Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles

  • Igor Ulitsky
  • Richard M. Karp
  • Ron Shamir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)

Abstract

We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention. Our method uses microarray gene expression profiles derived in clinical case-control studies to identify genes significantly dysregulated in disease specimens, combined with protein interaction data to identify connected sets of genes. Our core algorithm searches for minimal connected subnetworks in which the number of dysregulated genes in each diseased sample exceeds a given threshold. We have applied the method in a study of Huntington’s disease caudate nucleus expression profiles and in a meta-analysis of breast cancer studies. In both cases the results were statistically significant and appeared to home in on compact pathways enriched with hallmarks of the diseases.

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References

  1. 1.
    Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D.: How to infer gene networks from expression profiles. Molecular Systems Biology 3, 78 (2007)Google Scholar
  2. 2.
    van’t Veer, L., Dai, H., van de Vijver, M., He, Y., Hart, A., Mao, M., Peterse, H., van der Kooy, K., Marton, M., Witteveen, A., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)CrossRefGoogle Scholar
  3. 3.
    Segal, E., Friedman, N., Kaminski, N., Regev, A., Koller, D.: From signatures to models: understanding cancer using microarrays. Nat Genet 37(suppl.), S38–S45 (2005)CrossRefGoogle Scholar
  4. 4.
    Subramanian, A., Tamayo, P., Mootha, V., Mukherjee, S., Ebert, B., Gillette, M., Paulovich, A., Pomeroy, S., Golub, T., Lander, E., Mesirov, J.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102, 15545–15550 (2005)CrossRefGoogle Scholar
  5. 5.
    Rapaport, F., Zinovyev, A., Dutreix, M., Barillot, E., Vert, J.: Classification of microarray data using gene networks. BMC Bioinformatics 8, 35 (2007)CrossRefGoogle Scholar
  6. 6.
    Ulitsky, I., Shamir, R.: Identification of functional modules using network topology and high-throughput data. BMC Systems Biology 1 (2007)Google Scholar
  7. 7.
    Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19, I264–I272 (2003)CrossRefGoogle Scholar
  8. 8.
    Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18, S233–S240 (2002)Google Scholar
  9. 9.
    Rajagopalan, D., Agarwal, P.: Inferring pathways from gene lists using a literature-derived network of biological relationships. Bioinformatics 21, 788–793 (2005)CrossRefGoogle Scholar
  10. 10.
    Cabusora, L., Sutton, E., Fulmer, A., Forst, C.: Differential network expression during drug and stress response. Bioinformatics 21, 2898–2905 (2005)CrossRefGoogle Scholar
  11. 11.
    Nacu, S., Critchley-Thorne, R., Lee, P., Holmes, S.: Gene expression network analysis and applications to immunology. Bioinformatics 23, 850 (2007)CrossRefGoogle Scholar
  12. 12.
    Liu, M., Liberzon, A., Kong, S., Lai, W., Park, P., Kohane, I., Kasif, S.: Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genetics 3, e96+ (2007)CrossRefGoogle Scholar
  13. 13.
    Breitling, R., Amtmann, A., Herzyk, P.: Graph-based iterative group analysis enhances microarray interpretation. BMC Bioinformatics 5, 100 (2004)CrossRefGoogle Scholar
  14. 14.
    Chuang, H., Lee, E., Liu, Y., Lee, D., Ideker, T.: Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3 (2007)Google Scholar
  15. 15.
    Hochbaum, D.S.: Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. In: Hochbaum, D.S. (ed.) Approximation algorithms for NP-hard problems, PWS, Boston, pp. 94–143 (1997)Google Scholar
  16. 16.
    Dobson, G.: Worst-case analysis of greedy heuristics for integer programming with non-negative data. Mathematics of Operations Research 7, 515–531 (1982)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Shuai, T., Hu, X.: Connected set cover problem and its applications. In: Cheng, S.-W., Poon, C.K. (eds.) AAIM 2006. LNCS, vol. 4041, pp. 243–254. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (1990)MATHGoogle Scholar
  19. 19.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)CrossRefGoogle Scholar
  20. 20.
    Hodges, A., Strand, A., Aragaki, A., Kuhn, A., Sengstag, T., Hughes, G., Elliston, L., Hartog, C., Goldstein, D., Thu, D., et al.: Regional and cellular gene expression changes in human Huntington’s disease brain. Human Molecular Genetics 15, 965 (2006)CrossRefGoogle Scholar
  21. 21.
    Kaltenbach, L., Romero, E., et al.: Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet 3, e82 (2007)CrossRefGoogle Scholar
  22. 22.
    Rockabrand, E., Slepko, N., Pantalone, A., Nukala, V., Kazantsev, A., Marsh, J., Sullivan, P., Steffan, J., Sensi, S., Thompson, L.: The first 17 amino acids of Huntingtin modulate its sub-cellular localization, aggregation and effects on calcium homeostasis. Human Molecular Genetics 16, 61 (2007)CrossRefGoogle Scholar
  23. 23.
    Borrell-Pagès, M., Zala, D., Humbert, S., Saudou, F.: Huntington’s disease: from huntingtin function and dysfunction to therapeutic strategies. Cellular and Molecular Life Sciences (CMLS) 63, 2642–2660 (2006)CrossRefGoogle Scholar
  24. 24.
    Giuliano, P., De Cristofaro, T., et al.: DNA damage induced by polyglutamine-expanded proteins. Human Molecular Genetics 12, 2301–2309 (2003)CrossRefGoogle Scholar
  25. 25.
    Hoshino, M., Tagawa, K., et al.: Histone deacetylase activity is retained in primary neurons expressing mutant huntingtin protein. J. Neurochem. 87, 257–267 (2003)CrossRefGoogle Scholar
  26. 26.
    Butler, R., Bates, G.: Histone deacetylase inhibitors as therapeutics for polyglutamine disorders. Nat. Rev. Neurosci. 7, 784–796 (2006)CrossRefGoogle Scholar
  27. 27.
    Ferrante, R., Kubilus, J., Lee, J., Ryu, H., Beesen, A., Zucker, B., Smith, K., Kowall, N., Ratan, R., Luthi-Carter, R., et al.: Histone deacetylase inhibition by sodium butyrate chemotherapy ameliorates the neurodegenerative phenotype in Huntington’s disease mice. Journal of Neuroscience 23, 9418–9427 (2003)Google Scholar
  28. 28.
    Borovecki, F., Lovrecic, L., Zhou, J., Jeong, H., Then, F., Rosas, H.D., Hersch, S.M., Hogarth, P., Bouzou, B., Jensen, R.V., Krainc, D.: Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proc. Natl. Acad. Sci. USA 102, 11023–11028 (2005)CrossRefGoogle Scholar
  29. 29.
    Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H., Farmer, P., Praz, V., Haibe-Kains, B., et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 98, 262–272 (2006)CrossRefGoogle Scholar
  30. 30.
    Affar, E., Gay, F., Shi, Y., Liu, H., Huarte, M., Wu, S., Collins, T., Li, E., Shi, Y.: Essential Dosage-Dependent Functions of the Transcription Factor Yin Yang 1 in Late Embryonic Development and Cell Cycle Progression. Molecular and Cellular Biology 26, 3565–3581 (2006)CrossRefGoogle Scholar
  31. 31.
    Begon, D., Delacroix, L., Vernimmen, D., Jackers, P., Winkler, R.: Yin Yang 1 Cooperates with Activator Protein 2 to Stimulate ERBB2 Gene Expression in Mammary Cancer Cells. Journal of Biological Chemistry 280, 24428–24434 (2005)CrossRefGoogle Scholar
  32. 32.
    Li, L., Shaw, P.: Autocrine-mediated activation of STAT3 correlates with cell proliferation in breast carcinoma lines. Journal of Biological Chemistry 277, 17397–17405 (2002)CrossRefGoogle Scholar
  33. 33.
    Futreal, P., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., Stratton, M.: A census of human cancer genes. Nature Reviews Cancer 4, 177–183 (2004)CrossRefGoogle Scholar
  34. 34.
    Efron, B., Tibshirani, R.: An introduction to the bootstrap. Chapman & Hall, New York (1993)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Igor Ulitsky
    • 1
  • Richard M. Karp
    • 2
  • Ron Shamir
    • 1
  1. 1.School of Computer ScienceTel-Aviv UniversityTel-AvivIsrael
  2. 2.International Computer Science InstituteBerkeley 

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