The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies
Open accessPublished version of a preprint
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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“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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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Fitzmaurice, C., Allen, C., Barber, R. M., Barregård, L., Bhutta, Z. A., Brenner, H., Dicker, D., Chimed‐Ochir, O., Dandona, R., Dandona, L., et al. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2016.5688 · Cited by 6,318
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
A Landscape of Pharmacogenomic Interactions in Cancer
Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090
DrugBank 4.0: shedding new light on drug metabolism
Law, V., Knox, C., Djoumbou-Feunang, Y., Jewison, T., Guo, A. C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1068 · Cited by 1,995
Pharmacogenomics Knowledge for Personalized Medicine
Whirl‐Carrillo, M., McDonagh, E. M., Hebert, J. M., Gong, L., Sangkuhl, K., Thorn, C. F., Altman, R. B., & Klein, T. E. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.96 · Cited by 1,791
Biomarkers in Cancer Staging, Prognosis and Treatment Selection
Ludwig, J. A., & Weinstein, J. N. (2005) · Nature reviews. Cancer · doi:10.1038/nrc1739 · Cited by 1,682
Hoadley, K. A., Yau, C. S. F., Wolf, D. M., Cherniack, A. D., Tamborero, D., Ng, S., Leiserson, M. D. M., Niu, B., McLellan, M. D., Uzunangelov, V., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.049 · Cited by 1,537
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,520
Research works printed but not linked: 2
- 2016, Reproducible pharmacogenomic profiling of cancer cell line panels, Nature, 533, 333, 10.1038/nature17987 · DOI
- 2015, ExPASy - Cellosaurus, Cellosaurus
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Machine learning approaches to drug response prediction: challenges and recent progress
Adam, G. A., Rampášek, L., Safikhani, Z., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2020) · npj Precision Oncology · doi:10.1038/s41698-020-0122-1 · Cited by 459
Arora, R., Cao, C., Kumar, M., Sinha, S., Chanda, A., McNeil, R., Samuel, D., Arora, R. K., Matthews, T. W., Chandarana, S. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40271-4 · Cited by 363
Larsson, P., Engqvist, H., Biermann, J., Rönnerman, E. W., Forssell-Aronsson, E. B., Kovács, A., Karlsson, P., Helou, K., & Parris, T. Z. (2020) · Scientific Reports · doi:10.1038/s41598-020-62848-5 · Cited by 239
Artificial intelligence, machine learning, and drug repurposing in cancer
Tanoli, Z., Vähä-Koskela, M. J. V., & Aittokallio, T. A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1883585 · Cited by 229
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Health Informatics › 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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