LINCS@HMS

1. LINCS@HMS Pharmaco-response Signatures and Disease Mechanism Timothy Mitchison, Peter Sorger, Caroline Shamu Harvard Medical School Nathanael Gray Dana Farber Cancer Institute Cyril Benes, Daniel Haber, Massachusetts General Hospital Joshua Stuart University California , Santa Cruz (Avi Maayan) Mt. Sinai, NY LINCS Fall Meeting – October 26, 2011

2. LINCS@HMS Outline • Data being collected at HMS LINCS Center • Why collect data this way? • A typical data set • Organizing and accessing the data • Promises and conceptual challenges • Impact and outreach

3. LINCS@HMS Goals of the HMS LINCS Center Collect rich response data: Provide rich data sets for validation of protocols and development of standards, algorithms and informatic systems Integrate cell-biology and genomics: Demonstrate that coupling (i) high-throughput biochemical and cell-based data to (ii) expression/genomic data will uncover novel biology involved in disease and drug response. Create signatures and understand mechanism: Develop pathway-aware signatures of cellular response to (pharmacological) perturbation. Show signatures can uncover novel pharmacological mechanism and explain variation in response.

4. LINCS@HMS Rich response data Biochemical pathways Single-cell imaging Multiplex biochemical data Drug binding to kinome pAkt Cell fate data Ligand

5. LINCS@HMS Approaches in the HMS LINCS Center • Focus on drugs and medicinal chemistry: assemble annotated collection of kinase inhibitors (clinical and new) and measure biochemical specificity using industry standard kinome profiling assays. • Collect multi-factorial pharmaco-response signatures: capture the complexity of response in time and space using imaging, multiplex biochemistry and transcriptional assays. • Apply to cancer and other diseases: including rheumatoid arthritis, liver disease, and mitochondrial disease. • Develop informatics standards (ultimately a pipeline): to collect, analyze and disseminate diverse experimental data • Develop pathway-focused mathematical models: at different levels of resolution as means to create predictive pharmaco-response-signatures (PRSs).

6. LINCS@HMS Key features of the HMS center • Adaptive approach: Support diverse and changing assay and data types; adapt ongoing data collection to previous results. 2. Integration: • of methods and reagents across seven laboratories • of biochemical, imaging and expression assays • of chemical and cell-level annotation • of multiple nodes in signaling network • of protein, mRNA and genome data 3. Leverage: Leverage existing efforts in participating labs by adding LINCS standards, informatics and assays to ongoing projects (federally and industrially funded).

7. LINCS@HMS Data being collected at HMSLINCS Center

8. LINCS@HMS Typical Experimental Design signal transduction possible therapies 20-50 Signaling proteins genotypes Multiple drugs Primary cells Tumor cell lines cellular responses Multiple cytokines growth factors Cell State - apoptosis, growth, senescence etc.) “microenvironment” Transcription (L1000 assays)* Sequence Transcriptional state (Stuart Lab) GI50 Data * With Broad LINCS Center

9. LINCS@HMS Lysate-array, xMAP/Luminex, ELISA assays to measure mean response Cell response

10. LINCS@HMS Image-based measurements reveal dispersion in response Bjorn Millard

11. LINCS@HMS Linking immediate-early signals to transcriptional response Ligand Cell Type Signal SKBR3 Cells + EGF The joint project will generate the only dataset linking immediate-early signaling to transcription across diverse perturbations and cell lines Transcription

12. Phenotypic data on perturbagen response LINCS@HMS ~1000 Tumor Cell lines 200 Compounds-3 doses Number of lines Tissue of origin ~106 Data Points on Cell Killing Leveraging the Cell Line Collection of the Center for Molecular Therapeutics/Wellcome Trust /Sanger Institute

13. LINCS@HMS Perturbagen response determinants Unresponsive states: Not in M phase during assay Quiescent Targets: Plk1 Aurora A,B Kinesin-5 MPS1 Microtubules Pathways: Mitotic entry Mitotic exit Mitotic spindle assembly Spindle assembly checkpoint Apoptosis Modulators of drug availability: Drug efflux pumps Drug sequestration

14. LINCS@HMS Measuring perturbagen (drug) specificity phospho-Erk glial cell line-derived neurotrophic factor on MCF7 cells phospho-Erk Heregulin on MCF7 cells Cell level data We will generate the largest public database of small molecule selectivity profiles based on the multiplex biochemical assays used in pharma Pattricelli et al (2007) Biochemistry. 46:350-8. Cravatt et al (2010) Nat Rev Cancer. 2010 10:630-8

15. LINCS@HMS Structure of HMS LINCS Data

16. LINCS@HMS Why collect data this way?State of the artProven utilityCaptures critical features of response

17. LINCS@HMS Broad LINCS Center (contemporary genomics)

18. LINCS@HMS HMS LINCS Center (contemporary biochemistry)

19. LINCS@HMS Structure of conventional biochemical data

20. LINCS@HMS Application: discovery of inducible autocrine cascades

21. LINCS@HMS Application: cell specific signaling networks

22. LINCS@HMS Application: cell specific signaling networks HepG2 Focus HPH HHL5

23. LINCS@HMS Clustering cell –specific networks based on pathway topology and drug responses Compared to gene expression clusters

24. LINCS@HMS Response features and single-cell data • Cell-cell variation: Heterogeniety in response in addition to mean response - important when cell cycle is major factor. • Outlier populations: Uncover responses of outlier populations including those corresponding to undifferentiated or “tumor stem cells.” • Assay protein localization : Monitor changes in protein localization, such as nuclear translocation of transcription factors upon perturbation. • Monitor cell state changes: Assay states and morphological transitions such as cycle arrest, induction of apoptosis, senescence, EMT etc. with D. Flusberg

25. LINCS@HMS Characterizing perturbagen responses at a single-cell level by imaging Non-treated + drug 1 + drug 2 DAPI NucView LTR Mitotic index DNA replication Apoptosis Cell size Viability Phospo-states Localization (NB: images of various control compounds) 5637 cells

26. LINCS@HMS Fractional responses of cells to perturbagens (TRAIL) (survivors) (survivors) Allow cells to recover Treat Treat 2-3 day recovery for TRAIL exposure in culture

27. LINCS@HMS Dynamic responses of lapatinib sensitive and resistant breast cancer cells to perturbagens 21MT1 [1h] Cell Number 0 -11 -10 -9 -8 -7 -6 -5 Lapatinib log [M] log [ppERK] BT474 [24h] 21MT1 [24h] Cell Number Cell Number 0 -11 -10 -9 -8 -7 -6 -5 Lapatinib log [M] 0 -11 -10 -9 -8 -7 -6 -5 Lapatinib log [M] log [ppERK] log [ppERK] 1 mm Lapatinib 1 mM Meki

28. LINCS@HMS Impact of cell-to-cell variability on dose-response TRAIL Recover Fractional killing

29. LINCS@HMS LINCS Software System for Quantitative Image Analysis • Cell images encode large amounts of biological data beyond the obvious measurements of interest • Will provide image analysis community with reference data • Requires validated antibodies

30. LINCS@HMS A typical data set

31. LINCS@HMS Cue-signal response dataset – breast cancer (70% done)

32. LINCS@HMS Key questions • How diverse is ligand response? Map to subtypes (Basal A, Basal B, Her2+ etc) • Are receptor protein levels correlated to RNA levels or to ligand response? What about a protein/gene signature? • Among cell lines that show similar ligand responses, how diverse are the transcriptional responses? • Do Her2+ lines show similar responsiveness to diverse ErbB ligands? Predict Trastuzumab sensitivity or resistance mechanisms? • More generally – does ligand response correlate to sensitivity or predict combination drug responses? • Is variation in response at single-cell level predictive or resistance mechanisms? Mario Niepel

33. LINCS@HMS Some data: Ubiquitous Responsiveness to ErbB Ligands p-Erk p-Erk p-Erk EGF Receptor expression levels HRG Her2 amplified TrastuzumabR VEGF Time no ligand 1 ng/ml 100 ng/ml

34. LINCS@HMS Common, sporadic and rare responses Log10 (fold change) Common: HRG Responders Rare: PDGF RespondersHS578T – Basal B MDA MB 157– Basal B Sporadic: VEGF Responders pAKT-S(473) at 10 min 9/14/2014 Slide 34/44

35. LINCS@HMS Variation in diversity of transcriptional responses PCA Analysis by Avi Maayan (Mt Sinai)

36. LINCS@HMS Not only cancer cells: CSR analysis of primary synovial fibroblasts rheumatoid arthritis v. normal RA t (min) Normal Cue WNT5A WNT3A POLYIC IL6 IL1 TNFa IGF EGF Visfatin Adiponectin Leptin 10 WNT5A WNT3A POLYIC IL6 IL1 TNFa IGF EGF Visfatin Adiponectin Leptin 30 WNT5A WNT3A POLYIC IL6 IL1 TNFa IGF EGF Visfatin Adiponectin Leptin Gsk3 CREB Tyk2 JNK p90RSK p38MAPK NFkBp65 Erk1 MEK Src HSP27 STAT3 p53(S15) p70S6 cJun PDGFRb STAT6 TrkA Akt STAT2 Erk2 STAT3 p53(S37) Gsk3 CREB Tyk2 JNK p90RSK p38MAPK NFkBp65 Erk1 MEK Src HSP27 STAT3 p53(S15) p70S6 cJun PDGFRb STAT6 TrkA Akt STAT2 Erk2 STAT3 p53(S37) 90

37. LINCS@HMS Organizing and accessing the data

38. LINCS@HMS Informational Web Site

39. LINCS@HMS HMS LINCS Database

40. LINCS@HMS SDCubes: a new approach to management ofhigh content data SD cubes merge the HDF5 data standard (from remote earth sensing with XML to achieve efficient file-based storage of Unlimited size based on OWL-compliant ontologies

41. Informatics to store and disseminate image data LINCS@HMS We will create the largest (only?) public database of single-cell data on cellular responses to therapeutic drugs.

42. LINCS@HMS Preliminary informatics pipeline for collecting, managing and distributing single-cell signatures and data Now 2012 2013

43. LINCS@HMS Progress on data release: completing an assay set typically takes 12-24 months Biochemical Signatures Acquire compounds Format 6 mo 6 mo Release Assay in vitro 6 mo Cue-signal response analysis Single-cell assays Repeat -QC 12 mo 3 mo Format 6 mo Population-average assays Release 12 mo Transcription assays 3 mo 3 mo

44. LINCS@HMS Promises and challenges

45. LINCS@HMS Challenges facing HMS LINCS Center • Acquisition of large-scale data sets is occurring in the absence of a robust analytical or informatics platform – tracking, analyzing, publishing data is tricky • Value of analyzing immediate-early pathways biochemically on a large scale is not yet known – how dense does data need to be to infer pathways? • Relative merits of single-cell and population average data sets must be established. • Relating transcriptional (Broad) and biochemical/cell-level data (HMS) will require new statistical approaches and modeling tools

46. LINCS@HMS Comparing PARADIGM and Biochemical Networks ERK Component of the Basal Pathway in PARADIGM Comparing biochemical pathways to PARADIGM pathway concepts developed by Josh Stuart and used in the Santa Cruz Genome Browser

47. LINCS@HMS EREG HBEGF Interaction maps are not biochemical networks ERBB4 But highly discrepant by source And not interpretable biochemically With classical bow-tie features An receptor rich interactome CE(i,j) Macrophage CellMap I2D GeneGo NCI-PID Reactome STRING 0.8 Cytosolic Kinases 0.5 1.0 0.3 CellMap Macrophage I2D GeneGo Reactome STRING NCI-PID 0.2 0.1 Receptors and binding proteins Transcription Factors

48. LINCS@HMS Impact and outreach

49. LINCS@HMS Building links to relevant communities • Clinicians and cancer biologists: e.g. SU2C breast Cancer consortium (led by Joe Gray and Dennis Slamon). • Medicinal chemists in industry and academe (LINCS@HMS advisors) • Computational biologists involved in pathway engineering (DREAM competition) • Cell Biologists and microscopists:

50. Publications LINCS@HMS • Yang R, Niepel M, Mitchison TK, and Sorger PK (2010). Dissecting Variability in Responses to Cancer Chemotherapy through Systems Pharmacology. ClinPharmacolTher 88, 34-38. PMC2941986 PMID: 20520606. • Millard BL, Niepel M, Menden MP, Muhlich JL, and Sorger PK (2011). Adaptive Informatics for Multifactorial and High-Content Biological Data. Nat Methods 8, 487-492. PMC3105758 PMID: 21516115. • Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, and Stolovitzky G (2011). Crowdsourcing Network Inference: The Dream Predictive Signaling Network Challenge. Sci Signal 4, mr7. PMID: 21900204. • Zhang T, Inesta-Vaquera F, Niepel M, Zhang J, Ficarro S, Machleidt T, Xie T, Marto JA, Kim N, Sim T, Laughlin JD, Park H, LoGrasso PV, Patricelli M, Sorger PK, Alessi DR, and Gray NS (2011). Discovery of Potent and Selective Covalent Inhibitors of Jnk. Chemistry & Biology (accepted, in press) • Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q, Iorio F, Milano RJ, Bignell GR, Tam AT, Davies H, Stevenson JA, Barthorpe S, Lutz SR, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O’Brien P, Price S, Hur W, Yang W, Deng X, Butler A, Choi HG, Chang JW, Baselga J, Stamenkovic I, Engelman JA, Sharma SV, Saez-Rodriguez J, Gray NS, Settleman J, Futreal PA, Haber DA, Stratton MR, Ramaswamy S, McDermott U, and Benes CH The Genomics of Drug Sensitivity in Cancer. Nature (in review) • Alagesan B, Contino G, Guimaraes A, Corcoran R, Deshpande V, Wojtkiewicz G, Greninger P, Brown R, Chu G, Ying H, Hezel A, Wong KK, Liu Q, DePinho R, Loda M, Weissleder R, Benes C, Engelman J, and Bardeesy N Combined Mek and Pi3k Inhibition Induces Cell Death and Tumor Regression in Mouse Models of Pancreatic Cancer. Cancer Discovery (submitted) • Ni J, Liu Q, Xie S, Carlson C, Thanh V, Vogel KW, Riddle SM, Benes CH, Eck M, Roberts TM, Gray NS, and Zhao JJ Functional Characterization of the Anti-Cancer Potential of a P110β Isoform-Selective Pi3k Inhibitor. Nature Chemical Biology (in progress) • Tang Y, Xie T, Moerke N, Shamu CE, and Mitchison TJ A Novel Single-Cell Dye-Based Imaging Assay for Studying Multi-Dimensional Pharmacological Responses in Human Tumor Cell Lines to Small-Molecule Anti-Cancer Drugs. (in progress)