Epigenetic Instability-Based Metrics in Cell-Free DNA for Early Cancer Detection.

Abstract

Cancers present significant DNA methylation changes, which arise in a stochastic manner, marked by extensive epigenetic variation, indicative of high epigenetic instability. We aimed to evaluate the utility of epigenetic instability for cell-free DNA (cfDNA)-based cancer detection. Through analysis of cancer DNA methylation datasets (n = 2,084), we identified a set of 269 CpG island regions that robustly captures this instability in a cancer-specific manner. We developed metrics to measure this epigenetic instability, termed the epigenetic instability index (EII), for cancer screening via cfDNA methylation. Machine learning classifiers using the EII of these 269 regions efficiently identified breast and lung cancers from cfDNA, differentiating even stage IA lung adenocarcinoma with ∼81% sensitivity and early-stage breast cancer with ∼68% sensitivity, both at 95% specificity. Our studies demonstrate that quantifying epigenetic instability is a novel, capable approach to distinguishing cancer from normal cases using cfDNA, performing better than standard approaches using absolute methylation changes. The epigenetic instability-based approaches for cancer detection developed here, along with their validation in independent datasets, support further development and the potential for future clinical application of these strategies in cancer screening.