Southwest Oncology Group study S0413: a phase II trial of lapatinib (GW572016) as first-line therapy in patients with advanced or metastatic gastric cancer
Bibliographic record
Abstract
BACKGROUND: Lapatinib (GW572016) is a dual tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2/ErbB2), which are reported as overexpressed in 15%-45% of gastric cancers, making them potential targets. PATIENTS AND METHODS: The primary objective of this study was to assess response rate. Secondary objectives included overall survival (OS), toxicity, and the relationship of EGFR, ErbB2, and markers of angiogenesis with clinical outcome. Lapatinib was administered to chemonaive metastatic gastric cancer patients at a dose of 1500 mg orally daily for 28 days. RESULTS: The study enrolled 47 patients from February 2005 until May 2006. Four patients (9%) had a confirmed partial response (PR), 1 (2%) had an unconfirmed PR, and 10 (23%) had stable disease. Median (95% confidence interval) time to treatment failure was 1.9 (1.6-3.1) months and OS was 4.8 (3.2-7.4) months. Significant adverse events: one grade 4 cardiac ischemia/infarction, one grade 4 fatigue, and one grade 4 emesis. One treatment-related death was due to central nervous system ischemia. An exploratory analysis of markers revealed gene expression of HER2, interleukin (IL)-8 and genomic polymorphisms IL-8, and vascular endothelial growth factor correlated with OS. CONCLUSIONS: Lapatinib is well tolerated, with modest single-agent activity in advanced/metastatic gastric cancer patients. Potential molecular correlatives were identified which warrant further validation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".