Anti‐CD73 therapy impairs tumor angiogenesis
Bibliographic record
Abstract
CD73 is an ecto-nucleotidase overexpressed in various types of tumors that catabolizes the generation of extracellular adenosine, a potent immunosuppressor. We and others have shown that targeted blockade of CD73 can rescue anti-tumor T cells from the immunosuppressive effects of extracellular adenosine. Another important function of extracellular adenosine is to regulate adaptive responses to hypoxia. However, the importance of CD73 for tumor angiogenesis and the effect of anti-CD73 therapy on tumor angiogenesis remain unknown. In this study, we demonstrated that CD73 expression on tumor cells and host cells contribute to tumor angiogenesis. Our data revealed that tumor-derived CD73 enhances the production of vascular endothelial growth factor (VEGF) by tumor cells that host-derived CD73 is required for in vivo angiogenic responses and that endothelial cells require CD73 expression for tube formation and migration. Notably, the pro-angiogeneic effects of CD73 relied on both enzymatic and non-enzymatic functions. Using a mouse model of breast cancer, we demonstrated that targeted blockade of CD73 with a monoclonal antibody significantly decreased tumor VEGF levels and suppressed tumor angiogenesis in vivo. Taken together, our study strongly suggests that targeted blockade of CD73 can significantly block tumor angiogenesis, and further supports its clinical development for cancer treatment.
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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) | 0.002 | 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".