• Proc. Natl. Acad. Sci. U.S.A. · Sep 2019

    Comprehensive genomic profiling of glioblastoma tumors, BTICs, and xenografts reveals stability and adaptation to growth environments.

    • Yaoqing Shen, Cameron J Grisdale, Sumaiya A Islam, Pinaki Bose, Jake Lever, Eric Y Zhao, Natalie Grinshtein, Yussanne Ma, Andrew J Mungall, Richard A Moore, Xueqing Lun, Donna L Senger, Stephen M Robbins, Alice Yijun Wang, Julia L MacIsaac, Michael S Kobor, H Artee Luchman, Samuel Weiss, Jennifer A Chan, Michael D Blough, David R Kaplan, J Gregory Cairncross, Marco A Marra, and Jones Steven J M SJM Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4S6; sjones@bcgsc.ca. .
    • Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4S6.
    • Proc. Natl. Acad. Sci. U.S.A. 2019 Sep 17; 116 (38): 19098-19108.

    AbstractGlioblastoma multiforme (GBM) is the most deadly brain tumor, and currently lacks effective treatment options. Brain tumor-initiating cells (BTICs) and orthotopic xenografts are widely used in investigating GBM biology and new therapies for this aggressive disease. However, the genomic characteristics and molecular resemblance of these models to GBM tumors remain undetermined. We used massively parallel sequencing technology to decode the genomes and transcriptomes of BTICs and xenografts and their matched tumors in order to delineate the potential impacts of the distinct growth environments. Using data generated from whole-genome sequencing of 201 samples and RNA sequencing of 118 samples, we show that BTICs and xenografts resemble their parental tumor at the genomic level but differ at the mRNA expression and epigenomic levels, likely due to the different growth environment for each sample type. These findings suggest that a comprehensive genomic understanding of in vitro and in vivo GBM model systems is crucial for interpreting data from drug screens, and can help control for biases introduced by cell-culture conditions and the microenvironment in mouse models. We also found that lack of MGMT expression in pretreated GBM is linked to hypermutation, which in turn contributes to increased genomic heterogeneity and requires new strategies for GBM treatment.

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