Simultaneous engineering of natural killer cells for CAR transgenesis and CRISPR-Cas9 knockout using retroviral particles
Bibliographic record
Abstract
Natural killer (NK) cells are potent cytotoxic innate lymphocytes that can be used for cancer immunotherapy. Since the balance of signals from activating and inhibitory receptors determines the activity of NK cells, their anti-tumor activity can be potentiated by overexpressing activating receptors or knocking out inhibitory receptors via genome engineering, such as chimeric antigen receptor (CAR) transgenesis and CRISPR-Cas9-mediated gene editing, respectively. Here, we report the development of a one-step strategy for CRISPR-Cas9-mediated gene knockout and CAR transgenesis in NK cells using retroviral particles. We generated NK cells expressing anti-epidermal growth factor receptor (EGFR)-CAR with simultaneous TIGIT gene knockout using single transduction and evaluated the consequence of the genetic modifications in vitro and in vivo . Taken together, our results demonstrate that retroviral particle-mediated engineering provides a strategy readily applicable to simultaneous genetic modifications of NK cells for efficient immunotherapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".