Journal Article

DZNE-2026-00904

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.

Calina, T. G. ; Grafenhorst, E. ; Schallenberg, S. ; Möbs, M. ; Daenekas, B. ; Sabo, A. A. ; Chen, B. ; Koch, I. ; Janik, T. ; Churchman, M. ; Gourley, C. ; Christie, E. L. ; Vasilescu, C. ; Herlea, V. ; Schmitt, W. D. ; Leopold, N. ; Heitz, F. ; Vlaski, A. ; Tolkach, Y. ; Bremmer, F. ; Klauschen, F. ; Büttner, R. ; Ströbel, P. ; Burandt, E. ; Sauter, G. ; Heppner, F. L.DZNE* ; Taube, E. T. ; Sehouli, J. ; Braicu, I. E. ; Euskirchen, P. ; Horst, D. ; Gorringe, K. L. ; Capper, D. ; Dragomir, M. P.

2026
Cell Press Cambridge, MA

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Please use a persistent id in citations: doi:10.1016/j.xcrm.2026.102941

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-step machine-learning classifier that distinguishes MOC from EOM with 95.5% accuracy. External validation of this classifier on 21 MOCs and 24 EOMs yields an accuracy of 91.11% for differentiating MOC from EOM. These findings establish an epigenetic framework for mucinous ovarian tumors and provide a robust clinical classification tool.

Keyword(s): Humans (MeSH) ; Female (MeSH) ; DNA Methylation: genetics (MeSH) ; Ovarian Neoplasms: genetics (MeSH) ; Ovarian Neoplasms: pathology (MeSH) ; Ovarian Neoplasms: classification (MeSH) ; Ovarian Neoplasms: diagnosis (MeSH) ; Adenocarcinoma, Mucinous: genetics (MeSH) ; Adenocarcinoma, Mucinous: pathology (MeSH) ; Adenocarcinoma, Mucinous: classification (MeSH) ; Adenocarcinoma, Mucinous: diagnosis (MeSH) ; Machine Learning (MeSH) ; Neoplasm Metastasis (MeSH) ; Epigenesis, Genetic (MeSH) ; Middle Aged (MeSH) ; DNA methylation ; epigenetics ; machine learning ; metastasis ; molecular subtypes ; mucinous borderline ovarian tumor ; mucinous ovarian cancer

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Contributing Institute(s):

1. Neuroimmunology (AG Heppner)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

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