HALT MBC: HER2 suppression with the addition of lapatinib to trastuzumab in HER2-positive metastatic breast cancer (LPT112515).
Bibliographic record
Abstract
TPS658 Background: Dual blockade of HER2 with the combination of trastuzumab (T) and lapatinib (L) enhances antitumor activity in HER2-positive breast cancer (BC) preclinical models due to the complementary mechanisms of action of the 2 agents. In patients (pts) with T-treated HER2-positive metastatic BC (MBC), treatment with the combination was associated with longer progression-free (PFS) and overall survival (OS) compared with L alone. In pts with stage II/III BC, preoperative treatment with the combination plus paclitaxel (P) resulted in significantly higher pathologic complete response rates compared with P combined with either agent alone. This evidence supports the concept of dual HER2 blockade as a treatment strategy for HER2-positive MBC. The present study is designed to evaluate whether the addition of L improves PFS among women with HER2-positive MBC receiving T as maintenance therapy. Methods: In this open-label, Phase III study, 280 pts will be stratified by line of treatment (first/second) and hormone receptor status (positive/negative), then randomized 1:1 to receive maintenance treatment with either L (1000 mg qd, continuously) in combination with T (6 mg/kg once every 3 weeks [q3w]) or T (6 mg/kg q3w) alone. Pts will receive study treatment until disease progression, death, discontinuation due to adverse events, or other reasons. The primary endpoint is PFS; secondary endpoints are OS, clinical benefit rate, and safety. Key eligibility criteria include pts with HER2-positive MBC who have completed 12 to 24 weeks of first- or second-line treatment with T plus chemotherapy with an objective response or stable disease at time of chemotherapy discontinuation. Pts with stable brain metastases are eligible if entering the study on second-line treatment. Efficacy endpoints will be analyzed in the ITT population. A total of 193 PFS events is required to detect a 50% increase in median PFS (hazard ratio=0.67) for L plus T compared with T alone (median PFS 27 vs 18 weeks, respectively). The hypothesis will be tested using a 1-sided test with 80% power and a type I error of 0.025. The trial is currently open for accrual in the United States and Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".