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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Robert Clarke, Ph.D., D.Sc. Systems Biology in Cancer Research Study of an organism viewed as an integrated and interacting network of genes, proteins, and biochemical reactions that give rise to life…* *Lee Hood - Institute for Systems Biology A systems biology approach is required to integrate knowledge from cancer biology with computational and mathematical modeling ● 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 Systems Biology Research Cycle Endocrinologist 94: 13, 2010 Biological cycle Integration with modeling

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Robert Clarke, Ph.D., D.Sc. Resistance to Endocrine Therapies 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) Age (Menopausal Status) Risk Reduction 1 Recurrence: <50 years (ER+)45 ± 8% Recurrence: years (ER+)54 ± 5% Recurrence (ER-)6 ± 11% (not significant) Death: any cause <50 years (ER+)33 ± 6% Death: any cause years (ER+)32 ± 10% Death: any cause (ER-)-3 ± 11% (not significant) } Benefit from TAM 1 Proportional reduction in the 10-year risk of recurrences or death from the Early Breast Cancer Trialists Group meta analyses

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi ● 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 Are all Tamoxifen Failures the Same? Computational Modeling: task = classification health care cost (treatment) ER+ human cost (mortality) ER-

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi 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 benchmarks 2  Clinical characteristics – n=131 cases; >95% ER+; almost all Invasive Ductal Carcinomas – Tamoxifen only after surgery and radiotherapy – ≥15 years of clinical follow-up 1 Venet 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 2 Mackay et al., JNCI, 2011 report that the molecular subgroup classifications for the LumA, LumB, and normal-like breast cancer subgroups are not robust

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Early (≤3 yr) and Later (≥5 yr) TAM Recurrences AccuracySpecificitySensitivityAUCPPVNPVHazard RatioP-value < BC Loi et al. AccuracySpecificitySensitivityAUCPPVNPVHazard RatioP-value Sensitivity 1 - Specificity % Survival Time 1 - Specificity Sensitivity % Survival 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)

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Approach to Network Modeling ● 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 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) Chen et al. Nucl Acid Res, in press, 2013Wang et al., J Mach Learn Res, in press, 2013Yu et al, Bioinformatics, in revision, 2013 Gusev et al., Cancer Informatics, 12: 31-51, 2013Gu et al. Bioinformatics, 28: , 2012Tyson et al., Nature Rev Cancer, 11: , 2011 Zhang et al., PLoS ONE, 5 (4): e10268, 2010Yu et al., J Mach Learn Res, 11; , 2010Chen et al., Bioinformatics, 26: , 2010 Zhang et al., Bioinformatics 25: , 2009Clarke et al., Nature Rev Cancer 8: 37-49, 2008Wang et al., Bioinformatics, 23: , 2007 Computational modeling Physical 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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi 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 function 1 – most tumors acquiring endocrine resistance retain ERα expression 2 – responses to 2 nd and 3 rd line endocrine therapies are relatively common 2 – small molecule inhibitors and RNAi against ERα inhibit resistant cells 3 ● We don’t know precisely how ERα signaling is regulated or wired ● We need an ERα-driven network model to guide our studies 1 Johnson et al., Nat Med, Clarke et al. Pharmacol Rev, Kuske et al., Endocr Relat Cancer, 2006 Wang et al., Cancer Cell, 2006 ERα

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Roadmap for Modeling ER-Related Signaling John Tyson et al., Nature Rev Cancer, 2011 Primary Inputs/Regulators Estrogen Receptors Growth Factor Receptors (e.g., EGFR; Her2) 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 Outputs

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi  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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi ER and EGFR/HER2 Crosstalk ParametersDescriptionValue γ EPI Rate of EPI reaching its steady state 3×10 −4 min -1 γ GFR Rate of GFR reaching its steady state 5×10 −2 min -1 ω EPI Basal inhibition of EPI −1.92 ω GFR Basal inhibition of GFR −4−4 ω E2ER Basal inhibition of E2ER −2.1 ω ERP Basal inhibition of ERP −1.5 ω EPI,GFR EPI activation by GFR 6 ω GFR,GFR GFR activation by EPI 5 ω GFR,E2ER GFR inhibition by E2ER −2−2 ω GFR,ERP GFR activation by ERP 1.85 ω GFR,GFRover GFR activation by GFRover 0.15 ω E2ER,E2 E2ER activation by E2 3 ω ERP,GFR GFR activation by ERP 3 E2 E2 level in MCF7 cells 1 (normal); 0 (E2-depleted cells) ERT Parameter determining total ER level in MCF7 cells 1 (normal); >1 (ER-overexpressed cells) GFRover Excess GFR in GFR-transfected MCF7 cells 0 (normal); >0 (GFR-transfected cells) 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 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 Chun Chen et al., FEBS Lett, 2013 Mathematical Modeling: task = nature of ER regulation of cell fate

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi ER is a Bistable Switch for EGFR/HER2 Crosstalk John Tyson et al., Nature Rev Cancer, 2011 Bistability: resting in two different minimum states separated by a maximum ● 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 Mathematical Modeling: task = nature of ER regulation of cell fate Chun Chen et al., FEBS Lett, 2013

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Robert Clarke, Ph.D., D.Sc. Minimum Action Paths characterize state transitions Intermittent therapy opens a 2 nd response window Shifting E2 dose response Ligand Dependent Ligand Supersensitive Ligand Independent Phenotype Transitions Support Intermittent Therapy Chun Chen et al., in preparation Mathematical Modeling: task = ER-driven phenotype transitions

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Robert Clarke, Ph.D., D.Sc. ● 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? Factors Affecting Endocrine Responsiveness

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi 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 – 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 Minetta Liu et al., in review Bai Zhang et al., in preparation Number of nodes Computational Modeling: task = network topology Genes Gene Ontologyp-value 23/50“Apoptosis” 2.9E-13 14/50“Cell proliferation” 6.8E-5 Minetta Liu (Georgetown; Mayo) Mike Dixon; Bill Miller (Edinburgh) Jason Xuan (Virginia Tech) Joseph Wang (Virginia Tech) Circles = nodes Lines = edges NFκB BCL2 AKT MAPK EGFR ERα red = overexpressed in ‘Early’ green = overexpressed in ‘Late’ SRC ERβ AR Circles = nodes Lines = edges red = overexpressed in ‘Early’ green = overexpressed in ‘Late’ yellow = inconsistent MAPK ERα SRC ERβ AR BCL2 EGFR

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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Gene NameGene Symbol 1 Differencep-value Genes Up-regulated in LCC9 vs. LCC1 Cathepsin DCTSD5-fold<0.001 X-box Binding Protein-1 (TF)XBP14-fold<0.001 B-cell CLL/lymphoma 2BCL24-fold<0.001 Epidermal growth factor receptorEGFR2-fold0.002 Heat Shock Protein 27HSBP12-fold0.001 NFκB (p65) (TF)RELA2-fold<0.05 Genes Down-regulated in LCC9 vs. LCC 1 Death Associated Protein 6DAXX6-fold0.049 Early Growth Response-1 (TF)EGR13-fold<0.05 Interferon Regulatory Factor-1 (TF)IRF12-fold<0.05 Tumor Necrosis Factor-αTNF2-fold<0.05 TNF-Receptor 1TNFRSF1A2-fold<0.05 Data are mean values of the relative level of expression for each gene to the nearest integer; 1 HUGO Gene Symbols UPR = Unfolded Protein Response; TF = transcription factor Selected from molecular comparison of sensitive (LCC1) vs. stable resistant variant (LCC9) autophagy UPR apoptosis Some Early Changes are Retained Zhiping Gu et al., Cancer Res, 2002 Todd Skaar et al, J Steroid Biochem Mol Biol, 1998 apoptosis/UPR apoptosis UPR/apoptosis

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi SymbolGene NameChangep-value# CREs APBB2amyloid beta (A4) precursor protein-binding BCL2B-cell CLL/lymphoma CRKv-crk sarcoma virus CT10 oncogene homolog ESR1estrogen receptor alpha (ERα) * IL24interleukin < MYCv-myc myelocytomatosis viral oncogene homolog PHLDA2pleckstrin homology-like domain, family A, member S100A6S100 calcium binding protein A6 (calcyclin) XRCC6X-ray repair complementing defective repair 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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Some Retained Changes are Functionally Important XBP1(s) confers Antiestrogen ResistanceXBP1 cDNA increases BCL2XBP1 siRNA reduces BCL2 Rebecca Riggins et al., Mol Cancer Ther, 2005 Bianca Gomez et al., FASEB J, 2007 Inhibition of both BCL2 and BCLW is better BECN1 (siRNA) and 3-MA each reverse antiestrogen resistance when combined with BCL2 (YC137) inhibition apoptosisproliferation Anatasha Crawford et al., PLoS ONE, 2010 Yanxia Ning et al., Mol Cancer Ther, 2010

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi BCL2 and Total-BH3 Predicts Level of Apoptosis Mathematical Modeling: task = explore role of BCL2 family in apoptosis Bill Bauman, Tongli Zhang in preparation 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 17 nonlinear ordinary differential equations and 44 parameters for the various molecular species

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Coordinated Functions: BCL2 Family and Cell Fate Apoptosis (cell death) Autophagy (cell survival) altered cell metabolism?

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi 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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi VehicleICI 182, 780 Monodansylcadaverine-labeled Vesicles XBP1(s) Induces Pro-Survival Autophagy LC3-GFP expression MCF7/XBP1 VehicleMCF7/XBP1 1uM FAS MCF7/EV 1uM FasMCF7/EV Vehicle Ayesha Shajahan et al., submitted

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi  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?

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi High Confidence Interaction Network METABOLITE GENE/PROTEIN MET. – PROT./MET. PROT. – PROT. Mapping metabolome onto transcriptome (LCC1 vs. LCC9) Insulin/IGF signaling Cell survival signaling Energy metabolism Ayesha Shajahan et al., submitted

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Antiestrogens Reduce Intracellular ATP ATP levels drop with treatment in sensitive cells Resistant cells have lower basal ATP levels that are refractory to endocrine treatment ATP Vehicle=ethanol and no E2 E2=17β-estradiol TAM=Tamoxifen FAS=Fulvestrant/Faslodex PAC=Paclitaxel Ayesha Shajahan et al., submitted

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Complete medium Glutamine (no glucose) medium MYC, Glutamine, and UPR Enable LCC9 Survival Ayesha Shajahan (GU) et al., submitted UPR Activation

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

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

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Metabolic Adaptations System Coordination: Network Modeling John Tyson et al., Nature Rev Cancer, 2011

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Summary ● 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)

COMPREHENSIVE CANCER CENTER at GEORGETOWN UNIVERSITY Lombardi Acknowledgments J. Michael DixonUniversity of Edinburgh, Breast Unit William R. MillerUniversity of Edinburgh, Breast Unit Lorna RenshawUniversity of Edinburgh, Breast Unit Andrew SimmsUniversity of Edinburgh, Breast Unit Alexey LarionovUniversity of Edinburgh, Breast Unit Bill Baumann Engineering & Computer Science Chun ChenEngineering & Computer Science Li ChenEngineering & Computer Science Iman TavasollyBiological Sciences & Virginia Bioinformatics Institute John TysonBiological Sciences & Virginia Bioinformatics Institute Anael VerdugoBiological Sciences & Virginia Bioinformatics Institute Yue Wang Engineering & Computer Science Jianhua Xuan Engineering & Computer Science Bai Zhang Engineering & Computer Science Erica Golemis Rochelle Nasto Ilya Serebriiskii Harini AiyerAmrita CheemaSandra Jablonski Younsook ChoKatherine Cook Yongwei Zhang Ahreej EltayebCaroline FaceyLou Weiner Leena Hilakivi-ClarkeRong HuSubha Madhavan Mike JohnsonLu JinYuriy Gusev Habtom RessomRebecca B. Riggins Robinder Gauba Jessica SchwartzAyesha ShajahanMinetta Liu (now at Mayo) Anni WärriAlan Zwart U54-CA ICBP Center for Cancer Systems Biology 29XS194 NCI In Silico Research Center of Excellence R01-CA131465; R01-CA KG BC BC The patients who contributed to the clinical studies