interface-CancerDAP

interface-CancerDAP predicts individual anticancer drug responses by integrating gene expression, copy number variation, and DNA methylation to identify subpathway signatures for personalized cancer therapy.

Key Features:

• Subpathway Signature Identification: Identifies 46 distinct subpathway signatures associated with individual responses to anticancer drugs derived from analysis of five cancer–drug response datasets.
• Multi-Omics Integration: Integrates gene expression, copy number variation, and DNA methylation profiles to model molecular determinants of drug response.
• Validation and Reliability: Validates identified subpathway signatures using two independent datasets to assess robustness.
• Clinical Outcome Stratification: Stratifies patients by subpathway signatures to predict varying clinical outcomes and serve as prognostic biomarkers.
• Mechanism Similarity Analysis: Analyzes subpathways associated with cellular responses to 191 anticancer drugs from CellMiner, covering 2751 subpathways, to assess mechanism similarity of drug actions.

Scientific Applications:

• Prediction of individualized drug responses: Enables prediction and analysis of individualized responses to anticancer drugs for precision medicine research.
• Mechanistic studies of drug action: Supports investigation of molecular mechanisms underlying drug efficacy and resistance by linking subpathway signatures to cellular responses.
• Patient stratification for therapy: Aids stratifying patients for tailored therapies based on identified subpathway signatures.
• Biomarker discovery: Facilitates identification of potential prognostic biomarkers associated with clinical outcomes.

Methodology:

Integrates gene expression, copy number variation, and DNA methylation data; analyzes five cancer–drug response datasets to derive 46 subpathway signatures; validates signatures using two independent datasets; and analyzes subpathways associated with cellular responses to 191 anticancer drugs from CellMiner covering 2751 subpathways.