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PLoS One. 2017 Oct 12;12(10):e0185650. doi: 10.1371/journal.pone.0185650. eCollection 2017.

Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens.

Author information

1
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
2
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
3
Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America.
4
Cancer Biology Program, Fox Chase Cancer Center; Philadelphia, Pennsylvania, United States of America.
5
Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
6
Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
7
Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America.

Abstract

Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitizing agents.

PMID:
29023490
PMCID:
PMC5638242
DOI:
10.1371/journal.pone.0185650
[Indexed for MEDLINE]
Free PMC Article

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