Plasmatic MMP9 released from tumor-infiltrating neutrophils is predictive for bevacizumab efficacy in glioblastoma patients: an AVAglio ancillary study
Bibliographic record
Abstract
We previously identified matrix metalloproteinase 2 (MMP2) and MMP9 plasma levels as candidate biomarkers of bevacizumab activity in patients with recurrent glioblastoma. The aim of this study was to assess the predictive value of MMP2 and MMP9 in a randomized phase III trial in patients with newly diagnosed glioblastoma and to explore their tumor source. In this post hoc analysis of the AVAglio trial (AVAGlio/NCT00943826), plasma samples from 577 patients (bevacizumab, n = 283; placebo, n = 294) were analyzed for plasma MMP9 and MMP2 levels by enzyme-linked immunosorbent assay. A prospective local cohort of 38 patients with newly diagnosed glioblastoma was developed for analysis of tumor characteristics by magnetic resonance imaging and measurement of plasma and tumor levels of MMP9 and MMP2. In this AVAglio study, MMP9, but not MMP2, was correlated with bevacizumab efficacy. Patients with low MMP9 derived a significant 5.2-month overall survival (OS) benefit with bevacizumab (HR 0.51, 95% CI 0.34-0.76, p = 0.0009; median 13.6 vs. 18.8 months). In multivariate analysis, a significant interaction was seen between treatment and MMP9 (p = 0.03) for OS. In the local cohort, we showed that preoperative MMP9 plasma levels decreased after tumor resection and were correlated with tumor levels of MMP9 mRNA (p = 0.03). However, plasma MMP9 was not correlated with tumor size, invasive pattern, or angiogenesis. Using immunohistochemistry, we showed that MMP9 was expressed by inflammatory cells but not by tumor cells. After cell sorting, we showed that MMP9 was expressed by CD45+ immune cells. Finally, using flow cytometry, we showed that MMP9 was expressed by tumor-infiltrating neutrophils. In conclusion, circulating MMP9 is predictive of bevacizumab efficacy and is released by tumor-infiltrating neutrophils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".