State of the Biomarker Science in Ovarian Cancer: A National Cancer Institute Clinical Trials Planning Meeting Report
Bibliographic record
Abstract
PURPOSE: Despite therapeutic advances in the treatment of ovarian cancer (OC), 5-year survival remains low, and patients eventually die from recurrent, chemotherapy-resistant disease. The National Cancer Gynecologic Cancer Steering Committee identified the integration of scientifically defined subgroups as a top strategic priority in clinical trial planning. METHODS: A group of experts was convened to review the scientific literature in OC to identify validated predictive biomarkers that could inform patient selection and treatment stratification. Here, we report on these findings and their potential for use in future clinical trial design on the basis of hierarchal evidence grading. RESULTS: mutations and homologous recombination deficiency to predict poly (ADP-ribose) polymerase inhibitor response are supported in OC by the highest level of evidence. Additional biomarkers of response to agents targeting the pathways above have been identified but require prospective validation. CONCLUSION: Although a number of biomarkers of response to various agents in OC have been described in the literature, high-level evidence for the majority is lacking. This report highlights the unmet need for identification and validation of predictive biomarkers to guide therapy and future trial design in OC.
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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 | 0.024 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".