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. 2025 Apr;39(4):967-971.
doi: 10.1038/s41375-025-02533-6. Epub 2025 Mar 10.

Subgrouping germinal center-derived B-cell lymphomas based on machine learning-deduced DNA methylation modules

Selina Glaser  1 , Anja Fischer  1 , Juan Emilio Martínez-Manjón  2 , Cristina López  1   3   4   5 , Helene Kretzmer  6   7   8   9 , Birgit Burkhardt  10 , Daniel Hübschmann  11   12   13   14 , Michael Hummel  15 , Wolfram Klapper  16 , Julia Kolarova  1   3 , Markus Kreuz  17 , German Ott  18 , Bernhard Radlwimmer  19 , Maciej Rosolowski  17 , Matthias Schlesner  20 , Andreas Rosenwald  21 , Stephan Stilgenbauer  22 , Rabea Wagener  1   3 , Igor Zwir  2   23 , Lorenz Trümper  24 , Ralf Küppers  14   25 , Peter Lichter  19 , Ole Ammerpohl  1   3 , Coral Del Val #  2   23 , Reiner Siebert #  26   27 ; ICGC MMML-Seq consortium
Affiliations

Subgrouping germinal center-derived B-cell lymphomas based on machine learning-deduced DNA methylation modules

Selina Glaser et al. Leukemia. 2025 Apr.
No abstract available

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Conflict of interest statement

Competing interests: The authors declare no competing interests. Ethics approval: The MMML and ICGC MMML-Seq studies have been approved by the Institutional Review Board of the Medical Faculties of the University of Kiel (403/05 and A150/10), Ulm (349/11 for ICGC MMML-Seq) and of the recruiting centers. Informed consent from the patients or their legal guardians was obtained in accordance with the respective regulations of the institutional review boards. All methods were performed in accordance with the relevant guidelines and regulations.

Figures

Fig. 1
Fig. 1. DNA methylation profiling and characterization of germinal center-derived B-cell lymphomas.
Using PGMRA on DNA methylation array data from germinal center-derived B-cell lymphomas (FL, DLBCL, FL-DLBCL), we identified 300 CpGs, which were subsequently organized into four modules (M1-M4) and seven methylation patterns (MPs) through k-means clustering. A Heatmap depicting DNA methylation levels of the 300 CpGs differentiating seven MPs (MP1-7). Sample features are annotated at the top of the heatmap. The mutation clusters were calculated for the entire dataset and DLBCL cases separately as described by Hübschmann et al. [8]. The TCC was calculated based on WGS data. Rows represent individual CpGs, and columns represent samples. CpG sites and samples are organized according to the k-mean clustering. B Radar plots illustrate the key defining features of each MP, including lymphoma classification, patient age, cell-of-origin (COO), BCL2 or BCL6 rearrangements, and mutational clusters identified by Hübschmann et al. C Boxplots display the distribution of biological age, Horvath’s epigenetic age, Ki-67 expression, and proliferation history (based on the epiCMIT package) across MPs. For statistical testing a pairwise Wilcoxon rank sum test with Bonferroni correction was applied (see Supplementary Tables S3 and S4). FL Follicular lymphoma, DLBCL Diffuse large B-cell lymphoma, ABC activated B-cell-like, GCB germinal center B-cell-like, TCC tumor cell content, WGS whole genome sequencing, r rearranged, n. a. not applicable.
Fig. 2
Fig. 2. UMAP analysis of the 300 CpGs in germinal center-derived B-cell lymphomas.
UMAP analysis based on 300 CpGs identified by PGMRA, using Manhattan distance and 15 neighbors. The UMAP plots are colored according to various features: lymphoma classification, median DNA methylation levels, methylation patterns (MPs) and tumor cell content based on whole genome sequencing. FL Follicular lymphoma, DLBCL Diffuse large B-cell lymphoma.
See this image and copyright information in PMC

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