(single-cell RNAseq) An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma

Background: Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises four subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts. Methods: We used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis (NTA) and electron microscopy (EM), to investigate intercellular communication in the MB tumor niche. Results: Tumor cells of the SHH-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from otherwise very different MB subgroups or subtypes. Conclusions: With EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups. Overall design: We used a mouse model, Atoh1-cre:SmoM2, to profile the transcriptome of SHH-MB by single cell RNA-sequencing (scRNA-seq). In this system, Atoh1-cre drives expression of a constitutively active Smo allele (SmoM2) in granule neuron precursors (GNPs), resulting in fortuitous activation of downstream target genes and subsequent formation of SHH-MB in the cerebellum. Tumors from four littermates were isolated at postnatal day 20, dispersed into single cells and further processed for scRNA-seq. Quality-filtered sequence data were merged and subjected to dimensionality reduction and cell cycle regression to mitigate the effects of cell cycle heterogeneity. This resulted in a total of 4,749 cells (mean sequence reads 59,566 ± 7,821 per cell), which represent tumor cells as well as tumor-associated non-tumor cells including immune cells and glia cells.