Microbiome metabolites modulate MAIT cell response to immune checkpoint blockade in non-small cell lung cancer (NSCLC) 3096
Bibliographic record
Abstract
Abstract Description Mucosal-associated invariant T (MAIT) cells are innate-like T cells that recognize the microbial metabolite 5OPRU when presented on MR1, a non-classical MHC-like molecule. Two subsets of MAIT cells have been described: Tbet+ MAIT1, which infiltrate the tumor microenvironment (TME) and may exert anti-tumor effects via IFNγ and granzyme B, and RORγt+ MAIT17, which produce IL-17A which may hinder anti-tumor immunity. The commensal microbiome influences response to immune checkpoint blockade (ICB) therapy in NSCLC. However, the role of MAIT subsets as a mediator of this process remains unclear. An orthotopic lung tumor model in B6/MAIT-CAST mice was used to characterize the effects of microbial metabolites on MAIT subsets during ICB therapy. 5OPRU was administered intranasally with ICB and appropriate controls. Mice treated with 5OPRU alone developed larger tumors associated with increased IL-17A+ MAIT17 and dysfunctional PD1+ MAIT1 populations. In contrast, mice receiving ICB + 5OPRU had smaller tumors compared with ICB alone and exhibited a higher frequency of cytotoxic (IFNγ+, granzyme B+, PD1-) MAIT1. Conventional CD4/CD8 T cells and unconventional γδT/NKT cells showed no cytokine differences across groups, suggesting these results are MAIT-dependent. These findings suggest that microbial metabolites like 5OPRU modulate MAIT subsets and may enhance anti-tumor responses during ICB therapy. Funding Sources Canadian Institutes of Health Research (CIHR). Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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) | 0.002 | 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".