Bibliographic record
Abstract
BACKGROUND: Uveal melanoma (UM) is a disease that affects approximately five people per million in the United States. This disease metastasises predominantly to the liver, and treatment options following the clinical detection of these sequelae are limited. Vascular endothelial growth factor-A (VEGF-A) is the primary activator of tumour angiogenesis and functions by binding to VEGF-Receptor 2 (VEGF-R2) and is often required for tumour growth beyond 2-3 mm. The purpose of this study was to investigate the expression of VEGF-A and the primary VEGF-R2 in three UM cell lines. Furthermore, we investigated the effects of VEGF-A inhibition on receptor activation and production of other cytokines. Finally, the effects of VEGF-A inhibition on the proliferation, migration, and invasion in the cell lines were ascertained. MATERIALS: Three UM cell lines (92.1, OCM-1, and UW-1) were incubated with and without the addition of 100 μg/mL of bevacizumab. VEGF-A expression under both conditions was determined by sandwich enzyme-linked immunosorbent assay (ELISA), and phosphorylated VEGF-R2 expression was determined using western blot. The effects of VEGF-A inhibition on 20 cytokines (IL-1a, IL-2, IL-5, IL-8, IL-12p70, GM-CSF, IFNy, CCL3, MMP-9, TNF-a, IL-1b, IL-4, IL-6, IL-10, IL-13, GRO, MCP-1, MIP-1b, and RANTES) were determined using a multiplex sandwich ELISA. Proliferation rates before and after treatment were evaluated via sulforhodamine B assay, and migration and invasion assays implementing the Boyden chamber technique, the latter with artificial extracellular matrix, were used to assess their respective abilities. The Student's t-test was used to compare changes in cytokine expression following VEGF-A inhibition. Analysis of variance was used to compare changes in the functional abilities of three uveal melanoma cell lines following VEGF-A inhibition. A P-value < 0.05 was considered statistically significant. RESULTS: All three cell lines produced copious amounts of VEGF-A in culture (92.1, 11785.5 ± 231.8 pg/μL; OCM-1, 4608.0 ± 324.0 pg/μL; UW-1, 8309.3 ± 634.5 pg/μL), which was reduced to undetectable levels following the administration of bevacizumab (P< 0.05). Similarly, detectable phosphorylated VEGF-R2 was present in all cells, which was reduced significantly in all cell lines following bevacizumab treatment (107525.2 ± 8602.0 versus 1024.5 ± 98.2, 46587.3 ± 4192.9 versus 12821.1 ± 1666.7, and 60394.3 ± 4026.4 versus 6908.2 ± 607.2; 92.1, OCM-1, and UW-1, respectively; P< 0.05). Of the cytokines investigated, only MMP-9 and CCL3 were ubiquitously altered across all three cell lines following bevacizumab treatment; they were upregulated (CCL3: 1072.50 ± 18.77 pg/mL versus 1281.00 ± 72.34 pg/mL; 22.5 ± 7.85 pg/mL versus 62.00 ± 9.16 pg/mL; 20.33 ± 6.35 pg/mL versus 35.00 ± 6.22 pg/mL; control versus bevacizumab; MMP-9: 25.50 ± 5.47 pg/mL versus 88.25 ± 13.38 pg/mL; 19.75 ± 4.14 pg/mL versus 45.25 ± 8.36 pg/mL; 3.25 ± 1.09 pg/mL versus 19.25 ± 3.77 pg/mL; control versus bevacizumab; 92.1, OCM-1, and UW-1, respectively; P< 0.05). Bevacizumab significantly reduced the proliferation of one cell line (92.1: 0.405 ± 0.012 versus 0.509 ± 0.033; bevacizumab versus control; values OD; P< 0.05), the migration of two cell lines (92.1: 0.071 ± 0.003 versus 0.115 ± 0.003; OCM-1: 0.049 ± 0.005 versus 0.117 ± 0.014; bevacizumab versus control; values OD; P< 0.05), and did not significantly affect invasion. CONCLUSION: Despite the significant reduction in phosphorylated VEGF-R2 levels, bevacizumab did not have a dramatic impact on the functional abilities of the three UM cell lines studied. Our results indicate that compensatory mechanisms, such as the upregulation of MMP-9 and CCL-3, following bevacizumab administration may mitigate its effects on these abilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.779 | 0.680 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".