title: 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 DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.
authors.forename: Naiqian
authors.initials: N
authors.lastname: Zhang
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China.
authors.forename: Haiyun
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Fang
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
authors.forename: X Shirley
authors.initials: XS
authors.lastname: Liu
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical
chemical_list: D000970:Antineoplastic Agents