BACKGROUND Current methods for the detection and surveillance of bladder cancer (BCa) are often invasive and/or possess suboptimal sensitivity and specificity, especially in early-stage, minimal, and residual tumors.METHODS We developed an efficient method, termed utMeMA, for the detection of urine tumor DNA methylation at multiple genomic regions by MassARRAY. We identified the BCa-specific methylation markers by combined analyses of cohorts from Sun Yat-sen Memorial Hospital (SYSMH), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) database. The BCa diagnostic model was built in a retrospective cohort (n = 313) and validated in a multicenter, prospective cohort (n = 175). The performance of this diagnostic assay was analyzed and compared with urine cytology and FISH.RESULTS We first discovered 26 significant methylation markers of BCa in combined analyses. We built and validated a 2-marker–based diagnostic model that discriminated among patients with BCa with high accuracy (86.7%), sensitivity (90.0%), and specificity (83.1%). Furthermore, the utMeMA-based assay achieved a great improvement in sensitivity over urine cytology and FISH, especially in the detection of early-stage (stage Ta and low-grade tumor, 64.5% vs. 11.8%, 15.8%), minimal (81.0% vs. 14.8%, 37.9%), residual (93.3% vs. 27.3%, 64.3%), and recurrent (89.5% vs. 31.4%, 52.8%) tumors. The urine diagnostic score from this assay was better associated with tumor malignancy and burden.CONCLUSION Urine tumor DNA methylation assessment for early diagnosis, minimal, residual tumor detection and surveillance in BCa is a rapid, high-throughput, noninvasive, and promising approach, which may reduce the burden of cystoscopy and blind second surgery.FUNDING This study was supported by the National Key Research and Development Program of China and the National Natural Science Foundation of China.
Xu Chen, Jingtong Zhang, Weimei Ruan, Ming Huang, Chanjuan Wang, Hong Wang, Zeyu Jiang, Shaogang Wang, Zheng Liu, Chunxiao Liu, Wanlong Tan, Jin Yang, Jiaxin Chen, Zhiwei Chen, Xia Li, Xiaoyu Zhang, Peng Xu, Lin Chen, Ruihui Xie, Qianghua Zhou, Shizhong Xu, Darryl Luke Irwin, Jian-Bing Fan, Jian Huang, Tianxin Lin
Workflow indicating study design.