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Medulloblastoma (MB) is a heterogeneous malignant childhood brain cancer, classified into four molecular subgroups, WNT, SHH, Group3, and Group4, based on transcriptional, genetic, and clinical features. We aimed to understand the tumor microenvironment of Group 3 and Group 4 tumors utilizing single-cell transcriptome data. We identified four neoplastic cell states, including stemness (TNF-α), proliferative, mesenchymal and quiescence, and 11 broad lymphoid and myeloid cell types from single-cell transcriptomes. We applied CIBERSORTx to enumerate cell type proportion and CODEFACS on 73 bulk MB RNAseq profiles to determine cell type-specific abundance and gene expression profiles. We noted significant associations (both positive and negative) between the abundance of neoplastic cell states and microenvironmental cell types in both group3 and group4 MB. For example, the invasive cell state negatively correlated with dendritic cells in Group3 and Group4 tumors, but the invasive cell state was oppositely correlated with a macrophage subset in group 3 versus group 4 tumors, Survival analysis showed a significant association of higher proliferative cell state with poorer survival in Group3 MB while in group4 elevated invasive cell state, as well as elevated α, NK, and CD4-T cell abundance showed trends for poorer prognosis. Using Ecotyper, we determined cellular ecotypes based on cell type-specific transcriptome states. An ecotype containing B cell, hematopoietic, dendritic, and proliferative cell states was associated with poor prognosis in group3 and group4 tumors. We further analyzed ligand-receptor interaction between cell type states of poor prognosis ecotype; and found several interactions between ligands from immune cells (SEMA4G, LAMB2) and receptors on neoplastic cells (PLXNB2). Conversely, extracellular matrix ligands on neoplastic cells (COL1A1/2 and FN1) were found on this analysis potentially associated with integrin receptors on immune cells. Overall, our study highlights the potential biologic and clinical significance of neoplastic and immune cell states in group3/4 MB.

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.

INTRODUCTION Interactions between neoplastic cell characteristics and the immune microenvironment is an important yet understudied area in diffuse gliomas. We examined this by analyzing single cell RNAseq data (n=290,738) from 35 glioma patients (in-house and GSE182109) including 24 IDH-wildtype and 11 IDH-mutant tumors at the single cell level. Cells were assigned to specific classes, including neoplastic cell states and myeloid components, based on established methods. We used these data to develop a signature matrix to deconvolve bulk RNAseq data from TCGA diffuse gliomas. RESULTS Clustering of neoplastic cells revealed 4 cell states: proneural, mesenchymal, proliferative and cancer signaling. Elevated proneural cell state was, as expected, positively correlated with IDH-mutant tumors, and elevated mesenchymal cell state was correlated with IDH wildtype tumors (both p< 0.01, chi-square), Among IDH-mutant tumors, 1p/19q co-deletion was associated with the proneural state. Fourteen distinct myeloid cell subtypes were identified, and among them several were enriched for either microglial or macrophage (or both) markers. We examined relationships of neoplastic cell state with myeloid cell type abundance and significant associations were identified in both IDH-mutant tumors and IDH-wildtype tumors between specific myeloid cell states and myeloid clusters. Among IDH-mutant tumors, 1p/19q-co-deletion status was positively associated with abundance of a specific microglial cell cluster but negatively associated with abundance of 3 other myeloid cell clusters, suggesting substantial microenvironmental differences based on 1p/19q status in IDH-mutant tumors. In survival analyses, a specific myeloid cluster group proved interesting, with increased abundance of this group showing improved survival in both IDH-mutant and -wildtype tumors, in a manner that was enriched in specific neoplastic cell states. Overall, results suggest specific interactions between neoplastic cell characteristics, (cell states, IDH- and 1p/19q status), with myeloid cell and highlight the need of further studies understand the biological significance of the immune cell environment in diffuse gliomas.

The tumor micro-environment (TME) plays an important role in the biology of cancer, including gliomas. Single cell studies have highlighted the role of specific TME components in gliomas, and the methods to deconvolve bulk profiling data may serve to complement these studies on clinically annotated tumors. In this study, we estimated cell type proportions in 3 large glioma datasets (TCGA, CGGA-325, CGGA-693) using CIBERSORTx. Using a signature matrix comprising 22 immune cell types, we identified IDH mutation status-specific immune cell distributions and found that the proportions of 10 cell types were significantly different between IDHmut and IDHwt tumors across the 3 datasets. Looking further within IDHmut tumors, we found that monocytes were enriched in 1p/19q non-co-deleted tumors across the 3 glioma datasets, consistent with prior single cell studies. We then examined estimated gene expression among immune cell types relative to IDH mutation status and found clear separation of gene expression in 15 of 22 cell types in all 3 datasets. When we applied these 22 gene expression signatures in each tumor sample onto cluster-of-cluster analyses to identify tumor groups with distinct immune signature patterns, we found that samples were distributed largely according to the IDH status in all 3 datasets, confirming that immune cell expression is distinct based on IDH status. Among IDH-specific groups, cluster-of-cluster analyses showed that immune cell-based cluster groups had distinct survival outcomes, and that IDHwt samples were distributed significantly based on tumor grades as well as based on EGFR overexpression. Among IDHmut tumors, the distributions of tumor grade and 1p/19q co-deletion status were significantly different in the immune-based clusters in 2 of the 3 datasets examined. Overall, these results highlight the biological and clinical significance of the immune cell environment in gliomas, including distinctions based on IDH mutation status as well as prognosis within IDH-specific groups.