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Contributed by Marc W. Kirschner, February 24, 2014 (sent for review January 15, 2014)
Significance
Protein kinase inhibitors represent a major class of anticancer drugs, which are notoriously unspecific. Efforts to exploit the polypharmacology of inhibitors for target deconvolution have met with little success. Our significant contribution is to apply regularized regression to kinase expression and kinase profiling on a large set of inhibitors. By selecting a set of optimally designed kinase inhibitors that span a broad range of kinase specificities, we identified relevant kinases from our model in six cell lines; we then empirically validated a set of specific kinases that regulate cancer cell migration. Using this model, we predict a cell type-specific response to previously untested inhibitors. Broadly, these approaches should prove useful in identifying novel targets and in rational cancer therapy.
Abstract
Polypharmacology (action of drugs against multiple targets) represents a tempting avenue for new drug development; unfortunately, methods capable of exploiting the known polypharmacology of drugs for target deconvolution are lacking. Here, we present an ensemble approach using elastic net regularization combined with mRNA expression profiling and previously characterized data on a large set of kinase inhibitors to identify kinases that are important for epithelial and mesenchymal cell migration. By profiling a selected optimal set of 32 kinase inhibitors in a panel against six cell lines, we identified cell type-specific kinases that regulate cell migration. Our discovery of several informative kinases with a previously uncharacterized role in cell migration (such as Mst and Taok family of MAPK kinases in mesenchymal cells) may represent novel targets that warrant further investigation. Target deconvolution using our ensemble approach has the potential to aid in the rational design of more potent but less toxic drug combinations.
- systems pharmacology
- regularized regression
- perturbation biology
- predictive modeling
- cancer cell migration
Footnotes
↵1T.S.G. and L.P. contributed equally to this work.
- ↵2To whom correspondence should be addressed. E-mail: marc{at}hms.harvard.edu.
Author contributions: T.S.G., L.P., and M.W.K. designed research; T.S.G. and L.P. performed research; T.S.G., L.P., and M.W.K. analyzed data; and T.S.G., L.P., and M.W.K. wrote the paper.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1403080111/-/DCSupplemental.
Exploiting polypharmacology
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