Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Pharmacogenomics

Background:

• Individualized drug response prediction is crucial for personalized cancer treatment and advancing precision medicine.
• Large-scale multi-omics data offers significant potential for precision cancer therapy.

Purpose of the Study:

• To develop a pipeline for identifying subpathway signatures for predicting individual anticancer drug responses.
• To integrate multiple genetic and epigenetic alterations for comprehensive analysis.

Main Methods:

• Proposed a pipeline integrating gene expression, copy number variation, and DNA methylation data.
• Identified subpathway signatures using five cancer-drug response datasets.
• Validated the reliability of identified signatures in independent datasets.

Main Results:

• Identified 46 subpathway signatures associated with individual anticancer drug responses.
• Demonstrated that multi-omics subpathway signatures significantly improve prediction performance.
• Uncovered essential roles of different omics types and functional associations in drug response.
• Discovered subpathway signatures as potential prognostic biomarkers through patient stratification.
• Provided a landscape of subpathways for 191 anticancer drugs and revealed drug action mechanism similarity.
• Developed CancerDAP, a web interface for exploring subpathways and drug responses.

Conclusions:

• Systematically identified and characterized subpathway signatures for individualized anticancer drug response prediction.
• These findings may advance precise cancer treatment and the understanding of molecular mechanisms of drug actions.