MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes

Abstract

We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.

CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data

“…The joint methylation status of individual sequencing reads, often referred to as epialleles (epigenetic alleles), captures the ‘phased information’ of CpG sites, and can represent the methylation haplotype (9,14,32). With the advancement of base-resolution sequencing techniques (such as WGBS), epialleles have recently been studied in several major lines of DNA methylation research (14,25,26,31–35), such as tumor clones and their phylogeny (26,33,34), intratumor heterogeneity (25,35), solid tissue studies (31), and tissue deconvolution of cfDNAs (14). Most of these studies proposed new measures based on epialleles, such as proportion of discordant reads (PDR) (25), Epipolymorphism (31), methylation entropy (32), and methylated haplotype load (MHL) (14).…”

Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types

“…By significantly relaxing the dependence on reference methylation profiles of constituent cell types compared to previous methods (Houseman et al, 2012), EDec enables deconvolution of complex tumor tissues where highly accurate references are unavailable. In contrast to reference-free methods (Houseman et al, 2016, 2014; Rahmani et al, 2016; Zheng et al, 2014; Zou et al, 2014), EDec’s indirect use of surrogate references greatly assisted in the interpretation of deconvolution results, allowing us to uncover more complex biological patterns than possible by applying other deconvolution techniques. Further, unlike previous methylation-based deconvolution methods, EDec does not require that each cell type be explained by a single component (e.g., cancerous epithelial fraction in the TCGA dataset was modeled by five different components), thus making it possible to model the full diversity of cancerous epithelial cells.…”

“…Laser capture microdissection (LCM), cell sorting, and other physical methods to isolate cell types from solid tumors for molecular profiling are technically challenging, and severely limit throughput (Debey et al, 2004). A number of methods for in silico deconvolution have been developed to address this problem using as input gene expression profiles (Aran et al, 2015; Gentles et al, 2015; Houseman and Ince, 2014; Kuhn et al, 2011; Li and Xie, 2013; Newman et al, 2015; Shen-Orr et al, 2010; Venet et al, 2001; Yoshihara et al, 2013; Zhong et al, 2013) and, more recently, DNA methylation profiles (Houseman et al, 2012, 2014, 2016; Zheng et al, 2014; Zou et al, 2014; Rahmani et al, 2016) of tissue homogenates. However, the ability of these methods to infer cell type composition of solid tumors and interpret the states of constituent cell types is limited, thus hampering the study of cellular states and cellular interactions within the tumor microenvironment.…”

Applications of Immunogenomics to Cancer

“…While most cancer genomics studies are focused on the cancerous cells in the tumor tissue, the impurities, such as stromal cells, endothelial cells, and immune cells, could have major impact on the development and progression of cancer. With genomic profiling, tumor purity could be estimated from DNA copy number (Carter et al, 2012), SNP allele frequency (Li and Li, 2014), RNA-seq (Yoshihara et al, 2013), or DNA methylation (Zhang et al, 2015; Zheng et al, 2014) data. Interestingly, these methods using orthogonal tumor profiling modalities yield very consistent tumor purity estimates, in distinct contrast to the estimates provided by pathologists, suggesting that molecular and morphological changes in the tumor do not appear simultaneously.…”