1. Benjamin Haibe-Kains Director, Bioinformatics and Computational Genomics Laboratory Scientific Advisor, Bioinformatics Core Facility Are pharmacogenomic studies useful for developing predictors of drug response?

2. Non-Responders Responders D C A B B Treat with conventional drugs Treat with alternative drugs Genomic data Genomic predictive biomarkers E Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Predicting therapeutic response of patients based on their genomic profiles

3. Adapted from Luo et al. Cell, 2009 Therapeutic strategies in cancer Benjamin Haibe-Kains QBBMM Conference 2013-09-20

4. Many drug compounds have been designed and many others are under development Success stories enabled to develop relevant therapeutic strategies and bring them to the clinic But the number of new (targeted) drugs being approved is dramatically slowing down Need for companion tests to identify patients who are likely to respond to targeted therapies Anticancer therapies Benjamin Haibe-Kains QBBMM Conference 2013-09-20

5. It is not sustainable to test thousands of compounds (and their combinations) in clinical trials One needs a different approach to screen the therapeutic potential of new compounds Cancer cell lines can be used as preclinical models:  Cheap and high-throughput  Simple models to investigate drugs’ mechanisms of action  Enable to build genomic predictors of drug response Drug screening in preclinical models Benjamin Haibe-Kains QBBMM Conference 2013-09-20

6. Current studies Most studies investigated isolated, small pharmacogenomic datasets Very few have been validated in independent experiments and in clinical samples Some are sadly famous: Anil Potti’s scandal at Duke University [forensic Bioinformatics by Baggerly and Coombes]  The solution may lie in analyzing large collections of cell lines from multiple datasets Benjamin Haibe-Kains QBBMM Conference 2013-09-20

7. Pharmacogenomic data Resistant vs. sensitive cell lines Benjamin Haibe-Kains QBBMM Conference 2013-09-20

8. Large pharmacogenomic datasets Benjamin Haibe-Kains QBBMM Conference 2013-09-20 The Cancer Cell Line Encyclopedia (CCLE) initiated by Novartis/Broad Institute 24 drugs 1036 cancer cell lines Large-scale studies have been recently published in Nature The Cancer Genome Project (CGP) initiated by the Sanger Institute 138 drugs 727 cancer cell lines

9. CGP CCLE Drugs: 15 drugs have been investigated both in CGP and CCLE CCLECGP 256471565 Cell lines: 471 cancer cell lines in common between CGP and CCLE PaclitaxelMicrotubules depolymerization inhibitor PD-0325901, AZD6244Mitogen-activated protein kinase kinase (MEK) inhibitor AZD0530 (Saracatinib)Proto-oncogene tyrosine-protein Src inhibitor Nutlin-3Ubiquitin-protein ligase MDM2 inhibitor NilotinibBCR-ABL fusion protein inhibitor 17-AAG (Tanespamycin)Heat shock protein (Hsp90) inhibitor PD-0332991CDK4/6-Cyclin D inhibitor PLX4720, SorafenibRAF kinase inhibitors Crizotinib, TAE684ALK kinase inhibitors Erlotinib, LapatinibEGFR/HER2 kinase inhibitors PHA-665752Proto-oncogene c-MET kinase inhibitor Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Gene expression: ~12,000 genes were commonly assessed using Affymetrix HG-U133A and Plus2 chips Mutation: 68 genes were screened for mutations in both CGP and CCLE

10. We used CGP data to train genomic predictors of drug response for the 15 drugs Gene expressions as input and IC 50 as output Genomic predictors of drug response We implemented five linear modeling approaches to build genomic predictors: SINGLEGENE RANKENSEMBLE RANKMULTIV MRMR ELASTICNET Benjamin Haibe-Kains QBBMM Conference 2013-09-20

11. Validation framework Benjamin Haibe-Kains QBBMM Conference 2013-09-20

12. Genomic predictors of drug sensitivity (IC 50 ) Benjamin Haibe-Kains QBBMM Conference 2013-09-20 CGP in 10-fold cross-validations

13. Genomic predictors of drug sensitivity (IC 50 ) Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Trained on CGP, tested on CCLE Common cell lines

14. Genomic predictors of drug sensitivity (IC 50 ) Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Trained on CGP, tested on CCLE New cell lines

15. Given the poor performance of our predictors we decided to explore consistency between CGP and CCLE Different cell viability assays: CGP: Cell Titer 96 Aqueous One Solution Cell (Promega)  amount of nucleic acids CCLE: Cell Titer Glo luminescence assay (Promega)  metabolic activity via ATP generation Differences in experimental protocols including range of drug concentrations tested estimator for summarizing the drug dose-response curve Different technologies for measuring genomic profiles (gene expressions and mutations) Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Consistency between CGP and CCLE

16. Spearman correlation at different levels Genomic data (gene expression) Drug sensitivity (IC 50 and AUC) Gene-drug associations Consistency measure Benjamin Haibe-Kains QBBMM Conference 2013-09-20 0 0.8 1 poor good 0.70.6 moderate substantial Correlation 0.5 fair Cohen’s Kappa coefficient for mutations

17. Consistency of gene expression profiles Benjamin Haibe-Kains2013-09-20 QBBMM Conference Good correlation

18. Consistency of mutational profiles Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Moderate agreement

19. Consistency of drug sensitivity (IC 50 ) Benjamin Haibe-Kains2013-09-20 QBBMM Conference

20. Consistency of drug sensitivity (AUC) Benjamin Haibe-Kains2013-09-20 QBBMM Conference

21. Consistency of drug sensitivity Benjamin Haibe-Kains2013-09-20 QBBMM Conference Poor Fair Moderate

22. In 2010, GlaxoSmithKline tested 19 compounds on 311 cancer cell lines 194 cell lines in common with CGP and CCLE 2 drugs in common, Lapatinib and Paclitaxel CCLE and GSK used the same pharmacological assay ( Cell Titer Glo luminescence assay, Promega ) GSK Cancer Cell Line Genomic Profiling Data Benjamin Haibe-Kains QBBMM Conference 2013-09-20

23. Comparison with GSK for Lapatinib Benjamin Haibe-Kains QBBMM Conference 2013-09-20

24. Comparison with GSK for Paclitaxel Benjamin Haibe-Kains QBBMM Conference 2013-09-20

25. Replicates in CGP Same assay, same protocol Benjamin Haibe-Kains QBBMM Conference 2013-09-20

26. Poor Fair Moderate Significant gene-drug associations FDR < 20% Consistency of gene-drug associations Model for gene-drug association: whereY = drug sensitivity G i = gene expression of gene i T = tissue type Benjamin Haibe-Kains QBBMM Conference 2013-09-20

27. To identify the most likely source of inconsistencies we intermixed the gene expressions and drug sensitivity measures between studies Original = [CGP g +CGP d ] vs. [CCLE g +CCLE d ] GeneCGP.fixed = [CGP g +CGP d ] vs. [CGP g +CCLE d ] GeneCCLE.fixed = [CCLE g +CGP d ] vs. [CCLE g +CCLE d ] DrugCGP.fixed = [CGP g +CGP d ] vs. [CCLE g + CGP d ] DrugCCLE.fixed = [CGP g +CCLE d ] vs. [CCLEg+CCLE d ] Source of inconsistencies Benjamin Haibe-Kains QBBMM Conference 2013-09-20

28. Source of inconsistencies Benjamin Haibe-Kains QBBMM Conference 2013-09-20

29. Gene expressions used to be noisy but years of standardization enabled reproducible measurements Some more work needed to make variant calling more consistent but we will get there Drug phenotypes appear to be quite noisy though This prevents us to characterize drugs’ mechanism of action and to build robust genomic predictors of drug response Needs for standardization in terms of pharmacological assay and experimental protocol New protocols may be needed (combination of assays + more controls) Take home messages Benjamin Haibe-Kains QBBMM Conference 2013-09-20

30. Nehme Hachem Rachad El-Badrawi Simon Papillon-Cavanagh Nicolas de Jay Jacques Archambault Acknowledgements Hugo Aerts John Quackenbush Andrew Beck Andrew Jin Nicolai Juul Birkbak

31. Thank you for your attention!

32. Frank Emmert-Streib (Queen’s University, Ireland) and I are editing a Special Issue on Network Inference Your contributions are welcome! One more thing … Benjamin Haibe-Kains QBBMM Conference 2013-09-20 Deadline: Sept 15

33. Appendix

34. We implemented five linear models to build genomic predictors: SINGLEGENE: Univariate linear regression model with the gene the most correlated to sensitivity [-log 10 (IC 50 )] RANKENSEMBLE: Average of the predictions of the top 30 models RANKMULTIV: Multivariate model with the top 30 genes MRMR: Multivariate model with the 30 genes most correlated and less redundant ELASTICNET: Regularized multivariate model (L1/L2 penalization) Modeling techniques Benjamin Haibe-Kains QBBMM Conference 2013-09-20

35. Benjamin Haibe-Kains2013-09-20 QBBMM Conference Consistency of gene expression profiles by tissue types

36. Consistency of drug sensitivity by tissue types Benjamin Haibe-Kains QBBMM Conference 2013-09-20 IC50 AUC

37. Consistency of mutation-drug associations Model for gene-drug association: whereY = drug sensitivity M i = presence of mutation in gene i T = tissue type Benjamin Haibe-Kains QBBMM Conference 2013-09-20

38. Consistency of drug sensitivity calling Benjamin Haibe-Kains QBBMM Conference 2013-09-20

39. Drug sensitivity in CGP IC50 AUC

40. Drug sensitivity in CCLE IC50 AUC

41. IC50 in CGP and CCLE

42. AUC in CGP and CCLE