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.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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 teacher head, 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".