Abstract 2435A: Identification and treatment data of xenografts representing TCGA-defined glioblastoma subtypes
Authors: Keir Stephen T. , Rasheed B. Ahmed , Hoadley Katherine A. , Roskoski Martin A. , Gasinski Danuta , Kwatra Madan M. , Friedman Henry S. , Bigner Darell D. 
Published: August 2015

The Cancer Genome Atlas (TCGA) Network recently catalogued recurrent genomic abnormalities in glioblastoma (GBM). This genomic profiling led 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 patient derived xenograft models that represent each TCGA subtype serves to expedite the testing of targeted molecular therapeutic strategies for the treatment of GBMs. 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 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. To date we have identified at least 2 patient derived xenografts for each TCGA-defined subtype. These xenografts lines have realiable growth patterns with a tumor take of 80+%. Drug testing has been completed and includes tumor growth responses to the following single agents: temozolomide, bevacizumab, BCNU, cytoxan, irinotecan, carboplatin, erlotinib, CCNU, procabazine and vincristine. 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. 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.

Citation Format: Stephen T. Keir, B. Ahmed Rasheed, Katherine A. Hoadley, Martin A. Roskoski, Danuta Gasinski, Madan M. Kwatra, Henry S. Friedman, Darell D. Bigner. Identification and treatment data of xenografts representing TCGA-defined glioblastoma subtypes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2435A. doi:10.1158/1538-7445.AM2015-2435A