Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers
Menée à partir d'échantillons sanguins prélevés sur 591 patientes avec ou sans lésion cancéreuse de l'ovaire ou masse annexielle bénigne, cette étude évalue la performance d'un modèle d'apprentissage automatique, utilisant les caractéristiques du fragmentome de l'ADN libre circulant ainsi que les concentrations sériques des protéines HE4 et CA-125, pour détecter précocement la maladie
Ovarian cancer is a leading cause of death for women worldwide in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I–IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I–IV. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC=0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.
Cancer Discovery , article en libre accès, 2023