TM-08 * DIFFERENTIAL VASCULAR PATTERN OF GLIOMA STEM CELLS
Bibliographic record
Abstract
Treatment of the glioblastoma (GBM) tumors with anti-angiogenic therapies has not significantly improved patients’ survival. The lack of clear response is partly due to the heterogeneous nature of GBMs. Such heterogeneity is evident in different molecular subtypes, in tumor microenvironment including hypoxia and tumor metabolic profiles and also at the level of tumor vascularity, such as microvascular density (MVD) and permeability. While heterogeneity in vasculature results in heterogeneous metabolic profile of the tumor cells, no data is available explaining the effect of metabolic status on tumor vasculature. Deciphering the underlying mechanism of tumor vascular heterogeneity is needed, as it can improve design of targeted therapies. We aimed to establish the differential pattern of tumor vascularity in GBM xenografts of different glioma stem cells (GSCs). Furthermore, we investigated whether GSC tumor vasculature is regulated by changes in glucose metabolism. GSCs were isolated from operative GBM samples and their in-vitro angiogenic profile established using angiogenic arrays and western blot analysis. Glucose metabolism was altered by generating HK2 knockdown of each GSC line and used to generate intracranial xenografts. Tumor growth parameters, overall survival and the in-vivo vascular properties were determined using MRI characteristics and histological analysis. Our results showed that survival rate of the mice and tumor growth pattern varied between different GSCs. MVD and expression of angiogenic factors were different amongst different GSCs and in their matched xenografts. Furthermore, reduced glucose metabolism resulted in reduced MVD and increased animal survival, in GSCs with high MVD. GSC cell lines demonstrate significant variability in their vascular profile. Our data suggests that inhibition of glycolysis may be effective in GBMs with high MVD or perhaps regions of the tumor with highest vascular profile. Effective therapeutic strategies would require individual tumor vascular and metabolic status to be taken into account, prior to any therapeutic intervention.
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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 | 0.001 |
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".