Erlotinib therapy after initial platinum doublet therapy in patients with EGFR wild type non-small cell lung cancer: results of a combined patient-level analysis of the NCIC CTG BR.21 and SATURN trials.
Bibliographic record
Abstract
BACKGROUND: The clinical benefit of erlotinib in treating epidermal growth factor receptor (EGFR) wildtype non-small cell lung cancer (NSCLC) has been questioned. We examined the impact of erlotinib in confirmed EGFR wildtype patients in two placebo-controlled phase III trials: the National Cancer Institute of Canada Clinical Trials Group BR.21 (BR.21) and Sequential Tarceva in Unresectable Non-Small Cell Lung Cancer (SATURN) trials. METHODS: Combined re-analysis of progression-free survival (PFS) and overall survival (OS) in patients with known wildtype EGFR, estimated by Kaplan-Meier curves and compared by two-sided log-rank test. Cox proportional hazards model was used to estimate hazard ratios (HR) adjusted for potential confounders. Additional analyses assessed comparability of patients with known and unknown EGFR mutation status to determine generalizability of the two study populations. RESULTS: Mutation status was known in 25% (n=184 of 731) of the BR.21, and 49% (n=437 of 889) of the SATURN populations, of which 82% (n=150) and 89% (n=388) respectively had wildtype EGFR. HR for PFS was 0.71 (95% CI, 0.59-0.85; P<0.01) and for OS was 0.72 (95% CI, 0.59-0.88; P<0.01). Baseline characteristics and outcome (PFS and OS) distributions were similar for patients with known and unknown EGFR status, suggesting generalizability of the EGFR wildtype data. Erlotinib benefit was sustained in all clinical subsets. CONCLUSIONS: Erlotinib provided a consistent and significant improvement in survival for patients with EGFR wildtype NSCLC in both studies, individually and in combination. The benefit of erlotinib does not appear to be limited to patients with activating mutations of EGFR.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".