Cell-Free DNA Kinetics in a Pre-Clinical Model of Head and Neck Cancer
Bibliographic record
Abstract
In cancer patients, circulating tumour-derived DNA (ctDNA) levels imperfectly reflect disease burden apparent on medical imaging. Further evaluation of ctDNA levels over time is needed to better understand the correlation with tumour growth and therapeutic response. We describe ctDNA kinetics within an orthotopic, immunocompetent preclinical rabbit model of local-regionally advanced head and neck squamous cell carcinoma (HNSCC). Monitoring primary tumour and metastatic lymph node volume by computed tomography (CT), we observed a correlation between ctDNA levels and tumour burden. We found that ctDNA detection could precede evidence of tumour on CT. Sensitivity and specificity of ctDNA detection in this model was 90.2% (95% C.I.: 76.9-97.3%) and 85.7% (95% C.I.: 67.3-96.0%), respectively. Rapid tumour growth followed by auto-necrosis and tumour volume contraction produced a spike in ctDNA levels, suggesting that viable tumour cells may be required for sustained ctDNA release. Following surgical resection, both ctDNA and total plasma DNA were correlated with recurrent tumour volume. Our results reveal the complex kinetic behaviour of ctDNA and total plasma DNA upon tumour growth or surgery. This pre-clinical model could be useful for future studies focused on elucidating mechanisms of ctDNA release into the circulation from primary and metastatic sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.001 |
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".