LAG3 marks activated but hyporesponsive NK cells
Bibliographic record
Abstract
Abstract Natural Killer (NK) cells are critical for immunosurveillance yet become dysfunctional in contexts such as chronic stimulation by viral infections or cancer. This phenomenon is similar to T cell exhaustion but less well characterized, which limits therapeutic interventions. As shown for T cells, NK cells often display an increased expression of immune checkpoint proteins (ICP) following chronic stimulation, and ICP blockade therapies are currently being explored for several cancer types, which have remarkable patient benefits. Nevertheless, the nature of ICP expression in NK cells is still poorly documented. In this study, we aimed to identify the conditions that lead to and the phenotype of immune checkpoint LAG3 (Lymphocyte-activation gene 3) expressing NK cells. Using various experimental models, we found that LAG3 is expressed by murine NK cells upon activation in different contexts, including in response to cancer and acute viral infections. LAG3 marks a subset of immature, proliferating and activated cells, which, despite activation, have a reduced capacity to respond to a broad range of stimuli. Further characterization also revealed that LAG3+ NK cells exhibit a transcriptional signature similar to that of exhausted CD8+ T cells. Taken together, our results support the use of LAG3 as a marker of dysfunctional NK cells across diverse chronic and acute inflammatory conditions.
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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) | 0.003 | 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".