NRG/RTOG 1122: A phase 2, double‐blinded, placebo‐controlled study of bevacizumab with and without trebananib in patients with recurrent glioblastoma or gliosarcoma
Bibliographic record
Abstract
BACKGROUND: Targeting vascular endothelial growth factor (VEGF) alone does not improve overall survival (OS) in recurrent glioblastoma (rGBM). The angiopoiein (Ang)-TIE2 system may play a role in tumor survival under VEGF inhibition. We conducted a phase 2, double-blinded, placebo-controlled trial of bevacizumab plus trebananib (a novel Fc fusion protein that sequesters Ang1/Ang2) over bevacizumab alone in rGBM. METHODS: Patients ≥18 years of age with a Karnofsky performance status ≥70 and GBM or variants in first or second relapse were randomized to bevacizumab 10 mg/kg every 2 weeks plus trebananib 15 mg/kg every week or bevacizumab plus placebo. The primary endpoint was 6-month progression-free survival (PFS). RESULTS: After an initial 6-patient lead-in cohort confirmed the safety of combining bevacizumab and trebananib, 115 eligible patients were randomized to the control (n = 58) or experimental treatment (n = 57). In the control arm, 6-month PFS was 41.1%, median survival time was 11.5 months (95% CI, 8.4-14.2 months), median PFS was 4.8 months (95% CI, 3.8-7.1 months), and radiographic response (RR) was 5.9%. In the experimental arm, 6-month PFS was 22.6%, median survival time was 7.5 months (95% CI, 6.8-10.1 months), median PFS was 4.2 months (95% CI, 3.7-5.6 months), and RR was 4.2%. The rate of severe toxicities was not significantly different between arms. CONCLUSION: The combination of bevacizumab and trebananib was well tolerated but did not significantly improve 6-month PFS rate, PFS, or OS for patients with rGBM over bevacizumab alone. The shorter PFS in the experimental arm with a hazard ratio of 1.51 (P = .04) suggests that the addition of trebananib to bevacizumab is detrimental.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".