A phenotypical map of disseminated hepatocellular carcinoma suggests clonal constraints in metastatic sites
Bibliographic record
Abstract
AIMS: Access to tissue in patients with hepatocellular carcinoma (HCC) is limited compared to other malignancies, particularly at advanced stages. This has precluded a thorough characterisation of molecular drivers of HCC dissemination, particularly in relation to distant metastases. Biomarker assessment is restricted to early stages, and paired primary-metastatic comparisons between samples from the same patient are difficult. METHODS AND RESULTS: We report the evaluation of 88 patients with HCC who underwent autopsy, including multiregional sampling of primary and metastatic sites totalling 230 nodules analysed. The study included morphological assessment, immunohistochemistry and mutation status of the TERT promoter, the most frequently mutated gene in HCC. We confirm a strong predilection of HCC for lung dissemination, including subclinical micrometastases (unrecognised during imaging and macroscopic examinations) in 30% of patients with disseminated disease. Size of dominant tumour nodule; multinodularity; macrovascular invasion; high histological, nuclear and architectural grades; and cellular crowding were associated with the presence of extrahepatic metastasis. Among the immunohistochemistry markers tested, metastatic nodules had significantly higher K19 and EpCAM expression than primary liver tumours. Morphological and immunohistochemical features showed that metastatic HCC could be traced back to the primary tumour, sometimes to a specific hepatic nodule. CONCLUSIONS: This study suggests limited heterogeneity in metastatic sites compared to primary tumour sites.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".