Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Open accessPublished version of a preprint
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. Cancer Discov; 5(11); 1210–23. ©2015 AACR. See related commentary by Gray and Mills, p. 1130. This article is highlighted in the In This Issue feature, p. 1111
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Models that integrate several features predict drug sensitivity better than models based on one gene.
“logic models – generated using the LOBICO (“Logic Optimization for Binary Input to Continuous Output”computational approach- that combine multiple input features such as mutations in cancer genes, gene fusions, recurrent copy number aberrations and binarized pathway activity scores (derived from gene expression profiling outperform single-gene models for prediction of drug sensitivity153, 154.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [153]
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References 52 · Cited by 813
- Authors
- Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., Alexander, B., Li, A., Montgomery, P., Wawer, M. J., Kuru, N., Kotz, J. D., Hon, C. S.-Y., Munoz, B., Liefeld, T., Dančík, V., Bittker, J. A., Palmer, M., Bradner, J. E., Shamji, A. F., Clemons, P. A., & Schreiber, S. L.
- Affiliations
- Broad Institute (US)
- Sabancı Üniversitesi (TR)
- 1Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts.
- Corresponding author
- Shamji, Alykhan F.
- Clemons, Paul A.
- Schreiber, Stuart L.
- ORCID
- Seashore-Ludlow, Brinton
- Rees, Matthew G.
- Cheah, Jaime H.
- Cokol, Murat
- Price, Edmund V.
- Soule, Christian K.
- Gould, Joshua
- Alexander, Benjamin
- Montgomery, Philip
- Wawer, Mathias J.
- Kuru, Nurdan
- Kotz, Joanne D.
- Hon, C. Suk-Yee
- Liefeld, Ted
- Dančík, Vlado
- Bittker, Joshua A.
- Bradner, James E.
- Clemons, Paul A.
- Schreiber, Stuart L.
- Venue
- Cancer Discovery
- Published
- 2015-11-01
- Volume
- 5
- Issue
- 11
- Pages
- 1210-1223
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- PMC
- PMC4631646
- OpenAlex
- W2175970000
- Cited by
- 813
- References
- 52
- Citations by year
- 2026: 63 · 2025: 72 · 2024: 79 · 2023: 97 · 2022: 122 · 2021: 89 · 2020: 73 · 2019: 80 · 2018: 52 · 2017: 53 · 2016: 30 · 2015: 2
- FWCI
- 47.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Discovery (publishedVersion)
- PubMed (publishedVersion)
- Sabanci University (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (5rc2ca148399-02)
- National Cancer Institute (U01CA176152, RC2-CA148399)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- cancer cell line sensitivity, precision oncology, drug repurposing, cancer dependency, KRAS-mutant cancer, neuroblastoma, mechanism of action
- MeSH terms
- Antineoplastic Agents, Cell Survival, Dose-Response Relationship, Drug, Drug Screening Assays, Antitumor, Drug Synergism, Humans, Mutation, Neoplasms, Gene Expression Regulation, Neoplastic, Cluster Analysis, Drug Resistance, Neoplasm, Computational Biology, Cell Line, Tumor, Protein Kinase Inhibitors, Cell Proliferation, Small Molecule Libraries, Datasets as Topic
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