Sunitinib Plus Erlotinib Versus Placebo Plus Erlotinib in Patients With Previously Treated Advanced Non–Small-Cell Lung Cancer: A Phase III Trial
Bibliographic record
Abstract
PURPOSE: Sunitinib plus erlotinib may enhance antitumor activity compared with either agent alone in non-small-cell lung cancer (NSCLC), based on the importance of the signaling pathways involved in tumor growth, angiogenesis, and metastasis. This phase III trial investigated overall survival (OS) for sunitinib plus erlotinib versus placebo plus erlotinib in patients with refractory NSCLC. PATIENTS AND METHODS: Patients previously treated with one to two chemotherapy regimens (including one platinum-based regimen) for recurrent NSCLC, and for whom erlotinib was indicated, were randomly assigned (1:1) to sunitinib 37.5 mg/d plus erlotinib 150 mg/d or to placebo plus erlotinib 150 mg/d, stratified by prior bevacizumab use, smoking history, and epidermal growth factor receptor expression. The primary end point was OS. Key secondary end points included progression-free survival (PFS), objective response rate (ORR), and safety. RESULTS: In all, 960 patients were randomly assigned, and baseline characteristics were balanced. Median OS was 9.0 months for sunitinib plus erlotinib versus 8.5 months for erlotinib alone (hazard ratio [HR], 0.922; 95% CI, 0.797 to 1.067; one-sided stratified log-rank P = .1388). Median PFS was 3.6 months versus 2.0 months (HR, 0.807; 95% CI, 0.695 to 0.937; one-sided stratified log-rank P = .0023), and ORR was 10.6% versus 6.9% (two-sided stratified log-rank P = .0471), respectively. Treatment-related toxicities of grade 3 or higher, including rash/dermatitis, diarrhea, and asthenia/fatigue were more frequent in the sunitinib plus erlotinib arm. CONCLUSION: In patients with refractory NSCLC, sunitinib plus erlotinib did not improve OS compared with erlotinib alone, but the combination was associated with a statistically significantly longer PFS and greater ORR. The incidence of grade 3 or higher toxicities was greater with combination therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".