Supraphysiological FOXP3 expression in human CAR-Tregs results in improved stability, efficacy, and safety of CAR-Treg products for clinical application
Bibliographic record
Abstract
The forkhead family transcription factor (FOXP3) is an essential regulator for the development of regulatory T cells (Tregs) and orchestrates both suppressive function and Treg lineage identity. Stable expression of FOXP3 enables Tregs to maintain immune homeostasis and prevent autoimmunity. However, under pro-inflammatory conditions, FOXP3 expression in Tregs can become unstable, leading to loss of suppressive function and conversion into pathogenic T effector cells. Therefore, the success of adoptive cell therapy with chimeric antigen receptor (CAR) Tregs is highly dependent on the stability of FOXP3 expression to ensure the safety of the cell product. To warrant the stable expression of FOXP3 in CAR-Treg products, we have developed an HLA-A2-specific CAR vector that co-expresses FOXP3. The transduction of isolated human Tregs with the FOXP3-CAR led to an increase in the safety and efficacy of the CAR-Treg product. In a hostile microenvironment, under pro-inflammatory and IL-2-deficient conditions, FOXP3-CAR-Tregs showed a stable expression of FOXP3 compared to Control-CAR-Tregs. Furthermore, additional exogenous expression of FOXP3 did not induce phenotypic alterations and dysfunctions such as cell exhaustion, loss of functional Treg characteristics or abnormal cytokine secretion. In a humanized mouse model, FOXP3-CAR-Tregs displayed an excellent ability to prevent allograft rejection. Furthermore, FOXP3-CAR-Tregs revealed coherent Treg niche-filling capabilities. Overexpression of FOXP3 in CAR-Tregs has thereby the potential to increase the efficacy and reliability of cellular products, promoting their clinical use in organ transplantation and autoimmune diseases.
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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 | 0.001 | 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".