HPV16 Intratypic Variants in Head and Neck Cancers: A North American Perspective
Bibliographic record
Abstract
Human papillomavirus (HPV) is the major causative agent for cervical and many head and neck cancers (HNCs). HPVs randomly acquire single nucleotide polymorphisms (SNPs) that may become established via positive selection. Within an HPV type, viral isolates differing by <2% in the L1 region are termed “variants” and classified based on combinations of SNPs. Studies in cervical cancer demonstrate clear differences between HPV16 intratypic variants in terms of persistence of infection, tumor histology, cancer risk, and death. Much less is known about the frequency of HPV16 variants in HNC, and their effects on clinical outcomes. We combined HPV16 positive (HPV16+) HNC samples from a local Southwestern Ontario, Canada cohort with those from the Cancer Genome Atlas to create a larger North American cohort of 149 cases with clinical data and determined the distribution of intratypic variants and their impact on clinical outcomes. Most isolates were lineage A, sublineage A1, or A2, with roughly half exhibiting the T350G polymorphism in E6. Univariable analysis identified significant differences between 350T and 350G intratypic variants in clinical T, N, and O staging, as well as disease-free survival. Multivariable analysis failed to identify any clinical factor as a statistically significant covariate for disease-free survival differences between 350T and 350G. Significant differences in several measures of B-cell mediated immune response were also observed between 350T and 350G intratypic variants. We suggest that HPV genetic variation may be associated with HNC clinical characteristics and may have prognostic value.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".