An integrated approach to anti-cancer drug sensitivity prediction

Noah Berlow, Saad Haider, Qian Wan, Mathew Geltzeiler, Lara E. Davis, Charles Keller, Ranadip Pal

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.

Original languageEnglish (US)
Article number6808481
Pages (from-to)995-1008
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
StatePublished - Nov 1 2014


  • Drug sensitivity prediction
  • Personalized cancer therapy

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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