CDRgator

CDRgator integrates gene expression signatures of cancer drug resistance to enable comparative analysis and elucidation of molecular mechanisms underlying resistance.

Key Features:

• Resistance signatures from transcriptomic profiles: Includes resistance signatures derived from transcriptomic data of cancer cells and patient samples paired with their drug-resistant counterparts across more than 30 different cancer drugs.
• Drug sensitivity dataset signatures: Incorporates drug resistance group signatures sourced from two large drug sensitivity datasets encompassing approximately 1,000 cancer cell lines.
• Clustering Analysis: Performs clustering analysis to group similar gene expression profiles associated with resistance.
• Multidimensional Scaling (MDS): Uses multidimensional scaling to visualize similarities and dissimilarities among high-dimensional resistance signatures.
• Pathway Analysis: Conducts pathway analysis to identify biological pathways implicated in drug resistance.
• Meta-analysis of independent resistance models: Supports meta-analysis by integrating independent resistance models across datasets to identify conserved signatures.

Scientific Applications:

• Comparative analysis of resistance signatures: Facilitates comparison of gene expression changes between drug-sensitive and drug-resistant states to identify resistance-associated genes.
• Mechanism elucidation: Aids investigation of mechanisms such as target alteration, activation of alternative signaling pathways, epithelial–mesenchymal transition (EMT), and epigenetic changes.
• Identification of conserved pathways: Enables discovery of common pathways and molecular processes across drugs and cell lines linked to resistance.
• Integration across datasets: Allows synthesis of findings from transcriptomic and drug sensitivity datasets to support broader conclusions about resistance mechanisms.

Methodology:

Extracts resistance signatures from transcriptomic data and drug sensitivity datasets and applies clustering, multidimensional scaling (MDS), pathway analysis, and meta-analysis to characterize and integrate resistance signatures.