The response to brain tumor-derived growth factors is altered in radioresistant human brain endothelial cells
Bibliographic record
Abstract
INTRODUCTION: Radioresistant brain tumor vasculature is thought to hamper the efficiency of adjuvant cancer therapies. However, little is known regarding the signalling pathways involved in the angiogenic response to brain tumor-derived growth factors in irradiated human brain microvascular endothelial cells (HBMEC). The goal of this study is to assess the effect of ionizing radiation (IR) on HBMEC survival, migration and tubulogenesis. METHODS: HBMEC were cultured and irradiated at sublethal single doses. Cell survival was assessed by nuclear cell counting and flow cytometry. HBMEC migration in response to brain tumor-derived growth factors (U-87 GF) and tubulogenesis were assayed using modified Boyden chambers and Matrigel, respectively. RESULTS: We observed that single administration of 3-10 Gy IR doses only reduced cell survival by 30%. Radioresistant HBMEC overexpressed RhoA, a small GTPase protein regulating cellular adhesion and migration, and Rho-kinase (ROK), a serine-threonine protein kinase and one of RhoA's major targets. HBMEC migration was induced by vascular endothelial growth factor (VEGF), but even more so in response to sphingo-sine-1-phosphate (S1P) and to U-87 GF. Following IR exposure, HBMEC basal migration increased more than two-fold, whereas the response to S1P and to U-87 GF was significantly diminished. Similarly, the inhibitor of ROK Y-27632 decreased HBMEC migration in response to S1P and U-87 GF. Overexpression of RhoA decreased tubulogenesis, an effect also observed in irradiated HBMEC. CONCLUSION: Our results suggest that radioresistant HBMEC migration response to tumor-secreted growth factors and tubulogenesis are altered following IR. The RhoA/ROK signalling pathway is involved in the IR-altered angiogenic functions and may represent a potential molecular target for enhancing the impact of radiotherapy on tumor-associated endothelial cells.
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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 | 0.000 |
| 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) | 0.000 | 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".