Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens
Name
journal.pone.0185650.pdf
Size
3.16 MB
Format
Adobe PDF
Checksum (MD5)
1e33f513d812b5a36f1d3922b797adac
Author(s)
Beeharry, Neil
Fink, Lauren
Bhattacharjee, Vikram
Huang, Shao-shan Carol
Zhou, Yan
Yen, Tim
Ursu, Oana
Gosline, Sara Calafell
Fraenkel, Ernest
Date Issued
October 2017
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Ursu, Oana et al. “Network Modeling of Kinase Inhibitor Polypharmacology Reveals Pathways Targeted in Chemical Screens.” Edited by Qiming Jane Wang. PLOS ONE 12, 10 (October 2017): e0185650 © 2017 Ursu et al
Version
Final published version
Abstract
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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 chemosensitiz-ing agents.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0185650
