Individualized prognosis stratification in muscle invasive bladder cancer: A pairwise TP53-derived transcriptome signature

TP53 is the most frequently mutated gene in muscle invasive bladder cancer (MIBC) and there are two gene signatures regarding TP53 developed for MIBC prognosis. However, they are limited to immune genes only and unable to be used individually across platforms due to their quantitative manners. We used 827 gene expression profiles from seven MIBC cohorts with varied platforms to build a pairwise TP53-derived transcriptome signa-ture, 13 gene pairs (13-GPs). Since the 13-GPs model is a single sample prognostic predictor, it can be applied individually in practice and is applicable to any gene-expression platforms without specific normalization re-quirements. Survival difference between high-risk and low-risk patients stratified by the 13-GPs test was sta-tistically significant (HR range: 2.26-2.76, all P < .0001). Discovery and validation sets showed that the 13-GPs was an independent prognostic factor after adjusting other clinical features (HR range: 2.21-2.82, all P < .05). Moreover, it was a potential supplement to the consensus molecular classification of MIBC to further stratify the LumP subtype (patients with better prognoses). High-and low-risk patients by the 13-GPs model presented distinct immune microenvironment and DDR mutation rates, suggesting that it might have the potential for immunotherapy. Being a general approach to other cancer types, this study demonstrated how we integrated gene variants with pairwise gene panels to build a single sample prognostic test in translational oncology.

Automated summary

TP53 is the most frequently mutated gene in muscle invasive bladder cancer (MIBC), and two gene signatures have been developed for MIBC prognosis. However, they are limited to immune genes and cannot be used individually across platforms. This study developed a 13-gene pair model that can be used as a single-sample prognostic predictor in MIBC, applicable to any gene-expression platform without specific normalization requirements. The model showed statistically significant survival differences between high-risk and low-risk patients and could potentially be used for immunotherapy.