Molecular analysis of the epidermal growth factor receptor (EGFR) gene and protein expression in patients treated with erlotinib in National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) trial BR.21
Bibliographic record
Abstract
7007 Background: BR.21 demonstrated a significant survival benefit for patients with advanced NSCLC who received erlotinib vs placebo (Shepherd, ASCO 2004). While EGFR expression is common in NSCLC, protein expression by immunohistochemistry (IHC) does not appear to predict response to EGFR tyrosine kinase inhibitors (TKI). Point mutations and/or short deletions in exons 18–22 of the EGFR gene may predict response to EGFR TKIs. In BR.21, EGFR expression by IHC did not predict response. The predictive value of EGFR mutations (MUT) or gene amplification (AMP) on survival have not yet been reported. Methods: 731 patients were randomized to BR.21 (488 erlotinib, 243 placebo). IHC for EGFR expression was evaluated for 325 patients in formalin-fixed, paraffin embedded tumor specimens. 213/325 samples were suitable for molecular analyses for MUT and/or fluorescent in situ hybridization (FISH) studies for AMP. Results: 184/325 samples showed >10% EGFR staining by IHC. The HR for survival was 0.7 for patients with >10% EGFR expression compared to 0.9 for those without. EGFR gene data presently are available for 106/213 patients; this subset was significantly more likely to have had adenocarcinoma (adeno), >1 prior regimen, > time from diagnosis to randomization. 12 mutations/deletions in exons 19 and 21 were identified (10 adeno) in the 106 patients. FISH was performed successfully in 68/106 patients; 20 samples had AMP arising from polysomic gain or high copy number AMP (14/20 adeno). 4 patients had MUT + AMP, 3 MUT without AMP and in 5 patients with MUT, FISH was not successful. In this preliminary subset of 106 patients, 3 had complete or partial response; all 3 patients had AMP but no MUT. Analyses of additional EGFR gene exons and of the remaining samples are ongoing and will be correlated with survival. Conclusion: Erlotinib appears to have a greater effect on survival in patients with EGFR protein expression. In a small, preliminary subset, EGFR amplification appears a better predictor of response than EGFR mutation. Additional gene assays and survival analyses will be completed shortly. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration OSI OSI
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".