Inhibition of VEGF induces cellular senescence in colorectal cancer cells
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) inhibitors, such as bevacizumab, have improved outcomes in metastatic colorectal cancer (CRC). Recent studies have suggested that VEGF can delay the onset of cellular senescence in human endothelial cells. As VEGF receptors are known to be upregulated in CRC, we hypothesized that VEGF inhibition may directly influence cellular senescence in this disease. In our study, we observed that treatment with bevacizumab caused a significant increase (p < 0.05) in cellular senescence in vitro in several CRC cells, such as MIP101, RKO, SW620 and SW480 cells, compared to untreated or human IgG-treated control cells. Similar results were also obtained from cells treated with a VEGFR2 kinase inhibitor Ki8751. In vivo, cellular senescence was detected in MIP101 tumor xenografts from 75% of mice treated with bevacizumab, while cellular senescence was undetectable in xenografts from mice treated with saline or human IgG (p < 0.05). Interestingly, we also observed that the proportion of senescent cells in colon cancer tissues obtained from patients treated with bevacizumab was 4.4-fold higher (p < 0.01) than those of untreated patients. To understand how VEGF inhibitors may regulate cellular senescence, we noted that among the two important regulators of senescent growth arrest of tumor cells, bevacizumab-associated increase in cellular senescence coincided with an upregulation of p16 but appeared to be independent of p53. siRNA silencing of p16 gene in MIP101 cells suppressed bevacizumab-induced cellular senescence, while silencing of p53 had no effect. These findings demonstrate a novel antitumor activity of VEGF inhibitors in CRC, involving p16.
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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) | 0.001 | 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".