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

Abstract

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.

Keywords: BTICs; genome; glioblastoma; therapy; transcriptome.

Fig. 1.

Distribution of somatic aberrations in genes found significantly altered in previous GBM studies. Aberration types are encoded by different colors. Letters are used when the aberration is not shared in a pair or trio. C, private in a BTIC; D, private in a tumor and xenograft in a trio; F, private in a BTIC and xenograft in a trio; P, private in a tumor and BTIC in a trio; T, private in a tumor; X, private in a xenograft. MGMT and EGFR vIII status tiles are split when a tumor (Upper) and BTIC (Lower) differ. Loss of tumor suppresser and gain of oncogene are well-conserved in pairs and trios. The private aberrations often have low functional impact, such as shallow copy changes or a variant of unknown significance, or passenger mutations in hypermutated samples. (A) Aberrations in paired tumor and BTIC samples. (B) Aberrations in trios of tumor–BTIC–xenograft samples.

Fig. 2.

Principal-component analysis plot of tumors, BTICs, and xenografts based on RNA expression. Color indicates sample type, while shape represents subtype. Samples show higher similarity within that same sample type, suggesting the growth condition has a higher impact on the gene expression than the individual genetic makeup.

Fig. 3.

Global differences in DNA methylation profiles between BTICs and matched GBM tumors. (A) Heatmap of sample-to-sample correlations of global DNA methylation profiles from matched GBM tumors and BTICs shows that samples largely cluster by tissue source. There was no apparent clustering by sample subtype. (B) Scatterplot of PC1 versus PC2 scores shows that the first 2 PCs are largely associated with tissue source (BTIC vs. GBM tumor) followed by sample subtype classification.

Fig. 4.

Gene ontology biological process terms enriched in the top 10% of DEGs up-regulated in each sample type relative to one another. Terms were ranked by false discovery rate (FDR) and the top 10 are shown for each comparison. The vertical black line represents the threshold for a significant P-value cutoff (FDR < 0.05). (A) DEGs up-regulated in tumors relative to BTICs. (B) DEGs up-regulated in tumors relative to xenografts. (C) DEGs up-regulated in BTICs relative to xenografts. (D) DEGs up-regulated in xenografts relative to BTICs. (E) DEGs up-regulated in BTICs relative to tumors. A plot for DEGs up-regulated in xenografts relative to tumors is absent, as the DEG set from this comparison produced no enriched terms.

Fig. 5.

Cibersort cell fractions (A) and xCell enrichment scores (B) across multiple cell types with classical, mesenchymal, and proneural subtypes shown as red, green, and blue boxes and points, respectively. Only selected cell types of interest and with a median value greater than 0.001 from xCell are shown in B; for all cell types, see SI Appendix, Fig. S7. Two outlier values were excluded from B.

Fig. 6.

MGMT expression and MMR deficiency as 2 mutually exclusive mechanisms of TMZ resistance. MGMT expression levels are indicated by RPKM value, and mutation load is shown by the number of somatic mutations per megabase from posttreatment samples. Samples with >20 mutations per Mb have a very low level of MGMT RNA expression (<1 RPKM), and they all contain a somatic MSH loss-of-function mutation and have a hypermutation phenotype. Samples with MGMT expression over 1 RPKM have a low mutation load (<20 mutations per Mb).