The ability to measure genome-wide expression holds great promise for characterizing cells and distinguishing diseased from normal tissues. Thus far, microarray technology has only been useful for measuring relative expression between two or more samples, which has handicapped the ability of microarrays to classify tissue types. We have designed the first method that successfully measures absolute gene expression and show that by simultaneously analyzing data from hundreds of samples it is possible to demarcate expressed from unexpressed genes. The utility of the method is demonstrated by defining a gene expression barcode for human and mouse tissues, which is then used to differentiate diseased tissue from normal tissue. With clinical data, we find near perfect predictability of normal from diseased tissue for three cancer studies and one Alzheimer's disease study. The barcode method can also be used as a clinical prognostic test as we demonstrate with breast cancer data. This proposal seeks to improve the barcode algorithm by validating its findings, optimizing the algorithm's performance, making the algorithm available to the community in a biologist-friendly format and extending its use as a diagnostic and prognostic test to other cancers. This work will be completed using a bioinformatics approach by comparing the barcode's results to publicly available high-throughput data from serial analysis of gene expression (SAGE) and expressed sequence tag (EST) experiments. Since the algorithm can work on any microarray platform, with any species and any cell or tissue type, it is important to make it widely available to biologists, which will require joining various algorithms and automating the analysis pipeline. Then the scientific community can efficiently use the barcode to study cancer and other cellular diseases and develop diagnostic and prognostic tests for clinical use. Relevance: This proposal aims to further develop a novel algorithm that will potentially help in the diagnosis and treatment of cancers and other cellular diseases. ? ? ?
| McCall, Matthew N; Jaffee, Harris A; Zelisko, Susan J et al. (2014) The Gene Expression Barcode 3.0: improved data processing and mining tools. Nucleic Acids Res 42:D938-43 |
| McCall, Matthew N; Uppal, Karan; Jaffee, Harris A et al. (2011) The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res 39:D1011-5 |
