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      Recurrent chromosomal gains and heterogeneous driver mutations characterise papillary renal cancer evolution

      research-article
      1 , 2 , 3 , 4 , 4 , 1 , 3 , 3 , 1 , 3 , 5 , 6 , 1 , 1 , 1 , 1 , 1 , 1 , 7 , 7 , 7 , 8 , 9 , 10 , 11 , 12 , 12 , 13 , 13 , 13 , 13 , 14 , 15 , 15 , 15 , 16 , 17 , 18 , 18 , 19 , 20 , 21 , 22 ,   23 , 2 , a , 3 , 5 , b , 1 , 24
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          Abstract

          Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behaviour. Here we sequence the genomes or exomes of 31 pRCCs, and in four tumours, multi-region sequencing is undertaken. We identify BAP1, SETD2, ARID2 and Nrf2 pathway genes ( KEAP1, NHE2L2 and CUL3) as probable drivers, together with at least eight other possible drivers. However, only ~10% of tumours harbour detectable pathogenic changes in any one driver gene, and where present, the mutations are often predicted to be present within cancer sub-clones. We specifically detect parallel evolution of multiple SETD2 mutations within different sub-regions of the same tumour. By contrast, large copy number gains of chromosomes 7, 12, 16 and 17 are usually early, monoclonal changes in pRCC evolution. The predominance of large copy number variants as the major drivers for pRCC highlights an unusual mode of tumorigenesis that may challenge precision medicine approaches.

          Abstract

          Papillary renal cell carcinoma (pRCC) is a subtype of kidney cancer characterized by highly variable clinical behaviour. Here the authors sequence either the genomes or exomes of 31 pRCCs and identify several genes in sub-clones and large copy number variants in major clones that may be important drivers of pRCC.

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          Most cited references51

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          MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

          We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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            ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

            High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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              Signatures of mutational processes in human cancer

              All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                19 March 2015
                : 6
                : 6336
                Affiliations
                [1 ]Molecular and Population Genetics Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford , Roosevelt Drive, Oxford OX3 7BN, UK
                [2 ]Department of Biomedicine, Research Group Human Genomics, University of Basel , Mattenstrasse 28, 4058 Basel, Switzerland
                [3 ]Translational Cancer Therapeutics Laboratory, London Research Institute, Cancer Research UK , 44, Lincoln’s Inn Fields, London WC2A 3LY, UK
                [4 ]Bioinformatics and Biostatistics, London Research Institute, Cancer Research UK , 44, Lincoln’s Inn Fields, London WC2A 3LY, UK
                [5 ]University College London Cancer Institute and Hospitals , Huntley Street, London WC1E 6DD, UK
                [6 ]Faculty of Mechanical Engineering, Institute of Mathematics and Physics, Slovak University of Technology , Namestie slobody 17, 812 31 Bratislava, Slovakia
                [7 ]Department of Medicine, The Royal Marsden NHS Foundation Trust , 203 Fulham Road, London SW3 6JJ, UK
                [8 ]Department of Urology, The Royal Marsden NHS Foundation Trust , 203 Fulham Road, London SW3 6JJ, UK
                [9 ]School of Medicine, University of Queensland , Brisbane, Australia
                [10 ]Department of Histopathology, The Royal Marsden NHS Foundation Trust , 203 Fulham Road, London SW3 6JJ, UK
                [11 ]Experimental Histopathology, London Research Institute, Cancer Research UK , 44, Lincoln’s Inn Fields, London WC2A 3LY, UK
                [12 ]Urology Centre, Guy’s and St Thomas’s Hospital NHS Foundation Trust , Great Maze Pond, London SE1 9RT, UK
                [13 ]Advanced Sequencing Laboratory, London Research Institute, Cancer Research UK , 44, Lincoln’s Inn Fields, London WC2A 3LY, UK
                [14 ]Genomic analysis of tumour development, Instituto de Biomedicina y Biotecnología de Cantabria (CSIC-UC-Sodercan), Departamento de Biología Molecular, Universidad de Cantabria , 39011 Santander, Spain
                [15 ]Department of Histopathology, Guy’s and St Thomas’s Hospital NHS Foundation Trust , Great Maze Pond, London SE1 9RT, UK
                [16 ]Department of Oncology, Uro-Oncology Research Group, University of Cambridge , Cambridge CB2 0RE, UK
                [17 ]Department of Urology, University Hospitals , Birmingham B15 2TH, UK
                [18 ]Institute for Pathology, University Hospital Basel , Schönbeinstrasse 40, 4003 Basel, Switzerland
                [19 ]Department of Oncology, Cancer and Haematology Centre, Churchill Hospital, Oxford University Hospitals , Oxford OX3 7LJ, UK
                [20 ]Department of Medical Genetics, University of Cambridge , Cambridge CB2 0QQ, UK
                [21 ]Department of Histopathology, Imperial College London, Hammersmith Hospital , London W12 0HS, UK
                [22 ]Department of Histopathology, Medical Research Institute, University of Dundee Medical School, Ninewells Hospital , Dundee DD1 9SY, UK
                [23 ]Hypoxia Biology Laboratory, Henry Wellcome Building for Molecular Physiology, Nuffield Department of Clinical Medicine, University of Oxford , Roosevelt Drive, Oxford OX3 7BN, UK
                [24 ]NIHR Comprehensive Biomedical Research Centre, University of Oxford , Roosevelt Drive, Oxford OX3 7BN, UK
                Author notes
                [*]

                These authors contributed equally to this work

                Article
                ncomms7336
                10.1038/ncomms7336
                4383019
                25790038
                3f2245a8-b105-4d7c-8677-9ffd14187afc
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 18 July 2014
                : 21 January 2015
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