1. vs

2. home.ccr.cancer.gov Personalized medicine-The goal

3. 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

5. Pharmacogenomic profiling in cancer cells

6. FDA-approved targeted cancer drugs in clinical use

7.  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

8. Resistant vs. sensitive cell lines Pharmacogenomic data

9. 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

10.  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

13.  Spearman correlation at different levels Gene/mutation - drug associations Drug phenotypes (IC 50 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

14. DrugCell line Gene mutation Gene expression Intersection between the pharmacogenomic studies in terms of drugs, cell lines and genes drugs, cell lines and genes

15. A systematic screen in cancer cell lines identifies therapeutic biomarkers Drug-gene interaction Biomarkers of drug sensitivity and resistance

16. Ewing’s sarcoma cell lines are sensitive to PARP inhibition Multi-feature genomic signatures of drug response 17-AAG(HSP90 inh)

17. The cancer cell line Encyclopedia

18. AEW541 : IGF1 inhibitor IGF1 : major growth factor of myeloma Predictive modeling of pharmacological sensitivity using CCLE genomic data

19. AHR expression may denote a tumour dependency targeted by MEK inhibitors in NRAS-mutant cell lines Predicting sensitivity to topoisomerase I inhibitors Low expression of AHR High expression of AHR

20. Both studies also demonstrated that employing modern machine learning algorithms 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 viable pre-clinical system for identifying functional biomarkers underlying drug sensitivity or resistance and for suggesting patient selection strategies for clinical trial design

21. Consistency between gene expression profiles of cell lines in CGP and CCLE studies cell lines in CGP and CCLE studies Array platform -CGP : Genechip HG-U133A -CCLE : Genechip HG-U133PLUS2 Good correlation

22. Consistency between gene mutation profiles of cell lines in CGP and CCLE studies cell lines in CGP and CCLE studies Moderate correlation

23. 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 as measured at the facilities of the MGH and the WTSI Fair correlation MGH : Massachusetts General Hospital WTSI : Wellcome Trust sanger Institute

24. Consistency between drug sensitivity data published in CGP and CCLE studies in CGP and CCLE studies 471 cell line, 15 drugs

25. Consistency of AUC values between CGP and CCLE

26. Consistency of IC 50 values within the range of tested concentrations between CGP and CCLE between CGP and CCLE

27. IC50 AUC Correlations of the sensitivity measures for 15 drugs, across tissue types across tissue types

28. Model for gene-drug association: whereY = drug sensitivity G i = gene expression of gene i T = tissue type  = regression coefficients strength of gene-drug association : quantified by  I Consistency of gene-drug associations

29. Poor Fair Moderate

30. Consistency of associations of genomics features with drug sensitivity drug sensitivity

31. What is the source of inconsistency across 2 datasets? across 2 datasets? Genomic data ? Genomic data ? or or Drug response measure?

32. g:gene expression d: drug sensitivity Original : [CGP g +CGP d ] vs. [CCLE g +CCLE d ] 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) drug sensitivity measurement  the most likely source of inconsistencies is drug sensitivity measurement Effects on consistency by intermixing CGP and CCLE data

33.  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

34. Comparison with GSK for Lapatinib for Lapatinib Comparison with GSK for Paclitaxel for Paclitaxel

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

36. 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

37. As computational approaches for modeling therapeutic response become increasingly common in research and translational applications, a study is warranted to systematically assess different modeling approaches, and recommend best practices for future applications the use of elastic net or ridge regression applied to continuous valued response data, summarized 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 cytotoxic 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 values

40. five linear models to build genomic predictors Single gene: Univariate linear regression model with the gene the most correlated to sensitivity [-log 10 (IC 50 )] 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