Enhancing T cell responses and tumour immunity by vaccination with peptides conjugated to a weak NKT cell agonist
Bibliographic record
Abstract
Activated NKT cells can stimulate antigen-presenting cells leading to enhanced peptide antigen-specific immunity. However, administration of potent NKT cell agonists like α-galactosylceramide (α-GalCer) can be associated with release of high levels of cytokines, and in some situations, hepatotoxicity. Here we show that it is possible to provoke sufficient NKT cell activity to stimulate strong antigen-specific T cell responses without these unwanted effects. This was achieved by chemically conjugating antigenic peptides to α-galactosylphytosphingosine (α-GalPhs), an NKT cell agonist with very weak activity based on structural characterisation and biological assays. Conjugation improved delivery to antigen-presenting cells in vivo, while use of a cathepsin-sensitive linker to release the α-GalPhs and peptide within the same cell promoted strong T cell activation and therapeutic anti-tumour responses in mice. The conjugates activated human NKT cells and enhanced human T cell responses to a viral peptide in vitro. Accordingly, we have demonstrated a means to safely exploit the immunostimulatory properties of NKT cells to enhance T cell activation for virus- and tumour-specific immunity.
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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".