Tissue-resident MAIT17 cells are associated with angiogenic pathways and tumor recurrence in non-small cell lung cancer (NSCLC) 3966
Bibliographic record
Abstract
Abstract Description Interest in unconventional T cells in anti-tumor immunity is growing due to their established rapid effector functions, non-MHC-restricted nature, and unique ligand repertoire. Mucosal-associated invariant T cells (MAIT) are αβT cells that recognize non-peptide metabolites presented by MHC-related protein 1 (MR1) and can be activated independent of the TCR via cytokines. The role of MAIT cells in protecting the lung against bacterial and viral infections is well described, but their role in the human lung tumor microenvironment remains unclear. To investigate, surgical resections of tumor and normal adjacent tissue from treatment-naïve NSCLC patients were profiled using CITE-Seq and spectral flow cytometry. Distinct MAIT phenotypes were identified, including Th17-like (MAIT17) cells enriched for angiogenesis and tissue repair signatures, and Th1-like cells linked to cytotoxicity. Both subsets were significantly more activated in the tumor compared to matched normal adjacent tissue. Activated MAIT17 cells in normal adjacent tissue of ever-smokers was associated with greater tumor recurrence risk, and all T cell subsets showed increased IL17A production upon ex vivo stimulation, suggesting a global inflammatory response to smoking-induced tissue damage. These findings indicate a potentially pathogenic role for MAIT17 cells in driving tissue repair processes in normal adjacent lung tissue and propagating a tumor-promoting lung microenvironment even after tumor resection. Funding Sources Research is supported by the 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".