CAR-NK cell therapy in acute myeloid leukemia: safer, smarter, and moving toward clinical translation
Bibliographic record
Abstract
Acute myeloid leukemia (AML) remains a highly lethal hematologic malignancy with limited durable treatment options and poor long-term survival. Allogeneic stem cell transplantation provides a curative approach for select patients, but outcomes are constrained by relapse and transplant-related morbidity. Chimeric antigen receptor (CAR) T-cell therapy, transformative in several lymphoid cancers, has shown limited efficacy in AML due to disease heterogeneity, antigen overlap with normal hematopoiesis, and treatment-related toxicities such as cytokine release syndrome and neurotoxicity. Natural killer (NK) cells offer an alternative cellular platform with intrinsic antitumor properties and a favorable safety profile. CAR-engineered NK (CAR-NK) cells combine the innate ability of NK cells to recognize malignant cells with the antigen specificity of CARs, offering targeted cytotoxicity while minimizing the risks of graft-versus-host disease and severe cytokine-driven toxicities. The use of allogeneic NK sources, including peripheral blood, umbilical cord blood, induced pluripotent stem cells, and NK cell lines, further supports the development of scalable “off-the-shelf” therapies. In this review, we summarize recent advances in CAR-NK therapy for AML. We discuss NK cell biology and CAR-NK design, evaluate emerging clinical trial data, and highlight key considerations for therapeutic development, including target antigen selection, NK cell sources, and engineering strategies to enhance persistence and function. Finally, we explore combination approaches and adjuvant agents that may overcome disease heterogeneity and immune evasion. Together, these insights underscore the potential of CAR-NK cells as a safer and more versatile next-generation immunotherapy in AML.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".