A Multicenter Phase II Clinical Trial of Lapatinib (GW572016) in Hormonally Untreated Advanced Prostate Cancer
Bibliographic record
Abstract
OBJECTIVES: Lapatinib (GW572016) is a selective and potent dual tyrosine kinase inhibitor of the epidermal growth factor 1 (EGFR) and 2 (HER2), approved in the treatment of HER2 positive breast cancer. Since EGFR and HER2 overexpression has also been seen in prostate cancer and appears to correlate with a worse clinical outcome, Lapatinib may represent a novel therapeutic strategy in prostate cancer. This Phase II multicenter clinical trial is the first to evaluate Lapatinib in early stage, hormonally untreated recurrent or metastatic prostate cancer. METHODS: Eligible patients received lapatinib 1500 mg PO daily until progression. The primary end point was prostate specific antigen (PSA) response. Archival tumor tissue was collected for EGFR and HER2 analysis. RESULTS: A total of 23 patients, median age 67, ECOG PS 0-2, mean baseline PSA 7.5, were evaluable for response. In total, 125 cycles were administered. The most frequent adverse events were lymphopenia, fatigue, rash, dyspepsia, and diarrhea. Grade 3+ increased alanine aminotransferase (ALT) was reported in 2 patients, and grade 4 blurry vision in 1 patient. No PSA responses were seen. Median time to progression (TTP) was 4.6 months and 6 months progression-free estimate was 44.5%. CONCLUSIONS: Lapatinib was well tolerated but like other EGFR- and HER2-targeted agents in advanced HRPC failed to show significant antitumor activity even in this very early stage hormonally untreated population.
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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) | 0.001 | 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".