Detection of human papillomavirus type 16 in oropharyngeal squamous cell carcinoma using droplet digital polymerase chain reaction
Bibliographic record
Abstract
BACKGROUND: The incidence of oropharyngeal squamous cell carcinoma caused by oncogenic HPV (HPV-OPSCC) is rising worldwide. HPV-OPSCC is commonly diagnosed by RT-qPCR of HPV-16 E6 and E7 oncoproteins or by cyclin-dependent kinase inhibitor 2A, multiple tumor suppressor 1 (p16) immunohistochemistry (IHC). Droplet digital PCR (ddPCR) has been recently reported as ultra-sensitive and highly precise method of nucleic acid quantification for biomarker analysis. We aimed to validate this method for the detection of HPV-16 E6 and E7 in HPV-OPSCC. METHODS: Participants were recruited from January 2015-November 2015 at initial presentation to the University of Alberta Head and Neck Oncology Clinic. RNA was extracted, purified and quantified from prospectively collected participant tissues, and ddPCR was performed with fluorescent probes detecting HPV-16 E6 and E7. Results from ddPCR were compared with p16 IHC performed by clinical pathology as standard of care. RESULTS: Head and neck tissues were prospectively obtained from 68 participants including 29 patients with OPSCC, 29 patients with non-OPSCC and 10 patients without carcinoma. 79.2% of patients with OPSCC were p16 positive. The sensitivity and specificity of ddPCR HPV E6/E7 compared with p16 IHC in OPSCC was 91.3 and 100%, respectively. The amount of target RNA used was ≤1 ng, 20-50 times lower than reported by other for RT-qPCR HPV E6/E7. CONCLUSIONS: The ddPCR of HPV E6/E7 is a novel and highly specific method of detecting HPV-16 in OPSCC. Cancer 2016;122:1544-51. © 2016 American Cancer Society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".