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Dynamic modularity in protein interaction networks predicts breast cancer outcome

Abstract

Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may be useful as an indicator of breast cancer prognosis.

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Figure 1: Evidence of dynamic network modularity in the human interactome.
Figure 2: Structural and functional features of intermodular and intramodular hubs.
Figure 3: Differences in dynamic network properties in breast cancer tumors.
Figure 4: Dynamic network properties predict breast cancer outcome.

References

  1. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).

    Article  Google Scholar 

  2. Brown, K.R. & Jurisica, I. Online predicted human interaction database. Bioinformatics 21, 2076–2082 (2005).

    Article  CAS  Google Scholar 

  3. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  Google Scholar 

  4. Chatr-aryamontri, A. et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–D574 (2007).

    Article  CAS  Google Scholar 

  5. von Mering, C. et al. STRING 7–recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 35, D358–D362 (2007).

    Article  CAS  Google Scholar 

  6. Fraser, H.B. Modularity and evolutionary constraint on proteins. Nat. Genet. 37, 351–352 (2005).

    Article  CAS  Google Scholar 

  7. Han, J.D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004).

    Article  CAS  Google Scholar 

  8. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  9. de Lichtenberg, U., Jensen, L.J., Brunak, S. & Bork, P. Dynamic complex formation during the yeast cell cycle. Science 307, 724–727 (2005).

    Article  CAS  Google Scholar 

  10. Tengowski, M.W., Feng, D., Sutovsky, M. & Sutovsky, P. Differential expression of genes encoding constitutive and inducible 20S proteasomal core subunits in the testis and epididymis of theophylline- or 1,3-dinitrobenzene-exposed rats. Biol. Reprod. 76, 149–163 (2007).

    Article  CAS  Google Scholar 

  11. Thomas, M.K., Yao, K.M., Tenser, M.S., Wong, G.G. & Habener, J.F. Bridge-1, a novel PDZ-domain coactivator of E2A-mediated regulation of insulin gene transcription. Mol. Cell. Biol. 19, 8492–8504 (1999).

    Article  CAS  Google Scholar 

  12. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  13. Yip, K.Y., Yu, H., Kim, P.M., Schultz, M. & Gerstein, M. The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks. Bioiformatics 22, 2968–2970 (2006).

    Article  CAS  Google Scholar 

  14. Yu, H., Greenbaum, D., Xin Lu, H., Zhu, X. & Gerstein, M. Genomic analysis of essentiality within protein networks. Trends Genet. 20, 227–231 (2004).

    Article  CAS  Google Scholar 

  15. Puntervoll, P. et al. ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res. 31, 3625–3630 (2003).

    Article  CAS  Google Scholar 

  16. Letunic, I. et al. SMART 5: domains in the context of genomes and networks. Nucleic Acids Res. 34, D257–D260 (2006).

    Article  CAS  Google Scholar 

  17. Karnoub, A.E. & Weinberg, R.A. Ras oncogenes: split personalities. Nat. Rev. Mol. Cell Biol. 9, 517–531 (2008).

    Article  CAS  Google Scholar 

  18. McKusick, V.A. Mendelian inheritance in man and its online version, OMIM. Am. J. Hum. Genet. 80, 588–604 (2007).

    Article  CAS  Google Scholar 

  19. Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    Article  CAS  Google Scholar 

  20. van de Vijver, M.J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).

    Article  CAS  Google Scholar 

  21. Roukos, D.H. Prognosis of breast cancer in carriers of BRCA1 and BRCA2 mutations. N. Engl. J. Med. 357, 1555–1556, author reply 1556.

  22. Soderlund, K. et al. Intact Mre11/Rad50/Nbs1 complex predicts good response to radiotherapy in early breast cancer. Int. J. Radiat. Oncol. Biol. Phys. 68, 50–58 (2007).

    Article  Google Scholar 

  23. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  Google Scholar 

  24. Chang, H.Y. et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2, E7 (2004).

    Article  Google Scholar 

  25. Liu, R. et al. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N. Engl. J. Med. 356, 217–226 (2007).

    Article  CAS  Google Scholar 

  26. Sorlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl. Acad. Sci. USA 100, 8418–8423 (2003).

    Article  CAS  Google Scholar 

  27. Easton, D.F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).

    Article  CAS  Google Scholar 

  28. Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    Article  CAS  Google Scholar 

  29. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    Article  CAS  Google Scholar 

  30. Buyse, M. et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J. Natl. Cancer Inst. 98, 1183–1192 (2006).

    Article  CAS  Google Scholar 

  31. Haibe-Kains, B. et al. Comparison of prognostic gene expression signatures for breast cancer. BMC Genomics 9, 394 (2008).

    Article  Google Scholar 

  32. Bertin, N. et al. Confirmation of organized modularity in the yeast interactome. PLoS Biol. e153 (2007).

  33. von Mering, C. et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002).

    Article  CAS  Google Scholar 

  34. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  35. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge Lars Juhl Jensen, EMBL-Heidelberg, for critical assessment of this manuscript. We would like to thank L. Attisano for critical review and helpful discussions. Work in J.L.W.'s lab was supported by funds from Genome Canada through the Ontario Genomics Institute and the Ontario chapter of the Canadian Breast Cancer Fund. D.W.-F. was supported by an Natural Sciences and Engineering Research Council of Canada operating grant assigned to Q.M. J.L.W. is a CRC chair and an International Scholar of the Howard Hughes Medical Institute. I.W.T. is supported by a fellowship from the Canadian Breast Cancer Foundation.

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Authors and Affiliations

Authors

Contributions

I.W.T. contributed to the development of the project, the execution of all experiments and the writing of the manuscript. R.L. contributed to experiments in Figure 2 and writing of the manuscript. D.W.-F. and Q.M. contributed to the development and implementation of the prognosis classification algorithm and the cross-validation strategies, as well as to the writing the manuscript. Y.L. contributed programming support for data in Figures 1, 2, 3, 4. C.P. & D.F. contributed to the semantic similarity experiment in Supplementary Figure 2b. S.B. contributed to the development of the statistical frameworks throughout the manuscript and writing the manuscript. T.P. contributed to writing of the manuscript. J.L.W. contributed to the development of the project and the writing of the manuscript.

Corresponding author

Correspondence to Jeffrey L Wrana.

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Supplementary Figures 1–9 and Supplementary Methods (ZIP 16019 kb)

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Taylor, I., Linding, R., Warde-Farley, D. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27, 199–204 (2009). https://doi.org/10.1038/nbt.1522

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