CBIO-17. STAT3 IS A CENTRAL REGULATOR OF PROLIFERATION, INVASION, MIGRATION AND METABOLISM IN GBM TUMOR INITIATING CELLS
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most aggressive adult primary brain tumor. Currently, treatment for GBM involves surgical resection, chemotherapy, and radiation, resulting in a median survival of 15 months. This poor outcome highlights the need for improved therapeutic approaches to treat GBM patients. The signal transducer and activator of transcription 3 (STAT3) pathway is abnormally active in GBM, primarily in the mesenchymal subtype. STAT3 regulates various cellular processes including: proliferation, migration, invasion and resistance to therapy. The Weiss lab has established a large collection of brain tumor initiating cell (BTIC) lines derived from GBM patients. Here, we are focused on the role of STAT3 as a central regulator of the above cellular processes and of metabolism in GBM BTIC lines. We show that activation of the STAT3 pathway using exogenous cytokines leads to an increase in proliferation, invasion, and migration of BTICs. Using time-lapse imaging of 3D sphere invasion into a matrix, we observed that chemical inhibitors of JAK2 (an activator of STAT3) and of STAT3 decrease invasion of BTICs. Interestingly, our analysis of GBM patient sample gene expression data available from The Cancer Genome Atlas (TCGA) further demonstrates that a STAT3 gene expression signature correlates significantly with various metabolic pathways. We hypothesize that targeting the STAT3 pathway will decrease proliferation, invasion, and migration while simultaneously modulating BTIC metabolism. Initial work has shown a range of sensitivity to inhibitors of glycolysis and glutaminolysis in a genetically diverse set of 10 BTIC lines. Ongoing work is being performed to assess the effect of activating or inhibiting the STAT3 pathway to modulate the sensitivity of BTICs to these inhibitors. Overall, we have observed that STAT3 is a central hub in GBM BTICs that modulates proliferation, invasion, migration and metabolism. Currently, we are continuing in vivo experiments using BTIC orthotopic xenografts in mice.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".