Letter to the Editor: 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, the utLIFE-UC model was developed on a bladder cancer cohort (n = 150) and validated in The Cancer Genome Atlas (TCGA) bladder cancer cohort (n = 674) and an upper tract urothelial carcinoma (UTUC) cohort (n = 22). The utLIFE-UC model could discriminate 92.8% of UCs with 96.0% specificity and was robustly validated in the BLCA_TCGA and UTUC cohorts. Furthermore, compared to cytology, utLIFE-UC improved the sensitivity of bladder cancer detection (p < 0.01). In the MRD cohort, utLIFE-UC could distinguish 100% of patients with residual disease, showing superior sensitivity compared to cytology (p < 0.01) and fluorescence in situ hybridization (FISH, p < 0.05). This study shows that utLIFE-UC can be used to detect UC with high sensitivity and specificity in patients with early-stage cancer or MRD. The utLIFE-UC is a cost-effective, rapid, high-throughput, noninvasive, and promising approach that may reduce the burden of cystoscopy and blind surgery.

Automated summary

Current methods for early detection and MRD monitoring of urothelial carcinoma (UC) are invasive or suboptimal in sensitivity. A new workflow called utLIFE, based on UC-specific mutations and large copy number variations, can detect UC with high sensitivity and specificity. It has been validated in bladder cancer and upper tract urothelial carcinoma cohorts, showing improved sensitivity compared to cytology and fluorescence in situ hybridization (FISH).