HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

Area of Science:

• Computational biology
• Genomics
• Transcriptomics

Background:

• Identifying cancer driver genes is critical for developing targeted therapies.
• Integrating multi-omics data presents a significant challenge in cancer research.

Purpose of the Study:

• To introduce HIT'nDRIVE, a novel computational method for identifying patient-specific cancer driver genes.
• To leverage genomic and transcriptomic data for improved therapeutic development and precision oncology.

Main Methods:

• HIT'nDRIVE solves the random walk facility location problem using multihitting time in gene interaction networks.
• The method integrates genomic and transcriptomic data to pinpoint influential, sequence-altered genes.
• Applied to 2200 tumors across four cancer types and a pan-cancer cell line panel.

Main Results:

• Identified numerous clinically actionable driver genes across diverse cancer types.
• Demonstrated accurate tumor phenotype prediction using HIT'nDRIVE-identified driver gene modules.
• Discovered breast cancer subtype-specific driver modules linked to patient survival outcomes.
• Accurately predicted drug efficacy in pan-cancer cell lines based on driver genes and modules.

Conclusions:

• HIT'nDRIVE effectively identifies cancer driver genes and modules from multi-omics data.
• The method facilitates precision oncology by enabling phenotype prediction and drug efficacy assessment.
• HIT'nDRIVE supports clinical decision-making by contextualizing complex genomic and transcriptomic information.