Sense-and-Respond Payload Delivery Using a Novel Antigen-Inducible Promoter Improves Suboptimal CAR-T Activation
Bibliographic record
Abstract
Chimeric antigen receptor (CAR)-T cell therapies demonstrate the clinical potential of lymphocytes engineered with synthetic properties. However, CAR-T cells are ineffective in most solid tumors, partly due to inadequate activation of the infused lymphocytes at the site of malignancy. To selectively enhance antitumor efficacy without exacerbating off-target toxicities, CAR-T cells can be engineered to preferentially deliver immunostimulatory payloads in tumors. Here, we report a novel antigen-inducible promoter for conditional payload expression in primary human T cells. In therapeutic T cell models, the novel NR4A-based promoter induced higher reporter gene expression than the conventional NFAT-based promoter under weakly immunogenic conditions, where payload expression is most needed. Minimal activity was detected from the inducible promoters in the absence of antigen and after withdrawal of stimulation. As a functional proof-of-concept, we used the NR4A-based promoter to express cytokines in an antimesothelin CAR-T model with suboptimal stimulation and observed improved proliferation compared to T cells engineered with the conventional NFAT promoter or CAR alone. Our system achieves CAR-directed payload expression under weakly immunogenic conditions and could enable the next generation of cell therapies with enhanced antitumor efficacy.
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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) | 0.001 | 0.000 |
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".