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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
Wang, Jinhua
Zhang, Tinghu
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.
author_facet 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
Wang, Jinhua
Zhang, Tinghu
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.
author_sort Iorio, Francesco
collection PubMed
description 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.
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spelling pubmed-49674692016-08-04 A Landscape of Pharmacogenomic Interactions in Cancer 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 Wang, Jinhua Zhang, Tinghu 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. Cell Resource 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. Cell Press 2016-07-28 /pmc/articles/PMC4967469/ /pubmed/27397505 http://dx.doi.org/10.1016/j.cell.2016.06.017 Text en © 2016 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Resource
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
Wang, Jinhua
Zhang, Tinghu
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.
A Landscape of Pharmacogenomic Interactions in Cancer
title A Landscape of Pharmacogenomic Interactions in Cancer
title_full A Landscape of Pharmacogenomic Interactions in Cancer
title_fullStr A Landscape of Pharmacogenomic Interactions in Cancer
title_full_unstemmed A Landscape of Pharmacogenomic Interactions in Cancer
title_short A Landscape of Pharmacogenomic Interactions in Cancer
title_sort landscape of pharmacogenomic interactions in cancer
topic Resource
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4967469/
https://www.ncbi.nlm.nih.gov/pubmed/27397505
http://dx.doi.org/10.1016/j.cell.2016.06.017
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