In EBioMedicine
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.
Wang Ping, Shi Yue, Zhang Jianye, Shou Jianzhong, Zhang Mingxin, Zou Daojia, Liang Yuan, Li Juan, Tan Yezhen, Zhang Mei, Bi Xingang, Zhou Liqun, Ci Weimin, Li Xuesong
2023-Feb-07
Diagnosis, Machine learning, Molecular diagnostics, Relapse monitoring, Tumor markers, Urothelial carcinoma
