53. TUCATINIB VS PLACEBO ADDED TO TRASTUZUMAB AND CAPECITABINE FOR PATIENTS WITH PREVIOUSLY TREATED HER2+ METASTATIC BREAST CANCER (MBC) WITH BRAIN METASTASES (BM) (HER2CLIMB)
Bibliographic record
Abstract
Abstract BACKGROUND HER2CLIMB (NCT02614794) primary results have been reported previously (Murthy, NEJM 2019). We report results of exploratory efficacy analyses in pts with brain metastases (BM). METHODS All HER2+ MBC pts enrolled had a baseline brain MRI. Pts with BM were eligible and randomized 2:1 to receive tucatinib (TUC) or placebo, in combination with trastuzumab and capecitabine. Efficacy analyses were performed by applying RECIST 1.1 to the brain based on investigator evaluation. CNS-PFS and OS were evaluated in BM pts overall. Intracranial (IC) confirmed ORR-IC and DOR-IC were evaluated in BM pts with measurable IC disease. After isolated brain progression, pts could continue study therapy until second progression, and time from randomization to second progression or death was evaluated. RESULTS Overall, 291 pts (48%) had BM at baseline: 198 (48%) in the TUC arm and 93 (46%) in the control arm. There was a 68% reduction in risk of CNS-PFS in the TUC arm (HR: 0.32; P<0.0001). Median CNS-PFS was 9.9 mo in the TUC arm vs 4.2 mo in the control arm. Risk of overall death was reduced by 42% in the TUC arm (OS HR: 0.58; P=0.005). Median OS was 18.1 mo vs 12.0 mo. ORR-IC was higher in the TUC arm (47.3%) vs the control arm (20.0%). Median DOR-IC was 6.8 mo vs 3.0 mo. In pts with isolated brain progression who continued study therapy after local treatment (n=30), risk of second progression or death was reduced by 71% (HR: 0.29), and median time from randomization to second progression or death was 15.9 mo vs 9.7 mo, favoring the TUC arm. CONCLUSIONS In pts with previously treated HER2+ MBC with BM, TUC in combination with trastuzumab and capecitabine doubled the ORR-IC, reduced risk of IC progression or death by two-thirds and reduced risk of death by nearly half.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".