TP53-based tripartite classification reveals three distinct mutation patterns across pan-cancer.

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cancer exhibits significant genetic heterogeneity.
• Understanding gene mutation patterns across diverse cancer types is crucial for targeted therapies.
• The role of TP53 mutations and their impact on broader genomic landscapes require comprehensive analysis.

Purpose of the Study:

• To perform a comprehensive pan-cancer analysis of gene mutation frequencies.
• To identify significantly mutated genes and their distribution patterns across various cancers.
• To explore the impact of mutated transcription factors and signaling pathways on cancer phenotypes and therapeutic strategies.

Main Methods:

• Utilized a large dataset of 45,259 cancer samples from 41,988 patients.
• Classified samples based on TP53 mutation status (TP53_top, TP53_plus, Non_TP53).
• Conducted intersection analysis of mutated genes, identified significantly mutated genes, and mapped regulatory and signaling pathways.

Main Results:

• Identified 95 significantly mutated genes and 80,524 unique mutations, categorized into SinglePoint-SingleCan, SinglePoint-MultiCan, and MultiPoints-MultiCan.
• Discovered 26 mutated transcription factors and 476 downstream targets within 19 regulatory networks (TP53-Solo, TP53-Multi, Non_TP53).
• Mapped 47 mutated signaling pathway networks across 52 cancers, revealing diverse dysregulated phenotypes.

Conclusions:

• The study delineates two precision medicine strategies: SinglePoint specificity and MultiPoints compatibility.
• Identified potential therapeutic targets within mutated regulatory and signaling networks.
• Provides a blueprint for developing precision therapies based on integrated pan-cancer genotypes and phenotypes.