PS2 - 217 Treatment of Glioblastoma Brain Tumor-Initiating Cells with Microglial and Macrophage-Derived Cytokines
Bibliographic record
Abstract
The most common brain cancer, glioblastoma (GBM), also has the worst five-year survival rates of all cancers. This is due in part to cancer stem cells, known as brain tumor initiating cells (BTICs), which initiate GBM and are resistant to current therapies. Thus, it is important to study therapeutics targeting BTICs. Innate immune cells, microglia and macrophages (MMs), are also important GBM-associated cells. GBM influences MMs to promote tumor growth. However, my research group discovered that an old drug, amphotericin B, re-stimulates MMs to secrete cytokines that inhibit BTIC self-renewal (Nature Neurosci 17:46-55, 2014). Hence, I hypothesize these cytokines are potential GBM treatments. Methods: Twenty genetically diverse BTIC lines were exposed to interleukin-8 (IL8), monocyte chemoattractant protein-1 (MCP1), and tumor necrosis factor-alpha (TNF), the cytokines secreted most abundantly by amphotericin-stimulated MMs. Assays for proliferation, differentiation, apoptosis, and cell cycle arrest were performed. BTICs were then co-cultured with human GBM-derived MMs stimulated with TNF. Lastly, the relationship between TNF receptors (TNFR1/2) and presumed BTICs was determined in frozen human GBM sections by immunofluorescence. Results: TNF had the most potent inhibitory effect on BTIC proliferation. Also, TNF was able to increase BTIC differentiation, and induce apoptosis and G1 cell cycle arrest. In MM-BTIC co-culture, TNF stimulated MMs to decrease sphere formation. Interestingly, in human GBM tissue TNFR1 co-labeled with OLIG2, a major transcription factor expressed in presumed BTICs, implicating TNF as a BTIC-specific treatment. Conclusion: TNF is a promising candidate for the treatment of 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) | 0.001 | 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".