Previously documented mutations in the epidermal growth factor receptor (EGFR) gene in a non-small cell lung cancer (NSCLC) population treated with gefitinib are not associated with response
Bibliographic record
Abstract
7163 Background: Mutations in the tyrosine kinase domain of EGFR that may correlate with clinical features and response of NSCLC to EGFR tyrosine kinase inhibitors have been described. But, varying methodologies have contributed to an uncertain relationship between EGFR mutational status and response. This study sought to characterize EGFR mutations in microdissected tumour tissue from pts with advanced NSCLC treated with gefitinib and correlate their clinical data. Methods: Biopsy material from pts treated with gefitinib for advanced NSCLC at the British Columbia Cancer Agency was analyzed. Malignant cells (cytology specimens) or tissue (paraffin embedded biopsies) was reviewed and tumour cells isolated by laser- capture microdissection or manual scrape. Genomic DNA was extracted and exons coding for the EGFR tyrosine kinase domain (18 - 24) were amplified by PCR and sequenced. When insufficient, the priority was 18, 19, 21, followed by 20, 23, 22, and 24. EGFR mutational analyses were correlated with response to gefitinib and clinical features. Results: 61 pts were identified, 14 (23%) radiological responders (CR, PR): 10 Asian, 10 female, 8 non-smokers, 8 adenocarcinoma, 2 BAC. Of 51 tumour samples available, 39 had adequate tissue for sequencing analysis. EGFR copy number by FISH is pending. Laser-capture microdissection allowed for high quality DNA to be extracted almost exclusively from tumour. Exons 18, 19, 20, 21, 22, 23 and 24 have been sequenced in 37, 34, 30, 33, 12, 10, and 13 patients, respectively. 4 mutations were identified: 2 in 2 non-smoking Asian pts (exon 19; deletion or substitution of L747-T751) and 2 in Caucasians (exon 20 point mutation resulting in a L798F substitution). None of these pts had a response to gefitinib. Conclusions: As with other series, most responders were female, non-smokers of Asian origin. Our results support the relationship between Asian ethnicity and EGFR mutations but question the role of EGFR mutational status in predicting response. Prospective studies will need to focus the detection of additional genetic features using accurate and reproducible techniques before recommendations for selecting populations to be treated can be made. No significant financial relationships to disclose.
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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) | 0.002 | 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".