An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules
By A. Basu, Nicole E. Bodycombe, Jaime H. Cheah, Edmund V. Price, Ke Liu, Giannina I. Schaefer, R. Ebright, Michelle L Stewart, D. Ito, Stephanie Wang and 19 more. Cell, 2013
761 citations recorded by the source corpus.
Summary The high rate of clinical response to protein kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell-line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: 1) associate with specific cancer-genomic alterations and 2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl2-family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and accelerate discovery of drugs matched to patients by their cancer genotype and lineage.
Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/d952ae2b5a7334f76437e457b8d23c9abea9ceb1
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In Computational Theory and Mathematics
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Citations in the corpus
- A Landscape of Pharmacogenomic Interactions in Cancer (2016, background): “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).”
- Broad targeting of resistance to apoptosis in cancer (2015, background): “[33] Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al.”
- Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, background): “4a–c and 6).”
- Next-Generation Machine Learning for Biological Networks. (2018, neutral): “Extensive multi-omics data 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 a”
- Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset (2015, neutral): “Log-transformed duplicate data were averaged during normalization of luminescence values to vehicle (DMSO) treatment and background (media-only) wells, and at each compound concentration, a D-score was calculated (9), which after re-exponentiation we used as the percent-viability (PV) score.”
- Regulation of Ferroptotic Cancer Cell Death by GPX4 (2014, background): “328 Cell 156, 317–331, January 16, 2014 ª2014 Elsevier Inc. logistical regression analysis [Basu et al., 2013]).”