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.