Clinicopathologic features and prognostic implications of epidermal growth factor receptor (EGFR) gene mutations detected by denaturing high-performance liquid chromatography (dHPLC) in non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
7594 Background: Somatic mutations of the EGFR gene predict sensitivity to erlotinib and gefitinib and confer a favorable prognosis in patients (pts) with NSCLC. We have recently shown that dHPLC is an efficient and more sensitive method for mutation screening compared with DNA sequencing. The goal of this study was to describe the relationship between EGFR status (mutation status and type) and clinicopathologic factors. Methods: Tumor samples were analysed for EGFR exon 19 deletions and exon 21 L858R point mutations. DNA was extracted from paraffin-embedded tumor specimens and genotyped using dHPLC. The results were correlated with gender, smoking status, pathologic subtype, disease stage, and overall survival. Results: 215 NSCLC pts were genotyped. Mutations were present in 25% of cases (54 of 215). Among pts with mutations, 70% (38 of 54) had an exon 19 mutation whereas 30% had EGFR L858R. EGFR mutations were more common in women (31% vs 14%; p=0.008), in nonsmokers than ever-smokers (54% vs 17%; p<0.001), in adenocarcinomas/BAC than with other NSCLC histologies (31% vs 11%; p=0.004) and more frequently detected in advanced-stage than in early-stage disease (36% vs 15%; p=0.001). Median survival times of pts with stage IIIB-IV disease with and without EGFR mutations were 20 and 14 months, respectively. Those with exon 19 deletion mutations had a longer median survival than pts with L858R point mutations. Data describing the impact of tyrosine kinase inhibitor therapy on survival outcomes are pending and will be presented. Conclusion: dHPLC is a reliable tool for EGFR mutation detection. Mutations were preferentially observed in women, nonsmokers, adenocarcinomas/BAC and in patients with advanced disease. Pts with mutations experienced improved survival and those harboring deletions fared better than those with point mutations. These observations warrant confirmation in large prospective trials and exploration of the biological mechanisms of the differences between mutation types. No significant financial relationships to disclose.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".