A landscape of pharmacogenomic interactions in cancer

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.This work was funded by the Wellcome Trust (086375 and 102696). F.I. was supported by the European Bioinformatics Institute and Wellcome Trust Sanger Institute post-doctoral (ESPOD) program. T.A.K. was supported by the National Cancer Institute (U24CA143835) and the Netherlands Organization for Scientific Research. D.T. was supported by the People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union (FP7/2007-2013; 600388) and the Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO´ ). N.L.-B. was supported by La Fundacio ´ la Marato´ de TV3. M.E. was funded by the European Research Council (268626), the Ministerio de Ciencia e Innovacion (SAF2011-22803), the Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence (PIE13/00022), the Spanish Cancer Research Network (RD12/0036/0039), the Health and Science Departments of the Catalan Government Generalitat de Catalunya 2014-SGR 633, and the Cellex Foundation. U.M. was supported by a Cancer Research UK Clinician Scientist Fellowship. We thank Aiqing He for expression data and Ilya Shmulevich for assistance with the LOBICO framework. We thank P. Campbell, M. Ranzani, J. Brammeld, M. Petljak, F. Behan, C. Alsinet Armengol, H. Francies, V. Grinkevich, and A. ‘‘Lilla’’ Mupo for useful comments. P.R.-M., H.C., and H.d.S. are employees and shareholders of Bristol-Myers Squibb. Research in the M.J.G. lab is supported in part with funding from AstraZeneca

Systematic identification of genomic markers of drug sensitivity in cancer cells.

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies