MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma - PubMed (original) (raw)

. 2014 Aug 26;111(34):12550-5.

doi: 10.1073/pnas.1405839111. Epub 2014 Aug 11.

David J Pisapia 2, Hani R Malone 1, Hannah Goldstein 1, Liang Lei 2, Adam Sonabend 1, Jonathan Yun 1, Jorge Samanamud 1, Jennifer S Sims 1, Matei Banu 1, Athanassios Dovas 2, Andrew F Teich 2, Sameer A Sheth 1, Guy M McKhann 1, Michael B Sisti 1, Jeffrey N Bruce 3, Peter A Sims 4, Peter Canoll 5

Affiliations

MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma

Brian J Gill et al. Proc Natl Acad Sci U S A. 2014.

Abstract

Glioblastomas (GBMs) diffusely infiltrate the brain, making complete removal by surgical resection impossible. The mixture of neoplastic and nonneoplastic cells that remain after surgery form the biological context for adjuvant therapeutic intervention and recurrence. We performed RNA-sequencing (RNA-seq) and histological analysis on radiographically guided biopsies taken from different regions of GBM and showed that the tissue contained within the contrast-enhancing (CE) core of tumors have different cellular and molecular compositions compared with tissue from the nonenhancing (NE) margins of tumors. Comparisons with the The Cancer Genome Atlas dataset showed that the samples from CE regions resembled the proneural, classical, or mesenchymal subtypes of GBM, whereas the samples from the NE regions predominantly resembled the neural subtype. Computational deconvolution of the RNA-seq data revealed that contributions from nonneoplastic brain cells significantly influence the expression pattern in the NE samples. Gene ontology analysis showed that the cell type-specific expression patterns were functionally distinct and highly enriched in genes associated with the corresponding cell phenotypes. Comparing the RNA-seq data from the GBM samples to that of nonneoplastic brain revealed that the differentially expressed genes are distributed across multiple cell types. Notably, the patterns of cell type-specific alterations varied between the different GBM subtypes: the NE regions of proneural tumors were enriched in oligodendrocyte progenitor genes, whereas the NE regions of mesenchymal GBM were enriched in astrocytic and microglial genes. These subtype-specific patterns provide new insights into molecular and cellular composition of the infiltrative margins of GBM.

Keywords: glioma; microenvironment; tumor heterogeneity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

MRI screen captures show the radiographically localized sampling of CE (_A_′–_A_′′′) and NE (_B_′–_B_′′′) regions of GBM, and micrographs of the corresponding biopsies show the histological features of the highly cellular core (A) and the infiltrative margin (B) of the tumor (stained with hematoxylin and eosin). _A_′ and _B_′ show the axial FLAIR, A′′ and B′′ show the sagittal T1 with contrast, and _A_′′′ and _B_′′′ show the coronal T1 with contrast. The green crosshairs mark the biopsy sites. Quantitative analysis of CE (blue bars) and NE (tan bars) samples shows significant differences in cellular density (C) and the presence of histopathological hallmarks of high-grade glioma (D). Immunohistochemical analysis for NeuN shows numerous positive neurons in samples from NE regions (E) and only rare entrapped neurons in samples from the CE regions (F). Quantitative analysis of theses stains shows significant differences in the fractional abundance of NeuN+ neurons in NE vs. CE samples (G). In C, D, and G, **P < 0.001; ***P < 0.0001; ****P < 0.00001.

Fig. 2.

Fig. 2.

RNA-seq–based expression profiling showing the expression of the Verhaak classifier genes across 92 samples (39 CE, 36 NE, and 17 NB). The samples were clustered by using Spearman correlation into five major clusters. Two clusters are predominantly composed of NE and NB samples, and the other three clusters are predominantly composed of CE samples, with samples correlating with proneural, classical, and mesenchymal subtypes. The NE clusters contain the majority of neural samples. The colored bars above the heatmap show the sample origin (upper bar: NB, yellow; NE, green; CE, pink) and the subtype classification (lower bar: neural, brown; proneural, green; classical, blue; mesenchymal, red). The heatmap shows high levels of expression as red and low levels as green.

Fig. 3.

Fig. 3.

Computational deconvolution reveals cell type-specific expression profiles. (A) The heat map shows the expression of cell-type genes in the six cell types that were deconvolved from the NB and NE samples: High expression is yellow, and low expression is black. The lists of cell type-specific genes were derived from previous studies (11, 15, 16). (B) We performed a hypergeometric test to assess the significance of the cell type enrichment of each gene list compared with deconvolution of the whole transcriptome. Each of the six gene lists showed the most significant enrichment in the expected cell type (highlighted in red). In addition, microglia show significant enrichment for genes expressed by reactive astrocytes and neurons show significant enrichment for OPC genes (each highlighted in green). The list of cell type-specific genes and associated cell type-specific expression profiles are provided in

SI Appendix, Table S3

.

Fig. 4.

Fig. 4.

Heatmaps showing the deconvolved cellular distribution of gene expression for differentially expressed genes (P < 0.05) comparing normal brain (NB) to NE of proneural GBMs (A), NE of classical GBMs (B), NE of Primary mesenchymal GBMs (C), and NE of Recurrent mesenchymal GBMs (D). For each gene (rows), the expression level is normalized across cell types (columns) so that the value in the heat map reflects its fractional abundance in a given cell type. To obtain these cellular distributions, we deconvolved the NE and NB samples in aggregate and obtained a single average cellular distribution estimate for each gene. Although differential expression information was not provided to the deconvolution algorithm, all four heatmaps show a sharp transition in cellular composition between genes that are expressed at higher levels in the NE tumor tissue vs. genes that are expressed at higher levels in normal brain. The small heat maps that appear underneath each image represent the fraction of the total number of differentially expressed genes in each sample group are predominantly expressed in each of the six cell types.

Fig. 5.

Fig. 5.

iPAGE gene ontology analysis of differentially expressed genes comparing normal brain (NB) to NE of proneural GBMs (A), NE of classical GBMs (B), NE of Primary mesenchymal GBMs (C), and NE of Recurrent mesenchymal GBMs (D). The gene ontology categories highlighted in red are associated with genes that are highly expressed in the NE tissue relative to normal brain, whereas those in green are associated with genes that are highly expressed in normal brain relative to NE tissue. Gene ontology categories in black are associated with genes that are not differentially expressed. Although cell cycle and proliferation-related pathways dominate the proneural and classical NE, an immune response/inflammatory signature dominates the mesenchymal NE. These expression signatures are consistent with the cellular distributions that we estimated for differentially expressed genes by deconvolution analysis.

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