Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Year: 2015
Type: article
Abstract: UNLABELLED: 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.
Source: Cancer Discovery
Authors: Brinton Seashore-Ludlow, Matthew G. Rees, Jaime H. Cheah, Murat Cokol, Edmund V. Price, Matthew E. Coletti, Victor Jones, Nicole E. Bodycombe, Christian K. Soule, Joshua Gould, Benjamin Alexander, Ava Li, Philip Montgomery, Mathias J. Wawer, Nurdan Kuru, Joanne D. Kotz, C. Suk-Yee Hon, Benito Munoz, Ted Liefeld, Vlado Dančík, Joshua A. Bittker, Michelle Palmer, James E. Bradner, Alykhan F. Shamji, Paul A. Clemons, Stuart L. Schreiber
Institutions: Broad Institute, Sabancı Üniversitesi, Harvard University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2175970000