Comparison of molecular testing methods for the detection of <i>EGFR</i> mutations in formalin-fixed paraffin-embedded tissue specimens of non-small cell lung cancer
Bibliographic record
Abstract
AIM: To conduct a methods correlation study of three different assays for the detection of mutations at EGFR gene in human formalin-fixed paraffin-embedded tumour (FFPET) specimens of non-small cell lung carcinomas (NSCLC). METHODS: We conducted a 2-site method comparison study of two european conformity (CE) in vitro diagnostic (IVD)-marked assays, the cobas EGFR Mutation Test and the Therascreen EGFR29 Mutation Kit, and 2× bidirectional Sanger sequencing. We blind-tested 124 NSCLC FFPET specimens with all three methods; the cobas test was performed at both sites. Positive (PPA) and negative percent agreements (NPA) were determined for the cobas test versus each of the other two methods. Specimens yielding discordant test results between methods were further tested using quantitative massively parallel pyrosequencing (MPP). RESULTS: PPA between cobas and Sanger was 98.8%; NPA was 79.3%. Overall there were seven discordant results. MPP confirmed an exon 19 deletion in two cases and L858R mutation in four cases. PPA between cobas and Therascreen was 98.9% and NPA was 100%. There was one discordant result. Reproducibility of the cobas test between the two sites was 99.2%. CONCLUSIONS: The invalid rates for the cobas test and Therascreen were lower than Sanger sequencing. The cobas and Therascreen assays showed a high degree of concordance, and both were more sensitive for the detection of exon 19 deletion and L858R mutations than Sanger. The cobas test was highly reproducible between the two testing sites, used the least amount of DNA input and was the only test with automated results reporting.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".