Journal Article

DKFZ-2025-01181

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors.

Yuan, D. ; Jugas, R. ; Pokorna, P. ; Sterba, J. ; Slaby, O. ; Schmid, S. ; Siewert, C. ; Osberg, B. ; Capper, D.DKFZ* ; Halldorsson, S. ; Vik-Mo, E. O. ; Zeiner, P. S. ; Weber, K.DKFZ* ; Harter, P. N. ; Thomas, C. ; Albers, A. ; Rechsteiner, M. ; Reimann, R. ; Appelt, A. ; Schüller, U. ; Jabareen, N. ; Mackowiak, S. ; Ishaque, N. ; Eils, R. ; Lukassen, S. ; Euskirchen, P.DKFZ*

2025
Nature Research London

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Please use a persistent id in citations: doi:10.1038/s43018-025-00976-5

Abstract: DNA methylation-based classification of (brain) tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations used methylation microarrays for data generation, while most current classifiers rely on a fixed methylation feature space. This makes them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework that can accurately classify tumors using sparse methylomes obtained on different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models regarding accuracy and computational requirements while still being explainable. We use crossNN to train a pan-cancer classifier that can discriminate more than 170 tumor types across all organ sites. Validation in more than 5,000 tumors profiled on different platforms, including nanopore and targeted bisulfite sequencing, demonstrates its robustness and scalability with 99.1% and 97.8% precision for the brain tumor and pan-cancer models, respectively.

Note: 2025 Jul;6(7):1283-1294

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Contributing Institute(s):

1. DKTK Koordinierungsstelle Berlin (BE01)
2. DKTK Koordinierungsstelle Frankfurt (FM01)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Appears in the scientific report 2025

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DEAL Nature ; Essential Science Indicators ; IF >= 20 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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