ProsTAV, a novel blood‐based test for biopsy decision management in significant prostate cancer

Abstract

AbstractBackgroundCurrent pathways in early diagnosis of prostate cancer (PCa) can lead to unnecessary biopsy procedures. Here, we used telomere analysis to develop and evaluate ProsTAV®, a risk model for significant PCa (Gleason score >6), with the objective of improving the PCa diagnosis pathway.MethodsThis retrospective, multicentric study analyzed telomeres from patients with serum PSA 3–10 ng/mL. High‐throughput quantitative fluorescence in‐situ hybridization was used to evaluate telomere‐associated variables (TAVs) in peripheral blood mononucleated cells. ProsTAV® was developed by multivariate logistics regression based on three clinical variables and six TAVs. The predictive capacity and accuracy of ProsTAV® were summarized by receiver operating characteristic (ROC) curves and its clinical benefit with decision curves analysis.ResultsTelomeres from 1043 patients were analyzed. The median age of the patients was 63 years, with a median PSA of 5.2 ng/mL and a percentage of significant PCa of 23.9%. A total of 874 patients were selected for model training and 169 patients for model validation. The area under the ROC curve of ProsTAV® was 0.71 (95% confidence interval [CI], 0.62–0.79), with a sensitivity of 0.90 (95% CI, 0.88–1.0) and specificity of 0.33 (95% CI, 0.24–0.40). The positive predictive value was 0.29 (95% CI, 0.21–0.37) and the negative predictive value was 0.91 (95% CI, 0.83–0.99). ProsTAV® would make it possible to avoid 33% of biopsies.ConclusionsProsTAV®, a predictive model based on telomere analysis through TAV, could be used to increase the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/mL.