NR4A3 deficiency in CD8 <sup>+</sup> T cells improves adoptive T cell therapy of cancer
Bibliographic record
Abstract
ABSTRACT NR4A3 is a transcription factor that is rapidly induced in CD8 + T cells following antigenic recognition. We have previously shown that NR4A3 deficiency induces an early molecular program that promotes memory generation and enhances effector functions, which are two essential attributes for the success of adoptive cell therapy (ACT). Therefore, we tested the hypothesis that Nr4a3 -/- CD8 + T cells would have outstanding efficacy in ACT of cancer. Our results show that ACT of melanoma-bearing mice with Nr4a3 -/- effector CD8 + T cells provides a better tumor control than their wild-type counterpart. The therapeutic effect observed with Nr4a3 -/- effector CD8 + T cells is even better than the one observed with ACT of Nr4a3 +/+ effector CD8 + T cells in combination with anti-PD-L1 treatment. scRNA-seq analysis reveals a huge heterogeneity of tumor-infiltrating lymphocytes (TILs) states following ACT. The better tumor control observed with ACT of Nr4a3 -/- CD8 + effectors without anti-PD-L1 treatment correlates with an enrichment of TILs within the clusters that are associated with the anti-PD-L1 response of wild-type TILs. Moreover, the clusters that are enriched in Nr4a3 -/- TILs are the ones that are enriched for effector functions. Furthermore, Nr4a3 -/- and Nr4a3 +/+ effectors generate distinct progenitor populations. Pseudotime analysis suggests that these progenitors have different differentiation trajectories, which may explain why ACT with Nr4a3 -/- effectors is more efficient. Therefore, modulation of NR4A3 activity may represent a new strategy to generate long-lived and highly functional T cells for ACT. One sentence summary NR4A3 deficiency improves anti-tumor CD8+ T cell response
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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) | 0.001 | 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".