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High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. (Gyllensten)ORCID iD: 0000-0002-5056-9137
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-3445-6551
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2019 (English)In: Communications biology, ISSN 2399-3642, Vol. 2, article id 221Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019. Vol. 2, article id 221
Keywords [en]
Diagnostic markers, Machine learning, Ovarian cancer
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:uu:diva-388611DOI: 10.1038/s42003-019-0464-9ISI: 000472439200002PubMedID: 31240259OAI: oai:DiVA.org:uu-388611DiVA, id: diva2:1334275
Funder
Swedish Foundation for Strategic Research Swedish Research CouncilSwedish Cancer SocietyVinnova
Note

These authors contributed equally: Stefan Enroth, Malin Berggrund

Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-08-07Bibliographically approved

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Enroth, StefanBerggrund, MalinOlovsson, MattsStålberg, KarinGyllensten, Ulf B.
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