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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".