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Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells

[ Vol. 13 , Issue. 5 ]

Author(s):

Hui Liu, Libo Luo, Zhanzhan Cheng, Jianjiang Sun, Jihong Guan, Jie Zheng* and Shuigeng Zhou*   Pages 437 - 443 ( 7 )

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.

Keywords:

Drug combination, sparse representation, group structure, drug sensitivity, cancer cells, drug-kinase.

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, School of Computer Engineering, Nanyang Technological University, 639798, Department of Computer Science and Technology, Tongji University, Shanghai 201804, Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu 213011



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