ArticleProstate cancer and prostatic diseases2026
Validation of the Prostatype® P-score for predicting prostate cancer specific mortality in a multiethnic U.S. veterans cohort.
Article in Prostate cancer and prostatic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe Prostatype® Test evaluates expression levels of three stem cell genes (IGFBP3, F3, and VGLL3), which are combined with PSA, stage, and grade to calculate P-score. Previous research found P-score accurately predicts prostate cancer (PC) specific mortality (PCSM) in patients with newly diagnosed clinically localized PC. We evaluated the performance of P-score to predict PCSM in a large, multiethnic cohort from the Veterans' Administration (VA).
methodsAfter pathologic review to ensure sufficient tumor tissue, formalin-fixed paraffin-embedded (FFPE) biopsy cores from patients with newly diagnosed PC at the Durham VA were sent to an academic medical center. There, cores were sectioned, RNA extracted, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests conducted for IGFBP3, F3, VGLL3, and GAPDH (control). Results were combined with clinical data to generate P-scores. The association between P-score and PCSM was evaluated using c-index, Cox and Fine-Gray models, and decision curve analysis (DCA).
resultsHigher P-scores were significantly associated with a higher risk of PCSM (HR = 1.48 per 1 unit increase in P-score, 95% CI: 1.20-1.84, p <0.001) and accurately estimated PCSM (c-index = 0.87). Adding clinical variables to P-score only incrementally improved accuracy. The DCA indicated P-score provided net clinical benefit for patients with PCSM risk between 5% and ~50%. As P-score strongly correlated with risk group, we tested the value of P-score in intermediate-risk patients specifically, where it significantly predicted PCSM (HR 1.43, 95% CI: 1.09-1.86, p = 0.009).
conclusionIn this American cohort of veterans, P-score significantly predicted PCSM. Adding clinical variables minimally improved accuracy. Accuracy remained high in intermediate-risk patients, wherein there is arguably the greatest need for better risk stratification. Given P-scores can be generated rapidly in-house using a standardized RT-qPCR assay, P-score represents a robust new tool to risk-stratify newly diagnosed patients for PC death, thereby minimizing mismatched treatments.
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