Abstract 4878: Enhancing non-invasive detection of urothelial carcinoma through combined cytology and methylation profiling of urinary cell-free DNA

Abstract Detection of urothelial carcinoma (UC) faces a significant challenge in sensitivity due to the limitations of existing methodologies such as cytology. Identifying tumors through epigenetics pattern presents a promising advancement in diagnostic precision. This study aims to explore a non-invasive and more sensitive strategy by analyzing urinary cell-free DNA (ucfDNA) methylation profiles in conjunction with traditional urine cytology. The cohort for this study were collected from 20 patients with UC and 17 patients with benign or non-tumor lesions which histopathologic and cytopathologic results were reviewed by pathologist. Additional 16 cancer samples and 40 healthy donor samples were collected from independent research centers. We employed PredicineEPIC, a comprehensive whole-genome methylation assay, to investigate the DNA methylation characteristics in urine samples. The dataset was splited into 1:2 proportions for modeling and validation. The methylation signals were fed to three machine learning algorithms for determining the most effective classification model. Differentiating cancer and benign samples through urinary cytology has high specificity (1.0) but low sensitivity (0.3). Then we examined differentially methylated regions and implemented four most abnormally methylated regions as classify features. We found that the Gradient Boosting Machine (GBM) model achieved optimal performance in the validation set, with an AUC of 0.93, sensitivity of 0.81 (21/26), and specificity of 1.0 (36/36). The combination of methylation- and urine cytology-based models raised 2% prediction accuracy without reducing precision, suggesting that this assay could be applied to augment the cytology testing for UC detection. This study proposes a new and promising approach, leveraging cytology combined with epigenetic profiles of urinary cell-free DNA, for the precise diagnosis of urothelial carcinoma. Citation Format: Huan Zhao, Hang Dong, Haoran Tang, Zhihui Zhang, Na Wei, Jingjing Xu, Pan Du, Shidong Jia, Ting Xiao, Huiqin Guo. Enhancing non-invasive detection of urothelial carcinoma through combined cytology and methylation profiling of urinary cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4878.