Relative Abundance of <i>EGFR</i> Mutations Predicts Benefit From Gefitinib Treatment for Advanced Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Our aim was to determine whether abundance of epidermal growth factor receptor (EGFR) mutations in tumors predicts benefit from treatment with EGFR-tyrosine kinase inhibitors (TKIs) for advanced non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: We detected EGFR mutations in 100 lung cancer samples using direct DNA sequencing and amplification refractory mutation system (ARMS). Mutation-positive tumors by both methods carried high abundance of EGFR mutations. Tumors that were mutation positive by ARMS but mutation negative by direct DNA sequencing harbored low abundance of EGFR mutations. Mutation-negative tumors by both methods carried wild-type EGFR. All patients received gefitinib treatment. The correlation between EGFR mutation abundance and clinical benefit from gefitinib treatment was analyzed. RESULTS: Of 100 samples, 51 and 18 harbored high and low abundances of EGFR mutations, respectively; 31 carried wild-type EGFR. Median progression-free survival (PFS) was 11.3 (95% CI, 7.4 to 15.2) and 6.9 months (95% CI, 5.5 to 8.4) in patients with high and low abundances of EGFR mutations, respectively (P = .014). Median PFS of patients with low abundance of EGFR mutations was significantly longer than that of those with wild-type tumors (2.1 months; 95% CI, 1.0 to 3.2; P = .010). Objective response rates (ORRs) were 62.7%, 44.4%, and 16.1%, and overall survival (OS) rates were 15.9 (95% CI, 13.4 to 18.3), 10.9 (95% CI, 2.7 to 19.1), and 8.7 months (95% CI, 4.6 to 12.7) for patients with high abundance of EGFR mutations, low abundance of EGFR mutations, and wild-type EGFR, respectively. The difference between patients with high and low abundances of EGFR mutations was not significant regarding ORR and OS. CONCLUSION: The relative EGFR mutation abundance could predict benefit from EGFR-TKI treatment for advanced NSCLC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".