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Enregistrement W4414453786 · doi:10.1101/2025.09.21.677560

Effective allogeneic natural killer cell therapy for pancreatic adenocarcinoma avails conserved activating receptors and evades HLA I-driven inhibition

2025· preprint· en· W4414453786 sur OpenAlexafffund
Stacey N. Lee, Riley J. Arseneau, Thomas Arnason, Jeanette E. Boudreau

Notice bibliographique

RevuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Langueen
DomaineImmunology and Microbiology
ThématiqueImmune Cell Function and Interaction
Établissements canadiensBeatrice Hunter Cancer Research InstituteNova Scotia Health AuthorityDalhousie University
Organismes subventionnairesCanadian Institutes of Health ResearchDalhousie UniversityBeatrice Hunter Cancer Research InstituteCancer Research Institute
Mots-clésFlow cytometryImmune systemReceptorImmunotherapyCellNatural killer cellHuman leukocyte antigenLymphokine-activated killer cell

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Background At diagnosis, ∼80% of pancreatic ductal adenocarcinomas (PDAC) have metastasized. Relapse is thus common even among patients that undergo surgical resection, the only curative option. PDAC progresses rapidly, and existing immunotherapies have been ineffective. We hypothesized that natural killer (NK) cell immunotherapies could be effective against PDAC because they recognize conserved and heterogeneous features associated with cellular stress and transformation, and can seek out metastases distal to the primary tumour site. Here, we aim to define the key features of NK cells as effective agents for PDAC immunotherapy. Methods We used TCGA PDAC Firehose data and flow cytometry to predict and measure the most common activating or inhibitory ligands available on PDAC for NK cell activation. To ascertain how the tumour might alter expression of these ligands during treatment, inflammation or immune pressure, we measured expression of NK ligands at rest, or after exposure to immune cells or inflammation. To test and rank the functional importance of these dynamic ligands in the recognition, killing and control of PDAC< we used co-culture, antibody-blocking and an NK-competent humanized mouse model. Results Leveraging the known sequential acquisition of mutations as a surrogate for disease progression, we observed a progressive loss of transcript expression for activating NK cell ligands and chemoattractants. Exposure of PDAC to NK cells or IFN-γ, an inflammatory stimulus, drove dynamic changes in expression of both activating and inhibitory ligands. In vitro co-culture assays revealed a redundancy in the activating receptors engaged in NK:PDAC interactions, but that HLA-KIR signalling dominantly interrupted anti-PDAC activity. In NK-competent humanized mice, adoptively transferred, unselected, unmodified NK cells slowed tumour growth in a dose-dependent manner, but NK cells selected to avoid HLA I-driven inhibition were the most competent effectors for PDAC control. Conclusions Although there is redundancy among activating ligand:receptor pairs for recognizing PDAC tumours, but interactions between KIR and HLA define the extent to which anti-tumour activity can proceed. During tumour progression, and in response to immunotherapy. NK:tumour interactions drive upregulation of HLA I molecules. Thus, educated NK cells from HLA I-disparate donors may be the most effective allogeneic NK immunotherapy for PDAC. What is already known on this topic NK cells can be safely transferred across allogeneic barriers, so allogeneic therapy is possible. Since PDAC tumours progress very rapidly, there is insufficient time for engineered cell therapies. Although PDAC is typically considered to be an immunologically “cold” tumour, more recent studies have revealed that sub-tumour microenvironments can contain clusters of immune cells, and that the presence of immune cells in PDAC is associated with good prognosis. What this study adds We explore how the naturally-occurring heterogeneity of activating and inhibitory receptor expression with and between individuals impacts recognition of PDAC tumour cells. We find that the natural cytotoxicity receptors and NKG2D are among the most likely activating receptors to be expressed on NK cells responding to PDAC. However, interactions between inhibitory killer immunoglobulin-like receptors (KIR) and class I human leukocyte antigens (HLA) dominantly inhibit NK cell killing. To enable NK cell reactivity without the liability of inhibition, we demonstrate that NK cells can be selected intentionally from allogeneic HLA-mismatched donors, where they retain programmed functionality, but are ignorant to the HLA-driven signals for inhibition present on the tumour cells. How this study might affect research, practice or policy Diversity among NK cell functions is driven by interactions between HLA I molecules and KIR, with the remaining receptor-ligand partnerships mostly conserved across people. KIR:HLA I interactions are predictable, and there is a limited range of potential combinations, so definitions of key functions would empower mass-production of NK cells from a limited range of healthy donors that could be used as off-the-shelf cellular immunotherapy. Our study provides key exclusion criteria that will inform these selections. Lee Graphical abstract

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,218
Écart entre enseignants0,207 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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