Gefitinib or Placebo in Combination with Tamoxifen in Patients with Hormone Receptor–Positive Metastatic Breast Cancer: A Randomized Phase II Study
Bibliographic record
Abstract
PURPOSE: Increased growth factor signaling may contribute to tamoxifen resistance. This randomized phase II trial assessed tamoxifen plus placebo or the epidermal growth factor receptor inhibitor gefitinib in estrogen receptor (ER)-positive metastatic breast cancer. EXPERIMENTAL DESIGN: Patients with newly metastatic disease or recurred after adjuvant tamoxifen (stratum 1), or recurred during/after adjuvant aromatase inhibitor (AI) or after failed first-line AI (stratum 2), were eligible. Primary variables were progression-free survival (PFS; stratum 1) and clinical benefit rate (CBR; stratum 2). A 5% or more improvement in response variables with gefitinib was considered to warrant further investigation. Outcome was correlated with biomarkers measured on the primary tumor. RESULTS: In stratum 1 (n = 206), the PFS HR (gefitinib:placebo) was 0.84 (95% CI, 0.59-1.18; median PFS 10.9 versus 8.8 months). In the stratum 1 endocrine therapy-naïve subset (n = 158) the HR was 0.78 (95% CI, 0.52-1.15), and the prior endocrine-treated subgroup (n = 48) 1.47 (95% CI, 0.63-3.45). In stratum 1, CBRs were 50.5% with gefitinib and 45.5% with placebo. In stratum 2 (n = 84), CBRs were 29.2% with gefitinib and 31.4% with placebo. Biomarker analysis suggested that in stratum 1 there was greater benefit with gefitinib in patients who were ER-negative or had lower levels of ER protein. CONCLUSIONS: In stratum 1, the improved PFS with gefitinib plus tamoxifen met the protocol criteria to warrant further investigation of this strategy. In stratum 2, there was a numerical disadvantage for gefitinib; additional investigation after AI therapy is not warranted. Studies of predictive biomarkers are needed to subset appropriate patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".