Urine-based epigenetic profiling for detection of clear cell renal cell carcinoma: A prospective study.

Abstract

e16545 Background: Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer that poses a significant challenge in early detection and differentiation from other subtypes in clinical practice. Liquid biopsy using epigenomic signatures represents a potential solution for non-invasive cancer detection and disease surveillance. Methods: In this prospective study, aiming to collect matched tissue-plasma-urine samples from 200 ccRCC patients and 50 non-tumor donors, we enrolled 14 patients with ccRCC and 10 patients with benign kidney lesions. Matched samples (tissue, blood, and urine) were collected and analyzed using the DNA methylation assay, PredicineEPIC. An in-house developed bioinformatics algorithm was used to investigate the genome-wide epigenomic profiles, and a machine-learning model was built for tumor prediction based on tumor-specific methylation patterns. Further enrollment is ongoing. Results: In the exploratory cohort, tissue-based differential methylated regions (DMRs) profiling differentiated tumors from normal renal tissues with high accuracy. The ccRCC-specific methylation patterns were constructed with 637 optimized methylation features, and the machine learning classifier was trained. The prediction efficacy was tested in a larger-scale TCGA-KIRC dataset, which demonstrated high sensitivity and specificity (AUC = 0.985). Compared with the use of blood, urinary cell-free DNA-based methylation profiling allowed for the differentiation of ccRCC patients from healthy controls and patients with benign kidney lesions. Conclusions: The current preliminary study demonstrates the technical feasibility of urine-based methylation study in the identification of patients with ccRCC. Our findings highlight the potential of urine-based methylation profiling in kidney cancer detection and disease surveillance.