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      Genomic Evolution of Breast Cancer Metastasis and Relapse

      research-article
      1 , 2 , 23 , 3 , 4 , 23 , 1 , 5 , 6 , 1 , 7 , 1 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 13 , 14 , 1 , 15 , 1 , 7 , 1 , 15 , 16 , 1 , 1 , 1 , 1 , 1 , 2 , 16 , 17 , 17 , 1 , 18 , 6 , 18 , 19 , 20 , 16 , 21 , 22 , 3 , 4 , 23 , , 1 , 23 , 24 , ∗∗
      Cancer Cell
      Cell Press
      breast cancer, metastasis, relapse, genomics, somatic mutation

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Patterns of genomic evolution between primary and metastatic breast cancer have not been studied in large numbers, despite patients with metastatic breast cancer having dismal survival. We sequenced whole genomes or a panel of 365 genes on 299 samples from 170 patients with locally relapsed or metastatic breast cancer. Several lines of analysis indicate that clones seeding metastasis or relapse disseminate late from primary tumors, but continue to acquire mutations, mostly accessing the same mutational processes active in the primary tumor. Most distant metastases acquired driver mutations not seen in the primary tumor, drawing from a wider repertoire of cancer genes than early drivers. These include a number of clinically actionable alterations and mutations inactivating SWI-SNF and JAK2-STAT3 pathways.

          Graphical Abstract

          Highlights

          • Metastases mostly disseminate late from primary breast tumors, keeping most drivers

          • Drivers at relapse sample from a wider range of cancer genes than in primary tumors

          • Mutations in SWI-SNF complex and inactivated JAK-STAT signaling enriched at relapse

          • Mutational processes similar in primary and relapse; radiotherapy can damage genome

          Abstract

          By sequencing primary, locally relapsed, and metastatic breast cancers, Yates et al. show that clones seeding metastasis or relapse disseminate late from primary tumors but continue to acquire mutations, including clinically actionable alterations and mutations inactivating the SWI/SNF and JAK2-STAT3 pathways.

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          Most cited references17

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          Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database.

          The tumor suppressor gene TP53 is frequently mutated in human cancers. More than 75% of all mutations are missense substitutions that have been extensively analyzed in various yeast and human cell assays. The International Agency for Research on Cancer (IARC) TP53 database (www-p53.iarc.fr) compiles all genetic variations that have been reported in TP53. Here, we present recent database developments that include new annotations on the functional properties of mutant proteins, and we perform a systematic analysis of the database to determine the functional properties that contribute to the occurrence of mutational "hotspots" in different cancer types and to the phenotype of tumors. This analysis showed that loss of transactivation capacity is a key factor for the selection of missense mutations, and that difference in mutation frequencies is closely related to nucleotide substitution rates along TP53 coding sequence. An interesting new finding is that in patients with an inherited missense mutation, the age at onset of tumors was related to the functional severity of the mutation, mutations with total loss of transactivation activity being associated with earlier cancer onset compared to mutations that retain partial transactivation capacity. Furthermore, 80% of the most common mutants show a capacity to exert dominant-negative effect (DNE) over wild-type p53, compared to only 45% of the less frequent mutants studied, suggesting that DNE may play a role in shaping mutation patterns. These results provide new insights into the factors that shape mutation patterns and influence mutation phenotype, which may have clinical interest.
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            Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft

            Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumor progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis, and a xenograft derived from the primary tumor. The metastasis contained two de novo mutations and a large deletion not present in the primary tumor, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumor mutations, and displayed a mutation enrichment pattern that paralleled the metastasis (16 of 20 genes). Two overlapping large deletions, encompassing CTNNA1, were present in all three tumor samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared to the primary tumor suggest that secondary tumors may arise from a minority of cells within the primary.
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              Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline.

              To provide recommendations on appropriate use of breast tumor biomarker assay results to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer.
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                Author and article information

                Contributors
                Journal
                Cancer Cell
                Cancer Cell
                Cancer Cell
                Cell Press
                1535-6108
                1878-3686
                14 August 2017
                14 August 2017
                : 32
                : 2
                : 169-184.e7
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
                [2 ]Department of Clinical Oncology, Guys and St Thomas' NHS Trust, London SE1 9RT, UK
                [3 ]Section of Oncology, Department of Clinical Science, University of Bergen, Bergen, Norway
                [4 ]Department of Oncology, Haukeland University Hospital, Bergen, Norway
                [5 ]Big Data Institute, University of Oxford, Oxford OX3 7BN, UK
                [6 ]Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
                [7 ]European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, UK
                [8 ]Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM 87545, USA
                [9 ]Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
                [10 ]University of New Mexico Comprehensive Cancer Center, Albuquerque, NM 87102, USA
                [11 ]The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
                [12 ]Department of Human Genetics, University of Leuven, 3000 Leuven, Belgium
                [13 ]Department of Pathology, Haukeland University Hospital, Bergen, Norway
                [14 ]The Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
                [15 ]Computational Oncology, Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Institute, New York, NY 10065 USA
                [16 ]Division of Cancer Studies, Faculty of Life Sciences and Medicine, King's College London, London SE1 9RT, UK
                [17 ]Breast Cancer Translational Research Laboratory, Université Libre de Bruxelles, Institut Jules Bordet, Bd de Waterloo 121, 1000 Brussels, Belgium
                [18 ]Erasmus MC Cancer Institute and Cancer Genomics Netherlands, Erasmus University Medical Center, Department of Medical Oncology, Rotterdam, the Netherlands
                [19 ]Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA
                [20 ]Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [21 ]Breast Cancer Now Research Unit, King's College London, London SE1 9RT, UK
                [22 ]The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
                Author notes
                [∗∗ ]Corresponding author pc8@ 123456sanger.ac.uk
                [23]

                These authors contributed equally

                [24]

                Lead Contact

                Article
                S1535-6108(17)30297-0
                10.1016/j.ccell.2017.07.005
                5559645
                28810143
                eeee45a5-4aba-49c2-a521-f45d243a9b4d
                © 2017 The Authors

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

                History
                : 21 December 2016
                : 13 May 2017
                : 14 July 2017
                Categories
                Article

                Oncology & Radiotherapy
                breast cancer,metastasis,relapse,genomics,somatic mutation
                Oncology & Radiotherapy
                breast cancer, metastasis, relapse, genomics, somatic mutation

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