NOT-Gated CD93 CAR T Cells Effectively Target AML with Minimized Endothelial Cross-Reactivity
Bibliographic record
Abstract
Abstract Chimeric antigen receptor (CAR) T cells hold promise for the treatment of acute myeloid leukemia (AML), but optimal targets remain to be defined. We demonstrate that CD93 CAR T cells engineered from a novel humanized CD93-specific binder potently kill AML in vitro and in vivo but spare hematopoietic stem and progenitor cells (HSPC). No toxicity is seen in murine models, but CD93 is expressed on human endothelial cells, and CD93 CAR T cells recognize and kill endothelial cell lines. We identify other AML CAR T-cell targets with overlapping expression on endothelial cells, especially in the context of proinflammatory cytokines. To address the challenge of endothelial-specific cross-reactivity, we provide proof of concept for NOT-gated CD93 CAR T cells that circumvent endothelial cell toxicity in a relevant model system. We also identify candidates for combinatorial targeting by profiling the transcriptome of AML and endothelial cells at baseline and after exposure to proinflammatory cytokines. Significance: CD93 CAR T cells eliminate AML and spare HSPCs but exert on-target, off-tumor toxicity to endothelial cells. We show coexpression of other AML targets on endothelial cells, introduce a novel NOT-gated strategy to mitigate endothelial toxicity, and demonstrate use of high-dimensional transcriptomic profiling for rational design of combinatorial immunotherapies. See related commentary by Velasquez and Gottschalk, p. 559. This article is highlighted in the In This Issue feature, p. 549
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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 | 0.001 | 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".