title: Multitask learning improves prediction of cancer drug sensitivity.
abstract: Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy.
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Tri-Institutional Program in Computational Biology and Medicine, New York, New York, USA.
authors.forename: Han
authors.initials: H
authors.lastname: Yuan
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
authors.forename: Ivan
authors.initials: I
authors.lastname: Paskov
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
authors.forename: Hristo
authors.initials: H
authors.lastname: Paskov
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
authors.forename: Alvaro J
authors.initials: AJ
authors.lastname: González
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
authors.forename: Christina S
authors.initials: CS
authors.lastname: Leslie
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D002470:Cell Survival / Q000187:drug effects; D019295:Computational Biology / Q000379:methods*; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D006801:Humans; D057285:Precision Medicine / Q000379:methods; D015203:Reproducibility of Results
chemical_list: D000970:Antineoplastic Agents