Exon 19 Deletion Mutations of Epidermal Growth Factor Receptor Are Associated with Prolonged Survival in Non–Small Cell Lung Cancer Patients Treated with Gefitinib or Erlotinib
Bibliographic record
Abstract
PURPOSE: Somatic mutations in the epidermal growth factor receptor (EGFR) have been detected in patients with non-small cell lung cancer (NSCLC) and are associated with sensitivity to treatment with gefitinib or erlotinib. Our study explored the relationship between the two most common types of somatic EGFR mutations, exon 19 deletions and the L858R point mutation, and outcomes of patients following treatment with gefitinib or erlotinib. EXPERIMENTAL DESIGN: Tumor specimens obtained before treatment with gefitinib or erlotinib were analyzed for EGFR mutations. Patients with exon 19 deletion or L858R mutations were identified. The response rate, time to progression, and overall survival were determined for the two groups. RESULTS: We identified 36 patients with NSCLC and an EGFR mutation who were treated with gefitinib or erlotinib. Patients with an exon 19 deletion had a significantly longer overall survival compared with patients with an L858R mutation (38 versus 17 months; P = 0.04). There were also trends toward higher response rate (73% versus 50%) and improved time to progression (24 versus 10 months) for the patients with an exon 19 deletion, although these were not independently significant in a multivariate analysis. A difference in response rate for patients treated with gefitinib compared with erlotinib was also noted [18 of 23 (78%) versus 3 of 9 (33%); P = 0.04]. No obvious difference in time to progression or overall survival was noted between gefitinib- and erlotinib-treated patients. CONCLUSIONS: Patients with NSCLC and EGFR exon 19 deletions have a longer survival following treatment with gefitinib or erlotinib compared with those with the L858R mutation. Pooling of greater numbers of patients and completion of prospective trials are needed to further define the predictive and prognostic roles of different EGFR mutations with respect to treatment with gefitinib, erlotinib, and other EGFR inhibitors.
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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 | 0.001 |
| 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".