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Benjamin Haibe-Kains
Director, Bioinformatics and Computational Genomics LaboratoryScientific Advisor, Bioinformatics Core Facility
Are pharmacogenomic studies useful for developing predictors of drug response?
Non-Responders
Responders
D
C
A
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
Adapted from Luo et al. Cell, 2009
Therapeutic strategies in cancer
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
• 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
• 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-throughputSimple 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
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
Pharmacogenomic data
Resistant vs. sensitive cell lines
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
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
CGP CCLE
• Drugs: 15 drugs have been investigated both in CGP and CCLE
CCLECGP
256 471 565
• Cell lines: 471 cancer cell lines in common between CGP and CCLE
Paclitaxel Microtubules depolymerization inhibitor
PD-0325901, AZD6244 Mitogen-activated protein kinase kinase (MEK) inhibitor
AZD0530 (Saracatinib) Proto-oncogene tyrosine-protein Src inhibitor
Nutlin-3 Ubiquitin-protein ligase MDM2 inhibitor
Nilotinib BCR-ABL fusion protein inhibitor
17-AAG (Tanespamycin) Heat shock protein (Hsp90) inhibitor
PD-0332991 CDK4/6-Cyclin D inhibitor
PLX4720, Sorafenib RAF kinase inhibitors
Crizotinib, TAE684 ALK kinase inhibitors
Erlotinib, Lapatinib EGFR/HER2 kinase inhibitors
PHA-665752 Proto-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
• We used CGP data to train genomic predictors of drug response for the 15 drugs
• Gene expressions as input and IC50 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
Validation framework
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Genomic predictors of drug sensitivity (IC50)
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
CGP in 10-fold cross-validations
Genomic predictors of drug sensitivity (IC50)
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Trained on CGP, tested on CCLECommon cell lines
Genomic predictors of drug sensitivity (IC50)
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Trained on CGP, tested on CCLENew cell lines
• 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
• Spearman correlation at different levels• Genomic data (gene expression)
• Drug sensitivity (IC50 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
Consistency of gene expression profiles
Benjamin Haibe-Kains 2013-09-20QBBMM Conference
Good correlation
Consistency of mutational profiles
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Moderate agreement
Consistency of drug sensitivity (IC50)
Benjamin Haibe-Kains 2013-09-20QBBMM Conference
Consistency of drug sensitivity (AUC)
Benjamin Haibe-Kains 2013-09-20QBBMM Conference
Consistency of drug sensitivity
Benjamin Haibe-Kains 2013-09-20QBBMM Conference
Poor
Fair
Moderate
• 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
Comparison with GSK for Lapatinib
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Comparison with GSK for Paclitaxel
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Replicates in CGPSame assay, same protocol
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Poor
Fair
Moderate
Significant gene-drug associationsFDR < 20%
Consistency of gene-drug associationsModel for gene-drug association:where Y = drug sensitivity
Gi = gene expression of gene i
T = tissue type
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
• 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]
Source of inconsistencies
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Source of inconsistencies
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
• 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
• 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
Thank you for your attention!
• 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
Appendix
• 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)
Modeling techniques
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Benjamin Haibe-Kains 2013-09-20QBBMM Conference
Consistency of gene expression profilesby tissue types
Consistency of drug sensitivityby tissue types
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
IC50AUC
Consistency of mutation-drug associationsModel for gene-drug association:where Y = drug sensitivity
Mi = presence of mutation in gene i
T = tissue type
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Consistency of drug sensitivity calling
Benjamin Haibe-Kains QBBMM Conference 2013-09-20
Drug sensitivity in CGP
IC50
AUC
Drug sensitivity in CCLE
IC50
AUC
IC50 in CGP and CCLE
AUC in CGP and CCLE