Diet derived immunogenic metabolites modulate the activation and expression of checkpoints on CD8+ T-cells 4431
Bibliographic record
Abstract
Abstract Description Immunotherapy has improved the treatment of numerous cancers but is ineffective for a subset of patients, whilst some types of cancer are largely resistant. The incidence of immune related adverse effects (IRAE) further limit its use. Agents capable of improving the efficacy of cancer immunotherapy but without significantly increasing IRAE are needed. The active metabolites of Vitamin A and Vitamin D, along with the short chain fatty acid butyrate have demonstrated varied immune modulatory properties however their role in the anti-tumour immune response mediated by CD8+ T-cells, either alone or in combination, is incompletely understood. We have characterised the effects of these metabolites on CD8+ T-cells and found that they increase the polyfunctional activation of these cells whilst altering the expression of checkpoints including PD-1, CTLA-4, TIGIT and Lag-3. These effects were mediated by altered expression of and interaction between their cognate nuclear receptors. These metabolites were also observed to modulate the function of common-γ chain cytokines on the activation and proliferation of CD8+ T-cells. Finally, the presence of these metabolites in the plasma of melanoma patients undergoing immune checkpoint blockade was measured and associated with markers of T-cell function. These data support further investigation of combinations of immunogenic metabolites as safe, low-cost adjunct therapies to improve the efficacy of cancer immunotherapy. Funding Sources Supported by grants awarded by the Institute of Cancer Vaccines and Immunotherapy and the St Georges Hospital charity. Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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) | 0.006 | 0.001 |
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".