Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

By Brinton Seashore-Ludlow, M. Rees, Jaime H. Cheah, Murat Cokol, Edmund V. Price, Matthew E Coletti, V. Jones, Nicole E. Bodycombe, Christian K. Soule, Joshua Gould and 16 more. Cancer Discovery, 2015

689 citations recorded by the source corpus.

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).

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c5caae58071bc275bf53c0d17e13d8a1f7efb2de

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Citations in the corpus

Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “Therefore, most of the learning studies to date have been done using large panels of cancer cell lines (Garnett et al. 2012; Barretina et al. 2012; Seashore-Ludlow et al. 2015; Iorio et al. 2016), although there are also a few recent examples aiming at clinical treatment predictions in patient…”
Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress (2018, methodology): “We excluded 6 cell lines that either hadmismatched or non-confident classifications, resulting in 43 lines analyzed (1 assayed in duplicate by theCTRP).”
Next-Generation Machine Learning for Biological Networks. (2018, neutral): “…from drug treatments (Barretina et al., 2012; Basu et al., 2013; Garnett et al., 2012; Goodspeed et al., 2016; Musa et al., 2017; Rees et al., 2016; Seashore-Ludlow et al., 2015; Shoemaker, 2006; Yang et al., 2013), together with large amounts of genotypic data collected and stored in repositories…”
A Landscape of Pharmacogenomic Interactions in Cancer (2016, methodology): “Nonetheless, our results represent a comprehensive attempt to describe the landscape of clinically relevant pharmacogenomics interactions in cellular models of cancer, complementing previous efforts (Barretina et al., 2012; Basu et al., 2013; Garnett et al., 2012; Seashore-Ludlow et al., 2015).”
A Next Generation Connectivity Map: L1000 platform and the first 1,000,000 profiles (2017, background): “That is, while any given member of an MOA class would likely have a multitude of targets, integrating signatures across several examples of an MOA class would sharpen the on-target signal, while diminishing off-target effects, as has been proposed previously (Seashore-Ludlow et al., 2015).”