Activation of NOTCH Signaling by Tenascin-C Promotes Growth of Human Brain Tumor-Initiating Cells
Bibliographic record
Abstract
Abstract Oncogenic signaling by NOTCH is elevated in brain tumor-initiating cells (BTIC) in malignant glioma, but the mechanism of its activation is unknown. Here we provide evidence that tenascin-C (TNC), an extracellular matrix protein prominent in malignant glioma, increases NOTCH activity in BTIC to promote their growth. We demonstrate the proximal localization of TNC and BTIC in human glioblastoma specimens and in orthotopic murine xenografts of human BTIC implanted intracranially. In tissue culture, TNC was superior amongst several extracellular matrix proteins in enhancing the sphere-forming capacity of glioma patient-derived BTIC. Exogenously applied or autocrine TNC increased BTIC growth through an α2β1 integrin-mediated mechanism that elevated NOTCH ligand Jagged1 (JAG1). Microarray analyses and confirmatory PCR and Western analyses in BTIC determined that NOTCH signaling components including JAG1, ADAMTS15, and NICD1/2 were elevated in BITC after TNC exposure. Inhibition of γ-secretase and metalloproteinase proteolysis in the NOTCH pathway, or silencing of α2β1 integrin or JAG1, reduced the proliferative effect of TNC on BTIC. Collectively, our findings identified TNC as a pivotal initiator of elevated NOTCH signaling in BTIC and define the establishment of a TN-α2β1-JAG1-NOTCH signaling axis as a candidate therapeutic target in glioma patients. Cancer Res; 77(12); 3231–43. ©2017 AACR.
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 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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".