Phase III, Double-Blind, Controlled Trial of Atamestane Plus Toremifene Compared With Letrozole in Postmenopausal Women With Advanced Receptor-Positive Breast Cancer
Bibliographic record
Abstract
PURPOSE: To compare time to progression (TTP) with a steroidal aromatase inhibitor (AI) atamestane (ATA) combined with toremifene (TOR; complete estrogen blockade) versus letrozole (LET) in receptor-positive advanced breast cancer (ABC). PATIENTS AND METHODS: Eligibility included postmenopausal receptor-positive ABC and adjuvant hormonal therapy completed more than 12 months prior to study entry. Participants received daily ATA 500 mg with TOR 60 mg (ATA + TOR), or letrozole 2.5 mg (LET). The primary end point was TTP, whereas secondary objectives included objective response (OR), overall survival (OS), and time to treatment failure (TTF). The study had 80% power to detect a 25% increase in TTP assuming a TTP of 9.4 months in the LET population. RESULTS: A total of 865 patients were randomly assigned (434 to ATA + TOR and 431 to LET) in 60 centers in the United States, Canada, Russia, and Ukraine. Baseline characteristics were balanced. Median TTP was identical in the two arms at 11.2 months (P < .92). Median TTF was similar at 9.24 months (ATA + TOR) versus 10.44 months (LET). The hazard ratios (LET/ATA + TOR) were 1.00 (95% CI, 0.92 to 1.08) for TTP, 0.99 (95% CI, 0.92 to 1.06) for TTF, and 0.98 (95% CI, 0.87 to 1.11) for OS. OR occurred in 30% of patients receiving ATA + TOR and in 36% of patients receiving LET (P < .1). Adverse events (AEs) were similar for patients receiving ATA + TOR versus LET, and serious AEs were 10% v 11%, respectively. CONCLUSION: TTP for patients receiving ATA + TOR was identical to that for patients receiving LET, representing the first endocrine therapy comparable to LET in ABC. Unlike in the Anastrozole, Tamoxifen, and Combined trial, addition of an antiestrogen did not decrease efficacy of the AI. Future studies of AIs in combination with more effective selective estrogen receptor modulators or selective receptor downregulators is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".