The Complex Tumor Microenvironment in Ovarian Cancer: Therapeutic Challenges and Opportunities
Bibliographic record
Abstract
The tumor microenvironment (TME) in ovarian cancer (OC) has much greater complexity than previously understood. In response to aggressive pro-angiogenic stimulus, blood vessels form rapidly and are dysfunctional, resulting in poor perfusion, tissue hypoxia, and leakiness, which leads to increased interstitial fluid pressure (IFP). Decreased perfusion and high IFP significantly inhibit the uptake of therapies into the tumor. Within the TME, there are numerous inhibitor cells, such as myeloid-derived suppressor cells (MDSCs), tumor association macrophages (TAMs), regulatory T cells (Tregs), and cancer-associated fibroblasts (CAFs) that secrete high numbers of immunosuppressive cytokines. This immunosuppressive environment is thought to contribute to the lack of success of immunotherapies such as immune checkpoint inhibitor (ICI) treatment. This review discusses the components of the TME in OC, how these characteristics impede therapeutic efficacy, and some strategies to alleviate this inhibition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".