A Methylation Density Binary Classifier for Predicting and Optimizing the Performance of Methylation Biomarkers in Clinical Samples

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 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 clinically-useful biomarker for ovarian cancer.