Feeder Cells at the Interface of Natural Killer Cell Activation, Expansion and Gene Editing
Bibliographic record
Abstract
Genome engineered natural killer (NK) cell therapies are emerging as a promising cancer immunotherapy platform with potential advantages and remaining uncertainties. Feeder cells induce activation and proliferation of NK cells via cell surface receptor-ligand interactions, supported by cytokines. Feeder cell expanded NK cell products have supported several NK cell adoptive transfer clinical trials over the past decade. Genome engineered NK cell therapies, including CAR-NK cells, seek to combine innate and alloreactive NK cell anti-tumor activity with antigen specific targeting or additional modifications aimed at improving NK cell persistence, homing or effector function. The profound activating and expansion stimulus provided by feeder cells is integral to current applications of clinical-scale genome engineering approaches in donor-derived, primary NK cells. Herein we explore the complex interactions that exist between feeder cells and both viral and emerging non-viral genome editing technologies in NK cell engineering. We focus on two established clinical-grade feeder systems; Epstein-Barr virus transformed lymphoblastoid cell lines and genetically engineered K562.mbIL21.4-1BBL feeder cells.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".