Tumors resurrect an embryonic vascular program to escape immunity
Bibliographic record
Abstract
Tumors can escape immunity through multiple mechanisms, one of which is by enforcing a state of unresponsiveness of the tumor vasculature to inflammatory cytokines. This results in a lack of adhesiveness of angiogenic endothelial cells for immune cells and thus compromised immunity. This type of escape from immunity, called tumor endothelial cell anergy, is the result of exposure to angiogenic growth factors. Angiogenesis is a hallmark not only of cancer but also of embryonic development. It is assumed that angiogenesis-induced suppression of adhesion molecules is a regulatory function to provide an embryo with immune privileged conditions and allow uninterrupted growth and development. It is becoming clear that similar conditions are used by tumors to evade the immune system and ensure progressive growth. Gaining enhanced insight into these immune-privileged conditions is important as endothelial cell anergy can be overcome by angiogenesis inhibitors, an application that is rapidly emerging as a successful strategy to improve immunotherapy. The literature on endothelial adhesion molecule expression and leukocyte-vessel wall interactions during embryonic and fetal development is sparse, but available data allow the hypothesis that tumors, through angiogenesis, enforce an embryonic-like gene expression program in endothelial cells to suppress leukocyte infiltration and compromise antitumor immunity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".