Liquid Biopsy Method Pinpoints Disease Source From a Single Drop of Blood

Researchers now report a method that profiles cell-free material in plasma to trace disease sources from a single drop of blood, demonstrating improved tissue-of-origin resolution and classification performance with potential utility across oncology applications.

Current blood-based tests often detect signals without clearly identifying where they arise, constraining clinical decision-making. Early detection and screening for colorectal cancer remain particularly challenging, with a need for assays that operate on minimal sample volumes. Lymphoma subtyping and risk assessment also require more granular, biology-informed readouts that reflect disease aggressiveness.

Peking University (PKU; Beijing, China) researchers developed cf-EpiTracing, a platform that captures high-sensitivity epigenetic fingerprints from 50 microliters of plasma to determine tissue of origin, classify disease, and forecast outcomes. The research describes how cf-EpiTracing integrates multimodal epigenomic features from cell-free chromatin and applies machine learning to model disease-specific signatures. The approach is designed to overcome tissue-origin ambiguity that limits current liquid biopsies.

The method profiles multiple histone modifications carried by cell-free chromatin to generate detailed epigenetic fingerprints. By combining these multimodal features, cf-EpiTracing identifies the specific tissues driving disease activity. The machine-learning framework enables detection and tissue tracing from trace amounts of blood.

In early diagnosis and screening for colorectal cancer, cf-EpiTracing achieved up to 97.6% accuracy in training samples and 92.2% in independent validation samples. The platform also distinguished lymphoma subtypes and predicted patient outcomes more effectively than existing clinical tests. In diffuse large B‑cell lymphoma, cf-EpiTracing detected stronger signals of CD34‑positive cells in plasma, potentially reflecting bone marrow involvement and disease aggressiveness.

The study was published in Nature on March 4, 2026. The work was led at Peking University’s College of Future Technology with clinical contributions from the Department of Hematology, PKU Third Hospital. The article indicates the platform can identify tissue drivers of disease, distinguish lymphoma subtypes, and outperform existing tests in outcome prediction.

Future directions include integrating cf-EpiTracing with other cell-free modalities such as DNA methylation, sequence mutations, and chromatin topology. The authors state that combining modalities could enhance precision for diagnosing complex diseases and for monitoring cellular dynamics during disease progression and treatment in large patient cohorts. The report suggests this strategy could broaden noninvasive testing across multiple clinical scenarios.