Exploring the Clinical Impact of Predictive Biomarkers in Serous Ovarian Carcinomas
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. Although initial response rates to standard platinum-based treatment are at 70-80%, long-term response in advanced EOC disease is rarely achieved with the development of chemoresistance and recurrence, contributing to overall survival rates below 45%. Additional challenges stem from EOC heterogeneity, reflecting at least five histological subtypes, each with different underlying molecular characteristics and clinicopathology that have significant implications in treatment effectiveness and management. Since the last decade, technologies in genomics, proteomics and pathology have been deployed to find reliable clinical markers that can identify patients sensitive to standard chemotherapy treatments and stratify patients for more suitable targeted therapies. These efforts have identified several molecular markers of prognostic value that have been validated as biomarkers, such as BRCA and KRAS mutations, or are currently under investigation in clinical trials, such as CD8 T cells, immune checkpoint inhibitors and progesterone receptor. Recent advancements in biomarker research have also revealed new targets that have expanded treatment options, introducing poly (ADP-ribose) polymerase (PARP) inhibitors, anti-angiogenic agents, inhibitors targeting signaling pathways, and immunotherapy to improve maintenance therapies or enhance first-line therapy. This review presents a summary of current biomarkers, in clinical use or under evaluation, demonstrating a potential to inform on patient selection for treatment efficacy and predict response to EOC therapies, with particular focus on the serous subtypes, including high-grade and low-grade serous carcinomas.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".