Communications Biology · 2019 · 121 citations · 35 references
MedicineGynecologyOncologyEarly DetectionGynecologic OncologyProteomicsCancer BiomarkersCancer ProteomicsOvarian CancerProteomic BiomarkersProteomic ProfilingTumor MarkersBiomarker Signature AnalysisBiomarker Signature DiscoveryNovel Circulating BiomarkersOvarian Cancer BiomarkersOvarian Cancer DetectionOvarian Cancer Early DetectionOvarian Tumor BiomarkersProtein Biomarker Screening
Ovarian cancer is usually detected at a late stage, and the overall 5‑year survival is only 30‑40%. The study aims to identify novel biomarkers for early detection and improved diagnosis of ovarian cancer. Using proximity extension assay, the authors profiled 593 plasma proteins in three cohorts, applied a combinatorial strategy to select an 11‑protein signature plus age, and developed it into a multiplex PEA test reporting absolute concentrations. In an independent cohort, the signature achieved an AUC of 0.94, PPV of 0.92, sensitivity of 0.85, and specificity of 0.93 for detecting ovarian cancer stages I‑IV, demonstrating high accuracy for diagnostic and screening use.
Ovarian cancer is usually detected at a late stage and the overall 5-year survival is 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 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I-IV. 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.
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pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin, Natacha Turck, Alexandre Hainard et al. · BMC Bioinformatics · 2011 · 14K citations · Full text