The Impact of Homologous Recombination Deficiency on First-line Adjuvant Chemotherapy and First-line PARPi Maintenance Therapy in Chinese Patients with Epithelial Ovarian Cancer
Bibliographic record
Abstract
Abstract Homologous recombination deficiency (HRD) testing has been approved by FDA for selecting epithelial ovarian cancer (EOC) patients who may benefit from the first-line poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy. However, the effects of HRD on the clinical outcomes of first-line chemotherapy and first-line PARPi maintenance therapy have not been rigorously evaluated in Chinese EOC patients. Here, we developed an HRD assay and applied it to two large Chinese EOC patient cohorts. In the first-line adjuvant chemotherapy cohort (FACT, N = 380), HRD status significantly improved PFS (median, 15.6 months vs. 9.4 months; HR, 0.688; 95% CI, 0.526 to 0.899; P = 0.003) and OS (median, 89.5 months vs. 60.9 months; HR, 0.636; 95% CI, 0.423 to 0.955; P = 0.008). In the first-line PARPi maintenance therapy cohort (FPMT, N = 83), HRD status significantly improved PFS (median, NA vs 12 months; HR, 0.438; 95% CI, 0.201 to 0.957; P = 0.033) and OS (median, NA vs NA months; HR, 0.12; 95% CI, 0.029 to 0.505; P = 0.001). Our results demonstrate that HRD status is a significant predictor for PFS and OS in both first-line chemotherapy and first-line PARPi maintenance therapy, providing strong real-world evidence for conducting genetic testing and improving clinical recommendations for Chinese EOC patients.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".