Revisiting the Complexity of the Ovarian Cancer Microenvironment—Clinical Implications for Treatment Strategies
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the leading cause of death among gynecological malignancies in North American women. Given that EOC encompasses a broad class of tumors consisting of a variety of different histologic and molecular subtypes, which generates genetically and etiologically distinct tumors, several challenges arise during treatment of patients with this disease. Overlaying this complexity is the contribution of supporting cells, particularly stromal components such as fibroblasts and immune infiltrates that collectively create a microenvironment that promotes and enhances cancer progression. A notable example is the induction of angiogenesis, which occurs through the secretion of pro-angiogenic factors by both tumor and tumor-associated cells. The recent development of angiogenic inhibitors targeting tumor vasculature, which have been shown to improve patient outcome when combined with standard therapy, has launched a paradigm shift on how cancer patients should be treated. It is evident that future clinical practices will focus on the incorporation of therapies that antagonize the protumoral effects of such microenvironment contributors. Herein, an overview of the varying tumor-host interactions that influence tumor behavior will be discussed, in addition to the recent efforts undertaken to target these interactions and their potential to revolutionize EOC patient care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".