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      Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations

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          Abstract

          Several studies using genome-wide molecular techniques have reported various degrees of genetic heterogeneity between primary tumours and their distant metastases. However, it has been difficult to discern patterns of dissemination owing to the limited number of patients and available metastases. Here, we use phylogenetic techniques on data generated using whole-exome sequencing and copy number profiling of primary and multiple-matched metastatic tumours from ten autopsied patients to infer the evolutionary history of breast cancer progression. We observed two modes of disease progression. In some patients, all distant metastases cluster on a branch separate from their primary lesion. Clonal frequency analyses of somatic mutations show that the metastases have a monoclonal origin and descend from a common ‘metastatic precursor’. Alternatively, multiple metastatic lesions are seeded from different clones present within the primary tumour. We further show that a metastasis can be horizontally cross-seeded. These findings provide insights into breast cancer dissemination.

          Abstract

          Tumour heterogeneity is well-known; however, studies analysing the progression from primary to metastatic disease are still limited. Here, the authors used phylogenetic analyses and found that for some patients there are multiple seeding events from the primary tumour accompanied by cross-seeding between metastases.

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          Most cited references58

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                20 April 2017
                2017
                : 8
                : 14944
                Affiliations
                [1 ]Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles , Bld de Waterloo 121, 1000 Brussels, Belgium
                [2 ]Laboratory of Translational Genetics, Vesalius Research Center, VIB , Campus Gasthuisberg, O&N IV Herestraat 49, 3000 Leuven, Belgium
                [3 ]Laboratory of Translational Genetics, Department of Oncology, Katholieke Universiteit Leuven , O&N IV Herestraat 49, 3000 Leuven, Belgium
                [4 ]Second Department of Pathology, Semmelweis University , Üllői út 93, 1091 Budapest, Hungary
                [5 ]Department of Pathology, Institut Jules Bordet , Bld de Waterloo 121, 1000 Brussels, Belgium
                [6 ]2nd Department of Pathology, MTA-SE Tumor Progression Research Group, Semmelweis University , Üllői út 93, 1091 Budapest, Hungary
                [7 ]Semmelweis University Cancer Center, Semmelweis University , Tömő u. 25-29, 1083 Budapest, Hungary
                [8 ]Surgical and Molecular Tumor Pathology Centre, National Institute of Oncology , Ráth György u. 7-9, 1122 Budapest, Hungary
                [9 ]University of Genova and Istituto di Cura a Carattere Clinico e Scientifico Azienda Ospedaliera Universitaria San Martino—Instituto Nazionale Tumori , Largo Rosanna Benzi 10, 16132 Genoa, Italy
                [10 ]Yale University , Cedar Street 333, New Haven, Connecticut 05620, USA
                [11 ]Department of Medical Oncology, Institut Jules Bordet, Université Libre de Bruxelles , Bld de Waterloo 121, 1000 Brussels, Belgium
                Author notes
                [*]

                These authors contributed equally to this work.

                [†]

                These authors jointly supervised this work.

                Author information
                http://orcid.org/0000-0002-8249-3632
                Article
                ncomms14944
                10.1038/ncomms14944
                5474888
                28429735
                6f9243dd-25e9-4ed4-bb0f-7f6bfa699ee5
                Copyright © 2017, The Author(s)

                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
                : 03 May 2016
                : 15 February 2017
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