Mutual information analysis as a tool to assess the role of aneuploidy in the generation of cancer-associated differential gene expression patterns.

Most human tumors are characterized by: (1) an aberrant set of chromosomes, a state termed aneuploidy; (2) an aberrant gene expression pattern; and (3) an aberrant phenotype of uncontrolled growth. One of the goals of cancer research is to establish causative relationships between these three important characteristics. In this paper we were searching for evidence that aneuploidy is a major cause of differential gene expression. We describe how mutual information analysis of cancer-associated gene expression patterns could be exploited to answer this question. In addition to providing general guidelines, we have applied the proposed analysis to a recently published breast cancer-associated gene expression matrix. The results derived from this particular data set provided preliminary evidence that mutual information analysis may become a useful tool to investigate the link between differential gene expression and aneuploidy.