UCseek: ultrasensitive early detection and recurrence monitoring of urothelial carcinoma by shallow-depth genome-wide bisulfite sequencing of urinary sediment DNAResearch in context
Ping Wang,
Yue Shi,
Jianye Zhang,
Jianzhong Shou,
Mingxin Zhang,
Daojia Zou,
Yuan Liang,
Juan Li,
Yezhen Tan,
Mei Zhang,
Xingang Bi,
Liqun Zhou,
Weimin Ci,
Xuesong Li
Affiliations
Ping Wang
Department of Urology, Peking University First Hospital, Beijing, 100034, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Yue Shi
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
Jianye Zhang
Department of Urology, Peking University First Hospital, Beijing, 100034, China; Institute of Urology, Peking University, Beijing, 100034, China
Jianzhong Shou
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
Mingxin Zhang
Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China
Daojia Zou
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Yuan Liang
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
Juan Li
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Yezhen Tan
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Mei Zhang
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
Xingang Bi
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; Corresponding author.
Liqun Zhou
Department of Urology, Peking University First Hospital, Beijing, 100034, China; Institute of Urology, Peking University, Beijing, 100034, China; National Urological Cancer Center, Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing, 100034, China; Corresponding author.
Weimin Ci
CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China; Corresponding author.
Xuesong Li
Department of Urology, Peking University First Hospital, Beijing, 100034, China; Institute of Urology, Peking University, Beijing, 100034, China; National Urological Cancer Center, Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing, 100034, China; Corresponding author.
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.
