4.8 Article

A Landscape of Pharmacogenomic Interactions in Cancer

期刊

CELL
卷 166, 期 3, 页码 740-754

出版社

CELL PRESS
DOI: 10.1016/j.cell.2016.06.017

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资金

  1. Wellcome Trust [086375, 102696]
  2. European Bioinformatics Institute
  3. Wellcome Trust Sanger Institute post-doctoral (ESPOD) program
  4. National Cancer Institute [U24CA143835]
  5. Netherlands Organization for Scientific Research
  6. People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union [600388]
  7. Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO)
  8. La Fundacio la Marato de TV3
  9. European Research Council [268626]
  10. Ministerio de Ciencia e Innovacion [SAF2011-22803]
  11. Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence [PIE13/00022]
  12. Spanish Cancer Research Network [RD12/0036/0039]
  13. Health Department of the Catalan Government Generalitat de Catalunya [2014-SGR 633]
  14. Science Department of the Catalan Government Generalitat de Catalunya [2014-SGR 633]
  15. Cellex Foundation
  16. Cancer Research UK Clinician Scientist Fellowship
  17. AstraZeneca
  18. ICREA Funding Source: Custom
  19. Medical Research Council [1246915] Funding Source: researchfish

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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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