Computational Methods for Subtyping of Tumors and Their Applications for Deciphering Tumor Heterogeneity

  • Shihua ZhangEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1878)


With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.

Key words

Bioinformatics Cancer genomics Cancer subtype Cancer stratification Machine learning Model and algorithm 



This work has been supported by the National Natural Science Foundation of China [No. 11661141019, 61621003, 61422309 and 61379092], the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) [XDB13040600], and CAS Frontier Science Research Key Project for Top Young Scientist [No. QYZDB-SSW-SYS008].


  1. 1.
    Parker JS et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167CrossRefGoogle Scholar
  2. 2.
    Curtis C et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486:346–352CrossRefGoogle Scholar
  3. 3.
    Wu G, Stein L (2012) A network module-based method for identifying cancer prognostic signatures. Genome Biol 13:R112CrossRefGoogle Scholar
  4. 4.
    McLendon R et al (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061–1068CrossRefGoogle Scholar
  5. 5.
    Shen R, Olshen AB, Ladanyi M (2009) Integrative Clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25:2906–2912CrossRefGoogle Scholar
  6. 6.
    Zhang S, Liu CC, Li W, Shen H, Laird PW, Zhou XJ (2012) Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res 40:9379–9391CrossRefGoogle Scholar
  7. 7.
    Zhang S, Li Q, Liu J, Zhou XJ (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics 27:i401–i409CrossRefGoogle Scholar
  8. 8.
    Li W, Zhang S, Liu CC, Zhou XJ (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics 28:2458–2466CrossRefGoogle Scholar
  9. 9.
    Paquet ER, Hallet MT (2015) Absolute assignment of breast cancer intrinsic molecular subtype. J Natl Cancer Inst 107:dju357. Scholar
  10. 10.
    Verhaak RG et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17:98–110CrossRefGoogle Scholar
  11. 11.
    Liu Z, Zhang XS, Zhang S (2014) Breast tumor subgroups reveal diverse clinical predictive power. Sci Rep 4:4002CrossRefGoogle Scholar
  12. 12.
    Hofree M, Shen JP, Carter H, Gross A, Ideker T (2013) Network-based stratification of tumor mutations. Nat Methods 10:1108–1115CrossRefGoogle Scholar
  13. 13.
    Liu Z, Zhang S (2015) Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features. BMC Genomics 16:503CrossRefGoogle Scholar
  14. 14.
    Liu Z, Zhang S (2014) Toward a systematic understanding of cancers: a survey of the pan-cancer study. Front Genet 5:194PubMedPubMedCentralGoogle Scholar
  15. 15.
    Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C (2013) Emerging landscape of oncogenic signatures across human cancers. Nat Genet 45:1127–1133CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina

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