Characterizing Endocrine Status, Tumor Hypoxia and Immunogenicity for Therapy Success in Epithelial Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer is predominantly diagnosed at advanced stages which creates significant therapeutic challenges. As a result, the 5-year survival rate is low. Within ovarian cancer, significant tumor heterogeneity exists, and the tumor microenvironment is diverse. Tumor heterogeneity leads to diversity in therapy response within the tumor, which can lead to resistance or recurrence. Advancements in therapy development and tumor profiling have initiated a shift from a "one-size-fits-all" approach towards precision patient-based therapies. Here, we review aspects of ovarian tumor heterogeneity that facilitate tumorigenesis and contribute to treatment failure. These tumor characteristics should be considered when designing novel therapies or characterizing mechanisms of treatment resistance. Individual patients vary considerably in terms of age, fertility and contraceptive use which innately affects the endocrine milieu in the ovary. Similarly, individual tumors differ significantly in their immune profile, which can impact the efficacy of immunotherapies. Tumor size, presence of malignant ascites and vascular density further alters the tumor microenvironment, creating areas of significant hypoxia that is notorious for increasing tumorigenesis, resistance to standard of care therapies and promoting stemness and metastases. We further expand on strategies aimed at improving oxygenation status in tumors to dampen downstream effects of hypoxia and set the stage for better response to therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".