Afatinib as First-line Treatment of Older Patients With EGFR Mutation-Positive Non-Small-Cell Lung Cancer: Subgroup Analyses of the LUX-Lung 3, LUX-Lung 6, and LUX-Lung 7 Trials
Bibliographic record
Abstract
Background Afatinib is approved in the US, Europe, and several other regions for first-line treatment for epidermal growth factor receptor mutation-positive ( EGFR m + ) non-small-cell lung cancer (NSCLC). Patients and Methods Treatment-naive patients with advanced EGFR m + NSCLC were randomized to afatinib (40 mg/d) versus cisplatin/pemetrexed (LUX-Lung 3 [LL3]) or cisplatin/gemcitabine (LUX-Lung 6 [LL6]), or versus gefitinib (250 mg/d; LUX-Lung 7 [LL7]). We report subgroup analyses according to age, including 65 years or older versus younger than 65 years (preplanned; LL3/LL6) and additional cutoffs up to 75 years and older (exploratory; LL7). Progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were evaluated. Results Among the 134 of 345 (39%) and 86 of 364 (24%) patients aged 65 years and older in LL3 and LL6, median PFS was improved with afatinib versus chemotherapy (LL3: hazard ratio [HR], 0.64 [95% confidence interval (CI), 0.39-1.03]; LL6: HR, 0.16 [95% CI, 0.07-0.39]). Afatinib significantly improved OS versus chemotherapy in elderly patients with Del19 + NSCLC in LL3 (HR, 0.39 [95% CI, 0.19-0.80]). Among the 40 of 319 patients (13%) aged 75 years or older in LL7, median PFS (HR, 0.69 [95% CI, 0.33-1.44]) favored afatinib, consistent with the overall population. Afatinib-associated AEs in older patients were consistent with the overall populations. Conclusions Subgroup analyses of the LL3, LL6, and LL7 trials show that afatinib is an effective and tolerable treatment for patients with EGFR m + NSCLC, independent of age.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".