Background: Current methods for the detection and surveillance of urothelial carcinomas (UCs) are often invasive, costly, and not effective for low-grade, early-stage, and minimal residual disease (MRD) tumors. We aimed to develop and validate a model from urine sediments to predict different grade and stage UCs with low cost and high accuracy.
Methods: We collected 167 samples, including 90 tumors and 77 individuals without tumors, as a discovery cohort. We assessed copy number variations and methylation values for them and constructed a diagnostic classifier to detect UC, UCseek, by using an individual read-based method and support vector machine. The performance of UCseek was validated in an independent cohort derived from three hospitals (n = 206) and a relapse cohort (n = 42) for monitoring recurrence.
Findings: We constructed UCseek, which could predict UCs with high sensitivity (92.7%), high specificity (90.7%), and high accuracy (91.7%) in the independent validation set. The accuracy of UCseek in low-grade and early-stage patients reached 91.8% and 94.3%, respectively. Notably, UCseek retained great performance at ultralow sequencing depths (0.3X-0.5X). It also demonstrated a powerful ability to monitor recurrence in a surveillance cohort compared with cystoscopy (90.91% vs. 59.09%).
Interpretation: We optimized an improved approach named UCseek for the noninvasive diagnosis and monitoring of UCs in both low- and high-grade tumors and in early- and advanced-stage tumors, even at ultralow sequencing depths, which may reduce the burden of cystoscopy and blind second surgery.
Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.
Keywords: Diagnosis; Machine learning; Molecular diagnostics; Relapse monitoring; Tumor markers; Urothelial carcinoma.
Copyright © 2023. Published by Elsevier B.V.