Ex Vivo Expanded Human NK Cells Survive and Proliferate in Humanized Mice with Autologous Human Immune Cells
Bibliographic record
Abstract
Adoptive immune cell therapy is emerging as a promising immunotherapy for cancer. Particularly, the adoptive transfer of NK cells has garnered attention due to their natural cytotoxicity against tumor cells and safety upon adoptive transfer to patients. Although strategies exist to efficiently generate large quantities of expanded NK cells ex vivo, it remains unknown whether these expanded NK cells can persist and/or proliferate in vivo in the absence of exogenous human cytokines. Here, we have examined the adoptive transfer of ex vivo expanded human cord blood-derived NK cells into humanized mice reconstituted with autologous human cord blood immune cells. We report that ex vivo expanded NK cells are able to survive and possibly proliferate in vivo in humanized mice without exogenous cytokine administration, but not in control mice that lack human immune cells. These findings demonstrate that the presence of autologous human immune cells supports the in vivo survival of ex vivo expanded human NK cells. These results support the application of ex vivo expanded NK cells in cancer immunotherapy and provide a translational humanized mouse model to test the lifespan, safety, and functionality of adoptively transferred cells in the presence of autologous human immune cells prior to clinical use.
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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) | 0.000 | 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".