A Landscape of Pharmacogenomic Interactions in Cancer

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Author
Iorio, Francesco
Knijnenburg, Theo A.
Vis, Daniel J.
Bignell, Graham R.
Menden, Michael P.
Schubert, Michael
Aben, Nanne
Gonçalves, Emanuel
Barthorpe, Syd
Lightfoot, Howard
Cokelaer, Thomas
Greninger, Patricia
van Dyk, Ewald
Chang, Han
de Silva, Heshani
Heyn, Holger
Deng, Xianming
Egan, Regina K.
Liu, Qingsong
Mironenko, Tatiana
Mitropoulos, Xeni
Richardson, Laura
Moran, Sebastian
Sayols, Sergi
Soleimani, Maryam
Tamborero, David
Lopez-Bigas, Nuria
Ross-Macdonald, Petra
Esteller, Manel
Gray, Nathanael S.
Haber, Daniel A.
Stratton, Michael R.
Benes, Cyril H.
Wessels, Lodewyk F.A.
Saez-Rodriguez, Julio
McDermott, Ultan
Garnett, Mathew J.
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1016/j.cell.2016.06.017Metadata
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Iorio, F., T. Knijnenburg, D. Vis, G. Bignell, M. Menden, M. Schubert, N. Aben, et al. 2016. “A Landscape of Pharmacogenomic Interactions in Cancer.” Cell 166 (3): 740-754. doi:10.1016/j.cell.2016.06.017. http://dx.doi.org/10.1016/j.cell.2016.06.017.Abstract
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.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4967469/pdf/Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:29002552
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