Ras-mediated missing-self recognition via NKR-P1B:Clr-b interactions facilitates detection of oncogenic transformation and cancer immunosurveillance
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are involved in tumor immune surveillance, immunity to infection, and various pathological conditions. One mode of NK cell recognition, ‘missing-self’ recognition, is triggered by downregulation of inhibitory self ligands on target cells, thereby decreasing the net signaling threshold to promote NK cell activation. Clr-b, ligand for the NKR-P1B inhibitory NK receptor, is downregulated during cellular transformation, viral infection, and genotoxic stress, in turn promoting MHC-independent missing-self recognition. However, the mechanisms underlying the loss of Clr-b during oncogenic transformation remain unknown. Here, we demonstrate that proto-oncogene, Ras, is directly involved in Clr-b downregulation on mouse fibroblasts via the Raf/MEK/ERK and PI3K pathways. We show that c-myc overexpression also promotes Clr-b downregulation. To examine a link between the loss of Clr-b and tumour progression using a spontaneous B cell lymphoma model, we backcrossed the Emu-c-myc transgene onto a Clr-b−/− background, then assessed the survival rates of transgenic littermates. Preliminary results reveal a trend towards accelerated tumor progression of Clr-b+/+ littermates versus Clr-b+/− littermates, suggesting a potential protective role for Clr-b loss in tumour immune surveillance, one revealed under loss-of-heterozygosity conditions. Notably, previous results using Nkrp1b−/− mice also suggest that NKR-P1B-mediated inhibition may also facilitate deleterious tumour immune escape. Collectively, our data suggest that Clr-b downregulation during oncogenic transformation plays a role in the detection of oncogenic transformation and cancer immunosurveillance by 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".