Mass Spectrometry-Based Biomarkers to Detect Prostate Cancer: A Multicentric Study Based on Non-Invasive Urine Collection without Prior Digital Rectal Examination

Simple Summary Prostate cancer is the most frequent cancer type and one of the leading causes of death in men globally. Multiple biomarkers analyzed in urine have been proposed for detecting prostate cancer, in an effort to reduce unnecessary and invasive biopsies. Nevertheless, these biomarkers are based on sampling after prior digital rectal examination and/or prostate massage. Considering the need for more convenient urine sampling, in this study, we investigated endogenous urinary peptides in patients with prostate cancer compared to those with non- malignant (non- cancerous) prostatic diseases. A multidimensional biomarker model was developed based on 181 significant peptides that can detect whether a patient has high probability to bear a tumor in the prostate. Based on the results, the biomarker model including 181 biomarkers showed good accuracy in detecting prostate cancer and has the potential to improve clinical management of men with a suspicion of prostate cancer, by reducing the need for invasive biopsies. (1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.