Identify Non-Mutational p53 Functional Deficiency in Human Cancers

Abstract

An accurate assessment of TP53s functional status is critical for cancer genomic medicine. However, there is a significant challenge in identifying tumors with non-mutational p53 inactivations that are not detectable through DNA sequencing. These undetected cases are often misclassified as p53-normal, leading to inaccurate prognosis and downstream association analyses. To address this issue, we build the support vector machine (SVM) models to systematically reassess p53s functional status in TP53 wild-type (TP53WT) tumors from multiple TCGA cohorts. Cross-validation demonstrates the excellent performance of the SVM models with a mean AUC of 0.9822, precision of 0.9747, and recall of 0.9784. Our study reveals that a significant proportion (87-99%) of TP53WT tumors actually have compromised p53 function. Additional analyses uncovered that these genetically intact but functionally impaired (termed as predictively reduced function of p53 or TP53WT-pRF) tumors exhibit genomic and pathophysiologic features akin to p53 mutant tumors: heightened genomic instability and elevated levels of hypoxia. Clinically, patients with TP53WT-pRF tumors experience significantly shortened overall survival or progression-free survival compared to those with TP53WT-pN (predictive normal function of p53) tumors, and these patients also display increased sensitivity to platinum-based chemotherapy and radiation therapy.