Abstract A049: NKG2D and NKp30 activating receptors drive allogeneic natural killer cell responsiveness against pancreatic cancer
Bibliographic record
Abstract
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.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".