Precise engineering of chimeric antigen receptor expression levels defines T cell identity and function
Bibliographic record
Abstract
Abstract Chimeric Antigen Receptor (CAR) T therapy is a potent treatment for haematological malignancies, but T cell exhaustion reduces its efficacy in many patients. Although high CAR transgene levels appear to drive T cell exhaustion, the relationship between CAR expression levels, T cell function, and transcriptional identity is yet to be mapped at high resolution. Here, we harness a high-resolution microRNA-based control system to precisely modulate CAR transgene expression levels and assess the impact on T cell activation, gene expression and function. By post-transcriptionally modulating CAR abundance, we show that differential CAR levels significantly impact T cell proliferation, cytokine production and tonic signalling. T cells with high CAR expression become strongly activated even at low target antigen densities, while those with low CAR expression are triggered only by high concentrations of their target. Single-cell RNA sequencing of primary T cells expressing a broad range of CAR transcript levels revealed global transcriptional programmes that become dysfunctional with increased CAR abundance, expanding our understanding of T cell exhaustion. Notably, we identified a narrow CAR expression range where the exhaustion transcriptional state is not triggered, demonstrating that T cell exhaustion can be controlled by fine-tuning CAR levels. This work demonstrates that CAR expression levels are key determinants of T cell transcriptional identity and function and introduces a tractable method to precisely tune CAR expression and T cell activity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".