Prediction of cancer drug sensitivity using high-dimensional omic features.

TitlePrediction of cancer drug sensitivity using high-dimensional omic features.
Publication TypeJournal Article
Year of Publication2017
AuthorsChen, Ting-Huei, and Wei Sun
JournalBiostatistics
Volume18
Issue1
Pagination1-14
Date Published2017 01
ISSN1468-4357
KeywordsAntineoplastic Agents, Cell Line, Tumor, Humans, Models, Biological, Neoplasms
Abstract

A large number of cancer drugs have been developed to target particular genes/pathways that are crucial for cancer growth. Drugs that share a molecular target may also have some common predictive omic features, e.g., somatic mutations or gene expression. Therefore, it is desirable to analyze these drugs as a group to identify the associated omic features, which may provide biological insights into the underlying drug response. Furthermore, these omic features may be robust predictors for any drug sharing the same target. The high dimensionality and the strong correlations among the omic features are the main challenges of this task. Motivated by this problem, we develop a new method for high-dimensional bilevel feature selection using a group of response variables that may share a common set of predictors in addition to their individual predictors. Simulation results show that our method has a substantially higher sensitivity and specificity than existing methods. We apply our method to two large-scale drug sensitivity studies in cancer cell lines. Both within-study and between-study validation demonstrate the good efficacy of our method.

DOI10.1093/biostatistics/kxw022
Alternate JournalBiostatistics
Original PublicationPrediction of cancer drug sensitivity using high-dimensional omic features.
PubMed ID27324412
PubMed Central IDPMC5255052
Grant ListR01 GM105785 / GM / NIGMS NIH HHS / United States
P30 CA015704 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
R03 CA167684 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: