Establishment, Identification And Treatment Data Of Tcga Glioblatoma Xenograft Subtypes

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA 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 helps researchers and clinicians to better understand GBMs with the potential to personalize treatment options and explore different therapeutic approaches that each subtype may require. In addition, the TCGA identified possible mechanisms that can cause some GBM tumors to become resistant to therapy, including the standard of care alkylating agent temozolomide (TMZ). For this project, we established xenografts using patient derived tissue and passaged these xenografts until reliable growth characteristics were obtained. Established GBM xenografts were classified into TCGA-defined subtypes first by obtaining global gene expression data using the GeneChip Human Genome U133A 2.0 array from Affymetrix. Microarray data was then quantile normalized and probes were summarized as gene expression levels using RMA. Then data was log2 transformed and genes 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 data as the training dataset. Using established xenografts from each subtype, a panel of standard of care treatment agents (including TMZ) was assessed by delay in tumor growth and by tumor regression. Statistical analysis was performed using a SAS statistical analysis program, the Wilcoxon rank order test for growth delay, and Fisher's exact test for tumor regression. Data regarding each xenografts BRAF, EGFR, EGFRvIII, IDH1, PIK3CA, PIK3R1, PTEN, RB1, TERT and TP53 status is reported within. The identification of these valid xenograft models represents an important contribution toward the ability of studying GBM subtypes, in particular for modeling and predicting therapeutic response. Citation Format: Stephen T. Keir, B Ahmed Rasheed, Katherine A. Hoadley, Martin A. Roskoski, Danuta Gasinski, Patrick J. Killela, Hai Yan, Madan M. Kwatra, Henry S. Friedman, Darell D. Bigner. Establishment, identification and treatment data of TCGA glioblatoma xenograft subtypes. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 838. doi:10.1158/1538-7445.AM2014-838