Postprogression Outcomes for Osimertinib versus Standard-of-Care EGFR-TKI in Patients with Previously Untreated EGFR-mutated Advanced Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract Purpose: In the phase III FLAURA study, third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) osimertinib significantly improved progression-free survival (PFS) versus standard-of-care (SoC) EGFR-TKI (gefitinib or erlotinib) in patients with previously untreated EGFR (exon 19 deletion or L858R) mutation-positive advanced non–small cell lung cancer (NSCLC). Interim overall survival (OS) data were encouraging, but not formally statistically significant at current maturity (25%). Here we report exploratory postprogression outcomes. Patients and Methods: Patients were randomized 1:1 to receive osimertinib (80 mg orally, once daily) or SoC EGFR-TKI (gefitinib 250 mg or erlotinib 150 mg, orally, once daily). Treatment beyond disease progression was allowed if the investigator judged ongoing clinical benefit. Patients receiving SoC EGFR-TKI could cross over to receive osimertinib after independently confirmed objective disease progression with documented postprogression T790M-positive mutation status. Results: At data cutoff (June 12, 2017), 138 of 279 (49%) and 213 of 277 (77%) patients discontinued osimertinib and SoC EGFR-TKI, respectively, of whom 82 (59%) and 129 (61%), respectively, started a subsequent treatment. Median time to discontinuation of any EGFR-TKI or death was 23.0 months [95% confidence interval (CI), 19.5–not calculable (NC)] in the osimertinib arm and 16.0 months (95% CI, 14.8–18.6) in the SoC EGFR-TKI arm. Median second PFS was not reached (95% CI, 23.7–NC) in the osimertinib arm and 20.0 months (95% CI, 18.2–NC) in the SoC EGFR-TKI arm [hazard ratio (HR), 0.58; 95% CI, 0.44–0.78; P = 0.0004]. Conclusions: All postprogression endpoints showed consistent improvement with osimertinib versus SoC EGFR-TKI, providing further confidence in the interim OS data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".