The inhibitory NKR-P1B receptor regulates NK cell-mediated mammary tumor immunosurveillance in mice
Bibliographic record
Abstract
Natural killer (NK) cells are an important component of anti-cancer immunity, and their activity is regulated by an array of activating and inhibitory receptors. In mice, the inhibitory NKR-P1B receptor is expressed in NK cells and recognizes the C-type lectin-related protein-b (Clr-b) ligand. NKR-P1B:Clr-b interactions represent a ‘missing-self’ recognition system to monitor cellular levels of Clr-b on healthy and diseased cells. Here, we report an important role for NKR-P1B:Clr-b interactions in tumor immunosurveillance in MMTV-PyVT mice, which develop spontaneous mammary tumors. MMTV-PyVT mice on NKR-P1B-deficient genetic background developed mammary tumors earlier than on wild-type (WT) background. A greater proportion of tumor-infiltrating NK cells downregulate expression of the transcription factor Eomesodermin (EOMES) in NKR-P1B-deficient mice compared to WT mice. Tumor-infiltrating NK cells also downregulated CD49b expression but gain CD49a expression and exhibit effector functions, such as granzyme B upregulation and proliferation in mammary tumors. However, unlike the EOMES+ NK cells, the EOMES‒ NK cell subset is unable to respond to further in vitro stimulation and exhibits phenotypic alterations associated with immune dysfunction. These alterations included increased expression of PD-1, LAG-3, and TIGIT and decreased expression of NKp46, Ly49C/I, CD11b, and KLRG-1. Furthermore, tumor-infiltrating NKR-P1B-deficient NK cells exhibited an elevated dysfunctional immune phenotype compared to WT NK cells. These findings demonstrate that the NKR-P1B receptor plays an important role in mammary tumor surveillance by regulating anti-cancer immune responses and functional homeostasis in NK cells.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".