Immunological Profiling of Mutational and Transcriptional Subgroups in Pediatric and Adult High-Grade Gliomas

Author Manuscript Published OnlineFirst on July 2, 2019; DOI: 10.1158/2326-6066.CIR-18-0939 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Immunological profiling of mutational and transcriptional subgroups in pediatric and adult high-grade gliomas

Michael Bockmayr1,2,4, Frederick Klauschen2, Cecile L. Maire3, Stefan Rutkowski1, Manfred Westphal3, Katrin Lamszus3, Ulrich Schüller1,4,5*, Malte Mohme3*

1 Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 2 Charité – University Medicine Berlin, Humboldt University Berlin and Berlin Institute of Health, Institute of Pa- thology, Berlin, Germany 3 Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 4 Research Institute Children's Cancer Center Hamburg, Hamburg, Germany. 5 Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

* these authors contributed equally

Corresponding Authors: Malte Mohme, M.D. Department of Neurosurgery University Medical Center Hamburg-Eppendorf Martinistr. 52, 20246 Hamburg, Germany Tel.: +49 40 7410-0 Fax: +49 40 7410-58121 [email protected]

Ulrich Schüller, M.D. Research Institute Children´s Cancer Center Hamburg Martinistr. 52, N63 (HPI), 20251 Hamburg, Germany Tel.: +49 40 4260 51240 Fax.: +49 40 7410 40350 [email protected]

Words: 5468 (excl. abstract, translational relevance) Running Title: Immune profiling of pediatric and adult gliomas Key Words: high-grade glioma, glioblastoma, K27, G34, T cells, immune, gene expression, immune profile

Abstract Immunological treatment strategies are under investigation for high-grade gliomas. In order to invigorate a tumor-specific immune response it is required to determine relevant immunological pathways. We therefore investigated the immunological phenotypes within different subgroups of high-grade gliomas, with focus on rare genetic subgroups of pediatric and adolescent patients to identify potentially targetable mechanisms. We gathered published gene expression data from 1135 high-grade glioma patients and applied a machine learning tech- nique to determine their transcriptional (mesenchymal, classical, neural, proneural) and mutational (K27, G34, IDH, WT) subtype. Gene signatures of infiltrating immune cells and functional immune pathways were evaluated in correlation to histological diagnosis, age, transcriptional, and mutational subgroups. Our analysis identified four distinct microenvironmental signatures of immune cell infiltration (immune 1-4), which can be stratified into vascular, monocytic/stromal, monocytic/T cell– and APC/NK/T cell–dominated immune clusters. Im- mune cell expression profiles correlated with transcriptional and mutational subgroups but were independent of age and histological diagnosis. By including functional pathways and correlating the expression of immunostimulatory and -inhibitory receptor-ligand interactions, we were able to define the immunological microenvironment and identify possible immunological subtypes associated with poor prognosis. In addition, comparison of overall survival with the immunological landscape and with checkpoint molecules revealed correlations within the transcriptional and mutational subgroups, highlighting the potential application of PD- 1/PD-L1 checkpoint inhibition in K27-mutated tumors. Our study shows that transcriptional and mutational subgroups are characterized by distinct immunological tumor microenvironments, demonstrating the immunological heterogeneity within high-grade gliomas and sug- gesting an immune-specific stratification for upcoming immunotherapy trials.

Introduction High-grade gliomas are defined by aggressive and infiltrative growth, reducing the life expectancy in pediatric and adult patients (1). Despite multimodal treatment, including neu- rosurgical resection and combination of radio- and chemotherapy, tumor recurrence is a cer- tainty (1). Although treatment modalities such as immunotherapeutic approaches with checkpoint inhibitors have failed to show efficacy in a phase III study in recurrent glioblastomas (2,3), ongoing studies are trying to harness the ability of the immune system to counteract tumor growth and induce long-term remission (4). To identify which patients might benefit from checkpoint inhibition or other immunotherapeutic approaches, such as tumor vaccines, chimeric antigen receptor (CAR) T-cell therapy, autologous T-cell transfer or cytolytic virus injections, we must better understand the glioma-specific immune microenvironment (5). Large scale immunological profiling of gene expression data in other cancer entities has yielded information to improve precision immunotherapy (6–8). Unfortunately, the intricate cellular, genetic, and transcriptional heterogeneity of malignant gliomas adds complexity to efforts to define targetable immunological profiles (9,10).

The WHO classification in 2016 identified subcategories among high-grade gliomas (11). Molecular profiling, especially by isocitrate dehydrogenase (IDH) gene mutation analysis (12), improves understanding of subgroup-specific aggressiveness and prediction of overall survival in adult patients. Transcriptomic studies have identified additional subgroups. Phillips et al. described different molecular subgroups of astrocytoma and their prognostic relevance, then Verhaak and colleagues refined this classification by introducing a gene expression-based molecular classification of glioblastoma that stratifies tumors into mesenchymal, classical, neural and proneural subgroups (13,14). These groups share features of biological aggressiveness and response to therapy (13). The proneural subgroup was further subdivided into cGIMP and non-cGIMP methylated tumors (15). The neural subtype has been excluded in many subsequent studies due to suspected contamination of tumor samples with normal tissue (14,16).

In addition to the groups defined by transcriptional subtypes or mutations in the IDH gene in adult tumors, genomic sequencing studies also identified lineage-defining aberrations, such as the somatic mutations of the histone 3 variants in the positions G34 and K27 (17–19). Furthermore, pediatric glioblastoma can be stratified by additional molecular subgroups according to their RTK or MYCN pathway aberrations (19). The K27 and G34 mutations in histone 3 were predominantly found in pediatric and adolescent glioblastoma, highlighting their biological distinctiveness and the need to stratify high-grade gliomas according to their mutational profile.

Accumulating data suggest that molecularly distinct glioma subgroups differ in their tumor microenvironment and their immuno-stromal profiles (20,21). Investigations of the TCGA dataset, predominantly including IDH wild-type glioblastomas, revealed heterogeneity in the composition of the immune environment between mutational and transcriptional subgroups (22). Wang and colleagues demonstrated a subgroup-dependent macrophage/microglia infiltration according to the gene-signature, with increased infiltration in NF1- mutated tumors (23). This was also confirmed by gene-enrichment analysis for transcriptional subtypes by Doucette et al. (24) and by our group by gene-expression profiling for different molecular subgroups in medulloblastoma (25).

Our analysis evaluates the subtype-specific immune microenvironment across a cohort of pediatric and adult high-grade gliomas, comparing mutational and transcriptional profiles in order to identify immune infiltration patterns and functional pathways mediating local immunosuppression.

Materials and Methods All data analyses were performed using the statistical programming language R (26) including the packages survival, caret, randomForest, igraph, gplots and Rtsne [https://CRAN.R-project.org/package=survival, https://CRAN.R-project.org/package=caret, http://igraph.org, https://CRAN.R-project.org/package=gplots, http://www.jmlr.org/papers/v9/vandermaaten-08a.html, https://cran.r- project.org/package=randomForest].

Datasets, preprocessing and batch effect removal Raw gene expression data (.CEL files) from high-grade gliomas including eight series on the Affymetrix Human Genome U133 Plus 2.0 Array and one series on the Affymetrix HT_HG-U133A array were downloaded from Gene Expression Omnibus (GEO) (Fig.1a and Supplementary Table S1)(27). To minimize batch effects, the fRMA normalization, which includes a batch effect correction, was used as implemented in the R-package fRMA (28) using the input vectors from the R-packages hthgu133ahsentrezgfrmavecs and hgu133plus2-hsentrezgfrmavecs [http://bioconductor.org/packages/release/data/annotation/html/hthgu-133afrmavecs.html, http://bioconductor.org/packages/release/data/annotation/html/hgu133-plus2frmavecs.html] and the custom CDF files hthgu133ahsentrezgcdf and hgu133plus2hsentrezgcdf (version 19)(29). All data originating from the same center/study were transformed to median 0 as there were still remaining batch effects (Supplementary Fig. S1a). Since the data were not

homogenous with respect to mutational profile, localization and grade, no adjustment of the variability of each gene was performed between series measured on the same microarray. However, the mean absolute deviation, which is less influenced by outliers than the standard deviation was aligned between the two microarray platforms for each gene separately. After batch effect removal, clustering of the data revealed concordance of similar tumors (with respect to diagnosis, mutational profile or transcriptional subgroup) from different datasets. Duplicates defined by a Pearson correlation > 0.999 were removed yielding a final dataset of 79 anaplastic astrocytomas (AA), 28 diffuse intrinsic pontine gliomas (DIPG) and 1028 glioblastomas (GBM). The further analysis was restricted to the 12061 genes repre- sented on both arrays. Regarding the information on the IDH subgroup, the study by Gravendeel et al. performed sequencing of the IDH1 locus only, although TCGA datasets included sequencing information of the complete IDH gene (13,30).

Computation of immune and stromal signatures To obtain specific signatures of tumor-infiltrating immune and stromal cells, we first defined candidate genes from the signatures published by Becht et al. (31) and Danaher et al. (32) for 10 microenvironmental cell populations (MCP): T cells, CD8+ T cells, cytotoxic lymphocytes, B lineage cells, NK cells, monocytic lineage cells, myeloid dendritic cells, neutrophils, fibroblasts-like and endothelial cells (Supplementary Table S2 and S3). Second, all candidate genes were manually reviewed and those likely to overlap with glioma signatures were removed (Supplementary Table S2). In a final step, a correlation network was built by linking all genes with Pearson correlation over 0.4 for each signature separately. Only those genes belonging to the largest correlation module were retained as final candidate genes, since uncorrelated genes are unlikely to be specifically expressed by a unique cell population (32).

Scores of tumor-infiltrating immune and stromal cell signatures were defined as the average values of the corresponding marker genes. Signatures for different immunological processes were obtained from Doucette et al. (24) and Thorsson et al. (33) and computed analogously (Supplementary Table S3). This analysis is based on gene expression data and no histological or flow-cytometric analysis was performed. Conclusions of the presence of different immune cells were drawn based on expression of genes or gene signatures.

Inference of mutational status and transcriptional subtype Inference of the mutational status (IDH, K27, G34 and WT) and the transcriptional group (classical (CL), mesenchymal (MN), proneural (PN) and neural (NE) were performed

with a random forest classifier coupled with a confidence estimation, using the R-package randomForest. The classifier was built on the 1000 most variable probes in the training sets with 1000 trees. Default settings were used for the remaining parameters. Selection of the most variable genes was based on the standard deviation computed for each training fold separately. As the frequency of IDH, K27, and G34 mutations was age-dependent in our dataset, separate classifiers were built for young patients (reference set: all WT (n = 42), K27 (n = 36) and G34 (n = 13) patients under 20 years, as well as 20 IDH-mutated patients under 30 years) and older patients (reference set: all IDH-mutated (n = 79) and all WT (n = 485) patients over 20 years). Patients with a known transcriptional subgroup from the TCGA cohort were chosen as reference for the transcriptional subgroup classifier. To consider imbalanced class sizes, each group size was reduced to at most twice the number of samples of the smallest class for the WT/K27/G34/IDH classifier by downsampling. Group size was reduced to the size of the smallest class for the two other classifiers. Estimates of the classification accuracy were obtained from 5-fold cross-validation. As no parameter optimization was performed, nested cross-validation was not required. Since raw classification results yielded accuracies under 90% for each task, we used tail probabilities to increase accuracy and samples with a low proportion of votes for the predicted class were excluded and considered unclassifiable. To this end, the lowest threshold value was determined for each classification task such that samples from the validation fold with prediction score over the threshold were correctly assigned with an accuracy of over 91%. This allowed increasing accuracies over 91% for each of the classification tasks. The random forest classifier was compared to a radial basis function (RBF) kernel support vector, which did not yield superior classification accuracy in this setting.

Heatmapping, differential expression, k-means clustering, t-SNE and survival analysis Data was trimmed at z-scores ± 3 prior to heatmap analysis. Heatmaps were generated using the average-linkage method and Pearson correlations as similarity measure. Significance of differential expression between subgroups was assessed using the Kruskal-Wallis test. Multiple testing correction was performed with the Benjamini-Hochberg method. Immuno-stromal clusters were defined using k-means clustering of the previously defined marker genes. The number of clusters (n = 6) was selected using the elbow method. For visualization, t-distributed stochastic neighbor embedding (t-SNE) was used with Euclidi- an distance, perplexity 50, 1000 iterations and a learning rate η = 100. Default values were chosen for the remaining parameters. Survival analysis was performed using the proportional hazards model as implemented by the coxph function from the R package survival. Expres- sion values for the different signatures were transformed to quartiles and included as numer-

ical values. Multiple testing correction for the cell populations/signatures was performed using the Benjamini-Hochberg (BH) method where appropriate if not otherwise indicated.

Results

Prediction of mutational and transcriptional subgroups of high-grade gliomas Our aim in the present study was to combine gene expression profiling data from the Affymetrix U133A and U133p2 gene array chips and analyze immune cell- and immune pathway expression profiles in pediatric and adult high-grade gliomas according to age as well as mutational and transcriptional subgroups. We identified nine studies with appropriate gene expression datasets available on gene expression omnibus (GEO) (Fig. 1a) (21,30,34– 40). After exclusion of doublets and other tumor entities not representing gliomas, overall 1135 cases were included in this study (Supplementary Table S1). In order to be able to compare gene expression profiles from different centers as well as platforms, we corrected for batch effects using fRMA preprocessing and additional normalization (Supplementary Fig. S1a, Methods). Next, the combined dataset was characterized in more detail to allow correlative analyses between different glioma subgroups. However, transcriptional subtype information was only available in 495 samples (not available (NA): n = 640) and the mutational status was only available in 663 samples (NA: n = 472). We therefore used existing gene expression datasets with known mutational- and transcriptional subtype to train a machine-learning algorithm, which is then able to predict the mutational- and transcriptional subtype of gene expression data sets with unknown mutational- and transcriptional subtype information. To do so, we applied a random forest approach to separately predict the mutational and transcriptional subgroups of the missing samples (Fig. 1b). Machine learning was able to predict the mutational subgroup, defined by either histone H3 mutations at positions G34 or K27, as well as IDH mutations (IDHmut), or tumors harboring a wild-type for IDH (WT) and no histone H3 mutations (17–19). Next, machine learning predicted the transcriptional subgroup, defined by classical- (CL), mesenchymal- (MN), neural- (NE) and proneural (PN) signatures, by extrapolating from the known expression profiles. In an additional in silico validation step, the model accuracy was determined by internal cross-validation (Fig. 1c). Using tail probabilities and applying a threshold of >91% prediction accuracy (Fig. 1d), we were able to predict the mutational subgroup in an additional 344 cases and the transcriptional subgroup in 560 additional cases based on the known gene expression profiles (Fig. 1e). The age distribution of the individual groups in shown in Supplementary Fig. S1b. We kept the neural phenotype in our prediction model despite the controversial literature in this regard (16). We did this although data suggest that a neural profile might be contaminated with large amounts of non- neoplastic brain cells. Including the neural group therefore enabled us to investigate the im-

mune microenvironment of high-grade gliomas that are particularly diffuse or infiltrated by normal brain cells.

Optimization of immune cell population signature on the glioma dataset To analyze the abundance of tumor-infiltrating immune cell populations by inferring from gene expression data, several gene signatures have been published using different tools . These approaches are based on gene expression signatures of the cell types under consideration and are mostly derived from pan-cancer analyses. However, some of the known signatures also include genes which are expressed during neuronal development or tumorigenesis of malignant brain tumors. We therefore manually reviewed these signatures and tested the correlation of the published immune cell population signatures in our dataset for internal correlation in order to extract a glioma-specific immune subset signature, which is independent of tissue-specific interferences (Fig. 2, Supplementary Table S2). Inspection of diverse marker signatures revealed several genes (n = 22/144) that were not immune cell-specific. Some of these genes involved in neural development or glioma biology [e.g. KLRC3 (42), EOMES (43), GREM1 (44)] (Supplementary Table S2). Fur- thermore, many signatures included genes with low or negative correlation in our dataset, which is in contradiction with the hypothesis that these genes are specifically expressed by a unique type of cells in high-grade glioma (Fig. 2, grey dots) (32). As genes with different co- expression patterns are unlikely to be specifically expressed by a single cell population, only those genes belonging to the largest connected component were retained as final markers for the following analyses (Fig. 2, orange dots).

Immune cell gene expression profiles define immunological clusters After optimization of the gene expression signatures of infiltrating immune cell populations, we analyzed the composition of immune- and stromal cell gene expression clusters in correlation to age, transcriptional and mutational subgroup (Fig. 3a). Unsupervised hierarchical clustering identified four major clusters, which segregated according to their cellular signature into four clusters: vascular (immune 1), monocytic and stromal (immune 2), monocytic and T-cell dominant (immune 3), and an immune cluster with a gene expression profile indicative of infiltration by antigen presenting cells (APC), NK- and T cells (immune 4) (Fig. 3a). Hierarchical clustering revealed that certain immune clusters correlated with the transcriptional subtype. “Immune 1” primarily correlate with the classical (CL) transcriptional subtype, whereas “immune 2 and 3” were dominated by a mesenchymal (MN) signature. “Im- mune 4” demonstrated a mixed picture with no association to a transcriptional subgroup. Fur- thermore, we observed an increase of immune cluster 4 in G34-mutated tumors, as well as

the expected increase of immune cluster 2 in mesenchymal tumors (Supplementary Fig. S2A and B). There was no obvious correlation of the immune clusters with age (Fig. 3A). Next, we analyzed the prevalence of each immune/stromal cell population, as defined by their abovementioned gene signature (Supplementary Table S3), individually according to mutational subgroup, age, transcriptional subgroup and histological diagnosis (Fig. 3b). Age did not have a significant influence on the gene expression of classical immune cell subsets (Fig. 3b, second row). Age did, however, affect the stromal component, as tumors of pediatric and young patients demonstrated significantly lower gene expression profiles of fibroblast- like/stroma (P = 1.9e-5) and in part endothelial signatures (P = 0.0016). Low expression of the fibroblast-like signature was most frequent in IDH-mutated tumors (Fig 3b). Association of stromal expression with age was therefore most likely due to the age-distribution of IDH- mutated tumors, which were most prevalent in young adults (Supplementary Fig. S1) and not to intrinsically age-related mechanisms, as the stromal expression signature was not observed in wild-type gliomas. The stromal component was associated with monocytic cells (Supplementary Fig. S2c). This association was primarily seen in the immune 2 cluster, which was increased in tumors with a mesenchymal transcriptional subtype (Supplementary Fig. S2). T-cell signatures were most prevalent in WT glioma and in tumors with mesenchymal signatures and were significantly less expressed in histone H3-mutated (K27 and G34) tumors (P = 0.0022) (Fig. 3b, first and third row). A similar pattern was observed for cytotoxic T lymphocyte (CTLs) genes. B lineage cell gene expression was strongest in G34-mutated tumors but was not influenced by the transcriptional subtype. Monocytic lineage clusters, which include microglia and tumor-infiltrating macrophages, were underrepresented in G34- mutated tumors (P = 2.5e-7), but overrepresented in gliomas with a mesenchymal signature (P = 7.9e-92). Signatures for myeloid dendritic cells (mDCs), neutrophils and NK cells on the other hand were expressed in G34-mutated and IDH-mutated tumors, although no correlations to a transcriptional subgroup were found. The endothelial component was increased in IDH-mutated and WT tumors (P = 0.0018) with a classical signature (P = 2.7e-5) indicating a higher vascularization in these tumors compared to histone H3-mutated gliomas. Some of the strongest differences were observed with the stroma/fibroblast-like gene signature, which was reduced in IDH-mutated tumors, but expressed in WT tumors (P = 5.2e-20) and tumors with a mesenchymal signature (P = 1.1e-66) (Fig. 3b, right column). Taken together, these data show an impact of the mutational and transcriptional subgroups on the immune cell composition, demonstrating that the tumor biology, driven by the transcriptional and mutational phenotype, rather than the age of the immune system or the age at tumor occurrence shapes the immune landscape.

t-SNE analysis reveals immune microenvironment profiles

Next, we analyzed the immune microenvironment in more detail by investigating both gene signatures for immune cell populations and functional pathways involved in immune activation or inhibition, which have been defined by glioma- or pancancer studies by Thors- son et al. and Doucette et al. (24,33). First, we performed a t-SNE analysis using individual gene expression signatures for immune cell populations which have been optimized for glioma samples (Fig. 2) We included published signatures for pathways involved in immune activation/inhibition (Supplementary Table S3) (Fig. 4a). K-means clustering for all samples (n = 1135) was undertaken to identify microenvironmental subtypes. A Kaplan-Meyer plot of the overall survival within these clustered subtypes is shown in Fig. 4b. Here, the different clusters show significant differences (P = 2.1e-10), which may, in part, be explained by the fact that clusters 1 (green) and 6 (grey) contained mostly IDH-mutated tumors, which have a better prognosis than K27, G34, or WT tumors (Fig. 4a+b). The progression free survival for clusters 1-6 (P = 0.0043) and tumor samples that have been collected after previous treatment, i.e. chemo- or radiotherapy, are shown in Supplementary Fig. S3a-d. No significant impact of previous treatment on the immune gene expression landscape, except for a de- crease of endothelial cell signatures on pre-treated samples (P = 2.3e-5), was observed in this analysis. In analogy to the data presented in Fig. 4a, t-SNE plots were drawn for the transcriptional and mutational subgroups, as well as for age and histological diagnosis (Fig. 4c-f). Gene expression profiles for the t-SNE plotted samples of the individual signatures of immune cell populations and functional pathways from the pancancer (33) and glioma analysis (24) are shown in Fig. 4g-i. The gene expression of the individual signatures are illustrated in Supplementary Fig. S4a and b. Immune gene t-SNE clusters correlated best with the transcriptional subgroup. Here, the mesenchymal (MN) and classical (CL) subtypes demonstrated the strongest expression of immune genes, contributing to polarization of the samples.

Cluster 4 was mainly defined by genes indicating an infiltration of regulatory T cells (Tregs), fibroblast-like cells and cells of monocytic and myeloid lineage (Fig. 4g, second row). A strong signature of cytotoxic T lymphocytes (CTL) or the corresponding CD8+ T-cell markers was only detected in a minority of these samples, indicating that a stroma-rich environment

correlates with strong Treg- and monocytic infiltration and precludes infiltration of potentially tumor-specific cytotoxic CD8+ T cells. No clear polarization to any cluster was found for other gene signatures, such as mDCs, neutrophils, B cells, or endothelial cells within the combined analysis of the microenvironment. Although almost all IDHmut gliomas are known to express a proneural transcriptional subtype and therefore have a rather good prognosis, the proneural type is associated with poor prognosis in wild-type GBM (45). When stratifying by their immunostromal gene signatures, these two biologically different types group together, indi-

cating, that the transcriptional phenotype has a stronger impact on the immune microenvironment than the mutational IDH status. Functional immune inhibition or activation, described by pathways of stimulatory and inhibitory receptor/ligand interaction, were primarily associated with clusters 4 and 2. Alt- hough the stimulatory receptor/ligand pathway were both expressed in the same tumor samples, the inhibitory receptor/ligand axis was discordantly expressed (Fig. 4h, middle and lower panel). Inhibitory ligands, including, TGFB1, VEGFA/B, and IL-10, among others, were predominantly found in tumors with a mesenchymal transcriptional subgroup and extended beyond the monocytic and regulatory T-cell gene environment. Samples with strong inhibitory ligand gene expression were especially low in CTL signatures. Although the expression of inhibitory receptors, such as PDCD1, CTLA4, LAG3 and KIR2DL1/3, can usually be attribut- ed to CD3+ T cells, their expression did not correlate with tumors rich in T-cell gene expression clusters and was mainly associated with tumors with low immune cell signatures and low stimulatory receptor/ligand pathways (Fig 4h). The inhibitory receptor expression repre- sented one of the most distinct clusters (Fig. 4h, red circle = cluster 6), distant from immuno- suppressor and immunosuppressive signaling signatures defined by Doucette et al. (Fig. 4i) or other glioma antigen and ARG1 + IDO signatures (Fig. 4j) (24). Solely the co-inhibitory pathways, which include PDCD1LG2 and SLAMF7 and glioma antigen-rich samples co- localized to the inhibitory receptor area. These samples, which express high amounts of glioma antigen and harbor cells with strong expression of PDCD1 and CTLA4, might identify tumors amenable to checkpoint inhibition treatment. Moreover, within this region, most IDH- mutated tumors can be found. Although they do not express large amounts of antigen, the tumors seem to depend on the inhibitory receptor pathway.

Tumor-specific immune response affects survival associated with age and subgroup Finally, we wanted to determine the impact of individual immune cell population signatures and functional immune pathways on the overall survival of high-grade gliomas. First, we stratified all tumor samples with available overall survival information (n = 1057) by diagnosis, age, transcriptional and mutational subgroup and depicted the hazard ratio of individual gene signatures as heatmap representations (Fig. 5a, Supplementary Table S4a). Across all samples, functional immune pathways, including immunosuppressors, immunoactivators, immunosuppressive cytokines, and checkpoints or immune effectors, among others, are all associated with worse overall survival. These effects are pronounced in tumors with GBM and anaplastic astrocytoma histology and in patients >40 years of age. The negative impact of functional immune pathways on the overall survival is strongest in tumors with proneural transcriptional phenotype, which also include IDHmut glioma, whereas the overall survival of mesenchymal tumors is independent of their immune microenvironment. This also holds true

for WT tumors, in which no association with worse survival and negative immune regulators was found. Improved survival is observed primarily in association with gene expression profiles of cytotoxic- (T- and NK cells) and antigen presenting cells (mDCs and B cells) or inhibitory receptors (PDCD1, CTLA4, LAG3) (Fig. 5a). These data show that the immune microenvironment in tumors with mesenchymal transcriptional subgroup or patients at young age (<19 years) does not appear to affect overall survival, potentially indicating that these tumors are characterized by an immunosuppressed environment. To identify the influence of immune cell signatures on overall survival (OS, Fig. 5b) as well as progression free survival (PFS, Supplementary Fig. S3d) and to determine a marker profile that could be used in clinical diagnostics in order to classify the different immune microenvironments, we defined 4 immune clusters (Fig. 3a). When we plotted the overall survival of each immune clusters, we were able to confirm that immune cluster 2 (monocytic/stromal) was associated with a significantly worse prognosis (Fig. 5b). Other immune clusters with an immune microenvironment rich in antigen presenting cell (APC) and cytolytic cells (NK and T cells) present the best overall survival. Because of continuing study of immune checkpoint inhibitors in clinical trials, we analyzed the association of major immune checkpoints with the different high-grade glioma groups (Fig. 5c, Supplementary Table S4b). Here, CD274 (PD-L1), CTLA4, PDCD1, and LAG3 demonstrated significant correlations. As expected, PD-L1, a marker, which is associated with immune escape, was associated with a worsened survival (Hazard ratio >1), whereas the other markers (LAG3, CTLA4) correlated with better survival. PD-L1 was in- formative in tumors with anaplastic astrocytoma histology and with K27 mutation. Some checkpoints appeared to have a contrary impact on overall survival, as TGFB1 expression resulted in better prognosis in tumors with classical transcriptional phenotype and a worse survival in patients under the age of 20 years. Taken together, these data demonstrate, that immunological phenotype refines response of high-grade gliomas to immunological treatment approaches.

Discussion Molecular stratification of high-grade gliomas is paving the way to a better understanding of tumor heterogeneity. Integrated molecular analyses allow stratification into different subtypes and prediction of clinical response to therapy (13). Analyses in adult glioblastomas suggest that genomically diverse subtypes display distinct microenvironmental profiles (20,22,24). It is therefore hypothesized that genetic driver mutations promote unique immunological landscapes (20,23). Given the diverse genomic alterations between pediatric and adult high-grade glioma, i.e. K27 and G34 mutations, as well as the potential clinical implications for the translational use of therapeutic regimens, including chemotherapy and immune

checkpoint inhibition, we compared the immunological microenvironment of high-grade gliomas across age groups and stratified by clinically relevant genetic subgroups. In our study, we demonstrate that different mutational and transcriptional subgroups shape the composition of the immune subset signatures, independent of age or major histological diagnoses. We identified four distinct immunological patterns of immune cell profiles, which are defined by either vascular (immune 1), monocytic/stromal (immune 2), monocytic/T-cell (immune 3) and APC/NK/T-cell (immune 4) dominant immune clusters. These clusters correlated with the different transcriptional subgroups of high-grade gliomas. Based on a TCGA gene expression dataset of 544 patients, previous work by Doucette et al. highlighted that the mesenchymal subgroup displays a gene expression profile reflective of a proinflammatory anti-tumor response (24). Our cohort of 1135 gene expression profiles further enabled stratification of the mesenchymal subgroup into two immunologically distinct infiltration clusters, as the prevalence of monocytic markers together with a stromal and endothelial composition (immune 2) could be distinguished from a monocytic lineage profile with reduced stromal component but increased cytotoxic Tcell signature (immune 3), respectively. This observation suggests that the mesenchymal subgroup may harbor a distinct immunological subtype with increased gene expression indicative for higher CTL infiltration, which might be amenable to immune checkpoint inhibition. Work published by Luoto et al. analyzed the immune cell composition of 156 glioblastoma RNAseq samples by TCGA to investigate the impact of genomic alterations on the immune microenvironment in IDH wild-type and IDH-mutated GBM (22). That study identifies three immune response groups, a “negative” macrophage, granulocyte, and macrophage dominated group in IDH1 mutated tumors, a “humoral”, lymphocyte-enriched signature in EGFR-gained samples and a “cellular-like”, macrophage infiltrated subgroup in EGFR-amplified tumors (22). We confirm the observation of a lower monocyte/macrophage signature component in IDH-mutated tumors. However, we did not see an IDH-associated immune phenotype, as in our cohort, K27 and G34-mutated gliomas correlated more with an immune cluster 4 phenotype (immune 4: APC/NK-/T cell). We showed that IDH wild-type and mutated tumors displayed the most distinct differences in the stromal/fibroblast-like cell signature. Yet, only IDH-mutated tumors seemed to leverage certain functional pathways, as they expressed “inhibitory receptors”, such as PD-1, CTLA4, LAG3, or KIRs, which concurs with the finding of a “negative regulation of lymphocyte function” in IDH tumors by Luoto et al. (22). In a global pancancer analysis, glioblastoma was classified as a lympho-

cyte-depleted, macrophage-enriched tumor entity with a suppressed TH1 axis (33). Although the monocytic- macrophage lineage signature was also evident in our analysis, we describe a subset of GBM with low expression of monocytic lineage gene cluster, as the vascular

(immune 1: vascular) and APC/NK-/T cell (immune 4) clusters were depleted of monocyte/macrophage signatures. The K27 and G34 do not seem to follow the glioblastoma pattern described by Thorsson et al. (33). We show that K27 and G34 tumors displayed lower gene expression signatures of monocytic lineage cell and harbored lower cytotoxic and overall T-cell gene expression compared to IDH-mutated and wild-type gliomas. The depletion of lymphocytic clusters in the K27 and G34 tumors is comparable to the “immunologically quiet” immune subtype described for low-grade gliomas in the pancancer analysis (33). However, the increased B cell gene expression prevalence in G34-mutated tumors in our study points + to a primary signatures indicative for CD4 TH2 cell infiltration, which therefore deviates from

the observed dominance of a TH1 response in low-grade glioma by Thorsson et al. (33). The abovementioned studies demonstrate that the molecular and transcriptional heterogeneity in malignant gliomas correlates with high variability of the immunological landscape (20,22,24,33). These studies emphasize the need for further stratification in order to individualize upcoming clinical studies. Our data demonstrate that age and in part histological diagnosis are not relevant for the immune subset composition. However, immune cell signatures and functional immune pathways are associated with the transcriptional subgroups (13,16). Especially the mesenchymal subgroup demonstrated immune activity and association with monocytic lineage profiles and immune clusters 2 and 3. The strongest correlation of immune-relevant cell signatures and functional pathways was, however, observed in the proneural subgroup. Here, immunosuppressive and tumor-associated macrophage-related pathways, as well as stromal/fibroblast-like components correlated with a decreased overall survival. Although the mesenchymal subgroup displayed an immunological component, almost no correlation with overall survival was present. In addition, immune gene expression did not correlate with either survival in IDH wild-type gliomas or with patient age below 20 years, implying an immunological “inertness” in these groups. We initially hypothesized that high-grade gliomas in children and young adults might demonstrate distinct immunological profiles due to a still developing or young immune system. Except for the above-described lack of correlation of immunological gene expressions with overall survival, we did not find any association to a specific immune cell pattern. How- ever, when dissecting high-grade gliomas of the young and adolescent patients into their molecular subgroups, i.e. K27 and G34-mutated tumors, opposing effects of immunological genes on overall survival became apparent. In the K27-mutated tumors, CD274 (PD-L1) and CTLA4 correlated with a worse prognosis, whereas opposite was true for G34-mutated tumors. Furthermore, G34 tumors appear to rely primarily on TGFB1 and HAVCR2 as pathways for immune escape. This finding implicates that checkpoint inhibition of the PD-1/PD-L1 pathway might be beneficial for patients with diffuse midline gliomas harboring histone H3 K27M mutations.

Our study shows that immunological heterogeneity in high-grade gliomas is associated with distinct transcriptional and mutational subgroups, independent of patient age. The immune cell signatures correlate with the transcriptional subgroup and can be clustered into in four immunological groups. The mesenchymal subgroup shows an immunological activation pattern, which does not correlate with overall survival, reflecting the inert immunobiology of this transcriptional subtype as well as IDH wild-type glioblastoma. The proneural subgroup, on the other hand, which includes IDH-mutated tumors, displays expression on “inhibitory immune receptors” that correlates with overall survival. K27 and G34 gliomas exhibit diverse immune pathway expression, with K27 tumors especially deprived of lymphocyte, NK cell and antigen presenting mDC signatures and presumably other mechanisms of immune escape, such as the PD-L1 pathway. Taken together, our study demonstrates that future immunotherapeutic strategies should take into account the distinct immunological landscapes that may modulate therapeutic efficacy, especially for rare mutational subgroups of high-grade gliomas.

Acknowledgments This study was supported by the Werner-Otto-Stiftung (to US) and the Fördergemeinschaft Kinderkrebszentrum Hamburg (to MB, US), the Anni-Hofmann Stiftung (to KL) and the Else Kröner-Fresenius Stiftung (to MM).

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Figure Legends Figure 1. Data collection and in silico prediction of transcriptional and mutation subtype. (a) We gathered data from nine different studies using microarray gene expression profiling. Numbers delineate high-grade glioma samples included in this study, whereas numbers in brackets display the overall number of available datasets within the described study. Next, we performed a random forest approach (schematically shown, b), followed by internal cross-validation (schematically shown, c) to predict the mutational and transcriptional subtype. (d) Cross tables illustrate prediction accuracy compared to known datasets. (e) Hor- izontal bar diagrams display the origin and cohort composition of mutational and transcriptional subtype before (left panels) and after (right panels) the application of the prediction algorithm. AA: anaplastic astrocytoma, DIPG: diffuse intrinsic pontine glioma, GBM: glioblastoma, G34: G34-mutated glioma, K27: K27-mutated glioma, IDH: IDH-mutated glioma, WT: IDH wild-type glioma, N/A: not available, CL: classic transcriptional subgroup, MN: mesenchymal transcriptional subgroup, NE: neural transcriptional subgroup, PN: proneural transcriptional subgroup, N/A: not available.

Figure 2. Optimization of glioma-specific immune cell subset signature. Due to the in- terference from immune signatures published by Becht et al. and Danaher et al. (31,32) with certain genes expressed by tumor cells themselves, we performed an internal correlation analysis of the presumable immune cell gene sets within our compiled glioma cohort. The correlation threshold was set at R = 0.4. Genes not correlating with each other (grey dots) were excluded from the analysis. In addition,15 genes from the published gene signatures with known glial/neuronal expression were excluded in advance and are not shown. 25 additional genes published for immune cell analysis were not included due to their unavailability on the microarray chip (Supplemental Table S2).

Figure 3. Immune cell signatures stratified by age, histological diagnosis and transcriptional as well as the mutational subtype. (a) Heatmap representation of an unsupervised hierarchical clustering of 10 immune cell subsets, based on their gene expression signature (see above). Four distinct immune clusters can be identified (APC: antigen presenting cells). (b) Gene expression of individual immune cell populations are illustrated according to mutational subtype, age, and transcriptional subtype. P-values are calculated by the Kruskal-

Wallis test. test. AA: anaplastic astrocytoma, DIPG: diffuse intrinsic pontine glioma, GBM: glioblastoma, G34: G34-mutated glioma, K27: K27-mutated glioma, IDH: IDH-mutated glioma, WT: IDH wild-type glioma, N/A: not available, CL: classic transcriptional subgroup, MN: mesenchymal transcriptional subgroup, NE: neural transcriptional subgroup, PN: proneural transcriptional subgroup.

Figure 4. T-SNE analysis of immune cell signatures and functional immune pathways. (a) K-means clustering of all gene expression profiles results in 6 distinct immunological subtypes. (b) Overall survival demonstrates impact of IDH-mutated tumors on cluster 1 and 6. C- F) T-SNE analysis according to transcriptional subgroup (c), mutational subgroup (d), age (e), and histological diagnosis (f). Individual plots demonstrate distribution of immune cell subsets (g), functional pathways published by Thorsson et al. (h) and Doucette et al. (i) and other pathways (j). The red dotted circle highlights the prevalent expression of inhibitory receptors in cluster 6. Individual gene signatures for the different immune cell subsets and pathways are listed in Supplementary Table S3, respectively. Certain signatures published by Doucette et al. that are not associated with an immunomodulatory function (see Supple- mentary Fig. S2) are shown in panel (g) and (j).

Figure 5. Subgroup-specific correlation of overall survival with gene expression profiles of immunological subsets and pathways. (a) Subgroup-specific hazard ratios according to histological diagnosis, age, transcriptional and mutational subtype. Individual gene signatures for the different immune cell subsets and pathways are listed in Supplementary Table S4. (b) Overall survival stratified by the four immune clusters identified in Fig. 3. P- value calculated by Log-Rank test. (c) Correlation of overall survival with the expression of immune checkpoint genes according to the known subgroups (Supplementary Table S4b). P- values: * = P < 0.05, ** = significant after Benjamini-Hochberg correction for the tested signatures *** = significant after Bonferroni corrected for all hypotheses. AA: anaplastic astrocytoma, DIPG: diffuse intrinsic pontine glioma, GBM: glioblastoma, G34: G34-mutated glioma, K27: K27-mutated glioma, IDH: IDH-mutated glioma, WT: IDH wild-type glioma, NA: not available, CL: classic transcriptional subgroup, MN: mesenchymal transcriptional subgroup, NE: neural transcriptional subgroup, PN: proneural transcriptional subgroup.

Abbreviations APC Antigen presenting cell CD Cluster of differentiation + CD4 conventional, non-Treg, CD4 T cells CL Classical transcriptional subtype

CTLA4 Cytotoxic T-lymphocyte-associated protein 4 CTL Cytotoxic T lymphocytes GBM Glioblastoma G34 Histone-H3 gene mutation at position 34 IDH Isocitrate dehydrogenase K27 Histone-H3 mutation at position 27 LAG3 Lymphocyte-activation gene 3 mDCs Myeloid dendritic cells MGMT O6-Methylguanin-DNA-Methyltransferase NK cell Natural killer cell PDCD1 Programmed death-1 PDCD1LG2 Programmed death-1 ligand 2 PD-L1 Programmed death-1 ligand-1 SLAMF7 SLAM family member 7 TCGA The Cancer Genome Atlas TGFβ Transforming growth factor β WT Wild-type

Downloaded from cancerimmunolres.aacrjournals.org on September 24, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 2, 2019; DOI: 10.1158/2326-6066.CIR-18-0939 Figure 1 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. a Study Origin n Histology Chip PMID GEO ID Ceccarelli 2016 TCGA 524 GBM HT-u133A 26824661 GSE83130 Rembrandt Los Angeles 241 GBM u133p2 19208739 GSE108474 Griesinger 2013 Denver 34 Pediatric Gliomas u133p2 24078694 GSE50161 Lee 2008 Los Angeles 26 (267) GBM mixed 18940004 GSE13041 Gravendeel 2009 Rotterdam 175 all Gliomas u133p2 19920198 GSE16011 Sturm 2012 Heidelberg 46 GBM u133p2 23079654 GSE36245 Sturm 2016 Heidelberg 16 (182) PNET, mixed u133p2 26919435 GSE73038 Paugh 2010 Memphis 48 Pediatric HGG u133p2 20479398 GSE19578 Paugh 2011 Memphis 25 DIPG + Brainstem u133p2 21931021 GSE26576

n = 1135 b d Mutational Prediction Instances Random Forest

... *1000 true class

Tree 1 Tree 2 Tree n

Class A Class B Class X G34 IDH K27 WT WT K27 IDH G34 % of predict- Majority Voting predicted class able cases

Final Class Subclass Prediction c

Training fold Test fold

Cross validation true class determination of model accuracy CL ME NE PN

PN NE ME CL % of predict- predicted class able cases

0% 50% 100%

Transcriptional Mutation Subtype Age [years] Diagnosis Mutation e subtype predicted predicted

TCGA (n=524)

Rembrandt (n=241)

Denver (n=34)

Los Angeles (n=26)

Rotterdam (n=175)

Heidelberg (n=62)

Memphis (n=73)

0 50 100 0 50 100 0 50 100 0 50 100 0 50 100 0 50 100%

0−19 (n=158) AA (n=79) G34 (n=15) CL (n=154) G34 (n=17) CL (n=295) K27 (n=42) MN (n=374) 20−39 (n=151) DIPG (n=28) K27 (n=37) MN (n=173) IDH (n=127) NE (n=155) 40−59 (n=425) GBM (n=1028) IDH (n=84) NE (n=61) WT (n=821) PN (n=231) 60−89 (n=401) WT (n=527) PN (n=107) N/A (n=472) N/A (n=640) N/A (n=128) N/A (n=80) Prediction accuracy: Prediction accuracy: >91% >91%

T cells CD8 T cells Cytotoxic lymphocytes NK cells B lineage IL21R MAL CD160 CD8B GZMH KIR2DL1 MS4A1 FCRL2 CTSW CR2 CD5 CD3E KLRB1 CTLA4 PRF1 TNFRSF17 CD19 KIR2DL3 KIR3DL2 PNOC KLRD1 NCR1 CD6 FLT3LG CD79A CD3G GZMA KIR3DL1 SPIB SIRPG NKG7 TCL1A IGKC CD3D TNFRSF25 GZMB KIR2DL4 ICOS BLK SH2D1A TRAT1 CD8A XCL1 CD22 CD28 GNLY PTGDR BANK1

Monocytic lineage Myeloid dendritic cells Neutrophils Endothelial cells Stroma / Fibroblast-like MEGF9 CSF1R CD84 CD1B COL6A1 SIGLEC5 FCGR3B PLXNA2 FPR3 CD1A CXorf36 PAMR1 ADAP2 SLC25A37 CD163 CXCR2 ACVRL1 MS4A4A CD1E HAL ROBO4 TFEC VWF COL3A1 CEACAM3 MYCT1 MMRN2 PGF COL1A1 HDC CD68 CXCR1 MMRN1 FCAR VNN3 TIE1 COL6A2 CLEC10A FAM124B EMCN PLA2G7 STEAP4 MS4A2 SDPR CLIC2 TAGLN CPA3 TNFRSF10C CA4 PALMD DCN TPSAB1 TECPR2 EDN1

included in signature excluded

Monocytic lineage Stroma / Fibroblast-like Endothelial cells Myeloid dendritic cells B lineage NK cells Neutrophils CD8 T cells T cells Cytotoxic lymphocytes

G34 IDH K27 WT N/A 0-19 20-39 40-59 60-89 CL MN NE PN N/A AA DIPG GBM

Immune 1 Immune 2 Immune 3 Immune 4 Immune Cluster (Vascular) (Monocytic / Stromal) (Monocytic / T cell) (APC / NK- / T cell) -3 0 3 z-scores b T cells CTLs B lineage Monocytic Myeloid dendritic Neutrophils NK cells Endothelial Stroma / lineage cells (mDCs) cells Fibroblast-like Mutation K27 G34 IDH tat io n WT Mu −0.5 0.5 −0.4 0.0 0.4 −0.3 0.0 0.2 −0.2 0.0 0.2 −3 −1 1 3 −0.3 0.0 0.2 0.4 −2 0 1 2 −0.4 0.0 0.4 −0.3 0.0 0.2 p = 0.0022 p = 0.0023 p = 0.0023 p = 2.5e−07 p = 0.00074 p = 0.00074 p = 0.0018 p = 0.0018 p = 5.2e−20

Age [years] 0−19 20−39 40−59 Age 60−89 −0.2 0.2 −0.5 0.5 1.0 −0.4 0.0 0.4 −0.3 0.0 0.2 −3 −1 1 3 −0.2 0.0 0.2 −2 0 1 2 −0.4 0.0 0.4 0.8 −0.3 0.0 0.2 p = 0.16 p = 0.068 p = 0.25 p = 0.18 p = 0.092 p = 0.79 p = 0.54 p = 0.0016 p = 1.9e−05

Transcriptional subtype CL MN NE subtype 0.0 0.5 1.0 PN −1 0 1 2 −2 0 2 4 Transcriptional −0.2 0.2 −0.5 0.5 −0.2 0.2 −0.2 0.0 0.2 −0.2 0.0 0.2 −0.4 0.0 0.4 p = 7.9e−19 p = 3.4e−47 p = 0.0036 p = 7.9e−92 p = 0.00084 p = 0.0036 p = 0.46 p = 2.7e−05 p = 1.1e−66

Diagnosis AA DIPG GBM Diagnosis −0.5 0.5 −0.2 0.0 0.2 −3 −1 1 3 −0.2 0.0 0.2 −2 0 1 2 −0.3 0.0 0.2 −0.4 0.0 0.4 −0.4 0.0 0.4 −0.3 0.0 0.2 p = 0.0063 p = 0.062 p = 0.043 p = 0.023 p = 0.01 p = 0.15 p = 0.99 p = 0.00021 p = 1.1e−14

Figure 4 a K-means Clustering b Overall Survival c Transcriptional Subtype d Mutation e Age [years] f Diagnosis Cl. 1 Cl. 4 Cl. 2 Cl. 5 Cl. 3 Cl. 6

.75 p = 2.1*e-10

.25

Cl. 1 Cl. 2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 0 12 24 36 48 60 CL MN NE PN K27 G34 IDH WT 0−19 20−39 40−59 60−89 DIPG AA GBM Time [months] g T cells CTL CD8+ T cells NK cells h Co-Inhibitory Co-Stimulatory i Immunosuppressors Immuneactivators j Glioma Antigen

Proinflammatory Immunosuppressive T regs Monocytic lineage Myeloid markers Stroma / Fibroblast-like Stimulatory Ligand Stimulatory Receptor Cytokines Signaling ARG1 + IDO

B cell lineage mDCs Neutrophils Endothelial cells Inhibitory Ligand Inhibitory Receptor TAM + Skewing Antigen Presentation Others

−4 −2 0 2 4 Z−scores

Immune cell populations Thorsson et al. Doucette et al.

Functional Immune Signatures

Downloaded from cancerimmunolres.aacrjournals.org on September 24, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 2, 2019; DOI: 10.1158/2326-6066.CIR-18-0939 Figure 5 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. a Subgroup-specific Hazard Ratios Diagnosis Age Transcriptional Subtype Mutation *** *** *** * ** * *** * * Immunosuppressors ** Immuneactivators *** *** * *** * Immunosuppressive cytokines and checkpoints ** * * * Immune effector *** ** * ** ** Immunosuppressive signaling *** ** * ** ** TAM + Skewing *** * ** * *** Fibroblasts Stimulatory ligand *** * *** ** *** * * Inhibitory ligand ** ** * * ** * Glioma antigen * * Antigen presentation T cell effector Innate immune * * * Monocytic lineage ** ** * ** Myeloid markers T regs Endothelial cells * Co−inhibitory Proinf cytokines Co−stimulatory Stimulatory receptor * * * CD8 T cells * Inhibitory metabolism ** * * * * * Myeloid dendritic cells ** ** * * B lineage Neutrophils Cytotoxic lymphocytes ** * * ** * T cells ** ** NK cells ** ** * * ** * Inhibitory receptor Survival

AA (n=77) al (n=218) al (n=145) Better Worse K27 (n=37)G34 (n=13) All (n=1057) DIPG (n=26) IDH (n=113)WT (n=769) GBM (n=954)0-19y (n=113) ymal (n=347) 20-39y (n=136)40-59y (n=413)60-89y (n=395) Neur 1/2 1 2 Proneur Classical (n=281) Hazard Ratio Mesench b OS by Immune Cluster c OS by Immune checkpoint molecules Immune 1 (Vascular) P = 0.0063 Endothelial cells BTLA

Immune 2 (Monocytic/Stromal) * HAVCR2 Monocytic lineage Stroma / Fibroblast-like * * TGFB1 Endothelial cells LAG3 T cells / CTL * ** * ** * *

Immune 3 (Monocytic/T cell) * ** ** CD274 Monocytic lineage Overall survival TIGIT T cells Immune 4 (APC / NK- / T cell) * * * * CTLA4 Myeloid DCs PDCD1 B cells * * * *

0.0 0.2 0.4 0.6 0.8 1.0 NK cells AA Hazard ratio 0 10 20 30 40 50 60 T cells / CTL K27 G34 IDH WT DIPG GBM0−19y 20−39y40−59y60−89y Neural All tumors ProneuralClassical Count

Time [months] 0 4 8 Mesenchymal 1/2 1 2 Value

Immunological profiling of mutational and transcriptional subgroups in pediatric and adult high-grade gliomas

Michael Bockmayr, Frederick Klauschen, Cecile L Maire, et al.

Cancer Immunol Res Published OnlineFirst July 2, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/2326-6066.CIR-18-0939

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