OS09.7.A GBM AGILE PLATFORM TRIAL FOR NEWLY DIAGNOSED AND RECURRENT GBM: RESULTS OF FIRST EXPERIMENTAL ARM, REGORAFENIB
Bibliographic record
Abstract
Abstract BACKGROUND GBM AGILE (NCT03970447;https://www.gcaresearch.org/research/gbm-agile) is a phase 3 Bayesian adaptive platform trial that efficiently tests multiple arms against common control, with 6 arms included to date. Primary endpoint is overall survival (OS). Stage 1 experimental arms are adaptively randomized against other arms. Demonstrated efficacy in stage 1 leads to fixed randomization stage 2. Stages 1 and 2 are combined for registration. Control randomization is fixed. Regorafenib, a multikinase-inhibitor, entered into GBM AGILE as the first arm and therefore was equally randomized against control. Regorafenib showed OS benefit in recurrent disease (RD) in randomized phase 2 REGOMA trial. MATERIAL AND METHODS Patient subtypes in GBM AGILE are newly diagnosed unmethylated (NDU), RD, and—not considered for regorafenib—ND methylated (NDM). Arm indications (signatures) are combinations of subtypes. Control is temozolomide (ND) and lomustine (RD). Efficacy is assessed by OS hazard ratio(HR), arm/control. Efficacy is demonstrated when Bayesian probability of benefit (HR< 1.00) ≥ 98% (roughly analogous P-value: 0.02). Futility occurs at any monthly analysis when Bayesian predictive power (PP) is < 25% for all signatures. Follow-up continues for 12 months after arm’s accrual stops. RESULTS When PP for all 3 pre-defined signatures was < 25%, regorafenib’s accrual was stopped for futility. Regorafenib/control sample sizes were 49/51, 127/128, 176/179 for signatures NDU, RD, and both. Respective PPs: 0.138, 0.030, 0.025—none close to 0.25. Respective mean HRs: 1.26, 1.25, 1.23. Probabilities of benefit (HR< 1.00): 0.35, 0.18, 0.17. At final analysis, mean HRs were 1.07, 1.08, 1.08 with final probabilities of benefit (HR< 1.00) equal to 0.421, 0.312, 0.296—none close to 0.98. CONCLUSION GBM AGILE efficiently and compellingly addressed regorafenib’s role in GBM, in RD and NDU. These findings are germane as they fail to confirm the REGOMA results in RD. GBM AGILE continues to efficiently assess other therapies, including utilizing concurrent and previously accrued controls. This abstract was accepted and previously presented at the 2023 SNO Annual Meeting and published in Neuro-Oncology, Volume 25, Issue Supplement_5, November 2023, Pages v97-v98.
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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 | 0.000 | 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".