Abstract GS1-01: Tucatinib vs placebo, both combined with capecitabine and trastuzumab, for patients with pretreated HER2-positive metastatic breast cancer with and without brain metastases (HER2CLIMB)
Bibliographic record
Abstract
Abstract Objectives: The primary analysis from the HER2CLIMB study will describe the efficacy and safety of tucatinib, trastuzumab, and capecitabine, a treatment regimen under investigation for patients (pts) with advanced HER2+ metastatic breast cancer (BC) refractory to standard-of-care regimens. Rationale: Fifteen to 20% of pts diagnosed with BC annually have overexpression or amplification of the HER2 receptor. While significant advances have been made in the treatment of pts with HER2+ BC, treatment of metastatic disease remains a clinical challenge for which no curative options are available. The management of HER2+ CNS metastases (which occur at any time during the disease course in 30-50% of those with HER2+ metastatic BC) remains an area of unmet clinical need. Tucatinib is an investigational, oral tyrosine kinase inhibitor (TKI) that is highly specific to HER2 with minimal inhibition of the EGFR receptor. In a Phase 1b study, tucatinib plus capecitabine and trastuzumab showed an acceptable toxicity profile and encouraging anti-tumour activity, including in pts with active brain metastases. Methodology: In this double-blind, international, multicenter study (NCT02614794), 612 pts with locally advanced or metastatic HER2+ BC previously treated with trastuzumab, pertuzumab, and T-DM1 were randomized 2:1 to receive tucatinib (300 mg BID) or placebo, in combination with capecitabine (1000 mg/m2 BID, Days 1–14 of each 21-day cycle) and trastuzumab (6 mg/kg once every 21 days). Pts with newly diagnosed, progressing, or stable brain metastases not requiring immediate local therapy were included. The primary endpoint is PFS per RECIST 1.1 by blinded independent central review for the first 480 pts enrolled. Secondary endpoints, including PFS in pts with brain metastases and OS, will be evaluated in all 612 pts. The primary and key secondary endpoints will be compared between treatment arms using a stratified log rank test; the hazard ratio from Cox regression model will also be reported. Anticipated Results: Baseline demographics and disease characteristics will be presented by treatment arms. PFS, response rates, and duration of response for pts receiving tucatinib vs placebo will be reported for the first 480 pts. Common AEs and SAEs will be reported for both treatment arms in all treated pts. Secondary objectives, including PFS in pts with brain metastases and OS, may be presented if the data are sufficiently mature. Citation Format: Rashmi Murthy, Sherene Loi, Alicia Okines, Elisavet Paplomata, Erika Hamilton, Sara Hurvitz, Nancy Lin, Virginia Borges, Vandana Gupta Abramson, Carey Anders, Philippe L Bedard, Mafalda Oliveira, Erik Jakobsen, Thomas Bachelot, Shlomit S Shachar, Volkmar Mueller, Sofia Braga, Francois P Duhoux, Richard Greil, David Cameron, Lisa Carey, Giuseppe Curigliano, Karen Gelmon, Gabriel Hortobagyi, Ian Krop, Sibylle Loibl, Mark Pegram, Dennis Slamon, Maria Corinna Palanca-Wessels, Luke Walker, Wentao Feng, Eric Winer. Tucatinib vs placebo, both combined with capecitabine and trastuzumab, for patients with pretreated HER2-positive metastatic breast cancer with and without brain metastases (HER2CLIMB) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS1-01.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".