The role of angiogenesis in Group 3 medulloblastoma pathogenesis and survival
Bibliographic record
Abstract
BACKGROUND: Of the 4 medulloblastoma subgroups, Group 3 is the most aggressive but the importance of angiogenesis is unknown. This study sought to determine the role of angiogenesis and identify clinically relevant biomarkers of tumor vascularity and survival in Group 3 medulloblastoma. METHODS: VEGFA mRNA expression and survival from several patient cohorts were analyzed. Group 3 xenografts were implanted intracranially in nude rats. Dynamic susceptibility weighted (DSC) MRI and susceptibility weighted imaging (SWI) were obtained. DSC MRI was used to calculate relative cerebral blood volume (rCBV) and flow (rCBF). Tumor vessel density and rat vascular endothelial growth factor alpha (VEGFA) expression were determined. RESULTS: Patient VEGFA mRNA levels were significantly elevated in Group 3 compared with the other subgroups (P < 0.001) and associated with survival. Xenografts D283, D341, and D425 were identified as Group 3 by RNA hierarchical clustering and MYC amplification. The D283 group had the lowest rCBV and rCBF, followed by D341 and D425 (P < 0.05). These values corresponded to histological vessel density (P < 0.05), rat VEGFA expression (P < 0.05), and survival (P = 0.002). Gene set enrichment analysis identified 5 putative genes with expression profiles corresponding with these findings: RNH1, SCG2, VEGFA, AGGF1, and PROK2. SWI identified 3 xenograft-independent categories of intratumoral vascular architecture with distinct survival (P = 0.004): organized, diffuse microvascular, and heterogeneous. CONCLUSIONS: Angiogenesis plays an important role in Group 3 medulloblastoma pathogenesis and survival. DSC MRI and SWI are clinically relevant biomarkers for tumor vascularity and overall survival and can be used to direct the use of antivascular therapies for patients with Group 3 medulloblastoma.
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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 | 0.001 | 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 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".