vs. home.ccr.cancer.gov personalized medicine-the goal
TRANSCRIPT
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vs
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home.ccr.cancer.gov
Personalized medicine-The goal
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Anti-cancer therapy
• 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
• 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
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Pharmacogenomic profiling in cancer cells
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FDA-approved targeted cancer drugs in clinical use
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The Cancer Cell Line Encyclopedia (CCLE) initiated by Novartis/Broad
Institute• 24 drugs• 1036 cancer cell lines
Large-scale studies have been published in Nature
The Cancer Genome Project (CGP) initiated by the Sanger Institute• 138 drugs• 727 cancer cell lines
Large Pharmacogenomic dataset
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Resistant vs. sensitive cell lines
Pharmacogenomic data
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• AUC – the area under the fitted dose response curve
• Activity area – the area above the fitted dose response curve
• EC50 – the concentration at which the compound reaches 50% of its maximum reduction in cell viability
• IC50 – the concentration at which the compound reaches 50% reduction in cell viability
AUC
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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)
Comparison of experimental protocols
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Comparison of experimental protocols
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Comparison of experimental protocols
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Spearman correlation at different levels• Gene/mutation - drug associations
• Drug phenotypes (IC50 and AUC)
• Gene(pathway) - drug associations
0 0.8 1
poor good
0.70.6
moderate substantial
Correlation
0.5
fair
Cohen’s Kappa coefficient for mutations and drug sensitivity calls
Consistency measure
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Drug Cell line
Gene mutation Gene expression
Intersection between the pharmacogenomic studies in terms of drugs, cell lines and genes
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A systematic screen in cancer cell lines identifies therapeutic biomarkers
Drug-gene interaction
Biomarkers of drug sensitivity and resistance
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Ewing’s sarcoma cell lines are sensitive to PARP inhibition
Multi-feature genomic signatures of drug response
17-AAG(HSP90 inh)
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The cancer cell line Encyclopedia
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AEW541 : IGF1 inhibitor
IGF1 : major growth factor of myeloma
Predictive modeling of pharmacological sensitivity using CCLE genomic data
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AHR expression may denote a tumour dependency targeted byMEK inhibitors in NRAS-mutant cell lines
Predicting sensitivity to topoisomerase I inhibitors
Low expression of AHR
High expression of AHR
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• Both studies also demonstrated that employing modern machine learning algo-
rithms to develop predictors of drug response based on molecular profiling
measurements of each tumor could effectively identify known pharmacogenomic
predictive biomarkers
• These proof-of-concept studies have established cell line-based screens as a vi-
able pre-clinical system for identifying functional biomarkers underlying drug
sensitivity or resistance and for suggesting patient selection strategies for clinical
trial design
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Consistency between gene expression profiles of cell lines in CGP and CCLE studies
Array platform- CGP : Genechip HG-U133A- CCLE : Genechip HG-U133PLUS2
Good correlation
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Consistency between gene mutation profiles of cell lines in CGP and CCLE studies
Moderate correlation
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IC 50 values of camptothecin for 252 cell lines screened within the CGP project, as measured at the facilities of the MGH and the WTSI
Fair correlation
MGH : Massachusetts General HospitalWTSI : Wellcome Trust sanger Institute
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Consistency between drug sensitivity data published in CGP and CCLE studies
471 cell line, 15 drugs
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Consistency of AUC values between CGP and CCLE
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Consistency of IC50 values within the range of tested concentrations between CGP and CCLE
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IC50AUC
Correlations of the sensitivity measures for 15 drugs, across tissue types
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Model for gene-drug association:where Y = drug sensitivity
Gi = gene expression of gene i
T = tissue type
b = regression coefficients
strength of gene-drug association : quantified by b I
Consistency of gene-drug associations
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Consistency of gene-drug associations
Poor
Fair
Moder-ate
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Consistency of associations of genomics features with drug sensitivity
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What is the source of inconsistency across 2 datasets?
Genomic data ?
or
Drug response measure?
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g:gene expressiond: drug sensitivity
• Original
: [CGPg+CGPd] vs. [CCLEg+CCLEd]
• GeneCGP fixed
: (CGPg+CGPd) vs (CGPg+CCLEd)
• GeneCCLE fixed
: (CCLEg+CGPd) vs (CCLEg+CCLEd)
• Drug CGP fixed
: (CGPd+CGPg) vs (CGPd+CCLEg)
• Drug CCLE fixed
: (CCLEd+CGPg) vs (CCLEd+CCLEg)
the most likely source of inconsistencies is drug sensitivity measurement
Effects on consistency by intermixing CGP and CCLE data
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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
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Comparison with GSK for Lapatinib
Comparison with GSK for Paclitaxel
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D E C E M B E R 2 0 1 3 | VO L 5 0 4 | N AT U R E
Conclusion
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• Gene expressions used to be noisy
Some more work needed to make variant calling more consistent
• Drug phenotypes appear to be quite noisy
This prevents 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)
Discussion
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• As computational approaches for modeling therapeutic response become increas-ingly common in research and translational applications, a study is warranted to systematically assess different modeling approaches, and recommend best prac-tices for future applications
the use of elastic net or ridge regression applied to continuous valued response data, summa-rized using the area under the fitted dose response curve, and using all molecular features (in particular, gene expression data)
pathway targeted compounds lead to more accurate predictors than classical broadly cyto-toxic chemotherapies
discordance in reported values across the 2 datasets for the same compounds and suggest that raw dose-response data should be made publicly available to facilitate comparison of the 2 datasets based on the same procedures for processing and summarizing dose-response val-ues
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five linear models to build genomic predictors
• Single gene: Univariate linear regression model with the gene the most
correlated to sensitivity [-log10(IC50)]
• Rankensemble: Average of the predictions of the top 30 models
• Rankmultic: Multivariate model with the top 30 genes
• MRMR: Multivariate model with the 30 genes most correlated and less
redundant
• Elastic net: Regularized multivariate model (L1/L2 penalization)
Modeling techniques