PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology

Area of Science:

• Computational biology
• Genomics
• Pharmacology

Background:

• Personalized oncology faces challenges in selecting pharmacotherapy based on genomics.
• Current computational drug prioritization methods are underdeveloped.

Purpose of the Study:

• To develop novel network-based methods for personalized pharmacotherapy prioritization in cancer.
• To improve drug selection by considering indirect interactions and gene driverness.

Main Methods:

• Developed PANACEA (PersonAlized Network-based Anti-Cancer therapy EvaluAtion), incorporating drug-gene networks and gene driverness scores.
• Utilized two scoring methods: distance-based and random walk with restart (propagation).
• Evaluated PANACEA using TCGA, GDSC, and clinical datasets.

Main Results:

• Top-ranked drugs identified by PANACEA are relevant for cancer pharmacotherapy.
• PANACEA successfully identified drugs to which cancer cell lines are sensitive.
• The approach demonstrated adequate performance in clinical settings with known drug responses.
• PANACEA outperformed existing methods like iCAGES and PanDrugs.

Conclusions:

• Network-based approaches enhance personalized pharmacotherapy prioritization in cancer.
• PANACEA offers a robust computational tool for selecting targeted cancer therapies.
• The developed R package is available for broader research application.