Validation of a urine- based proteomics test to predict clinically significant prostate cancer: complementing MRI pathway

Abstract

Purpose: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. One major clinical need is to accurately predict clinically significant PCa (csPCa). A proteomics based 19-biomarker model (19-BM) was previously developed using Capillary Electrophoresis -Mass Spectrometry (CE-MS) and validated in 1000 patients at risk for PCa. Here, our objective was to validate 19-BM in a multicentre prospective cohort of 101 biopsy-naive patients using current diagnostic pathways. Materials and Methods: Urine samples from 101 PCa patients were analysed through CE-MS. All patients underwent MRI using a 3-T system. The 19-BM score was estimated via a support vector machine-based software (MosaCluster; v1.7.0), employing previously established cut-off criterion of -0.07. Previously developed diagnostic nomograms were calculated along with MRI. Results: Independent validation of the 19-BM yielded a sensitivity of 77% and specificity of 85% (AUC:0.81). This performance surpasses that of PSA (AUC:0.56), and PSA density (AUC:0.69). For PI-RADS≤ 3 patients, the 19-BM showed a sensitivity of 86% and specificity of 88%. Integrating the 19-BM with MRI resulted in significantly better accuracy (AUC:0.90) compared to the individual investigations alone (AUC19BM=0.81; p=0.004 and AUCMRI:0.79; p=0.001). Examining the decision curve analysis, the 19-BM with MRI surpassed other approaches for the prevailing risk interval from 30% cut-off. Conclusions: 19-BM exhibited favourable reproducibility for prediction of csPCa. In PI-RADS≤3 patients the 19-BM correctly classified 88% of the patients with insignificant PCa at the cost of one csPCa patient that was missed. Utilising 19-BM test could prove valuable complementing MRI and reducing the need for unnecessary biopsies.