Transgene IL-6 Enhances DC-Stimulated CTL Responses by Counteracting CD4+25+Foxp3+ Regulatory T Cell Suppression via IL-6-Induced Foxp3 Downregulation
Bibliographic record
Abstract
Dendritic cells (DCs), the most potent antigen-presenting cells have been extensively applied in clinical trials for evaluation of antitumor immunity. However, the efficacy of DC-mediated cancer vaccines is still limited as they are unable to sufficiently break the immune tolerance. In this study, we constructed a recombinant adenoviral vector (AdVIL-6) expressing IL-6, and generated IL-6 transgene-engineered DC vaccine (DCOVA/IL-6) by transfection of murine bone marrow-derived ovalbumin (OVA)-pulsed DCs (DCOVA) with AdVIL-6. We then assessed DCOVA/IL-6-stimulated cytotoxic T-lymphocyte (CTL) responses and antitumor immunity in OVA-specific animal tumor model. We demonstrate that DCOVA/IL-6 vaccine up-regulates expression of DC maturation markers, secretes transgene-encoded IL-6, and more efficiently stimulates OVA-specific CTL responses and therapeutic immunity against OVA-expressing B16 melanoma BL6-10OVA in vivo than the control DCOVA/Null vaccine. Moreover, DCOVA/IL-6-stimulated CTL responses were relatively maintained in mice with transfer of CD4+25+Foxp3+ Tr-cells, but significantly reduced when treated with anti-IL-6 antibody. In addition, we demonstrate that IL-6 down-regulates Foxp3-expression of CD4+25+Foxp3+ Tr-cells in vitro. Taken together, our results demonstrate that AdV-mediated IL-6 transgene-engineered DC vaccine stimulates potent CTL responses and antitumor immunity by counteracting CD4+25+ Tr immunosuppression via IL-6-induced Foxp3 down-regulation. Thus, IL-6 may be a good candidate for engineering DCs for cancer immunotherapy.
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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 | 0.001 |
| 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".