Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data

Part of the Methods in Molecular Biology book series (MIMB, volume 1833)


Somatic copy number alterations (SCNAs) are profound in cancer genomes at different stages: oncogenesis, progression, and metastasis. Accurate detection and characterization of SCNA landscape at genome-wide scale are of great importance. Next-generation sequencing and SNP array are current technology of choice for SCNA analysis. They are able to quantify SCNA with high resolution and meanwhile raise great challenges in data analysis. To this end, we have developed an R package saasCNV for SCNA analysis using (1) whole-genome sequencing (WGS), (2) whole-exome sequencing (WES) or (3) whole-genome SNP array data. In this chapter, we provide the features of the package and step-by-step instructions in detail.

Key words

Copy number variation Somatic copy number alteration SAAS-CNV Next-generation sequencing Whole-genome sequencing Whole-exome sequencing SNP array Segmentation Cancer genome 


  1. 1.
    Zack TI, Schumacher SE, Carter SL et al (2013) Pan-cancer patterns of somatic copy number alteration. Nat Genet 45(10):1134–1140. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Kim TM, Xi R, Luquette LJ et al (2013) Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes. Genome Res 23(2):217–227. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wang K, Lim HY, Shi S et al (2013) Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma. Hepatology 58(2):706–717. CrossRefPubMedGoogle Scholar
  4. 4.
    Torrecilla S, Sia D, Harrington AN et al (2017) Trunk mutational events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma. J Hepatol 67(6):1222–1231. CrossRefPubMedGoogle Scholar
  5. 5.
    Uzilov AV, Ding W, Fink MY et al (2016) Development and clinical application of an integrative genomic approach to personalized cancer therapy. Genome Med 8(1):62. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Gusnanto A, Wood HM, Pawitan Y et al (2012) Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data. Bioinformatics 28(1):40–47. CrossRefPubMedGoogle Scholar
  7. 7.
    Pinkel D, Segraves R, Sudar D et al (1998) High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 20(2):207–211. CrossRefPubMedGoogle Scholar
  8. 8.
    Peiffer DA, Le JM, Steemers FJ et al (2006) High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res 16(9):1136–1148. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Campbell PJ, Stephens PJ, Pleasance ED et al (2008) Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat Genet 40(6):722–729. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Chiang DY, Getz G, Jaffe DB et al (2009) High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat Methods 6(1):99–103. CrossRefPubMedGoogle Scholar
  11. 11.
    Zhang Z, Hao K (2015) SAAS-CNV: a joint segmentation approach on aggregated and allele specific signals for the identification of somatic copy number alterations with next-generation sequencing data. PLoS Comput Biol 11(11):e1004618. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Kim HY, Choi JW, Lee JY et al (2017) Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data. Oncotarget 8(16):27277–27285. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Core Team R (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  14. 14.
    McKenna A, Hanna M, Banks E et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    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(5):491–498. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zhang NR, Siegmund DO, Ji H et al (2010) Detecting simultaneous changepoints in multiple sequences. Biometrika 97(3):631–645. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31(3):213–219. CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22(3):568–576. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale BiologyIcahn School of Medicine at Mount SinaiNew YorkUSA

Personalised recommendations