Molecular changes in epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) biopsies at time of progression compared to initial biopsy
Bibliographic record
Abstract
e22066 Background: EGFR mutations predict sensitivity and clinical outcome to tyrosine kinase inhibitors (TKI) in NSCLC. The two most commonly described mutations are Exon 19 deletion and Exon 21 L858R missense mutations. Genetic alterations over time have been described in other tumour types, but studies assessing EGFR genotypic changes with lung cancer progression are lacking. We sought to compare EGFR mutational status from lung tumors at time of recurrence or progression with the primary tumor. Methods: Using the Jewish General Hospital lung cancer database, of all patients diagnosed with NSCLC since 1999, those with biopsies at two different points in time were identified. All tumour samples were genotyped for EGFR exons 19 and 21 mutations using denaturing high performance liquid chromatography (dHPLC). Results: 29 patients were identified. Data for 12 patients, whose time of recurrence or progression varied between 4 months and 6 years, are available at this time. Of 12 patients, one had EGFR exon 19 mutation at time of diagnosis. One patient who initially displayed no EGFR mutation was found to have an exon 19 deletion at time of recurrence. The one with exon 19 at time of initial diagnosis continued to express exon 19 in the second biopsy. Conclusions: To our knowledge, this is the only study assessing changes in molecular genotype using dHPLC between primary and recurrent or progressive lung cancer biopsy specimens. Although sample size is small, it is evident that changes in EGFR mutational status can occur. Further prospective studies are required to determine how commonly molecular changes occur. 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".