ACTR-19. BASELINE PLASMA MATRIX METALLOPROTEINASE 9 (MMP9) PREDICTS OVERALL SURVIVAL (OS) BENEFIT FROM BEVACIZUMAB INDEPENDENTLY OF MOLECULAR SUBTYPES IN NEWLY DIAGNOSED GLIOBLASTOMA: RETROSPECTIVE ANALYSIS OF AVAglio
Bibliographic record
Abstract
Addition of bevacizumab to radiotherapy/temozolomide (RT/TMZ) improved progression-free survival (PFS) (vs placebo+RT/TMZ) but not OS in newly diagnosed glioblastoma (AVAglio/NCT00943826). However, a retrospective analysis of AVAglio suggested that patients with proneural glioblastoma derived an OS benefit from first-line bevacizumab. In recurrent glioblastoma, MMP9 and MMP2 plasma levels may predict bevacizumab activity. We retrospectively analyzed AVAglio data to examine whether MMP9/MMP2 baseline plasma levels predicted OS benefit from bevacizumab in newly diagnosed glioblastoma and potential relationships between MMP and glioblastoma molecular subtypes. MMP9 and MMP2 levels were assessed (ELISA; R&D Systems) in baseline plasma samples from 577/921 patients (AVAglio: placebo n=294/bevacizumab n=283). Molecular subtypes were analyzed by gene expression profiling (placebo n=167/bevacizumab n=164). Post-hoc analysis and PFS/OS multivariate models, including MMP9/2-treatment interactions, were performed. MMP9 distribution at baseline was comparable between arms (median: placebo 82.4ng/mL [range 5–3454]; bevacizumab 83.6ng/mL [range 10–3070]). Patient characteristics were generally balanced between subgroups (per quartile [Q]), including for known prognostic factors. Patients with low MMP9 (
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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) | 0.000 | 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) | 0.001 | 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 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".