DNA Methylation Analysis on Anal Swabs for Anal Cancer Screening in People Living With Human Immunodeficiency Virus
Bibliographic record
Abstract
BACKGROUND: High-resolution anoscopy (HRA) to prevent anal cancer is complex and screening capacity is limited. Previously, DNA methylation analysis of anal high-grade squamous intraepithelial lesion (HSIL) biopsies was shown to distinguish between HSIL with an increased and a low cancer risk, supporting personalized treatment. Here, methylation analysis on anal swabs was assessed to detect underlying HSIL with an increased cancer risk. METHODS: A cross-sectional series of paired anal swabs and biopsies of 215 persons with human immunodeficiency virus and swabs of 19 cancer patients were tested for 6 methylation markers. Data were analyzed by logistic regression analysis. The primary endpoint was methylation-positive biopsy HSIL (M+ HSIL), indicating increased cancer risk. Test performance of methylation markers, human papillomavirus (HPV), and/or cytology, and cancer detection and HRA referral, were calculated. RESULTS: Anal cancer swabs showed highest methylation. ZNF582 and panels ASCL1/ZNF582 and LHX8/ZNF582 yielded an area under the curve of 0.68-0.70 to detect underlying M+ HSIL. Methylation at 80% sensitivity corresponded to 43% fewer patients requiring HRA, without missing any cancers and detecting 79% of HPV-16-positive HSIL-AIN3. Methylation/HPV and cytology/HPV co-testing performed similarly. CONCLUSIONS: Methylation levels in anal swabs reflect underlying anal disease. Methylation analysis could reduce HRA referrals substantially, while maintaining a high sensitivity for M+ HSIL and detecting all cancers. These results encourage screening on anal swabs to preselect patients needing HRA.
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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 | 0.003 |
| 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".