A Landscape of Pharmacogenomic Interactions in Cancer
Francesco Iorio (European Molecular Biology Laboratory, Wellcome Trust Sanger Institute)
Theo A. Knijnenburg (Institute for Systems Biology)
Daniel J. Vis (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
GR Bignell (Wellcome Trust Sanger Institute)
Michael P. Menden (European Molecular Biology Laboratory, RWTH Aachen University)
Michael Schubert (European Molecular Biology Laboratory)
N.N. Aben (TU Delft - Pattern Recognition and Bioinformatics)
Emanuel Gonçalves (European Molecular Biology Laboratory)
Syd Barthorpe (Wellcome Trust Sanger Institute)
Lodewyk F. Wessels (TU Delft - Pattern Recognition and Bioinformatics)
G.B. More Authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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 cancerdriven 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. Logicbased 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.
help_outline