NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in...

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Understanding the collective impact of multiple genetic alterations on cancer phenotypes is complex.
• Genomic data analysis faces challenges due to mutation pattern complexity and variability.

Purpose of the Study:

• To present the NETwork-based Genotype-to-Phenotype Transformation (NetG2P) tool.
• To translate genomic information into pathway interaction networks for cancer research.
• To uncover critical oncogenic features linked to cancer prognosis.

Main Methods:

• Utilized network propagation to translate genomic data into pathway interaction networks.
• Conducted a pan-cancer analysis on The Cancer Genome Atlas dataset across 10 cancer types.
• Employed machine learning and explainable artificial intelligence models for prognosis prediction.

Main Results:

• Identified critical oncogenic features, primarily representing signaling crosstalk, as functional units for cancer prognosis.
• Revealed distinct "distributed" and "modular" network patterns among cancer types based on pathway interactions.
• Predicted novel drug targets and identified candidates for drug repurposing using cancer cell line data.

Conclusions:

• NetG2P generates patient-specific networks of critical oncogenic features.
• The tool aids in suggesting personalized treatments for cancer patients.
• This approach advances precision medicine in oncology.