Benjamin Haibe-Kains Director , Bioinformatics and Computational Genomics Laboratory

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

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

3. Therapeutic strategies in cancer Adapted from Luoet al. Cell, 2009 QBBMM Conference 2013-09-20

4. Anticancer therapies • 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 QBBMM Conference 2013-09-20

5. Drug screening in preclinical models • 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 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 QBBMM Conference 2013-09-20

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

8. Large pharmacogenomic datasets • 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 • The Cancer Cell Line Encyclopedia (CCLE) initiated by Novartis/Broad Institute • 24drugs • 1036 cancer cell lines QBBMM Conference 2013-09-20

9. CGP CCLE • Drugs: 15 drugs have been investigated both in CGP and CCLE • Cell lines: 471 cancer cell lines in common between CGP and CCLE • Gene expression: ~12,000 genes were commonly assessed using Affymetrix HG-U133A and Plus2 chips CGP CCLE • Mutation: 68 genes were screened for mutations in both CGP and CCLE 256 471 565 QBBMM Conference 2013-09-20

10. Genomic predictors of drug response • We used CGP data to train genomic predictors of drug response for the 15 drugs • Gene expressions as input and IC50 as output • We implemented five linear modeling approaches to build genomic predictors: • SINGLEGENE • RANKENSEMBLE • RANKMULTIV • MRMR • ELASTICNET QBBMM Conference 2013-09-20

11. Validation framework QBBMM Conference 2013-09-20

12. Genomic predictors of drug sensitivity (IC50) CGP in 10-fold cross-validations QBBMM Conference 2013-09-20

13. Genomic predictors of drug sensitivity (IC50) Trained on CGP, tested on CCLE Common cell lines QBBMM Conference 2013-09-20

14. Genomic predictors of drug sensitivity (IC50) Trained on CGP, tested on CCLE New cell lines QBBMM Conference 2013-09-20

15. Consistency between CGP and CCLE • 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) QBBMM Conference 2013-09-20

16. Consistency measure • Spearman correlation at different levels • Genomic data (gene expression) • Drug sensitivity (IC50 and AUC) • Gene-drug associations 0.8 0 0.6 0.7 1 0.5 Correlation fair substantial good poor moderate • Cohen’s Kappa coefficient for mutations QBBMM Conference 2013-09-20

17. Consistency of gene expression profiles Good correlation QBBMM Conference 2013-09-20

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

19. Consistency of drug sensitivity (IC50) QBBMM Conference 2013-09-20

20. Consistency of drug sensitivity (AUC) QBBMM Conference 2013-09-20

21. Consistency of drug sensitivity Moderate Fair Poor QBBMM Conference 2013-09-20

22. GSK Cancer Cell Line Genomic Profiling Data • 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) QBBMM Conference 2013-09-20

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

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

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

26. Consistency of gene-drug associations Model for gene-drug association: where Y = drug sensitivity Gi = gene expression of gene i T = tissue type Significant gene-drug associations FDR < 20% Moderate Fair Poor QBBMM Conference 2013-09-20

27. Source of inconsistencies • To identify the most likely source of inconsistencies we intermixed the gene expressions and drug sensitivity measures between studies • Original = [CGPg+CGPd] vs. [CCLEg+CCLEd] • GeneCGP.fixed = [CGPg+CGPd] vs. [CGPg+CCLEd] • GeneCCLE.fixed = [CCLEg+CGPd] vs. [CCLEg+CCLEd] • DrugCGP.fixed = [CGPg+CGPd] vs. [CCLEg+ CGPd] • DrugCCLE.fixed = [CGPg+CCLEd] vs. [CCLEg+CCLEd] QBBMM Conference 2013-09-20

28. Source of inconsistencies QBBMM Conference 2013-09-20

29. Take home messages • 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) QBBMM Conference 2013-09-20

30. Acknowledgements • NehmeHachem • Rachad El-Badrawi • Simon Papillon-Cavanagh • Nicolas de Jay • Jacques Archambault • Hugo Aerts • John Quackenbush • Andrew Beck • Andrew Jin • Nicolai JuulBirkbak

31. Thank you for your attention!

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

33. Appendix

34. Modeling techniques • We implemented five linear models to build genomic predictors: • SINGLEGENE: Univariate linear regression model with the gene the most correlated to sensitivity [-log10(IC50)] • 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) QBBMM Conference 2013-09-20

35. Consistency of gene expression profiles by tissue types QBBMM Conference 2013-09-20

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

37. Consistency of mutation-drug associations Model for gene-drug association: where Y = drug sensitivity Mi = presence of mutation in gene i T = tissue type QBBMM Conference 2013-09-20

38. Consistency of drug sensitivity calling 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