Discovery and preliminary validation of a new panel of personalized ovarian cancer biomarkers for individualized detection of recurrence
Bibliographic record
Abstract
Background: Following first-line treatment, over 80% of advanced ovarian cancer cases suffer recurrence. Treatment of patients with recurrence based on CA125 has not resulted in improvements in outcome postulating that we need biomarkers for earlier detection. A tumor-specific array of serum proteins with advanced proteomic methods could identify personalized marker signatures that detect relapse at a point where early intervention may improve outcome. Methods: For our discovery phase, we employed the proximity extension assay (PEA) to simultaneously measure 1,104 proteins in 120 longitudinal serum samples (30 ovarian cancer patients). For our validation phase, we used PEAs to concurrently measure 644 proteins (including 21 previously identified candidates, plus CA125 and HE4) in 234 independent, longitudinal serum samples (39 ovarian cancer patients). Results: We discovered 23 candidate personalized markers (plus CA125 and HE4), in which personalized combinations were informative of recurrence in 92% of patients. In our validation study, 21 candidates were each informative of recurrence in 3-35% of patients. Patient-centric analysis of 644 proteins generated a refined panel of 33 personalized tumor markers (included 18 validated candidates). The panel offered 91% sensitivity for identifying individualized marker combinations that were informative of recurrence. Conclusion: Tracking individualized combinations of tumor markers may offer high sensitivity for detecting recurrence early and aid in prompt clinical referral to imaging and treatment interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".