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Open Access Methodology article

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

Trish P Tran1, Edison Ong2, Andrew P Hodges1, Giovanni Paternostro12 and Carlo Piermarocchi23*

Author Affiliations

1 Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA

2 Salgomed Inc., Del Mar, CA 92014, USA

3 Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA

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BMC Systems Biology 2014, 8:74  doi:10.1186/1752-0509-8-74

Published: 25 June 2014

Abstract

Background

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 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.

Results

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 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.

Conclusions

We 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.

Keywords:
Drug response predictions; Kinase inhibitors; Elastic net regression; High throughput screening; Drug combination therapies