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      Genomic complexity is associated with epigenetic regulator mutations and poor prognosis in diffuse large B-cell lymphoma

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      a , b , a , b , c , c , c , d , b , b , e , f , g , h , i , j , k , l , m , n , n , o , p , q , r , s , t , c , u , v , a , w
      Oncoimmunology
      Taylor & Francis
      Tumor mutation burden, KMT2D, genomic instability, tumor microenvironment, PD-1, PD-L1, TP53, epigenetic, DLBCL, INDEL

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          ABSTRACT

          Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma with high mutation burdens but a low response rate to immune checkpoint inhibitors. In this study, we performed targeted next-generation sequencing and fluorescent multiplex immunohistochemistry, and investigated the clinical significance and immunological effect of mutation numbers in 424 DLBCL patients treated with standard immunochemotherapy. We found that KMT2D and TP53 nonsynonymous mutations (MUT) were significantly associated with increased nonsynonymous mutation numbers, and that high mutation numbers (MUT high) were associated with significantly poorer clinical outcome in germinal center B-cell-like DLBCL with wild-type TP53. To understand the underlying mechanisms, we identified a gene-expression profiling signature and the association of MUT high with decreased T cells in DLBCL patients with wild-type TP53. On the other hand, in overall cohort, MUT high was associated with lower PD-1 expression in T cells and PD-L1 expression in macrophages, suggesting a positive role of MUT high in immune responses. Analysis in a whole-exome sequencing dataset of 304 patients deposited by Chapuy et al. validated the correlation of MUT- KMT2D with genomic complexity and the significantly poorer survival associated with higher numbers of genomic single nucleotide variants in activated B-cell–like DLBCL with wild-type TP53. Together, these results suggest that KMT2D inactivation or epigenetic dysregulation has a role in driving DLBCL genomic instability, and that genomic complexity has adverse impact on clinical outcome in DLBCL patients with wild-type TP53 treated with standard immunochemotherapy. The oncoimmune data in this study have important implications for biomarker and therapeutic studies in DLBCL.

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          Most cited references45

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          Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden

          Background High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types. Methods In this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types. Results We demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB. Conclusions These results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0424-2) contains supplementary material, which is available to authorized users.
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            Mutational heterogeneity in cancer and the search for new cancer genes

            Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.
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              Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy

              Programmed cell death protein–1 (PD-1) and programmed cell death ligand–1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell–inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti–PD-1 monotherapy and combination immunotherapy regimens.
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                Author and article information

                Journal
                Oncoimmunology
                Oncoimmunology
                Oncoimmunology
                Taylor & Francis
                2162-4011
                2162-402X
                20 July 2021
                2021
                20 July 2021
                : 10
                : 1
                : 1928365
                Affiliations
                [a ]Department of Hematology and Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University; , Guangzhou, China
                [b ]Hematopathology Division and Department of Pathology, Duke University Medical Center; , Durham, North Carollina, USA
                [c ]Duke Cancer Institute; , Durham, North Caronlina, USA
                [d ]NeoGenomics Laboratories; , Aliso Viejo, California, USA
                [e ]Department of Pathology and Cell Biology, Columbia University Irving Medical Center and New York Presbyterian Hospital, New York, New York, USA;
                [f ]Department of Medicine, Section of Hematology, University of Verona; , Verona, Italy
                [g ]Department of Pathology, Institute of Pathology,University Hospital Basel; , Switzerland
                [h ]Clinical Department, Aalborg University Hospital; , Aalborg, Denmark
                [i ]Hematopathology Department, Mayo Clinic; , Rochester, Minnesota, USA
                [j ]Department of Pathology, Weill Medical College of Cornell University; , New York, New York, USA
                [k ]Department of Pathology and Genomic Medicine, The Methodist Hospital; , Houston, Texas, USA
                [l ]Department of Pathology, Cleveland Clinic; , Cleveland, Ohio, USA
                [m ]Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center; , Houston, Texas, USA
                [n ]Department of Pathology, Asan Medical Center, Ulsan University College of Medicine; , Seoul, Korea
                [o ]Lymphoma Unit, Department of Onco-Hematology, IRCCS San Raffaele Scientific Institute; , Milan, Italy
                [p ]Department of Pathology, Odense University Hospital; , Odense, Denmark
                [q ]Hematology & Oncology, Gundersen Lutheran Health System; , La Crosse, Wisconsin, USA
                [r ]Department of Pathology, Radboud University Nijmegen Medical Centre; , Nijmegen, Netherlands
                [s ]Pathology Department, Hospital Universitario Marqués de Valdecilla; , Santander, Spain
                [t ]Department of Medicine (Hematology and Oncology), Feinberg School of Medicine, Northwestern University; , Chicago, Illinois,USA
                [u ]Department of Medicine, Baylor College of Medicine; , Houston, Texas,USA
                [v ]Department of Hematology, The First Affiliated Hospital of Xiamen University; , Xiamen, Fujian,China
                [w ]Genomic Testing Cooperative, LCA; , Irvine, California,USA
                Author notes
                CONTACT Ken H. Young ken.young@ 123456duke.edu Duke University Medical Center, Division of Hematopathology and Department of Pathology, Duke Cancer Institute; , Durham, NC 27710, USA
                Hua You youhua307@ 123456163.com Affiliated Cancer Hospital & Institute of Guangzhou Medical University; , Guangzhou, China
                Maher Albitar malbitar@ 123456genomictestingcooperative.com Genomic Testing Cooperative LCA, 175 Technology Drive,;
                [†]

                These authors have contributed equally to this work and share first authorship.

                Author information
                https://orcid.org/0000-0002-7615-3949
                https://orcid.org/0000-0002-4515-9731
                https://orcid.org/0000-0002-5634-2937
                https://orcid.org/0000-0002-5755-8932
                Article
                1928365
                10.1080/2162402X.2021.1928365
                8293967
                34350060
                c60872f7-3712-4f6a-8a1c-6deae9048f3d
                © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 5, Tables: 3, References: 49, Pages: 1
                Categories
                Research Article
                Original Research

                Immunology
                tumor mutation burden,kmt2d,genomic instability,tumor microenvironment,pd-1,pd-l1,tp53,epigenetic,dlbcl,indel

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