Immunization of Malignant Melanoma Patients with Full-Length NY-ESO-1 Protein Using TLR7 Agonist Imiquimod as Vaccine Adjuvant
Bibliographic record
Abstract
T cell-mediated immunity to microbes and to cancer can be enhanced by the activation of dendritic cells (DCs) via TLRs. In this study, we evaluated the safety and feasibility of topical imiquimod, a TLR7 agonist, in a series of vaccinations against the cancer/testis Ag NY-ESO-1 in patients with malignant melanoma. Recombinant, full-length NY-ESO-1 protein was administered intradermally into imiquimod preconditioned sites followed by additional topical applications of imiquimod. The regimen was very well tolerated with only mild and transient local reactions and constitutional symptoms. Secondarily, we examined the systemic immune response induced by the imiquimod/NY-ESO-1 combination, and show that it elicited both humoral and cellular responses in a significant fraction of patients. Skin biopsies were assessed for imiquimod's in situ immunomodulatory effects. Compared with untreated skin, topical imiquimod induced dermal mononuclear cell infiltrates in all patients composed primarily of T cells, monocytes, macrophages, myeloid DCs, NK cells, and, to a lesser extent, plasmacytoid DCs. DC activation was evident. This study demonstrates the feasibility and excellent safety profile of a topically applied TLR7 agonist used as a vaccine adjuvant in cancer patients. Imiquimod's adjuvant effects require further evaluation and likely need optimization of parameters such as formulation, dose, and timing relative to Ag exposure for maximal immunogenicity.
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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".