‘Nur'turing tumor T cell tolerance and exhaustion: novel function for Nuclear Receptor Nur77 in immunity
Bibliographic record
Abstract
The nuclear receptor Nur77 is expressed in a multitude of tissues, regulating cell differentiation and homeostasis. Dysregulation of Nur77 signaling is associated with cancer, cardiovascular disease, and disorders of the CNS. The role of Nur77 in T cells has been studied for almost 30 years now. There is a clear appreciation that Nur77 is crucial for apoptosis of self-reactive T cells. However, the regulation and function of Nur77 in mature T cells remains largely unclear. In an exciting development, Nur77 has been recently demonstrated to impinge on cancer immunotherapy involving chimeric antigen receptor (CAR) T cells and tumor infiltrating lymphocytes (TILs). These studies indicated that Nur77 deficiency reduced T cell tolerance and exhaustion, thus raising the effectiveness of immune therapy in mice. Based on these novel insights, it may be proposed that regulation of Nur77 activity holds promise for innovative drug development in the field of cellular immunotherapy in cancer. In this review, we therefore summarize the role of Nur77 in T cell selection and maturation; and further develop the idea of targeting its activity in these cells as a potential strategy to augment current cancer immunotherapy treatments.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".