Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Abstract

Kun Zhang and colleagues present a metric called methylation haplotype load (MHL) that quantifies methylation patterns within blocks of tightly linked CpG dinucleotides. They show that the MHL can distinguish samples from different human somatic tissues and that it can be used to improve detection of cancer-derived circulating DNA and identify its tissue of origin. Kun Zhang and colleagues present a metric called methylation haplotype load (MHL) that quantifies methylation patterns within blocks of tightly linked CpG dinucleotides. They show that the MHL can distinguish samples from different human somatic tissues and that it can be used to improve detection of cancer-derived circulating DNA and identify its tissue of origin. Adjacent CpG sites in mammalian genomes can be co-methylated owing to the processivity of methyltransferases or demethylases, yet discordant methylation patterns have also been observed, which are related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that show highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks, after analysis of 61 whole-genome bisulfite sequencing data sets and validation with 101 reduced-representation bisulfite sequencing data sets and 637 methylation array data sets. Using a metric called methylation haplotype load, we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, using methylation haplotypes we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 patients with lung or colorectal cancer.