Harnessing connectivity in a large-scale small-molecule sensitivity dataset

Seashore-Ludlow, Brinton and Rees, Matthew G. and Cheah, Jaime H. and Çokol, Murat and Price, Edmund V. and Coletti, Matthew E. and Jones, Victor and Bodycombe, Nicole E. and Soule, Christian K. and Gould, Joshua and Alexander, Benjamin and Li, Ava and Montgomery, Philip and Wawer, Mathias J. and Kuru, Nurdan and Kotz, Joanne D. and Hon, C. Suk-Yee and Munoz, Benito and Liefeld, Ted and Dancik, Vlado and Bittker, Joshua A. and Palmer, Michelle and Bradner, James E. and Shamji, Alykhan F. and Clemons, Paul A. and Schreiber, Stuart L. (2015) Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discovery, 5 (11). pp. 1210-1223. ISSN 2159-8274 (Print) 2159-8290 (Online)

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Abstract

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). SIGNIFICANCE: We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.
Item Type: Article
Subjects: R Medicine > RM Therapeutics. Pharmacology
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Murat Çokol
Date Deposited: 24 Dec 2015 21:30
Last Modified: 23 Aug 2019 15:27
URI: https://research.sabanciuniv.edu/id/eprint/28721

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