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      Exome Sequencing in BRCA1- and BRCA2-Negative Greek Families Identifies MDM1 and NBEAL1 as Candidate Risk Genes for Hereditary Breast Cancer

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          Abstract

          Approximately 10% of breast cancer (BC) cases are hereditary BC (HBC), with HBC most commonly encountered in the context of hereditary breast and ovarian cancer (HBOC) syndrome. Although thousands of loss-of-function (LoF) alleles in over 20 genes have been associated with HBC susceptibility, the genetic etiology of approximately 50% of cases remains unexplained, even when polygenic risk models are considered. We focused on one of the least-studied European populations and applied whole-exome sequencing (WES) to 52 individuals from 17 Greek HBOC families, in which at least one patient was negative for known HBC risk variants. Initial screening revealed pathogenic variants in known cancer genes, including BARD1:p.Trp91* detected in a cancer-free individual, and MEN1:p.Glu260Lys detected in a BC patient. Gene- and variant-based approaches were applied to exome data to identify candidate risk variants outside of known risk genes. Findings were verified in a collection of Canadian HBOC patients of European ancestry (FBRCAX), in an independent group of Canadian BC patients (CHUM-BC) and controls (CARTaGENE), as well as in individuals from The Cancer Genome Atlas (TCGA) and the UK Biobank (UKB). Rare LoF variants were uncovered in MDM1 and NBEAL1 in Greek and Canadian HBOC patients. We also report prioritized missense variants SETBP1:c.4129G > C and C7orf34:c.248C > T. These variants comprise promising candidates whose role in cancer pathogenicity needs to be explored further.

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          Most cited references21

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          Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth.

          Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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            Realizing the promise of cancer predisposition genes.

            Genes in which germline mutations confer highly or moderately increased risks of cancer are called cancer predisposition genes. More than 100 of these genes have been identified, providing important scientific insights in many areas, particularly the mechanisms of cancer causation. Moreover, clinical utilization of cancer predisposition genes has had a substantial impact on diagnosis, optimized management and prevention of cancer. The recent transformative advances in DNA sequencing hold the promise of many more cancer predisposition gene discoveries, and greater and broader clinical applications. However, there is also considerable potential for incorrect inferences and inappropriate clinical applications. Realizing the promise of cancer predisposition genes for science and medicine will thus require careful navigation.
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              Recurrent SETBP1 mutations in atypical chronic myeloid leukemia.

              Atypical chronic myeloid leukemia (aCML) shares clinical and laboratory features with CML, but it lacks the BCR-ABL1 fusion. We performed exome sequencing of eight aCMLs and identified somatic alterations of SETBP1 (encoding a p.Gly870Ser alteration) in two cases. Targeted resequencing of 70 aCMLs, 574 diverse hematological malignancies and 344 cancer cell lines identified SETBP1 mutations in 24 cases, including 17 of 70 aCMLs (24.3%; 95% confidence interval (CI) = 16-35%). Most mutations (92%) were located between codons 858 and 871 and were identical to changes seen in individuals with Schinzel-Giedion syndrome. Individuals with mutations had higher white blood cell counts (P = 0.008) and worse prognosis (P = 0.01). The p.Gly870Ser alteration abrogated a site for ubiquitination, and cells exogenously expressing this mutant exhibited higher amounts of SETBP1 and SET protein, lower PP2A activity and higher proliferation rates relative to those expressing the wild-type protein. In summary, mutated SETBP1 represents a newly discovered oncogene present in aCML and closely related diseases.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                18 October 2019
                2019
                : 10
                : 1005
                Affiliations
                [1] 1Division of Molecular Biology and Genetics, Biomedical Sciences Research Center Al. Fleming , Vari, Greece
                [2] 2Department of Hygiene and Epidemiology, University of Ioannina Medical School , Ioannina, Greece
                [3] 3Department of Epidemiology and Biostatistics, Imperial College London , London, United Kingdom
                [4] 4Dalla Lana School of Public Health, University of Toronto , Toronto, ON, Canada
                [5] 5Women's College Research Institute, Women’s College Hospital , Toronto, ON, Canada
                [6] 6Centre de recherche du Centre hospitalier de l’Université de Montréal and Institut du cancer de Montréal , Montreal, QC, Canada
                [7] 7Department of Oncology, McGill University , Montreal, QC, Canada
                [8] 8Lady Davis Institute for Medical Research, Jewish General Hospital , Montreal, QC, Canada
                [9] 9Department of Medical Genetics, The Research Institute of the McGill University Health Centre , Montreal, QC, Canada
                [10] 10Department of Medicine, McGill University , Montreal, QC, Canada
                [11] 11Department of Human Genetics, McGill University , Montreal, QC, Canada
                [12] 12Cancer Research Program, The Research Institute of the McGill University Health Centre , Montreal, QC, Canada
                [13] 13McGill University and Genome Quebec Innovation Centre , Montreal, QC, Canada
                Author notes

                Edited by: John Frederick Pearson, University of Otago, New Zealand

                Reviewed by: Chiara Vardabasso, Gotham Therapeutics, United States; Emmanouil Dermitzakis, University of Geneva, Switzerland

                *Correspondence: Antigone S. Dimas, dimas@ 123456fleming.gr ; Jiannis Ragoussis, ioannis.ragoussis@ 123456mcgill.ca

                This article was submitted to Cancer Genetics, a section of the journal Frontiers in Genetics

                †Present Adress: Stavros Glentis, Division of Pediatric Hematology/Oncology, First Department of Pediatrics, University of Athens, Aghia Sophia Children’s Hospital, Athens, Greece

                Article
                10.3389/fgene.2019.01005
                6813924
                31681433
                8b647fed-fb25-457c-9a31-5bf5f3ba2ed9
                Copyright © 2019 Glentis, Dimopoulos, Rouskas, Ntritsos, Evangelou, Narod, Mes-Masson, Foulkes, Rivera, Tonin, Ragoussis and Dimas

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 March 2019
                : 20 September 2019
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 46, Pages: 11, Words: 5623
                Funding
                Funded by: Ministry of National Education and Religious Affairs 10.13039/501100006408
                Funded by: Stavros Niarchos Foundation 10.13039/501100004343
                Categories
                Genetics
                Original Research

                Genetics
                hereditary breast cancer,exome sequencing,greek population,candidate risk variants,mdm1,nbeal1

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