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    Abstract
     
    Breast cancer, as the top most common cause of cancer death in women, has already killed over 500,000 people worldwide. Therefore it is very important to find out indicators or markers that discriminate different disease outcomes in breast cancer prognosis and diagnosis. In 2007, Chuang et al used network-based approaches and identified subnetwork markers that can classify metastasis versus non-metastasis tumors. 
     
    Other than the conventional network-based method, in 2009, Taylor et al looked at the rewiring of human protein interaction network (interactome) that drives phenotypic transformations of oncogenic cells and directly affect disease outcome. They examined the dynamic structure of the interactome to determine whether changes in the organization of the interactome can be used to predict patient outcome. 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. 
     
     
    Papers to read (please click the numbers for the link)
     
    1 Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009 Feb;27(2):199-204. Epub 2009 Feb 1. PubMed PMID: 19182785.
     
     Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140. Epub 2007 Oct 16. PubMed PMID: 17940530; PubMed Central PMCID: PMC2063581.
     
    Bibliography (please click the numbers for the link)
     
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