CD33–CD123 IF-THEN Gating Reduces Toxicity while Enhancing the Specificity and Memory Phenotype of AML-Targeting CAR-T Cells
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Chimeric antigen receptor (CAR) T-cell therapy has remarkably succeeded in treating lymphoblastic leukemia. However, its success in acute myeloid leukemia (AML) remains elusive because of the risk of on-target off-tumor toxicity to hematopoietic stem/progenitor cells (HSPC) and insufficient T-cell persistence and longevity. Using a SynNotch circuit, we generated a high-precision “IF-THEN” gated logical circuit against the combination of CD33 and CD123 AML antigens and demonstrated antitumor efficacy against AML cell lines and patient-derived xenografts. Unlike constitutively expressed CD123 CAR-T cells, those expressed through the CD33 SynNotch circuit could preserve HSPCs and lower the risk of on-target off-tumor hematopoietic toxicity. These gated CAR-T cells exhibited lower expression of exhaustion markers (PD-1, TIM-3, LAG-3, and CD39), higher frequency of memory T cells (CD62L+CD45RA+), and enhanced expansion. Although targeting AML, the moderated circuit CAR signal also helped mitigate cytokine release syndrome, potentially addressing one of the ongoing challenges in CAR-T immunotherapy. Significance: Our study demonstrates the use of “IF-THEN” SynNotch-gated CAR-T cells targeting CD33 and CD123 in AML reduces off-tumor toxicity. This strategy enhances T-cell phenotype, improves expansion, preserves HSPCs, and mitigates cytokine release syndrome—addressing critical limitations of existing AML CAR-T therapies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it