TNFα secreted by glioma associated macrophages promotes endothelial activation and resistance against anti-angiogenic therapy
Bibliographic record
Abstract
One of the most prominent features of glioblastoma (GBM) is hyper-vascularization. Bone marrow-derived macrophages are actively recruited to the tumor and referred to as glioma-associated macrophages (GAMs) which are thought to provide a critical role in tumor neo-vascularization. However, the mechanisms by which GAMs regulate endothelial cells (ECs) in the process of tumor vascularization and response to anti-angiogenic therapy (AATx) is not well-understood. Here we show that GBM cells secrete IL-8 and CCL2 which stimulate GAMs to produce TNFα. Subsequently, TNFα induces a distinct gene expression signature of activated ECs including VCAM-1, ICAM-1, CXCL5, and CXCL10. Inhibition of TNFα blocks GAM-induced EC activation both in vitro and in vivo and improve survival in mouse glioma models. Importantly we show that high TNFα expression predicts worse response to Bevacizumab in GBM patients. We further demonstrated in mouse model that treatment with B20.4.1.1, the mouse analog of Bevacizumab, increased macrophage recruitment to the tumor area and correlated with upregulated TNFα expression in GAMs and increased EC activation, which may be responsible for the failure of AATx in GBMs. These results suggest TNFα is a novel therapeutic that may reverse resistance to AATx. Future clinical studies should be aimed at inhibiting TNFα as a concurrent therapy in GBMs.
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 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 | 0.000 |
| 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".