Read-level DNA methylation deconvolution enhances circulating tumor DNA detection.

Abstract

DNA methylation is a key epigenetic modification underlying cellular identity. Conventional methods based on CpG site-level data often lack sensitivity in detecting low-frequency methylation signals. Here, we present Alpha, a novel method combining unbiased segmentation with robust read-level identification of low frequency cell-type-specific methylation signals. Methylation markers identified by Alpha exhibited significant enrichment in regulatory genomic elements such as enhancers, active promoters, and transcription factor binding sites. In simulated cell-type admixtures, Alpha-derived markers demonstrated improved deconvolution performance, exhibiting lower error metrics compared to beta-value based methods (DSS), even with limited marker numbers (N < 50). We combined Alpha with a non-negative least squares approach (Alpha-NNLS) to enable sensitive detection of circulating tumor DNA (ctDNA) in simulated cell-free DNA from breast and colon cancers, outperforming existing read-level methylation-based tumor fraction estimation methods (CelFEER and UXM). We applied Alpha-NNLS to targeted bisulfite sequencing data from early-stage colon cancer plasma samples and demonstrated strong concordance with existing approaches (R2 = 0.98), supporting its potential for sensitive detection of ctDNA.