PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data

Area of Science:

• Computational biology
• Genomic medicine
• Bioinformatics

Background:

• Interpreting extensive tumor genomic alterations for personalized treatment is challenging.
• Identifying relevant biomarkers from complex molecular data requires advanced analytical tools.

Purpose of the Study:

• To introduce PanDrugs2, an upgraded methodology for interpreting tumor molecular alterations.
• To facilitate the selection of personalized therapeutic strategies by integrating multi-omics data and cancer genetic dependencies.

Main Methods:

• Integrated multi-omics analysis combining somatic and germline variants, copy number variation, and gene expression data.
• Incorporation of cancer genetic dependencies to identify novel therapeutic targets.
• Database update with >74K drug-gene associations from 23 sources.

Main Results:

• PanDrugs2 supports comprehensive analysis of somatic and germline variants, copy number variation, and gene expression.
• Identified therapeutic options for previously untargetable genes by considering cancer genetic dependencies.
• Generated an updated database with extensive drug-gene associations and a novel clinical decision-making report.

Conclusions:

• PanDrugs2 significantly advances the interpretation of tumor molecular alterations for personalized medicine.
• The integrated multi-omics approach and consideration of genetic dependencies offer broader therapeutic options.
• PanDrugs2 provides a valuable, freely accessible resource for clinical decision-making in oncology.