Mast cells boost anti-tumor potency of MAIT cells via inflammasome-dependent secretion of IL-18
Bibliographic record
Abstract
Mast cells (MC) serve as pivotal sentinels in the regulation of immune responses and inflammation, yet their function in lung adenocarcinoma (LUAD) remains largely neglected. To decode their heterogeneity, we perform single-cell transcriptomic analysis of LUAD-infiltrating MCs. Our study uncovers the complexity in MC composition and identifies 9 distinct states, including proinflammation, chemotaxis, and antigen presentation. The proinflammatory MC subset, characterized by high IL-18 expression, is associated with improved outcomes for LUAD patients. This pro-inflammatory property is regulated by the activation of NLRP3 inflammasome within MCs, resulting in the formation of GSDMD pores and successive pyroptosis. Moreover, these MCs enhance the innate-like anti-tumor activity of MAIT cells by upregulating NKG2D and IFN-γ through the cytokine-activation mechanism. Our results uncover an unappreciated state of MCs and describe an inflammasome-dependent, MC-mediated regulation of MAIT cells in LUAD. These findings diversify our understanding of the functional repertoire and mechanistic equipment of MCs and MAIT cells, and suggest a potential therapeutic target for cancer treatment. The immunomodulatory functions of mast cells (MCs) within the tumor microenvironment are elusive. Here, the authors present a transcriptomic characterization of LUAD-infiltrating MCs and identify a proinflammatory subset that contributes to the activation of cytotoxic MAIT cells by secreting IL-18 via NRLP3 activation and pyroptosis, ultimately boosting anti-tumor 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".