Cell-free DNA in blood originates from fragmented chromatin released by dying cells from both healthy and diseased tissues1,2. These fragments carry rich molecular modalities that can reveal pathological alterations in tissues of origin3–10. Here we develop cf-EpiTracing, a highly sensitive automated platform that profiles histone modifications in cell-free DNA from as little as 50 μl of human plasma. By integrating multimodal chromatin states with machine learning, cf-EpiTracing enables accurate deconvolution of cell types of origin. We generated 2,417 cf-EpiTracing profiles from plasma of 125 healthy individuals and 549 patients with inflammatory bowel disease, colorectal cancer, coronary heart disease or lymphoma. cf-EpiTracing enabled unbiased identification of primary diseased tissues and other organ involvement, stratification of B cell lymphoma subtypes with different genetic and epigenetic underpinnings, and detection of early-stage diseases or lesions. Surveying dynamics of epigenetic signatures uncovered disease transformation from follicular lymphoma to diffuse large B cell lymphoma. Further, cf-EpiTracing revealed genomic translocations and epigenetic alterations in patients with mantle cell lymphoma. Of note, our study leverages holistic epigenetic signatures, independently of knowledge of gene transcription, to accurately report recurrence risk and therapeutic response. Together, these findings establish cf-EpiTracing as an automated, non-invasive, epigenome-centric framework with broad applications in early diagnosis, molecular subtyping and prognostic prediction.
