Mechanisms underlying chemotherapy-induced vascular proliferation in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer is a leading cause of gynecological cancer-related death in Canadian women. Ovarian cancer is managed through surgical cytoreduction and carboplatin-based chemotherapy. Unfortunately, most patients often relapse or have reduced responses to initial chemotherapy. The mechanisms behind carboplatin resistance are poorly understood. In pilot studies, our group has observed vascular proliferation in patient samples following carboplatin treatment. The effectiveness of modulating neovascularization in combination with carboplatin has also been demonstrated in two large Phase 3 trials. In this study, I explore the underlying mechanisms of chemotherapy-induced vascular proliferation and potentially, tumour cell survival. I hypothesize that carboplatin induces angiogenic factors in ovarian cancer cells leading to microvascular endothelial cell survival.\nTo test my hypothesis, I screened for a variety of angiogenic factors in ovarian cancer cells and vascular endothelial cells following exposure to carboplatin. My results show that a number of angiogenic genes are upregulated in response to carboplatin exposure, including placental growth factor (PGF). Preclinical studies have shown that inhibition of PGF prevents tumour growth and metastasis. Therefore, I tested the effect of PGF and condition media prepared from ovarian cancer cells following carboplatin challenge on endothelial cell survival. My results show that PGF and ovarian cancer condition media facilitates endothelial cell survival. I also found that carboplatin may induce PGF expression in ovarian cancer cells through β-catenin activation.\nFindings from this study may help better understand the effects of carboplatin exposure on ovarian cancer. Furthermore, the results may provide additional targets to increase carboplatin sensitivity in ovarian cancer patients.
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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) | 0.002 | 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".