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http://hdl.handle.net/2445/114504
Title: | A landscape of pharmacogenomic interactions in cancer |
Author: | Iorio, Francesco Knijnenburg, Theo A. Vis, Daniel J. Bignell, Graham Menden, Michael P. Schubert, Michael Aben, Nanne Gonçalves, Emanuel Barthorpe, Syd Lightfoot, Howard Cokelae, Thomas Greninger, Patricia Dyk, Ewald van Chang, Han Silva, Heshani de Heyn, Holger Deng, Xianming Egan, Regina K. Liu, Qingsong Mironenko, Tatiana Mitropoulos, Xeni Richardson, Laura Wang, Jinhua Zhang, Tinghu Moran, Sebastian Sayols, Sergi Soleimani, Maryam Tamborero Noguera, David López Bigas, Núria Ross-Macdonald, Petra Esteller, Manel Gray, Nathanael S. Haber, Daniel A. Stratton, Michael R. Benes, Cyril H. Wessels, Lodewyk F. A. Saez-Rodriguez, Julia McDermott, Ultan Garnett, Mathew J. |
Keywords: | Càncer Oncogènesi Medicaments antineoplàstics Resistència als medicaments Farmacogenètica Genomes Fenotip Cancer Carcinogenesis Antineoplastic agents Drug resistance Pharmacogenetics Genomes Phenotype |
Issue Date: | 28-Jul-2016 |
Publisher: | Cell Press |
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 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. |
Note: | Reproducció del document publicat a: https://doi.org/10.1016/j.cell.2016.06.017 |
It is part of: | Cell, 2016, vol. 166, num. 3, p. 740-754 |
URI: | http://hdl.handle.net/2445/114504 |
Related resource: | https://doi.org/10.1016/j.cell.2016.06.017 |
ISSN: | 0092-8674 |
Appears in Collections: | Articles publicats en revistes (Ciències Fisiològiques) Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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