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A Landscape of Pharmacogenomic Interactions in Cancer

Francesco Iorio1, Theo A Knijnenburg2, Daniel J Vis3

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.

Cell
|July 12, 2016
PubMed

Insights

Cancer cell lines mirror tumor molecular changes, aiding drug discovery. This research links genetic alterations to drug responses, identifying new therapeutic strategies for specific cancer types.

Area of Science:

  • Genomics
  • Cancer Biology
  • Pharmacology

Background:

  • Cancer genome studies offer deep molecular insights but translating this to clinical therapies remains difficult.
  • Developing targeted therapies requires understanding the link between specific cancer alterations and drug efficacy.

Purpose of the Study:

  • To map cancer-driven molecular alterations from tumors to cell lines.
  • To correlate these alterations with sensitivity to a wide range of drugs.
  • To identify predictive models for drug response using integrated data.

Main Methods:

  • Analysis of 11,289 tumors across 29 tissues, integrating mutation, copy number, methylation, and gene expression data.
  • Mapping tumor alterations onto 1,001 molecularly annotated cancer cell lines.
  • Correlating cell line molecular profiles with sensitivity data for 265 drugs.
  • Utilizing logic-based modeling and machine learning to uncover drug response predictors.

Main Results:

  • Cancer cell lines effectively recapitulate oncogenic alterations found in tumors.
  • Numerous molecular alterations are significantly associated with drug sensitivity or resistance.
  • Tissue lineage plays a crucial role in mediating drug response.
  • Combinations of alterations can sensitize cells to specific drugs, and machine learning models highlight key data types for prediction.

Conclusions:

  • This study provides a valuable resource for linking cancer genotypes to cellular phenotypes.
  • The findings facilitate the identification of potential therapeutic options for distinct cancer subpopulations.
  • The integrated analysis highlights the utility of cell line models in preclinical drug development and personalized medicine.

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