Abstract A30: Integrated analysis of genomic data and annotations in cancer research

Many cancer researchers are currently generating heterogeneous genomic data sets containing mRNA expression, array based comparative genomic hybridization (aCGH), and other information from multiple patients. In the near future, data sets will likely include proteomics, methylomics, and high throughput sequencing data. Analytical tools that integrate these different data types to prioritize genes for further study have lagged, however. With this in mind, we have implemented a standalone software application that integrates CGH and mRNA expression profiling data. This application implements several different methods to generate ranked lists of genes and genomic regions based on breakpoint frequency, aberration frequency, and the impact of copy number changes on mRNA expression. The software is Java GUI based, with abstracted object models that can be extended as new genomic data types and annotations become available. We used this application to analyze aCGH datasets generated from 22 primary pancreatic tumor xenografts and mRNA expression datasets from 16 of the 22 samples. Genes which were located near a putative breakpoint and whose expression levels were high in a majority of the xenografts samples were considered putative targets. Preliminary IHC analysis of pancreas primary tumor tissue microarrays confirms one of these putative targets, Chemokine (CXC motif) Ligand 5 (CXCL5). By allowing cancer researchers to correlate genomic aberrations with expression data, we believe that this application will generate more meaningful targets for further biomarker identification and/or drug target discovery. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A30.