Abstract #4284: Blockade of VEGFR2/KDR increases malignancy in human epithelial ovarian carcinoma
Bibliographic record
Abstract
Human epithelial ovarian carcinoma (EOC) is the most lethal neoplasm affecting the female genital tract. Vascular Endothelial Growth Factor (VEGF) and its tyrosine kinase receptor KDR (VEGFR2) are overexpressed in these lesions. We previously demonstrated that this signaling loop provides a survival pathway for human EOC growth in suspension, similar to what would occur in ascites fluid that accumulates in the peritoneal cavity of the patient. Chemoresistance is a major problem for successful therapy of this cancer, however, some progress has recently been made using bevacizumab (Avastin; a monoclonal antibody against VEGF) in adjuvant settings. We therefore explored whether a more complete blockade of VEGF signaling would be an effective strategy for cancer control by knocking down KDR expression in chemoresistant OVCAR-3 EOC cells using siRNA. Cells with KDR knockdown demonstrated more aggressive subcutaneous growth in vivo. In addition, when cells lacking KDR were implanted into the peritoneal cavity of immune deficient mice, these cells enhanced the accumulation of ascites characterized by higher VEGF levels. The cells lacking KDR showed increased Neuropilin-1 expression and decreased expression of some adhesion proteins, notable cadherins and integrins. Evaluation of 80 clinical cases of EOC for NRP-1 versus KDR expression showed a significantly higher NRP-1:KDR ratio with cancer progression. Our findings reveal additional complexity of interaction between VEGF pathway molecules in ovarian cancer, and demonstrate the potential limitations of applying specific molecular techniques in a therapeutic setting. Citation Information: In: Proc Am Assoc Cancer Res; 2009 Apr 18-22; Denver, CO. Philadelphia (PA): AACR; 2009. Abstract nr 4284.
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 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) | 0.015 | 0.003 |
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".