Development and Validation of Urine-based Peptide Biomarker Panels for Detecting Bladder Cancer in a Multi-center Study

Purpose: Urothelial bladder cancer presents high recurrence rates, mandating continuous monitoring via invasive cystoscopy. The development of noninvasive tests for disease diagnosis and surveillance remains an unmet clinical need. In this study, validation of two urine-based biomarker panels for detecting primary and recurrent urothelial bladder cancer was conducted. Experimental Design: Two studies (total n = 1,357) were performed for detecting primary (n = 721) and relapsed urothelial bladder cancer (n = 636). Cystoscopy was applied for detecting urothelial bladder cancer, while patients negative for recurrence had follow-up for at least one year to exclude presence of an undetected tumor at the time of sampling. Capillary electrophoresis coupled to mass spectrometry (CE-MS) was employed for the identification of urinary peptide biomarkers. The candidate urine-based peptide biomarker panels were derived from nested cross-sectional studies in primary (n = 451) and recurrent (n = 425) urothelial bladder cancer. Results: Two biomarker panels were developed on the basis of 116 and 106 peptide biomarkers using support vector machine algorithms. Validation of the urine-based biomarker panels in independent validation sets, resulted in AUC values of 0.87 and 0.75 for detecting primary (n = 270) and recurrent urothelial bladder cancer (n = 211), respectively. At the optimal threshold, the classifier for detecting primary urothelial bladder cancer exhibited 91% sensitivity and 68% specificity, while the classifier for recurrence demonstrated 87% sensitivity and 51% specificity. Particularly for patients undergoing surveillance, improved performance was achieved when combining the urine-based panel with cytology (AUC = 0.87). Conclusions: The developed urine-based peptide biomarker panel for detecting primary urothelial bladder cancer exhibits good performance. Combination of the urine-based panel and cytology resulted in improved performance for detecting disease recurrence. (C) 2016 AACR.