Circumventing vascular barriers for effective immunotherapy in brain tumors – focus on glioblastoma
Bibliographic record
Abstract
Abstract Blood vessels play a fundamental and unique role in brain tumor pathogenesis, including by mediating interactions with the peripheral immune system. Despite this intimate connection, endogenous immune surveillance and multiple modalities of immunotherapy have thus far exerted relatively little impact on disease progression and patient survival in high-grade brain tumors, such as glioblastoma (GBM). This applies to both adults and children, where complex vascular processes have emerged as possible actionable targets beyond anti-angiogenesis. Indeed, vascular responses in GBM include angiogenic, non-angiogenic (cooption, vasectasia), and angiocrine interactions mediated by soluble factors and extracellular vesicles (EVs). It is still poorly understood why immune cells are excluded from the GBM tumor microenvironment and what barriers may operate at the immune-vascular interface which could be modified to improve immunotherapy outcomes. The emerging research directions include efforts to overcome the immune cell exclusion, defining molecular hallmarks of treatment susceptibility in subsets of patients, assessing different immune effectors, and rational temporal scheduling of immunotherapy administration relative to the effects of cytoreductive treatments. It is suggested that experimental insights into the interplay between vascular and immune cell compartments may serve as hypothesis-generating material for future clinical studies in 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 | 0.001 | 0.001 |
| 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".