Using the 6-base genome for full multiomic analysis of cfDNA through combined methylation and fragmentomic analysis to enhance classification of clinical cancer cfDNA samples

Sensitive detection of circulating tumour DNA (ctDNA) within the total pool of cell-free DNA (cfDNA) is crucial for the early diagnosis of cancer via liquid biopsy, and for monitoring ctDNA levels during treatment and remission. This enables earlier monitoring of treatment response, identification of minimal residual disease, and early detection of cancer. However, ctDNA typically represents only a minor fraction of overall cfDNA, posing significant challenges for conventional biomarker-based detection methods. Recently, fragmentomics-based approaches have shown promising results for ctDNA detection in liquid biopsy samples, with studies demonstrating that analysis of fragment size distributions across genomic regions, the frequency of 5' end motifs, and nucleosome positioning patterns near functional genomic sites can substantially enhance sensitivity and specificity. The advent of advanced methylomic profiling methods, such as 6-base sequencing with duet evoC, which distinguishes 5-methylcytosine (5mC) from 5-hydroxymethylcytosine (5hmC), has further expanded the analytical landscape. Integrating 5mC and 5hmC profiling with ctDNA detection strategies extends the spectrum of discernible fragment end motifs and provides orthogonal data layers to fragment size and nucleosome positioning analysis. These multidimensional epigenetic signatures hold the potential to markedly improve the resolution and accuracy of ctDNA detection. Here, we evaluate the impact of these additional epigenetic layers on ctDNA detection in liquid biopsy samples from a cohort of healthy volunteers and patients with different stages of colorectal cancer. We show that as well as different 5mC and 5hmC profiles between healthy, and early or late stage CRC, there are distinct differences in fragmentomics metrics when comparing across these groups. We identify regulatory regions displaying differences in nucleosome positioning in cfDNA derived from healthy individuals and CRC patients. Finally, we compare the ability of fragmentomics and epigenetic modalities (individually and in combination) to classify CRC patients. These classifiers evidence the utility of full multiomic datasets in cfDNA applications.