Experimental Models of Liquid Biopsy in Hepatocellular Carcinoma Reveal Clone‐Dependent Release of Circulating Tumor DNA
Bibliographic record
Abstract
Liquid biopsy, the molecular analysis of tumor components released into the bloodstream, has emerged as a noninvasive and resourceful means to access genomic information from cancers. Most data derived from translational studies showcase its numerous potential clinical applications. However, data from experimental models are scarce, and little is known about the underlying mechanisms and factors controlling the release of circulating tumor DNA (ctDNA) and cells (CTCs). This study aimed to model liquid biopsy in hepatocellular carcinoma xenografts and to study the dynamics of release of ctDNA and CTCs; this included models of intratumoral heterogeneity (ITH) and metastatic disease. We quantified ctDNA by quantitative polymerase chain reaction (PCR) targeting human long interspersed nuclear element group 1; targeted mutation analysis was performed with digital droplet PCR. CTCs were traced by flow cytometry. Results demonstrated the feasibility of detecting ctDNA, including clone‐specific mutations, as well as CTCs in blood samples of mice. In addition, the concentration of ctDNA and presence of tumor‐specific mutations reflected tumor progression, and detection of CTCs was associated with metastases. Our ITH model suggested differences in the release of DNA fragments impacted by the cell‐clone origin and the treatment. Conclusion: These data present new models to study liquid biopsy and its underlying mechanisms and highlighted a clone‐dependent release of ctDNA into the bloodstream.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".