Validation of Methods for Oropharyngeal Cancer HPV Status Determination in US Cooperative Group Trials
Bibliographic record
Abstract
Tumor human papillomavirus (HPV) status is a prognostic factor for oropharyngeal cancer, but classification methods are not standardized. Here we validate the HPV classification methods used in US cooperative group trials. Tumor DNA and RNA purified from 240 paraffin-embedded oropharyngeal cancers diagnosed from 2000 to 2009 were scored as evaluable if positive for DNA and mRNA controls by quantitative polymerase chain reaction (PCR). Eighteen high-risk (HR) HPV types were detected in tumors by consensus PCR, followed by HR-HPV E6/7 oncogene expression analysis by quantitative reverse transcriptase PCR. The sensitivity (S), specificity (SP), and positive (PPV) and negative predictive values (NPV) of p16 expression detected by immunohistochemistry (IHC) and HPV16 detected by in situ hybridization (ISH) were evaluated in comparison with HR-HPV E6/7 oncogene expression. Interrater agreement among 3 pathologists was evaluated by κ statistics. Of 235 evaluable tumors, 158 (67%; 95% confidence interval, 61.2-73.3) were positive for HR-HPV E6/7 oncogene expression [HPV type 16 (92%), 18 (3%), 33 (3%), 35 (1%), or 58 (1%)]. p16 IHC had high sensitivity (S 96.8%, SP 83.8%, PPV 92.7%, and NPV 92.5%), whereas HPV16 ISH had high specificity (S 88.0%, SP 94.7%, PPV 97.2%, and NPV 78.9%) for HR-HPV oncogene expression. Interrater agreement was excellent for p16 (κ=0.95 to 0.98) and HPV16 ISH (κ=0.83 to 0.91). Receiver operating curve analysis determined the cross-product of p16 intensity score and percentage of tumor staining to optimally discriminate HR-HPV E6/7-positive and HR-HPV E6/7-negative tumors. p16 IHC and HPV16 ISH assays show excellent performance, with high sensitivity and specificity, respectively. A new validated H-score for p16 IHC assessment is proposed. Appropriate assay choice depends on clinical implications of a false-positive or false-negative test.
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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 | 0.643 | 0.682 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".