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      Proteogenomics connects somatic mutations to signaling in breast cancer

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          Summary

          Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. We describe quantitative mass spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers of which 77 provided high-quality data. Integrated analyses allowed insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated against the Library of Integrated Network-based Cellular Signatures, thereby connecting CETN3 and SKP1 loss to elevated expression of EGFR, and SKP1 loss also to increased SRC. Global proteomic data confirmed a stromal-enriched group in addition to basal and luminal clusters and pathway analysis of the phosphoproteome identified a G Protein-coupled receptor cluster that was not readily identified at the mRNA level. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.

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          Most cited references19

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          Ubiquitin ligases: cell-cycle control and cancer.

          A driving force of the cell cycle is the activation of cyclin-dependent kinases (CDKs), the activities of which are controlled by the ubiquitin-mediated proteolysis of key regulators such as cyclins and CDK inhibitors. Two ubiquitin ligases, the SKP1-CUL1-F-box-protein (SCF) complex and the anaphase-promoting complex/cyclosome (APC/C), are responsible for the specific ubiquitylation of many of these regulators. Deregulation of the proteolytic system might result in uncontrolled proliferation, genomic instability and cancer. Cumulative clinical evidence shows alterations in the ubiquitylation of cell-cycle regulators in the aetiology of many human malignancies. A better understanding of the ubiquitylation machinery will provide new insights into the regulatory biology of cell-cycle transitions and the development of anti-cancer drugs.
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            Is Open Access

            Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.

            To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
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              LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

              For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                23 April 2016
                25 May 2016
                25 May 2016
                25 November 2016
                : 534
                : 7605
                : 55-62
                Affiliations
                [1 ]The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [2 ]Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York, NY 10016, USA
                [3 ]Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
                [4 ]Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai New York, NY 10029, USA
                [5 ]Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
                [6 ]Department of Medicine, The Genome Institute, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63108, USA
                [7 ]Department of Oncology-Pathology, Karolinska Institute, 171 76 Stockholm, Sweden
                [8 ]Lester and Sue Smith Breast Center, Dan L. Duncan Comprehensive Cancer Center and Departments of Medicine and Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
                [9 ]Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [10 ]Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri 63110, USA
                [11 ]Biostatistics Center, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, United States
                [12 ]Department of Biomedical Informatics and Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA
                [13 ]National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
                Author notes
                Correspondence and requests for materials should be addressed to P.M. ( pmertins@ 123456broadinstitute.org ), M.J.E. ( Matthew.Ellis@ 123456bcm.edu ), or S.A.C. ( scarr@ 123456broad.mit.edu )
                [#]

                List of participants in the NCI CPTAC and their affiliations appear in Supplementary Information.

                [*]

                These authors contributed equally to this work

                Article
                NIHMS778057
                10.1038/nature18003
                5102256
                27251275
                e8b41a12-57ce-4624-a3e6-3ed80e2410d9

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