Paper detail

Prediction of kinase inhibitor response using activity profiling, in-vitro screening, and elastic net regression

Many 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 new untested drugs and elucidate the role played by 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. We 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 method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compare different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations leads to predictive models, and predictivity can be improved by applying logarithmic transformation to the data. The method is applied to the A549 lung cancer cell line. A pathway enrichment analysis of the set of kinases identified by the method shows that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.