Columbia University

Technology Ventures

Identification of Genes Synergistically Associated with Disease from Continuous Gene Expression Data

Technology #m07-052

“Lead Inventors: Dimitris Anastassiou, Ph.D.; John Watkinson

Microarray Analysis Techniques Identify Genes Associated with Disease, not Cause of Disease Traditional microarray analysis techniques such as those that make use of clustering or support vector machines (SVMs) can be used to successfully identify genes that appear to be associated with disease. Although these techniques have been useful in medical diagnosis, they are less useful in identifying the underlying biological mechanisms responsible for disease because they do not provide any insight as to whether several genes are part of a single biological pathway.

Identification of Multiple Genes Associated with Disease Helps Identify Genetic Pathways Responsible for Disease The technology is a method for identifying pairs of genes whose association with disease is cooperative, i.e., insofar that the concurrent expression levels of the pair of genes rather than the individual expression levels of each gene are connected with the presence of disease. To do so, the technology quantifies the synergy of a gene pair with respect to a disease in terms of the difference between the information both genes concurrently provide about the disease and the sum of the information provided by each gene individually. By applying this measure of synergy to continuous gene expression levels, one can exhaustively evaluate a set of possible gene pairs and identify which pairs exhibit the highest synergy.

The technology was applied to publicly available prostate cancer expression data from a set of healthy and cancerous tissue samples. The results of this experiment were successfully validated by repeating the analysis after permuting the gene expression matrix obtained from the tissue samples.

Applications:
• The technology can be used to identify genes that are concurrently associated with a disease; this facilitates the identification of the genetic pathways responsible for disease.

Advantages:
• Unlike existing microarray data analysis techniques, the technology can identify multiple genes that are concurrently associated with a disease.

Patent Status: Patent Pending (US 12/022,862) ~ see link below.

Licensing Status: Available for Sponsored Research Support

Publications: Identification of gene interactions associated with disease from gene expression data using synergy networks, BMC Systems Biology, Vol. 2, No. 10, 2008.