Abstract B001: Harnessing invariant natural killer T cell cytotoxicity for cancer therapy
Bibliographic record
Abstract
Abstract Cytotoxic cells are major effectors in adoptive cell transfer (ACT) cancer therapies. Although autologous conventional T cells are commonly used in ACT therapies, other types of cells, such as invariant natural killer T (iNKT) cells, have become attractive for the design of off-the-shelf ACT therapies. Unlike conventional T cells, iNKT cells react to glycolipid antigens presented by CD1d, a non-polymorphic MHC I-like molecule. Based on their cytokine profile and transcription factor expression, three main helper iNKT subsets have been described in mice: iNKT1, iNKT2, and iNKT17. In addition, the cytotoxic response of iNKT cells has been reported, but this function is less characterized. In a previous transcriptomic study, we have shown that the iNKT1 subset is heterogeneous and it comprises two main clusters: iNKT1b, enriched in genes related to T helper 1, and iNKT1c, characterized by a cytotoxic gene profile. Here, we demonstrated that iNKT1c cells exhibit higher in vitro specific killing activity against EL4 tumor cells compared to iNKT1b cells. We identified IL-15 as one of the factors that boosts the cytotoxic functions of iNKT1c cells. Moreover, adoptive cell transfer of iNKT1c cells in mice previously injected with B16F10 melanoma cells resulted in reduced metastatic lung nodules. We also explored the presence of a distinct cytotoxic iNKT cell subset in humans by spectral flow cytometry and clustering analysis. From PBMCs of healthy donors, we identified a population of CD57+Tbethi iNKT cells that show a cytotoxic profile. Interestingly, this population can also be found infiltrating tumors in lung cancer patients. This study unraveled bona fide cytotoxic iNKT cells and provides insights for the design of future cancer therapies. Citation Format: Carolina de Amat Herbozo, Stephanie Wong, Meggie Kuypers, Jessica Matthews, Thomas Baranek, Sarah Crome, Adrian Sacher, Christophe Paget, Thierry Mallevaey. Harnessing invariant natural killer T cell cytotoxicity for cancer therapy [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 B001.
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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) | 0.003 | 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; 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".