Abstract 2998: Akt-activated endothelium constitute the niche for residual disease and resistance to bevacizumab in ovarian cancer
Bibliographic record
Abstract
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 anti-angiogenic therapies inhibit new blood vessel growth, induce endothelial cell apoptosis, and block the incorporation of haematopoietic and endothelial progenitor cells into new blood vessels. Despite in vitro and in vivo successes antivascular therapy with bevacizumab targeting VEGF 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 (OCC) 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. Citation Format: Bella Samia Guerrouahen, Jennifer Pasquier, Nadine Abou Kaoud, Marie-Claude Beauchamp, Mahtab Maleki, Pegah Ghiabi, Raphael Lis, Ahmed Saleh, Walter H. Gotlieb, Shahin Rafii, Arash Rafii. Akt-activated endothelium constitute the niche for residual disease and resistance to bevacizumab in ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2998. doi:10.1158/1538-7445.AM2014-2998
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".