STMC-31. STIMULATION OF MICROGLIA AND MACROPHAGES AND GROWTH ATTENUATION OF BRAIN TUMOR-INITIATING CELLS WITH TUMOR NECROSIS FACTOR-ALPHA
Bibliographic record
Abstract
Microglia and macrophages (M/Ms) are functionally plastic entities that are compelled by glioblastoma (GBM) to adopt anti-inflammatory phenotypes and become major players in GBM progression. Understanding how to reverse this compulsion and maintain a pro-inflammatory tumor microenvironment is critical to developing effective therapeutics for GBM. Our studies have uncovered that GBM-associated M/Ms (GAM/Ms) can be pharmacologically compelled to shed the influence of GBM and secrete inhibitory factors that decrease the proliferation of GBM stem cell (GSC) lines and xenografts (Nature Neurosci 17:46-55, 2014). GSCs are a cellular reservoir that support GBM treatment resistance so it is important to development therapeutics that target this population. Our recent studies show that the most potent M/M-secreted factor behind GSC inhibition is tumor necrosis factor-alpha (TNF). We found that TNF decreases GSC proliferation and self-renewal through cytotoxic effects as well as G1 cell cycle arrest. TNF also induces differentiation in molecularly diverse GSCs. Additionally, we found that TNF can compel freshly-isolated human GAM/Ms to adopt a pro-inflammatory phenotype and inhibit GSCs in co-culture. The TNF receptors, TNFR1/2, are differentially expressed on GSCs and M/Ms. TNFR1, associated with apoptosis, is expressed by GSCs, while TNFR2, associated with survival mechanisms, is expressed on GAM/Ms. Moreover, TNFR1 on GSCs co-labels with OLIG2, one of the most specific markers of stemness in GBM, supporting the notion that TNF can target GSCs. Normal brain expresses low to non-existent levels of TNFR1, lending further support to this notion. Given the lack of studies investigating the effect of TNF on GSCs and the immunomodulatory effects TNF can exert on GAM/Ms, we feel it is a promising strategy to harness the effects of this powerful pro-inflammatory cytokine against GBM.
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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) | 0.004 | 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".