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      HSP110 sustains chronic NF-κB signaling in activated B-cell diffuse large B-cell lymphoma through MyD88 stabilization

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          Key Points

          HSP110 sustains chronic NF-κB signaling in ABC-DLBCL through MyD88 stability. HSP110 is highly expressed in cells of patients with ABC-DLBCL and correlates with MyD88 expression.

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          Most cited references45

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma

            BACKGROUND Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. Gene-expression profiling has identified subgroups of DLBCL (activated B-cell–like [ABC], germinal-center B-cell–like [GCB], and unclassified) according to cell of origin that are associated with a differential response to chemotherapy and targeted agents. We sought to extend these findings by identifying genetic subtypes of DLBCL based on shared genomic abnormalities and to uncover therapeutic vulnerabilities based on tumor genetics. METHODS We studied 574 DLBCL biopsy samples using exome and transcriptome sequencing, array-based DNA copy-number analysis, and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on the co-occurrence of genetic alterations. RESULTS We identified four prominent genetic subtypes in DLBCL, termed MCD (based on the co-occurrence of MYD88 L265P and CD79B mutations), BN2 (based on BCL6 fusions and NOTCH2 mutations), N1 (based on NOTCH1 mutations), and EZB (based on EZH2 mutations and BCL2 translocations). Genetic aberrations in multiple genes distinguished each genetic subtype from other DLBCLs. These subtypes differed phenotypically, as judged by differences in gene-expression signatures and responses to immunochemotherapy, with favorable survival in the BN2 and EZB subtypes and inferior outcomes in the MCD and N1 subtypes. Analysis of genetic pathways suggested that MCD and BN2 DLBCLs rely on “chronic active” B-cell receptor signaling that is amenable to therapeutic inhibition. CONCLUSIONS We uncovered genetic subtypes of DLBCL with distinct genotypic, epigenetic, and clinical characteristics, providing a potential nosology for precision-medicine strategies in DLBCL. (Funded by the Intramural Research Program of the National Institutes of Health and others.)
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              Diffuse large B-cell lymphoma.

              Diffuse large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma worldwide, representing approximately 30-40% of all cases in different geographic regions. Patients most often present with a rapidly growing tumour mass in single or multiple, nodal or extranodal sites. The most common type of DLBCL, designated as not otherwise specified, represents 80-85% of all cases and is the focus of this review. There are also rare types of lymphoma composed of large B-cells, in aggregate about 15-20% of all neoplasms that are sufficiently distinctive to recognise separately. DLBCL not otherwise specified (referred to henceforth as DLBCL) is a heterogeneous entity in terms of clinical presentation, genetic findings, response to therapy, and prognosis. A major advance was the application of gene expression profiling (GEP) to the study of DLBCL which further clarified this heterogeneity and provided a rationale for subdividing cases into groups. The most popular system divides cases of DLBCL according to cell-of-origin into germinal centre B-cell like (GCB) and activated B-cell like (ABC) subtypes, with about 10-15% of cases being unclassifiable. Patients with the GCB subtype usually have better prognosis than patients with the ABC subtype. Although cell-of-origin is useful for predicting outcome, the GCB and ABC subtypes remain heterogeneous, with better and worse prognostic subsets within each group. Next generation sequencing (NGS) analysis of DLBCL has facilitated global identification of numerous and diverse genetic abnormalities in these neoplasms and has shown that GCB and ABC tumours have different mutation profiles. Although the therapy of patients with DLBCL is an active area of research, the current 5-year overall survival rate is 60-70% using standard-of-care frontline therapy. A precision medicine approach for the design of new therapies based on molecular findings in DLBCL is likely the best path forward. As pathologists, our role has expanded beyond diagnosis. We must perform a complete work-up of DLBCL cases. In addition to our traditional role in establishing the diagnosis, we need to analyse markers that provide information regarding prognosis and potential therapeutic targets. We also must ensure that adequate tissue is triaged for molecular studies which are essential for designing therapy regimens, particularly in the setting of disease relapse.
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                Author and article information

                Contributors
                Journal
                Blood
                American Society of Hematology
                0006-4971
                1528-0020
                August 02 2018
                August 02 2018
                : 132
                : 5
                : 510-520
                Affiliations
                [1 ]INSERM, LNC Unité Mixte de Recherche (UMR) 1231, Equipe Labellisée Ligue Nationale Contre le Cancer, Dijon, France;
                [2 ]Faculté des sciences de santé, Université Bourgogne Franche-Comté, Dijon, France;
                [3 ]INSERM U1111, Centre National de la Recherche UMR 5308, CIRI, EVIR Team, Université de Lyon-1, ENS de Lyon, Lyon, France;
                [4 ]Université Côte d’Azur, INSERM, C3M, Contrôle Métabolique des Morts Cellulaires, Nice, France;
                [5 ]Service Pathologie du Plateau de Biologie du Centre Hospitalier Universitaire (CHU) Dijon, Dijon, France;
                [6 ]Service de Biopathologie, Centre Léon Bérard, Lyon, France;
                [7 ]INSERM, UMR U1236, Université Rennes 1, Etablissement Français du Sang Bretagne, CHU de Rennes, Laboratoire d’Hématologie, Rennes, France;
                [8 ]INSERM, U918, Rouen, France;
                [9 ]Department of Immunology, University of Tübingen, Tübingen, Germany; and
                [10 ]Centre Georges François Leclerc, Dijon, France
                Article
                10.1182/blood-2017-12-819706
                29871863
                926d031d-d5c5-4196-92d3-d3b07bed6abc
                © 2018
                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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