Improving the clinical performance of blood-based DNA methylation biomarkers utilizing locus-specific epigenetic heterogeneity

Abstract

Aberrant DNA methylation is commonly heralded as a promising cancer biomarker; however, its inherently stochastic nature often leads to variable methylation patterns that can complicate the use of methylation 2 biomarkers for clinical diagnostics, particularly in dilute samples such as liquid biopsies. Here, we present a methylation density binary classifier, a statistical method for leveraging differential heterogeneous methylation to predict and optimize the performance of methylation biomarkers for clinical applications. We first developed and tested the classifier using methylation density profiles derived from reduced representation bisulfite sequencing reads of ovarian carcinoma at ZNF154, a recurrently methylated locus in multiple cancer types. We then used in silico simulations to predict the performance of the classifier in liquid biopsies and validated these predictions using quasi-digital melt curve analysis (DREAMing) of circulating cell-free DNA from individuals with versus without ovarian carcinoma. We found good agreement between predicted and observed classifier performance, and further demonstrated that implementation of this approach with ZNF154 outperformed CA-125 for use in etiologically-diverse ovarian cancer types. Our results indicate that methylation density profiles can be exploited to predict and facilitate implementation of methylation biomarkers for clinical applications, and that ZNF154 methylation shows promise as a clinicallyuseful biomarker for ovarian cancer.DNA (ctDNA), and associated epigenomic alterations, when shed from cancerous tissues into the blood (4). While whole methylome analyses, such as whole-genome bisulfite sequencing (WGBS) (5) andInfinium BeadArrays (6), have identified scores of differentially-methylated genomic loci in cancers (7), only a handful of individual loci have ultimately become effective for use as biomarkers in clinical applications (8,9). This is due to a number of technical hurdles involved in translating promising methylation biomarkers for use in liquid biopsies, including (i) the small proportion of plasma ctDNA relative to cell-free DNA (cfDNA) derived from healthy cells (10), (ii) heterogeneity of methylation at a given locus (11-14), (iii) age-associated accrual of methylation confounding marker selection (15), and (iv) differences in the yield of extracted tumor subtypes than measurement of blood CA-125, the most commonly-employed biomarker for monitoring EOC. Not only do these results suggest potential as an improved screening method for ovarian cancer, they more broadly offer a practical means of improving the diagnostic performance of DNA methylation-based biomarkers, particularly in challenging samples such as liquid biopsies. MATERIAL AND METHODS Datasets and samples450K Illumina Infinium HumanMethylation450 BeadChip datasets. Processed Illumina Human 450K data for EOCs (GEO accession GSE72021, described in (8)) from 221 tumor samples (171 serous, 18 endometrioid, 14 clear cell, 9 mucinous and 9 other histological cancer subtypes) and W...