Journal
CURRENT BIOINFORMATICS
Volume 13, Issue 5, Pages 437-443Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1574893613666180118104250
Keywords
Drug combination; sparse representation; group structure; drug sensitivity; cancer cells; drug-kinase
Funding
- National Natural Science Foundation of China (NSFC) [61672113, 61772367]
- National Key Research and Development Program of China [2016YFC0901704]
- MOE AcRF Tier 2 grant, Ministry of Education, Singapore [ARC 39/13, MOE2013-T2-1-079]
- Program of Shanghai Subject Chief Scientist [15XD1503600]
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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.
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