Systems Biology for Multiplatform Data Integration: An Overview

  • Elad ZivEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)


In this chapter, we consider some of the concepts behind multiplatform data integration. First, we examine the types of inferences that can be made using methods that integrate data types. Next, we discuss some broad considerations about methodologies. We conclude with the example of joint analyses of germ line genetic variation, gene expression and complex phenotypes. This chapter draws heavily from analyses that integrate datasets for inference on hereditary aspects of cancer susceptibility. However, these concepts should apply more broadly to other domains.

Key words

Models Prediction Cluster analysis 


  1. 1.
    Greenman C, Stephens P, Smith R et al (2007) Patterns of somatic mutation in human cancer genomes. Nature 446(7132):153–158CrossRefGoogle Scholar
  2. 2.
    van de Vijver MJ, He YD, van’t Veer LJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009CrossRefGoogle Scholar
  3. 3.
    Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826CrossRefGoogle Scholar
  4. 4.
    Gianni L, Zambetti M, Clark K et al (2005) Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol 23(29):7265–7277CrossRefGoogle Scholar
  5. 5.
    Krop I, Ismaila N, Andre F et al (2017) Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast Cancer: American Society of Clinical Oncology clinical practice guideline focused update. J Clin Oncol 35(24):2838–2847CrossRefGoogle Scholar
  6. 6.
    Vazquez AI, Veturi Y, Behring M et al (2016) Increased proportion of variance explained and prediction accuracy of survival of breast Cancer patients with use of whole-genome multiomic profiles. Genetics 203(3):1425–1438CrossRefGoogle Scholar
  7. 7.
    Galon J, Costes A, Sanchez-Cabo F et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313(5795):1960–1964CrossRefGoogle Scholar
  8. 8.
    Ayers M, Lunceford J, Nebozhyn M et al (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127(8):2930–2940CrossRefGoogle Scholar
  9. 9.
    Aguiar PN Jr, De Mello RA, Hall P, Tadokoro H, Lima Lopes G (2017) PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: updated survival data. Immunotherapy 9(6):499–506CrossRefGoogle Scholar
  10. 10.
    McGranahan N, Furness AJ, Rosenthal R et al (2016) Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351(6280):1463–1469CrossRefGoogle Scholar
  11. 11.
    Gibney GT, Weiner LM, Atkins MB (2016) Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol 17(12):e542–e551CrossRefGoogle Scholar
  12. 12.
    Perou CM, Jeffrey SS, van de Rijn M et al (1999) Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A 96(16):9212–9217CrossRefGoogle Scholar
  13. 13.
    Sorlie T, Perou CM, Tibshirani R et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19):10869–10874CrossRefGoogle Scholar
  14. 14.
    Sorlie T, Tibshirani R, Parker J et al (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 100(14):8418–8423CrossRefGoogle Scholar
  15. 15.
    Cancer Genome Atlas N (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70CrossRefGoogle Scholar
  16. 16.
    Robertson AG, Kim J, Al-Ahmadie H et al (2017) Comprehensive molecular characterization of muscle-invasive bladder Cancer. Cell 171(3):540–556 e525CrossRefGoogle Scholar
  17. 17.
    Hoadley KA, Yau C, Wolf DM et al (2014) Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158(4):929–944CrossRefGoogle Scholar
  18. 18.
    Turner N, Tutt A, Ashworth A (2004) Hallmarks of ‘BRCAness’ in sporadic cancers. Nat Rev Cancer 4(10):814–819CrossRefGoogle Scholar
  19. 19.
    Polak P, Kim J, Braunstein LZ et al (2017) A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat Genet 49(10):1476–1486CrossRefGoogle Scholar
  20. 20.
    Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500(7463):415–421CrossRefGoogle Scholar
  21. 21.
    Le DT, Durham JN, Smith KN et al (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357(6349):409–413CrossRefGoogle Scholar
  22. 22.
    Maxwell KN, Wubbenhorst B, Wenz BM et al (2017) BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers. Nat Commun 8(1):319CrossRefGoogle Scholar
  23. 23.
    Carter H, Marty R, Hofree M et al (2017) Interaction landscape of inherited polymorphisms with somatic events in Cancer. Cancer Discov 7(4):410–423CrossRefGoogle Scholar
  24. 24.
    Morley M, Molony CM, Weber TM et al (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430(7001):743–747CrossRefGoogle Scholar
  25. 25.
    Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437(7063):1365–1369CrossRefGoogle Scholar
  26. 26.
    Pickrell JK, Marioni JC, Pai AA et al (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464(7289):768–772CrossRefGoogle Scholar
  27. 27.
    Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 6(4):e1000888CrossRefGoogle Scholar
  28. 28.
    Hormozdiari F, van de Bunt M, Segre AV et al (2016) Colocalization of GWAS and eQTL signals detects target genes. Am J Hum Genet 99(6):1245–1260CrossRefGoogle Scholar
  29. 29.
    Gamazon ER, Segre AV, van de Bunt M et al (2018) Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet 50(7):956–967CrossRefGoogle Scholar
  30. 30.
    Gamazon ER, Wheeler HE, Shah KP et al (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47(9):1091–1098CrossRefGoogle Scholar
  31. 31.
    Gusev A, Ko A, Shi H et al (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48(3):245–252CrossRefGoogle Scholar
  32. 32.
    Mancuso N, Shi H, Goddard P, Kichaev G, Gusev A, Pasaniuc B (2017) Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am J Hum Genet 100(3):473–487CrossRefGoogle Scholar
  33. 33.
    Reshef YA, Finucane HK, Kelley DR et al (2018) Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 50(10):1483–1493CrossRefGoogle Scholar
  34. 34.
    Gusev A, Mancuso N, Won H et al (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50(4):538–548CrossRefGoogle Scholar
  35. 35.
    Raj T, Li YI, Wong G et al (2018) Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet 50(11):1584–1592CrossRefGoogle Scholar
  36. 36.
    Ratnapriya R, Sosina OA, Starostik MR et al (2019) Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat GenetGoogle Scholar
  37. 37.
    Wu L, Shi W, Long J et al (2018) A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet 50(7):968–978CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Division of General Internal Medicine, Department of Medicine, Helen Diller Family Comprehensive Cancer Center, Institute for Human GeneticsUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations