A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples

Abstract

Background: Colorectal cancer is the fourth cause of cancer-related deaths worldwide, though detection at early stages associates with good prognosis. Thus, there is a clear demand for novel non-invasive tests for the early detection of colorectal cancer and premalignant advanced adenomas, to be used in population-wide screening programs. Aberrant DNA methylation detected in liquid biopsies, such as serum circulating cell-free DNA (cfDNA), is a promising source of non-invasive biomarkers. This study aimed to assess the feasibility of using cfDNA pooled samples to identify potential serum methylation biomarkers for the detection of advanced colorectal neoplasia (colorectal cancer or advanced adenomas) using microarray-based technology.

Results: cfDNA was extracted from serum samples from 20 individuals with no colorectal findings, 20 patients with advanced adenomas, and 20 patients with colorectal cancer (stages I and II). Two pooled samples were prepared for each pathological group using equal amounts of cfDNA from 10 individuals, sex-, age-, and recruitment hospital-matched. We measured the methylation levels of 866,836 CpG positions across the genome using the MethylationEPIC array. Pooled serum cfDNA methylation data meets the quality requirements. The proportion of detected CpG in all pools (> 99% with detection p value < 0.01) exceeded Illumina Infinium methylation data quality metrics of the number of sites detected. The differential methylation analysis revealed 1384 CpG sites (5% false discovery rate) with at least 10% difference in the methylation level between no colorectal findings controls and advanced neoplasia, the majority of which were hypomethylated. Unsupervised clustering showed that cfDNA methylation patterns can distinguish advanced neoplasia from healthy controls, as well as separate tumor tissue from healthy mucosa in an independent dataset. We also observed that advanced adenomas and stage I/II colorectal cancer methylation profiles, grouped as advanced neoplasia, are largely homogenous and clustered close together.

Conclusions: This preliminary study shows the viability of microarray-based methylation biomarker discovery using pooled serum cfDNA samples as an alternative approach to tissue specimens. Our strategy sets an open door for deciphering new non-invasive biomarkers not only for colorectal cancer detection, but also for other types of cancers.

Keywords: Advanced adenomas; Circulating cell-free DNA; Colorectal cancer; DNA methylation; MethylationEPIC; Non-invasive diagnostic biomarkers; Pooled samples; Serum.

Fig. 1

Density distributions of methylation data. a Density distribution of the raw methylation beta and M values across the 866,836 CpG sites measured in the six pooled serum cfDNA pooled samples. b Density distribution of the beta values by probe type for all the interrogated CpG sites in pools A–F

Fig. 2

Identification of differential methylation. a Boxplot of global cfDNA methylation in NCF, AN, AA, and CRC pools. Global methylation is expressed as the average methylation rate for each pooled sample. The box plot represents the median (line across the box), interquartile range, and maximum and minimum values (whiskers). b Manhattan plot showing −log₁₀(p value) resulting from the differential methylation analysis for all the CpGs considered (703,653). The p values are sorted by chromosome coordinates. Significant DMPs between AN and NCF pooled samples with a FDR < 5% (5808) appear highlighted in darker color, above the red dashed line. c Volcano plot of differential methylation −log₁₀(p value) versus differences in methylation levels (Δbeta: obtained by subtracting the DNA methylation levels (beta values) of NCF from AN). Significant DMPs appear above the red dashed line (FDR 5%). Significant DMPs with a difference in the methylation levels greater than 10% (1384) are highlighted in color (135 hypermethylated DMPs in AN, orange dots: Δbeta > 0.1 and FDR < 5%; 1249 hypomethylated DMPs in AN, blue dots: Δbeta < − 0.1 and FDR < 5%). d Relative distribution of the 1385 DMPs with absolute Δbeta > 0.1 in relation to CpG islands (CGI) and across different genomic regions. The EPIC array categorizes probes following a functional classification into three major groups: promoter regions (5′UTR, TSS200, TSS1500, and first exons), intragenic regions (gene body and 3′UTR), and intergenic regions. TSS200, TSS1500: 200 and 1500 bp upstream the transcription start site, respectively. CGI-shore: sequences 2 kb flanking the CGI, CGI-shelf: sequences 2 kb flanking shore regions, opensea: sequences located outside these regions [30]

Fig. 3

Unsupervised analyses including the 1384 DMPs with |Δbeta| > 0.1. a Unsupervised hierarchical clustering and heatmap. Each column represents one pooled sample, and each row represents one of the DMPs (1384). The dendrogram was computed and reordered based on row means. Methylation values are displayed from 0 (red, unmethylated) to 1 (green, fully methylated). b Clustering using multidimensional scaling (MDS) based on the 1384 DMPs

Fig. 4

Unsupervised analyses performed on GSE48684 including the 518 DMPs shared by EPIC and 450K arrays. a Unsupervised hierarchical clustering and heatmap based on these 518 DMPs. Each column represents one tumor or mucosa sample from GSE48684, and each row represents one CpG. The dendrogram was computed and reordered based on row means. Methylation values are displayed from 0 (red, unmethylated) to 1 (green, fully methylated). b Clustering using multidimensional scaling (MDS) on tumor and mucosa samples from GSE48684 based on these 518 DMPs