High throughput proteomics identifies 484 high-accuracy plasma protein biomarker signatures for ovarian cancer

Abstract

Abstract Ovarian cancer is usually detected at a late stage with the 5-year survival at only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 981 proteins in patients with ovarian cancer and benign tumours, using the proximity extension assay. A novel combinatorial strategy was developed for identification of multivariate biomarker signatures, resulting in 484 mutually exclusive models out of which 448 did not contain the present biomarker MUCIN-16. The top-ranking model consisted of 14 proteins and had a AUC=0.95, PPV=1.0, sensitivity=0.99 and specificity=1.0 for detection of stage III-IV ovarian cancer in the discovery data, and an AUC=0.89, PPV=0.93, sensitivity=0.89 and specificity=0.95 in the replication data. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.