Decrease of VEGF-A in myeloid cells attenuates glioma progression and prolongs survival in an experimental glioma model
Bibliographic record
Abstract
BACKGROUND: Glioblastomas are highly vascularized tumors with a prominent infiltration of macrophages/microglia whose role in promoting glioma growth, invasion, and angiogenesis has not been fully elucidated. METHODS: The contribution of myeloid-derived vascular endothelial growth factor (VEGF) to glioma growth was analyzed in vivo in a syngeneic intracranial GL261 glioma model using a Cre/loxP system to knock out the expression of VEGF-A in CD11b + myeloid cells. Changes in angiogenesis-related gene expression profile were analyzed in mutant bone marrow-derived (BMD) macrophages in vitro. Furthermore, we studied the influence of macrophages on GL261 growth, invasiveness, and protein expression profile of angiogenic molecules as well as the paracrine effect of mutant macrophages on angiogenesis in vitro. RESULTS: Myeloid cell-restricted VEGF-A deficiency leads to a growth delay of intracranial tumors and prolonged survival. The tumor vasculature in mutant mice was more regular, with increased pericyte coverage. Expression analysis revealed significant downregulation of VEGF-A and slight upregulation of TGFβ-1 in BMD macrophages from mutant mice. Endothelial tube formation was significantly decreased by conditioned media from mutant macrophages. The expression of angiogenesis-related proteins in GL261 glioma cells in co-culture experiments either with wild-type or mutant macrophages remained unchanged, indicating that effects observed in vivo are due to myeloid-derived VEGF-A deficiency. CONCLUSIONS: Our results highlight the importance of VEGF derived from tumor-infiltrating myeloid cells for initiating vascularization in gliomas. The combination of antiangiogenic agents with myeloid cell-targeting strategies might provide a new therapeutic approach for glioblastoma patients.
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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".