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      Molecular Diagnosis of Primary Mediastinal B Cell Lymphoma Identifies a Clinically Favorable Subgroup of Diffuse Large B Cell Lymphoma Related to Hodgkin Lymphoma

      research-article
      1 , 5 , 6 , 1 , 6 , 15 , 7 , 7 , 6 , 7 , 7 , 8 , 9 , 9 , 10 , 11 , 11 , 12 , 13 , 13 , 13 , 14 , 14 , 15 , 16 , 17 , 16 , 18 , 16 , 20 , 16 , 19 , 16 , 21 , 1 , 1 , 22 , 22 , 2 , 3 , 5 , 4 , 1
      The Journal of Experimental Medicine
      The Rockefeller University Press
      gene expression profiling, microarray, outcome prediction, PMBL, DLBCL

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          Abstract

          Using current diagnostic criteria, primary mediastinal B cell lymphoma (PMBL) cannot be distinguished from other types of diffuse large B cell lymphoma (DLBCL) reliably. We used gene expression profiling to develop a more precise molecular diagnosis of PMBL. PMBL patients were considerably younger than other DLBCL patients, and their lymphomas frequently involved other thoracic structures but not extrathoracic sites typical of other DLBCLs. PMBL patients had a relatively favorable clinical outcome, with a 5-yr survival rate of 64% compared with 46% for other DLBCL patients. Gene expression profiling strongly supported a relationship between PMBL and Hodgkin lymphoma: over one third of the genes that were more highly expressed in PMBL than in other DLBCLs were also characteristically expressed in Hodgkin lymphoma cells. PDL2, which encodes a regulator of T cell activation, was the gene that best discriminated PMBL from other DLBCLs and was also highly expressed in Hodgkin lymphoma cells. The genomic loci for PDL2 and several neighboring genes were amplified in over half of the PMBLs and in Hodgkin lymphoma cell lines. The molecular diagnosis of PMBL should significantly aid in the development of therapies tailored to this clinically and pathogenetically distinctive subgroup of DLBCL.

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          Most cited references22

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Blockade of B7-H1 improves myeloid dendritic cell-mediated antitumor immunity.

            Suppression of dendritic cell function in cancer patients is thought to contribute to the inhibition of immune responses and disease progression. Molecular mechanisms of this suppression remain elusive, however. Here, we show that a fraction of blood monocyte-derived myeloid dendritic cells (MDCs) express B7-H1, a member of the B7 family, on the cell surface. B7-H1 could be further upregulated by tumor environmental factors. Consistent with this finding, virtually all MDCs isolated from the tissues or draining lymph nodes of ovarian carcinomas express B7-H1. Blockade of B7-H1 enhanced MDC-mediated T-cell activation and was accompanied by downregulation of T-cell interleukin (IL)-10 and upregulation of IL-2 and interferon (IFN)-gamma. T cells conditioned with the B7-H1-blocked MDCs had a more potent ability to inhibit autologous human ovarian carcinoma growth in non-obese diabetic-severe combined immunodeficient (NOD-SCID) mice. Therefore, upregulation of B7-H1 on MDCs in the tumor microenvironment downregulates T-cell immunity. Blockade of B7-H1 represents one approach for cancer immunotherapy.
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              A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma.

              To classify cancer specimens by their gene expression profiles, we created a statistical method based on Bayes' rule that estimates the probability of membership in one of two cancer subgroups. We used this method to classify diffuse large B cell lymphoma (DLBCL) biopsy samples into two gene expression subgroups based on data obtained from spotted cDNA microarrays. The germinal center B cell-like (GCB) DLBCL subgroup expressed genes characteristic of normal germinal center B cells whereas the activated B cell-like (ABC) DLBCL subgroup expressed a subset of the genes that are characteristic of plasma cells, particularly those encoding endoplasmic reticulum and golgi proteins involved in secretion. We next used this predictor to discover these subgroups within a second set of DLBCL biopsies that had been profiled by using oligonucleotide microarrays [Shipp, M. A., et al. (2002) Nat. Med. 8, 68-74]. The GCB and ABC DLBCL subgroups identified in this data set had significantly different 5-yr survival rates after multiagent chemotherapy (62% vs. 26%; P < or = 0.0051), in accord with analyses of other DLBCL cohorts. These results demonstrate the ability of this gene expression-based predictor to classify DLBCLs into biologically and clinically distinct subgroups irrespective of the method used to measure gene expression.
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                Author and article information

                Journal
                J Exp Med
                The Journal of Experimental Medicine
                The Rockefeller University Press
                0022-1007
                1540-9538
                15 September 2003
                : 198
                : 6
                : 851-862
                Affiliations
                [1 ]Metabolism Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892
                [2 ]Medicine Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892
                [3 ]Laboratory of Pathology, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892
                [4 ]Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892
                [5 ]Biometric Research Branch, Division of Cancer Treatment and Diagnosis, NCI, NIH, Bethesda, MD 20892
                [6 ]Department of Pathology, Hôpital Henri Mondor, 94000 Créteil, France
                [7 ]Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE 68198
                [8 ]Department of Preventive and Societal Medicine, University of Nebraska Medical Center, Omaha, NE 68198
                [9 ]Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE 68198
                [10 ]Department of Immunology, The Norwegian Radium Hospital, N-0310 Oslo, Norway
                [11 ]Department of Oncology, The Norwegian Radium Hospital, N-0310 Oslo, Norway
                [12 ]Department of Pathology, The Norwegian Radium Hospital, N-0310 Oslo, Norway
                [13 ]Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
                [14 ]Department of Pathology, University of Würzburg, 97070 Würzburg, Germany
                [15 ]British Columbia Cancer Center, Vancouver, British Columbia, Canada V5Z 4E6
                [16 ]Southwest Oncology Group, Oregon Health and Science University, Portland, OR 97239
                [17 ]Department of Pathology, Oregon Health and Science University, Portland, OR 97239
                [18 ]Department of Pathology, University of Arizona Cancer Center, Tucson, AZ 85724
                [19 ]Department of Medicine, University of Arizona Cancer Center, Tucson, AZ 85724
                [20 ]James P. Wilmot Cancer Center, University of Rochester School of Medicine, Rochester, NY 14642
                [21 ]Fred Hutchinson Cancer Research Center, Seattle, WA 98109
                [22 ]Bioinformatics and Molecular Analysis Section, CBEL, CIT, NIH, Bethesda, MD 20892
                Author notes

                Address correspondence to Louis M. Staudt, Metabolism Branch, CCR, NCI, Bldg. 10, Rm. 4N114, NIH, Bethesda, MD 20892. Phone: (301) 402-1892; Fax: (301) 496-9956; email: lstaudt@ 123456mail.nih.gov

                Article
                20031074
                10.1084/jem.20031074
                2194208
                12975453
                353c355c-0e7b-411c-b8c0-e46700aa3531
                Copyright © 2003, The Rockefeller University Press
                History
                : 6 June 2003
                : 25 August 2003
                : 27 August 2003
                Categories
                Article

                Medicine
                dlbcl,pmbl,gene expression profiling,outcome prediction,microarray
                Medicine
                dlbcl, pmbl, gene expression profiling, outcome prediction, microarray

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