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Germline Variation and Somatic Alterations in Ewing Sarcoma

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Ewing Sarcoma

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2226))

Abstract

Ewing sarcoma (EwS) is a rare bone or soft tissue tumor that occurs early in life and as such genetic variation is a major contributor to EwS risk. To date, genetic investigations have identified key somatic mutations and germline variants of importance for EwS risk. While substantial progress is being made in uncovering the genetic etiology of EwS, considerable gaps in knowledge remain. Herein, we provide a summary of methodological approaches for future genomic investigations of EwS. We anticipate this recommended analytical framework for germline and somatic investigations, along with genomic data from growing EwS case series, will aid in accelerating new genomic discoveries in EwS and expand knowledge of the genetic architecture of EwS.

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References

  1. Moore LE, Gold L, Stewart PA et al (2005) Parental occupational exposures and Ewing’s sarcoma. Int J Cancer 114:472–478

    Article  CAS  PubMed  Google Scholar 

  2. Valery PC, McWhirter W, Sleigh A et al (2002) Farm exposures, parental occupation, and risk of Ewing’s sarcoma in Australia: a national case-control study. Cancer Causes Control CCC 13:263–270

    Article  PubMed  Google Scholar 

  3. Grünewald TGP, Cidre-Aranaz F, Surdez D et al (2018) Ewing sarcoma. Nat Rev Dis Primer 4:5

    Article  Google Scholar 

  4. Fraumeni JF, Glass AG (1970) Rarity of Ewing’s sarcoma among U.S. Negro children. Lancet Lond Engl 1:366–367

    Article  Google Scholar 

  5. Jensen RD, Drake RM (1970) Rarity of Ewing’s tumour in Negroes. Lancet Lond Engl 1:777

    Article  CAS  Google Scholar 

  6. Jawad MU, Cheung MC, Min ES et al (2009) Ewing sarcoma demonstrates racial disparities in incidence-related and sex-related differences in outcome: an analysis of 1631 cases from the SEER database, 1973–2005. Cancer 115:3526–3536

    Article  PubMed  Google Scholar 

  7. Joyce MJ, Harmon DC, Mankin HJ et al (1984) Ewing’s sarcoma in female siblings. A clinical report and review of the literature. Cancer 53:1959–1962

    Article  CAS  PubMed  Google Scholar 

  8. Rahman N (2014) Realizing the promise of cancer predisposition genes. Nature 505:302–308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Turc-Carel C, Aurias A, Mugneret F et al (1988) Chromosomes in Ewing’s sarcoma. I. An evaluation of 85 cases of remarkable consistency of t(11;22)(q24;q12). Cancer Genet Cytogenet 32:229–238

    Article  CAS  PubMed  Google Scholar 

  10. Delattre O, Zucman J, Plougastel B et al (1992) Gene fusion with an ETS DNA-binding domain caused by chromosome translocation in human tumours. Nature 359:162–165

    Article  CAS  PubMed  Google Scholar 

  11. Sankar S, Lessnick SL (2011) Promiscuous partnerships in Ewing’s sarcoma. Cancer Genet 204:351–365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Postel-Vinay S, Véron AS, Tirode F et al (2012) Common variants near TARDBP and EGR2 are associated with susceptibility to Ewing sarcoma. Nat Genet 44:323–327

    Article  CAS  PubMed  Google Scholar 

  13. Machiela MJ, Grünewald TGP, Surdez D et al (2018) Genome-wide association study identifies multiple new loci associated with Ewing sarcoma susceptibility. Nat Commun 9:3184

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Grünewald TGP, Bernard V, Gilardi-Hebenstreit P et al (2015) Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite. Nat Genet 47:1073–1078

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Brohl AS, Patidar R, Turner CE et al (2017) Frequent inactivating germline mutations in DNA repair genes in patients with Ewing sarcoma. Genet Med Off J Am Coll Med Genet 19:955–958

    CAS  Google Scholar 

  16. Worch J, Cyrus J, Goldsby R et al (2011) Racial differences in the incidence of mesenchymal tumors associated with EWSR1 translocation. Cancer Epidemiol Biomarkers Prev 20:449–453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74:5463–5467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gangwal K, Sankar S, Hollenhorst PC et al (2008) Microsatellites as EWS/FLI response elements in Ewing’s sarcoma. Proc Natl Acad Sci U S A 105:10149–10154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Loh P-R, Genovese G, Handsaker RE et al (2018) Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. Nature 559:350–355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Machiela MJ, Zhou W, Sampson JN et al (2015) Characterization of large structural genetic mosaicism in human autosomes. Am J Hum Genet 96:487–497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Loh P-R, Danecek P, Palamara PF et al (2016) Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 48:1443–1448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Das S, Forer L, Schönherr S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Brody JA, Morrison AC, Bis JC et al (2017) Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nat Genet 49:1560–1563

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. McCarthy S, Das S, Kretzschmar W et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279–1283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Auton A, Abecasis GR, Altshuler DM et al (2015) A global reference for human genetic variation. Nature 526:68–74

    Article  PubMed  CAS  Google Scholar 

  27. Chen C-Y, Pollack S, Hunter DJ et al (2013) Improved ancestry inference using weights from external reference panels. Bioinforma Oxf Engl 29:1399–1406

    Article  CAS  Google Scholar 

  28. Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  29. Marchini J, Howie B, Myers S et al (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39:906–913

    Article  CAS  PubMed  Google Scholar 

  30. Loh P-R, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284–290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhou W, Nielsen JB, Fritsche LG et al (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet 50:1335–1341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Turner S (2018) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. In: J. Open Source Softw. https://doi.org/10.1101/005165. Accessed 27 Nov 2019

  33. Machiela MJ, Chanock SJ (2015) LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinforma Oxf Engl 31:3555–3557

    Article  CAS  Google Scholar 

  34. Machiela MJ, Chanock SJ (2018) LDassoc: an online tool for interactively exploring genome-wide association study results and prioritizing variants for functional investigation. Bioinforma Oxf Engl 34:887–889

    Article  CAS  Google Scholar 

  35. Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40:D930–D934

    Article  CAS  PubMed  Google Scholar 

  36. Boyle AP, Hong EL, Hariharan M et al (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma Oxf Engl 25:1754–1760

    Article  CAS  Google Scholar 

  38. DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Garrison E, Marth G (2012) Haplotype-based variant detection from short-read sequencing. ArXiv12073907 Q-Bio

    Google Scholar 

  40. Kim S, Scheffler K, Halpern AL et al (2018) Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods 15:591–594

    Article  CAS  PubMed  Google Scholar 

  41. Layer RM, Chiang C, Quinlan AR, Hall IM (2014) LUMPY: a probabilistic framework for structural variant discovery. Genome Biol 15:R84

    Article  PubMed  PubMed Central  Google Scholar 

  42. Chen X, Schulz-Trieglaff O, Shaw R et al (2016) Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32:1220–1222

    Article  CAS  PubMed  Google Scholar 

  43. Wala JA, Bandopadhayay P, Greenwald N et al (2018) SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res 28(4):581–591. https://doi.org/10.1101/gr.221028.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wu MC, Lee S, Cai T et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 91:224–237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lee S, Abecasis GR, Boehnke M, Lin X (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95:5–23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tate JG, Bamford S, Jubb HC et al (2019) COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 47:D941–D947

    Article  CAS  PubMed  Google Scholar 

  49. Landrum MJ, Lee JM, Riley GR et al (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42:D980–D985

    Article  CAS  PubMed  Google Scholar 

  50. Rentzsch P, Witten D, Cooper GM et al (2019) CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 47:D886–D894

    Article  CAS  PubMed  Google Scholar 

  51. Ioannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99:877–885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kim S, Jhong J-H, Lee J, Koo J-Y (2017) Meta-analytic support vector machine for integrating multiple omics data. BioData Min 10

    Google Scholar 

  53. Musa J, Cidre-Aranaz F, Aynaud M-M et al (2019) Cooperation of cancer drivers with regulatory germline variants shapes clinical outcomes. Nat Commun 10:1–10

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA

    Mitchell J. Machiela

  2. Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Thomas G. P. Grünewald

Authors
  1. Mitchell J. Machiela
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  2. Thomas G. P. Grünewald
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Corresponding author

Correspondence to Mitchell J. Machiela .

Editor information

Editors and Affiliations

  1. Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ) Hopp-Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Florencia Cidre-Aranaz

  2. Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), Hopp-Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Thomas G. P. Grünewald

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Machiela, M.J., Grünewald, T.G.P. (2021). Germline Variation and Somatic Alterations in Ewing Sarcoma. In: Cidre-Aranaz, F., G. P. Grünewald, T. (eds) Ewing Sarcoma . Methods in Molecular Biology, vol 2226. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1020-6_1

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  • DOI: https://doi.org/10.1007/978-1-0716-1020-6_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1019-0

  • Online ISBN: 978-1-0716-1020-6

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