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home.ccr.cancer.gov

Personalized medicine-The goal

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

Pharmacogenomic profiling in cancer cells

FDA-approved targeted cancer drugs in clinical use

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

Resistant vs. sensitive cell lines

Pharmacogenomic data

• 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

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

Comparison of experimental protocols

Comparison of experimental protocols

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

Drug Cell line

Gene mutation Gene expression

Intersection between the pharmacogenomic studies in terms of drugs, cell lines and genes

A systematic screen in cancer cell lines identifies therapeutic biomarkers

Drug-gene interaction

Biomarkers of drug sensitivity and resistance

Ewing’s sarcoma cell lines are sensitive to PARP inhibition

Multi-feature genomic signatures of drug response

17-AAG(HSP90 inh)

The cancer cell line Encyclopedia

AEW541 : IGF1 inhibitor

IGF1 : major growth factor of myeloma

Predictive modeling of pharmacological sensitivity using CCLE genomic data

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

• 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

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

Consistency between gene mutation profiles of cell lines in CGP and CCLE studies

Moderate correlation

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

Consistency between drug sensitivity data published in CGP and CCLE studies

471 cell line, 15 drugs

Consistency of AUC values between CGP and CCLE

Consistency of IC50 values within the range of tested concentrations between CGP and CCLE

IC50AUC

Correlations of the sensitivity measures for 15 drugs, across tissue types

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

Consistency of gene-drug associations

Poor

Fair

Moder-ate

Consistency of associations of genomics features with drug sensitivity

What is the source of inconsistency across 2 datasets?

Genomic data ?

or

Drug response measure?

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

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

Comparison with GSK for Lapatinib

Comparison with GSK for Paclitaxel

D E C E M B E R 2 0 1 3 | VO L 5 0 4 | N AT U R E

Conclusion

• 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

• 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

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