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Network modeling links breast cancer susceptibility and centrosome dysfunction

Abstract

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer–associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

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Figure 1
Figure 2: Generation of the XPRSS-Int data set.
Figure 3: Expression analysis of the XPRSS-Int data set in breast tumors.
Figure 4: Generation of the BCN and ranking of XPRSS-Int genes/proteins.
Figure 5: HMMR-centered interactome model.
Figure 6: BRCA1-BARD1–mediated polyubiquitination, HMMR-BRCA1 and HMMR-AURKA interactions and centrosome dysfunction.

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Acknowledgements

We thank members of our laboratories for discussion and comments on the manuscript; A. Merdes (Wellcome Trust Centre for Cell Biology) for anti-PCM1; B. Koch (Research Institute of Molecular Pathology) for anti-CSPG6 and anti-SMC1L1; K.-T. Jeang (National Institute of Allergy and Infectious Disease) for anti-MAD1L1 (Dap23); D.-Y. Jin (University Hong Kong) for anti-MAD1L1 (81d); E.A. Nigg (Max Planck Institute of Biochemistry) for anti-CEP2; D.R. Scoles (University California Los Angeles) for providing constructs; V. Joulov for sharing results before publication; C. McCowan, T. Clingingsmith and C. You for administrative assistance; and K. Salehi-Ashtiani, D. Szeto, R. Murray and C. Lin for characterizing the genomic structure of HMMR. L.M.S. was supported by a Department of Defense Breast Cancer Research Program fellowship and a grant from the National Cancer Institute (CA90281to J.D.P.). M.T. was supported by an award from the National Institutes of Health (NIH; K08-AG21613). K.C.G. received support from the US Army Medical Research Acquisition Activity (W23RYX-3275-N605) and NYSTAR (C040066). This work was supported by an NIH/National Cancer Institute (NCI) R33 grant (to M.V.), an NIH/NCI U01 grant (to S. Korsmeyer, S. Orkin, G. Gilliland and M.V.), an NIH/NCI ICBP grant (to J. Nevins and M.V.), an 'interactome mapping' grant from the NIH/National Human Genome Research Institute and the NIH/National Institute of General Medical Sciences (to F. Roth and M.V.), an NIH/NCI P30 grant (CA046592 to the University of Michigan), a Spanish Ministry of Education and Science grant (PR2006-0474 to V.M.) and awards from the Breast Cancer Research Foundation (BCRF13740) and the Niehaus, Southworth, Weissenbach Foundation (to K.O.) and the Koodish Foundation (to T.K.). We also acknowledge the role of the New York Cancer Project, supported by the Academic Medicine Development Company of New York.

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Contributions

Experiments and data analyses were coordinated by M.A.P., J.-D.J.H., L.M.S and K.N.S. Computational analyses were performed by J.-D.J.H., K.C.G., N.B. and K.V. Yeast two-hybrid analysis screens were performed by M.A.P., J.S.A., J.-F.R and N.A.-G. Biochemical experiments were performed by M.A.P., L.M.S., W.M.E., R.A.G. and B.S. Cell culture and immunofluorescence experiments were performed by M.A.P. and L.M.S. The case-control study in Israel was conceived and executed by G.R. Genotyping and statistical analyses of the case-control studies were performed by K.N.S., L.S.R., G.R., V.M., T.K., B.G., D.L., K.O. and S.B.G. M.A.P., X.S. and P.H. performed the HapMap genotype-haplotype and gene expression association analysis. V.A. provided biochemical analysis support, R.S.G. provided statistical support and M.T., C.L., K.L.N., B.L.W., M.E.C., D.E.H. and D.M.L. helped with overall interpretation of the data. The manuscript was written by M.A.P., J.-D.J.H., L.M.S., D.E.H., M.E.C., S.B.G., J.D.P. and M.V. The project was conceived by M.V. and codirected by S.B.G., J.D.P. and M.V.

Corresponding authors

Correspondence to Stephen B Gruber, Jeffrey D Parvin or Marc Vidal.

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Competing interests

D.M.L. is a research grantee of and a consultant to the Novartis Institute for Biomedical Research.

Supplementary information

Supplementary Text and Figures

Supplementary Methods, Supplementary Figs. 1–9, Supplementary Tables 3 and 6–8 (PDF 34268 kb)

Supplementary Table 1

LINT-Int. (XLS 141 kb)

Supplementary Table 2

XPRSS-Int. (XLS 1027 kb)

Supplementary Table 4

XPRSS-Int, BRCA1mut. (XLS 26 kb)

Supplementary Table 5 (XLS 99 kb)

Supplementary Table 9

Study genes and proteins. (XLS 27 kb)

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Pujana, M., Han, JD., Starita, L. et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 39, 1338–1349 (2007). https://doi.org/10.1038/ng.2007.2

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