Abstract B030: Characterizing MAIT cells in lung cancer: Insights from NSCLC and murine models
Bibliographic record
Abstract
Abstract Background: Mucosal-associated invariant T cells (MAIT) are unconventional αβT cells with a semi-invariant TCR that recognizes microbially-derived riboflavin metabolites presented by MHC-related protein 1 (MR1). Upon TCR engagement or cytokine stimulation, MAIT cells can rapidly secrete pro-inflammatory cytokines and cytotoxic molecules. The precise role of MAIT cells in the tumor-immune microenvironment remains unclear, as different reports have described them as either beneficial or detrimental to patient prognosis. Considering that MAIT cells constitute a notable proportion (2-4%) of the lung T cell repertoire in both humans and mice, the lung serves as an appropriate setting for ex vivo and in vivo MAIT cell characterization. Objective: To characterize the phenotype of MAIT cells in lung tumor and uninvolved tissue from non-small cell lung cancer (NSCLC) specimens and orthotopic murine models. Results: Surgical resections of primary tumor and normal adjacent tissue from 29 treatment-naïve NSCLC patients were profiled using a comprehensive 31-marker spectral flow cytometry panel. MAIT cells expressing activating and inhibitory receptors including CD69, PD-1, CTLA-4, and CD39, were enriched in tumor tissue. Conversely, cytotoxic MAIT cells expressing granzyme B, granulysin, and CD57, were enriched in normal adjacent tissue. For in vivo studies, lungs of B6-MAITCAST mice, which harbor higher frequencies of MAIT cells compared to conventional mouse strains, were orthotopically injected with CMT167 tumor cells or vehicle control. MAIT cells from lungs injected with CMT167 tumors also expressed higher levels of activating and inhibitory receptors, and decreased levels of granzyme B. Additionally, there was a slight decrease in IFNγ and IL-17A production upon stimulation in vitro with PMA/ionomycin compared to vehicle controls. Conclusion: In the context of lung cancer, MAIT cells are activated but may lose functionality within the tumor-immune microenvironment. Rationale: There is growing interest in the immunotherapeutic potential of unconventional T cells due to their rapid effector functions and non-MHC-restricted nature. By investigating the role of MAIT cells in the tumor microenvironment, we can comprehensively identify their biological advantages and limitations. Citation Format: Stephanie WY Wong, Carolina de Amat Herbozo, Adriana Vukosich-Pennell, Ben X Wang, Pamela S Ohashi, Adrian G Sacher. Characterizing MAIT cells in lung cancer: Insights from NSCLC and murine models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".