Natural killer (NK) cells inhibit systemic metastasis of glioblastoma cells and have therapeutic effects against glioblastomas in the brain
Bibliographic record
Abstract
BACKGROUND: Glioblastoma multiforme (GBM) is characterized by extensive local invasion, which is in contrast with extremely rare systemic metastasis of GBM. Molecular mechanisms inhibiting systemic metastasis of GBM would be a novel therapeutic candidate for GBM in the brain. METHODS: Patient-derived GBM cells were primarily cultured from surgical samples of GBM patients and were inoculated into the brains of immune deficient BALB/c-nude or NOD-SCID IL2Rgamma(null) (NSG) mice. Human NK cells were isolated from peripheral blood mononucleated cells and expanded in vitro. RESULTS: Patient-derived GBM cells in the brains of NSG mice unexpectedly induced spontaneous lung metastasis although no metastasis was detected in BALB/c-nude mice. Based on the difference of the innate immunity between two mouse strains, NK cell activities of orthotopic GBM xenograft models based on BALB/c-nude mice were inhibited. NK cell inactivation induced spontaneous lung metastasis of GBM cells, which indicated that NK cells inhibit the systemic metastasis. In vitro cytotoxic activities of human NK cells against GBM cells indicated that cytotoxic activity of NK cells against GBM cells prevents systemic metastasis of GBM and that NK cells could be effective cell therapeutics against GBM. Accordingly, NK cells transplanted into orthotopic GBM xenograft models intravenously or intratumorally induced apoptosis of GBM cells in the brain and showed significant therapeutic effects. CONCLUSIONS: Our results suggest that innate NK immunity is responsible for rare systemic metastasis of GBM and that sufficient supplementation of NK cells could be a promising immunotherapeutic strategy for GBM in the brain.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".