Assessment of ctDNA detection in prostate cancer using a 6-base epigenomics and fragmentomics approach.

Abstract

e17132 Background: Prostate cancer (PCa) is the second most frequently diagnosed cancer in men worldwide. Early detection is crucial for improving outcomes and reducing mortality. Circulating tumor DNA (ctDNA) is a promising tool for the early detection of various solid tumors. However, ctDNA shedding rates in early-stage PCa are lower compared to other tumor types, complicating the distinction between cancerous and non-cancerous DNA fragments and reducing sensitivity. A multidimensional cfDNA analysis may enhance sensitivity and facilitate early PCa detection. Methods: We analyzed cfDNA from localized (lPCa, n = 57), metastatic (mPCa, n = 36), and healthy controls (n = 14) using duet evoC platform (biomodal), which enables simultaneous assessment of genetic and epigenetic features, including 5mC and 5hmC. Regions of interest were identified from differentially methylation analysis of TCGA tumor–normal pairs, comparison of prostate tissue to healthy cfDNA and genes known to be expressed in prostate tissue. 5mC and 5hmC levels were extracted for these regions along withgenome wide fragmentomics features, focusing on fragment size and end-motifs. Classifier models were developed, including using read level approaches, and evaluated under cross-validation, with performance assessed at high-specificity thresholds. Results: Early results that showed PCa cfDNA methylation profiling was concordant with TCGA-derived tumour methylation whereas lPCa had more modest differences and that integration of 5mC and 5hmC enhanced discrimination overall. Fragmentomics analysis also provided consistent and reproducible discrimination across disease stages. Therefore it was decided to use read level analysis and the combination of methylation and fragmentomic features to develop multiomic classifiers to detect early and late-stage disease. Integration of those multiomic features led to improved performance for detection of prostate cancer across disease stages at high specificity. Conclusions: cfDNA methylation reflects tumor biology in advanced disease, whereas fragmentomic features provide greater robustness for early detection. Integrating complete methylation with 5mC and 5hmC and fragmentomics enhances sensitivity in localized prostate cancer and may accelerate development of non-invasive diagnostic tools.