Objectives: Head and neck squamous cell carcinoma (HNSCC) poses a diagnostic and therapeutic challenge worldwide and is associated with a poor survival rate. Due to the variability in the efficacy of treatments for HNSCC, new predictive biomarkers of therapy outcomes are needed. Recently, we developed an algorithm that employs the mutational profile of TP53 as an independent prognostic factor in HNSCC. In this study, we investigated its role as a predictive biomarker of treatment outcomes in HNSCC patients. We also tested the usefulness of two classification systems for TP53 mutational landscapes.
Materials and methods: Clinical and genomic data were retrieved from The Cancer Genome Atlas database. We built a multivariate stepwise backward binary regression model to assess the role of TP53 mutations in predicting therapeutic outcomes.
Results: Cases harbouring high-risk-of-death mutations reported an odds ratio of 3.301 for stable or progressive disease compared to wild-type cases, while no significant difference in treatment outcomes was found between cases with low-risk-of-death mutations and wild-type TP53. Our analysis found that older patients with a history of alcohol consumption had a higher risk of stable/progressive disease.
Conclusions: This study improves current evidence on the role of TP53 mutations in treatment response in HNSCC patients.
Keywords: TP53 genes; biomarkers; mutation; precision Medicine; squamous Cell Carcinoma of Head and Neck.
© 2023 The Authors. Oral Diseases published by Wiley Periodicals LLC.