The Effect of Bevacizumab on Human Malignant Melanoma Cells with Functional VEGF/VEGFR2 Autocrine and Intracrine Signaling Loops
Bibliographic record
Abstract
Receptors for the angiogenic factor VEGF are expressed by tumor cancer cells including melanoma, although their functionality remains unclear. Paired human melanoma cell lines WM115 and WM239 were used to investigate differences in expression and functionality of VEGF and VEGFR2 in vitro and in vivo with the anti-VEGF antibody bevacizumab. Both WM115 and WM239 cells expressed VEGF and VEGFR2, the levels of which were modulated by hypoxia. Detection of native and phosphorylated VEGFR2 in subcellular fractions under serum-free conditions showed the presence of a functional autocrine as well as intracrine VEGF/VEGFR2 signaling loops. Interestingly, treatment of WM115 and WM239 cells with increasing doses of bevacizumab (0-300 µg/ml) in vitro did not show any significant inhibition of VEGFR2 phosphorylation. Small-molecule tyrosine kinase inhibitor, sunitinib, caused an inhibition of VEGFR2 phosphorylation in WM239 but not in WM115 cells. An increase in cell proliferation was observed in WM115 cells treated with bevacizumab, whereas sunitinib inhibited proliferation. When xenografted to immune-deficient mice, we found bevacizumab to be an effective antiangiogenic but not antitumorigenic agent for both cell lines. Because bevacizumab is unable to neutralize murine VEGF, this supports a paracrine angiogenic response. We propose that the failure of bevacizumab to generate an antitumorigenic effect may be related to its generation of enhanced autocrine/intracrine signaling in the cancer cells themselves. Collectively, these results suggest that, for cancers with intracrine VEGF/ VEGFR2 signaling loops, small-molecule inhibitors of VEGFR2 may be more effective than neutralizing antibodies at disease control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".