Comprehensive genomic profiling of glioblastoma tumors, BTICs, and xenografts reveals stability and adaptation to growth environments
A partir de l'analyse du profil génomique de glioblastomes, de cellules souches tumorales et de xénogreffes orthotopiques, cette étude met en évidence l'influence, sur l'expression des ARN messagers et l'épigénome, des conditions de culture et du micro-environnement tumoral dans les modèles murins
Our comprehensive genomic comparison of matched tumor, brain tumor-initiating cells (BTICs), and xenografts will help inform the observations made when using these model systems and guide the experimental design for drug screening and hypothesis testing. In addition, paired genomic and transcriptomic data we generated for tumors and their matched in vitro and in vivo models provide valuable resources for the future study of glioblastoma at the genetic and molecular levels.Glioblastoma 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.
Proceedings of the National Academy of Sciences , résumé, 2018