Coupling Regulatory Networks and Microarays: Revealing Molecular Regulations of Breast Cancer Treatment Responses

  • Lefteris Koumakis
  • Vassilis Moustakis
  • Michalis Zervakis
  • Dimitris Kafetzopoulos
  • George Potamias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)


Moving towards the realization of genomic data in clinical practice, and following an individualized healthcare approach, the function and regulation of genes has to be deciphered and manifested. Two of the most significant forms of molecular data come form microarray gene expression sources, and gene interactions sources – as encoded in Gene Regulatory Networks (GRNs). The usual computational task is the gene selection procedure with the GRNs to be mainly utilized for annotation and enrichment purposes. In this study we present a novel perception of these resources. Initially we locate all functional path-modules encoded in GRNs and we try to assess which of them are compatible and match the gene-expression profiles of samples that belong to different phenotypes. The differential power of the selected path-modules is computed and their biological relevance is assessed. The whole approach was applied on a set of microarray studies with the target of revealing putative regulatory mechanisms that govern and putatively guide the treatment responses of BRCA patients. The results were quite satisfactory according to their biological and clinical relevance.


Breast Cancer Gene Regulatory Network Differential Power Breast Cancer Research BRCA Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ideker, T., Galitski, T., Hood, L.: A new approach to decoding life: systems biology. Annual Review of Genomics and Human Genetics 2, 343–372 (2001)CrossRefGoogle Scholar
  2. 2.
    Collins, F.S., Eric, D., Green, E.D., Guttmacher, A.E., Mark, S., Guyer, M.S.: A vision for the future of genomics research. Nature 422, 835–847 (2003)CrossRefGoogle Scholar
  3. 3.
    Simon, R., Radmacher, M.D., Dobbin, K., McShane, L.M.: Pitfalls in the Use of DNA Microarray Data for Diagnostic Classification. Journal of the National Cancer Institute 95(1), 14–18 (2003)CrossRefGoogle Scholar
  4. 4.
    Ambroise, C., McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS 99(10), 6562–6566 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Arkin, A., Ross, J.: Computational functions in biochemical reaction networks. Biophys. J. 67(2), 560–578 (1994)CrossRefGoogle Scholar
  6. 6.
    Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford Univ. Press, New York (1993)Google Scholar
  7. 7.
    Potamias, G., Koumakis, L., Moustakis, V.: Gene Selection via Discretized Gene-Expression Profiles and Greedy Feature-Elimination. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 256–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, Y., et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 365(9460), 671–679 (2005)Google Scholar
  9. 9.
    Sotiriou, C., et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 98(4), 262–272 (2006)CrossRefGoogle Scholar
  10. 10.
    Miller, L.D., et al.: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. PNAS 102(38), 13550–13555 (2005)CrossRefGoogle Scholar
  11. 11.
    Desmedt, C., et al.: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin. Cancer Res. 13(11), 3207–3214 (2007)CrossRefGoogle Scholar
  12. 12.
    Sutherland, R.L.: Endocrine resistance in breast cancer: new roles for ErbB3 and ErbB4. Breast Cancer Research 13(3), 106 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hutcheson, I.R., et al.: Heregulin beta1 drives gefitinib-resistant growth and invasion in tamoxifen-resistant MCF-7 breast cancer cells. Breast Cancer Research 9(4), R50 (2007)Google Scholar
  14. 14.
    Zhu, Y., Sullivan, L.L., Nair, S.S., Williams, C.C., Pandey, A.K., Marrero, L., Vadlamudi, R.K., Jones, F.E., et al.: Coregulation of estrogen receptor by ERBB4/HER4 establishes a growth-promoting autocrine signal in breast tumor cells. Cancer Research 66(16), 7991–7998 (2006)CrossRefGoogle Scholar
  15. 15.
    Sonne-Hansen, K., et al.: Breast cancer cells can switch between estrogen receptor alpha and ErbB signaling and combined treatment against both signaling pathways postpones development of resistance. Breast Cancer Research and Treatment 121(3), 601–613 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lefteris Koumakis
    • 1
  • Vassilis Moustakis
    • 1
    • 2
  • Michalis Zervakis
    • 3
  • Dimitris Kafetzopoulos
    • 4
  • George Potamias
    • 1
  1. 1.Institute of Computer Science, FORTHGreece
  2. 2.Department of Production EngineeringTechnical Univsrsity of ChaniaGreece
  3. 3.Department of Electronic and Computer EngineeringTechnical University of ChaniaGreece
  4. 4.Institute of Molecular Biology & Biotechnology, FORTHGreece

Personalised recommendations