Evaluation of denaturing high‐performance liquid chromatography as a rapid detection method for identification of epidermal growth factor receptor mutations in nonsmall‐cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Somatic mutations of the epidermal growth factor receptor (EGFR) gene in nonsmall-cell lung cancer (NSCLC) may predict responsiveness to tyrosine kinase inhibitors. These mutations are commonly identified using DNA sequencing methods. Although considered the gold standard, this approach is time-consuming. In addition, this approach requires large diagnostic specimens and a high ratio of tumor-to-normal-tissue DNA for optimal results. The use of denaturing high-performance liquid chromatography (dHPLC) as a method to screen for the 2 predominant EGFR mutations is reported. METHODS: Clinical specimens from 104 NSCLC patients were analyzed for EGFR mutations in exons 19 and 21. After DNA extraction and polymerase chain reaction (PCR), both direct sequencing and dHPLC were performed and the results were compared. RESULTS: Sequencing revealed a total of 7 mutations: 3 deletion mutations in exon 19 and 4 missense mutations in exon 21. dHPLC showed the presence of genomic alterations in 23 samples, including the 7 identified by sequencing plus 16 additional samples (10 in exon 19 and 1 in exon 21). dHPLC fractions were isolated, reamplified, and sequenced to confirm the results. In serial dilution studies, dHPLC was able to detect mutations in samples containing as little as 1.6% to 6.25% mutated DNA, whereas direct sequencing required at least 30%. CONCLUSIONS: dHPLC is an efficient and more sensitive method for screening for genomic alterations in exons 19 and 21 of the EGFR gene compared with direct sequence analysis. These data suggest that dHPLC should be implemented as a screening tool for detection of EGFR mutations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".