Network Analysis of Gene Expression Proles in Breast Cancer Cell Lines

Systems biology has fundamentally altered the way cancer research is performed. In a few short years we have progressed from northern blot analysis of a handful of genes at a time, to probing the transcriptome, the proteome, and the metabolome of tumor cells. To date, microarray analyses of the transcriptome of breast and other cancer types have yielded novel insights that have direct relevance to our understanding of cancer progression. Gene expression analysis of primary breast cancer tissue has resulted in the denition of discrete cluster groups that have prognostic signi- cance (Perou et al. 2000; Ross and Perou 2001; Sorlie et al. 2001; Goh et al. 2007). Specic genes have been identied that improved our understanding of both the important signaling pathways in breast cancer and the genetic basis for breast cancer progression (Bulavin et al. 2002; Hyman et al. 2002; Kauraniemi et al. 2001). To progress beyond these elegant yet descriptive analyses of the transcriptome, novel computational methods must be developed that integrates information on disease progression. Such approaches require an analysis of time series data for a more complete understanding of the system-level complexity of cancer.