TAMI-46. TNFα SECRETED BY GLIOMA ASSOCIATED MACROPHAGES PROMOTES ENDOTHELIAL ACTIVATION AND RESISTANCE AGAINST ANTI-ANGIOGENIC THERAPY
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) remains a universal fatal disease and improving survival remains a challenge. One of the most prominent features of GBM is hyper-vascularization, characterized by abnormally dilated, distorted, and leaky vessels. Bone marrow (BM)-derived macrophages are recognized to be an important host cell population that are actively recruited to the tumor and referred to as glioma-associated macrophages (GAMs). GAMs are found to associate closely with blood vessels, and thought to provide a critical role in tumor neo-vascularization. However, the mechanisms by which GAMs regulate and promote endothelial cells (ECs) in the process of tumor vascularization and response to anti-angiogenic therapy (AATx) is not understood. To understand how GBM, GAMs and EC interacts, we designed and developed a novel GBMs-GAMs-EC co-culture system as well as a unique in vivo model in which the BM of NOD/SCID mice were reconstituted with red fluorescent protein (RFP)-BM cells to create a chimeric mouse and then using intracranial injection of GFP-U87 cells to create a GBM xenograft. Our study is the first to demonstrate that glioma cells-secreted IL8 and CCL2 which stimulate GAMs to secrete TNFα and promote EC activation. TNFα inhibition led to normalization of the tumor vasculature and significant improvement in survival in glioma mice model. More importantly, we validate these findings using clinical data showing that TNFα is a predictor of overall survival and response to AATx in GBM patients. We further demonstrated that increased macrophage recruitment and upregulation of TNFα in GAMs activating EC in response to bevacizumab treatment is one of the critical molecular mechanisms underlying the failure of AATx in GBM. Collectively, these results provide compelling evidence to further explore the clinical impact of inhibiting TNFα concurrent with AATx.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".