Use of denaturing high performance liquid chromatography (dHPLC) for detection of EGFR mutations in patients with NSCLC
Bibliographic record
Abstract
10068 Background: Somatic mutations of the epidermal growth factor receptor (EGFR) gene may predict responsiveness to tyrosine kinase inhibitors in patients with non-small cell lung cancer (NSCLC). These mutations are commonly identified using DNA sequencing methods. Although considered the gold standard, this approach is expensive and time-consuming. In addition, a high ratio of tumour to normal tissue DNA is required for optimal results which is not often available in biopsies obtained from these patients. The primary objective of this study was to develop a rapid and sensitive method for screening of EGFR mutations. Materials and Methods: Clinical specimens from 110 NSCLC patients were analysed for EGFR mutations in exons 19 and 21. After DNA extraction and PCR, both dHPLC and direct sequencing were performed and results were compared. Results: Sequencing revealed a total of 7 (6%) mutations: 4 missense mutations in exon 21 and 3 deletion mutations in exon 19. dHPLC showed the presence of genomic alterations in 18 (16%) samples, including the 7 identified by sequencing plus 11 additional samples (10 in exon 19 and one in exon 21). dHPLC fractions were isolated, reamplified, and sequenced to confirm these results. In serial dilution studies, dHPLC was able to detect mutations in samples containing as little as 10% mutated DNA whereas direct sequencing required at least 30%. Conclusions: dHPLC is an efficient and accurate, as well as a a more sensitive method for screening of genomic alterations in exons 19 and 21 of the EGFR gene compared to direct sequencing. This data suggests that dHPLC should be implemented as a screening tool for detection of EGFR mutations. 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 | 0.001 | 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".