Inhibition of Notch Signaling in Glioblastoma Targets Cancer Stem Cells via an Endothelial Cell Intermediate
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is a highly heterogeneous malignant tumor. Recent data suggests the presence of a hierarchical organization within the GBM cell population that involves cancer cells with stem-like behavior, capable of repopulating the tumor and contributing to its resistance to therapy. Tumor stem cells are thought to reside within a vascular niche that provides structural and functional support. However, most GBM studies involve isolated tumor cells grown under various culture conditions. Here, we use a novel three-dimensional organotypic "explant" system of surgical GBM specimens that preserves cytoarchitecture and tumor stroma along with tumor cells. Notch inhibition in explants results in decreased proliferation and self-renewal of tumor cells but is also associated with a decrease in endothelial cells. When endothelial cells are selectively eliminated from the explants via a toxin conjugate, we also observed a decrease in self-renewal of tumor stem cells. These findings support a critical role for tumor endothelial cells in GBM stem cell maintenance, mediated at least in part by Notch signaling. The explant system further highlighted differences in the response to radiation between explants and isolated tumor neurospheres. Combination treatment with Notch blockade and radiation resulted in a substantial decrease in proliferation and in self-renewal in tumor explants while radiation alone was less effective. This data suggests that the Notch pathway plays a critical role in linking angiogenesis and cancer stem cell self-renewal and is thus a potential therapeutic target. Three-dimensional explant systems provide a novel approach for the study of tumor and microenvironment interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".