EGFR Tyrosine Kinase Mutation Testing in the Treatment of Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Non-small-cell lung cancer (nsclc) tumours with activating mutations of the epidermal growth factor receptor (efgr) tyrosine kinase are highly sensitized to the effects of oral tyrosine kinase inhibitors such as gefitinib and erlotinib, suggesting the possibility of targeted treatment of nsclc based on EFGR mutation status. However, no standardized method exists for assessing the EGFR mutation status of tumours. Also, it is not known if available methods are feasible for routine screening. To address that question, we conducted a validation study of methods used for detecting EGFR mutations in exons 19 and 21 at molecular laboratories located in five specialized Canadian cancer centres. METHODS: The screening methods were first optimized using cell lines harbouring the mutations in question. A validation phase using anonymized patient samples followed. RESULTS: The methods used at the sites were highly specific and sensitive in detecting both mutations in cell-line dna (specificity of 100% and sensitivity of at least 1% across all centres). In the validation phase, we observed excellent concordance between the laboratories for detecting mutations in the patient samples. Concordant results were obtained in 26 of 30 samples (approximately 87%). In general, the samples for which results were discordant were also less optimal, containing small amounts of tumour. CONCLUSIONS: Our results suggest that currently available methods are capable of reliably detecting exon 19 and exon 21 mutations of EFGR in tumour samples (provided that sufficient tumour material is available) and that routine screening for those mutations is feasible in clinical practice.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".