cfMIND: A read-level methylation framework for accurate non- invasive disease detection using cell-free DNA

Abstract

Plasma cell-free DNA (cfDNA) emerged as a promising non-invasive biomarker for cancers. However, reliable detection remains challenging due to the low abundance of tumor-derived cfDNA fragments and the dilution of informative methylation signals when aggregated into region-level features. Here, we propose a novel approach cfMIND, an efficient and robust machine-learning framework that leverages stratified read-level methylation signals to preserve rare cell-type-specific information and enhance detection sensitivity. By avoiding information loss inherent to conventional aggregation strategies, cfMIND enables more accurate and stable inference across diverse conditions. cfMIND is compatible with various cfDNA methylation sequencing technologies and cancer types. Across multiple cancer datasets (n = 868), cfMIND achieves high performance (AUROC up to 0.966) and maintains strong accuracy even at ultra-low sequencing depth (0.2x) and in early-stage cancers. Notably, cfMIND demonstrates exceptional robustness, generalizing effectively across cohorts and platforms without the need for model retraining. These results highlight its potential utility in heterogeneous experimental and clinically relevant settings. Beyond cancer detection, cfMIND is readily extendable to non-malignant diseases, as demonstrated by its ability to capture disease-associated methylation alterations in amyotrophic lateral sclerosis (ALS). Functional investigations on cfMIND-identified features further reveal enrichment in key regulatory regions implicated in disease pathogenesis and recapitulate tissue- and single-cell-level methylation and transcriptional programs underlying tumor biology. Collectively, cfMIND represents a significant advancement in the field, offering a broadly applicable, functionally interpretable, and high-resolution framework for non-invasive disease detection.