a systems biology view of endocrine...

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.


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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


Lombardi John Tyson, et al., Nature Rev Cancer, 2011

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


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


Lombardi Bianca Gomez et al., FASEB J, 2007

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


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


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


(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



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Clustering by Protein (iTRAC) Profiles

Molecular profiles (proteome) match phenotypic responses to ICI

Resistant Sensitive

Treatment Control = vehicle


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

a systems biology view of endocrine...

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