Comprehensive Analysis of Tumor Mutational Burden in Prostate Cancer: Multipublic Dataset Analysis

Tumor mutational burden (TMB) is a clinically relevant biomarker for predicting responsiveness to immune checkpoint inhibitors (ICIs). However, in prostate cancer (PCa), the mechanisms underlying TMB elevation and its prognostic utility across diverse treatment regimens remain poorly understood. This study aimed to investigate the associations between TMB and clinicopathological, genomic, treatment, and survival characteristics by analyzing several large public PCa datasets.

We retrospectively analyzed clinicopathological, genomic, therapeutic, and survival data from four published studies. Each dataset was independently assessed to explore associations between TMB and key variables. Statistical analyses included the Wilcoxon rank-sum test, log-rank test, and multivariate logistic regression to adjust for confounding factors. TMB-based stratification for survival and oncoplot analyses was performed using cut-off values of 5 or 10 mutations per megabase (Mb).

Higher TMB was independently associated with high Gleason score, neuroendocrine PCa, and prior exposure to hormone therapy or taxane chemotherapy. In the overall and hormone therapy cohorts, patients with TMB-high/intermediate (≥5 mutations/Mb) exhibited significantly poorer overall survival (OS). Oncoplot analysis revealed increased mutation frequencies in SPEN and ARID1A in patients with TMB-high (≥10 mutations/Mb), genes previously linked to ICI responsiveness. However, TMB alone did not reliably predict ICI outcomes. When microsatellite instability (MSI) high patients were included, TMB-high showed a borderline association with improved OS in the ICI-treated cohort (P = .051).

TMB may be influenced both tumor aggressiveness and treatment-induced mutational dynamics in PCa. Combined evaluation of TMB and MSI status may enhance immunotherapy stratification and prognostication.