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

Abstract

INTRODUCTIONː Prostate cancer (PCa) is the most frequently diagnosed cancer among men. A 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. This study aimed to validate 19-BM in a multicenter prospective cohort of 101 biopsy-naive patients using current diagnostic pathways. METHODSː Urine samples from 101 patients with PCa were analyzed using CE-MS. All patients underwent MRI using a 3-T system. The 19-BM score was estimated using support vector machine-based software (MosaCluster v1.7.0), employing a previously established cut-off criterion of -0.07. Previously developed diagnostic nomograms were calculated along with MRI. RESULTSː Independent validation of 19-BM yielded a sensitivity of 77% and a specificity of 85% (AUC:0.81). This performance surpassed those of PSA (AUC:0.56) and PSA density (AUC:0.69). For PI-RADS≤ 3 patients, 19-BM showed a sensitivity of 86% and a specificity of 88%. Integrating 19-BM with MRI resulted in significantly better accuracy (AUC:0.90) compared to individual investigations alone (AUC19BM=0.81; p=0.004 and AUCMRI:0.79; p=0.001). Examining the decision curve analysis, 19-BM with MRI surpassed other approaches for the prevailing risk interval from a 30% cut-off. CONCLUSIONSː 19-BM exhibited favorable reproducibility for the prediction of csPCa. In patients with PI-RADS≤3, 19-BM correctly classified 88% of the patients with insignificant PCa at the cost of one missed csPCa patient. Utilizing the 19-BM test could prove valuable in complementing MRI and reducing the need for unnecessary biopsies.