PS2 - 203 Bone Marrow-Derived Mesenchymal Stem Cells Reverse Ionizing Radiation-Induced Active State of Endothelial Cells in Glioblastoma
Bibliographic record
Abstract
As the central component of heightened vascularization in glioblastoma (GBM), endothelial cells (EC) are arguably the most responsive stromal cells in the tumour microenvironment during conventional treatment using ionizing radiation (IR). Although the inherent tumor tropism and angiogenic property of mesenchymal stem cells (MSC) has been documented in many cancer types including GBM, little is known about the function of MSC that are recruited to the GBM tumor microenvironment. The purpose of this study was to elucidate the effect of MSC on irradiated EC. Here we studied the influence of IR, MSC or MSC condition media (CM) on human umbilical vein endothelial cells (HUVECs). IR was found to up-regulate mRNA levels of CXCL5, CXCL10, ICAM1, VCAM1, VEGF-c and tissue factor (TF) in a dose-dependent manner whereas MSC co-culture boosted the expression of ICAM1, VCAM1, VEGF-c, TF, and MCP3. An additive effect of IR and MSC-culture was most detected with respect to ICAM1, TF and CXCL5 under 2Gy. MSC co-culture decreased IR-induced phospho-p53 in HUVECs at 15Gy. In addition, IR decreased the level of phospho-Akt in HUVECs as well as cell proliferation. Notably, both effects were countered by MSC CM. Interestingly, up-regulation of the same set of IR-driven genes in GBM was positively correlated with poor survival in the TCGA GBM database, a correlation that was lost if using gene list that was obtained from IR and MSC combine. These findings suggest that MSC promotes angiogenesis in GBM by overturning the IR-induced active state of endothelial cells and rendering them radio-resistant.
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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 | 0.001 |
| 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".