CellMinerCDB

CellMinerCDB integrates pharmacologic, genomic, transcriptomic, proteomic, and other multi-omic data from cancer cell line collections (including NCI-60, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, NCI/DTP small cell lung cancer, NCI Almanac, and NCATS) to enable analysis of drug responses and biomarker discovery in cancer pharmacogenomics.

Key Features:

• RNA Sequencing Data Integration: Incorporates RNA sequencing (RNA-seq) for the NCI-60 with composite and isoform transcript expression data and integrates RNA-seq with existing microarray data, highlighting isoform diversity for genes such as HRAS, PTEN, EGFR, RAD51, ALKBH2, BRCA1, ERBB2, TP53, FGFR2, and CTNND1.
• Proteomic Data Resource: Includes proteomic data generated using pressure cycling technology and SWATH mass spectrometry with reproducible detection and quantification of over 3,100 SwissProt proteotypic proteins, revealing stoichiometric relationships not evident at the mRNA level.
• Comprehensive Drug Activity Information: Provides activity information for 2,675 drugs and compounds (including NCATS entries) across 183 cancer cell lines, with 72 cell lines unique to NCATS and representation of previously understudied tissues of origin.
• Multi-Omic Data Integration: Integrates DNA copy number, methylation and mutation profiles, transcriptome data, protein levels, histone modifications, metabolites, CRISPR signatures, and additional molecular data for cross-database analyses.
• Advanced Analytical Tools: Supports multiple univariate and multivariate analyses, including linear regression and LASSO, to identify determinants of drug response and perform hypothesis testing.
• Pathway and Gene-Drug Correlations: Demonstrates correlations between gene-drug pairings and isoform expression linked by pathways or function, with examples including ALKBH2–benzaldehyde, AKT3–vandetanib, BCR–imatinib, CDK1 and CDK20–palbociclib, CASP1–imexon, and FGFR3–pazopanib.

Scientific Applications:

• Translational pharmacogenomics: Combines drug activity profiles with genomic and molecular data to study determinants of drug response and support translational research.
• Isoform-level transcriptomics: Reveals cell-specific isoform differences and their links to RNA processing and splicing genes using RNA-seq and isoform expression data.
• Proteomics-informed drug response prediction: Uses SWATH mass spectrometry–derived protein quantification to assess protein-level contributions to pathway activity and drug sensitivity beyond mRNA measures.
• Biomarker and target identification: Integrates multi-omic data to facilitate identification of biomarkers and therapeutic targets relevant to clinically used chemotherapeutic and targeted therapies for precision medicine.

Methodology:

Integrates publicly available genomic, molecular, and pharmacologic datasets from NCI-60, CCLE, GDSC, CTRP, NCI/DTP small cell lung cancer, NCI Almanac, and NCATS, and performs univariate and multivariate analyses including correlation analyses, linear regression, and LASSO.