Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens
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 pharmaceuti...
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| Format: | Article |
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Public Library of Science
2018
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| Online Access: | http://hdl.handle.net/1721.1/113260 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 |
| _version_ | 1826200243449888768 |
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| author | Beeharry, Neil Fink, Lauren Bhattacharjee, Vikram Huang, Shao-shan Carol Zhou, Yan Yen, Tim Ursu, Oana Gosline, Sara Calafell Fraenkel, Ernest |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Beeharry, Neil Fink, Lauren Bhattacharjee, Vikram Huang, Shao-shan Carol Zhou, Yan Yen, Tim Ursu, Oana Gosline, Sara Calafell Fraenkel, Ernest |
| author_sort | Beeharry, Neil |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:33:24Z |
| format | Article |
| id | mit-1721.1/113260 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:33:24Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1132602022-10-01T04:25:34Z Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens Beeharry, Neil Fink, Lauren Bhattacharjee, Vikram Huang, Shao-shan Carol Zhou, Yan Yen, Tim Ursu, Oana Gosline, Sara Calafell Fraenkel, Ernest Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ursu, Oana Gosline, Sara Calafell Fraenkel, Ernest 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. National Institutes of Health (U.S.) (Grant R01-GM089903) National Institutes of Health (U.S.) (Grant U01-CA184898) 2018-01-22T16:45:18Z 2018-01-22T16:45:18Z 2017-10 2017-05 2018-01-19T18:28:01Z Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/113260 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 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 http://dx.doi.org/10.1371/journal.pone.0185650 PLOS ONE Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0 application/pdf Public Library of Science PLoS |
| spellingShingle | Beeharry, Neil Fink, Lauren Bhattacharjee, Vikram Huang, Shao-shan Carol Zhou, Yan Yen, Tim Ursu, Oana Gosline, Sara Calafell Fraenkel, Ernest Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title | Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title_full | Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title_fullStr | Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title_full_unstemmed | Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title_short | Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| title_sort | network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens |
| url | http://hdl.handle.net/1721.1/113260 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 |
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