Concurrent transposon engineering and CRISPR/Cas9 genome editing of primary CLL-1 chimeric antigen receptor–natural killer cells
Bibliographic record
Abstract
BACKGROUND: Natural killer (NK) cell genome editing promises to enhance the innate and alloreactive anti-tumor potential of NK cell adoptive transfer. DNA transposons are versatile non-viral gene vectors now being adapted to primary NK cells, representing important tools for research and clinical product development. AIMS AND METHODS: We set out to generate donor-derived, primary chimeric antigen receptor (CAR)-NK cells by combining the TcBuster transposon system with Epstein-Barr virus-transformed lymphoblastoid feeder cell-mediated activation and expansion. RESULTS: This approach allowed for clinically relevant NK-cell expansion capability and CAR expression, which was further enhanced by immunomagnetic selection based on binding to the CAR target protein.The resulting CAR-NK cells targeting the myeloid associated antigen CLL-1 efficiently targeted CLL-1-positive AML cell lines and primary AML populations, including a population enriched for leukemia stem cells. Subsequently, concurrent delivery of CRISPR/Cas9 cargo was applied to knockout the NK cell cytokine checkpoint cytokine-inducible SH2-containing protein (CIS, product of the CISH gene), resulting in enhanced cytotoxicity and an altered NK cell phenotype. CONCLUSIONS: This report contributes a promising application of transposon engineering to donor-derived NK cells and emphasizes the importance of feeder mediated NK cell activation and expansion to current protocols.
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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".