Characterisation of anal intraepithelial neoplasia and anal cancer in <scp>HIV</scp>‐positive men by immunohistochemical markers p16, Ki‐67, <scp>HPV‐E4</scp> and <scp>DNA</scp> methylation markers
Bibliographic record
Abstract
Human papillomavirus (HPV)-induced anal intraepithelial neoplasia (AIN, graded 1-3) is highly prevalent in HIV-positive (HIV+) men who have sex with men (MSM), but only a minority of lesions progresses to cancer. Our study aimed to characterise comprehensively anal tissue samples from a cross-sectional series (n = 104) of HIV+ MSM and longitudinal series (n = 40) of AIN2/3 progressing to cancer using different biomarkers. The cross-sectional series consisted of 8 normal, 26 AIN1, 45 AIN2, 15 AIN3 and 10 anal squamous cell carcinoma. Tissue sections were immunohistochemically (IHC) stained for p16 (viral transformation marker), Ki-67 (cellular proliferation marker) and HPV-E4 (viral production marker). We evaluated the expression of IHC markers and compared it with DNA methylation, a marker for malignant transformation. E4 positivity decreased, whereas p16 and Ki-67 scores and methylation marker positivity increased (P values < .001) with increasing severity of anal lesions. Within AIN2, a heterogeneous biomarker pattern was observed concerning E4, p16 and methylation status, reflecting the biological heterogeneity of these lesions. In the longitudinal series, all AIN2/3 and carcinomas showed high p16 and Ki-67 expression, strong methylation positivity and occasional E4 positivity. We earlier showed that high methylation levels are associated with progression to cancer. The observed E4 expression in some AIN2/3 during the course of progression to cancer and absence of E4 in a considerable number of AIN1 lesions make the potential clinical significance of E4 expression difficult to interpret. Our data show that IHC biomarkers can help to characterise AIN; however, their prognostic value for cancer risk stratification, next to objective methylation analysis, appears to be limited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".