Epidermal growth factor receptor mutations detected by denaturing high‐performance liquid chromatography in nonsmall cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Somatic mutations in the epidermal growth factor receptor (EGFR) kinase domain are associated with sensitivity to EGFR-tyrosine kinase inhibitors (EGFR-TKI) in patients with nonsmall cell lung cancer (NSCLC). METHODS: The authors tested the possibility that nucleotide sequencing may be poorly suited for detection of mutations in tumor samples and found that denaturing high-performance liquid chromatography (dHPLC) was an efficient and more sensitive method for screening. RESULTS: These results suggested that some reports based on standard DNA sequencing techniques may have underestimated mutation rates. In the present report, the authors examined the relationship between the presence and type of EGFR mutations detected by dHPLC and various clinicopathologic features of NSCLC, including response to therapy with EGFR-TKI. Among 251 patients with advanced disease, 100 individuals received EGFR-TKI. Those whose tumors harbored a detectable EGFR kinase mutation were much more likely to have a partial response (PR) or stable disease (SD) with EGFR-TKI therapy than patients whose tumor contained no mutation (80% vs 35%; P = .001). Among the individual genotype subgroups, the frequency of a PR or SD was significantly different between patients with an exon 19 deletion compared with those with no detectable mutation (86% vs 35%; P < .001). Furthermore, patients whose tumors expressed an exon 19 mutant EGFR isoform exhibited a trend toward better EGFR-TKI response (86% vs 67%; P = .171) and improved survival compared with patients whose tumors expressed an exon 21 mutation. CONCLUSIONS: Our findings warrant confirmation in large prospective trials and exploration of the biological mechanisms of the differences between mutation types.
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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 | 0.002 |
| 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) | 0.000 | 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".