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© Research
Publication : Cell

A Landscape of Pharmacogenomic Interactions in Cancer

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Cell - 06 Jul 2016

Francesco Iorio, Theo A. Knijnenburg, Daniel J. Vis, Graham R. Bignell, Michael P. Menden, Michael Schubert, Nanne Aben, Emanuel Gonçalves, Syd Barthorpe, Howard Lightfoot, Thomas Cokelaer, Patricia Greninger, Ewald van Dyk, Han Chang, Heshani de Silva, Holger Heyn, Xianming Deng, Regina K. Egan, Qingsong Liu, Tatiana Mironenko, Xeni Mitropoulos, Laura Richardson, Jinhua Wang, Tinghu Zhang, Sebastian Moran, Sergi Sayols, Maryam Soleimani, David Tamborero, Nuria Lopez-Bigas, Petra Ross-Macdonald, Manel Esteller, Nathanael S. Gray, Daniel A. Haber, Michael R. Stratton, Cyril H. Benes, Lodewyk F.A. Wessels, Julio Saez-Rodriguez, Ultan McDermott, Mathew J. Garnett

Link to Pubmed [PMID] – 27397505

Cell. 2016 Jul 28;166(3):740-54.

Highlights

  • We integrate heterogeneous molecular data of 11,289 tumors and 1,001 cell lines
  • We measure the response of 1,001 cancer cell lines to 265 anti-cancer drugs
  • We uncover numerous oncogenic aberrations that sensitize to an anti-cancer drug
  • Our study forms a resource to identify therapeutic options for cancer sub-populations

Summary

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.

http://www.cell.com/fulltext/S0092-8674(16)30746-2