Title:Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells
VOLUME: 13 ISSUE: 5
Author(s):Hui Liu, Libo Luo, Zhanzhan Cheng, Jianjiang Sun, Jihong Guan, Jie Zheng* and Shuigeng Zhou*
Affiliation:Changzhou No. 7 People`s Hospital, Changzhou, Jiangsu 213011, Changzhou No. 7 People`s Hospital, Changzhou, Jiangsu 213011, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, Department of Computer Science and Technology, Tongji University, Shanghai 201804, School of Computer Science and Engineering, Nanyang Technological University, Nanyang 639798, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433
Keywords:Drug combination, sparse representation, group structure, drug sensitivity, cancer cells, drug-kinase.
Abstract:Background: Due to the intrinsic compensatory mechanism and cross-talks mong cellular
signaling pathways, single-target drugs often fail to inhibit the survival pathways in cancer cells. Some
multi-target combination drugs have demonstrated their high sensitivities and low side effects in cancer
therapies, and thus drawn intensive attentions from researchers and pharmaceutical enterprises.
Method: Although a few computational methods have been developed to infer combination drug
sensitivities based on drug-kinase interactions, they either depend on the binarization of drug-kinase
binding affinities, which would lead to the loss of weak drug-target inhibitions known to affect
significantly the anticancer effects, or disregard the functional group structure among the kinases
involved in cancer signalling pathways. In this paper, we employed a sparse linear model, uncertain
group sparse representation (UGSR), to infer essential kinases governing the cellular responses to drug
treatments in cancer cells, based on the massively collected drug-kinase interactions and drug sensitivity
datasets over hundreds of cancer cell lines. The inferred essential kinases can be subsequently used to
calculate the cancer cell sensitivities to combination drugs.
Results: The leave-one-out cross validations and two real cases show that our method achieve high
performance in predict drug sensitivities of combination drugs. Moreover, a user-friendly web interface
with interactive network viewer, tabular viewer and other graphical visualization plugins, has been
implemented to facilitate data access and interpretation.
