Abstract 5146: EGFL7 is a potent endogenous inhibitor of tumor angiogenesis
Bibliographic record
Abstract
Abstract Background: Tumor growth depends on establishment of new blood vessels through de novo angiogenesis, and blocking angiogenesis has proven to be an effective anti-cancer strategy. Epidermal growth factor-like 7 (EGFL7) is an endothelial-specific protein that is required for vascular tubulogenesis. Importantly, EGFL7 expression is increased during tumor growth, and recent evidence suggests that this may be due in part to the expression of EGFL7 by tumor cells. The precise function of EGFL7 in the endothelium and in the tumor microenvironment remains elusive, but we hypothesized that EGFL7 promotes the metastasis of HT1080 cells by modulating angiogenesis. To this end, EGFL7 was over-expressed in human fibrosarcoma HT1080 cells and its effect on angiogenesis, tumor growth and progression was assessed. Methods: Human fibrosarcoma HT1080 tumor cells were stably transfected with empty vector, EGFL7-GFP or EGFL7-myc. Cell proliferation was assessed by MTT assay. The effect of EGFL7 on tumor angiogenesis was assessed using HUVEC co-culture morphogenesis assays and a highly modified in vivo CAM angiogenesis assay. The effect of EGFL7 over-expression on tumor growth and metastasis was assessed using an avian embryo xenograft model system, whereby tumors were grown in the chorioallantoic membrane of shell-less chicken embryos. Metastasis was quantified using real-time PCR analysis to detect human alu sequences in distant organs including the brain, liver and lungs. Tumor vessel ultrastructure was examined by transmission electron microscopy and tumor vessel function was assessed using a real time vascular leak assay. Results: EGFL7 over-expression in HT1080 tumor cells did not affect their proliferation. HUVEC co-culture experiments demonstrated a significant decrease in branching morphogenesis (p<0.01). In vivo, EGFL7 over-expression significantly inhibited de novo angiogenesis in the CAM angiogenesis assay. Tumors over-expressing EGFL7 were significantly (p<0.01) smaller than controls. Additionally, we demonstrated that EGFL7 over-expression decreases spontaneous metastasis by more than 80% (p < 0.001). Transmission electron micrographs of the vasculature in EGFL7 over-expressing tumors revealed a disorganized endothelium with multiple layers of endothelial cells and compromised tight junctions. Vascular permeability assays indicated that this vasculature was significantly more permeable than controls, while structural integrity was maintained. Conclusion: Over-expression of EGFL7 in a metastatic fibrosarcoma reduced tumor growth and metastasis via a potent inhibition of angiogenesis. This was caused by a significant defect in the spatial organization of endothelium, which increased vascular permeability without affecting structural integrity. Our results identify EGFL7 as a potent endogenous inhibitor of angiogenesis, which may have implications for future therapeutic approaches. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5146. doi:10.1158/1538-7445.AM2011-5146
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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) | 0.000 | 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) | 0.006 | 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".