Computational identification of multi-omic correlates of anticancer therapeutic response
Open accessAbstract
BACKGROUND: A challenge in precision medicine is the transformation of genomic data into knowledge that can be used to stratify patients into treatment groups based on predicted clinical response. Although clinical trials remain the only way to truly measure drug toxicities and effectiveness, as a scientific community we lack the resources to clinically assess all drugs presently under development. Therefore, an effective preclinical model system that enables prediction of anticancer drug response could significantly speed the broader adoption of personalized medicine. RESULTS: Three large-scale pharmacogenomic studies have screened anticancer compounds in greater than 1000 distinct human cancer cell lines. We combined these datasets to generate and validate multi-omic predictors of drug response. We compared drug response signatures built using a penalized linear regression model and two non-linear machine learning techniques, random forest and support vector machine. The precision and robustness of each drug response signature was assessed using cross-validation across three independent datasets. Fifteen drugs were common among the datasets. We validated prediction signatures for eleven out of fifteen tested drugs (17-AAG, AZD0530, AZD6244, Erlotinib, Lapatinib, Nultin-3, Paclitaxel, PD0325901, PD0332991, PF02341066, and PLX4720). CONCLUSIONS: Multi-omic predictors of drug response can be generated and validated for many drugs. Specifically, the random forest algorithm generated more precise and robust prediction signatures when compared to support vector machines and the more commonly used elastic net regression. The resulting drug response signatures can be used to stratify patients into treatment groups based on their individual tumor biology, with two major benefits: speeding the process of bringing preclinical drugs to market, and the repurposing and repositioning of existing anticancer therapies.
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A joint matrix of multi-omics features including gene expression, copy number variation and mutation was used with random forest and support vector machine to predict anti-cancer drug response.
“For example, a joint matrix of multi-omics features (which included gene expression, copy number variation and mutation) was used with classical RF and SVM to predict anti-cancer drug response (Stetson et al., 2014).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Stetson et al., 2014
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Using machine learning approaches for multi-omics data analysis: A review
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- Authors
- Stetson, L. C., Pearl, T., Chen, Y., & Barnholtz-Sloan, J. S.
- Affiliations
- Case Comprehensive Cancer Center (US)
- Massachusetts Institute of Technology (US)
- Venue
- BMC Genomics
- Published
- 2014-10
- Volume
- 15
- Issue
- S7
- Pages
- 481-481
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4243102
- OpenAlex
- W2010773093
- Cited by
- 49
- References
- 37
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 8 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 4 · 2016: 1 · 2015: 3
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Update notices
- erratum (2015-06-30)
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- BioMed Central Ltd
- Funders
- National Science Foundation (DGEO 9517830)
- Case Comprehensive Cancer Center, Case Western Reserve University (P30 CA043703, CA043703)
- National Cancer Institute (P30CA043703)
- 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 › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- anticancer drug response prediction, cancer cell lines, pharmacogenomics, precision oncology, random forest, support vector machine, elastic net, drug repurposing, patient stratification, 17-AAG, erlotinib, lapatinib, paclitaxel, PLX4720, PD0325901, palbociclib, selumetinib, AZD0530
- MeSH terms
- Algorithms, Animals, Antineoplastic Agents, Drug Screening Assays, Antitumor, Humans, Models, Biological, Predictive Value of Tests, Computational Biology, Cell Line, Tumor, Datasets as Topic
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