Role of repulsive guidance signaling and GPR180 in pediatric low-grade glioma infiltration
Bibliographic record
Abstract
Pediatric low-grade gliomas (pLGGs) are the most common brain tumors in children with varying degrees of infiltration. Despite having a positive prognosis, if the standard treatment, gross total resection, is impossible due to tumor location or diffuseness, outcomes worsen. Development of targeted therapeutics for diverse subtypes of pLGGs is limited by a lack of genetic models. We generated five fly pLGG models using patient-derived fusion genes to investigate molecular subtype-specific pathology, and found glial overexpression of QKI::RAF1, associated with pilocytic astrocytomas and glioneuronal tumors, induced aberrant glial migration and infiltration. Both repulsive guidance signaling and GPR180/CG9304 mediated glial infiltration, which was suppressed by glial overexpression of Robo2 or PlexA/B, or knockdown of GPR180/CG9304. ROBO2 and GPR180/CG9304 were down and upregulated, respectively, in flies and patients with RAF fusions. Our study provides mechanistic insights into pLGG tumorigenesis and suggests targeting repulsive guidance signaling and GPR180/CG9304 as potential therapeutics for pLGG subtypes.
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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".