Concurrent transposon engineering and CRISPR/Cas9 genome editing of primary CLL-1 chimeric antigen receptor–natural killer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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