Resiquimod as an Immunologic Adjuvant for NY-ESO-1 Protein Vaccination in Patients with High-Risk Melanoma
Bibliographic record
Abstract
The Toll-like receptor (TLR) 7/8 agonist resiquimod has been used as an immune adjuvant in cancer vaccines. We evaluated the safety and immunogenicity of the cancer testis antigen NY-ESO-1 given in combination with Montanide (Seppic) with or without resiquimod in patients with high-risk melanoma. In part I of the study, patients received 100 μg of full-length NY-ESO-1 protein emulsified in 1.25 mL of Montanide (day 1) followed by topical application of 1,000 mg of 0.2% resiquimod gel on days 1 and 3 (cohort 1) versus days 1, 3, and 5 (cohort 2) of a 21-day cycle. In part II, patients were randomized to receive 100-μg NY-ESO-1 protein plus Montanide (day 1) followed by topical application of placebo gel [(arm A; n = 8) or 1,000 mg of 0.2% resiquimod gel (arm B; n = 12)] using the dosing regimen established in part I. The vaccine regimens were generally well tolerated. NY-ESO-1-specific humoral responses were induced or boosted in all patients, many of whom had high titer antibodies. In part II, 16 of 20 patients in both arms had NY-ESO-1-specific CD4⁺ T-cell responses. CD8⁺ T-cell responses were only seen in 3 of 12 patients in arm B. Patients with TLR7 SNP rs179008 had a greater likelihood of developing NY-ESO-1-specific CD8⁺ responses. In conclusion, NY-ESO-1 protein in combination with Montanide with or without topical resiquimod is safe and induces both antibody and CD4⁺ T-cell responses in the majority of patients; the small proportion of CD8⁺ T-cell responses suggests that the addition of topical resiquimod to Montanide is not sufficient to induce consistent NY-ESO-1-specific CD8⁺ T-cell responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".