Abstract INTRODUCTION Glioblastoma (GBM) tumors are highly heterogenous and plastic. To better understand how tumors respond to treatment, it is important to use immunocompetent mouse models. We've created various mouse GBM models with common genetic mutations found in different subtypes of GBM, including classical, proneural, mesenchymal, and BRAF-driven. METHODS Using a combination of CRISPR and PiggyBac Transposon constructs, we injected plasmid mixtures into the ventricles of the mouse brain during the post-natal period. Electroporation was then used to enable the plasmids to enter nearby neural stem cells, which led to the spontaneous formation of tumors (spontaneous model). We collected tumor cells from these models and injected them into the adult mouse brain to create the injected mouse GBM model. Here we use spatial and single cell transcriptome profiling approaches to characterize these models at endpoint. Spatial transcriptome data was collected using the Visium 10X platform and tumor spots were deconvoluted from non-malignant spots using consensus non-negative matrix factorization. Copy number alterations (CNAs) were inferred using non-malignant spots and single-cell clusters as a reference. RESULTS We found that copy number states are largely homogenous within each sample, supporting an early population expansion in vivo. CNAs found in spontaneous tumors are also found in the matched injected tumors, which in turn harbour additional CNAs, suggesting that further selection occurs in vitro. Some of the CNAs include relevant cancer genes, suggesting the observed clonal expansions are potentially driven by additional somatic alterations that cooperate with the engineered driver mutations. Studying these models using orthogonal methods will allow us to discover alterations that synergize with the original drivers and delineate principles of GBM initiation and progression in an immune-competent environment.