Abstract
Simple SummaryThe detection of DNA methylation changes in blood has emerged as a promising approach for cancer diagnosis and management. Our group has previously optimized a blood DNA methylation profiling technology that is based on affinity capture of methylated DNA, termed cfMBD-seq. The aim of this study was to assess the potential clinical feasibility of cfMBD-seq. We applied cfMBD-seq to the blood samples of cancer patients and identified methylation signatures that can not only discriminate cancer patients from cancer-free individuals but can also enable accurate multi-cancer classification. Our findings will help to expand on existing blood-based molecular diagnostic tests and identify novel methylation biomarkers for early cancer detection and classification.Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.
Highlights
Lung and colorectal cancer are among the most common causes of cancer-related deaths in the US, whereas pancreatic cancer is the deadliest form of solid malignancy with an alarming 10% five-year survival rate [1]
We investigated genome-wide methylation enrichment and found that the number of captured fragments without any CpG tandem accounted for only 1.47% (1.33%–1.59%) of high-quality reads (Figure 2b)
We found a median of 42.16% (39.47–45.15) of reads mapped to CpG islands, whereas CpG islands only accounted for 0.7% of the hg19 reference genome (Figure 2e,f and Figure S1c)
Summary
Lung and colorectal cancer are among the most common causes of cancer-related deaths in the US, whereas pancreatic cancer is the deadliest form of solid malignancy with an alarming 10% five-year survival rate [1]. The dismal mortality rates seen in patients with these malignancies are associated with advanced stage at the time of diagnosis. To improve the outcomes of this patient population, many technologies and assays that enable cancer detection at its early stage have been investigated. Circulating components are shed from multiple body sites, and the methylation patterns of cfDNA are consistent with the tissues they originated from [7]. In this context, systemic analysis of cfDNA methylation profiles is under development for early cancer detection, minimal residual disease monitoring, treatment response and prognosis assessment, and to determine the tissue of origin [8,9]
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