GENE-38. DISRUPTED ENDOTHELIAL CELL GENE EXPRESSION PROFILE PREDICTS GBM CLINICAL RESPONSE TO ANTI-ANGIOGENIC THERAPY
Bibliographic record
Abstract
Glioblastoma (GBM) is one of the most aggressive adult brain tumors, with the histopathologic hallmark of increased abnormal vasculature. While anti-angiogenesis therapy had shown value in pre-clinical and early clinical studies, in particular targeting vascular endothelial growth factor (VEGF), the durability of response to anti-angiogenic therapy varies and hence efficacy remains a limitation. There is data to suggest that certain subtypes of GBM however have a more effective response to anti-angiogenic therapy. Therefore, there is a need to identify prognostic markers that can determine the subpopulation of GBM patients that response to AA therapy. We established five genetic expression profiles (group A~E) by stimulating endothelial cells (ECs) with different combinations of ionizing radiation (IR) and mesenchymal stem cells (MSCs) before performing angiogenesis array analysis. Bioinformatics analysis of Group A~E identified the combination of HGF and CXCL10 alterations as the differentiators that separated the AVAglio patients into 3 groups (group 1~3). We found that GBM patients with high HGF and low CXCL10 levels had the worst clinical response to AA therapy. Further qPCR analysis of gene expression levels among different cell types revealed that GBM cells have the highest expression of HGF and the lowest expression of CXCL10 relative to ECs. Similar trend were detected in MSCs relative to ECs but to a lesser degree. Thus we propose that such genetic parameter in GBM could potentially be a prognostic marker for AA therapy.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".