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Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data
Author(s) -
Alexandra Schrader,
Katharina Meyer,
Neele Walther,
Ailine Stolz,
Maren Feist,
Elisabeth Hand,
Frederike von Bonin,
M. Evers,
Christian Köhler,
Katayoon Shirneshan,
Martina Vockerodt,
Wolfram Klapper,
Monika Szczepanowski,
Paul G. Murray,
Holger Bastians,
Lorenz Trümper,
Rainer Spang,
Dieter Kube
Publication year - 2016
Publication title -
oncotarget
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.373
H-Index - 127
ISSN - 1949-2553
DOI - 10.18632/oncotarget.9219
Subject(s) - breakpoint cluster region , germinal center , biology , cancer research , b cell receptor , gene , gene expression profiling , gene expression , b cell , cell cycle , lymphoma , microbiology and biotechnology , genetics , immunology , antibody
To discover new regulatory pathways in B lymphoma cells, we performed a combined analysis of experimental, clinical and global gene expression data. We identified a specific cluster of genes that was coherently expressed in primary lymphoma samples and suppressed by activation of the B cell receptor (BCR) through αIgM treatment of lymphoma cells in vitro. This gene cluster, which we called BCR.1, includes numerous cell cycle regulators. A reduced expression of BCR.1 genes after BCR activation was observed in different cell lines and also in CD10+ germinal center B cells. We found that BCR activation led to a delayed entry to and progression of mitosis and defects in metaphase. Cytogenetic changes were detected upon long-term αIgM treatment. Furthermore, an inverse correlation of BCR.1 genes with c-Myc co-regulated genes in distinct groups of lymphoma patients was observed. Finally, we showed that the BCR.1 index discriminates activated B cell-like and germinal centre B cell-like diffuse large B cell lymphoma supporting the functional relevance of this new regulatory circuit and the power of guided clustering for biomarker discovery.

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