Tumour necrosis factor‐α (TNF‐α) transgene‐expressing dendritic cells (DCs) undergo augmented cellular maturation and induce more robust T‐cell activation and anti‐tumour immunity than DCs generated in recombinant TNF‐α
Bibliographic record
Abstract
Tumour antigen presentation by dendritic cells (DCs) to T cells in lymphoid organs is crucial for induction of anti-tumour immune responses. It has been previously reported that tumour necrosis factor-alpha (TNF-alpha) is required for DC activation and subsequent induction of optimal immune responses, and thus DCs for anti-tumour vaccination are often generated by culture in exogenous TNF-alpha. In the present study, we investigated the effect on anti-tumour immunity of vaccination with Mut1 tumour peptide-pulsed DCs engineered to express a TNF-alpha transgene. Our data shows that transfection of DCs with recombinant adenovirus AdV-TNF-alpha resulted in greater maturation of the DCs than occurred with control DCs cultured in exogenous TNF-alpha, as determined by up-regulated expression of pro-inflammatory cytokines (e.g. interleukins 1beta and 18), chemokines [e.g. interferon-gamma-inducible protein-10 and macrophage inflammatory protein-1beta (MIP-1beta)], the CC chemokine receptor CCR7, and immunologically important cell surface molecules (CD40, CD86 and intercellular adhesion molecule-1). These transgenic DCs stimulated stronger allogeneic T-cell responses in vitro and T-cell activation in vivo; displayed 2.4-fold enhanced chemotactic responses to the MIP-3betain vitro (P<0.05); and, perhaps most importantly, trafficked into the draining lymph nodes dramatically (seven-fold, P<0.01) more efficiently than the control DCs. Our data also demonstrate that vaccination of mice with Mut1 peptide-pulsed, AdV-TNF-alpha-transfected DCs stimulated more efficient in vitro Mut1-specific CD8+ cytotoxic T-cell responses and solid tumour immunity in vivo, when compared to the in vitro TNF-alpha-cultivated DCs. Thus, DCs engineered to secrete TNF-alpha may offer a new strategy in DC cancer vaccines.
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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".