A Phase II Study of Pazopanib in Patients with Recurrent or Metastatic Invasive Breast Carcinoma: A Trial of the Princess Margaret Hospital Phase II Consortium
Bibliographic record
Abstract
PURPOSE: Angiogenesis is an important hallmark of breast cancer growth and progression. Pazopanib, an oral small molecule inhibitor of vascular endothelial growth factor receptor, platelet-derived growth factor receptor, and KIT, has activity across a range of solid tumors. We evaluated the activity of single-agent pazopanib in recurrent or metastatic breast cancer (MBC). PATIENTS AND METHODS: Patients with recurrent breast cancer or MBC, treated with up to two prior lines of chemotherapy, were eligible to receive pazopanib, 800 mg daily until progression. The primary endpoint was the objective response rate as measured by Response Evaluation Criteria in Solid Tumors. Secondary endpoints included time to progression, the stable disease rate, and toxicity. Using a two-stage design, confirmed response in three of 18 patients was required to proceed to stage 2. RESULTS: Twenty evaluable patients were treated, with a median age of 56 years; 70% were estrogen receptor positive, all were human epidermal growth factor receptor 2 negative. The majority had one or two prior lines of chemotherapy. One patient (5%) had a partial response, 11 (55%) had stable disease (SD) [four (20%) with SD > or = 6 months], and seven (35%) had progressive disease as their best response. One (5%) was not evaluable. The median time to progression was 5.3 months. Pazopanib did not cause significant severe toxicity aside from grade 3-4 transaminitis, hypertension, and neutropenia in three patients each (14% each) and grade 3 gastrointestinal hemorrhage in one patient (5%). CONCLUSION: Pazopanib provides disease stability in advanced breast cancer. The activity seen is comparable with that of other antiangiogenic agents in this setting. Pazopanib may be of interest for future studies in breast cancer, including in combination with other systemic agents.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".