Results from AMBER, a randomized phase 2 study of bevacizumab and bortezomib versus bortezomib in relapsed or refractory multiple myeloma
Bibliographic record
Abstract
BACKGROUND: Newer systemic therapies have significantly advanced the treatment of multiple myeloma, but additional agents are needed. Bortezomib is a proteasome inhibitor with efficacy in relapsed/refractory multiple myeloma that inhibits tumor angiogenesis, a process that has been implicated in multiple myeloma pathogenesis. METHODS: In AMBER("A Randomized, Blinded, Placebo-Controlled, Multicenter, Phase II Study of Bevacizumab in Combination With Bortezomib in Patients With Relapsed or Refractory Multiple Myeloma"), patients with relapsed or refractory multiple myeloma were randomized to receive bortezomib (1.3 mg/m(2) on days 1, 4, 8, and 11 of each 21-day cycle) and either placebo or bevacizumab (15 mg/kg on day 1 of each cycle) for up to 8 cycles. At completion, patients in the bortezomib-plus-bevacizumab arm could continue bevacizumab until they developed progressive disease or unacceptable toxicity. The primary endpoint was progression-free survival (PFS). RESULTS: The stratified hazard ratio of PFS for the bevacizumab-containing arm (n = 49) relative to the bortezomib monotherapy arm (n = 53) was 0.743 (95% confidence interval [CI], 0.43-1.28; P = .2804); the median PFS was 6.2 months (95% CI, 4.4-8.5 months) and 5.1 months (95% CI, 4.2-7.2 months), respectively; the overall response rates were 51% and 43.4% (P = .4029), respectively; and the median response duration was 6.9 months (95% CI, 4.73-11.83 months) and 6.0 months (95% CI, 4.86-8.31 months), respectively. Frequent adverse events occurred at similar rates across treatment arms, but hypertension, fatigue, and neuralgia occurred more frequently in the bevacizumab-containing arm. CONCLUSIONS: The addition of bevacizumab to bortezomib in unselected patients with pretreated multiple myeloma did not result in significant improvements in efficacy outcomes. The combination was well tolerated, and no new safety concerns for either agent were identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".