Blood and saliva‐derived <scp>ctDNA</scp> is a marker of residual disease after treatment and correlates with recurrence in human papillomavirus‐associated head and neck cancer
Bibliographic record
Abstract
BACKGROUND: There is an alarming increase in human papillomavirus-associated head and neck cancer (HNC), reaching epidemic levels. While patient prognosis is generally good, off-target treatment effects are associated with decreased quality of life. Thus, non-invasive strategies to predict treatment response and risk of recurrence could help de-escalate treatment. In this study, we tested circulating tumor (ct)DNA in liquid biopsies (blood/saliva) of HPV-positive HNC patients to assess treatment response and disease progression. METHODS: A total of 235 blood and saliva samples were collected from 60 HPV-positive and 17 HPV-negative HNC patients (control group) before and/or after treatment. Samples were analyzed using ddPCR for HPV16/18/31/33/35/45 and correlated with imaging and pathological examination. RESULTS: p < 0.00001), with high concordance between saliva and blood (93%). In matched samples, all patients positive for ctDNA before treatment showed significant reductions in ctDNA levels post treatment (p < 0.0001). All but one patient with persistent ctDNA after treatment showed residual tumor and subsequent recurrence. Finally, fragmentomic analysis revealed shifts in cell-free DNA fragment size after treatment, suggesting a complementary biomarker for treatment response. CONCLUSIONS: Blood and saliva were found to be good sources of HPV-ctDNA. The presence of ctDNA strongly correlated with treatment response, demonstrating clinical utility as a non-invasive biomarker to monitor tumor progression in HPV-positive HNC. Liquid biopsy based ctDNA testing could be an effective approach to predict recurrence and stratify patients for de-escalation of treatment, thereby improving quality of life.
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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) | 0.001 | 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".