Abstract A049: NKG2D and NKp30 activating receptors drive allogeneic natural killer cell responsiveness against pancreatic cancer
Notice bibliographique
Résumé
Abstract Typified by late-stage diagnosis and treatment resistance, pancreatic adenocarcinoma (PDAC) remains refractory to immunotherapy. Natural Killer (NK) cells are mediators of cancer immunosurveillance and may have a unique ability to infiltrate and target PDAC. NK cell-mediated target cell recognition is dependent on the integration of heterogeneous signals from germline-encoded receptors for activation and inhibition. By identifying the role that ligand expression plays in contributing to NK cell driven-PDAC targeting, we expect to define key receptors involved in NK-PDAC interaction and inform precise NK-based PDAC immunotherapy.We hypothesize that ligands expressed by PDAC determine the subsets of NK cells that will respond to the tumor and that these constitute “inclusion” and “exclusion” criteria for effective NK cell-based immunotherapies. In a novel humanized mouse model which supports human NK cells through transgenic HLA and human IL-15, we demonstrate dose- dependent control of primary PDAC xenografts. Human NK cells and mouse leukocytes infiltrate NK-treated PDAC tumors in vivo leading to significant tumor burden reduction and lengthened survival. To explore receptor-ligand partnerships relevant in the outcome of NK:PDAC interactions, we probed the PDAC FIREHOSE 2016 dataset (TCGA) for relationships between tumor genomes and expression of ligands for NK cells. KRAS with or without TP53 mutated PDAC showed differential gene upregulation of NKG2D ligands, MICA and ULBP2, as well as TRAIL-R2 and TRAIL-R1. Using flow cytometry, we found that these ligands are indeed expressed (albeit to differing extents) on PDAC cell lines and are among the most prominent activating ligands available for NK cells. In response to inflammatory signals, these NKG2D ligands’ expression decreased and expression of inhibitory NK cell ligands including HLA I and PD-L1 increased. Functionally, these changes corresponded to decreased degranulation of NK cells cocultured with PDAC, suggesting a shift in the balance toward inhibitory signals, and underscoring a need to maximize activation of NK cells while minimizing their capacity for inhibitory signaling. To define the most potent signals for NK-PDAC interactions, we used a 27-colour flow cytometry panel that revealed the most responsive NK cells to be those that co-express NKG2D and NKp30. Blocking either of these receptors interrupted PDAC killing and blocking inhibitory KIR-HLA interactions enhanced killing. Ongoing studies imply that the combination of NK cells armed with these activating receptors but lacking inhibitory receptors for the HLA I expressed on tumors convey the best tumor protection in our in vivo model. These results indicate that selection of allogeneic NK cell donors can enable simultaneous removal of inhibitory signals and support of activating receptor engagement to maximize NK-PDAC interactions. Since this novel strategy does not require extensive selection, it leaves a polyfunctional NK cell population for comprehensive targeting of the diverse tumor cells that comprise PDAC tumors. Citation Format: Stacey N Lee, Riley J Arseneau, Jeanette E Boudreau. NKG2D and NKp30 activating receptors drive allogeneic natural killer cell responsiveness against pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr A049.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».