Abstract
BackgroundMany kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.ResultsWe have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.ConclusionsWe have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.
Highlights
Many kinase inhibitors have been approved as cancer therapies
The 244 kinase inhibitors (KIs) of the EMD drug library were screened at 1000 nM individually and the treatment lasted for 72 hours
We have introduced an integrated experimental and computational methodology that identifies the role of specific kinases in the drug response of a given cell line
Summary
Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, providing a map of the strength of action of each compound on a large number of its targets. The sensitivity to PARP inhibitors of Ewing’s sarcoma cells with mutations in the EWS gene and to MEK inhibitors in NRAS-mutant cell lines with AHR expression have been predicted using analysis of variance and the elastic net method [6] and experimentally validated [7,8] In these analyses, the statistical variable associated to drugs was represented by the half maximal inhibitory concentration (IC50) in different cell lines. A method to predict drugs sensitivity in cell lines based on the integration of genomic data with molecular physico-chemical descriptors of the drugs has been recently proposed [9] Another useful type of information is the residual activity of kinases after interacting with a compound. Patient genetic profiles, and sensitivity of primary leukemia patient samples to kinase inhibitors were recently used by Tyner et al [10] to identify functionally important kinase targets and clarify kinase pathway dependence in cancer
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