MGMT modulates glioblastoma angiogenesis and response to the tyrosine kinase inhibitor sunitinib
Bibliographic record
Abstract
Angiogenesis inhibitors, such as sunitinib, represent a promising strategy to improve glioblastoma (GBM) tumor response. In this study, we used the O(6)-methylguanine methyltransferase (MGMT)-negative GBM cell line U87MG stably transfected with MGMT (U87/MGMT) to assess whether MGMT expression affects the response to sunitinib. We showed that the addition of sunitinib to standard therapy (temozolomide [TMZ] and radiation therapy [RT]) significantly improved the response of MGMT-positive but not of MGMT-negative cells. Gene expression profiling revealed alterations in the angiogenic profile, as well as differential expression of several receptor tyrosine kinases targeted by sunitinib. MGMT-positive cells displayed higher levels of vascular endothelial growth factor receptor 1 (VEGFR-1) compared with U87/EV cells, whereas they displayed decreased levels of VEGFR-2. Depleting MGMT using O(6)-benzylguanine suggested that the expression of these receptors was directly related to the MGMT status. Also, we showed that MGMT expression was associated with a dramatic increase in the soluble VEGFR-1/VEGFA ratio, thereby suggesting a decrease in bioactive VEGFA and a shift towards an antiangiogenic profile. The reduced angiogenic potential of MGMT-positive cells is supported by: (i) the decreased ability of their secreted factors to induce endothelial tube formation in vitro and (ii) their low tumorigenicity in vivo compared with the MGMT-negative cells. Our study is the first to show a direct link between MGMT expression and decreased angiogenicity and tumorigenicity of GBM cells and suggests the combination of sunitinib and standard therapy as an alternative strategy for GBM patients with MGMT-positive tumors.
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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 | 0.000 | 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) | 0.001 | 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".