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Dynamic modularity in protein interaction networks predicts breast cancer outcome


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.


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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



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.

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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).

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