当前，对于尿路上皮癌（UC）的早期检测和最小残留疾病（MRD）监测的方法具有侵入性和/或灵敏度不足的缺点。我们开发了一种高效的工作流程，命名为尿液肿瘤DNA多维生物信息学预测器(utLIFE)。通过使用UC特异性突变和大的拷贝数变异，utLIFE-UC模型在膀胱癌队列 (n = 150) 中开发，并在癌症基因组图谱 (TCGA) 膀胱癌队列 (n = 674) 和上尿路UC队列 (n = 22) 中进行了验证。utLIFE-UC模型能够区分92.8％的UC，具有96.0％的特异性，在BLCA_TCGA和UTUC队列中进行了可靠的验证。此外，与细胞学相比，utLIFE-UC提高了膀胱癌检测的灵敏度（p<0.01）。在MRD队列中，utLIFE-UC能够区分所有残留疾病的患者，显示了与细胞学(p<0.01)和荧光原位杂交(p<0.05)相比更高的灵敏度。本研究表明，utLIFE-UC可以用于在早期癌症或MRD的患者中高灵敏度和高特异性地检测UC。utLIFE-UC是一种经济实惠、快速、高通量、无创和有前途的方法，可减轻膀胱镜检查和盲目手术的负担。© 2023作者。

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.© 2023. The Author(s).