title: An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction.
abstract: 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.
authors.forename: Noah
authors.initials: N
authors.lastname: Berlow
authors.forename: Saad
authors.initials: S
authors.lastname: Haider
authors.forename: Qian
authors.initials: Q
authors.lastname: Wan
authors.forename: Mathew
authors.initials: M
authors.lastname: Geltzeiler
authors.forename: Lara E
authors.initials: LE
authors.lastname: Davis
authors.forename: Charles
authors.initials: C
authors.lastname: Keller
authors.forename: Ranadip
authors.initials: R
authors.lastname: Pal
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D002470:Cell Survival / Q000187:drug effects; D019295:Computational Biology / Q000379:methods*; D019008:Drug Resistance, Neoplasm; D051379:Mice; D012680:Sensitivity and Specificity; D023041:Xenograft Model Antitumor Assays
chemical_list: D000970:Antineoplastic Agents