DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification.

Autor:	Jurmeister P; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: Philipp.jurmeister@med.uni-muenchen.de., Leitheiser M; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Arnold A; Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany., Capilla EP; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Mochmann LH; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Zhdanovic Y; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Schleich K; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Jung N; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Chimal EC; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Jung A; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany., Kumbrink J; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany., Harter P; German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Neuropathology, Ludwig-Maximilians-Universität München, Munich, Germany., Prenißl N; Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany., Elezkurtaj S; Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany., Brcic L; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria., Deigendesch N; Department of Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland., Frank S; Department of Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland., Hench J; Department of Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland., Försch S; Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany., Breimer G; Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands., van Engen van Grunsven I; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Lassche G; Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands; Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., van Herpen C; Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands; Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., Zhou F; Department of Pathology, New York University Langone Health, School of Medicine, New York, New York., Snuderl M; Institute of Pathology, Friedrich-Alexander-University Erlangen-Nurnberg, University Hospital Erlangen, Erlangen, Germany., Agaimy A; Institute of Pathology, Friedrich-Alexander-University Erlangen-Nurnberg, University Hospital Erlangen, Erlangen, Germany., Müller KR; Machine Learning Group, Department of Software Engineering and Theoretical Computer Science, Technical University of Berlin, Berlin, Germany; Department of Artificial Intelligence, Korea University, Seoul, South Korea; Max Planck Institute for Informatics, Saarbrucken, Germany; BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany., von Deimling A; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany., Capper D; Department of Neuropathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany., Klauschen F; Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany; BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany., Ihrler S; DERMPATH München, Munich, Germany.
Jazyk:	angličtina
Zdroj:	Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2024 Dec; Vol. 37 (12), pp. 100625. Date of Electronic Publication: 2024 Sep 25.
DOI:	10.1016/j.modpat.2024.100625
Abstrakt:	Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor-specific mutations or gene fusions, there is an unmet need to identify additional diagnostic biomarkers for entities lacking specific alterations. In this study, we collected a comprehensive cohort of 363 cases encompassing 20 different salivary gland tumor entities and explored the potential of DNA methylation to classify these tumors. We were able to show that most entities show specific epigenetic signatures and present a machine learning algorithm that achieved a mean balanced accuracy of 0.991. Of note, we showed that cribriform adenocarcinoma is epigenetically distinct from classical polymorphous adenocarcinoma, which could support risk stratification of these tumors. Myoepithelioma and pleomorphic adenoma form a uniform epigenetic class, supporting the theory of a single entity with a broad but continuous morphologic spectrum. Furthermore, we identified a histomorphologically heterogeneous but epigenetically distinct class that could represent a novel tumor entity. In conclusion, our study provides a comprehensive resource of the DNA methylation landscape of salivary gland tumors. Our data provide novel insight into disputed entities and show the potential of DNA methylation to identify new tumor classes. Furthermore, in future, our machine learning classifier could support the histopathologic diagnosis of salivary gland tumors. (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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