Detection of HPV DNA in Saliva of Patients with HPV-Associated Oropharyngeal Cancer Treated with Radiotherapy
Bibliographic record
Abstract
BACKGROUND: To investigate the technical feasibility of RT-PCR and direct sequencing to quantify HPV DNA in the saliva of patients with Human-Papilloma-Virus related oropharyngeal cancer (HPV-OPC), the level of which is known to predict prognosis after treatment. METHODS: Nine patients with locally advanced HPV-OPC treated with definitive radiotherapy with chemotherapy or cetuximab, or radiotherapy alone between April 2016 and September 2017, were enrolled, two of whom also received induction chemotherapy. Saliva was collected before (baseline), during (mid-RT) and after (post-RT) radiotherapy. HPV-16 DNAs (E6 and E7) in saliva were quantified by RT-PCR and sequencing, the latter using a custom cancer panel. Correlations between HPV DNA levels and clinical outcomes were assessed. RESULTS: Compared to the baseline, the relative cycle threshold (Ct) value of E6 and E7 reduced at the point of mid-RT in the majority of the patients (100% and 75% for E6 and E7, respectively). Similarly, the relative Ct value from the baseline to post-RT reduced in 86% and 100% of the patients for E6 and E7, respectively. During the follow-up period, three patients (33%) experienced disease progression. The relative baseline Ct values of these three patients were in the top 4 of all the patients. The sequences of HPV DNA were detected in five (83%) of six samples of the baseline saliva that underwent DNA sequencing, along with several gene mutations, such as TP53,CDKN2A and PIK3CA. CONCLUSIONS: This study demonstrates that, in addition to detection and quantification of HPV DNA by RT-PCR, detection by sequencing of HPV-DNA using a customized cancer panel is technically possible.
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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 | 0.000 | 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 | 0.000 |
| 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".