Multi-modal liquid biopsy integrating cfDNA fragmentomics and serum biomarkers for the differential diagnosis of ovarian tumors.

Abstract

5573 Background: Ovarian cancer (OC) remains the leading cause of mortality among gynecological malignancies, primarily due to delayed diagnosis. Current diagnostic modalities, including imaging and serum biomarkers, exhibit suboptimal specificity, leading to unnecessary surgical interventions for benign adnexal masses. This study explores the clinical utility of cell-free DNA (cfDNA) fragmentomics, a high-dimensional genomic feature reflecting chromatin accessibility, in distinguishing malignant from benign ovarian tumors. Methods: This multi-center study enrolled 595 participants across three cohorts: a training set (n=236) and an internal validation set (n=172) from Yunnan Cancer Hospital, and an independent external validation set (n=136) from Beijing Obstetrics and Gynecology Hospital. Additionally, a prediction cohort included borderline ovarian tumors (BOT, n=51). Using whole-genome sequencing (WGS), we characterized cfDNA via four fragmentomic dimensions: copy number variations (CNVs), fragment size profiles (FSPs), nucleosome footprints (NFs), and genomic element perturbations (ARTEMIS). An ensemble model was trained to classify malignant ovarian cancer versus benign disease, and a generalized linear model was constructed by integrating fragmentomic scores with CA125 and HE4 levels to further improve the performance. Results: The cfDNA-only model demonstrated robust performance with Area Under the Curve (AUC) values of 0.937, 0.930, and 0.932 in the training, internal, and external cohorts, respectively. At 90% sensitivity in training (internal: 88.4%; external: 89.2%), specificities remained stable at 77.12%, 77.91%, and 78.87%. The model exhibited exceptional sensitivity for High-Grade Serous Ovarian Cancer (HGSOC) (>90%) with sensitivity increased with stages; early-stage (I–II) sensitivities were maintained ≥77%. Integrating the cfDNA fragmentomic score with CA125 and HE4 significantly enhanced AUCs to 0.961 (training), 0.964 (internal) and 0.964 (external). While maintaining high sensitivity (~90%), the combined model substantially increased specificity to 90.1% (training). Among 37 surgically treated stage I–III patients, higher pre-operative predictive scores were significantly associated with shorter disease-free survival (DFS) (p < 0.05), independent of age, stage, and histology. In BOT patients, 39.2% were classified as higher malignant-risk, consistent with an intermediate phenotype. Conclusions: The integration of cfDNA fragmentomics with traditional biomarkers provides a highly sensitive and specific non-invasive tool for the differential diagnosis of ovarian masses. This multi-modal approach significantly outperforms current clinical standards, offering a promising strategy for early detection and clinical risk stratification in ovarian cancer management.