CD38 knockout natural killer cells expressing an affinity optimized CD38 chimeric antigen receptor successfully target acute myeloid leukemia with reduced effector cell fratricide
Bibliographic record
Abstract
There is a strong biological rationale for the augmentation of allogeneic natural killer (NK) cell therapies with a chimeric antigen receptor (CAR) to enhance acute myeloid leukemia (AML) targeting. CD38 is an established immunotherapeutic target in multiple myeloma and under investigation as a target antigen in AML. CD38 expression on NK cells and its further induction during ex vivo NK cell expansion represents a barrier to the development of a CD38 CAR-NK cell therapy. We set out to develop a CD38 CAR-NK cell therapy for AML, first by using an NK cell line which has low baseline CD38 expression and subsequently healthy donor expanded NK cells. To overcome anticipated fratricide due to NK cell CD38 expression when using primary expanded NK cells, we applied CRISPR/Cas9 genome editing to disrupt the CD38 gene during expansion achieving a mean knockdown efficiency of 84%. The resulting CD38 KD expanded NK cells, after expression of an affinity optimized CD38 CAR, showed reduced NK cell fratricide and an enhanced ability to target primary AML blasts. Furthermore, the cytotoxic potential of CD38 CAR-NK cells was augmented by pre-treatment of the AML cells with all-trans retinoic acid which drove enhanced CD38 expression offering a rational combination therapy. These findings support the further investigation of CD38 KD - CD38 CAR-NK cells as a viable immunotherapeutic approach to the treatment of 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 | 0.000 | 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 | 0.000 | 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".