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      Interpreting whole genome and exome sequencing data of individual gastric cancer samples

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          Abstract

          Background

          Gastric cancer is the fourth most common cancer and the second leading cause of cancer death worldwide. In order to understand the genetic background, we sequenced the whole exome and the whole genome of one microsatellite stable as well as one microsatellite unstable tumor and the matched healthy tissue on two different NGS platforms. We here aimed to provide a comparative approach for individual clinical tumor sequencing and annotation using different sequencing technologies and mutation calling algorithms.

          Results

          We applied a population-based whole genome resource as a novel pathway-based filter for interpretation of genomic alterations from single nucleotide variations (SNV), indels, and large structural variations. In addition to a comparison with tumor genome database resources and a filtering approach using data from the 1000 Genomes Project, we performed pyrosequencing analysis and immunohistochemistry in a large cohort of 428 independent gastric cancer cases.

          Conclusion

          We here provide an example comparing the usefulness and potential pitfalls of different technologies for a clinical interpretation of genomic sequence data of individual gastric cancer samples. Using different filtering approaches, we identified a multitude of novel potentially damaging mutations and could show a validated association between a mutation in GNAS and gastric cancer.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-017-3895-z) contains supplementary material, which is available to authorized users.

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          Most cited references28

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          Patterns of somatic mutation in human cancer genomes.

          Cancers arise owing to mutations in a subset of genes that confer growth advantage. The availability of the human genome sequence led us to propose that systematic resequencing of cancer genomes for mutations would lead to the discovery of many additional cancer genes. Here we report more than 1,000 somatic mutations found in 274 megabases (Mb) of DNA corresponding to the coding exons of 518 protein kinase genes in 210 diverse human cancers. There was substantial variation in the number and pattern of mutations in individual cancers reflecting different exposures, DNA repair defects and cellular origins. Most somatic mutations are likely to be 'passengers' that do not contribute to oncogenesis. However, there was evidence for 'driver' mutations contributing to the development of the cancers studied in approximately 120 genes. Systematic sequencing of cancer genomes therefore reveals the evolutionary diversity of cancers and implicates a larger repertoire of cancer genes than previously anticipated.
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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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              Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants.

              We compared whole-exome sequencing (WES) and whole-genome sequencing (WGS) in six unrelated individuals. In the regions targeted by WES capture (81.5% of the consensus coding genome), the mean numbers of single-nucleotide variants (SNVs) and small insertions/deletions (indels) detected per sample were 84,192 and 13,325, respectively, for WES, and 84,968 and 12,702, respectively, for WGS. For both SNVs and indels, the distributions of coverage depth, genotype quality, and minor read ratio were more uniform for WGS than for WES. After filtering, a mean of 74,398 (95.3%) high-quality (HQ) SNVs and 9,033 (70.6%) HQ indels were called by both platforms. A mean of 105 coding HQ SNVs and 32 indels was identified exclusively by WES whereas 692 HQ SNVs and 105 indels were identified exclusively by WGS. We Sanger-sequenced a random selection of these exclusive variants. For SNVs, the proportion of false-positive variants was higher for WES (78%) than for WGS (17%). The estimated mean number of real coding SNVs (656 variants, ∼3% of all coding HQ SNVs) identified by WGS and missed by WES was greater than the number of SNVs identified by WES and missed by WGS (26 variants). For indels, the proportions of false-positive variants were similar for WES (44%) and WGS (46%). Finally, WES was not reliable for the detection of copy-number variations, almost all of which extended beyond the targeted regions. Although currently more expensive, WGS is more powerful than WES for detecting potential disease-causing mutations within WES regions, particularly those due to SNVs.
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                Author and article information

                Contributors
                d.esser@iem.uni-kiel.de
                niklas.holze@medizin.uni-luebeck.de
                jochen.haag@uksh.de
                s.schreiber@mucosa.de
                sandra.krueger@uksh.de
                v.warneke@web.de
                p.rosenstiel@mucosa.de
                +49(0)431-500-15501 , christoph.roecken@uksh.de
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                6 July 2017
                6 July 2017
                2017
                : 18
                : 517
                Affiliations
                [1 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Institute for Clinical Molecular Biology, , Christian-Albrechts-University, ; 24105 Kiel, Germany
                [2 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Institute of Pathology, , Christian-Albrechts-University, ; Arnold-Heller-Str. 3, Haus 14, D-24105 Kiel, Germany
                [3 ]ISNI 0000 0004 0646 2097, GRID grid.412468.d, Department of General Internal Medicine, , University Hospital Schleswig-Holstein, ; 24105 Kiel, Germany
                [4 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Institute for Experimental Medicine, , Christian-Albrechts-University, ; 24105 Kiel, Germany
                Article
                3895
                10.1186/s12864-017-3895-z
                5501078
                28683819
                66e2a65f-7fe1-4f09-abae-80ceda5aca97
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 1 January 2017
                : 22 June 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: Ro1173/11; Ro1173/12
                Award ID: Cluster of Excellence “Inflammation at Interfaces”
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: EU INTERREG IVA HIT-ID
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                gastric cancer,deep sequencing,gnas
                Genetics
                gastric cancer, deep sequencing, gnas

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