Afatinib for the Treatment of NSCLC with Uncommon EGFR Mutations: A Narrative Review
Bibliographic record
Abstract
Afatinib, the world's first irreversible ErbB family (containing four different cancer cell epidermal growth factor receptors, including EGFR, HER2, ErbB3, and ErbB4) inhibitor, is a second-generation oral epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). It can be used as a first-line treatment for locally advanced or metastatic non-small-cell lung cancer (NSCLC) with an EGFR-sensitive mutation or for patients with locally advanced or metastatic squamous lung cancer whose disease progresses during or after platinum-containing chemotherapy. Currently, with the use of third-generation EGFR-TKIs, afatinib is no longer clinically indicated as the first choice for patients with NSCLC who have EGFR-sensitive mutations. However, afatinib showed a considerable inhibitory effect in NSCLC patients with uncommon EGFR mutations (G719X, S768I, and L861Q) according to a combined post hoc analysis of the LUX-Lung2/3/6 trials. With the development of genetic testing technology, the detection rate of uncommon EGFR mutations is increasing. The aim of this paper is to describe in detail the sensitivity of rare EGFR mutations to afatinib and to provide information and a reference for those suffering from advanced NSCLC who have uncommon 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".