Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival

DOI DOI DOI DOI DOI ほか143件をすべて表示 一部だけ表示 被引用文献7件 オープンアクセス
  • S. Blenk
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • J. Engelmann
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • M. Weniger
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • J. Schultz
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • M. Dittrich
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • A. Rosenwald
    Institute for Pathology, Josef-Schneider-Str. 2, 97080 Würzburg, Germany.
  • H.K. Müller-Hermelink
    Institute for Pathology, Josef-Schneider-Str. 2, 97080 Würzburg, Germany.
  • T. Müller
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.
  • T. Dandekar
    Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland D-97074 Universität Würzburg, Germany.

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<jats:p> Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures. </jats:p><jats:p> We identify specific, activated B cell-like (ABC) and germinal center B cell-like (GCB) distinguishing genes. These include early (e.g. CDKN3) and late (e.g. CDKN2C) cell cycle genes. </jats:p><jats:p> Independently from previous classification by marker genes we confirm a clear binary class distinction between the ABC and GCB subgroups. An earlier suggested third entity is not supported. A key regulatory network, distinguishing marked over-expression in ABC from that in GCB, is built by: ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5. It predicts and supports the aggressive behaviour of the ABC subgroup. These results help to understand target interactions, improve subgroup diagnosis, risk prognosis as well as therapy in the ABC and GCB DLBCL subgroups. </jats:p>

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