Gabriela Alexe, Yorktown Heights
Title: Molecular profiles of breast cancer progression

Authors: Gul S Dalgin (1,*) , Gabriela Alexe(2,3,*), Gyan Bhanot(3,4,5,**),
Charles DeLisi(4,**)

Abstract. We develop a new robust technique to analyze microarray data
which uses a combination of principal components analysis and consensus
ensemble k-clustering 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.

1.    Mol. Bio., Cell. Bio. and Biochem. Prog., Boston University, Boston,
MA 02215, USA
2.    Computational Biology Center, TJ Watson IBM Research, Yorktown
Heights, NY 10598, USA; The Broad Institute of MIT and Harvard, 7 Cambridge
Center, Cambridge MA, 02142, USA
3.    Center for Systems Biology, Institute for Advanced Study, Princeton,
NJ 08540, USA
4.    Center for Advanced Genomic Technology, Department of Biomedical
Engineering, Boston University, Boston, MA 02215, USA
5.    Cancer Institute of New Jersey, 195 Little Albany Street, New
Brunswick, NJ 08903; BioMaPS Institute and Department of Biomedical
Engineering, Rutgers University, Piscataway, NJ 08854, USA
*   Joint first authors
** Correspondent authors:,