Urinary biomarkers for the detection of prostate cancer in patients with high-grade prostatic intraepithelial neoplasia.
High-grade prostatic intraepithelial neoplasia (HGPIN) is a recognized precursor stage of PCa. Men who present HGPIN in a first prostate biopsy face years of active surveillance including repeat biopsies. This study aimed to identify non-invasive prognostic biomarkers that differentiate early on between indolent HGPIN cases and those that will transform into actual PCa. We measured the expression of 21 candidate mRNA biomarkers using quantitative PCR in urine sediment samples from a cohort of 90 patients with initial diagnosis of HGPIN and a posterior follow up of at least two years. Uni- and multivariate statistical analyses were applied to analyze the candidate biomarkers and multiplex models using combinations of these biomarkers. PSMA, PCA3, PSGR, GOLM, KLK3, CDH1, and SPINK1 behaved as predictors for PCa presence in repeat biopsies. Multiplex models outperformed (AUC = 0.81-0.86) the predictive power of single genes, including the FDA-approved PCA3 (AUC = 0.70). With a fixed sensitivity of 95%, the specificity of our multiplex models was of 41-58%, compared to the 30% of PCA3. The PPV of our models (30-38%) was also higher than the PPV of PCA3 (27%), suggesting that benign cases could be more accurately identified. Applying statistical models, we estimated that 33% to 47% of repeat biopsies could be prevented with a multiplex PCR model, representing an easy applicable and significant advantage over the current gold standard in urine sediment. Using multiplex RTqPCR-based models in urine sediment it is possible to improve the current diagnostic method of choice (PCA3) to differentiate between benign HGPIN and PCa cases.