Human papillomavirus DNA prevalence and type distribution in anal carcinomas worldwide
Bibliographic record
Abstract
Knowledge about human papillomaviruses (HPV) types involved in anal cancers in some world regions is scanty. Here, we describe the HPV DNA prevalence and type distribution in a series of invasive anal cancers and anal intraepithelial neoplasias (AIN) grades 2/3 from 24 countries. We analyzed 43 AIN 2/3 cases and 496 anal cancers diagnosed from 1986 to 2011. After histopathological evaluation of formalin-fixed paraffin-embedded samples, HPV DNA detection and genotyping was performed using SPF-10/DEIA/LiPA25 system (version 1). A subset of 116 cancers was further tested for p16(INK4a) expression, a cellular surrogate marker for HPV-associated transformation. Prevalence ratios were estimated using multivariate Poisson regression with robust variance in the anal cancer data set. HPV DNA was detected in 88.3% of anal cancers (95% confidence interval [CI]: 85.1-91.0%) and in 95.3% of AIN 2/3 (95% CI: 84.2-99.4%). Among cancers, the highest prevalence was observed in warty-basaloid subtype of squamous cell carcinomas, in younger patients and in North American geographical region. There were no statistically significant differences in prevalence by gender. HPV16 was the most frequent HPV type detected in both cancers (80.7%) and AIN 2/3 lesions (75.4%). HPV18 was the second most common type in invasive cancers (3.6%). p16(INK4a) overexpression was found in 95% of HPV DNA-positive anal cancers. In view of the results of HPV DNA and high proportion of p16(INK4a) overexpression, infection by HPV is most likely to be a necessary cause for anal cancers in both men and women. The large contribution of HPV16 reinforces the potential impact of HPV vaccines in the prevention of these lesions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".