Effective allogeneic natural killer cell therapy for pancreatic adenocarcinoma avails conserved activating receptors and evades HLA I-driven inhibition
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".