Modeling Endocrine Resistance in Breast Cancer

1. Modeling Endocrine Resistance in Breast Cancer Robert Clarke, Ph.D., D.Sc. Professor of Oncology Director, Center for Cancer Systems Biology Dean for Research Georgetown University Medical Center

2. Systems Biology Research Cycle Endocrinologist 94: 13, 2010 Biological cycle Integration with modeling Systems Biology in Cancer Research A systems biology approach is required to integrate knowledge from cancer biology with computational and mathematical modeling Study of an organism viewed as an integrated and interacting network of genes, proteins, and biochemical reactions that give rise to life…* • Systems biology goals • interactions among the components of a biological system • how these interactions control system function and behavior • integrate and analyze complex data from multiple sources using interdisciplinary tools • build in silico models of system (network) function *Lee Hood - Institute for Systems Biology

3. Resistance to Endocrine Therapies 1Proportional reduction in the 10-year risk of recurrences or death from the Early Breast Cancer Trialists Group meta analyses 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) To understand how some ER+ breast cancers become (or already are at diagnosis) resistant to endocrine therapies, we invoke an integrated, multimodal,network hypothesis • network is modular and exhibits both redundancy and degeneracy • signaling is highly integrated and coordinates many cellular functions In the face of the stresses induced by endocrine therapies, the network modules of interest are those that regulate cell fate, i.e., the cell’s choice: • to live or die (e.g. control/execution of apoptosis, autophagy, necrosis) • if to live, whether or not to proliferate (i.e., cell cycle control/execution) } Benefit from TAM

4. Are all Tamoxifen Failures the Same? Computational Modeling: task = classification • Compare failures “on-treatment” (early; ≤3yrs) with those that • recurred (distant recurrence) later “off-treatment” (later; ≥5 yrs) • Construct molecular classifiers using gene expression microarray • data from breast tumors collected at diagnosis • integrated resampling workflow to ease the “gene selection bias” problem • Support Vector Machine with recursive feature elimination ER+ ER- human cost (mortality) health care cost (treatment)

5. Classifying Early vs. Later TAM Recurrences • Resampling approaches used to ease the “gene selection bias” problem • – training procedure (block a) • – validation step (block b) • Must outperform random gene sets of the same size (10,000 random sets)1 • Must meet n=7 pre-established performance benchmarks2 • Clinical characteristics • n=131 cases; >95% ER+; almost all Invasive Ductal Carcinomas • Tamoxifen only after surgery and radiotherapy • ≥15 years of clinical follow-up 1Venet et al., PLoS Comp Biol, 2011 report that >60% (in some cases 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, and normal-like breast cancer subgroups are not robust

6. Sensitivity % Survival 1 - Specificity Time Sensitivity % Survival 1 - Specificity Time Early (≤3 yr) and Later (≥5 yr) TAM Recurrences BC030280 Loi et al. 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)

7. Approach to Network Modeling • The module(s) of interest exist within an immense search space (the human • interactome) and we don’t know all of the genes/proteins in each module • Networks are high dimensional and so the data have unique properties, e.g., curse • of dimensionality; confound of multimodality; scale free; small world; etc. • Clarke et al., Nature Rev Cancer, 2008; Wang et al., Br J Cancer 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 functions • We apply both computational and mathematical modeling tools • computational modelscan 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 Physical modeling Chen et al. Nucl Acid Res, in press, 2013 Wang et al., J Mach Learn Res, in press, 2013 Yu et al, Bioinformatics, in revision, 2013 Gusevet al., Cancer Informatics, 12: 31-51, 2013Guet al. Bioinformatics, 28: 1990-1997, 2012 Tyson et al., Nature Rev Cancer, 11: 523-532, 2011 Zhang et al., PLoS ONE, 5 (4): e10268, 2010 Yu et al., J Mach Learn Res, 11;2141-2167, 2010 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 Wang et al., Bioinformatics, 23: 2024-2027, 2007

8. Network Modeling: Where to Start? • We have selected our key modules of interest • 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 development and function1 • most tumors acquiring endocrine resistance retain ERαexpression2 • 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 • We need an ERα-driven network model to guide our studies ERα 1Johnson et al., Nat Med , 2003 2Clarke et al. Pharmacol Rev, 2002 3Kuske et al., EndocrRelat Cancer, 2006 Wang et al., Cancer Cell, 2006

9. Roadmap for Modeling ER-Related Signaling Hypothesis: With the stresses induced by endocrine therapies, the network modules of interest are those that regulate cell fate, i.e., the breast cancer cell’s choice • to live or die (e.g. control/execute apoptosis, autophagy, necrosis, UPR) • if to live, whether or not to proliferate (i.e., cell cycle control/execution) Primary Inputs/Regulators Estrogen Receptors Growth Factor Receptors (e.g., EGFR; Her2) Primary Outputs John Tysonet al.,Nature Rev Cancer, 2011

10. ER is the most upstream regulator of cell fate decisions ER can be mutated, phosphorylated, degraded, recycled mutations appear to be relatively rare in clinical samples Fulvestrant acts by targeting the receptor for ubiquitin-mediated degradation ER can activated by ligand or by growth factors several growth factors and their receptors signaling to MAPKs that can activate ER through phosphorylation Regulation of ER activation may be a central determinant of endocrine responsiveness ERα as a “Master” Regulator of Cell Fate

11. ER and EGFR/HER2 Crosstalk Mathematical Modeling: task = nature of ER regulation of cell fate Primary data from multiple clones of MCF-7 cells transfected with either HER2 or EGFR and assayed for E2-dependent or E2-independent growth Liu et al., Breast Cancer Res Treat, 1995 Miller et al., Cell Growth Diff, 1994 Crosstalk between ER and GFR GFR = growth factor receptor (HER2 or EGFR) GFRover = transfected with GFR EPI = epigenetic components ERP = estrogen-independent E2ER= estrogen-dependent ERT = total ER levels Chun Chen et al., FEBS Lett, 2013

12. ER is a Bistable Switch for EGFR/HER2 Crosstalk Mathematical Modeling: task = nature of ER regulation of cell fate Bistability: resting in two different minimum states separated by a maximum John Tyson et al.,Nature Rev Cancer, 2011 • Breast cancer cells can switch reversibly and robustly between E2 • and GFR dependence • GFR can inhibit ER expression and/or activate (phosphorylate) any remaining ER • cells can eliminate or silence GFR plasmid (epigenetic) and upregulate ER • Model can explain some of the molecular heterogeneity in cell • populations • Blocking either pathway increases the likelihood that the other • pathway will be activated • E2-dependence  GFR-dependence (ER-independence) occurs • more easily/rapidly than the reverse Chun Chen et al., FEBS Lett, 2013

13. Phenotype Transitions Support Intermittent Therapy Mathematical Modeling: task = ER-driven phenotype transitions Shifting E2 dose response Ligand Dependent Ligand Supersensitive Ligand Independent Minimum Action Paths characterize state transitions Intermittent therapy opens a 2nd response window Chun Chen et al., in preparation

14. Factors Affecting Endocrine Responsiveness • What molecular events are associated with endocrine • resistance? • When are these changes acquired (early, late)? • Which changes are functionally/mechanistically important? • How do cells coordinate their functions to make and • execute a cell fate decision?

15. Number of nodes ERαSignaling: Early vs. Late Recurrences Computational Modeling: task = network topology • 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 • 2-steps per iteration (walk) • 300,000 iterations • 1,452 neighbors selected; n=50 are frequently visited • Model the n=50 using the microarray data and • MS/RW method AR Genes Gene Ontology p-value 23/50 “Apoptosis” 2.9E-13 14/50 “Cell proliferation” 6.8E-5 red = overexpressed in ‘Early’ green = overexpressed in ‘Late’ yellow = inconsistent red = overexpressed in ‘Early’ green = overexpressed in ‘Late’ EGFR SRC SRC NFκB BCL2 MAPK ERβ Minetta Liu (Georgetown; Mayo) Mike Dixon; Bill Miller (Edinburgh) Jason Xuan (Virginia Tech) Joseph Wang (Virginia Tech) MAPK ERβ ERα BCL2 AR EGFR ERα Circles = nodes Lines = edges Circles = nodes Lines = edges AKT Minetta Liu et al., in review Bai Zhang et al., in preparation

16. Some Changes are Acquired Early Computational Modeling: Differential Dependency Network (DDN) analysis • 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 XBP1 is a key component of the Unfolded Protein Response (UPR) BCL2 (large family) regulate apoptosis/survival Yue Wang et al., Bioinformatics, 2009

17. Some Early Changes are Retained Selected from molecular comparison of sensitive (LCC1) vs. stable resistant variant (LCC9) autophagy UPR apoptosis/UPR UPR UPR/apoptosis apoptosis apoptosis apoptosis apoptosis 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 ZhipingGuet al., Cancer Res, 2002 Todd Skaaret al, J Steroid BiochemMolBiol, 1998

18. 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

19. Some Retained Changes are Functionally Important XBP1(s) confers Antiestrogen Resistance XBP1 cDNA increases BCL2 XBP1 siRNA reduces BCL2 Inhibition of both BCL2 and BCLW is better BECN1 (siRNA) and 3-MA each reverse antiestrogen resistance when combined with BCL2 (YC137) inhibition Rebecca Riggins et al., Mol Cancer Ther, 2005 Bianca Gomez et al., FASEB J, 2007 proliferation apoptosis Anatasha Crawford et al., PLoS ONE, 2010 YanxiaNinget al.,Mol Cancer Ther, 2010

20. BCL2 and Total-BH3 Predicts Level of Apoptosis Mathematical Modeling: task = explore role of BCL2 family in apoptosis 17 nonlinear ordinary differential equations and 44 parameters for the various molecular species Model predicts %apoptosis and provides an approximate measure of responsiveness based on the concentrations of BCL2 and the total of all BH3 members of the BCL2 family PCD = programmed cell death/apoptosis Bill Bauman, Tongli Zhang in preparation

21. Coordinated Functions: BCL2 Family and Cell Fate Apoptosis (cell death) Autophagy (cell survival) altered cell metabolism?

22. Autophagy (self-eating) Normal process through which aged or damaged subcellular organelles are degraded and their components recycled into intermediate cellular metabolism BECN1 (siRNA) and 3-MA (inhibit autophagy) reverse antiestrogen resistance when combined with BCL2 (YC137) inhibition Anatasha Crawford et al., PLoS ONE, 2010

23. LC3-GFP expression Monodansylcadaverine-labeled Vesicles MCF7/EV Vehicle MCF7/EV 1uM Fas MCF7/XBP1 Vehicle MCF7/XBP1 1uM FAS Vehicle ICI 182, 780 XBP1(s) Induces Pro-Survival Autophagy Ayesha Shajahanet al.,submitted

24. Metabolome: collection of metabolites (~2500 identified in humans) e.g., within a cell reflects the physiological state of a cell Intermediates and products of metabolism (<1 kDa in size) e.g., amino acids, antioxidants, nucleotides, sugars, etc. Metabolites separated by mass and charge using UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) Data processed using Random Forest algorithm to identify most robust discriminant metabolites Coordinated Functions: Metabolism How does a cell coordinate its resources to allow execution of a cell fate decision?

25. High Confidence Interaction Network METABOLITE Mapping metabolome onto transcriptome (LCC1 vs. LCC9) GENE/PROTEIN MET. – PROT./MET. Insulin/IGF signaling PROT. – PROT. Cell survival signaling Energy metabolism Ayesha Shajahanet al.,submitted

26. Antiestrogens Reduce Intracellular ATP Vehicle=ethanol and no E2 E2=17β-estradiol TAM=Tamoxifen FAS=Fulvestrant/Faslodex PAC=Paclitaxel ATP ATP levels drop with treatment in sensitive cells Resistant cells have lower basal ATP levels that are refractory to endocrine treatment Ayesha Shajahanet al.,submitted

27. MYC, Glutamine, and UPR Enable LCC9 Survival Complete medium Glutamine (no glucose) medium UPR Activation Ayesha Shajahan (GU) et al.,submitted

28. Cellular Sensing of Nutrient/Energy Deprivation GRP78 and AMPK may be energy sensors and autophagy switches XBP1 BCL2  BCL2:BECN1 XBP1  BCL2:BECN1 may confer degenerancy on autophagy induction Katherine Cook et al., Cancer Res, 2012

29. A Mechanistic Topology of Endocrine Resistance Cellular metabolism may be an essential determinant of cell fate or Glutamine (poor vascularization; loss of growth factor stimulation, etc.) Autophagy UPR BECN1 BCL2, et al. Apoptosis GRP78 = HSPA5 = BiP Clarke et al., Cancer Res, 2012 Katherine Cook et al., Cancer Res, 2012

30. System Coordination: Network Modeling Metabolic Adaptations John Tyson et al.,Nature Rev Cancer, 2011

31. Systems biology approaches provide one way to explore phenotypes and to integrate cellular and molecular features to understand mechanism(s) Cells appear to experience EnR stress and can use GRP78 to activate the UPR, which then integrates signaling to determine cell fate inhibits apoptosis (e.g., antiapoptotic BCL2 family members) induces autophagy (e.g., BECN1, antiapoptotic BCL2 family members, AMPK, mTOR) initiates/coordinates changes in metabolism required to execute the cell fate decision Antiestrogens modify cellular energy metabolism leading to changes in glutamate/glutamine/glucose uptake and intracellular AMP levels autophagy also provides intermediate metabolites to fuel the cell fate decision ER acts as a bistable switching mechanism to affect phenotype, making intermittent therapy a more effective strategy Some early adaptations to treatment are retained in resistant cells Resistance may not require many new nodes but does change the nature/usage of existing edges among nodes (it’s mostly the same network of nodes, its just wired differently) Summary

32. Acknowledgments The patients who contributed to the clinical studies J. Michael DixonUniversity of Edinburgh, Breast Unit William R. MillerUniversity of Edinburgh, Breast Unit Lorna Renshaw University of Edinburgh, Breast Unit Andrew Simms University of Edinburgh, Breast Unit Alexey LarionovUniversity of Edinburgh, Breast Unit HariniAiyer Amrita Cheema Sandra Jablonski Younsook Cho Katherine Cook Yongwei Zhang AhreejEltayeb Caroline Facey Lou Weiner LeenaHilakivi-Clarke Rong Hu SubhaMadhavan Mike Johnson Lu Jin YuriyGusev HabtomRessom Rebecca B. Riggins RobinderGauba Jessica Schwartz Ayesha ShajahanMinetta Liu (now at Mayo) AnniWärri Alan Zwart Bill Baumann Engineering & Computer Science Chun Chen Engineering & Computer Science Li Chen Engineering & Computer Science ImanTavasolly Biological Sciences & Virginia Bioinformatics Institute John Tyson Biological Sciences & Virginia Bioinformatics Institute Anael Verdugo Biological Sciences & Virginia Bioinformatics Institute Yue Wang Engineering & Computer Science JianhuaXuanEngineering & Computer Science BaiZhang Engineering & Computer Science Erica Golemis Rochelle Nasto IlyaSerebriiskii KG090245 U54-CA149147 ICBP Center for Cancer Systems Biology 29XS194 NCI In Silico Research Center of Excellence R01-CA131465; R01-CA149653 BC073977 BC122874