First-line afatinib for advanced EGFRm+ NSCLC: Analysis of long-term responders in the LUX-Lung 3, 6, and 7 trials
Bibliographic record
Abstract
OBJECTIVES: In patients with advanced epidermal growth factor receptor mutation-positive (EGFRm+) non-small cell lung cancer (NSCLC), first-line afatinib significantly improved progression-free survival (PFS) and objective response vs. platinum-doublet chemotherapy in the phase III LUX-Lung 3 and LUX-Lung 6 trials, and significantly improved PFS, time to treatment failure and objective response vs. gefitinib in the phase IIb LUX-Lung 7 trial. We report post-hoc analyses of efficacy, safety and patient-reported outcomes (PROs) in afatinib long-term responders (LTRs) in these trials. METHODS: Treatment-naïve patients with stage IIIB/IV EGFRm + NSCLC randomized to afatinib in LUX-Lung 3/LUX-Lung 6/LUX-Lung 7 were included in the analysis. Patients treated with afatinib for ≥ 3 years were defined as LTRs. RESULTS: In LUX-Lung 3, LUX-Lung 6, and LUX-Lung 7, 24/229 (10%), 23/239 (10%) and 19/160 (12%) afatinib-treated patients were LTRs. Baseline characteristics were similar to the study populations, except for the proportions of women (LUX-Lung 3/LUX-Lung 6 only; 92/78% vs. 64% overall) and Del19-positive patients (63-79% vs. 49-58% overall). Median treatment duration among LTRs was 50, 56 and 42 months, and median PFS was 49.5, 55.5, and 42.2 months in LUX-Lung 3/LUX-Lung 6/LUX-Lung 7, respectively. Median overall survival could not be estimated. Frequency of afatinib dose reduction was consistent with the LUX-Lung 3/LUX-Lung 6/LUX-Lung 7 overall populations. PROs were stable in LTRs, with slight improvements after 3 years of afatinib treatment vs. baseline scores. CONCLUSIONS: In the LUX-Lung 3/LUX-Lung 6/LUX-Lung 7 trials, 10-12% of afatinib-treated patients were LTRs. Long-term afatinib treatment was independent of tolerability-guided dose adjustment and had no detrimental impact on safety or PROs.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".