Cancer Genome Characterization using Gene Expression and DNA Copy Number Analysis

Univ Of North Carolina Chapel Hill, Chapel Hill NC

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Abstract

[unreadable] DESCRIPTION (provided by applicant): With the publication of the human genome's complete sequence, in combination with advances in optics, microfabrication and computing, many of the previously described cancer causing genetic aberrations can now be characterized, or profiled, using high-density technologies. Although there has been a plethora of different assays/platforms developed, two platforms have been the most extensively utilized and include mRNA/gene expression profiling (DNA microarrays) and array-based Comparative Genomic Hybridization (aCGH microarrays) for measuring DNA copy number abnormalities. While these platforms were partially developed in academic labs, the commercial development of gene and aCGH microarrays has resulted in competitive pricing, high array quality, flexibility for the refinement of probes, and development of a large range of analytic tools. Our own experience in the development of these assays and in the application of them to the study of breast and lung cancer, has lead to the identification of novel subtypes of tumors and to the identification of a new tumor suppressor gene. This leads us to propose to use DNA microarray and aCGH arrays to characterize tumors provided through the Cancer Genome Characterization Centers. Specifically, we propose to characterize the provided human tumors using Agilent Technologies Inc. produced arrays with up to 244,000 features and to perform 1) gene expression microarray analysis containing all known human genes, 2) DNA microarrays containing approximately 1000 different viral and bacterial pathogens to test for the presence of infectious agents, 3) microarrays containing all known human MicroRNA, and 4) aCGH microarrays to determine DNA copy number changes. Our group has demonstrated expertise with all of these data types and with the analysis of these data. Importantly, we have also developed new data mining techniques that have allowed for integration and cross-validation of these comprehensive genomic data sets. [unreadable] [unreadable] [unreadable] [unreadable]