car4-

expressing embryonic stem cells at the GEJ that opportunistically migrate into the esophagus in

the absence of squamous epithelia. Taken together, these studies provide evidence that EAC

may arise via aberrant activity in stem cell pathways, irrespective of the origins of these

progenitors. Aim 3 extends this reasoning to therapeutic resistance in EAC by asking whether

the expression of a stem cell pathway—with an established role in gastrointestinal

development—is associated with EAC radioresistance.

25

1.11 Hedgehog Signalling Pathway (Aim 3)

The hypothesis that EAC is initiated and propagated by a subset of stem-like cells is further

supported by the observation that the Hedgehog (Hh) signalling pathway, a member of the

stem cell signalling network, is aberrantly activated in BE and EAC. Hh signalling regulates

embryonic development, with key roles in pattern formation and appendage development in

insects, and neural tube differentiation in vertebrates.127 In addition to patterning, Hh signalling

controls cell proliferation and differentiation in stem cells and stem-like progenitors. 128,129 In

adult life, Hh signalling mediates tissue homeostasis 130,131 and repair after injury.132

Mammalian systems have three Hh homologues: Sonic (SHH), Indian (IHH) and Desert Hh

(DHH). Of the three, SHH is the most studied in both developmental and pathologic contexts. It

is also the predominant ligand found in gastrointestinal development and carcinogenesis. In the

absence of Hh ligand, the 12-pass transmembrane receptor Patched-1 (PTCH1) inhibits

Smoothened (SMO), a 7-pass transmembrane protein with homology to G-protein coupled

receptors (Figure 2). SMO suppression permits the assembly of a cytoplasmic inhibitory

complex including Suppressor of Fused (SUFU), which targets the glioma-associated oncogene

homologue (GLI) family of transcription factors, GLI1, GLI2 and GLI3 for proteolytic cleavage.

Vertebrates have a second isoform of the receptor, PTCH2. While PTCH1 and PTCH2 have

similar affinities for all three ligands, PTCH2 has a decreased ability to inhibit SMO.133

HH ligands are released from the signalling cell through the 12-pass transmembrane protein

Dispatched (DISP). Ligand binding to either PTCH1 or PTCH2 alleviates PTCH-mediated

suppression of SMO, allowing SMO to translocate to the primary cilium. There, it causes the

26

dissociation of the inhibitory complex, permitting the GLI family of transcription factors to

accumulate in their full length forms.134

Regulation of gene transcription by GLI proteins is better understood for the Drosophila

homolog, Cubitus interruptus (Ci). The Ci protein is a composite of positive and negative

regulatory domains. In the absence of Hh signal, Ci is processed into a repressor form, while Hh

signalling stabilizes the full length activator form. The three mammalian GLI isoforms differ

significantly in their homology to Ci and consequently in their transcriptional functions. GLI2

and GLI3 are more closely related to Ci than GLI1, and can act as both transcriptional activators

and repressors.135 However, GLI2 appears to act more potently as an activator, since GLI2 loss-

of-function diminishes SHH-induced target gene expression in mouse embryonic fibroblasts.136

Conversely, GLI3 acts primarily as a transcriptional repressor, although it may also function as a

negative regulator of the pathway by upregulating PTCH1 and Hedgehog Interacting Protein

(HIP), a transmembrane protein that binds each of the three ligands and attenuates the Hh

signal.136 GLI1 is exclusively an activator, but its role in activating the pathway appears to be

less potent than that of GLI2. In fact, GLI1 but not GLI2 is dispensable for murine

development.137 While GLI1 appears to act in concert with GLI2 to activate the pathway, it

predominantly functions as readout of activated signaling.136

Hh target genes are involved in many cell functions, including cell cycle progression,

proliferation, differentiation, stem cell maintenance, epithelial-mesenchymal transitions, cell

adhesion, signal transduction, angiogenesis and apoptosis138,139 ( Figure2A).

A B

27

Of the cellular processes targeted by Hh signalling, proliferation, self-renewal and survival are

most relevant to this project and warrant elaboration. Cellular proliferation requires cyclin-

dependent kinase (CDK)-mediated progression through cell cycle checkpoints (Figure 2B). CDKs

associate with Cyclins A-E at different points in the cell cycle to promote progression. Cdc25

family members activate CDKs by dephosphorylating inhibitory phosphorylation sites. CDK

inhibitors such as p16, p19 and p27 inhibit the CDK-cyclin complexes.

N-Myc promotes cell cycle progression through p27 downregulation, and FOXM1 does so

through upregulation of Cdc25B and Cyclin B1. Hedgehog signalling promotes cell cycle

progression through GLI-binding to promoter and enhancer regions of the N-Myc, Cyclin D1,

and Cyclin D2 genes. The Cyclin E and FOXM1 genes are indirect GLI targets.139 Thus, Hedgehog

signalling promotes cellular proliferation through upregulation of multiple cell-cycle mediators.

Figure 2.The Hedgehog pathway. (A) Hh signalling cascade. See text for description of pathway. (B) The role of Hh signalling in cell cycle regulation. Adapted from [139].

28

Hedgehog signalling protects cells from apoptosis through

upregulation of anti-apoptotic BCL2, CFLAR and

downregulation of pro-apoptotic BIM, p19, p53, the BH3-

only protein NOXA and FAS proteins139 (Figure 3).

Finally, Hedgehog signalling cross-talks with WNT, RTK,

NOTCH and BMP/TGFβ signalling to regulate stem cell

renewal. These interactions are complex, and will not be

outlined in detail here. Nevertheless, several interactions

are worth highlighting. Bone morphogenetic protein (BMP) signalling is involved in both self-

renewal and lineage commitment of embryonic stem (ES) cells.140 Murine ES cells utilize

autocrine BMP signalling to promote self-renewal by collaborating with LIF-STAT3 to suppress

neural lineage differentiation.141 Similarly, Notch is required for the maintenance of many self-

renewing tissues including the brain 142, blood 143 and the gut.144 Hh signals from epithelial cells

induce mesenchymal BMP4 upregulation through FOXF1 upregulation.145 Furthermore, Hh

signals both positively and negatively mediate Notch signalling. Hh upregulates the Notch

ligand JAG2. It also represses p53, which upregulates the Notch receptor NOTCH1.146

Hedgehog signalling can follow one of three mechanisms: paracrine, reverse paracrine and

autocrine. In paracrine signalling, the ligand-secreting epithelial cell signals locally to the

stroma, which expresses both the receptors and effectors of the pathway. Signal transduction

in the stroma provides a selective growth advantage for the tumour through the upregulation

Figure 3. Hh signalling promotes cell survival. Adapted from [139]

29

of growth-promoting genes. Paracrine signalling has been observed in tumours of the

gastrointestinal tract, including pancreatic, esophageal and colon cancer.147–149 In reverse

paracrine signalling, seen in B cell lymphoma,150 bone marrow- and splenic stroma-derived

ligands activate the pathway in receptor-expressing tumour cells. In autocrine signalling,

tumours synthesize and respond to their own ligands. This mechanism has been observed in

small cell lung cancer and in some cancers of the digestive tract.151,152

1.12. Hedgehog pathway and EAC (Aim 3)

Hedgehog signalling between the endoderm and mesoderm is critical for the development of

the esophagus from the endodermally-derived gut.148 Intestinal columnar epithelium such as

that lining the early esophagus is maintained by SHH signalling.153,154 Squamous epithelium

does not appear in the esophagus until Hh signalling is downregulated.154 Berman et al found

endogenous overexpression of SHH and IHH in OE33, an esophageal adenocarcinoma cell line.

Pathway activity was induced by ligand overexpression rather than mutation, suggesting that

reactivation of an embryonic pathway is involved in carcinogenesis.152 Interestingly, the results

from this study suggested that Hh signalling in EAC followed an autocrine mechanism, with

ligands, receptor and effectors all expressed in the EAC cell line used.

A subsequent study by Ma et al found elevated levels of Hh pathway transcripts and proteins in

four of four primary human esophageal adenocarcinoma specimens, compared to matched

normal esophageal epithelium. Using a combination of in situ hybridization and

immunohistochemistry, they found that SHH was restricted to the tumour cells while PTCH1

protein was detected in both the tumour and stroma.155 This suggested that a paracrine

30

signalling mechanism similar to that found in esophageal development could mediate

epithelial-mesenchymal interactions during tumorigenesis. Furthermore, epithelial PTCH1

expression indicated that autocrine and paracrine signalling could occur simultaneously. In a

separate study, Wang and colleagues looked at tissue microarrays of esophagectomy

specimens representing normal squamous epithelium, BE, BE with low- to high-grade dysplasia

and adenocarcinoma. Immunohistochemistry did not detect SHH in normal squamous

epithelium but found ubiquitous expression of SHH in BE and EAC. Using a mouse

esophagojejunostomy model, this group further demonstrated that exposure to bile reflux

resulted in marked upregulation of Hh ligands in the mouse esophagus, accompanied by

stromal expression of PTCH1 near the resulting intestinal metaplasia.156

Most recently, Yang et al compared Hh staining in 174 primary human esophageal specimens

encompassing ESCC, EAC and their respective precursor lesions, squamous dysplasia and BE.

PTCH1 was expressed in 96% of human EAC specimens, compared to 38% of ESCC specimens.

21% of dysplastic lesions were positive for PTCH1, and these positive signals were restricted to

tissues with severe dysplasia or carcinoma in situ. In contrast, PTCH1 was detected in 58% of BE

tissues, with similar frequencies in tissues with and without dysplasia. Thus, while Hh signalling

appears to promote the formation of carcinoma in situ in ESCC, pathway activation may be

among the earliest events in the pathological progression to BE and ultimately EAC.157 Taken

together, the evidence suggests that EAC carcinogenesis may be modeled as development gone

awry. That is, exposure to chronic acid reflux and inflammation may reactivate embryonic

pathways in stem cell populations, giving rise to a more resistant albeit genetically unstable

epithelium.

31

While these studies unequivocally demonstrate reactivation of Hh signalling in EAC

carcinogenesis, it remains unclear whether pathway activity occurs in an autocrine or paracrine

pattern. Furthermore, of those studies performed in primary patient tissues, most have relied

on immunostaining with Hh antibodies. This technique offers a visual representation of the

spatial distribution of Hh signalling in tissue sections, but it suffers from several limitations.

First, it has a limited ability to quantify low levels of antigen expression, a particularly potent

weakness given the often remarkably low expression levels of certain Hh genes. Second, cross-

reactivity of antibodies is a concern, given the substantial sequence homology between SHH

and IHH and between PTCH1 and PTCH2. Most antibodies against Hh ligands are designed

against the highly conserved NH2 terminal 19kDa protein.134 Since ligand and receptor isoforms

may have distinct functions,133 reliable detection of each is desirable. Aim 3a seeks to

complement the evidence provided in these studies by using quantitative real time polymerase

chain reactions (qPCR) of Hh pathway genes in primary xenograft-derived tumour tissue. In

these tumours, malignant epithelium derives from the human patient, while normal

mesenchyme and endothelium—the tumour bed—is supplied by the mouse. Thus, species-

specific qPCR primers are used in Aim 3a to determine which cell types express each Hh

transcript.

1.13. Hedgehog pathway and radiation (Aim 3)

Since the Hh pathway appears to be activated in response to tissue injury, it seems reasonable

that it may be involved in the cellular response to radiation therapy. Several clinical studies

have supported this hypothesis. qRT-PCR analysis of biopsy specimens from cervical cancer

patients undergoing chemoradiotherapy revealed a correlation between up-regulation of SMO

32

and increased risk of locoregional recurrence, supporting a role for Hh signalling in tumour

repopulation after chemoradiotherapy (CRT).158 Additionally, in a large cohort of head and

neck cancer patients treated with radiation alone, increasing GLI1 expression measured by

immunohistochemistry correlated with poorer outcomes in time to disease progression, time to

metastasis and overall survival in a multivariate analysis.159 In a similar study of esophageal

squamous cell carcinoma patients treated with CRT, the absence of nuclear Gli1 staining in pre-

treated surgically excised tumours was associated with overall patient survival, and all patients

with nuclear Gli1 staining had distant lymph node metastases.160 Thus, clinical data from

several tumour sites suggest that Hh signalling promotes cancer regrowth and metastasis after

RT and CRT. However, since access to pretreatment tumour biopsies is scarce, many of these

studies rely on surgically-resected specimens and are consequently limited in their ability to

distinguish baseline Hh expression from therapy-induced pathway activation. A particular

strength of our xenograft model is the ability to probe Hh pathway activity at multiple time

points before and after irradiation.

Other studies have directly implicated Hh signalling in the cellular response to radiation.

Recently, an in vitro study of hepatocellular carcinoma demonstrated a radioprotective effect of

autocrine Hh signalling. SHH ligand, added either as recombinant protein or as a component of

conditioned medium from irradiated and non-irradiated cells protected subsequent cultured

cells from the effects of radiation. SHH antibody neutralization partially blocked

radioprotection, and GLI1 knockdown abolished this effect.161 Exogenous SHH ligand delayed

the disappearance of γH2AX foci after irradiation, and reduced the level of phosphorylated

CHK1 after irradiation. While the mechanisms of a putative Hh-mediated response to radiation

33

are poorly understood, it appears that pathway activity may contribute to radioresistance by

overriding cell cycle checkpoints despite DNA damage.

In response to DNA damage, tumour suppressor protein p53 is covalently modified and

stabilized, resulting in cell cycle arrest and in some cases, apoptosis. Hedgehog signalling after

DNA damage in transformed mouse embryonic fibroblasts (MEFs) inhibits p53 accumulation by

inducing phosphorylation and activation of the E3 ubiquitin-protein ligase MDM2, resulting in

p53 degradation. In addition, constitutively activated SMO augments p53 binding to MDM2.

Thus Hh signalling may promote cellular proliferation after DNA damage by overriding p53-

mediated cell cycle arrest.162

Further evidence that Hh signalling abrogates cell cycle checkpoints was provided by Fernandez

et al using a murine model of medulloblastoma. Radioresistant medulloblastoma cells occupy

the perivascular niche and express Yes-associated protein (YAP), a transcriptional co-activator

and a SHH target. YAP enables cells to progress through the G2/M checkpoint with damaged

DNA by promoting insulin-like growth factor 2 (IGF2) expression and AKT activation, resulting in

ATM-CHK2 inactivation. However, the bulk of these mechanistic studies used YAP-transfected

cells rather than SHH-induced upregulation of YAP, leaving open the possibility of a SHH-

independent role for YAP in the radiation response. Nevertheless, this data supports the

hypothesis that Hh signaling might promote radioresistance through cell cycle checkpoint

abrogation.163

In spite of this evidence, none of these reports directly implicate radiation-induced SHH in

34

cellular radioresistance. Furthermore, little is known about Hh expression as a response

mechanism to radiation in EAC. In a number of experiments, Sims-Mourtada et al showed that

in cell line-derived xenografts treated with chemoradiation, increases in Hh activity

immediately preceded increases in tumour proliferation rates. Furthermore, SHH stimulation

increases cyclin D1 expression and Rb phosphorylation. Hh inhibition decreases Cyclin D1 and

CDK4 expression with a resulting accumulation of cells in the G1 phase of the cell cycle. This

suggests that pathway activity promotes G1-S phase cell cycle transitions by increasing activity

of the cyclin-Rb axis. The implications of this finding are non-trivial, given the well-documented

observation that cells in S phase are more radioresistant compared to cells in G2 and M

phases.60 Finally, Hh blockade reduced the shoulder region of the radiation survival curve,

suggesting that repair of sublethal damage was inhibited. While this last observation was made

on cells from the validated EAC cell line BE-3, it was subsequently discovered that another cell

line used to generate the bulk of data in this study, believed to be derived from an EAC tumour,

was in fact derived from another disease site. While an elegant mechanistic demonstration of

the radioresistance-conferring effect of Hh signalling, these results have yet to be replicated in

validated models of EAC. Aims 3b and 3c seek to fill in these knowledge gaps by measuring

changes in Hh expression at multiple time points after irradiation, and by evaluating the ability

of Hh inhibition to radiosensitive EAC tumours.

1.14. Aims and Hypotheses

The overall objective of this project is to apply the cancer stem cell model to interrogate

multiple contributors to radioresistance in a panel of primary human EAC xenografts. These

factors include heterogeneous TIC frequencies, clonogenic frequencies and Hedgehog pathway

activity.

35

Aim 1 overall: To evaluate our primary xenografts as a model of intrinsic tumour

radiosensitivity and of clinically-observed heterogeneity in radioresistance.

Aim 1: To determine whether the radiation-induced specific growth delay of xenograft tumours

varies more between xenograft lines (primary patient tumours) or between multiple passages

within one xenograft line. We hypothesize that specific growth delay will remain consistent

across multiple passages within a tumour line, but vary across tumour lines, thereby reflecting

both the intrinsic radiosensitivity of each patient tumour and the heterogeneity in EAC tumour

radiosensitivity.

Aim 2 overall: To determine whether radiation therapy enriches for tumourigenic and/or

clonogenic cells in primary patient xenograft tumours.

Aim 2(a): To compare the in vivo tumour TIC frequency between irradiated and non-irradiated

xenograft tumours. Figure 4 is a schematic representation of a typical xenograft growth curve

and response to irradiation. Points A, A’, B and B’ represent the volumes at which limiting

dilution and clonogenic assays are performed. We hypothesize that EAC TICs are more

radioresistant than non-TICs and thus, irradiated tumours will have a higher TIC frequency than

non-irradiated controls after some time delay post-irradiation (see Figure 4: A’ > A). However,

after tumour recovery and repopulation, the TIC frequency will have returned to baseline (non-

irradiated) levels (Figure 4: B’ = B). Importantly, we expect that the TIC frequency will not

change in control tumours (Figure 4: A = B)

Aim 2(b): To compare the ex vivo clonogenic frequency between irradiated and non-irradiated

xenograft tumours. We hypothesize that the clonogenic cell population is enriched for TICs and

is therefore more radioresistant than the non-clonogenic population. Therefore, the clonogenic

36

frequency should be higher in irradiated compared to non-irradiated tumours (Figure 4: A’ > A).

However, we predict that, like the TIC frequency, the clonogenic frequency will return to

baseline levels after tumour repopulation (Figure 4: B’ = B; A = B).

Aim 3 overall: To study the role of Hedgehog pathway activity in the radiation-response of

xenograft tumours.

Aim 3(a): To determine how Hedgehog pathway expression is distributed between the tumour

and stroma in xenograft tumours, using qRT-PCR. Given previously published data using

antibody staining methods, and acknowledging an epithelial to mesenchymal signalling

mechanism during normal esophageal development, we hypothesize that SHH and IHH

transcripts will be detected largely in the human tumour cells, while pathway activation (i.e.

PTCH1 and GLI1) will be detected in the murine stromal cells.

Aim 3(b): To track the expression changes of Hh pathway components with time after radiation.

We hypothesize that ionizing radiation will induce upregulation of Hh pathway proteins prior to

and possibly during repopulation, reflecting a survival response of remaining TICs.

Aim 3(c): To investigate the effects of Hh pathway inhibition, with and without radiation, on the

growth delay of primary EAC cells in vivo. We hypothesize that administration of 5E1, a SHH

Figure 4. Relevant xenograft tumour volume points used for LDAs and clonogenic assays. A= control LDA1; A’= radiation LDA1; B= control LDA2; B’= radiation LDA2.

A

B’ B

A’

37

inhibitor will radiosensitize xenograft tumours and significantly increase the specific growth

delay compared to radiation or inhibitor alone.

38

Chapter 2: Primary xenografts as models of radiosensitivity and resistance (Aim 1)

AIM 1: To evaluate our primary xenografts as a model of intrinsic tumour radiosensitivity and of

clinically-observed heterogeneity in radioresistance.

The background and rationale of Aim 1 are discussed in Sections 1.6 and 1.7.

2.1. Aim 1 Methods

2.1.1. Patient samples

Primary human esophageal adenocarcinoma specimens from patient esophagectomies were

obtained fresh from the University Health Network Tumour Tissue Bank. Specimens were

collected according to the institutional human ethical guidelines. Specimens were used

immediately for xenograft implantation, or else stored overnight at four degrees Celsius until

implantation the following day. Clinical data was also collected, including the patient’s age,

gender, date of diagnosis, date of recurrence, treatment regimen, tumour location and

differentiation grade, disease-free survival, overall survival, and treatment outcome. These data

are summarized in Table 3.

Tissue bank

ID

Xenograft line

Patient tumour

site

Differentiation grade

Patient Gender

Age at diagnosis

Disease stage at

diagnosis

Pre-operative

chemo

Pre-operative

rad

Disease-free

survival (days)

Overall survival (days)

Dead or alive (as

of March 2012)

59046 2 GEJ/ Gastric Cardia

Moderate F 86 2 No No 71 805 Alive

61057 3 GEJ Poor M 77 3 No No 165 312 Alive

60045 4 GEJ Moderate M 46 3 Yes No 403 786 Dead

60516 5 GEJ/Gastric Cardia

Poor F 70 3 No No 83 270 Alive

62325 6 GEJ Moderate M 57 3 No No 242 461 Alive

60745 7 Lower third/distal esophagus

Not available M 51 3 Yes No 107 193 Alive

63862 8 GEJ Moderate M 65 3 Yes Yes 43 258 Alive

Table 3. Primary patient specimens established as xenograft lines

39

2.1.2. Development of xenograft model

Xenograft experiments followed an Animal Use Protocol approved by the University Health

Network Animal Care Committee. Primary tumour samples cut into fragments roughly 2-3 mm

in each dimension were implanted subcutaneously on the abdomen or hind flank of NOD/SCID

mice that were a minimum of 3 weeks old. During surgical implantation, mice were kept under

general anesthesia with isofluorane. Tumours that engrafted were grown to a maximum of

1.5cm in the longest dimension, at which point the mice were sacrificed, the tumours were

excised and small fragments were passaged to a new cohort of mice. Mice were kept in

ventilated cages of five mice each and were given irradiated food and water ad libitum.

Engrafted primary tumours between passages three and 13 were used for xenograft irradiation

experiments. For each experiment, tumour fragments were implanted subcutaneously into the

right hind flank of 30-60 NOD/SCID mice. Once palpable, biweekly tumour measurements were

made using calipers. Tumour size was calculated using the formula for ellipsoid volume:164

Volume = length × width2 × 0.52

When tumours reached an average volume of approximately 400mm3, mice were randomly

sorted into a control (non-irradiated) or irradiated group. In general, at least ten mice were

used per treatment arm. When mice were sacrificed at different time points for a specific

experiment, it was calculated that eight to ten mice would be available for analysis at the end

of the xenograft growth curve, even after accounting for a 5- 10% premature animal death rate.

40

2.1.3. Precision irradiation: Identification of appropriate radiation doses

Radiation dose toxicity was evaluated by delivering 2, 4, 8 or 10Gy of precision radiation to the

hind flank of non-tumour bearing NOD/SCID mice. Irradiated mice were monitored for two

months for signs of radiation injury, including hair loss, erythema, dermatitis, tissue edema,

ulcers, weight loss, impaired motor skills or death. 4Gy was the highest dose that did not

produce any signs of radiation damage and was therefore selected for further xenograft

studies.

During irradiation, mice were restrained in a custom-made plastic jig with the tumour-bearing

leg extended from the abdomen and exposed to the radiation beam. 4Gy of X-rays was

delivered at a dose rate of 3.07 Gy/min using an XRAD 225 kVp precision irradiator, fitted with a

2mm thick copper filter to remove low-energy X-rays. A 2.5cm diameter columnator was used

to direct the radiation beam to the tumour bearing leg. Half of the dose was delivered from

above the tumour, and half from below. Radiation was given under ambient air breathing

conditions without anesthetic.

2.1.4. Assessment of tumour bed effect

Xenograft Line 3 was expanded into 23 NOD/SCID mice at the third passage. Tumour volume

measurements were made biweekly. When tumours reached an average volume of 400mm3,

mice were randomly sorted into three groups: control (non-irradiated), 4Gy or 8Gy of ionizing

radiation. Studies of tumour implantations into pre-irradiated tissue beds have established that

the TBE becomes apparent at a threshold dose of about 5Gy, and maintains a dose dependent

effect on tumour growth rate from 5Gy to 20Gy.165 Thus, 4Gy was chosen from the results of

41

the toxicity study, and 8Gy was chosen because it lay above the 5Gy threshold. Weekly tumour

volume measurements were continuously made until tumours reached the volume limit,

became ulcerated, or until the slopes (i.e. tumour growth rates) of the irradiated groups

stabilized.

A TBE alters the tumour growth rate by damaging normal tissue endothelium and epithelium.

Thus, if present in our xenograft model, a TBE would manifest in the growth curve as a post-

radiation slope (Figure 5, green curve) less than that of the non-irradiated controls (blue curve).

If no TBE occurs, then irradiated tumours will recapitulate the growth rate of the control

tumours (i.e. the slopes will be parallel) after some period of growth delay (a “plateau” phase).

This is graphically represented in Figure 5.

Figure 5. Tumour bed effect schematic.

42

2.1.5. Xenograft growth delay

Volume measurements were continued biweekly following irradiation until tumours reached

the volume endpoint. The specific growth delay was calculated as: (TR-TC)/TC, where TR and TC

are the times taken after irradiation for the control and irradiated tumours to triple their

volumes as measured at the time of radiation. By normalizing the growth delay to the control

tripling time, the specific growth delay can be interpreted as the number of control tumour

tripling times required for irradiated tumours to triple their own volume. In contrast to the

growth delay, the specific growth delay permits comparisons between tumour lines (or

passages) with different intrinsic growth rates.166,167

2.1.6. Statistical analysis

A linear mixed effect repeated measures model was applied to tumour volume measurements.

This model-based approach to testing the effect of radiation accounts for the repeated and

longitudinal nature of tumour measurements. Furthermore, it enables statistical inference on

the effect of irradiation through estimation of model parameters. Finally, this model can

account for outlying tumour growth profiles as well as missing data points arising from

mortality or premature sacrifice of animals. The linear mixed-effects model of longitudinal

tumour volume is formulated as:

TimeTreatmentTimeTreatmentTimeVolume TTTrteatmentTimeTreatmentTime ,2,1*0 *)log(

Here, the dichotomous Treatment covariate is 0 for control tumours and 1 for irradiated

tumours. The discrete Time covariate represents the number of days elapsed since irradiation.

43

The fixed component of this mixed-effects model is represented by the terms; 0 represents

the tumour volume of each group at the time of irradiation; Treatment represents the difference

in the average tumour volume between irradiated and control tumours over the duration of

the experiment; Time represents the rate of tumour volume growth, and

TreatmentTime*

represents the difference in the growth rate between irradiated and control tumours. The

random effects component of the model (i,1 and

i,2 ) captures the observed variation in

tumour volumes of individual mice (as opposed to the treatment group as a whole).

To determine whether a TBE is present in our model, the slopes of the curves for each

treatment (or control) group were compared using the Wald test. The model was first applied

to all data points to measure the overall effect of irradiation on tumour volume. The model was

subsequently applied only to data points in the plateau phase of the growth curve, and then to

all data points during the repopulation phase, in order to assess the effect of radiation on the

slopes during these two respective growth phases.

To determine whether radiation significantly delayed tumour growth, we tested the null

hypothesis that treatment has no effect on tumour growth rate ( 0: *0 TrteatmentTimeH ) using

SAS 9.0 statistical software.

To calculate tumour growth delay, we first obtained a model-based estimate of tumour volume

at the time of radiation, represented by 0V̂ . Tumour volume was assumed to be equivalent for

both groups at the time of irradiation. We then calculated the time taken for the average

44

volume for each treatment group to reach 30V̂ . The growth delay was calculated from the

modeled parameters as:

)ˆˆ(ˆ

)3/1log(||

*

3,3, 00

TreatmentTimeTimeTime

VVolumeControlTreatmentVVolumeRadiationTreatment TimeTimeGD

.

The specific growth delay is given by:

1ˆˆ

ˆ

|*3, 0

TreatmentTimeTime

Time

VVolumeControlTreatmentTime

GDSGD

and the standard error of the SGD is given by:

2

*

***

22

*

)ˆˆ(

)ˆ,ˆ(ˆˆ2)ˆ(ˆ)ˆ(ˆ)(

TreatmentTimeTime

TreatmentTimeTimeTreatmentTimeTimeTreatmentTimeTimeTimeTreatmentTime CovVarVarSGDSE

To test for a trend in SGD across passages (a “passage effect”), a linear regression was fitted to

the SGDs calculated for each passage within a line, and the regression slope was tested for

equivalency with zero ( 0:0 passageH ).

To test for a difference in SGD between xenograft lines, the ANOVA test was applied to the

SGDs of each experiment. The ANOVA test was also applied to the mean SGDs for each

xenograft Line.

45

2.2. Aim 1 Results

2.2.1. A tumour bed effect, if present, is negligible at low radiation doses in our xenograft

model

Figure 6 presents the growth of Line 3 passage 3 tumours that received 0Gy, 4Gy or 8Gy of

radiation. The linear mixed effect model was first applied to all data points in order to measure

the overall effect of irradiation on tumour volume (Figure 7A). A Wald test of the resultant

curves showed that the slopes of each of the treatment groups was significantly different from

that of the control, suggesting that radiation had a significant effect in both groups. However,

the difference in slopes of the 4Gy and 8Gy treatment groups was not significantly different

when tested over the whole xenograft observation period. Similar results were obtained when

the model was applied only to data points within the plateau phase of the growth curve

(between days 38 and 60), indicating that the overall effect of radiation is most pronounced

during this phase.

Figure 6. There is no significant TBE at low radiation doses in Line 3 passage 3

RT

↓

0

500

1000

1500

2000

2500

3000

0 20 40 60 80 100 120

Tum

ou

r vo

lum

e (m

m^3

)

Days since Implantation

Line 3 passage 3: Assay for TBE

Control

4Gy

8Gy

Plateau Repopulation

46

The model was subsequently applied only to data points within the repopulation phase (after

day 60 in the 4Gy and 8Gy arms), in order to evaluate a potential tumour-bed effect (Figure 7B).

Control measurements from Day 40 onwards were used for comparison. None of the pair-wise

comparisons revealed a statistically significant change in slope relative to controls. Thus, no

significant tumour bed effect was observed at either the 4Gy or 8Gy dose in xenograft line 3.

However, these experiments were performed on one xenograft line only, and further

experiments are required to confirm these results in additional lines. Furthermore, it is possible

that our experiments lacked the power to detect subtle changes in slope, i.e. small tumour bed

effects after 8Gy of radiation.

p<0.0001

p = 0.74

p<0.0001

Days since radiation Days since radiation

Figure 7. Linear mixed effect model on TBE experiment. (A) Linear regression applied on all datapoints. There is a significant effect of each radiation dose, but no significant difference between the two doses. (B) Linear regression of datapoints during repopulation phase. There is no significant difference in any of the slopes.

A B

p = 0.89

p = 0.37

p = 0.46

Tum

ou

r vo

lum

e (m

m3 )

47

2.2.2. Precision irradiation delays xenograft tumour growth

In total, 7 xenograft lines were treated with precision irradiation, 6 of which were treated on

multiple passages. The growth curves are presented in Appendix A. Radiation significantly

delayed tumour growth in all 7 lines. Only passage 4 of Line 6 showed a non-significant

response to irradiation. The results of these statistical tests are summarized in Table 4.

Line Passage P value

2 8 0.0065

2 10 0.0005

3 3a <.0001

3 3b 0.0003

3 4 0.041

4 4 <.0001

4 5 <.0001

4 6 <.0001

4 7 <.0001

5 3 0.0156

6 3 0.0448

6 4 0.0911

7 4 <.0001

7 5 <.0001

8 3 <.0001

8 4 <.0001

Table 4. Effect of radiation on tumour growth. P values <0.05 indicate a significant change in slope of irradiated versus non-irradiated tumours using all time points after treatment.

48

2.2.3. No passage effect on radiation growth delay was detected in our xenograft models

Figure 8 presents two examples of the how SGD was obtained from the linear mixed effect

model. Model parameters including slopes and intercepts were obtained from the green and

blue lines. Table 5 lists the GD and SGD for each passage. Figure 9 presents this data graphically.

Line Passage Volume at

radiation GD

(days) SGD

(fold) Standard

error SGD

2 8 386 7.5 0.47 0.19

2 10 336 18.7 1.14 0.43

3 3A 362 26.2 1.21 0.31

3 3B 342 33.5 2.05 0.72

3 4 397 34.7 1.10 0.92

4 4 413 23.7 1.08 0.32

4 5 413 25.8 0.91 0.30

4 6 372 20.8 0.75 0.18

5 3 314 16.4 0.56 0.28

6 3 458 17.6 0.49 0.29

6 4 334 9.7 0.29 0.20

7 4 516 11.1 0.56 0.13

7 5 641 13.7 0.73 0.13

8 3 770 35.5 1.98 0.77

8 4 238 9.2 0.63 0.17

Table 5. Growth delay (GD) and specific growth delay (SGD) by tumour line and passage

Figure 8. Two representative samples of growth delay derivation using the mixed effect model. (A) Tumour growth of control and treated mice from Line 7 passage 5 plotted as tumour volume over time. (1B) Tumour growth plotted as the natural logarithm of tumour volume over time. (2A) Tumour growth of Line 4 passage 6. (2B) Natural logarithm of tumour growth in Line 4 passage 6. The green and blue lines represent the overall growth of control and treated tumours, respectively. The intersection of the blue and green lines is the starting volume at the time of irradiation. The red line is the volume corresponding to 3x the starting volume. The GD is calculated from the time delay between the green and blue lines each intersecting the red line.

1A 1B 2A 2B

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6 7 8

Spec

ific

gro

wth

del

ay

Passage

Line 2

Line 3

Line 4

Line 6

Line 7

Line 8

3 3a 3b 4 5 6 8 10

Figure 9. Specific growth delay by tumour line and passage

49

We speculated that the passage number of a xenograft experiment might impact the growth

delay (GD) and specific growth delay (SGD). Linear regression of the SGDs across all passages

within each line showed no significant trend with passage (Table 6).

2.2.4. Specific growth delay cannot quantitatively distinguish between xenograft tumour lines

Since there was no significant passage effect on SGD, we calculated an average SGD for each

tumour line (Table 7). These values are plotted in Figure 10. An ANOVA of the average SGD

revealed no significant difference between xenograft lines (p=0.15). Thus, our xenograft model

cannot quantitatively distinguish between patient tumours based on xenograft radiosensitivity.

Figure 10. Average SGD for each line. Line 6 had the shortest SGD, while Line 3 had the longest SGD. Lines 2, 4, 5 and 7 had intermediate SGDs. The average SGD calculated for Line 8 is questionable (discussed in section 3.3)

Table 7. Average SGD by line. The SGD is normalized to the time required for control tumours to triple their original volume. Thus, SGD can be interpreted as the number of control tumour tripling times required for irradiated tumours to grow by the same amount.

Line P value

2 0.261

3 0.402

4 0.284

5 N/A

6 0.456

7 0.373

8 0.204

Line SGD (fold) Standard error

2 0.804 0.237

3 1.454 0.402

4 0.915 0.158

5 0.558 0.282

6 0.389 0.177

7 0.643 0.091

8 1.305 0.393

Table 6. Linear regression analysis for the effect of passage on SGD. None of the tests yielded significance. Thus, H0 cannot be rejected, and it is concluded that passage number does not affect SGD.

50

2.3. Aim 1 Discussion

There has been significant debate over the role of endothelial cell damage in determining

overall tumour radioresponse. To evaluate the suitability of using NOD/SCID xenografts as a

model of radiosensitivity, it was therefore necessary to evaluate the effect of radiation on the

tumour bed.

A study of tumour growth kinetics in xenograft Line 3 suggests that there was no strong tumour

bed effect at low doses of ionizing radiation. There was an apparent reduction in tumour

growth rate during repopulation after an 8Gy dose, however these kinetics were statistically

indistinguishable from those of tumours repopulating after a 4Gy dose. The difference in slope

between these two treatment arms was slight. Nevertheless, experiments were performed on

one xenograft line only, and further investigations in additional lines are required to conclude

that a TBE is absent entirely. Furthermore, it is possible that statistically significant changes in

slope might have been found with the inclusion of more mice in each arm of the study. For

these reasons, 4Gy was used as the standard dose for all subsequent xenograft radiation

studies. No significant change in slope during tumour repopulation was seen in any subsequent

xenograft growth curve (see Appendix A). This provides further evidence that 4Gy was an

appropriate dose for our model.

These results, while performed on a single xenograft line, are supported by published literature.

Originally, Garcia-Barros et al showed that tumours grown in apoptosis-resistant acid

sphingomyelinase (asmase-/-) deficient mice were more radioresistant than the same tumours

grown in wild-type mice, suggesting that microvascular damage was indeed a significant

51

determinant of tumour radioresponse.79 Gerweck et al subsequently challenged this conclusion

by demonstrating that tumours established in nude mice using a DNA-PKcs-/- tumour cell line

were more radiosensitive than tumours established from radioresistant DNA-PKcs+/+ cells. Since

relative radiosensitivity between the isogenic tumours was proportional to their intrinsic

radiosensitivity as measured in vitro, they concluded that intrinsic tumour cell radiosensitivity

was the major determinant of tumour radiation response.81 In a follow-up paper, the same

group reported a growth delay ratio between isogenic tumours in SCID mice that was not equal

to the ratio of their respective intrinsic radiosensitivies, suggesting that stromal damage may

contribute to the radiation response in some models.168 While this appeared to contradict

earlier observations that the TCD50 was not associated with a SCID phenotype,78 it may be that

endothelial cell damage conferred by the SCID mutation increases tumour growth delay but

does not affect tumour cure as measured by TCD50. However, a more recent study by Garcia-

Barros et al failed to show enhanced tumour growth delay in SCID mice compared to

SV129/C57BL/6 wild-type mice for either MCA/129 fibrosarcomas or B16 melanomas, calling

into question a causal relationship between irradiated SCID endothelium and enhanced tumour

growth delay. Building on their earlier results,80 this group maintained that the effect of stromal

damage on tumour response was accounted for by asmase-mediated endothelial cell apoptosis,

rather than impaired DNA damage repair.

Taken together, published findings suggest that macroscopic observations of a putative tumour

bed effect depend both on context (mouse strain, tumour cell type) and on the parameter used

to measure radiation response. If TCD50 is indeed independent of a TBE, we may justifiably use

NOD/SCID mice to measure the TIC frequency, since previous studies have shown a convincing

52

correlation between TCD50 and TIC frequency.101 While the effect of tumour bed radiation

damage on tumour growth delay remains controversial, our study focuses exclusively on a

comparative analysis of primary patient tumours. Thus, if growth delay is prolonged by a TBE,

this effect would likely be similar across all our xenograft models, negating a TBE as a

confounding factor in our comparisons. That is, the magnitude of the growth delay would not

reflect the true values, but the ranking of the models in order of relative radiosensitivity would

remain the same.

We sought to evaluate (a) the in vivo radiosensitivity of our xenograft tumours and (b) the

ability of our xenograft model to distinguish between the intrinsic radiosensitivities of multiple

patient tumours. To evaluate and quantify tumour radioresponse, we first modeled tumour

growth after irradiation as linear growth in the log scale. This estimation does not distinguish

between the plateau and regrowth phases in growth curve. Rather, the control and irradiated

arms share an intercept (volume at the time of radiation) yet differ in their slopes. The

significance of this change in slope was used as an initial test of radiosensitivity. By this method,

all passages showed a response to radiation with the exception of Line 6 passage 4. Passage 3

of the same line was marginally significant (p = 0.045). We therefore concluded that Line 6 was

the most radioresistant relative to other models in our panel.

We next sought to measure the growth delay (GD) and specific growth delay (SGD) for each

passage of each line. We reasoned that SGD measurements would provide both a descriptive

assessment of heterogeneity in our tumour panel and a means to quantify this heterogeneity.

The SGDs ranged from 0.4 to 1.5 fold relative to the control tripling time, with Line 6 and Line 3

53

at the radioresistant and radiosensitive ends of this spectrum, respectively (Figure 11). This

result was in agreement with our previous observation that Line 6 is relatively radioresistant.

Importantly, we noted that the initial tumour volumes (at the time of irradiation) varied across

passages in some lines, and that significant differences in starting volume impacted the GD and

SGD (See Line 8 in Table 5). It is possible that a larger starting volume contributes to a larger GD

by increasing the tripled volume (3V0) above a particular threshold. This threshold may

represent a point at which continued tumour growth is limited by nutrient and oxygen supply.

Since irradiated tumours face more endothelial cell damage than control tumours, this putative

“volume threshold” may be more pronounced in large irradiated tumours compared to

controls. In other words, a 3V0 of approximately 1000mm3 may impose a lesser burden on

irradiated tumours than a 3V0 of 1500mm3 due to differences in levels of hypoxia and nutrients

at the two volume endpoints. Furthermore, a 3V0 above the volume endpoint outlined in our

Animal Use Protocol (AUP) requires the mixed effect model to extrapolate past real data points,

thereby introducing error into the calculated SGD. Taken together, these observations suggest

that reliable quantification of SGD can only be obtained from passages with approximately

Figure 11. Spectrum of radiosensitivity among xenograft lines

Relatively resistant Intermediate Relatively sensitive

54

equal starting volumes. For this reason, the average SGD of Line 8 should be interpreted with

caution.

Figures 9-11 (p. 48-49, 53) portray the range of radiosensitivities among the seven lines used.

However, statistical testing revealed that differences in SGD among xenograft lines were non-

significant. Thus, SGD is not quantitatively capable of distinguishing between patient-derived

tumour lines. The reduced sensitivity of our model to quantitatively distinguish patient

tumours is in part a factor of the numbers of mice used. For economical reasons, mouse

numbers were limited such that the total number of mice remaining at the end of a xenograft

experiment would be approximately ten per group. In some experiments, fewer than ten mice

per group remained due to animal morbidity, tumour burden, or because gene expression

analyses required more mice to be sacrificed than originally estimated. Such cases resulted in

truncated growth curves and missing data points that may have introduced significant error

into the linear regression. With these mouse numbers, growth delays such as those seen in Line

6 were too small to detect statistical significance. For those lines that did show statistically

significant growth delays, limited mouse numbers reduced the ability of the model to

distinguish between the average growth delays of each line.

The methods used to irradiate xenografts may also account for the reduced sensitivity of our

model to discriminate between tumours. Because our second aim was to interrogate TIC

enrichment at a distinct point after irradiation, we chose a single-dose rather than fractionated

treatment regimen, and we used a dose rate of 3.07 Gy/min. Studies have demonstrated that

the use of low-dose rate irradiation (0.01-0.05 Gy/min) in cell lines increases the spread of

55

clonogenic survival measurements, thereby enhancing the ability to detect slight differences in

radiosensitivity.169 Similarly, in a study of five human cell lines, a series of 5 and 6 fractions of

2Gy gave a more precise and reproducible measure of radiosensitivity than a single dose at

2Gy.170 These methods incorporate the influence of cellular repair of sublethal damage into the

radiosensitivity measurements, and are therefore better able to discriminate between

clonogenic survival curves. While it remains unclear whether low-dose rate or fractionated

irradiation can increase the spread of SGD measurements, future efforts may consider

incorporating these considerations into our radiosensitivity model.

Ultimately, the biological characteristics of each patient tumour may manifest in an insufficient

effect size to detect such differences, or the models may indeed all have the same degree of

radiosensitivity. Additional experiments are required to determine whether the effect size

between patient tumours is significant. Despite the resulting lack of statistical significance, our

mathematical model is robustly capable of detecting a radiation-induced growth delay within

each experiment. Thus, while our xenograft model may be less well-suited for studies of patient

heterogeneity (i.e. distinct patient subgroups classified by therapeutic response), it remains a

valuable tool for preclinical studies investigating the effects of radiation on tumour growth both

alone and in combination with other agents. This strength alone renders our model a valuable

contribution to the field of preclinical radiation studies of EAC, since few such models currently

exist. Furthermore, we utilize our model for precisely this purpose in Aim 3, and demonstrate

that Hedgehog inhibitors in combination with radiation may have an important future role in

the clinical management of EAC.

56

Chapter 3: Enrichment of tumourigenic and clonogenic cells through radiotherapy (Aim 2)

3. AIM 2: To determine whether radiation therapy enriches for tumourigenic and/or clonogenic

cells in primary patient xenograft tumours.

The background and rationale of Aim 2 are discussed in Sections 1.8 - 1.10

3.1. Aim 2 Methods

3.1.1. Limiting dilution assay

Limiting dilution assays (LDAs) were performed on control and treated mice in order to

determine changes in TIC frequency with radiation. In a typical radiation response curve, the

average volume of irradiated tumours plateaus after treatment (Figure 12). Interpreted within

the context of a TIC model, this plateau theoretically reflects an equilibrium between cell death

in the non-TIC population and the survival and/or proliferation of the much smaller TIC fraction.

Thus, we hypothesize that the point of maximum TIC enrichment occurs at the end of this

plateau phase, immediately prior to repopulation (Figure 12: point A’). This point of maximum

TIC enrichment was chosen as a target for the first LDA on the irradiated group. The second LDA

was performed after repopulation, when the tumours approached their maximum average

volume (Figure 12: B’). The clonogenic assays were performed at the same points along the

growth curve.

It is unclear whether the TIC frequency is dependent on tumour volume. Thus, to control for

this possibility, LDAs and clonogenic assays were performed on irradiated and non-irradiated

groups at the same volume (different time points). Thus, point A in Figure 12 represents the

control LDA used as a baseline for comparison with A’. Similarly, point B represents the control

57

LDA used as a baseline for comparison with B’. We hypothesized that the TIC frequency at A’

would be greater than at both A and B’.

Figure 13 is a schematic representation of the LDA methods. When average tumour volume

reached the target for each LDA, a minimum of three mice per group were sacrificed. The

tumours were excised, mechanically minced and enzymatically digested into single cell

suspension using 300 units/mL collagenase, 100 units/mL hyaluronidase (Stemcell

Technologies) and 125 units/mL deoxyribonuclease I (Worthington) in DMEM or Media 199

supplemented with penicillin and streptomycin. Cell suspensions were incubated with 1-2 mL of

red blood cell lysis buffer, rinsed with and re-suspended in phosphate buffered saline (PBS)

with 2% fetal bovine serum (FBS).

The percentage of stromal cells varied between tumour lines. It was conceivable that stromal

percentage could also vary between different treatment groups within a tumour line. Since

LDAs and clonogenic assays rely on a cell count that accurately reflects the number of epithelial

cells used, it was necessary to deplete cell suspensions of any mouse-derived stromal cells.

H2Kd is the major histocompatibility complex for NOD/SCID mice, and labels all nucleated cells

A

B’ B

A’

Figure 12. Relevant xenograft tumour volume points used for LDAs and clonogenic assays. A= control LDA1; A’= radiation LDA1; B= control LDA2; B’= radiation LDA2.

58

in the mouse. Since red blood cells were lysed in the previous step, it was reasonable to assume

that all remaining mouse cells expressed H2Kd. Thus, cell suspensions were incubated with

biotinylated rat anti-mouse H2Kd antibody (BD Biosciences) (1:2000 dilution), followed by anti-

biotin microbeads (Miltenyi Biotec) (1:10). Cells were then passed through a magnetic column

to deplete the effluent of all magnetically-labeled H2K+ cells. Viable cells were counted using

trypan blue dye exclusion.

Successful H2K depletion was confirmed by flow cytometry. Effluent cells were stained with

mouse IgG blocking antibody, followed by streptavidin-PE-Cy7, anti-EpCAM-APC, and DAPI

stain. Cells stained with all antibodies minus one (“fluorescence minus one”) were used as

gating controls. Data were collected on a BD LSRII Analytical Flow Cytometer and analyzed using

FlowJo software. The percentage of cells negative for H2K was recorded for future back-

calculations of actual number of tumour cells injected. Sample flow cytometric plots of pre-

depletion cell suspensions, column-adhering cell suspensions and effluent suspensions are

presented in Appendix B.

The remaining effluent tumour cells were re-suspended in MatrigelTM (BD Biosciences) at ten-

fold dilutions of 1x105, 1x104 and 1x103 cells/100uL. Each cell suspension was injected

subcutaneously into the right and left hind flanks of five NOD/SCID mice in 100uL injection

volumes. Thus, a total of ten injections in five mice were performed for each cell concentration.

Mice were monitored for tumour formation for up to six months. If a tumour on one flank

reached the volume limit or developed an ulcer before the other flank could be scored, the

mouse was sacrificed and the injection on the second flank was removed from the analysis. The

59

fraction of injections at each cell dose that gave rise to tumours was recorded. Extreme Limiting

Dilution Analysis (ELDA) software (available online at http://bioinf.wehi.edu.au/software/elda/)

was used to calculate the TIC fraction.

Figure 13. Xenograft limiting dilution assay. Once tumours reach the targeted volume, 2-3 mice are sacrificed, the tumours are excised and digested into single-cell suspension. Mouse-derived cells are magnetically depleted and the remaining tumour cells are injected at discrete doses into 3 cohorts of 5 mice each. Each mouse receives 2 injections for a total of 10 injections per dose.

3.1.2. Clonogenic assay Recent evidence suggests that long term culturing in serum promotes cellular differentiation.65

Furthermore, it is well documented that stem cells are highly sensitive to O2. In particular, high

oxygen tension increases the differentiation rate of certain stem cell populations.171 It is

possible that limited energy supply under hypoxia allows only strict DNA replication (otherwise

known as self-renewal) rather than activation of differentiation programs. Since the purpose of

this assay was to measure the fraction of cells possessing the stem-like property of

clonogenicity, two culturing conditions were of interest: classical DMEM supplemented with

fetal bovine serum (FBS) and cultured in ambient air, as well as a defined serum-free media

(DSFM) cultured at physiological oxygen levels (2% O2). Initially, all clonogenic assays were

60

performed in both conditions. After repeated observation that the latter better supported

colony growth (data not shown), assays were performed strictly under serum-free, low oxygen

conditions.

DSFM was prepared using the following reagents: DMEM/F12+Glutamax (Invitrogen), 1X B27

minus Vitamin A (Invitrogen), 1X non-essential amino acids (Gibco), lipids (Sigma), 4ug/mL

heparin (Sigma), 1mM N-Acetyl-cysteine (Sigma), 10mM Hepes (Sigma), 10ng/mL FGF

(Invitrogen), 10ng/mL EGF (Invitrogen), 100units/mL and 100ug/mL of penicillin and

streptomycin, respectively (Invitrogen).

Cells were prepared for clonogenic assays following the methods described above for the LDA.

After magnetic depletion of H2K+ cells and verification by FACS, cells were re-suspended in

either DSFM or DMEM in three discrete cell dilutions. Cells were typically seeded at densities of

5x104, 1x104 and 1x103 cells per well, however these densities varied with each experiment,

depending on the number of available effluent cells. Cells were plated on 24-well plates coated

with rat tail collagen (BD Biosciences) at 5ug/cm2. Media was changed 3-4 days after initial

plating, and cells remained in culture for up to four weeks, or until visible colonies formed.

Colonies containing >50 cells were counted under a microscope.

3.2. Aim 2 Results

3.2.1. Radiation may enrich the TIC fraction prior to repopulation in some EAC tumours

Limiting dilution assays were performed on Lines 2, 3 and 4 at passages 10, 4 and 5,

respectively. Lines 3 and 4 showed a >10-fold and >6-fold enrichment of the TIC fraction at

61

point A’ (day 73 and day 64 for Lines 3 and 4, respectively) (Figure 14A and Figure 15A). These

time points roughly corresponded to periods late in the plateau phase of the xenograft growth

curves, prior to repopulation of the irradiated tumours.

Results from the first LDA performed on Line 2 were not as conclusive. While the irradiated

tumours presented a TIC frequency of 1 in 17,790 cells, the control group yielded non-

interpretable results, since tumours grew more frequently at the intermediate dose than at the

highest dose (Figure 16A). Thus, radiation-induced TIC enrichment could not be evaluated in

Line 2.

In Line 3, the second LDA (performed at the volume endpoints for each group) yielded non-

interpretable results in both the control and radiation groups (Figure 14B). In Line 4, the

enriched TIC frequency was sustained even after tumour repopulation (point B’). Curiously, the

TIC frequency in the control arm increased almost 5-fold at point B (Figure 15B). In Line 2, point

B yielded a TIC frequency of 1 in 3,328 control cells (Figure 16B). This was compared to a post-

repopulation TIC frequency of 1 in 9,650 irradiated cells (point B’). The difference between

these two frequencies was insignificant. In addition, the apparent enrichment within the

irradiated tumours before and after repopulation (from 1:17,790 to 1:9,650) was insignificant.

It therefore appears that radiation may enrich the tumourigenic cell population immediately

after treatment in some EAC tumours. It remains inconclusive whether the TIC frequency

returns to baseline levels after tumour repopulation. Results from the second LDA in Lines 2

and 4 suggest that this might not be the case.

62

Control LDA time point 1

Dose Tested Response

99600 10 8

9960 10 6

996 8 2

Confidence Intervals

Lower Estimate Upper

63298 30676 14867

Radiation LDA time point 1

Dose Tested Response

95000 7 7

9500 9 8

950 10 5

Confidence Intervals

Lower Estimate Upper

6341 2928 1352

A

Control LDA time point 1

Dose Tested Response

96000 8 5

9600 10 7

960 10 2

Confidence Intervals

Lower Estimate Upper

Error Error Error

Radiation LDA time point 1

Dose Tested Response

95800 10 2

9580 8 3

958 9 2

Confidence Intervals

Lower Estimate Upper

Error Error Error

B

Control LDA1

↓ RT ↓

↑

Radiation LDA1

0

500

1000

1500

2000

0 20 40 60 80 100 120

Tum

ou

r V

olu

me

(mm

^3)

Days since Implantation

Line 3 (P4): 1st LDA

Control

Radiation

Control LDA2

↓

RT ↓

Radiation LDA2

↓

0

500

1000

1500

2000

0 20 40 60 80 100 120

Tum

ou

r V

olu

me

(mm

^3)

Days since Implantation

Line 3 (P4): 2nd LDA

Control

Radiation

Figure 14. Limiting dilution assay Line 3 passage 4. Tumours were irradiated at an average volume of 470mm3. (A) LDA1,

corresponding to points A and A’ in Figure 5. (B) LDA2, corresponding to points B and B’ in Figure 5.

63

Control LDA time point 1

Dose Tested Response

67541 9 5

13508 10 7

1351 10 3

Confidence Intervals

Lower Estimate Upper

60719 31968 16831

Radiation LDA time point 1

Dose Tested Response

88182 9 8

8818 10 10

882 10 7

Confidence Intervals

Lower Estimate Upper

9951 4871 2384

A

Control LDA time point 2

Dose Tested Response

100000 10 9

10000 10 9

1000 10 5

Confidence Intervals

Lower Estimate Upper

15915 7113 3179

Radiation LDA time point 2

Dose Tested Response

98500 9 9

9850 10 7

985 10 6

Confidence Intervals

Lower Estimate Upper

9110 4559 2282

B

Control LDA1

↓ RT ↓

↑ Radiation

LDA1

0

500

1000

1500

2000

0 20 40 60 80 100 120

Tum

ou

r vo

lum

e (m

m^3

)

Days since Implantation

Line 4 (P5): 1st LDA

Control

Radiation

Control LDA2 →

RT ↓

Radiation LDA2

←

0

500

1000

1500

2000

0 20 40 60 80 100 120

Tum

ou

r vo

lum

e (m

m^3

)

Days since Implantation

Line 4 (P5): 2nd LDA

Control

Radiation

Figure 15. Limiting dilution assay Line 4 passage 5. Tumours were irradiated at an average volume of 514mm3. (A) LDA1,

corresponding to points A and A’ in Figure 5. (B) LDA2, corresponding to points B and B’ in Figure 5.

64

Control LDA time point 1

Dose Tested Response

47440 10 5

9488 10 10

948 10 5

Confidence Intervals

Lower Estimate Upper

Error Error Error

Radiation LDA time point 1

Dose Tested Response

32016 10 7

8000 10 5

1600 10 2

Confidence Intervals

Lower Estimate Upper

32266 17790 9809

Control LDA2

↓ RT ↓

↑ Radiation

LDA1 0

500

1000

1500

2000

2500

0 20 40 60 80 100

Tum

ou

r V

olu

me

(mm

^3)

Days since Implantation

Line 2 (P10): 1st LDA

Control

Radiation

Control LDA time point 1

Dose Tested Response

31600 10 10

7900 10 8

1580 10 6

Confidence Intervals

Lower Estimate Upper

6273 3328 1766

Radiation LDA time point 1

Dose Tested Response

33400 10 10

8350 10 9

1670 10 6

Confidence Intervals

Lower Estimate Upper

17467 9650 5331

Control LDA2

↓

RT ↓

Radiation LDA2

←

0

500

1000

1500

2000

2500

0 20 40 60 80 100

Tum

ou

r V

olu

me

(mm

^3)

Days since Implantation

Line 2 (P10): 2nd LDA

Control

Radiation

A

B

Figure 16. Limiting dilution assay Line 2 passage 10. Tumours were irradiated at an average volume of 480mm3. (A) LDA1,

corresponding to points A and A’ in Figure 5. (B) LDA2, corresponding to points B and B’ in Figure 5.

65

3.2.2. The ability of radiation to enrich the clonogenic fraction was not demonstrated

Ex vivo clonogenic assays were performed on xenograft Line 2 at passage 10, Line 3 at passage 6

and Line 4 at passages 6 and 7. Results from the clonogenic assays are summarized in Table 8.

All but one passage failed to yield clonogenicity results for various reasons. The clonogenic

assays performed on Line 2 passage 10 were lost to fungal infection in the tissue culture plates.

Cells derived from Line 4 did not adapt well to tissue culture conditions. Of the small

percentage of cells that adhered, very few formed colonies and none formed colonies larger

than 50 cells. In addition, the clonogenic assay performed on Line 4 passage 7 was lost to

bacterial contamination. An in vivo streptococcus pneumoniae infection was subsequently

found in several mice harbouring tumours from Line 4. Thus, it is likely that cells from Line 4

were unable to adhere to and proliferate in tissue culture condition due to a mouse-derived

infection.

Line Passage Control frequency at Point A (%)

Radiation frequency at Point A’ (%)

Control frequency at Point B (%)

Radiation frequency at Point B’ (%)

2 10 Fungal infection Fungal infection No colonies No colonies 3 6 0.332±0.016 0.097±0.006 0.026±0.001 0.005±0.001 4 6 No colonies Bacterial infection No colonies No colonies

7 Bacterial infection Bacterial infection Bacterial infection Bacterial infection Table 8. Clonogenic assays. Points A, A’, B and B’ correspond to points on the xenograft growth curve as indicated in Figure 11.

Clonogenic frequencies were obtained in Line 3 passage 6 only (Figure 17). The clonogenic

frequency of control tumours was 0.332%±0.016 at point A and 0.026%±0.001 at point B. The

corresponding clonogenic frequencies in irradiated tumours were 0.097%±0.006 and

0.005%±0.001 at points A’ and B’ respectively. Thus, radiation did not appear to enrich the

clonogenic cell population in Line 3. Rather, the clonogenicity of irradiated tumours appeared

to be reduced relative to controls at both points on the xenograft growth curve.

66

3.3. Aim 2 Discussion

TICs have been identified in many solid tumours, but no study to date has identified this

putatively radioresistant cell population in EAC. The results reported here from LDAs on two

radiosensitive xenograft lines suggest that radiation enriches for tumourigenic cells prior to

tumour repopulation. To our knowledge, this is the first demonstration of enhanced TIC

frequencies during the radiation-induced plateau period of the growth curve. However, in

several of the LDAs performed the frequency of “negative events” was <37%, indicating that a

more accurate calculation of TIC frequency would have been obtained by extending the LDA to

lower cell doses. Thus additional studies are required to verify these results in a larger number

of samples, and using limiting dilution assays that include a lower cell dose than the minimum

dose used here.

The translational significance of radiation-induced TIC enrichment is non-trivial, particularly in

EAC. Clinical management of EAC relies heavily on chemoradiotherapy, yet EAC tumours are

notoriously resistant to cure by these methods. Residual TICs left after radiotherapy may be the

A: 0.33%

↓

B: 0.03%

↓

RT

↓

↑ A':

0.1%

B': 0.01%

↓

0

500

1000

1500

2000

0 20 40 60 80 100 120

Tum

ou

r V

olu

me

(mm

^3)

Days since Implantation

Line 3 (P4)

Control

Radiation

Figure 17. Clonogenic assay Line 3. Tumour were irradiated at an average volume of 470mm3.

67

drivers of disease progression, emphasizing the need to integrate TIC-targeted agents into

current treatment regimens. These preliminary results warrant further experimentation in

additional xenograft lines.

We hypothesized that the tumourigenic capacity of irradiated xenografts would resemble that

of non-irradiated xenografts after tumour repopulation (points B and B’ in Figure 12). We

proposed that at this point, the TICs enriched after irradiation would have undergone multiple

asymmetric cell divisions in order to repopulate the tumour with transit amplifying and

terminally-differentiated cells. However, only Line 4 yielded interpretable results at the second

LDA, and these did not indicate a return to baseline TIC levels in the irradiated tumours. Rather,

irradiated tumours sustained a high tumourigenic capacity, and control tumours developed

enhanced tumourigenicity. It is unclear what may have contributed to these observations. It is

possible that radiation selected for or generated radioresistant clones with a higher

tumourigenic capacity. If conferred with a survival advantage, these cells could contribute

disproportionately to the repopulated tumour. However, since the TIC frequency also increased

in the control arm, this is an unlikely explanation. Rather, other technical and/or biological

factors may be accountable, including the selection of poorly-representative xenograft tumours

for LDAs, inaccurate cell counts, or an intrinsic increase in TIC frequency as the tumour grows

(i.e. size-associated changes in the TIC frequency).

Of particular interest is the reversal in LDA response rates at intermediate and high doses in

Lines 2 and 3. In these cases, more tumours grew at intermediate or even low doses than at the

highest dose. Similar results have been obtained by separate work in our lab using

68

chemotherapy to enrich for TICs. We hypothesize that an inhibitory effect may be present at

the higher doses in some tumours. This inhibitory molecule could derive from residual

contaminating mouse cells, such as a secreted matrix or inflammatory factor, or from the

tumour cells themselves. In this case, the lower cell doses would have a diluted concentration

of the inhibitor and would better support tumour growth. Once again, further studies are

required to elucidate the mechanisms involved in this dose-response inversion.

We sought to determine whether the clonogenic frequency increases after irradiation. We

reasoned that the TIC population is contained within the clonogenic fraction. Thus, TIC

enrichment might yield clonogen enrichment. Of the clonogenic assays performed, only one

yielded interpretable results. These results suggest that the clonogenic frequency is reduced

both after irradiation (Figure 12: point A’) and at the tumour volume endpoints (Figure 12:

points B and B’). The reliability of these results is questionable. While TICs may be contained

within the clonogenic population, it is likely that the non-TIC clonogenic compartment,

consisting of transit amplifying and differentiated cells, is radiosensitive. In fact, the high

proliferative capacity of these cells suggests that substantial cell kill should be expected within

the clonogenic population. To truly observe TIC enrichment with an ex vivo clonogenic assay,

one would likely have to perform a re-plating assay in which colonies formed in the first assay

are seeded again in a second passage. By testing the self-renewal of clonogens, a re-plating

assay introduces an extra functional test and may therefore be more appropriate for future TIC

studies.

69

Chapter 4: Hedgehog pathway in the response to irradiation

4. AIM 3: To study the role of Hedgehog pathway activity in the radiation-response of xenograft

tumours.

4.1. Aim 3 Methods

4.1.1. PCR primer design

To accomplish the objectives of Aim 3, it was necessary that each primer be designed twice,

once for the mouse transcript and once for the human transcript. mRNA sequences were

obtained from NCBI for the following Hedgehog-related genes: SHH and IHH (ligands), PTCH1

and PTCH2 (receptors), GLI1 and SMO (effectors). In addition, primers were designed for a

panel of eight potential housekeeping genes: GAPDH, ACTB, HPRT1, GUSB, HSP90AB1, YWHAZ,

L32 and RPLP0. Mouse and human sequences for each gene were aligned using NCBI blast, and

regions of low homology were identified. The online Primer3 software was used to find short

sequences within these regions that satisfy specified requirements of primer size (20-22bp),

product size (90-150bp) melting temperature (57-63 degrees C) and %GC (40-60%). Output

sequences were then blasted against the whole genome of each species to ensure species-

specificity. Sequence synthesis was outsourced (Integrated DNA Technologies). The species

specificity of each synthesized primer was tested by RT-PCR on a sample of normal mouse

esophagus and the human EAC cell line, OE33. Primers that cross-amplified in both species, or

that produced doublet dissociation curves were redesigned. The primer sequences used are

listed in Table 9.

70

Transcript Human Mouse

SHH F: cagaggagtctctgcactacga R: cgtagtacacccagtcgaagc

F: cactatgagggtcgagcagtg R: gtggatgtgagctttggattc

IHH F: acaaagcatgggacactggt R: catgccaagctgtgaaagagt

F: gcattgctctgtcaagtctga R: tctcctggctttacagctgac

GLI1 F: acacatatggacctggctttg R: ctgccctatgtgaagccctat

F: tcctctcattccacaggacag R: ctggtatgggagttcctggtt

PTCH1 F: caccgacacacacgacaatac R: gcatggtaatctgcgtttcat

F: ctgctgggtgtactgatgctt R: agcagaaccagtccattgaga

PTCH2 F: tcttctacatggggctgacc R: gtatttgtcgtgcagccattc

F: ctccgctcaggtcattcagat R: ggaggcaaaatggtgactaca

SMO F:tgagtgggatttgttttgtgg R: ctcctcggatgaggaagtagc

F: agattgtttgccgagcagat R: ccacgaaccagactactccag

ACTB F: ttgctatccaggctgtgctat R: agggcatacccctcgtagat

F: cgttgacatccgtaaagacctc R: gtgctaggagccagagcagta

HPRT1 F: ctagttctgtggccatctgct R: gcccaaagggaactgatagtc

F: ctgtggccatctgcctagtaa R: gacaatctaccagagggtaggc

YWHAZ F: aatgcttcacaagcagagagc R: tgcttgttgtgactgatcgac

F: ctgcctacatattggtgtgtg R: tttgtgtcacagcctcacaag

HSP90AB1 F:gtcttctgctggaggttcctt R:ctttgacccgcctctcttcta

F:gaggcagacaaaaacgacaaa R:tgagaaaccagaggagagcag

GAPDH F:gacctgccgtctagaaaaacc R: accacctggtgctcagtgtag

F: aaattcaacggcacagtcaag R: tttgatgttagtggggtctcg

L32 F: atcgctcacaatgtttcctcc R: aaacagaaaacgtgcacatga

F: gccattgtagaaagagcagca R: tgcacacaagccatctactca

GUSB F: gactgcagcggtctgtacttc R: aaagacgcacttccaacttga

F: gtcaacttcaggttcccagtg R: tcccgataggaagggtgtagt

RPLP0 F: gaaactctgcattctcgcttc R: actcgtttgtacccgttgatg

F: caaagctgaagcaaaggaaga R: attaagcaggctgacttggttg

Table 9. PCR primers designed for Hedgehog gene expression analysis with internal controls

4.1.2. Housekeeping gene selection

RT-PCR requires the use of internal reference genes whose expression levels do not change

with experimental intervention, in this case, irradiation. Multiple studies have demonstrated

that expression levels of commonly-used housekeeping genes can fluctuate under cellular

stress conditions such as irradiation, hypoxia, induced differentiation and passaging.172–174 To

ensure selection of proper endogenous controls, eight housekeeping genes were evaluated for

their stable expression following irradiation in three separate xenograft tumour lines (lines 2, 3

and 4).

71

Primers were designed for the following housekeeping genes: ACTB, GAPDH, HPRT1, GUSB,

HSP90AB1, YWHAZ, RPLP0 and L32. Irradiated and control samples from Lines 2, 3 and 4, frozen

in OCT and stored at -80°C, were used to test the housekeeping genes. The results of these

experiments are discussed below.

4.1.3. Hedgehog gene expression

The time points interrogated for Hh expression varied between xenograft experiments, and

were chosen based on the number of available animals in each treatment group. Generally, the

time points of interest aligned with the volume points used for the in vivo TIC and ex vivo

clonogenic studies. Thus, at least one time point during the plateau phase of the irradiated

growth curve was chosen, with a volume-matched control. Volume endpoints (after tumour cell

repopulation) were also chosen, with volume-matched controls. In addition, several time points

immediately after irradiation (24 hours, 48 hours, 1 week) were added based on sufficient

numbers of mice. For the first three experiments (Lines 2, 3 and 4), 1-2 mice were sacrificed at

each time point. For subsequent experiments (lines 6, 7 and 8), 2-3 mice were sacrificed at each

time point.

Mice were sacrificed and the tumours were immediately excised and frozen at -80°C in

optimized cutting temperature (OCT) compound (Sakura Finetek). 10µm-thick sections were cut

from frozen specimens using a cryostat. Additional sections were cut and stained with

hematoxylin and eosin. A pathologist assessed the percentage of tumour epithelium in each

sample.

72

RNA was extracted from frozen tissue sections using the RNeasy Mini Kit (Qiagen) with on-

column DNA digestion. RNA concentration was measured using a Nanodrop® ND-1000

Spectrophotometer (Thermo Scientific). RNA quality was evaluated using the Agilent 2100

Bioanalyzer, which provides a RNA Integrity Number (RIN) as a measure of RNA degradation. As

RNA degrades, the ratio of the 28S to 18S ribosomal RNA decreases. The RIN is an integer

between 1 and 10 that accounts for both the 28S:18S ratio, as well as the entire electrophoretic

trace. RNA samples with RINs above 8 were used for qRT-PCR experiments, although most

samples had RINs above 9.

RNA was reverse-transcribed using the iScript™ cDNA Synthesis Kit (Bio Rad). Depending on the

amount of available RNA in each experiment, 2-10ng of cDNA from each tumour sample was

combined with RT2 SYBR® Green ROX™ qPCR Mastermix (Qiagen) and the primer solution for a

total volume of 10uL per well. Each sample was run in triplicate wells. PCR was performed on an

Applied Biosystems 7900HT Fast Real-Time PCR System (Life Technologies). Amplification data

was analyzed using Sequence Detection Systems (SDS) software version 2.3. For each sample,

Hh gene expression was normalized to the geometric mean of the housekeeping genes (∆Ct).

The ∆∆Ct was calculated as the difference between the ∆Ct of each irradiated sample and the

∆Ct of its volume-matched control sample. The fold difference in expression was calculated as

fold difference = 2-∆∆Ct.

73

4.1.4. 5E1 validation and toxicity study

The ability of Hedgehog inhibition to radiosensitize xenograft tumours was assessed by in vivo

administration of 5E1, an antibody for the biologically-active N-terminus of SHH.175 5E1 was

cultured from a hybridoma obtained from the laboratory of Dr. Thomas Jessell in New York.

5E1 toxicity in NOD/SCID mice was evaluated at three doses: 10mg/kg, 20mg/kg and 30mg/kg.

This range was chosen based on previous reports, as well as the quantity of antibody obtained

from the hybridoma culture. Three to five mice were each given a single intraperitoneal

injection of 5E1 antibody. While the proposed experiment required weekly dosing, there was

insufficient antibody obtained from culture to perform multiple dosing in the toxicity analyses.

Mice were monitored for six weeks. No mice died, however mice that received the highest dose

showed an average 10.7% weight loss. Thus, 20mg/kg was selected for the experiment.

The 5E1 antibody was validated by the following method: two tumour-bearing mice were

injected with 5E1 at each of the three doses listed above. In addition, two tumour-bearing mice

were injected with a PBS control. One mouse from each of the 5E1 groups and one control

mouse were sacrificed 24 hours after injection and the remaining mice were sacrificed 1 week

after 5E1 (or PBS) administration. The tumours were excised and RNA was extracted by the

same methods described in Section 4.1.3. The expression levels of SHH, IHH, GLI1, PTCH1,

PTCH2 and SMO were evaluated by qRT-PCR. ACTB was used as a single housekeeping gene.

While there was no detectable change in expression levels in the human tumour cells after 5E1

administration, mouse GLI1 and mouse PTCH1 were significantly downregulated at 24 hours

and 1 week after 5E1 administration at all three doses (Figure 18).

74

4.1.5. In vivo Hh inhibition in a xenograft model

Passage 6 of the primary human xenograft tumour line 7 was expanded into 80 NOD/SCID mice.

The implantation procedure followed that previously described. Mice were monitored for two

weeks, at which point tumours were palpable. Biweekly tumour measurements were made,

and when tumours reached an average volume of 260mm3, the mice were randomly sorted into

5 groups: radiation control (0Gy), 5E1 control (intraperitoneal (i.p.) injection of mouse

polyclonal IgG), 5E1 alone (20mg/kg), radiation alone (4Gy), and combined 5E1+radiation

(20mg/kg, 4Gy). An IgG group was included to control for the effects of off-target 5E1 antibody

binding. Mice receiving 5E1 were given a single i.p. injection of the inhibitor. Mice in the IgG

group received a single injection of IgG at the same concentration and volume as the 5E1

injections. 24 hours later, mice in the radiation and 5E1+radiation groups received 4Gy of

precision irradiation to the tumour-bearing leg. Biweekly measurements of tumour volume

0

0.05

0.1

0.15

0.2

0.25

%H

ou

seke

epin

g ge

ne

exp

ress

ion

H-SHH H-IHH H-GLI1 H-PTCH1 H-PTCH2 H-SMO

Human primers

Mouse primers

Figure 18. 5E1 validation. Human and mouse primers were used to detect expression of pathway genes in xenograft tumours of Line 7 passage 5 after 10, 20 or 30mg/kg 5E1. Mice were sacrificed at 1 and 7 days after 5E1 injection to measure sustainability of Hh inhibition.

75

were made until tumours reached the volume endpoint, or until the average tumour volume of

each group tripled in value from the time of radiation.

4.1.6. Statistical analysis

To determine whether the expression of Hedgehog transcripts fluctuated significantly after

irradiation, we took the natural logarithm of the fold difference in expression between

irradiated and control samples. The mean fold difference of the two to three samples per time

point after irradiation was calculated. A z-test was used to test the difference of this mean from

zero ( 0)ln(:0 foldH ).

4.2. Aim 3 Results

4.2.1. ACTB, HPRT1, HSP90AB1 and YWHAZ are appropriate housekeeping genes for EAC

radiation studies

Housekeeping gene (HKG) stability was evaluated on control and irradiated tumours from Lines

2, 3 and 4. The ∆Ct between each irradiated tumour sample and a baseline control (non-

irradiated) sample was calculated for all candidate genes. In addition, the ∆Ct between a

control sample at the volume endpoint and a baseline control sample was calculated in order to

determine if housekeeping gene expression was stable across volume changes within the

control arm. Gene expression stability was evaluated by plotting the ∆Ct values for each cDNA

sample across all candidate genes (Figure 19). If all candidate housekeeping genes were stably

expressed after irradiation (or after substantial tumour growth), the ∆Ct for one xenograft

sample would be constant, represented by a line with a slope of 1. This constant ∆Ct would

simply represent the difference in cDNA concentration of the irradiated and control sample.

76

Any housekeeping gene for which the ∆Ct deviated from this horizontal line would therefore be

interpreted to have unstable expression after irradiation.

Each of the eight candidate housekeeping genes was evaluated at multiple time points after

irradiation in three separate xenograft tumour lines (lines 2, 3 and 4). Most samples displayed

an approximately linear trend with a slope of zero (Figure 19). A line of best fit was plotted for

each sample, and each housekeeping gene was scored based on the number of times it fell

significantly above or below this line (Table 10). With the three highest scores of 10, 10 and 9,

GUSB, L32 and RPLP0 most frequently deviated from the estimated line and were immediately

rejected. GAPDH, with a score of 9, was retained for subsequent analysis because of its wide

use in published literature. With the remaining candidate genes, different combinations of

three were used to calculate geometric means for each sample. A previously rejected

housekeeping gene, RPLP0 was used as surrogate “gene of interest” and its expression levels in

each sample were normalized using each of the putative geometric means. These geometric

means were evaluated for their ability to “flatten” the RPLP0 expression curve (Figure 20).

Ultimately, two combinations of housekeeping genes were selected as suitable endogenous

controls: HPRT1/HSP90AB1/YWHAZ, and ACTB/HSP90AB1/YWHAZ. Interestingly, recent work

from another group within our institution has demonstrated that the former combination of

HKGs is stably expressed under hypoxia (personal communication, M. Koritzinsky). However,

while this combination is most stably expressed in the three tumour lines tested (lines 2, 3, 4),

subsequent tumour lines used for gene expression experiments (lines 6, 7, 8) showed variable

levels of HPRT1 expression. Thus, ACTB, HSP90AB1 and YWHAZ were chosen as HKGs in

subsequent experiments.

77

Figu

re

19:

Ho

use

keep

ing

gen

e (H

KG

) ra

dia

tio

n

stab

ility

in

3

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Eac

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ine

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∆C

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etw

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ion

are

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ross

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es w

ith

in a

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, th

e ∆

Ct

bet

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s sh

ou

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78

Line Species Sample ACTB GAPDH GUSB HPRT L32 RPLP0 HSP90 YWHAZ

2

Human C/27d X X X X X

R/6hr X X

R/1d X X

R/3d X X X X

R/9d X X X

Mouse C /27d X X

R/6hr X X X X

R/1d X X X X

R/3d X X X X

3 R/9d X X X

Human C/33d X

R/26d X X X

R/67d X X

Mouse C/33d X X X

R/26d X

R/67d X X

4 Human C/36d X

R/48hr X

R/7d X X

R/15d X X X

Mouse C/36d X

R/48hr X X

R/7d X X

R/15d X X

Total score 3 9 10 8 10 9 5 5

Table 10. Housekeeping gene radiation stability score. C= control; R= Radiation; d= days after irradiation. Each housekeeping gene was scored based on the number of times it fell above or below a perceived best-fit horizontal line for each sample. Those with the highest scores were deemed unstable before and after irradiation and therefore unsuitable as housekeeping genes. ACTB, GAPDH, HPRT, HSP90 and YWHAZ were used for subsequent analysis.

79

Figure 20. Selection of best housekeeping gene combination using radiation stability in 3 tumour lines. The geometric mean of 3 housekeeping genes is used to normalize the expression of a 4

th gene, RPLP0. The blue line is the difference of the green and red

lines, and its “flatness” represents the stability of the housekeeping gene combination. (A)The geometric mean of ACTB, HSP90AB1 and YWHAZ, and (B) of HPRT, HSP90AB1 and YWHAZ were selected as suitable for endogenous controls. (C) The geometric mean of GAPDH, HSP90AB1 and YWHAZ was rejected as a normalizer because it introduced kinks into the ∆Ct line (blue).

80

4.2.2. Hedgehog expression in EAC xenografts displays a predominantly epithelial-to-

mesenchymal paracrine mechanism (Aim 3a)

Histologic examination confirmed the presence of both epithelia and stroma in all samples used

for qRT-PCR. The percent epithelia of samples used at each time point is summarized in Table

11.

Reverse-transcription followed by quantitative real-time PCR of six xenograft tumour lines

revealed that the expression of Hh ligands, Sonic and Indian Hedgehog was detected

predominantly in the patient-derived tumour epithelium. In contrast, both of the pathway’s

receptors, PTCH1 and PTCH2, as well as the co-receptor/activator SMO and transcription factor

GLI1 were predominantly expressed in the host-derived stromal cells (Figure 21). Nevertheless,

low-level expression of PTCH1/2, GLI1 and SMO was detected in human cells in five of the six

lines (Line 3 excluded), suggesting that tumour cells may be capable of receiving and

Line (passage)

Treatment Time after

radiation (days) # Samples % tumour epithelium

6(4)

Control 0 2 60-70%

Control 7 3 70-80% Control 48 3 60-90%

Radiation 1 1 70% Radiation 15 2 60% Radiation 21 2 60-80% Radiation 48 3 80%

7(5)

Control 0 2 70%

Control 7 3 70-80% Control 22 3 80%

Radiation 1 2 70% Radiation 7 3 70-80% Radiation 14 3 70-80% Radiation 38 3 80%

8(4)

Control 0 3 80-90%

Control 7 3 90% Control 28 3 90%

Radiation 1 3 80-90% Radiation 7 3 80% Radiation 14 3 80-90% Radiation 35 3 80-90%

Table 11. Histologic quantification of percent tumour epithelium in xenograft samples

81

transducing the Hh signal. Similarly, slight expression of both ligands was detected in the

stroma of each line except for Lines 2 and 4, neither of which expressed SHH.

The expression levels of Hh transcripts varied among xenograft lines and between genes within

each line. Lines 2, 3 and 4 showed the highest overall expression, with transcript levels ranging

from 1-20% those of the housekeeping genes. In contrast, Lines 6, 7 and 8 displayed ligand and

receptor/effector expression levels less than 1% and 5% of housekeeping gene levels,

respectively (see y-axes in Figure 21).

82

0

0.2

0.4

0.6

0.8

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 6 (62325) passage 4

Human

Mouse

0

4

8

12

16

20

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 4 (60045) passage 7

Human

Mouse

A B

C D

E F

Hed

geh

og

exp

ress

ion

as

per

cen

t h

ou

seke

ep

ing

gen

es

Figure 21. Localization of Hh transcripts in epithelium versus stroma of untreated tumours from 6 xenograft lines. Note that each y-axis uses a scale appropriate for the expression levels of that xenograft line. (A-C) Each bar represents the average of 8-9 control (unirradiated) xenograft tumours taken from 3 different volumes along the growth curve. For qRT-PCR, 10ng of RNA were loaded in each well. (D-F) Each bar is the average of 3-6 tumours from 3 different volumes. 2-5ng of RNA were loaded in each well for qRT-PCR. Housekeeping genes used for A-D were ACTB, YWHAZ and HSP90AB1. HPRT1 was used in lieu of ACTB for E and F.

0

1

2

3

4

5

6

7

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 3 (61057) passage 6

Human

Mouse

0

0.5

1

1.5

2

2.5

3

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 7 (60745) passage 5

Human

Mouse

0

0.4

0.8

1.2

1.6

2

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 8 (63862) passage 4

Human

Mouse

0

5

10

15

20

25

SHH IHH PTCH1 PTCH2 GLI1 SMO

Line 2 (59046) passage 10

Human

Mouse

83

4.2.3. Radiation upregulates both autocrine and paracrine Hh expression in some EAC tumours

(Aim 3b)

Since our studies interrogate transcript levels in irradiated tumours compared to a reference of

non-irradiated controls, we use the terms “upregulation” and “downregulation” to refer to

positive and negative changes in gene expression relative to non-irradiated controls, making no

inference on the subsequent levels of protein in the cell. In each of the three xenograft lines

investigated, at least five transcripts were significantly upregulated in either mouse or human

cells after irradiation. The genes themselves, the cell type demonstrating the expression

changes, and the levels and timing of fluctuation were heterogeneous across the lines. To map

out the autocrine and/or paracrine characteristics of this expression, we generated one-colour

heat maps with mouse and human data displayed separately. Two hypothetical heat maps—

one displaying autocrine signalling and the other paracrine signalling—is presented in Figure 22.

Transcript

Days since radiation

Human Mouse

1 7 14 38 1 7 14 38

SHH

IHH

PTCH1

PTCH2

GLI1

SMO

Transcript

Days since radiation

Human Mouse

1 7 14 38 1 7 14 38

SHH

IHH

PTCH1

PTCH2

GLI1

SMO

Figure 22. Autocrine and paracrine Hh signalling displayed in one-colour heat maps. In the top panel, ligands, receptors and effectors are all upregulated in the epithelium. In the bottom panel, ligands are upregulated in epithelium while pathway activation occurs in the stroma.

Autocrine

Paracrine

84

Figures 23-28 present the gene expressions as one-colour heat maps and bar graphs for each of

the three lines. Fold changes correspond to the relative expression of irradiated tumours

compared to volume-matched controls. Statistically significant upregulations of ≥1.5-fold are

coloured in red. The same data is presented as bar graphs, where the natural logarithm of the

fold change is used to visually distinguish upregulation and downregulation. Data is presented

line by line.

Line 8 Passage 4 (Figures 23-24):

IHH showed a progressive increase in expression after irradiation, reaching a 6-fold

upregulation relative to controls two weeks after irradiation. This was followed by a sharp 8-

fold downregulation at the volume endpoint (38 days after irradiation). SHH expression

mirrored this trend, with approximately 2-fold upregulation at one and two weeks, followed by

a return to baseline levels at day 38. In addition, GLI1 was approximately 2-fold upregulated in

human tumour cells at one week, two weeks and 38 days. A modest 1.5-fold upregulation was

seen in PTCH1 at one and two weeks after irradiation.

Stromal GLI1, PTCH1 and PTCH2 were all approximately 2-fold upregulated for the first two

weeks after irradiation, and PTCH1 reached a 3-fold upregulation at day 14. While SHH was

almost 2-fold upregulated in the same cells one week after radiation, no significant changes

were seen in IHH.

Line 6 Passage 4 (Figures 25-26):

Similar expression patterns were seen in Line 6. IHH was 3-fold upregulated 15 days after

irradiation. This was followed by a modest 1.6-fold upregulation in SHH at day 21. In addition,

85

GLI1 was 7-fold upregulated in human tumour cells at day 21. A 2-fold GLI1 upregulation

appeared to be maintained at the volume endpoint (day 48) with a trend towards significance

(p=0.055). No significant upregulation was seen in either PTCH1 or PTCH2 in human cells.

The stromal compartment in Line 6 also showed similar expression patterns to those of Line 8.

PTCH1 was significantly upregulated at all time-points probed. PTCH2 showed a marked 22-fold

upregulation at day 1, a 5-fold upregulation at day 15, and a return to baseline levels at day 48.

GLI1 was 2-fold upregulated at day 1, subsequently returned to baseline and was ultimately 2-

fold downregulated at day 48. IHH was 4-fold upregulated in stromal cells at day 15.

Line 7 Passage 5 (Figures 27-28):

Line 7 exhibited a unique expression profile, with a sharp increase in Hh transcripts consistent

with a pathway response in tumour cells immediately following irradiation. This was followed

by subsequent downregulation or return to baseline. GLI1 and PTCH2 were 3-fold and 6-fold

upregulated at day 1, respectively. SHH, IHH and PTCH1 were either unchanged or showed

decreased expression relative to controls in the human epithelial compartment throughout the

observation period.

In the stromal compartment, SHH was 14-fold and 9-fold upregulated one day after irradiation

and at the volume endpoint (day 38), respectively. No statistical significance was found in the

apparent fluctuations in IHH transcript levels. GLI1, PTCH1 and PTCH2 were all either

unchanged on downregulated for all time points after irradiation.

86

Transcript

Days since radiation

Human Mouse

1 7 14 38 1 7 14 38

SHH 1.03 2.02 1.89 1.00 0.17 1.81 0.11 1.78

IHH 1.42 3.99 6.20 0.12 1.04 0.87 0.78 4.86

PTCH1 0.85 1.60 1.52 0.43 1.85 2.13 2.77 0.87

PTCH2 0.62 1.16 0.74 0.62 1.70 1.30 1.88 1.10

GLI1 0.63 1.69 2.05 1.55 1.87 1.54 1.94 0.72

SMO 0.54 0.71 0.73 1.15 1.31 0.82 0.91 0.97

Figure 23. Gene expression changes in Line 8 passage 4. Statistically significant ≥1.5-fold upregulations are coloured in red.

Figure 24. Bar graphs of gene expression changes in Line 8 passage 4. Asterisks mark statistically-significant up- or downregulations.

Line 8 passage 4

87

Transcript

Days since radiation

Human Mouse

1 15 21 48 1 15 21 48

SHH 0.39 0.18 1.61 0.57 1.23 N/A * 1.39

IHH 0.59 3.38 0.29 0.22 8.33 4.16 N/A 5.18

PTCH1 1.41 0.92 0.74 0.71 2.63 6.37 3.45 1.75

PTCH2 * 0.91 2.03 0.55 22.44 4.86 0.17 1.32

GLI1 2.92 2.47 6.71 1.92 1.98 1.11 0.90 0.52

SMO 0.32 0.41 1.07 2.69 0.97 0.52 1.24 1.48

Figure 25. Gene expression changes in Line 6 passage 4. Statistically significant ≥1.5-fold upregulations are coloured in red. Asterisks represent genes that were detected in irradiated samples but not in the volume-matched controls. Thus, while a fold difference could not be calculated, upregulation was detected.

Ligands

Receptors

Activators

& targets

Figure 26. Bar graphs of gene expression changes for Line 6 passage 4. Asterisks mark statistically significant up- or downregulations.

Line 6 passage 4

88

Transcript

Days since radiation

Human Mouse

1 7 14 38 1 7 14 38

SHH 0.82 0.91 0.80 0.81 14.30 0.20 1.13 8.62

IHH 0.93 0.51 0.53 0.83 N/A 1.44 2.11 4.92

PTCH1 0.80 1.00 0.79 0.71 1.00 0.56 0.37 0.72

PTCH2 6.13 0.43 0.39 0.97 0.79 0.44 0.35 0.87

GLI1 3.30 0.49 0.53 1.49 0.92 0.57 0.56 0.84

SMO 68.05 0.23 1.50 0.55 0.92 0.75 0.76 1.22

Figure 27. Gene expression changes in Line 7 passage 5. Statistically significant ≥1.5-fold upregulations are coloured in red.

Ligands

Receptors

Activators

& targets

Figure 28. Bar graphs of gene expression changes in Line 7 passage 5. Asterisks mark statistically significant up- or downregulations

Line 7 passage 5

89

4.2.4. Single dose 5E1 inhibits stromal Hh activation for up to one week

To validate 5E1 as an inhibitor of Hh signalling in our xenograft model, we treated mice bearing

tumours from Line 7 passage 5 with a single dose of 5E1 at 20mg/kg and measured the

expression levels of Hh pathway components by RT-PCR. Two tumours from untreated mice

were used as controls. Across the three doses used, GLI1 was an average 20-fold and 13-fold

downregulated 24 hours and one 1 week after 5E1 administration, respectively. Similarly,

PTCH1 showed an average 9-fold and 7-fold downregulation at 24 hours and one week after

5E1 injection (Figure 18). In contrast, SMO expression was unchanged by 5E1 inhibition. This

result was expected given that SMO is not a target of the pathway. Thus, 5E1 inhibits Hh

signalling in our xenograft model of EAC.

4.2.5. The ability of 5E1 to radiosensitize EAC xenografts was not demonstrated and warrants

further study (Aim 3c)

Prior to selecting a xenograft line for a 5E1 inhibition study, untreated tumours from three lines

(Lines 6, 7 and 8) were tested for baseline Hh expression using qRT-PCR. All three expressed Hh

transcripts (Figure 21). Line 7 was selected for the 5E1 inhibition study. 5E1 failed to increase

the growth delay of both irradiated and non-irradiated tumours relative to radiation alone and

IgG control, respectively (Figure 29).

90

Figure 29. 5E1 failed to radiosensitize xenograft tumours from Line 7 passage 6

4.3. Aim 3 Discussion

4.3.1. Hedgehog expression in EAC xenografts is suggestive of paracrine signalling

While the activation of Hh signalling has been well characterized in EAC, studies have produced

conflicting results describing the direction of signalling in this tumour. Autocrine signalling was

reported by Berman et al using the commercially available validated EAC cell line OE33.152 In

contrast, one study using a mouse esophagojejunostomy model reported paracrine activation

of the pathway.156 Yet another study found expression patterns consistent with both autocrine

and paracrine mechanisms in primary patient samples.155

Our study uses qRT-PCR to evaluate the distribution of Hh transcripts between the epithelial

and stromal compartments of patient-derived primary xenografts. As we have not incorporated

a measure of protein readout (i.e. nuclearization of GLI), we cannot conclude that the presence

of a transcript indicates pathway activation, even if elevated relative to controls, as is the case

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90

Tum

ou

r vo

lum

e (m

m^3

)

Days since Implantation

Line 7 passage 6 treated with 5E1 and radiation

Control

IgG

5E1

RT

5E1+RT

91

in our post-radiation gene expression experiments. Nevertheless, we found that xenograft

tumours display lower transcript levels after treatment with 5E1, suggesting that changes in

transcript levels correspond to changes in pathway activity. With this in mind, our data from

patient-derived xenografts agree with the results of the latter reports outlined above, and

support both paracrine and autocrine signalling in EAC, with the former playing the

predominant role.

The apparent contradiction between our results and those of Berman et al may be resolved

through a consideration of the experimental techniques used. Use of fresh patient samples

suggests that while both signalling mechanisms are present, a paracrine pattern predominates.

It is likely that long-term culture conditions impose selective pressures on EAC cells to become

self-sufficient in their use of the Hh pathway. Thus, lacking a stromal support network, OE33

cells may have capitalized upon and enhanced an already-present albeit low-level autocrine

mechanism to support their own growth.

4.3.2. Hedgehog is involved in the radiation response of EAC tumours

With evidence amassing in both the cancer stem cell and Hedgehog fields, carcinogenesis is

increasingly being viewed as the misuse of homeostatic mechanisms involved in tissue repair

and stem cell self-renewal. This model is particularly appealing for EAC, which develops from

chronic acid reflux-induced injury to esophageal epithelium. By extension, irradiation of EAC

tumours may provoke the same stem cell-driven responses found during normal repair.

Evidence from multiple tumour sites suggests that Hh signalling is involved in the radiation

response (see section 1.13.) However, little is known about the pathway’s role in the

92

radioresponse of EAC tumours. Our results demonstrate that Hh transcripts are significantly

modulated after irradiation of primary EAC xenografts.

Of the three xenograft lines tested, two (Line 6 and Line 8) showed a distinct increase in

expression of pathway transcripts in an epithelial-to-mesenchymal pattern suggestive of

paracrine signalling. Both SHH and IHH were significantly upregulated in Line 8 tumour cells one

week after irradiation. Despite a significant decrease in the number of transcripts

(“downregulation”) at several time points, the expression of both ligands was also upregulated

in Line 6 at two to three weeks following irradiation. In both Line 6 and Line 8 PTCH1, PTCH2

and GLI1 were upregulated in stromal cells.

Curiously, stromal receptor upregulation preceded epithelial ligand upregulation in Line 6

(Figure 25). Receptor upregulation could be interpreted as an autocrine response to stromal

IHH. However, this upregulation does not reach significance until day 15, at which point

tumour-derived IHH is also upregulated. Alternatively, stromal receptor upregulation may

reflect a response to epithelial ligand upregulation at time points in between or prior to those

tested in this experiment.

In both Line 6 and Line 8, epithelial ligand expression was accompanied by epithelial GLI1

upregulation, suggesting an underlying autocrine signalling mechanism in addition to the

paracrine activation discussed above. The use of both paracrine and autocrine Hh signalling has

been reported in other organ systems. SHH signalling occurs in an autocrine loop within the

epithelial compartment of prostate tumours, while normal prostate development utilizes the

93

pathway in a paracrine fashion between epithelia and stroma.176 Similarly, embryonic lung

epithelial cells communicate with adjacent mesenchyme using paracrine Hh signals. 177

However, epithelial Hh activation occurs in small clusters of cells later in lung development, and

in small numbers of the basal layer epithelial cells in adult bronchial epithelium. Furthermore,

this autocrine signalling mechanism is detectable in lung tissue during repair of acute airway

injury and in small cell lung cancer (SCLC). The authors of this report speculated that autocrine

Hh activation following tissue injury represents a regenerative response within the progenitor

cell population in lung tissue, and could play a role in SCLC carcinogenesis.151 Thus, while

paracrine Hh signalling in tumours may represent the misuse an embryonic development

pathway, autocrine signalling may reflect the adaptation of a cancer cell to profound Hh

dependency during inflammation, tissue repair or carcinogenesis.

Alternatively, underlying autocrine Hh signalling may represent the predominant signalling

mechanism of the TIC compartment. TICs from several disease sites have been shown to rely on

either autocrine or reverse paracrine (mesenchymal to epithelial) Hh signalling for self-renewal

and proliferation. Blockade of SMO function inhibits the self-renewal of TICs from multiple

myeloma,178 glioblastoma,179,180 colon cancer,181 gastric cancer,182 breast cancer,183 and chronic

myeloid leukaemia.184,185 Thus, TICs from multiple different tumour types rely on an active Hh

pathway through autocrine or reverse-paracrine signalling. It is possible that EAC TICs are

equally reliant on Hh pathway activation. The upregulated autocrine expression observed after

irradiation may reflect an increased burden on EAC TICs to self-renew and repopulate the

tumour.

94

As part of the rationale for investigating a putative TIC population in EAC tumours, we cited

recent findings that the intestinal stem cell marker Lgr5 is ubiquitously expressed in EAC and

marks gastric progenitors from which the EAC cell of origin may derive (see section 1.10). It is

interesting to note that Lgr5 is also a target of the Hh pathway and appears to promote cellular

proliferation and tumour formation in basal cell carcinoma.186 Furthermore, Lgr5+ hair follicle

stem cells communicate within their own population and with other follicular stem cell

populations through autocrine and paracrine Hh signalling, respectively.187 Thus, a bimodal Hh

signalling pattern has been established in the putative EAC cell of origin.

Taken together, there is substantial evidence that autocrine Hh signalling plays an important

role in stem cell and cancer stem cell self-renewal and in tissue repair. Thus, paracrine

expression patterns observed here may reflect a proliferative and/or anti-apoptotic response of

bulk or transit-amplifying EAC tumour to radiation-induced tissue injury, or it may represent

epithelial-to-mesenschymal communication between EAC TICs and the tumour

microenvironment. Since Hedgehog signalling has documented roles in both proliferation and

cell survival, this interpretation is plausible. The observed autocrine expression patterns may be

the response of a radiation-activated TIC population that must maintain self-renewing divisions

while repopulating the tumour.

Despite an initial spike in epithelial SMO expression following irradiation in Line 7, levels of the

co-receptor remain relatively unchanged across all three lines. Since SMO has not been

reported as a target of the pathway, these results are reasonable. Furthermore, stromal SMO

95

expression was unchanged after pathway inhibition with 5E1 (Figure 18). Thus, SMO expression

is pathway-independent.

The radiation-induced expression changes in Line 7 deviated from the pattern established by

Lines 6 and 8. SHH ligand in Line 7 appeared to be upregulated in the stromal compartment,

while PTCH2 and GLI1 were upregulated in tumour cells following irradiation, suggesting a

reverse-paracrine mechanism in this model. However, we cannot rule out the possibility of an

autocrine mechanism in this case. That is, epithelial expression of PTCH2, GLI1 and SMO may be

a response to undetected ligand expression in tumour cells within the first 24 hours after

irradiation (prior to the first time point interrogated). Regardless, Hh pathway expression

patterns do not appear to follow a conventionally paracrine mechanism in Line 7. Furthermore,

while Lines 6 and 8 showed a delayed albeit sustained upregulation of Hh transcripts after

irradiation, Line 7 showed acute, transient upregulation that was not sustained past the first 24

hours after irradiation. The remaining pathway components are largely downregulated or

statistically equivalent to baseline (control) expression levels, suggesting that the Hh pathway is

not active after irradiation in Line 7. That pathway activation—whether autocrine or reverse

paracrine—may not even be functional in this line is supported by results obtained from Aim

3(c). SHH inhibition with 5E1 antibody alone failed to induce a significant growth delay in Line 7

xenografts relative to control tumours. In addition, 5E1 failed to radiosensitize Line 7 xenografts

compared to radiation alone. Thus, Hh signalling may not be a critical component of the

radiation response in Line 7, and the unique expression patterns in this line should be

interpreted with caution.

96

Chapter 5: Limitations, alternatives and future directions

The limitations of the present studies shed light on alternative methods and questions we

might have pursued. Looking forward, the knowledge gained from these studies—both those

that succeeded and those that did not—will guide further exploration within each of the three

aims.

The overarching objective of the present studies was to probe multiple factors in the radiation

response of EAC tumours (radiosensitivity, TICs and Hh signalling) using clinically-reflective

experimental models. There were, however, several limitations inherent in the xenograft

models used.

First, our panel of seven xenograft lines may not represent the full spectrum of radiosensitivity

observed clinically. For instance, no single line was completely non-responsive to irradiation.

Future xenograft irradiation studies in an expanded panel of primary patient tumours will

determine whether a wider spectrum of radiosensitivity exists. This will allow us to probe the

radiation response of patient tumours in relation to parameters such as the TIC frequency and

to Hh transcript levels suggestive of pathway activation. Our panel of seven tumours currently

lacks the power to detect such correlations. In addition, our model is unable to detect SGD

differences between tumour lines. We suspect that insufficient animal numbers can account for

this. Initially, our xenograft experimental design incorporated all three aims into one

experiment. That is, one xenograft irradiation experiment would provide the materials to assess

SGD, TIC enrichment and Hh pathway activity. In retrospect, combining these aims into one

97

experimental passage may have compromised our ability to detect statistically significant

differences in tumour growth kinetics, since mice were continuously removed from the growth

delay experiment for TIC and gene expression experiments. Thus, increasing the number of

animals per experiment, as well as re-evaluating the mathematical models used to assess

tumour growth kinetics will increase the power of our xenograft model.

Second, our use of single-dose, high dose-rate precision irradiation deviates from clinical

treatment modalities. Patients receive multiple fractions of low-dose irradiation over an

extended treatment period. This permits normal tissue to repair damaged DNA in between

fractions, resulting in reduced patient toxicity. Initially, fractionated irradiation did not seem

feasible for our studies, since limiting dilution assays, clonogenic assays and gene expression

analyses were planned for two distinct points in the growth curve: during the plateau phase

and after repopulation. Fractionation would have delayed repopulation (and perhaps even

cured some mice), resulting in lengthy experiments and missing data points. Furthermore,

initial radiation studies were combined with chemotherapy and chemoradiation studies (not

reported here), both of which used single-dose chemotherapy. For these reasons, we selected a

single-dose irradiation scheme.

In retrospect, it would likely have been both feasible and desirable to incorporate fractionated

irradiation into our xenograft models. Since fractionated radiotherapy preferentially spares

benign tissue, interactions between tumour and stroma during fractionated therapy may differ

from tumour-stroma interactions after a single dose of radiation. In particular, by allowing

normal tissue to repair sublethal damage, and by allowing radioresistant tumour cells to

98

redistribute into sensitive phases of the cell cycle, fractionated irradiation may decrease the

proportion of tumour to stroma (i.e. the % tumour epithelium) compared to single-dose

therapy. Thus, measurements of Hh expression after fractionated irradiation may more closely

mirror the human radiation response. This has important implications for the translational

relevance of our work. If Hh inhibitors are to be integrated into standard clinical management

of EAC, preclinical Hh studies should aim to mimic the human treatment regimens as closely as

possible.

In addition to fractionation, future work should consider incorporating low-dose rate irradiation

into xenograft studies. As discussed in Section 2.3, these methods incorporate a measure of

sublethal cellular repair, and may therefore increase the sensitivity of our xenograft model to

slight differences in specific growth delay between patient tumours.

A third limitation of the xenograft model involves the use of NOD/SCID mice, a decision that

was made based on available resources. Chemotherapeutic studies in our laboratory resulted in

the establishment of an in-house NOD/SCID breeding colony. Our economical decision to use

the same strain for radiation studies may have undermined our efforts to recapitulate clinically-

relevant radiation responses. As discussed above, normal tissue is presumed to repair damaged

DNA more efficiently than malignant cells during fractionated radiotherapy. Being incapable of

DNA double-strand break repair, NOD/SCID mice may differ from the human system in terms of

the normal tissue response to irradiation. We showed on a macroscopic level that this genetic

defect did not compromise our ability to measure xenograft growth kinetics. However, on a

cellular and molecular level, this stromal defect may have affected our gene expression and TIC

99

studies. For example, it is widely accepted that oxygen tension fluctuates in a tumour following

irradiation. Acute hypoxia may result from endothelial cell death, but subsequent angiogenesis

permits tumour reoxygenation. This observation forms the rationale for combined

chemoradiotherapy, since radiation-induced debulking and angiogenesis can improve drug

delivery. However, it is possible that an irradiated NOD/SCID tumour bed has a reduced

capacity to stimulate angiogenesis and tumour reoxygenation relative to other model systems

or to humans. Thus, irradiated tumours in NOD/SCID mice may remain chronically hypoxic. This

microenvironmental difference between the murine and human systems would likely affect the

TIC frequency, since hypoxia is a component of the TIC niche. Furthermore, changes in oxygen

tension have profound effects on gene expression, and may result in Hh expression changes

that differ from those seen in irradiated human tissue. This genetic difference between human

and mouse stroma could be of particular concern in our models, given our observation of a

predominantly paracrine signalling mechanism.

Future xenograft irradiation studies should consider using a mouse strain with a benign tissue

radiation response that more closely resembles that of human tissue. The RAG2γc double

knockout strain, which lacks the SCID mutation, offers a suitable alternative to NOD/SCIDs.

We have chosen to model EAC radioresistance within the framework of the cancer stem cell

theory. Due to a lack of validated stem cell markers in EAC, we have used a functional assay of

tumour-initiating capability to detect TICs in this cancer. Our studies of TICs and clonogenic cells

were limited in several respects. First, our LDA results suggest but do not conclusively show that

the radioresistant cells are the same population as the TICs. It is possible that radioresistant

100

non-TICs are equally capable of driving tumour regrowth (during the repopulation phase of the

growth curve) and are therefore relevant therapeutic targets. Second, our LDA results are

limited in number. We demonstrated TIC enrichment in two xenograft Lines. Additional TIC

studies should be pursued in remaining xenograft lines in order to corroborate the findings

presented here. A final limitation of our TIC studies emerges from a potential inhibitory effect

at intermediate and high LDA doses. This suggests that factors other than the intrinsic TIC

frequency can modulate LDA outcomes. This observation has not yet been reported in

published literature, and may shed light on inherent weaknesses in the techniques used to

identify TICs.

Our data on Hh expression is limited in several ways. First, missing information about pathway

regulation at the protein level precludes us from making conclusions about pathway signalling.

The use of qRT-PCR in combination with immunohistochemistry would permit more conclusive

observations of pathway activation in response to radiotherapy. Furthermore, we have

measured the transcript levels of GLI1 only, since it is the most reliable readout of pathway

activity. Since GLI2 and GLI3 appear to be the predominant transcriptional activators of the

pathway, analysis of their expression levels and protein localization within the cell would

provide a more robust indication of pathway activation.

Second, our studies on baseline Hh expression in EAC (Section 4.2.2) do not include normal

esophageal epithelium as negative controls. This, however, would be a difficult comparison to

make, since normal squamous and malignant esophageal epithelia differ in cell phenotype (i.e.

squamous versus columnar). Ideally, matched controls from normal gastric cardia could provide

101

a reasonable measure of baseline expression prior to malignant transformation. Finally, our

data on Hh signalling after irradiation is currently restricted to two xenograft lines. The results

from these studies are promising and warrant further investigation in additional lines. If

possible, future studies should aim to demonstrate that Hh signalling promotes EAC TIC self-

renewal, either through functional assays (LDAs, re-plating clonogenic assays) or through

biomarker staining (Lgr5, Oct4, Nanog etc.). In addition, future studies should explore

alternative roles for Hedgehog in the radiation response. For example, current work in our

laboratory is focused on the ability of Hh inhibition to radiosensitize cell-line derived EAC cells

via cell cycle redistribution.

The present studies describe a set of EAC xenograft models with heterogeneous

radiosensitivities. These models are therefore valuable tools for preclinical studies of (a) the in

vivo EAC tumour radiation response, and (b) combined modality therapies incorporating novel

targeted agents. We sought to determine whether in vivo inhibition of Sonic Hh could increase

the growth delay of irradiated tumours. However, due to time constraints and the transient

availability of xenograft tumours for implantation, we unknowingly selected a xenograft line

that does not appear to upregulate Hh signalling in response to irradiation. It is therefore a

limitation of our study that experimentation was not sequential. Had gene expression data

been available at the time, a more appropriate line would have been selected for Hh inhibition.

Moving forward, our xenograft lines will continue to be used for Hh inhibition studies. Our

laboratory has obtained two clinically-used Smoothened inhibitors (LDE225, Novartis and BMS-

833923, BMS). Future work will aim to incorporate these targeted agents, as well as a research-

102

grade GLI1 antibody, into a radiation-based treatment scheme in our xenograft model. Our

immediate aim is to demonstrate that Hh inhibition in Lines 6 and 8 can increase the growth

delay and specific growth delay of irradiated tumours relative to radiation or inhibitor alone. A

demonstration of the radiosensitizing effect of Hh inhibition would have profound clinical-

translational implications.

It may be the case that Hh inhibition does not “prime” cells for irradiation, but prevents

repopulation after radiotherapy. In other words, it is unclear whether Hh inhibition would act

syngergistically or additively in combination with radiation. Future work should aim to

distinguish between these two effects in order to optimize the timing and dosing of pathway

inhibition. These initiatives will be complemented by further efforts to map out the radiation-

induced gene expression changes both in additional xenograft lines, and at additional time

points after radiotherapy. Ultimately, a more robust understanding of the role and timing of

upregulated Hh signalling after irradiation will guide clinical decision-making in a multi-modality

therapeutic setting.

103

Chapter 6: Conclusion

Despite the pervasive clinical radioresistance of EAC tumours, little is known about the

underlying mechanisms driving this resistance. A paucity of validated preclinical models has

contributed to this knowledge deficit.

We have described seven primary EAC xenograft models that, while encompassing a range of

radiosensitivities, are statistically indistinguishable from each other based on specific growth

delay. Nevertheless, a qualitative assessment of the range of GD and SGD values, both within

and between xenograft lines leaves open the possibility that subtle differences in intrinsic

radiosensitivity may be detected with increasing sample sizes and further optimization of

experimental techniques.

Using this model, we have demonstrated that ionizing radiation enriches the tumourigenic

population in two patient-derived EAC tumours. It remains inconclusive whether the TIC

frequency returns to baseline following tumour recovery after radiotherapy. Our finding that

radiation may enrich the TIC component of a solid tumour has not been reported in the

literature, and is therefore valuable not only from a clinical-translational perspective, but as a

proof-of-principle that LDAs are a valuable tool for probing the cell phenotypic factors

contributing to radioresistance.

Building on a foundation of evidence that the Hh pathway is activated in EAC, we next used our

xenograft model to interrogate the response of this pathway to irradiation. We found that in

104

the absence of radiation, Hh expression patterns in EAC suggest a predominantly paracrine

mechanism, corroborating published evidence from studies using other detection techniques.

Furthermore, we found that Hh expression is upregulated following irradiation in two primary

xenograft lines, suggesting that the pathway may be activated in response to radiotherapy.

Upregulation occurred in both autocrine (tumour cell to tumour cell) and paracrine (tumour to

stroma) patterns. A third xenograft line appeared to upregulate Hh pathway components in a

reverse paracrine direction, however pathway inhibition with 5E1 in this line did not delay

tumour growth or regrowth following radiotherapy, calling into question the functionality of

the pathway in this line.

Taken together, the results from these studies support the hypothesis that EAC radioresistance

can be modeled in parallel with EAC carcinogenesis. That is, embryonic development pathways

usurped during EAC development may take on a heightened role in the tumour response to

irradiation. In this case, pathway upregulation may reflect a widespread anti-apoptotic and/or

proliferative response following radiation-induced DNA damage. Alternatively, upregulation

following irradiation may be restricted to TICs and TIC niches, reflecting an acute signal to

promote TIC self-renewal in the face of widespread cytotoxicity. Since no EAC TIC markers have

been found to date, it remains unclear whether Hh signalling exclusively supports the EAC TIC

compartment. For this reason, efforts at measuring TIC frequencies with LDAs before and after

Hh inhibition should be pursued.

Despite the recent failure of SMO inhibitor IPI-929 in a phase II clinical trial of pancreatic

cancer, our data support findings in both EAC and other tumour sites that SMO inhibitors have

105

an important place in the clinical management of Hh-expressing cancers. We observed

upregulation of Hh transcripts in the first two to three weeks after irradiation (during the

plateau phase of the growth curve), suggesting that clinical integration of a SMO inhibitor after

radiotherapy may improve patient response. However, further efforts are needed to both

buttress our reported findings, and to determine whether Hh inhibition is most effective when

used before or after radiotherapy.

106

Appendix A: Xenograft growth curves for all seven models

107

108

\

109

Appendix B: Sample flow cytometric plots of H2K depletion for limiting dilution and clonogenic assays

EpC

AM

-AP

C

H2K-PECy7

Line 4 (P6)

Line 3(P4)

Line 2(P10)

Pre-depletion Cells remaining in column Effluent

110

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