Epigenetic analysis of cell-free DNA by fragmentomic profiling

Abstract

Cell-free DNA (cfDNA) fragmentation patterns contain important molecular information linked to tissues of origin. We explored the possibility of using fragmentation patterns to predict cytosine-phosphate-guanine (CpG) methylation of cfDNA, obviating the use of bisulfite treatment and associated risks of DNA degradation. This study investigated the cfDNA cleavage profile surrounding a CpG (i.e., within an 11-nucleotide [nt] window) to analyze cfDNA methylation. The cfDNA cleavage proportion across positions within the window appeared nonrandom and exhibited correlation with methylation status. The mean cleavage proportion was ∼twofold higher at the cytosine of methylated CpGs than unmethylated ones in healthy controls. In contrast, the mean cleavage proportion rapidly decreased at the 1-nt position immediately preceding methylated CpGs. Such differential cleavages resulted in a characteristic change in relative presentations of CGN and NCG motifs at 5′ ends, where N represented any nucleotide. CGN/NCG motif ratios were correlated with methylation levels at tissue-specific methylated CpGs (e.g., placenta or liver) (Pearson’s absolute r > 0.86). cfDNA cleavage profiles were thus informative for cfDNA methylation and tissue-of-origin analyses. Using CG-containing end motifs, we achieved an area under a receiver operating characteristic curve (AUC) of 0.98 in differentiating patients with and without hepatocellular carcinoma and enhanced the positive predictive value of nasopharyngeal carcinoma screening (from 19.6 to 26.8%). Furthermore, we elucidated the feasibility of using cfDNA cleavage patterns to deduce CpG methylation at single CpG resolution using a deep learning algorithm and achieved an AUC of 0.93. FRAGmentomics-based Methylation Analysis (FRAGMA) presents many possibilities for noninvasive prenatal, cancer, and organ transplantation assessment.

Early detection of malignant and pre-malignant peripheral nerve tumors using cell-free DNA fragmentomics

“…Still, motif diversity score (MDS), an aggregate measure of motif distribution through normalized Shannon entropy, was not significantly different between clinical states ( Figure 4b-c ). Recently, Zhou et al 30 demonstrated that cfDNA fragment cleavage patterns could be quantified by non-negative matrix factorization of 4-mer end motifs into “founder” end motif profiles (F-profiles). We found that indeed, motifs contributed non-randomly to F-profiles ( Supplemental Figure 1 ), F-profile contributions to cfDNA samples differed between clinical states ( Figure 4d ), and that specific F-profiles were more accurate than individual motifs in differentiating clinical states.…”

High tissue-specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA

“…Of course, other models integrating other descriptors, eg, epigenetic marks [36] or fragment sizes [17], other loci and based on a more diverse set of data must be designed to provide a tool usable at the bedside. We hope that our findings, in particular, those with regards to the use of noncoding RNAs, will help improve the sensitivity of ctDNA detection and help widespread liquid biopsy applications.…”

Machine learning to detect the SINEs of cancer

“…In cancer cells, they participate in structural changes, probably through homologous recombination given their widespread distribution throughout the genome and highly similar sequences (2). Moreover, Alu elements are hypomethylated early during tumor progression (3)(4)(5)(6)(7)(8)(9), and this feature has been incorporated into methods for the earlier detection of cancer through plasma cell-free DNA (cfDNA) analysis (10). Alu elements also reflect the altered fragmentation patterns found in the cfDNA of patients with cancer: One of the first plasma multicancer biomarkers used quantitative polymerase chain reaction (qPCR) to calculate the ratio of short and long Alu segments (11)(12)(13).…”