Plasma Cell-free DNA Methylomes for Hepatocellular Carcinoma Detection and Monitoring After Liver Resection or Transplantation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".