Epidermal growth factor receptor gene amplification and expression in disseminated pediatric low-grade gliomas
Bibliographic record
Abstract
OBJECT: Pediatric low-grade gliomas (LGGs) are the largest group of central nervous system neoplasms in children. Although these tumors are generally benign, 5 to 10% of patients with pediatric LGGs present with leptomeningeal dissemination. The genetic and biological nature of these tumors is poorly understood. The authors looked for certain molecular abnormalities that may differentiate disseminated gliomas from the other pediatric LGGs. METHODS: Comparative genomic hybridization (CGH) was applied to 18 pediatric LGGs. Six cases featuring disseminated pediatric LGGs were compared with 12 control cases involving nondisseminated pediatric LGGs. Fluorescence in situ hybridization (FISH) analysis and immunohistochemical analysis were used to highlight further specific genetic targets. The CGH revealed multiple chromosomal abnormalities in five of six cases with disseminated gliomas and in six of 12 control cases. No correlation was found between the number of chromosomal abnormalities and dissemination status. Amplification of chromosome 7 was noted in four of six cases with disseminated gliomas as opposed to one of 12 control cases (p = 0.02). The FISH analysis revealed epidermal growth factor receptor (EGFR) amplification in one case negative to chromosome 7 amplification by CGH, raising the amplification cases to five of six (p = 0.0038). Immunohistochemical analysis for EGFR was positive in six of six cases and in two of 12 control cases (p = 0.0015). At the end of a mean follow-up period of 7.2 years, all patients with disseminated gliomas are alive with variable but slow disease progression. CONCLUSIONS: The high rate of EGFR gene amplification and protein expression in disseminated pediatric LGGs is intriguing and may have implications for our understanding of the role of EGFR in glioma genesis. Targeted therapies may be available for these children. Larger-scale studies are needed to establish further these findings.
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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 | 0.001 | 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 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".