Article ; Online: UCseek
EBioMedicine, Vol 89, Iss , Pp 104437- (2023)
ultrasensitive early detection and recurrence monitoring of urothelial carcinoma by shallow-depth genome-wide bisulfite sequencing of urinary sediment DNAResearch in context
2023
Abstract: Summary: 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 ...
Abstract | Summary: 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. |
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Keywords | Urothelial carcinoma ; Molecular diagnostics ; Tumor markers ; Diagnosis ; Relapse monitoring ; Machine learning ; Medicine ; R ; Medicine (General) ; R5-920 |
Subject code | 616 |
Language | English |
Publishing date | 2023-03-01T00:00:00Z |
Publisher | Elsevier |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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