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Molecular Profiles Of Breast Cancer Progression Gabriela Alexe IBM Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA. Email: email@example.com We develop a new robust technique to analyze microarray data which uses a combination of principal components analysis and consensus ensemble kclustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset from Ma et al. (2003) which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. A major result of our analysis is that different sets of patients seem to progress to the same final phenotype along different functional pathways. Our findings are validated on external gene expression microarray datasets.