A CCR4 antagonist combined with vaccines induces antigen-specific CD8+ T cells and tumor immunity against self antigens
Bibliographic record
Abstract
Regulatory T cells (Tregs) may impede cancer vaccine efficacy in hematologic malignancies and cancer. CCR4 antagonists, an emergent class of Treg inhibitor, have been shown to block recruitment of Tregs mediated by CCL22 and CCL17. Our aim was to demonstrate the ability of a CCR4 antagonist (a small chemical molecule identified in silico) when combined with vaccines to break peripheral tolerance controlled by Tregs, a prerequisite for the induction of CD8(+) T cells against self Ags. Immunization of transgenic or normal mice expressing tumor-associated self Ags (Her2/neu, OVA, gp100) with a CCR4 antagonist combined with various vaccines led to the induction of effector CD8(+) T cells and partial inhibition of tumor growth expressing self Ags in both prophylactic and therapeutic settings. The CCR4 antagonist was more efficient than cyclophosphamide to elicit anti-self CD8(+) T cells. We also showed that the population of Tregs expressing CCR4 corresponded to memory (CD44(high)) and activated (ICOS(+)) Tregs, an important population to be targeted to modulate Treg activity. CCR4 antagonist represents a competitive class of Treg inhibitor able to induce functional anti-self CD8(+) T cells and tumor growth inhibition when combined with vaccines. High expression of CCR4 on human Tregs also supports the clinical development of this strategy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".