Oligodendrogliomas result from the expression of an activated mutant epidermal growth factor receptor in a RAS transgenic mouse astrocytoma model.
Bibliographic record
Abstract
A significant proportion of human malignant gliomas exhibit amplification, overexpression, or mutations of the epidermal growth factor receptor (EGFR). To define the functional role(s) of the EGFR in the pathogenesis of gliomas, we established transgenic mice that express both wild-type (wt) and mutant (EGFRvIII) EGFR molecules using the human glial fibrillary acidic protein (GFAP) promoter. Both GFAP-EGFR(wt) and GFAP-EGFRvIII transgenic mice demonstrated increased numbers of astrocytes compared with control littermates, however, developed normally without formation of gliomas. To determine whether EGFR overexpression could modify the tumor phenotype in our previously reported GFAP-V(12)Ha-ras transgenic mouse astrocytoma model, mice expressing both activated RAS and EGFR were developed. GFAP-V(12)Ha-ras;GFAP-EGFRvIII, but not GFAP-V(12)Ha-ras;GFAP-EGFR(wt) double transgenic mice, had decreased survival with fifty percent of the mice dead at 2-4 weeks from gliomas, compared with 12-16 weeks for the GFAP-V(12)Ha-ras mice. Furthermore, GFAP-V(12)Ha-ras;GFAP-EGFRvIII mice developed oligodendrogliomas and mixed oligoastrocytoma tumors, instead of the fibrillary astrocytomas observed in GFAP-V(12)Ha-ras mice. In addition to yielding a spontaneous model of infiltrating oligodendroglioma, this study demonstrates that astrocyte-specific expression of EGFRvIII alone is insufficient for gliomagenesis but rather contributes to glioma progression in the context of existing predisposing genetic changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".