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A Systems Biology View of Endocrine Resistance Robert Clarke, Ph.D., D.Sc., F.R.S.Biol., F.R.S.Chem., F.R.S.Med. (U.K.)
Professor, Department of Oncology
Co-Director, Breast Cancer Research Program
Dean for Research
Georgetown University Medical Center
COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY
Lombardi
DISCLOSURES
I am a consultant for American
Gene Technologies.
COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY
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225,000 newly diagnosed cases of invasive breast cancer annually (USA)
40,000 American women (~500,000 worldwide) die of breast cancer each year
One breast cancer death (on average) every 13 minutes in the USA
70% of new breast cancer cases express ER (estrogen receptor alpha; ESR1)
~50% of all breast cancer deaths are from ER+ disease
Benefit
from TAM }
Age (Menopausal Status) Risk Reduction1
Recurrence: <50 years (ER+) 45 ± 8%
Recurrence: 60-69 years (ER+) 54 ± 5%
Recurrence (ER-) 6 ± 11% (not significant)
Death: any cause <50 years (ER+) 33 ± 6%
Death: any cause 60-69 years (ER+) 32 ± 10%
Death: any cause (ER-) -3 ± 11% (not significant) 1Proportional reduction in the 10-year risk of recurrences
(Early Breast Cancer Trialists Group meta analyses)
Breast Cancer, Tamoxifen, and Clinical Outcomes
Resistance (de novo)
Dormancy (acquired resistance)
Adapted from: Demicheli, et al., BMC Cancer, 2010
ER+
ER-
Resistance
Dormancy
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Robert Clarke, Ph.D., D.Sc.
Cancer Systems Biology: Hypotheses
● Rather than being synonymous with bioinformatics, computational or
mathematical biology, systems biology sits uniquely at their nexus – how a systems components interact to control its function and behavior
– integrate complex, often high dimensional data from multiple sources
– predictive multiscale models of system (network) function
Systems Biology Research Cycle Endocrinologist 94: 13, 2010
Biological cycle
Integration with modeling
We invoke an integrated, multimodal, gene network hypothesis – network is modular and exhibits redundancy
– signaling is highly integrated and coordinates many cellular functions
Network modules of interest are those that regulate cell fate – to live or die
– if to live, whether or not to proliferate (i.e., cell cycling)
– if to die, how to die (e.g. apoptosis, autophagy, necrosis)
Responsiveness is not intrinsic to individual cells but can be shared
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Approach to Network Modeling
Fu et al., Scientific Reports, 5: srep13955, 2015 Wang et al., Scientific Reports, 6: srep18909, 2016 Chen et al., Nucl Acid Res, e65, 2016
Wang et al., Bioinformatics, 31: 137-139, 2014 Chen et al., PLoS ONE, 9 (11): e112143, 2014 Yu et al, Bioinformatics, 30: 431-433, 2014
Chen et al. Nucl Acid Res, 431: e42, 2013 Wang et al., J Mach Learn Res, 14: 2899-2904, 2013 Gusev et al., Cancer Informatics, 12: 31-51, 2013
Tyson et al., Nature Rev Cancer, 11: 523-532, 2011 Yu et al., J Mach Learn Res, 11;2141-2167, 2010 Gu et al. Bioinformatics, 28: 1990-1997, 2012
Chen et al., Bioinformatics, 26: 1426-1422, 2010 Zhang et al., Bioinformatics, 25: 526-532, 2009 Clarke et al., Nature Rev Cancer, 8: 37-49, 2008
● We take a systems biology approach to integrate knowledge from cancer biology
with computational and mathematical modeling to make both qualitative and
quantitative predictions on how a system (breast cancer) functions
● We develop and apply both computational and mathematical modeling tools – computational models can find local topologies or modules within high dimensional data
using multiple different methods (top down)
– mathematical models can represent local topologies or modules by a series of
differential equations, stochastic reaction networks, etc. (bottom up)
Computational modeling Mathematical modeling
● The module(s) of interest exist within an immense search space (the human
interactome); we don’t know all of the genes/proteins/metabolites in each module
● Networks are high dimensional and the data have unique properties, e.g., curse of
dimensionality; confound of multimodality; scale free; small world Clarke et al., Nature Rev Cancer, 2008
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Guiding Questions for Systems Modeling
● What is the quantitative modeling framework?
● Are all Tamoxifen failures the same? – resistance vs. dormancy
● What’s the role of ERα? – properties as a molecular switch
– activation status (inactive vs. ligand vs. growth factor)
– most breast cancer that acquire TAM resistance are ER+
● For resistance, when are mechanistically relevant changes
acquired? – do changes occur early (hours, days)
– are changes that arise early retained
● What coordinated functions contribute to endocrine
responsiveness and how are these integrated? – unfolded protein response, autophagy, apoptosis, metabolism
COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY
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Quantitative Modeling Framework
Cell functions (not all are shown) are captured as modules or groups of modules, e.g., UPR (unfolded
protein response) is a single module, whereas Cell Fate has several integrated modules (cell cycle,
apoptosis, autophagy, etc.)
What may drive altered cell fate decisions with endocrine therapy?
Cell
fate
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● Compare early recurrences (≤3yrs) at distant sites (outside the
breast) with those that recurred later (≥5 yrs)
● Early vs. late cases for building a computational predictor – n=131 cases; 95% ER+; almost all IDC; all collected at diagnosis
– Tamoxifen the only systemic therapy (after surgery + radiotherapy)
– ≥15 years of clinical follow-up
● Building the predictor – address dimensionality and reduce gene selection bias1
– outperform random gene sets of the same size (10,000 random sets)2
– SVM with recursive feature elimination in crossvalidation workflow
– meet n=7 pre-established performance benchmarks3
● Independent validation dataset
– similar patient population with TAM as the only systemic therapy
– long term follow-up (≥15 years)
– same microarray platform (Affymetrix)
Are Early and Late Recurrences the Same?
1Clarke et al., Nature Rev Cancer, 2008 1Venet et al., PLoS Comp Biol, 2011 report that >60% (up to 90%) of breast cancer molecular predictors are no better than random gene sets
2Mackay et al., JNCI, 2011 report that the molecular subgroup classifications for the LumA, LumB, LumC, and normal-like subgroups are not statistically robust
Predicting Late Recurrence in ER+ Breast Cancer
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Early (≤3 yr) vs. Late (≥5 yr) TAM Recurrences
Accuracy Specificity Sensitivity AUC PPV NPV Hazard Ratio P-value
0.90 0.95 0.81 0.87 0.91 0.89 3.45 <0.0001
BC030280 (training dataset)
Loi et al. (independent validation dataset)
Accuracy Specificity Sensitivity AUC PPV NPV Hazard Ratio P-value
0.77 0.83 0.74 0.81 0.88 0.67 3.11 0.0004
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Sensi
tivit
y
1 - Specificity
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
THSD4THSD4
ERBB4
SLC7A8RBM24KIAA1467CMYA5STK32B
MAOBNCOA7MUM1L1DEGS2FLJ14959EFHC2CX3CR1PTGER3PTGER3PTGER3ITGA8MS4A7MS4A7SEC14L2FERD3LTNNI1C1orf86STK35RNF133ZNF704
MGC52498
LOC283079KCNJ12LOC651964
RHDC8orf12OFCC1
SLC6A6NAP1L4GGNOR10A3PRO0471C12orf65LOC440292 /// LOC647995LOC150763
DCLK3IKZF1LOC284801
CR1LTMEM4BATF2LRP8SOD2C1orf187SLC7A5PNPLA3ME3ATXN7L1C1orf96LOC144874PLCH1ADAMTS1BCL2L14USP36
RFX3LOC728683TAAR3STXBP5LRAB6BRASD2
% S
urv
ival
Time
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1-specificity
sensitiv
ity
1 - Specificity
Sensi
tivit
y
% S
urv
ival
Time
Performance exceeds all (n=7)
pre-established benchmarks in
both datasets (and outperforms all
of 10,000 randomly selected gene
sets)
Minetta Liu (Georgetown; Mayo)
Mike Dixon; Bill Miller (Edinburgh)
Jason Xuan (Virginia Tech)
Joseph Wang (Virginia Tech)
(submitted)
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Network Modeling: The Role of ER
● We have selected our key modules of interest in our hypothesis – live or die (e.g., apoptosis, autophagy, necrosis)
– proliferate or growth arrest (i.e., cell cycling)
● We know that ERα is relevant and will coordinate several cell functions – key regulator in normal mammary gland function1
– tumors acquiring endocrine resistance generally retain ERα expression2
– responses to 2nd and 3rd line endocrine therapies are relatively common2
– small molecule inhibitors and RNAi against ERα inhibit resistant cells3
● We don’t know precisely how ERα signaling is regulated or wired
● ERα is a transcription factor and we have transcriptome data
1Johnson et al., Nat Med , 2003 2Clarke et al. Pharmacol Rev, 2002
3Wang et al., Cancer Cell, 2006; Kuske et al., Endocr Relat Cancer, 2006; Katherine Cook et al., FASEB J, 2014
ERα
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ERα Signaling: Early vs. Late Recurrences
● Identify closest protein partners to ERα using a novel
Random Walk (RW) based algorithm with Metropolis
Sampling (MS; Markov Chain-Monte Carlo) technique to
walk 8 PPI (protein-protein interaction) databases – 1,452 neighbors selected; n=50 are frequently visited
Model the n=50 using the microarray data we used to
study early vs. late recurrences in both datasets
Build a consensus signaling topology
Num
ber
of
nodes
Minetta Liu (Georgetown; Mayo)
Mike Dixon; Bill Miller (Edinburgh)
Jason Xuan (Virginia Tech)
Joseph Wang (Virginia Tech)
(submitted)
Circles = nodes
Lines = edges
red = overexpressed in ‘Early’
green = overexpressed in ‘Late’
MAPK
ERα
SRC
ERβ
AR
BCL2
EGFR
CDK1
Cyclin A2
How might early and late recurrences be different?
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ERα Signaling: Apoptosis and Proliferation
Genes Gene Ontology p-value
23/50 Apoptosis (cell survival) 2.9E-13
14/50 Cell proliferation (cell cycling) 6.8E-5
Genes differentially regulated in early vs. late recurrence (emergence from dormancy)
TAM treatment or E2 withdrawal increase
apoptosis and reduce proliferation (Ki67) (modified from Johnston et al., 1999)
MCF-7 xenografts
Anastrazole reduces proliferation (Ki67) (modified from Dowsett et al., 2002)
ER+ breast tumors
Antiestrogens and aromatase inhibitors induce significant
growth arrest (Ki67) in patients’ tumors and experimental models
Figures from review by Urruticoechea et al., 2005
Might endocrine therapies induce dormancy?
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Robert Clarke, Ph.D., D.Sc.
Control of Signaling: Estrogen Response Landscape
Mathematical Modeling: task = nature of the ER switch (trace, low, high)
Chun Chen, et al., FEBS Lett, 2013
Chun Chen et al., Interface Focus, 2014
U=-ln(Pss) (Pss = steady state of the probability density function)
high
low
trace
trace
low
high
– Lines show minimum action paths between basins
– ERM and GFR are modeled using stochastic differential
equations (SDEs)
– E2ER applies quasi-equilibrium approximation
– White balls show the lowest (attractor) states
● ER acts as a bistable switch (rests in two different minimum states separated by a maximum)
● Cells can switch reversibly and robustly between E2 and GFR (growth factor) dependence
● E2-dependence → GFR-dependence (E2-independence) occurs more easily than the reverse
● Model can explain some of the molecular heterogeneity in cell populations
● Model predicts that intermittent treatment will be more effective than constant treatment
Ttreat = duration at Low E2
Tbreak = duration at high E2
PI = proliferation index
log10N = average cell number
Start with 1,000 cells
a
b
hypersensitive
resistant
independent
sensitive
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Represent the local structures of a network by a set of local conditional
probability distributions – decompose the entire expression profile
into a series of local networks (nodes; parents) – local dependency is learned
– local conditional probabilities are estimated from linear regression model
– allow more than one conditional probability distribution per node
– Lasso technique is used to limit overfitting
Identify motifs and “hot spots” within motifs – time series data from T47D cells ± E2; ± Fulvestrant (Lin et al., Genome Biol, 2004)
– key nodes identified include AKT, XBP1, NFκB, several BCL2 family members,
several MAPKs
Bai Zhang et al., Bioinformatics, 2009
plasma membrane
cytosol
nucleus
extracellularly exposed
plasma membrane
cytosol
nucleus
extracellularly exposed
XBP1 is a key component of the
Unfolded Protein Response (UPR)
Computational Modeling: Differential Dependency Network (DDN) analysis
Some Changes are Acquired Early
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Gene Name Gene Symbol1 Difference p-value
Genes Up-regulated in LCC9 vs. LCC1
Cathepsin D CTSD 5-fold <0.001
X-box Binding Protein-1 (TF) XBP1 4-fold <0.001
B-cell CLL/lymphoma 2 BCL2 4-fold <0.001
Epidermal growth factor receptor EGFR 2-fold 0.002
Heat Shock Protein 27 HSBP1 2-fold 0.001
NFκB (p65) (TF) RELA 2-fold <0.05
Genes Down-regulated in LCC9 vs. LCC1
Death Associated Protein 6 DAXX 6-fold 0.049
Early Growth Response-1 (TF) EGR1 3-fold <0.05
Interferon Regulatory Factor-1 (TF) IRF1 2-fold <0.05
Tumor Necrosis Factor-α TNF 2-fold <0.05
TNF-Receptor 1 TNFRSF1A 2-fold <0.05
Data are mean values of the relative level of expression for each gene to the nearest integer; 1HUGO Gene Symbols
UPR = Unfolded Protein Response; TF = transcription factor
Selected from molecular comparison of sensitive (LCC1) vs. stably resistant (LCC9)
autophagy
UPR
UPR
apoptosis
apoptosis
apoptosis
Some Early Changes are Retained
Zhiping Gu et al., Cancer Res, 2002
apoptosis/UPR
apoptosis/autophagy
UPR/apoptosis
apoptosis
Modeling Late Features of Antiestrogen Resistance
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Symbol Gene Name Change p-value # CREs
APBB2 amyloid beta (A4) precursor protein-binding -1.3 0.001 1
BCL2 B-cell CLL/lymphoma-2 3.1 0.029 3
CRK v-crk sarcoma virus CT10 oncogene homolog -2.0 0.003 2
ESR1 estrogen receptor alpha (ERα) 2.8 0.040 0*
IL24 interleukin 24 -9.7 <0.001 1
MYC v-myc myelocytomatosis viral oncogene homolog 1.6 0.04 1
PHLDA2 pleckstrin homology-like domain, family A, member 2 -3.3 0.004 2
S100A6 S100 calcium binding protein A6 (calcyclin) 2.3 0.001 1
XRCC6 X-ray repair complementing defective repair 6 1.6 0.016 1
XBP1(s) May Control Some Retained Changes
*several ATF6 sites that may be regulated by ATF6:XBP1 heterodimers
Bianca Gomez et al., FASEB J, 2007
Rong Hu et al., Mol Cell Biol, 2015
BCL2 regulation by XBP1 further implies use of the UPR to control cell survival
XBP1 induces ER and also binds to ER acting as a coactivator — feed-forward amplification activity
MYC is known to affect cellular metabolism — XBP1 regulation of MYC may link UPR to the regulation of metabolic homeostasis
— MYC regulated changes in metabolism could fuel the resistance phenotype
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Some Retained Changes are Functionally Important
FAS = Faslodex; Fulvestrant; ICI 182,780
TAM = Tamoxifen
Estrogen-independence is phenotypically similar
to aromatase inhibitor resistance
XBP1(s) confers Estrogen Independence
MCF7/XBP1
MCF7/c
MCF7/XBP1
MCF7/c
XBP1(s) confers Antiestrogen Resistance
T47D/XBP1
T47D/c
T47D/XBP1T47D/XBP1
T47D/cT47D/c
EtOH TAM FAS
Rela
tiv
e A
po
pto
sis
0
2
4
6
8
10MCF7/c
MCF7/XBP1
p<0.001 for ANOVA,
*p<0.05
*
*Annexin V
Bianca Gomez et al., FASEB J, 2007
XBP1 activation in the UPR (spliced XBP1 is a
transcription factor)
XBP1 and TAM recurrence n=100 cases
Davies et al., 2008
(qPCR based study)
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Coordinated Functions: Unfolded Protein Response (UPR)
• Several of the genes identified implicate the unfolded protein response (UPR) in
endocrine responsiveness
• UPR can be triggered by decrease in oxygen, nutrients, (e.g., glucose), etc. and
initially protects the cell until the stress subsides
• UPR is a quality control system that recognizes improperly folded proteins and
refolds them, or facilitates their degradation, in response to stress
• Energy is required for protein folding - induction of the UPR implies that endocrine
therapies reduce cellular energy store
• GRP78 is the primary upstream regulator of all three arms (PERK, ATF6, IRE1)
Primary sensor is GRP78
(Glucose Regulated Protein78
BiP; HSPA5)
Endoplasmic
Reticulum Nucleus
Three arms of the UPR
PERK, ATF6, IRE1α
XBP1 is a key effector in two
arms of the UPR
Figure adapted from Szegezdi et al., 2006
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GRP78 is upregulated in Antiestrogen Resistance
Katherine Cook et al., Cancer Res, 2012
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GRP78 and BCL2 are upregulated in LCC9 cells
Both GRP78 (upstream in UPR) and BCL2 (downstream) are increased in resistant LCC9 cells
from Szegezdiet et al. 2006
Katherine Cook et al., Cancer Res, 2012
In canonical UPR, BCL2 is regulated by
CHOP and/or JNK
BCL2
Actin
LCC1 LCC9
ctrl. TAM ICI TUN ctrl. TAM ICI TUN
BiP/GRP78
downstream
upstream
Our in silico DDN subnetwork model predicts that BCL2 is regulated by XBP1s
BCAR3MAPK3
ABCB11
NFKB1
NFKB2ESR2
BIK
MAPK13
HOXA10
EBAG9
CAV3
F12CGA
BCL2
XBP1
Plasma Membrane
Cytosol
Nucleus
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Rebecca Riggins et al., Mol Cancer Ther, 2005
Anatasha Crawford et al., PLoS ONE, 2010
XBP1 siRNA reduces BCL2
in LCC9 cells
LCC9 cells have lost BCL2 regulation BCLW is also upregulated
in LCC9 cells
XBP1 regulates autophagy
Autophagy Apoptosis
BECN1 (siRNA) and 3-MA optimally restore
antiestrogen sensitivity when combined with BCL2
inhibition
UPR→XBP1(s) →BCL2: Autophagy/Apoptosis Integration
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UPR-Autophagy Integration
Iman Tavasolly et al., CPT Pharmacometrics Syst Pharmacol, in press
Katherine Cook & Clarke Front Pharmacol submitted
Katherine Cook et al., Cancer Res, 2012
glucose
GRP78
XBP1
GRP78
XBP1
GRP78
Chloroquine
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Chloroquine Resensitizes Antiestrogen Resistant Tumors
Katherine Cook et al., Clin Cancer Res, 2014
CQ is more effective with
TAM than with ICI
CQ is more effective with
TAM than with ICI
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GRP78 Alters Antiestrogen Responsiveness
Katherine Cook et al., Cancer Res, 2012
TAM = 4-hydroxyTamoxifen
ICI = ICI 182780, Faslodex, Fulvestrant
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METABOLITE
GENE/PROTEIN
MET. – PROT./MET.
PROT. – PROT.
Blocking autophagy reduces inputs into intermediate metabolism (so we mapped metabolome onto transcriptome)
Insulin/IGF signaling
Cell survival signaling
Energy metabolism
More Coordinated Functions: Metabolism
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Endocrine Therapies Induce Energy Deprivation
Glucose uptake is comparable between
E2-treated (red) sensitive cells and resistant cells
(independent of endocrine treatment)
Glucose uptake is not affected by antiestrogen treatment
in resistant cells but is suppressed in sensitive cells
LCC1 LCC9
Glu
co
se
Up
take
re
lative
to
LC
C1
Ve
hic
le
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Vehicle
E2
TAM
FAS
PAC Glucose
ATP levels drop with treatment in sensitive cells
Resistant cells have lower basal ATP levels that are
refractory to endocrine treatment
LCC1 LCC9
AT
P levels
rela
tive to L
CC
1 V
ehic
le
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Vehicle
E2
TAM
FAS
PAC ATP
E2=17β-estradiol
TAM=Tamoxifen
FAS=Fulvestrant/Faslodex
PAC=Paclitaxel
Vehicle=ethanol and no E2
Ayesha Shajahan-Haq et al., Molecular Cancer, 2014
Surojeet Sengupta, Rong Hu, in review
MCF7 vs. LCC1 LCC1 vs. LCC9
MCF7 –E2 vs. MCF7 +E2
Altered regulation of select members of the SLC family
including induction of SLC2A1 (GLUT1)
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Complete medium Glutamine (no glucose) medium
MYC, Glutamine, and UPR Enable LCC9 Survival
UPR Activation
Ayesha Shajahan et al., Mol Cancer, 2014
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Metabolic Adaptations
System Coordination: Network Modeling
Tyson et al., Nature Rev Cancer, 2011
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Cell fate regulation is a highly integrated and coordinated system
Glucose, Cell Metabolism, and Endocrine Resistance
Clarke et al., Cancer Res, 2012
Katherine Cook et al., Cancer Res, 2012
(poor vascularization; loss of growth factor stimulation, etc.)
GRP78 = HSPA5 = BiP
BCL2,
et al.
BECN1
Apoptosis
UPR
Autophagy
Proliferation
Metabolism
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Resistant Cells Can Communicate their Phenotype
As few as 1:20 resistant cells enable some sensitive cells to survive ICI
Surojeet Sengupta, Rong Hu, in review
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Clustering by Protein (iTRAC) Profiles
Molecular profiles (proteome) match phenotypic responses to ICI
Resistant Sensitive
Treatment Control = vehicle
Surojeet Sengupta, Rong Hu, in review
Cell Fate (i) Apoptosis
Glucose (influx)
ERα action Endocrine
Therapy
Cell Fate (ii) Prosurvival Autophagy
Cell Fate (ii) Prodeath Autophagy
Chloroquine
Cell Fate (iii) Proliferation
Metformin
GRP78
Gedatolisib
Stress Response
(i) Unfolded Protein Response
Metabolism
Sensing (i) Glucose
Glucose regulated proteins (GRPs)
AMPK (ATP)
ATP
Metabolism (i) Glucose
Glucose transport
Glycolysis
Gluconeogenesis
TCA
Cycle
Unfolded Proteins
ULK1 complex
mTOR complex
AKT
PI3K
Palbociclib
Modules
Drugs
Everolimus
Venetoclax
Deoxyglucose
IT139
Alpelisib
CB839 Glutaminase
Targeting: Many Options
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Acknowledgments
J. Michael Dixon University of Edinburgh, Breast Unit
William R. Miller University of Edinburgh, Breast Unit
Lorna Renshaw University of Edinburgh, Breast Unit
Andrew Simms University of Edinburgh, Breast Unit
Alexey Larionov University of Edinburgh, Breast Unit
Bill Baumann Engineering & Computer Science
Chun Chen Engineering & Computer Science
Li Chen Engineering & Computer Science
Iman Tavasolly Biological Sciences & Virginia Bioinformatics Institute
John Tyson Biological Sciences & Virginia Bioinformatics Institute
Anael Verdugo Biological Sciences & Virginia Bioinformatics Institute
Yue Wang Engineering & Computer Science
Jianhua Xuan Engineering & Computer Science
Bai Zhang Engineering & Computer Science
Harini Aiyer Amrita Cheema Sandra Jablonski
Younsook Cho Katherine Cook Yongwei Zhang
Ahreej Eltayeb Caroline Facey Louis Weiner
Leena Hilakivi-Clarke Rong Hu Subha Madhavan
Mike Johnson Lu Jin Yuriy Gusev
Habtom Ressom Rebecca B. Riggins Robinder Gauba
Jessica Schwartz Ayesha N. Shajahan Minetta C. Liu (now at Mayo)
Anni Wärri Louis M. Weiner Alan Zwart
U54-CA149147 ICBP Center for Cancer Systems Biology
29XS194 NCI In Silico Research Center of Excellence
P30 CA51008 NCI Cancer Center Support Grant
U01-CA-184902; R01-CA131465; R01-CA149653
The patients who contributed to the clinical studies
Zhen Zhang
Erica Golemis
Ilya Serebriiskii
Edna Cukierman
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