Investigating the cytotoxicity of expanded NK and CD70-targeting CAR-NK cells against brain tumor cells 3722
Bibliographic record
Abstract
Abstract Description Glioblastoma multiforme (GBM) and medulloblastoma (MB) are aggressive brain cancers that demonstrate poor patient survival despite intense interventions. Natural Killer (NK) cells are promising in cancer immunotherapy due to their potent antigen-independent cytolytic functions. A chimeric antigen receptor (CAR) can further enhance their tumour specificity. CD70 is a favorable target for CAR-NK cells given its key role in tumor recurrence. Here, we evaluated the in vitro cytotoxicity of ex vivo expanded NK and anti-CD70 CAR-NK cells against GBM and MB. The CAR-NK cells were generated through CRISPR/Cas9 gene editing and adeno-associated viral vector gene delivery. Peripheral blood-derived human NK and CAR-NK cells were expanded using IL-21-expressing feeder cells, and their cytotoxicity was assessed in response to GBM and MB cell lines/patient-derived cells. Expanded NK cells displayed high cell killing of both GBM and MB tumor targets. However, expansion led to rapid surface expression of CD70 on anti-CD70 CAR-NK cells, resulting in their fratricide-mediated depletion. To address this, CD70 knock-out (KO) anti-CD70 CAR-NK cells were generated, which successfully expanded in culture and reliably expressed the CAR receptor. These CAR-NK cells displayed enhanced targeting of highly CD70+ patient-derived GBM cells. This work underscores the in vitro cytotoxic potential of CD70KO anti-CD70 CAR-NK cells against GBM, with their in vivo tumor killing to be further investigated. Funding Sources Supported by a Canadian Institutes of Health Research (CIHR) grant, a Canada Graduate Scholarship - Master’s (CGS-M), and an Ontario Graduate Scholarship (OGS). Topic Categories Immune Mechanisms of Human Disease (HUM)
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".