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      Germline Mutation in MUS81 Resulting in Impaired Protein Stability is Associated with Familial Breast and Thyroid Cancer

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          Abstract

          Multiple primary thyroid cancer (TC) and breast cancer (BC) are commonly diagnosed, and the lifetime risk for these cancers is increased in patients with a positive family history of both TC and BC. Although this phenotype is partially explained by TP53 or PTEN mutations, a significant number of patients are negative for these alterations. We judiciously recruited patients diagnosed with BC and/or TC having a family history of these tumors and assessed their whole-exome sequencing. After variant prioritization, we selected MUS81 c.1292G>A (p.R431H) for further investigation. This variant was genotyped in a healthy population and sporadic BC/TC tissues and investigated at the protein level and cellular models. MUS81 c.1292G>A was the most frequent variant (25%) and the strongest candidate due to its function of double-strand break repair. This variant was confirmed in four relatives from two families. MUS81 p.R431H protein exhibited lower expression levels in tumors from patients positive for the germline variant, compared with wild-type BC, and normal breast and thyroid tissues. Using cell line models, we showed that c.1292G>A induced protein instability and affected DNA damage response. We suggest that MUS81 is a novel candidate involved in familial BC/TC based on its low frequency in healthy individuals and proven effect in protein stability.

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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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            In silico prediction of splice-altering single nucleotide variants in the human genome

            In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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              The UCSC Xena Platform for cancer genomics data visualization and interpretation

              UCSC Xena is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. The investigator-driven platform consists of a web-based Xena Browser and turn-key Xena Hubs. Xena showcases seminal cancer genomics datasets from TCGA, Pan-Cancer Atlas, PCAWG, ICGC, GTEx, and the GDC; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data modalities, including SNPs, INDELs, large structural variants, CNV, gene and other types of expression, DNA methylation, clinical and phenotypic annotations. A researcher can host their own data securely via private hubs running on a laptop or behind a firewall, with visual and analytical integration occurring only within the Xena Browser. Browser features include the high performance Visual Spreadsheet, dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and bookmarks.
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                20 May 2020
                May 2020
                : 12
                : 5
                : 1289
                Affiliations
                [1 ]Faculty of Medicine, Sao Paulo State University, UNESP, Botucatu SP 18618-687, Brazil; maisapinheiro12@ 123456gmail.com
                [2 ]International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; flupinacci@ 123456accamargo.org.br (F.C.S.L.); karinamirsant@ 123456gmail.com (K.M.S.); biomarchi@ 123456gmail.com (F.A.M.); tatianebasso2015@ 123456gmail.com (T.R.B.); marianabisarro@ 123456yahoo.com.br (M.B.d.R.); mroffe@ 123456accamargo.org.br (M.R.); ghajj@ 123456cipe.accamargo.org.br (G.N.M.H.); lpkowalski@ 123456accamargo.org.br (L.P.K.)
                [3 ]Department of Surgery and Orthopedics, Experimental Research Unity, Faculty of Medicine, São Paulo State University, UNESP, Botucatu SP 18618-687, Brazil; sandradrigo@ 123456gmail.com
                [4 ]Department of Veterinary Surgery and Anesthesiology, São Paulo State University, UNESP, Botucatu SP 18618-681, Brazil; carlos.e.alves@ 123456unesp.br
                [5 ]Department of Genetics and Evolutionary Biology, University of São Paulo, USP, São Paulo SP 05508-090, Brazil; soniacsandrade@ 123456ib.usp.br
                [6 ]Department of Clinical Genetics, Vejle University Hospital, 7100 Vejle, Denmark; mads.jorgensen@ 123456rsyd.dk
                [7 ]Department of Genetics and Morphology, Institute of Biological Sciences, University of Brasília, UnB, Brasília DF 70910-900, Brazil; rolando.andre@ 123456unb.br
                [8 ]Krembil Research Institute, UHN, University of Toronto, Toronto, ON M5G 2C4, Canada; juris@ 123456ai.utoronto.ca
                [9 ]Institute of Neuroimmunology, Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
                [10 ]Cancer Genetics Unit, Centro de Oncologia, Hospital Sirio Libanês, São Paulo SP 01308-050, Brazil; miachatz@ 123456gmail.com
                [11 ]Department of Clinical Genetics, Vejle University Hospital, Institute of Regional Health Research, University of Southern Denmark, 5000 Odense, Denmark
                Author notes
                Author information
                https://orcid.org/0000-0002-9112-1179
                https://orcid.org/0000-0002-6326-7795
                https://orcid.org/0000-0001-6747-6642
                https://orcid.org/0000-0002-6702-6139
                https://orcid.org/0000-0002-1302-5261
                https://orcid.org/0000-0003-4742-7047
                https://orcid.org/0000-0001-7247-3067
                https://orcid.org/0000-0002-2507-946X
                https://orcid.org/0000-0001-6894-1219
                https://orcid.org/0000-0003-4637-5687
                Article
                cancers-12-01289
                10.3390/cancers12051289
                7281423
                32443704
                443743fa-95af-4116-96fe-cb24c85e60d2
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 March 2020
                : 12 May 2020
                Categories
                Article

                exome sequencing,mus81,breast cancer,thyroid cancer,functional analysis,familial cancer

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