Ultrasensitive detection of oncogenic human papillomavirus in oropharyngeal tissue swabs
Bibliographic record
Abstract
BACKGROUND: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) caused by oncogenic human papillomavirus (HPV) is rising worldwide. HPV-OPSCC is commonly diagnosed by RT-qPCR of HPV E6 and E7 oncoproteins or by p16 immunohistochemistry (IHC). Droplet digital PCR (ddPCR) has been recently reported as an ultra-sensitive and highly precise method of nucleic acid quantification for biomarker analysis. To validate the use of a minimally invasive assay for detection of oncogenic HPV based on oropharyngeal swabs using ddPCR. Secondary objectives were to compare the accuracy of ddPCR swabs to fresh tissue p16 IHC and RT-qPCR, and to compare the cost of ddPCR with p16 IHC. METHODS: OC/OPSCC patients, and healthy controls undergoing tonsillectomy. All underwent an oropharyngeal swab with ddPCR for quantitative detection of E6 and E7 mRNA. Surgical specimens had p16 IHC performed. Agreement between ddPCR and p16 IHC was determined for patients with p16 positive and negative OC/OPSCC as well as for healthy control patients. The sensitivity and specificity of ddPCR of oropharyngeal swabs were calculated against p16 IHC for OPSCC. RESULTS: OCSCC, and 25 healthy controls. The sensitivity and specificity of ddPCR of oropharyngeal swabs against p16 IHC were 92 and 98% respectively, using 20-50 times less RNA than that required for conventional RT-qPCR. Overall agreement between ddPCR of tissue swabs and p16 of tumor tissue was high at ĸ = 0.826 [0.662-0.989]. CONCLUSION: Oropharyngeal swabs analyzed by ddPCR is a quantitative, rapid, and effective method for minimally invasive oncogenic HPV detection. This assay represents the most sensitive and accurate mode of HPV detection in OPSCC without a tissue biopsy in the available literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".