Therapeutic Vaccination against Human Papillomavirus Type 16 for the Treatment of High-Grade Anal Intraepithelial Neoplasia in HIV+ Men
Bibliographic record
Abstract
PURPOSE: Anal cancer is increasing in HIV+ men who have sex with men (MSM). Treatment options for its precursor, high-grade anal intraepithelial neoplasia (HGAIN), are suboptimal. In this phase I to II dose-finding study, we assessed the safety and efficacy of the human papillomavirus type 16 (HPV16) synthetic long peptide vaccine (SLP-HPV-01) in HIV+ MSM with HPV16-positive HGAIN. PATIENTS AND METHODS: Four dosage schedules (1-5-10; 5-10-20; 10-20-40; and 40-40-40-40 μg) of SLP-HPV-01 were administered intradermally with a 3-week interval in 10 patients per dose level (DL). In each dose group, 5 patients also received 1 μg/kg pegylated IFNα-2b subcutaneously. Primary endpoints were safety and regression of HGAIN at 3, 6, and 12 months. RESULTS: Eighty-one of 134 screened patients (60%) had HPV16-negative HGAIN lesions, leaving 53 eligible patients. Thirteen patients were excluded, leaving 40 men. The vaccine was well tolerated. One patient developed a generalized rash. The highest dosage level induced the strongest immune responses. There was no indication for stronger reactivity in the IFNα groups. Up to 18 months of follow-up, 8/38 intention-to-treat patients had a complete clinical and histologic response and one had a partial response (in total 9/38, 23.7%). At the highest dosage level, the clinical response was 4/10 (40%). Stronger immune responses were detected among clinical responders. CONCLUSIONS: The highest DL is safe, immunogenic, and associated with clinical responses to HPV16-induced lesions. However, as the majority of HGAIN is caused by the other HPV types, further studies should aim at pan-HPV vaccination to prevent or treat HGAIN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".