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U.S. National Institutes of Health
Last Updated: 03/05/10

Greg J. Riggins, Duke University Medical Center
“A Molecular Classification of Brain Tumors”

Abstract: There is a predicted 0.44 percent lifetime risk of dying from a malignant brain tumor in the US. An overall five-year survival of less than 30 percent attests to our lack of ability to effectively treat these cancers. Malignant brain tumors are a very heterogeneous group of tumors, and a logical area to apply a rational molecular-based classification system. We plan to address classification and response to therapy as the two main goals of this grant. First, we plan to develop and test a gene expression based classification of malignant gliomas and embryonal CNS tumors. We will look within large histologically similar groups such as glioblastomas and medulloblastomas, to sub-classify these tumors. Second, we will develop for the malignant gliomas; a gene expression based test that predicts response to therapy. Here the emphasis will be to make it possible to select the chemotherapy that has the greatest chances of success, prior to starting treatment. Our approach for identifying RNA levels that predict class or response will be to first generate candidate using Serial Analysis of Gene Expression (SAGE). Our experience with SAGE as part of the Cancer Genome Anatomy Project (CGAP) indicates that this is a powerful way to initially assess all the expressed genes. By performing a limited sequence SAGE analysis spread out over 68 brain tumors, we can create a cost and labor effective comprehensive profile of the candidate genes most likely to predict class or response. The CGAP infrastructure for this analysis is in place and running, including low-cost high throughput sequencing, ideal for this pursuit It will be necessary to test or verify each candidate gene in a large independent set of tumors. For this purpose we have chosen real-time quantitative PCR and tissue microarrays. Real-time PCR has the advantage of being able to produce accurate transcript levels, rapidly and economically, from multiple small samples. Tissue microarrays have the advantage of being able to assay protein, RNA or DNA levels, determine the location of the expressing cell in the tissue, and utilize fixed archived samples from hundreds of tumors simultaneously. We will be collaborating with the recognized leaders for this technology to produce comprehensive brain tumor tissue microarrays. We have led the way in public release of gene expression data, in part because the absolute and digital transcript levels from SAGE adapt well to data sharing. All SAGE data will be immediately posted with CGAP using our web site, SAGEmap (, with a similar site for tissue microarray and real-time PCR data.