A phase II multicenter trial comparing two schedules of lapatinib (LAP) as first or second line monotherapy in subjects with advanced or metastatic non-small cell lung cancer (NSCLC) with either bronchioloalveolar carcinoma (BAC) or no smoking history
Bibliographic record
Abstract
7611 Background: LAP (GW572016) is an oral reversible, dual tyrosine kinase inhibitor of EGFR (ERBB1) and HER2/neu (ERBB2). This study was designed to test the activity of 2 dose schedules of LAP in chemotherapy naïve pts with NSCLC; it was amended to target patients with either BAC or no smoking history in the first or second line and to evaluate the relationship of mutations in target genes to responses. Methods: LAP was given orally 1,500 mg once (QD) or 500 mg twice daily (BID) until progression or intolerance. Safety and efficacy (RECIST) were assessed every 4 & 8 weeks. The primary endpoint was response. The target (BAC/no smoking) and non- target populations were assessed for efficacy, and tumor tissue was analyzed for ERBB1 and ERBB2 mutations and/or amplifications. Results: The study was stopped for futility after 131 pts were randomized (65 QD, 66 BID). Median age 66 (range 32–86); female 56%; BAC 20%, No BAC 71%; previously untreated 98.5%; current/former smokers 70%, never smoker 30%. There were no complete responses. Of 56 pts in the target population, 1 (2%) achieved partial response (PR), 11 (20%) had stable disease (SD) of ≥24 wks; in the non-target population, 1 pt had a PR (1.3%) and 12 (16%) had SD of ≥24 wks. 3 pts had ERBB1 mutations (G719S, S768I, KRAS G12S; L858R and T790M; L858R) but none of them responded. There were no ERBB2 mutations. Three of 77 pts evaluated had ERBB1 gene copy increase (none of whom responded) and 2 had ERBB2 gene copy increase (one had a 51% decrease in tumor size). The most common adverse events were grade 1/2 diarrhea, nausea, rash, vomiting and fatigue, and were similar in both groups. Conclusions: LAP was well-tolerated, with no notable difference in toxicity between the QD and BID groups. Very few responses were seen, stable disease was sometimes prolonged. The prevalence of mutations was low even in the target population. Given the preclinical synergy between LAP and other agents, further studies will be necessary to determine whether LAP is active in combination with other agents for the treatment of NSCLC. [Table: see text]
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".