A phase II biomarker assessment of tivozanib in oncology (BATON) trial in patients (pts) with advanced renal cell carcinoma (RCC).
Bibliographic record
Abstract
TPS4686 Background: Tivozanib is an oral, potent, and selective tyrosine kinase inhibitor targeting all three vascular endothelial growth factor (VEGF) receptors; it showed efficacy and tolerability in a Phase II trial in pts with advanced RCC (Nosov et al. JCO 2011;29[18S]:4550). Preclinical studies have identified a 42-gene tivozanib resistance signature (Jie et al. EORTC-NCI-AACR 2010; abstract 608) that may be predictive of tivozanib sensitivity. This study (NCT01297244) is designed to evaluate these potential biomarkers for tivozanib activity in pts with advanced RCC. Methods: Pts with advanced RCC (stratified as clear cell or non-clear cell) who underwent nephrectomy and received ≤1 prior systemic treatment (no prior VEGF- or mammalian target of rapamycin–targeted therapy) entered this open-label, single-arm study conducted in the United States and Canada beginning January 2011. Pts receive tivozanib 1.5 mg/d orally (3 weeks on, 1 week off schedule). Primary objectives include correlation of biomarkers in blood (e.g. VEGF, hepatocyte growth factor [HGF]) and tumor tissue (e.g. CD68, hypoxia-induced factor, VEGF, HGF, and gene expression profiles) with clinical activity and/or treatment-related toxicity, and 6-month progression-free survival (PFS) rate. Secondary objectives include overall response rate (ORR), PFS, safety and tolerability, and pharmacokinetics. Contingency table methods will be used for biomarker correlation with ORR, and Cox proportional-hazards regression for correlation with PFS. Sample size (100 pts) is estimated based on clinical considerations and the precision with which 6-month PFS can be estimated. Biomarkers were defined at study outset; the requirement of prior nephrectomy ensured availability of primary tumors as controls for correlative analysis. As of January 2012, the study completed enrollment of 100 pts, demonstrating a large number of pts can be enrolled to a biomarker study with well-defined candidate genes and critical inclusion criteria to facilitate compliance. Tivozanib biomarkers evaluated in this study, along with those evaluated in other solid tumors, may play an important role in optimizing patient selection.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".