Tumor Environment Dictates Medulloblastoma Cancer Stem Cell Expression and Invasive Phenotype
Bibliographic record
Abstract
The neural precursor surface marker CD133 is thought to be enriched in brain cancer stem cells and in radioresistant DAOY medulloblastoma-derived tumor cells. Given that membrane type-1 matrix metalloproteinase (MT1-MMP) expression is a hallmark of highly invasive, radioresistant, and hypoxic brain tumor cells, we sought to determine whether MT1-MMP and other MMPs could regulate the invasive phenotype of CD133(+) DAOY cells. We found that when DAOY medulloblastoma or U87 glioblastoma cells were implanted in nude mice, only those cells specifically implanted in the brain environment generated CD133(+) brain tumors. Vascular endothelial growth factor and basic fibroblast growth factor gene expression increases in correlation with CD133 expression in those tumors. When DAOY cultures were induced to generate in vitro neurosphere-like cells, gene expression of CD133, MT1-MMP, MMP-9, and MDR-1 was induced and correlated with an increase in neurosphere invasiveness. Specific small interfering RNA gene silencing of either MT1-MMP or MMP-9 reduced the capacity of the DAOY monolayers to generate neurospheres and concomitantly abrogated their invasive capacity. On the other hand, overexpression of MT1-MMP in DAOY triggered neurosphere-like formation which was further amplified when cells were cultured in neurosphere medium. Collectively, we show that both MT1-MMP and MMP-9 contribute to the invasive phenotype during CD133(+) neurosphere-like formation in medulloblastoma cells. Increases in MMP-9 may contribute to the opening of the blood-brain barrier, whereas increased MT1-MMP would promote brain tumor infiltration. Our study suggests that MMP-9 or MT1-MMP targeting may reduce the formation of brain tumor stem cells.
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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) | 0.001 | 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 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".