DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.

Teodor G Calina; Elena Grafenhorst; Simon Schallenberg; Markus Möbs; Bjarne Daenekas; Alexandru A Sabo; BaoQing Chen; Ines Koch; Tobias Janik; Michael Churchman; Charlie Gourley; Elizabeth L Christie; Catalin Vasilescu; Vlad Herlea; Wolfgang D Schmitt; Nicolae Leopold; Florian Heitz; Angelina Vlaski; Yuri Tolkach; Felix Bremmer; Frederick Klauschen; Reinhard Büttner; Philipp Ströbel; Eike Burandt; Guido Sauter; Frank L Heppner; Eliane T Taube; Jalid Sehouli; Ioana E Braicu; Philipp Euskirchen; David Horst; Kylie L Gorringe; David Capper; Mihnea P Dragomir

Journal article

DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.

Teodor G Calina, Elena Grafenhorst, Simon Schallenberg, Markus Möbs, Bjarne Daenekas, Alexandru A Sabo, BaoQing Chen, Ines Koch, Tobias Janik, Michael Churchman, Charlie Gourley, Elizabeth L Christie, Catalin Vasilescu, Vlad Herlea, Wolfgang D Schmitt, Nicolae Leopold, Florian Heitz, Angelina Vlaski, Yuri Tolkach, Felix Bremmer Show all

Cell Rep Med | Elsevier BV | Published : 2026

DOI: 10.1016/j.xcrm.2026.102941

Open access

Abstract

Mucinous ovarian carcinoma (MOC) is an epithelial ovarian cancer subtype that is frequently misclassified as extraovarian mucinous metastasis (EOM) because of overlapping features. To address this diagnostic challenge, we perform genome-wide DNA methylation profiling of 58 MOCs, 38 EOMs, and 18 mucinous borderline ovarian tumors (mBOTs) collected from six institutions. Methylation analysis defines two mBOT groups, one epigenetically similar to normal ovary and one resembling MOC. Unsupervised clustering reveals two distinct MOC methylation subtypes with potential prognostic relevance in the internal cohort. Using these data together with 389 external profiles, we develop and validate a three..

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University of Melbourne Researchers

Elizabeth Christie Author

Kylie Gorringe Author

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Keywords

32 Biomedical and Clinical Sciences

Human Genome

Mucinous Ovarian Cancer

3211 Oncology and Carcinogenesis

Mucinous Borderline Ovarian Tumor

Machine Learning and Artificial Intelligence