Format

Send to

Choose Destination
Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7.

A Landscape of Pharmacogenomic Interactions in Cancer.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
2
Institute for Systems Biology, Seattle, WA 98109, USA; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands.
3
Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands.
4
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
5
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany.
6
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
7
Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands; Department of EEMCS, Delft University of Technology, Delft 2628 CD, the Netherlands.
8
Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
9
Genetically Defined Diseases and Genomics, Bristol-Myers Squibb Research and Development, Hopewell, NJ 08534, USA.
10
Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain.
11
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA.
12
Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain.
13
Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain.
14
Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain; Department of Physiological Sciences II of the School of Medicine, University of Barcelona, L'Hospitalet 08908, Barcelona, Catalonia, Spain.
15
Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
16
Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands; Department of EEMCS, Delft University of Technology, Delft 2628 CD, the Netherlands; Cancer Genomics Netherlands, Uppsalalaan 8, Utrecht 3584CT, the Netherlands.
17
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK. Electronic address: um1@sanger.ac.uk.
18
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK. Electronic address: mg12@sanger.ac.uk.

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.

PMID:
27397505
PMCID:
PMC4967469
DOI:
10.1016/j.cell.2016.06.017
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center