Revisiting the Role of Antiandrogen Strategies in Ovarian Cancer
Bibliographic record
Abstract
Abstract Learning Objectives After completing this course, the reader will be able to: Explain the role of the androgen axis in the development of ovarian cancer.Discuss the potential compounds with anti-androgen activity that can be assessed for the treatment of patients with ovarian cancer. CME This article is available for continuing medical education credit at CME.TheOncologist.com Androgen receptors are frequently expressed in epithelial ovarian cancer (EOC). Their role in the development of EOC is not fully understood. In the present review we first discuss the epidemiological data linking a hyperandrogen state to a higher risk for ovarian cancer, second describe in vitro studies of the role of androgens in influencing the growth of EOC, and finally review the completed clinical trials with compounds that exploit the androgen axis in patients with ovarian cancer. The therapeutic approaches that inhibit androgen signaling have so far produced only modest response rates. In the light of new data regarding the role of androgen stimulation in the evolution of EOC and the emergence of new compounds used for the treatment of other hormone-driven malignancies, such as prostate and breast cancer, we provide suggestions for new studies of antiandrogen therapeutics in the treatment of EOC. A specific example is the new agent abiraterone. In addition, we propose a panel of molecules that could be assessed as potential biomarkers that may aid patient selection for this approach in the future.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".