Predictive Relevance of Circulating miR-622 in Patients with Newly Diagnosed and Recurrent High-Grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
BACKGROUND: Identifying patients with high-grade serous ovarian cancer (HGSOC) who will respond to treatment remains a clinical challenge. We focused on miR-622, a miRNA involved in the homologous recombination repair (HRR) pathway, and we assessed its predictive value in serum prior to first-line chemotherapy and at relapse. METHODS: Serum miR-622 expression was assessed in serum prior to first-line platinum-based chemotherapy in a prospective multicenter study (miRNA Serum Analysis, miRSA, NCT01391351) and a retrospective cohort (Biological Resource Center, BRC), and was also studied at relapse. Progression-free survival (PFS) and overall survival (OS) were used as primary and secondary endpoints prior to first-line chemotherapy and OS as a primary endpoint at relapse. RESULTS: The group with high serum miR-622 expression was associated with a significantly lower PFS (15.4 versus 24.4 months; adjusted HR 2.11, 95% CI 1.2 3.8, P = 0.015) and OS (29.7 versus 40.6 months; adjusted HR 7.68, 95% CI 2.2-26.2, P = 0.0011) in the miRSA cohort. In the BRC cohort, a high expression of miR-622 was also associated with a significantly lower OS (22.8 versus 35.9 months; adjusted HR 1.98, 95% CI 1.1-3.6, P = 0.026). At relapse, high serum miR-622 was associated with a significantly lower OS (7.9 versus 20.6 months; adjusted HR 3.15, 95% CI 1.4-7.2, P = 0.0062). Serum miR-622 expression is a predictive independent biomarker of response to platinum-based chemotherapy for newly diagnosed and recurrent HGSOC. CONCLUSIONS: These results may open new perspectives for HGSOC patient stratification and monitoring of resistance to platinum-based and poly(ADP-ribose)-polymerase-inhibitor-maintenance therapies, facilitating better and personalized treatment decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".