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      Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal

      research-article
      1 , 2 , 20 , 1 , 20 , 1 , 20 , 1 , 20 , 1 , 20 , 3 , 20 , 4 , 20 , 5 , 20 , 2 , 20 , 6 , 1 , 2 , 2 , 7 , 5 , 8 , 9 , 1 , 10 , 10 , 10 , 2 , 5 , 11 , 11 , 1 , 2 , 10 , 12 , 13 , 13 , 2 , 2 , 1 , 1 , 7 , 3 , 14 , 14 , 15 , 16 , 17 , 17 , 18 , 18 , 18 , 17 , 18 , 17 , 18 , 1 , 7 , 19 , 21 , , PEACE, the TRACERx Renal Consortium
      Cell
      Cell Press
      renal cell cancer, metastasis, evolution of metastasis, oligometastasis, solitary metastasis, cytoreductive nephrectomy, metastasectomy, chromosome instability, loss of 9p

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          Summary

          Clear-cell renal cell carcinoma (ccRCC) exhibits a broad range of metastatic phenotypes that have not been systematically studied to date. Here, we analyzed 575 primary and 335 metastatic biopsies across 100 patients with metastatic ccRCC, including two cases sampledat post-mortem. Metastatic competence was afforded by chromosome complexity, and we identify 9p loss as a highly selected event driving metastasis and ccRCC-related mortality (p = 0.0014). Distinct patterns of metastatic dissemination were observed, including rapid progression to multiple tissue sites seeded by primary tumors of monoclonal structure. By contrast, we observed attenuated progression in cases characterized by high primary tumor heterogeneity, with metastatic competence acquired gradually and initial progression to solitary metastasis. Finally, we observed early divergence of primitive ancestral clones and protracted latency of up to two decades as a feature of pancreatic metastases.

          Graphical Abstract

          Highlights

          • Evolutionary study of matched primary metastasis biopsies from 100 ccRCC cases

          • Metastasis competence is afforded by chromosome complexity, but not driver mutation load

          • The hallmark genomic drivers of ccRCC metastasis are loss of 9p and 14q

          • Punctuated and branched evolution result in distinct patterns of metastases

          Abstract

          A multi-center prospective study and two validation cohorts of matched primary metastasis biopsies from 100 patients with clear-cell renal cell carcinoma provides a comprehensive picture of the genetic underpinnings and the evolutionary patterns of metastasis.

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          Most cited references66

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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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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                19 April 2018
                19 April 2018
                : 173
                : 3
                : 581-594.e12
                Affiliations
                [1 ]Translational Cancer Therapeutics Laboratory, the Francis Crick Institute, London NW1 1AT, UK
                [2 ]Renal and Skin Units, the Royal Marsden Hospital NHS Foundation Trust, London SW3 6JJ, UK
                [3 ]Department of Pathology, Cruces University Hospital, Biocruces Institute, University of the Basque Country, Barakaldo, Spain
                [4 ]Department of Urology, the Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
                [5 ]Urology Centre, Guy’s & St Thomas’ NHS Foundation Trust, London, UK
                [6 ]Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London NW1 1AT, UK
                [7 ]Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK
                [8 ]Department of Cellular Pathology, Guy’s & St Thomas’ NHS Foundation Trust, London SE1 7EH, UK
                [9 ]Department of Pathology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
                [10 ]Department of Oncology, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
                [11 ]Experimental Histopathology Laboratory, the Francis Crick Institute, London NW1 1AT, UK
                [12 ]Department of Pathology, Complejo Hospitalario de Navarra, 31008 Pamplona, Spain
                [13 ]Department of Pathology, University College London Hospitals, London WC1E 6DE, UK
                [14 ]Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [15 ]Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [16 ]Molecular Oncology, Department of Medicine, Siteman Cancer Center, Washington University, St. Louis, MO, USA
                [17 ]Roche Sequencing Solutions, Madison, Research & Development, Madison, WI, 53719, USA
                [18 ]Ventana Medical Systems, Tucson, AZ 85755, USA
                [19 ]Department of Medical Oncology, University College London Hospitals, London NW1 2BU, UK
                Author notes
                []Corresponding author charles.swanton@ 123456crick.ac.uk
                [20]

                These authors contributed equally

                [21]

                Lead Contact

                Article
                S0092-8674(18)30389-1
                10.1016/j.cell.2018.03.057
                5938365
                29656895
                91039f5b-5cbf-4fbe-8ea6-80056e87b235
                © 2018 Francis Crick Institute

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 25 September 2017
                : 6 March 2018
                : 20 March 2018
                Categories
                Article

                Cell biology
                renal cell cancer,metastasis,evolution of metastasis,oligometastasis,solitary metastasis,cytoreductive nephrectomy,metastasectomy,chromosome instability,loss of 9p

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