A modular transcriptome map of mature B cell lymphomas

Background: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disea
Background: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma.
Methods: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics.
Results: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on "double-hit" MYC and BCL2 transformed lymphomas.
Conclusions: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.
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Author:Henry Löffler-Wirth, Markus Kreuz, Lydia Hopp, Arsen Arakelyan, Andrea Haake, Sergio B. Cogliatti, Alfred Christian Feller, Martin-Leo Hansmann, Dido Lenze, Peter Möller, Hans Konrad Müller-Hermelink, Erik Fortenbacher, Edith Willscher, German Ott, Andreas Rosenwald, Christiane Pott, Carsten Schwänen, Heiko Trautmann, Swen Wessendorf, Harald Stein, Monika Szczepanowski, Lorenz Trümper, Michael Hummel, Wolfram Klapper, Reiner Siebert, Markus Löffler, Hans Binder
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=31039827
Parent Title (English):Genome medicine
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Year of Completion:2019
Date of first Publication:2019/04/30
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:German Cancer Aid consortium Molecular Mechanisms for Malignant Lymphoma
Release Date:2019/06/17
Tag:B cell malignancies; Gene regulation; Machine learning; Molecular subtypes; Tumor heterogeneity
Issue:1, Art. 27
First Page:1
Last Page:20
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
HeBIS PPN:45393949X
Dewey Decimal Classification:610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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