Reconstructing cancer drug response networks using multitask learning

BMC Syst Biol. 2017 Oct 10;11(1):96. doi: 10.1186/s12918-017-0471-8.


Background: Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer.

Results: The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug.

Conclusions: Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

Keywords: LINCS; Machine learning; TCGA.

MeSH terms

  • Antineoplastic Agents / pharmacology*
  • Antineoplastic Agents / therapeutic use
  • Computational Biology / methods*
  • Machine Learning*
  • Neoplasms / drug therapy*
  • Neoplasms / genetics
  • Survival Analysis
  • Treatment Outcome


  • Antineoplastic Agents