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 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".