Sequencing of therapy following first-line afatinib in patients with EGFR mutation-positive non-small cell lung cancer
Bibliographic record
Abstract
OBJECTIVES: With the availability of several epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs), sequential therapy could potentially render EGFR mutation-positive non-small cell lung cancer a chronic disease in some patients. In this retrospective analysis of EGFR mutation-positive (Del19/L858R) patients receiving first-line afatinib in LUX-Lung 3, 6, and 7, we assessed uptake of, and outcomes following, subsequent therapies including the third-generation EGFR TKI, osimertinib. METHODS: Post-progression therapy data were prospectively collected during follow-up. Molecular testing of tumours at progression/discontinuation of afatinib was not mandatory. Duration of subsequent therapies, and survival following osimertinib, were calculated with Kaplan-Meier estimates. RESULTS: Among 553 patients who discontinued first-line afatinib, second-, third- and fourth-line therapy was administered in 394 (71%), 265 (48%), and 156 (28%) patients. The most common post-progression therapy was platinum-based chemotherapy (46%). Thirty-seven patients received subsequent osimertinib, 10 as second-line treatment. Median progression-free survival on afatinib in these 37 patients was 21.9 months. Median duration of osimertinib therapy was 20.2 months; median overall survival was not reached after a median follow-up of 4.7 years. CONCLUSIONS: Most patients treated with first-line afatinib received subsequent therapy. Although limited by sample size, enrichment, and a retrospective nature, data from patients who received sequential afatinib and osimertinib are encouraging, warranting further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".