Molecular Study of Malignant Gliomas Treated with Epidermal Growth Factor Receptor Inhibitors: Tissue Analysis from North American Brain Tumor Consortium Trials 01-03 and 00-01
Bibliographic record
Abstract
PURPOSE: We investigated the molecular effect of the epidermal growth factor receptor (EGFR) inhibitors erlotinib and gefitinib in vivo on all available tumors from patients treated on North American Brain Tumor Consortium trials 01-03 and 00-01 for recurrent or progressive malignant glioma. EXPERIMENTAL DESIGN: EGFR expression and signaling during treatment with erlotinib or gefitinib were analyzed by Western blot and compared with pre-erlotinib/gefitinib-exposed tissue or unexposed controls. Tumors were also analyzed for EGFR mutations and for other genomic abnormalities by array-based comparative genomic hybridization. Clinical data were used to associate molecular features with tumor sensitivity to erlotinib or gefitinib. RESULTS: Erlotinib and gefitinib did not markedly affect EGFR activity in vivo. No lung signature mutations of EGFR exons 18 to 21 were observed. There was no clear association between erlotinib/gefitinib sensitivity and deletion or amplification events on array-based comparative genomic hybridization analysis, although novel genomic changes were identified. CONCLUSIONS: As erlotinib and gefitinib were generally ineffective at markedly inhibiting EGFR phosphorylation in these tumors, other assays may be needed to detect molecular effects. Additionally, the mechanism of erlotinib/gefitinib sensitivity likely differs between brain and lung tumors. Finally, novel genomic changes, including deletions of chromosomes 6, 21, and 22, represent new targets for further research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".