Decoding TP53 Variants: A Statistical and Computational Approach to Prioritize Pathogenic Mutations in Cancer Biology

Abstract

Background:
TP53, often referred to as the “guardian of the genome,” is a critical tumor suppressor gene that maintains cellular integrity by regulating the cell cycle, DNA repair, and apoptosis. Mutations in TP53 are among the most frequent alterations in cancer and are associated with tumor progression, therapeutic resistance, and poor prognosis. Given the widespread clinical significance of TP53 variants, understanding their functional impact using computational tools has become an essential step in cancer research.Aim: This study aims to comprehensively analyze TP53 variants using a dataset of genomic alterations, focusing on predictive pathogenicity metrics such as SIFT, PolyPhen, CADD, REVEL, MetaLR, and Mutation Assessor. Additionally, the study identifies trends in predictive scores, examines inter-tool correlations, and prioritizes high-risk variants for further clinical investigation.Materials and Methods: A dataset of 3,830 TP53 variants was analyzed. Predictive tools were employed to assess the functional consequences of these variants. Descriptive statistics, correlation analysis, and prioritization criteria based on high CADD (>20), low REVEL (<0.2), and damaging SIFT (≤0.05) scores were applied. Visualizations, including scatter plots and score distributions, were generated to highlight critical insights.Results:Descriptive analysis revealed that a majority of TP53 variants have high CADD scores (>20), indicating significant functional impact. SIFT scores clustered near 0, suggesting that many variants are predicted to be damaging. REVEL scores, however, skewed toward lower values, creating discrepancies with CADD. Correlation analysis demonstrated strong agreement between CADD, MetaLR, and Mutation Assessor scores, while REVEL showed weaker correlation with CADD. Variants with high CADD and low REVEL scores were prioritized, as they may represent novel candidates requiring functional validation.Conclusion:
This study highlights the diversity and complexity of TP53 variants in cancer, underscoring the importance of integrating multiple predictive scores to assess their pathogenic potential. Variants with discordant predictions between tools may represent unique targets for further experimental validation. Such analyses are essential for identifying clinically actionable TP53 mutations and advancing precision oncology.