循环肿瘤DNA（ctDNA）在膀胱癌患者中用于分子残余病（MRD）检测的灵敏度仍然不足。为解决这个问题，我们专注于疾病最接近的生物体液尿液，并分析了74名局部膀胱癌患者的尿液肿瘤DNA。我们将超低覆盖全基因组测序（ULP-WGS）与深度测序（uCAPP-Seq）的尿液癌症个性化分析相结合，实现了敏感的MRD检测和预测总生存率。与血浆ctDNA相比，尿液游离DNA（cfDNA）中的变异等位基因频率、推断的肿瘤突变负荷和拷贝数衍生的肿瘤分数水平显著预测了病理完全缓解状态。一种结合这些尿液cfDNA衍生因素的随机森林模型，采用一次留一验证，对于预测残留疾病相对于黄金标准手术病理的敏感性为87％。根据Kaplan-Meier分析，对于被该模型预测为MRD的患者，无进展生存（HR = 3.00，p = 0.01）和总生存（HR = 4.81，p = 0.009）都显著恶化，这得到Cox回归分析的证实。对于浸润性肌层、新辅助化疗和保留验证亚组进行的额外生存分析印证了这些发现。总之，我们对74名局部膀胱癌患者的尿液样本进行了个性化分析，并利用尿液cfDNA多组学方法进行了敏感的MRD检测和准确的生存预测。©2023年，作者（们）。

Circulating tumor DNA (ctDNA) sensitivity remains subpar for molecular residual disease (MRD) detection in bladder cancer patients. To remedy this problem, we focused on the biofluid most proximal to the disease, urine, and analyzed urine tumor DNA in 74 localized bladder cancer patients. We integrated ultra-low-pass whole genome sequencing (ULP-WGS) with urine cancer personalized profiling by deep sequencing (uCAPP-Seq) to achieve sensitive MRD detection and predict overall survival. Variant allele frequency, inferred tumor mutational burden, and copy number-derived tumor fraction levels in urine cell-free DNA (cfDNA) significantly predicted pathologic complete response status, far better than plasma ctDNA was able to. A random forest model incorporating these urine cfDNA-derived factors with leave-one-out cross-validation was 87% sensitive for predicting residual disease in reference to gold-standard surgical pathology. Both progression-free survival (HR = 3.00, p = 0.01) and overall survival (HR = 4.81, p = 0.009) were dramatically worse by Kaplan-Meier analysis for patients predicted by the model to have MRD, which was corroborated by Cox regression analysis. Additional survival analyses performed on muscle-invasive, neoadjuvant chemotherapy, and held-out validation subgroups corroborated these findings. In summary, we profiled urine samples from 74 patients with localized bladder cancer and used urine cfDNA multi-omics to detect MRD sensitively and predict survival accurately.© 2023. The Author(s).