Clinical Utility of Urine DNA for Noninvasive Detection and Minimal Residual Disease Monitoring in Urothelial Carcinoma

Current methods for the early detection and minimal residual disease (MRD) monitoring of urothelial carcinoma (UC) are invasive and/or possess suboptimal sensitivity. We developed an efficient workflow named urine tumor DNA multidimensional bioinformatic predictor (utLIFE). Using UC-specific mutations and large copy number variations, utLIFE-UC model was developed on bladder cancer cohort (n = 150) and validated in The Cancer Genome Atlas (TCGA) bladder cancer cohort (n=674) and upper tract urothelial carcinoma (UTUC) cohort (n=22). The utLIFE-UC model could discriminate 92.8% UC with 96.0% specificity, and validated robustly in BLCA_TCGA and UTUC cohorts. Furthermore, compared to cytology, utLIFE-UC achieved a great improvement in detection of non–muscle-invasive bladder cancer (NMIBC, p<0.01). In the MRD cohort, utLIFE-UC could distinguish 100% of patients with residual disease, showing superior sensitivity than cytology (p<0.01) and fluorescence in situ hybridization (FISH, p<0.05). utLIFE-UC is a promising method for early diagnosis and MRD monitoring in UC. This study shows that utLIFE-UC can detect urothelial carcinoma with high sensitivity and specificity in patients in early-stage cancer or with minimal residual disease, is a cost-effective, rapid, high-throughput, noninvasive, and promising approach that may reduce the burden of cystoscopy and blind surgery.