Pharmacological control of CAR T cells through CRISPR-driven rapamycin resistance
Bibliographic record
Abstract
ABSTRACT Chimeric antigen receptors (CARs) reprogram T cells to recognize and target cancer cells. Despite remarkable responses observed with CAR-T cell therapy in patients with hematological malignancies, CAR-T cell engineering still relies mostly on randomly integrating vectors, limiting the possibilities of fine-tuning T cell function. Here, we designed a CRISPR-based marker-free selection strategy to simultaneously target a therapeutic transgene and a gain-of-function mutation to the MTOR locus to enrich cells resistant to rapamycin, a clinically used immunosuppressant. We readily engineered rapamycin-resistant (RapaR) CAR-T cells by targeting CAR expression cassettes to the MTOR locus. Using in vitro cytotoxicity assays, and a humanized mouse model of acute lymphoblastic leukemia, we show that RapaR-CAR-T cells can efficiently target CD19 + leukemia cells in presence of immunosuppressing doses of rapamycin. Furthermore, our strategy allows multiplexed targeting of rapamycin-regulated immunoreceptors complexes (DARICs) to the MTOR and TRAC loci to pharmacologically control CAR-T cells’ activity. We foresee that our approach could both facilitate the enrichment of CRISPR-engineered CAR-T cells ex vivo and in vivo while improving tumor eradication.
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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) | 0.002 | 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".