1 / 42

Benjamin Haibe-Kains Director , Bioinformatics and Computational Genomics Laboratory

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

megan
Download Presentation

Benjamin Haibe-Kains Director , Bioinformatics and Computational Genomics Laboratory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related