Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain

Abstract

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

Figure 1

Overview of data analyzed (a) showing datasets used, batch correction and construction of Brain-UMAP. (b) UMAP of complete dataset including adult gliomas from The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG), The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) and The Chinese Glioma Genome Atlas (CGGA), pediatric tumors and GTEx-defined normal brain. (c) UMAP showing unique clustering of GTEx-defined brain-regions.

Figure 2

UMAP for adult glioma showing patients colored in by (a) TCGA-GBM and TCGA-LGG patients (b) age at diagnosis (c) Chr 7 gain/Chr10 loss in patients. (d) Chr 1p/19q co-deletion status in patients (e) IDH1 mutation (f) TP53 mutation (g) ATRX mutation. (h) UMAP identifying the 3 distinct adult glioma subtypes—IDH wildtype, Astrocytoma and Oligodendrogliomas.

Figure 3

Co-localization of adult gliomas from two publicly available datasets TCGA and CGGA. Top panel shows (a) grade, (c) IDH mutation status and (e) 1p19q co-deletion status for adult gliomas from TCGA colored in, and adult gliomas from CGGA greyed out. Bottom panel shows (b) grade, (d) IDH mutation status and (f) 1p19q co-deletion status for adult gliomas from CGGA colored in, and adult gliomas from TCGA greyed out. (g) Survival analysis for adult glioma subtypes IDH wildtype(red), Astrocytoma (blue) and Oligodendroglioma (green) from TCGA and CGGA shown in solid and dotted lines respectively. (h) Prediction of survival time (in years) using a nearest neighbor approach shows a gradient for adult gliomas.

Figure 4

(a) UMAP of pediatric tumors (b) Updated coloring of the Brain-UMAP showing pediatric tumors and three subtypes for the adult gliomas.

Figure 5

Visualization of GSVA Pathway scores across Brain-UMAP for cancer pathways and cellular processes.

Figure 6

Visualization of gene expression profiles for genes from the Reactome mismatch repair pathway across the Brain-UMAP.

Figure 7

(a) UMAP of pediatric tumors and adult glioma subtypes from TCGA. Coloring in UMAP of pediatric tumors and adult glioma subtypes from TCGA by (b) number of point mutations and (c) number of gene fusions per tumor (d) number of genes with copies gained per tumor and (e) number of genes with copies deleted per tumor.

Figure 8

Integration and visualization of genomic information such as gene expression, mutation, copy number and gene fusions at a single gene level across Brain-UMAP for 5 genes—EGFR, PTEN, CIC, BRAF and ALK.