A systems approach to marker guided therapy in breast cancer

1. A systems approach to marker guided therapyin breast cancer Joe W. Gray, Ph.D. Lawrence Berkeley National Laboratory University of California, San Francisco

2. A systems approach to marker guided therapyin breast cancer Breast cancer overview and statement of the problem An in vitro systems approach to match treatment to “ome” Improving and testing the model

3. A systems approach to marker guided therapyin breast cancer Breast cancer overview and statement of the problem An in vitro systems approach to match treatment to “ome” Improving and testing the model

4. Stage Distribution and 5-year Relative Survival by Stage at Diagnosis for 1999-2006, All Races, Females World wide incidence - 1,150,000/yr Worldwide mortality - 410,000/yr SEER Registry

5. Overall goal • Improve treatment by identifying molecular subtype markers that • predict resistance to existing therapies • predict response to experimental therapies

6. Hundreds of compounds are approved or well along in the developmental pipeline How do we find the most effective for breast cancer?

7. International cancer genomics efforts are substantially increasing the number of recognizable cancer subtypes that may respond differentially to specific therapies

8. State of the breast cancer genome • Remarkable genomic and epigenomic heterogeneity between and within tumors • Hundreds of genes and gene networks are deregulated in ways that contribute to cancer pathophysiology • Subtypes are defined by aberrations at multiple levels:mutation, structure, copy number, chromatin modification, ncRNA, … • Subtypes defined by recurrent aberrations are associated with outcome • Response varies with subtype

9. State of the breast cancer genome • Remarkable genomic and epigenomic heterogeneity between and within tumors • Hundreds of genes and gene networks are deregulated in ways that contribute to cancer pathophysiology • Subtypes are defined by aberrations at multiple levels:mutation, structure, copy number, chromatin modification, ncRNA, … • Subtypes defined by recurrent aberrations are associated with outcome • Response varies with subtype Copy num. Mutation Protein RNA

10. State of the breast cancer genome • Remarkable genomic and epigenomic heterogeneity between and within tumors • Hundreds of genes and gene networks are deregulated in ways that contribute to cancer pathophysiology • Subtypes are defined by aberrations at multiple levels:mutation, structure, copy number, chromatin modification, ncRNA, … • Subtypes defined by recurrent aberrations are associated with outcome • Response varies with subtype

11. How do we make the optimal match between drug and subtype? ??? Associations

12. A systems approach to marker guided therapyin breast cancer Breast cancer overview and statement of the problem An in vitro systems approach to match treatment to “ome” Improving and testing the model

13. We use a collection of 50+ breast cancer cell lines to model the molecular diversity of primary tumors • Therapeutic approaches can be tested quickly to identify subtype specific responses • Model can be characterized at great molecular depth to identify predictive markers • Model can be manipulated to test predictions

14. To what extent do the cell lines represent what we know about breast cancer?

15. Cell lines model gene expression subtypes, recurrent copy number chances and mutations

16. We have assessed ~100 therapeutic strategies in 50 cell lines Emphasis on signaling pathways

17. Establishing associations between response and molecular subtypes UCSC Cancer Genome Browser Molecular features Biological features

18. Approximately half of compounds tested show significant molecular subtype specificity We are especially interesting in identifying genomic drivers for molecular response GI50 Associations Associations Cell line Kuo, Guan, Hu, Bayani 2007

19. Most effective targeted agents are linked to genomic markers that predict response *Except VEGFR and proteosome inhibitors

20. ~25% of compounds are significantly associated with genome copy number abnormalities Spellman, Sadanandam, Kuo

21. Kuo, Spellman, Sadanandam Platinum, anti-metabolites and anti-mitotic apparatus protein inhibitors effective in basal subtype cells Luminal Basal Claudin-low Sensitive Resistant PI3K inhibitor PI3K inhibitor PI3K inhibitor AURK inhibitor PLK1 inhibitor

22. Response to mitotic apparatus inhibitors is associated with transcriptional upregulation of a network of mitotic apparatus genes Mao, Hu et al

23. Why does this network exist?

24. Expression of mitotic apparatus genes is associated with amplification of transcription factors that target mitotic apparatus genes Christina Clark, Carlos Caldas FOXM1 SOX9 ZEB1 MYC

25. All genes in the mitotic apparatus signature are targeted by these transcription factors Mao, Curtis, 2010

26. Kuo, Spellman, Sadanandam EGFR, ERBB2, PI3K inhibitors, HDACs effective in luminal subtype cells Luminal Basal Claudin-low Hierarchical clustering of 31 significant subtype specific drugs and BrCa cell lines. PI3K inhibitor PI3K inhibitor PI3K inhibitor AURK inhibitor PLK1 inhibitor

27. Luminal subtype preference for ERBB2 and AKT pathway inhibitors “explained” by the subtype specificity of activating genomic aberrations X GI50 Lapatinib AKTi PIK3CA GI50 PTEN

28. Aberrations interact - AKT inhibitors synergize with lapatinib in ERBB2+, PIK3CAmt cells Korkola, Cooper, et al 2010

29. A systems approach to marker guided therapyin breast cancer Breast cancer overview and statement of the problem An in vitro systems approach to match treatment to “ome” Improving and testing the model

30. Complicating factors • Microenvironment • Response not durable • Response heterogeneity

31. The microenvironment modulates response to ERBB2 targeted drugs AU565 ERBB2 amp SKBR3 ERBB2 amp 2D monolayer 3D matrigel HCC1569 ERBB2 amp BT549 ERBB2 norm Wiegelt, et al., Breast Cancer Res Treat 122:35–43, 2010

32. The microenvironment modulates the signaling network HER3 HER2 microenvironment b1-integrin cytosol PI3K a,b,g,d IRS1 PDK1 RAF Akt MEK TSC2 MAPK Rheb PRAS40 mTorC1 mTorC2 S6K1 PKCα nucleus S6 HER3, PDK1, Akt, … Inhibition of microenv. signaling also should modulate response COX2, CREB, cJun, NFkB, ATF2, ER, Tcf/Lef, Rb, AP1, cFos, CXCR4, ETS, HIF1a, MYC -> CBX5

33. Inhibition of b1-integrin signaling enhances response to ERBB2 targeted drugs in 3D but not 2D AU565 ERBB2 amp SKBR3 ERBB2 amp AIIB2 None HCC1569 ERBB2 amp BT549 ERBB2 norm Wiegelt, et al., Breast Cancer Res Treat 122:35–43, 2010

34. Microenvironment dependent response may explain why treatment of metastatic disease is difficultCan we identify microenvironment independent therapies?

35. This motivates assessment of pathway function in situ Britt Marie Ljung

36. TOF-SIMS “ome” imaging Immunohistochemistry or in situ hybridization with mass tag labeled reagents. Each tag is a color. Primary Ion Beam Tag 1 Map Total Area Spectrum Tag 2 map m/z 256 256 Sample

37. More complications ERBB2 inhibition is not durable Amin et al, Science TM 2010; 2: 16ra7.

38. Understanding response dynamics HER3 HER2 microenvironment b1-integrin cytosol PI3K a,b,g,d IRS1 PDK1 RAF Akt MEK TSC2 MAPK Rheb PRAS40 mTorC1 mTorC2 S6K1 PKCα nucleus Mills, Moasser et al S6 HER3, PDK1, Akt, … COX2, CREB, cJun, NFkB, ATF2, ER, Tcf/Lef, Rb, AP1, cFos, CXCR4, ETS, HIF1a, MYC -> CBX5

39. Statistical and dynamic modeling to understand long term behavior Center for Cancer Systems Biology Signaling occurs in 3 dimensions Network behavior is context dependent Need to understand the emergent properties of complex, cross coupled systems • ODE model for short term effects (Soulaiman Itani) • A hybrid Boolean-ODE model using to model longer term effects[Chen 2009] (Young-Hwan Chang) Tomlin lab

40. Molecular responses are heterogeneous – a partial explanation for lack of durability? Digital v. analogue drug responses Sorger et al

41. A systems approach to marker guided therapyin breast cancer TCGA/ICGC projects are defining a growing number of distinct subtypes In vitro systems suggest at least half of all therapeutic compounds show subtype specificity Improving the model - Modeling the microenvironment, heterogeneity and long term durability

42. Collaborators Clinical science (I SPY etc) Laura Esserman (UCSF) Laura Van’t Veer (UCSF) Rick Baehner (UCSF) Nola Hylton (UCSF) John Park (UCSF) Hope Rugo (UCSF) Britt Marie Ljung (UCSF) Hubert Stoppler (UCSF) Fred Waldman (UCSF) Cell line system biology Wen-Lin Kuo Jim Korkola Nick Wang Nora Bayani Brian Cooper Mara Jeffress Anna Lapuk Demetris Iacovides Mina Bissell Martha Stampfer Terry Speed (UCB) Claire Tomlin (UCB)Michael Korn (UCSF) Frank McCormick (UCSF) Gordon Mills (MDACC) Yiling Lu (MDACC) Peter Sorger (Harvard) Genome biology Paul Spellman Anguraj Sadanandam Laura Heiser Shannon Dorton Jing Huang Steffen Durinck Obi Griffith Lakshmi Jakkula Francois Pepin Andy Wyrobek David Haussler (UCSC) Josh Stuart (UCSC) Project management Heidi Feiler Shradda Ravani Mitotic apparatus networks Zhi Hu Jian Hua Mao Shenda Gu Barbara Weber (GSK – then) Richard Wooster (GSK) Christina Clark (Cambridge) Carlos Caldas (Cambridge) NCI Center for Cancer Systems Biology, The Cancer Genome Atlas, CPTAC, Bay Area Breast Cancer SPORE, Atwater foundation, GSK, Roche, Millenium, Pfizer, Progen, Cytokinetics, Cell Biosciences, DOD Innovator, SU2C