ArticleJournal of the American Medical Informatics Association : JAMIA
Comparison and validation of genomic predictors for anticancer drug sensitivity.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.ACS pharmacology & translational science · 2023Article
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Multiplex gene quantification as digital markers for extremely rapid evaluation of chemo-drug sensitivity.Patterns (New York, N.Y.) · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.Biomolecules · 2020Article
- Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data.Frontiers in oncology · 2020Review
- Machine learning approaches to drug response prediction: challenges and recent progress.NPJ precision oncology · 2020Review
- Article
- Dr.VAE: improving drug response prediction via modeling of drug perturbation effects.Bioinformatics (Oxford, England) · 2019Article
- Evaluating the consistency of large-scale pharmacogenomic studies.Briefings in bioinformatics · 2019Review
- Predict drug sensitivity of cancer cells with pathway activity inference.BMC medical genomics · 2019Article
- The germline genetic component of drug sensitivity in cancer cell lines.Nature communications · 2018Article
- Hub genes in a pan-cancer co-expression network show potential for predicting drug responses.F1000Research · 2018Article
- Article
- Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies.Scientific reports · 2017Article
- Gene isoforms as expression-based biomarkers predictive of drug response in vitro.Nature communications · 2017Article
- Safikhani et al. reply.Nature · 2016Article
- Context-specific functional module based drug efficacy prediction.BMC bioinformatics · 2016Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundAn enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel.
methodsTwo large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase.
resultsSixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1.
conclusionThese results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.
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