Urine cell-free DNA multi-omics to detect MRD and predict survival in bladder cancer patients

Abstract

Circulating tumor DNA sensitivity remains subpar for minimal residual disease (MRD) detection in bladder cancer patients. To remedy this, we focused on the biofluid most proximal to the disease, urine, and analyzed urine tumor DNA (utDNA) 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 tumor fraction levels in urine cfDNA significantly predicted pathologic complete response status. A random forest model integrating these factors with leave-one-out cross-validation was 87% sensitive for predicting MRD. 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 cell-free DNA multi-omics to sensitively detect MRD and accurately predict survival.