Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

ABSTRACT: The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or D

SUBMITTER: Zhang N 

PROVIDER: S-EPMC4587957 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature