Improved deconvolution of circulating tumor DNA from shallow whole-genome methylation sequencing using CelFiE-ISH
DNA methylation is a powerful biomarker for cancer detection and tissue classification, but clinical samples are complex mixtures of cell types, making accurate cell-type deconvolution challenging-especially when tumor-derived DNA is rare. Reference-based and machine-learning deconvolution methods leverage comprehensive methylation atlases to estimate cell-type composition, yet their performance varies by algorithm, sequencing depth, and data modality. Critically, deconvolution accuracy depends on the selection of informative genomic markers, with low-coverage WGS posing substantial limitations for detecting rare tumor fractions. Here, we perform low-coverage (0.2-0.3x) ONT sequencing on plasma samples from a range of epithelial malignancies including lung, breast, colorectal, and pancreatic cancers-across a range of tumor fractions to investigate the impact of marker selection and improved deconvolution using the haplotype-aware CelFiE-ISH algorithm.