Page 104 - Molecular features of low-grade developmental brain tumours

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twice with PBS, resuspended in PBS containing 1 mg/ml propidium iodide and 1 g/ml RNase A and incubated for 10 min at 37°C. Cell cycle analysis was performed using a FACSCanto Flow Cytometer equipped with FACSDiva software (BD Biosciences, San Jose, CA, USA) and data analysis was performed using FlowJo 7.6 (FlowJo LLC, Ashland, OR, USA). Viable cells showing a DNA content between G1 and G2 (S-phase) were selected as proliferative population.
Statistical analysis
Statistical analysis was performed with GraphPad Prism software (Graphpad software Inc., La Jolla, CA) using the non-parametric Mann-Whitney U test or, for multiple groups, the non- parametric Kruskal-Wallis test followed by Mann-Whitney U test. Correlations were assessed with R using the Spearman’s rank correlation test. An adjusted p-value<0.05 was considered statistically significant.
Data availability
The data that support the findings of this study are openly available on the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under the accession number: EGAS00001003787.
Results
The protein-coding transcriptome of SEGAs
To characterize the transcriptome profile of SEGAs RNA-Seq was performed on total RNA extracted from SEGA samples and control brain samples. The analysis included 19 SEGA samples from 17 TSC patients and 2 patients with no other signs of TSC (all surgical specimens) and 8 area-matched periventricular controls (autopsy specimens) without a history of seizures or other neurological disease (See materials and methods and Table 1). After quality assessment and filtering ~37 million paired-end reads remained per sample, of which ~88% mapped to the GRCh38 reference genome. A principal component analysis (PCA) revealed that the major source of variability in gene expression was the diagnosis (SEGA or control; Figure 1a), which was confirmed by a Spearman’s correlation matrix of the gene expression showing that the control samples and SEGA samples clustered separately (Figure 1b). No specific clustering was seen based on the TSC mutation (Figure 1b). To assess other potential confounders on the transcriptome profile of the samples a principal variance component analysis (PVCA) was performed. When all control and SEGA samples were assessed, as expected, the major contributor to the variance between the samples was the diagnosis (Supplementary Figure 1a). Assessment of clinical features such as, mutation, age, gender, brain area, drug-treatment, mTORC1 inhibitors, epilepsy, presence of other TSC lesions or country of origin demonstrated that no single variable or two-way interaction was a single major contributor to the variance seen in the transcriptome profiles of the SEGAs (Supplementary Figure 1b).
Differential gene expression analysis revealed 9400 DEGs (adjusted p-value<0.05) in SEGA compared to control tissue, of which 4621 genes were over-expressed and 4779