Resiquimod as an Immunologic Adjuvant for NY-ESO-1 Protein Vaccination in Patients with High-Risk Melanoma
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it