CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set
By W. Reinhold, M. Sunshine, Hongfang Liu, Sudhir Varma, K. Kohn, Joel Morris, J. Doroshow, Yves Pommier. Cancer Research, 2012
783 citations recorded by the source corpus.
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 US National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacological response platforms. In this report we introduce a CellMiner1 web application designed to improve use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,217 genes and 360 microRNAs along with activity reports for 18,549 chemical compounds including 91 drugs approved by the US 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.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2a3305b5ec1c5166ebc91d5c54ced1245ff919e3
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In Cancer Research
In Molecular Biology
- Marinka Zitnik
- Kexin Huang
- Jure Leskovec
- Cao Xiao
- Jimeng Sun
- Tianfan Fu
- Yusuf H. Roohani
- Yves Pommier
Citations in the corpus
- Stereospecific PARP trapping by BMN 673 and comparison with olaparib and rucaparib (2013, background): “Cell lines are colored according to tissue of origin (41).”
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “…features are downloaded from NCI’s CellMiner interface: 25,723 gene features capturing transcript expression levels averaged from five microarray platforms, 627 microRNA expression features and 3171 proteomic features that capture the abundance levels of a subset of proteins (Reinhold et al. 2012).”
- Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties (2012, background): “8 predict drug responses in various contexts [21-24].”