CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set
Open accessAbstract
High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.
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- Authors
- Reinhold, W. C., Sunshine, M., Liu, H., Varma, S., Kohn, K. W., Morris, J., Doroshow, J., & Pommier, Y.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Reinhold, William C.
- Pommier, Yves
- ORCID
- Venue
- Cancer Research
- Published
- 2012-07-15
- Volume
- 72
- Issue
- 14
- Pages
- 3499-3511
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC3399763
- OpenAlex
- W2103196126
- Cited by
- 887
- References
- 47
- Citations by year
- 2026: 39 · 2025: 79 · 2024: 119 · 2023: 150 · 2022: 152 · 2021: 55 · 2020: 35 · 2019: 35 · 2018: 44 · 2017: 32 · 2016: 41 · 2015: 42 · 2014: 36 · 2013: 18 · 2012: 6
- FWCI
- 13.36
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 CA999999)
- National Cancer Institute (Y99 CA999999)
- U.S. National Library of Medicine (R01LM009959)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- CellMiner, web-based data analysis tool, gene expression patterns, microRNA expression, multidrug resistance, doxorubicin, miR-17-92 cluster, EGFR tyrosine kinase inhibitors, erlotinib, gefitinib, afatinib, lapatinib, pattern matching, cancer cell lines
- MeSH terms
- Antineoplastic Agents, Drug Evaluation, Humans, RNA, United States, Gene Expression, Databases, Factual, Information Storage and Retrieval, Computational Biology, Internet, Genomics, MicroRNAs, Microarray Analysis, National Cancer Institute (U.S.)
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