PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies
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Abstract
Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.
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Neither cBioPortal, PharmacoDB nor COSMIC curate samples to map unique identifiers or link samples and models from the same patient, which can be important for identifying mis-identified or cross-contaminated samples.
“Third, neither cBioPortal, PharmacoDB nor COSMIC curate samples to map unique identifiers or link samples and models from the same patient, which can be important for identifying mis-identified or cross-contaminated samples.”
From Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models (2019) · cites this paper as [35]
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- Authors
- Smirnov, P., Kofia, V., Maru, A., Freeman, M., Ho, C., El-Hachem, N., Adam, G.-A., Ba-alawi, W., Safikhani, Z., & Haibe-Kains, B.
- Affiliations
- University Health Network (CA)
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-10-09
- Volume
- 46
- Issue
- D1
- Pages
- D994-D1002
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5753377
- OpenAlex
- W2759360483
- Cited by
- 155
- References
- 39
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 16 · 2023: 15 · 2022: 15 · 2021: 35 · 2020: 23 · 2019: 17 · 2018: 10
- FWCI
- 10.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Cancer Research Society
- Princess Margaret Cancer Foundation
- Ontario Institute for Cancer Research
- Genentech
- Stand Up To Cancer
- Canadian Institutes of Health Research
- Canadian Cancer Society Research Institute
- 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 › Biomedical Text Mining and Ontologies
- Keywords
- cancer pharmacogenomics, anticancer drug screening, drug sensitivity, database curation, drug response prediction, data integration, in vitro drug sensitivity
- MeSH terms
- Pharmacogenomic Testing, Antineoplastic Agents, Dose-Response Relationship, Drug, Drug Screening Assays, Antitumor, Humans, User-Computer Interface, Cell Line, Tumor, Data Mining, Databases, Pharmaceutical
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