Akt-Activated Endothelium Constitutes the Niche for Residual Disease and Resistance to Bevacizumab in Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer is the second leading cause of cancer-related death in women worldwide. Despite optimal cytoreduction and adequate adjuvant therapies, initial tumor response is often followed by relapse suggesting the existence of a tumor niche. Targeted therapies have been evaluated in ovarian cancer to overcome resistant disease. Among them, antiangiogenic therapies inhibit new blood vessel growth, induce endothelial cell apoptosis, and block the incorporation of hematopoietic and endothelial progenitor cells into new blood vessels. Despite in vitro and in vivo successes, antivascular therapy with bevacizumab targeting VEGF-A has limited efficacy in ovarian cancer. The precise molecular mechanisms underlying clinical resistance to anti-VEGF therapies are not yet well understood. Among them, tumor and stromal heterogeneity might determine the treatment outcomes. The present study investigates whether abnormalities in the tumor endothelium may contribute to treatment resistance to bevacizumab and promote a residual microscopic disease. Here, we showed that ovarian cancer cells activate Akt phosphorylation in endothelial cells inducing resistance to bevacizumab leading to an autocrine loop based on FGF2 secretion. Altogether, our results point out the role of an activated endothelium in the resistance to bevacizumab and in the constitution of a niche for a residual disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".