Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

Abstract

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Keywords: CIBERSORTx; Deconvolution; Glioma; Malignant cell-state; Prognosis; Tumor microenvironment.

Fig. 1

An overview of the infiltrated immune cells in glioma and its subtypes. A Differentially represented cell types between IDH-MUT (green) and IDH-WT (orange) tumors are depicted by using boxplots. Similarly, B boxplots representing relative abundance of the cell types between Chr1p/19q codeleted (cyan) and non-codeleted (dark green) samples in IDH-MUT tumors. C Forest plots displaying prognostic association of M2 macrophages in both IDH-WT and IDH-MUT tumors. The abbreviations used for each cell type is described in Additional file 13: Table_S1

Fig. 2

Gene expression clustering and the separation between IDH-MUT and IDH-WT tumors. A UMAPs denoting 15 independent gene expression clusters identified in all 3 datasets harboring a clear separation between IDH-MUT (green) and IDH-WT (orange) tumors. B Most significant pathways and related genes derived from selected cell types demonstrates comparative transcriptional profiles between IDH-WT and IDH-MUT tumors

Fig. 3

Cluster of cluster analysis of immune based clusters. A Heatmaps representing the hierarchical clustering of LM22 based clusters broadly differentiating IDH-WT tumors from IDH-MUT. B Histograms showing quantitative distribution IDH specific tumors across each cluster

Fig. 4

Tumor groups with distinct immune signatures based on selected cell types. A Heatmaps representing hierarchical clustering of the clusters identified from selected immune cell types from IDH-WT tumors. The above annotation bars representing the distributions of cluster assignments, tumor grade, MGMT promoter methylation, EGFR expression, TERT expression (surrogating the status of TERT promoter mutation) and Chr7 gain & Chr10 loss followed by their Kaplan–Meier curves denoting their survival differences and B forest plots displaying prognostic association of these immune based clusters for IDH-WT. Similarly, C heatmaps representing the hierarchical clustering of the clusters identified from selected immune cell types from IDH-MUT tumors. The above annotation bars representing the distributions of cluster assignments, tumor grade and Chr1p/19q loss with Kaplan–Meier curves below the heatmap denoting their survival differences and D forest plots displaying prognostic association of these immune based clusters for IDH-MUT tumors

Fig. 5

Deconvolving malignant cell states and their interaction with immune based clusters. A Differential distributions of IDH-WT specific malignant cell-states between the two immune-based survival groups are depicted by boxplots. B Scatter plots representing a positive correlation between the proportions of M0 macrophages and MES-like component of IDH-WT tumors across all 3 datasets. C Scatter plots representing a significant positive correlation between the expressions of OSM from macrophages and OSMR from malignant cells in IDH-WT tumors identified by scRNA based deconvolution. D Boxplots depicting differential distribution of epithelial-mesenchymal transition markers between the two immune-based survival groups. Similarly, E boxplots depicting differential distribution of STAT3 signaling markers between the two immune-based survival groups

Fig. 6

Immunotherapy-treated glioma datasets. A Stacked bar plots representing relative proportion of 10 broader category of immune cell types from LM10 across samples treated with checkpoint inhibitor. B Stacked bar plots representing the relative proportions of 4 malignant cell-states across all immunotherapy treated samples. C Forest plot representing the association between their dichotomous immune cell fractions and their overall survival. D Monocyte-based gene expression clusters represented by tSNE followed by E Kaplan–Meier curves denoting distinct survival between the two clusters. F Boxplots depicting differential distribution of epithelial-mesenchymal transition markers between the two immune-based survival groups derived from monocytes from samples treated with checkpoint inhibitor. G Scatter plots representing significant positive correlations between the epithelial-mesenchymal transition and the T cell exhaustion scores both in patients treated with a checkpoint inhibitor, as well as larger cohorts. H Forest plot representing the insignificant or inconsistent association between their dichotomous monocytic fractions in non-immunotherapy or non-checkpoint inhibitor treated IDH-WT tumors (from TCGA, CGGA325 and CGGA693) and their overall survival

Fig. 7

Enrichment for genes from mesenchymal pathways in M0 macrophages while M2 macrophages demonstrated the enrichment for immunological pathways in A IDH-WT and B IDH-MUT tumors