An Integrative MuSiCO Algorithm: From the Patient-Specific Transcriptional Profiles to Novel Checkpoints in Disease Pathobiology

  • Anastasia Meshcheryakova
  • Philip Zimmermann
  • Rupert Ecker
  • Felicitas Mungenast
  • Georg Heinze
  • Diana MechtcheriakovaEmail author
Part of the RNA Technologies book series (RNATECHN)


Strong efforts are invested in the field of cancer and other multifactorial diseases to evaluate the applicability of gene expression patterns for identification of novel disease-relevant checkpoints and nomination of promising biomarkers for disease and/or targets. Deciphering the disease complexity demands the implementation of a holistic approach, which covers the levels of the biological hierarchy from molecules to functional gene network(s) and biological pathways and further to disease (patho)mechanisms and clinical relevance. In this chapter we describe the systems biology-based integrative algorithm, named by us as MuSiCO/fromMultigeneSignature to Patient-OrientatedClinicalOutcome, and discuss its applicability for translational research. This innovative approach is based on the implementation of consecutive analytical modules integrating advanced gene expression profiling of clinical patient specimens, prognostic/predictive modeling, digital pathology, and systems biology. It consolidates in-depth expertise from diverse scientific and medical disciplines and hereby bridges systems biology and systems medicine to maximize the benefit of the patient.


MuSiCO algorithm Multigene signature Gene expression profiling Statistical modeling for survival prediction and therapy response AID/APOBEC gene family Sphingolipid system Systems biology Next generation digital pathology Personalized medicine 

List of Abbreviations


Activation-induced cytidine deaminase


Colorectal cancer metastasis in the liver


Epithelial to mesenchymal transition


from Multigene signature to patient-orientated clinical outcome


Regions of interest


  1. Baak JPA (1991) Manual of quantitative pathology in cancer diagnosis and prognosis. Springer, BerlinGoogle Scholar
  2. Becht E, Giraldo NA, Dieu-Nosjean MC, Sautes-Fridman C, Fridman WH (2016) Cancer immune contexture and immunotherapy. Curr Opin Immunol 39:7–13CrossRefPubMedGoogle Scholar
  3. Conticello SG (2012) Creative deaminases, self-inflicted damage, and genome evolution. Ann N Y Acad Sci 1267:79–85CrossRefPubMedGoogle Scholar
  4. Courvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV (2011) Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol 64:993–1000CrossRefPubMedGoogle Scholar
  5. De Bin R, Herold T, Boulesteix AL (2014) Added predictive value of omics data: specific issues related to validation illustrated by two case studies. BMC Med Res Methodol 14:117CrossRefPubMedPubMedCentralGoogle Scholar
  6. De Bin R, Janitza S, Sauerbrei W, Boulesteix AL (2016) Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Biometrics 72:272–280CrossRefPubMedGoogle Scholar
  7. Dunkler D, Michiels S, Schemper M (2007) Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis? Eur J Cancer 43(4):745–751CrossRefPubMedGoogle Scholar
  8. Efron B, Tibshirani R (1997) Improvements on cross-validation: the.632+ bootstrap method. J Am Stat Assoc 92:548–560Google Scholar
  9. Fridman WH, Pages F, Sautes-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12:298–306CrossRefGoogle Scholar
  10. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313(5795):1960–1964CrossRefGoogle Scholar
  11. Gillet JP, Calcagno AM, Varma S, Davidson B, Bunkholt Elstrand M et al (2012) Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma. Clin Cancer Res 18(11):3197–3206CrossRefPubMedPubMedCentralGoogle Scholar
  12. Gleiss A, Zeillinger R, Braicu EI, Trillsch F, Vergote I et al (2016) Statistical controversies in clinical research: the importance of importance. Ann Oncol 27(7):1185–1189CrossRefPubMedGoogle Scholar
  13. Gleiss A, Oberbauer R, Heinze G (2017) An unjustified benefit: immortal time bias in the analysis of time-dependent events. Transpl Int.
  14. Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New YorkCrossRefGoogle Scholar
  15. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344CrossRefPubMedGoogle Scholar
  16. Heinze G, Wallisch C, Dunkler D (2018) Variable selection – a review and recommendations for the practicing statistician. Biom J 60(3):431–449Google Scholar
  17. Mechtcheriakova D, Sobanov Y, Holtappels G, Bajna E, Svoboda M et al (2011) Activation-induced cytidine deaminase (AID)-associated multigene signature to assess impact of AID in etiology of diseases with inflammatory component. PLoS One 6(10):e25611CrossRefPubMedPubMedCentralGoogle Scholar
  18. Mechtcheriakova D, Svoboda M, Meshcheryakova A, Jensen-Jarolim E (2012) Activation-induced cytidine deaminase (AID) linking immunity, chronic inflammation, and cancer. Cancer Immunol Immunother 61:1591–1598CrossRefPubMedPubMedCentralGoogle Scholar
  19. Meshcheryakova A, Tamandl D, Bajna E, Stift J, Mittlboeck M et al (2014) B cells and ectopic follicular structures: novel players in anti-tumor programming with prognostic power for patients with metastatic colorectal cancer. PLoS One 9:e99008CrossRefPubMedPubMedCentralGoogle Scholar
  20. Meshcheryakova A, Svoboda M, Tahir A, Kofeler HC, Triebl A et al (2016) Exploring the role of sphingolipid machinery during the epithelial to mesenchymal transition program using an integrative approach. Oncotarget 7(16):22295–22323CrossRefPubMedPubMedCentralGoogle Scholar
  21. Muramatsu M, Kinoshita K, Fagarasan S, Yamada S, Shinkai Y et al (2000) Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme. Cell 102:553–563CrossRefGoogle Scholar
  22. Okazaki IM, Hiai H, Kakazu N, Yamada S, Muramatsu M et al (2003) Constitutive expression of AID leads to tumorigenesis. J Exp Med 197:1173–1181CrossRefPubMedPubMedCentralGoogle Scholar
  23. Peduzzi P, Concato J, Feinstein AR, Holford TR (1995) Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 48:1503–1510CrossRefPubMedGoogle Scholar
  24. Schemper M (2003) Predictive accuracy and explained variation. Stat Med 22:2299–2308CrossRefPubMedGoogle Scholar
  25. Shen-Orr SS, Gaujoux R (2013) Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 25:571–578CrossRefPubMedGoogle Scholar
  26. Smith GC, Seaman SR, Wood AM, Royston P, White IR (2014) Correcting for optimistic prediction in small data sets. Am J Epidemiol 180:318–324CrossRefPubMedPubMedCentralGoogle Scholar
  27. Svoboda M, Meshcheryakova A, Heinze G, Jaritz M, Pils D et al (2016) AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer. BMC Genomics 17:643CrossRefPubMedPubMedCentralGoogle Scholar
  28. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385–395CrossRefPubMedGoogle Scholar
  29. Uno H, Cai T, Pencina MJ, D’Agostino RB, Wei LJ (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med 30:1105–1117PubMedPubMedCentralGoogle Scholar
  30. Van Houwelingen JC, Le Cessie S (1990) Predictive value of statistical models. Stat Med 9:1303–1325CrossRefPubMedGoogle Scholar
  31. Verweij PJ, Van Houwelingen HC (1994) Penalized likelihood in Cox regression. Stat Med 13:2427–2436CrossRefPubMedGoogle Scholar
  32. Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165:710–718CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anastasia Meshcheryakova
    • 1
  • Philip Zimmermann
    • 2
  • Rupert Ecker
    • 3
  • Felicitas Mungenast
    • 1
  • Georg Heinze
    • 4
  • Diana Mechtcheriakova
    • 1
    Email author
  1. 1.Department of Pathophysiology and Allergy ResearchCenter for Pathophysiology, Infectiology and Immunology, Medical University of ViennaViennaAustria
  2. 2.Nebion AGZürichSwitzerland
  3. 3.TissueGnosticsViennaAustria
  4. 4.Section for Clinical BiometricsCenter for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaViennaAustria

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