IDENTIFICATION AND PRELIMINARY TREATMENT DATA OF XENOGRAFTS REPRESENTING TCGA-DEFINED SUBTYPES

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Abstract

BACKGROUND: The Cancer Genome Atlas (TCGA) Network recently catalogued recurrent genomic abnormalities in glioblastoma (GBM). This genomic profiling lead to the molecular classification of GBMs into four subtypes: Proneural, Neural, Classical and Mesenchymal. The importance of identifying these subtypes stands to help researchers and clinicians to better understand GBMs with the potential to personalize treatment options and explore different therapeutic approaches that each subtype may require. As such, the development of preclinical xenograft models that represent each TCGA subtype serves to expedite the evaluation of targeted therapeutic strategies for the treatment of GBMs. METHODS: For this project, established xenografts from patient derived tissue were passaged until reliable growth characteristics were obtained. Established GBM xenografts were classified into TCGA-defined subtypes first by obtaining global gene expression data using Affymetrix. Microarray data was then normalized and probes were summarized as gene expression levels using RMA. Then data was log2 transformed and genes were median centered. Xenografts were subsequently classified into one of four previously defined subtypes as described using Classification to the Nearest Centroid (ClaNC) with the TCGA GBM training dataset. Using established xenografts from each subtype, a preliminary panel of treatment agents was assessed by delay in tumor growth and by tumor regression in athymic mice bearing subcutaneous tumors. Statistical analysis was performed using a personalized SAS statistical analysis program, the Wilcoxon rank order test for growth delay, and Fisher's exact test for tumor regression. RESULTS: To date, our lab has established and identified 2 patient derived xenografts for each TCGA-defined subtype. These xenografts lines have reliable growth patterns with a tumor take rate >80%. Phase I drug testing has been completed and includes tumor growth responses to the following single agents: temozolomide, bevacizumab, BCNU, cytoxan, and irinotecan. CONCLUSIONS: A panel of patient derived xenografts with reliable growth characteristics that represent the four TCGA-defined GBM subtypes has been established and utilized for drug evaluation. The identification of these valid xenograft models represents an important contribution for modeling and predicting therapeutic response. We have currently completed the first round of drug testing within each subtype and look to expand testing to include additional agents as well as select combinations. SECONDARY CATEGORY: n/a.

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