Performance of PCA3 and TMPRSS2:ERG Within the Prostate Cancer Prevention Trial Risk Calculator Version 2 in a Lithuanian Cohort.

BACKGROUND

Prostate cancer (PCa) remains a significant health concern due to its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models.

OBJECTIVE

This study aimed to evaluate the performance of incorporating urinary biomarkers - prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T:E) - into the Prostate Cancer Prevention Trial Risk Calculator version 2 (PCPTRC2) in a Lithuanian cohort to enhance the detection of clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

A single-centre prospective study included 246 men scheduled for initial prostate biopsy between January 2021 and August 2024 due to elevated total PSA levels or abnormal digital rectal examination (DRE). Following ethical approval and informed consent, urinary samples were collected post-DRE and analysed for PCA3 and T:E. Each patient's risk was calculated using the basic PCPTRC2 and updated versions incorporating biomarkers. Biopsies were performed based on multiparametric magnetic resonance imaging (mpMRI) findings.

RESULTS

Of 209 biopsy samples analysed, 111 (53.1%) were diagnosed with csPCa. The AUC for PCa detection was 59.6% for the original PCPTRC2, improving to 76.2% with PCA3 and further to 79.5% when both PCA3 and T:E were included. Both updated versions demonstrated significantly higher sensitivity compared to the original (p<0.001). However, no significant differences were noted in distinguishing csPCa from non-csPCa.

CONCLUSION

Incorporating PCA3 and T:E into PCPTRC2 substantially enhances diagnostic accuracy for detecting PCa in biopsy-naïve patients. Despite limitations, these findings underscore the potential for optimizing risk calculators in clinical practice, advocating for larger cohorts to validate these results.