CAR-T Cell Therapy for Acute Myeloid Leukemia: Where Do We Stand Now?
Bibliographic record
Abstract
Background: Patients with refractory and relapsed acute myeloid leukemia (R/R AML) face a dismal prognosis. CAR-T therapy has emerged as a potential treatment option. This study assesses the available clinical evidence on CAR-T in R/R AML, focusing on safety and efficacy outcomes. Methods: We included studies on CAR-T therapy for R/R AML published from June 2014 to January 2025. Data on patient and disease characteristics, CAR-T constructs, response rates, post-CAR-T allogeneic HSCT (allo-HSCT), and safety outcomes were analyzed. Results: Twenty-five CAR-T clinical trials involving 296 patients were identified. The most frequently targeted antigens were CD33, CD123, and CLL-1, while CD7, CD19, NKG2D, and CD38 were also explored. Responses were heterogeneous and often short-lived when not consolidated with allo-HSCT. Cytokine release syndrome and neurotoxicity were generally low grade and manageable. Prolonged and severe myelosuppression was a frequent limiting toxicity, often requiring allo-HSCT to restore hematopoiesis. Disease progression was the leading cause of death, followed by infections. Conclusions: CAR-T cell therapy may represent a feasible therapeutic strategy, particularly as bridging to allo-HSCT to mitigate myelotoxicity and improve long-term outcomes. Nevertheless, it remains in the early stages of development and faces significant efficacy and safety challenges that must be addressed in future trials to enable the expansion of this promising therapeutic approach for a population with high unmet medical needs.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".