Objective: To evaluate the utility of cfMeDIP-seq for detecting hepatocellular carcinoma (HCC) and monitoring recurrence following curative-intent liver surgery.
Summary background data: HCC remains a leading cause of cancer mortality, with high recurrence rates after surgery. Current surveillance depends on imaging and tumor-informed genomics, both limited by sensitivity and tissue access. A tumor-agnostic, noninvasive cfDNA-based method could significantly improve clinical management.
Methods: 236 cfDNA samples were collected at surgery (b-HCC, n=89) and follow-up (f-HCC, n=112) from 89 HCC patients undergoing liver transplantation (n=57) or resection (n=32), plus 35 healthy controls (CTL). cfMeDIP-seq was performed followed by machine learning to: (i) develop an HCC-specific classifier in a discovery cohort (52 b-HCC vs. 35 CTL); (ii) test the classifier in a validation cohort of 37 patients; and (iii) assign an HCC methylation score (HMS) reflecting the probability of a sample containing HCC-derived cfDNA. Relationships between HMS and clinical variables were assessed.
Results: The classifier identified HCC with 97% sensitivity and 99% specificity in the discovery cohort and 97% accuracy in the validation cohort. Baseline HMS >0.9 was associated with higher recurrence risk (HR 3.43, 95% CI 1.30-9.06, P=0.013). HMS decreased by 3-44% (median 17%) within 13 weeks post-surgery. HMS trajectories diverged for recurrent and non-recurrent patients, with HMS rise indicating clinical recurrence. HMS was independent of other clinicopathologic variables.
Conclusion: Tumor-agnostic cfDNA methylomes accurately detect HCC and predict recurrence after liver resection or transplantation. This approach may have important implications for HCC diagnosis, treatment, and monitoring.
Keywords: cell-free DNA; hepatocellular carcinoma; methylomes; prediction; recurrence.
Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.