Context Sensitive Modeling of Cancer Drug Sensitivity.
Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.
Department of Biomedical Informatics, Columbia University, New York, New York, 10032, United States of America; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, New York, 10027, United States of America.
Bo-Juen
BJ
Chen
Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, New York, 10027, United States of America.
Oren
O
Litvin
Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States of America.
Lyle
L
Ungar
Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, New York, 10027, United States of America.
Dana
D
Pe'er
D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D019008:Drug Resistance, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy*
D000970:Antineoplastic Agents