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      MYD88 mutations identify a molecular subgroup of diffuse large B-cell lymphoma with an unfavorable prognosis

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          Abstract

          The 2016 World Health Organization classification defines diffuse large B-cell lymphoma (DLBCL) subtypes based on Epstein-Barr virus (EBV) infection and oncogenic rearrangements of MYC/BCL2/BCL6 as drivers of lymphomagenesis. A subset of DLBCL, however, is characterized by activating mutations in MYD88/CD79B. We investigated whether MYD88/CD79B mutations could improve the classification and prognostication of DLBCL. In 250 primary DLBCL, MYD88/CD79B mutations were identified by allele-specific polymerase chain reaction or next-generation-sequencing, MYC/BCL2/BCL6 rearrangements were analyzed by fluorescence in situ hybridization, and EBV was studied by EBV-encoded RNA in situ hybridization. Associations of molecular features with clinicopathologic characteristics, outcome, and prognosis according to the International Prognostic Index (IPI) were investigated. MYD88 and CD79B mutations were identified in 29.6% and 12.3%, MYC, BCL2, and BCL6 rearrangements in 10.6%, 13.6%, and 20.3%, and EBV in 11.7% of DLBCL, respectively. Prominent mutual exclusivity between EBV positivity, rearrangements, and MYD88/CD79B mutations established the value of molecular markers for the recognition of biologically distinct DLBCL subtypes. MYD88-mutated DLBCL had a significantly inferior 5-year overall survival than wild-type MYD88 DLBCL (log-rank; P=0.019). DLBCL without any of the studied aberrations had superior overall survival compared to cases carrying ≥1 aberrancy (log-rank; P=0.010). MYD88 mutations retained their adverse prognostic impact upon adjustment for other genetic and clinical variables by multivariable analysis and improved the prognostic performance of the IPI. This study demonstrates the clinical utility of defining MYD88-mutated DLBCL as a distinct molecular subtype with adverse prognosis. Our data call for sequence analysis of MYD88 in routine diagnostics of DLBCL to optimize classification and prognostication, and to guide the development of improved treatment strategies.

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          Most cited references30

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          Cumulative incidence in competing risks data and competing risks regression analysis.

          T. J. Kim (2007)
          Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of cumulative incidence, the log-rank test for comparison of cumulative incidence curves, and the standard Cox model for the assessment of covariates lead to incorrect and biased results. In this article, we discuss competing risks data analysis which includes methods to calculate the cumulative incidence of an event of interest in the presence of competing risks, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. A hypothetical numeric example and real data are used to compare those three methods in the competing risks data analysis to their respective counterparts in the standard survival analysis. The source and magnitude of bias from the Kaplan-Meier estimate is also detailed.
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            A Multiprotein Supercomplex Controlling Oncogenic Signaling in Lymphoma

            Summary B cell receptor (BCR) signaling has emerged as a therapeutic target in B cell lymphomas, but inhibiting this pathway in diffuse large B cell lymphoma (DLBCL) has benefited only a subset of patients 1 . Gene expression profiling identified two major DLBCL subtypes, known as germinal center (GC) B cell-like (GCB) and activated B cell-like (ABC) 2,3 , with inferior outcomes following immunochemotherapy in ABC. Autoantigens drive BCR-dependent activation of NF-κB in ABC DLBCL through a kinase cascade of SYK, BTK and PKCβ to promote the assembly of the CARD11-BCL10-MALT1 (CBM) adapter complex that recruits and activates IκB kinase (IKK) 4,5,6 . Genome sequencing revealed gain-of-function mutations targeting the CD79A and CD79B BCR subunits and the Toll-like receptor (TLR) signaling adapter MYD88 5,7 , with MYD88L265P being the most prevalent isoform. In a clinical trial, the BTK inhibitor, ibrutinib, produced responses in 37% of ABC cases 1 . The most striking response rate (80%) was observed in tumors with both CD79B and MYD88 L265P mutations, but how these mutations cooperate to promote dependence on BCR signaling remains unclear. Herein, we used genome-wide CRISPR-Cas9 screening and functional proteomics to understand the molecular basis for exceptional clinical responses to ibrutinib. We discovered a new mode of oncogenic BCR signaling in ibrutinib-responsive cell lines and biopsies, coordinated by a multiprotein supercomplex formed by MYD88, TLR9, and the BCR (My-T-BCR). The My-T-BCR co-localizes with mTOR on endolysosomes, where it drives pro-survival NF-κB and mTOR signaling. Inhibitors of BCR and mTOR signaling cooperatively decreased My-T-BCR supercomplex formation and function, providing mechanistic insight into their synergistic toxicity for My-T-BCR+ DLBCL cells. My-T-BCR complexes characterized ibrutinib-responsive malignancies and distinguished ibrutinib responders from non-responders. Our data provide a roadmap for the rational deployment of oncogenic signaling inhibitors in molecularly-defined subsets of DLBCL.
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              Diffuse large B-cell lymphoma genotyping on the liquid biopsy

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                Author and article information

                Journal
                Haematologica
                Haematologica
                haematol
                Haematologica
                Haematologica
                Ferrata Storti Foundation
                0390-6078
                1592-8721
                February 2020
                23 May 2019
                : 105
                : 2
                : 424-434
                Affiliations
                [1 ]Department of Hematology, Amsterdam University Medical Center, University of Amsterdam
                [2 ]Lymphoma and Myeloma Center Amsterdam-LYMMCARE, and Cancer Center Amsterdam (CCA), Amsterdam
                [3 ]Department of Hematology, Leiden University Medical Center, Leiden
                [4 ]Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden
                [5 ]Department of Pathology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam
                [6 ]Department of Pathology, Leiden University Medical Center, Leiden
                [7 ]Department of Internal Medicine & Hematology, Onze Lieve Vrouwe Gasthuis, Amsterdam
                [8 ]Department of Pathology, Onze Lieve Vrouwe Gasthuis, Amsterdam
                [9 ]Department of Internal Medicine & Hematology, Albert Schweitzer Hospital, Dordrecht
                [10 ]Department of Internal Medicine & Hematology, Tergooi Hospital, Hilversum
                [11 ]Department of Internal Medicine & Hematology, Deventer Hospital, Deventer
                [12 ]Department of Internal Medicine & Hematology, Waterland Hospital, Purmerend
                [13 ]Department of Medical Oncology & Hematology, Antoni van Leeuwenhoekziekenhuis, Amsterdam
                [14 ]Department of Hematology, University Medical Center Groningen, Groningen, the Netherlands
                Author notes
                Correspondence: JOOST S. VERMAAT, j.s.p.vermaat@ 123456lumc.nl
                Article
                1050424
                10.3324/haematol.2018.214122
                7012469
                31123031
                bcdd14a1-1d7f-44b6-8e91-9f63bd7676e2
                Copyright© 2020 Ferrata Storti Foundation

                Material published in Haematologica is covered by copyright. All rights are reserved to the Ferrata Storti Foundation. Use of published material is allowed under the following terms and conditions:

                https://creativecommons.org/licenses/by-nc/4.0/legalcode. Copies of published material are allowed for personal or internal use. Sharing published material for non-commercial purposes is subject to the following conditions:

                https://creativecommons.org/licenses/by-nc/4.0/legalcode, sect. 3. Reproducing and sharing published material for commercial purposes is not allowed without permission in writing from the publisher.

                History
                : 24 December 2018
                : 22 May 2019
                Categories
                Article
                Non-Hodgkin Lymphoma

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