CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics. Issue Volume 49:Issue D1(2021) (16th November 2020)

• Record Type: Journal Article
• Authors: Luna, Augustin; Elloumi, Fathi; Varma, Sudhir; Wang, Yanghsin; Rajapakse, Vinodh N; Aladjem, Mirit I; Robert, Jacques; Sander, Chris; Pommier, Yves; Reinhold, William C
• Is Part Of: Nucleic acids research. Volume 49:Issue D1(2021)
• Page Start: D1083
• Page End: D1093
• Publication Date: 2020-11-16
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkaa968
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb ) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.