Immunological features in de novo and recurrent glioblastoma associate with survival outcomes

Abstract

Glioblastoma (GBM) is an immunologically “cold” tumor characterized by poor responsiveness to immunotherapy. Standard-of-care for GBM is surgical resection followed by chemoradiotherapy and maintenance chemotherapy. However, tumor recurrence is the norm, and recurring tumors are found frequently to have acquired molecular changes (e.g. mutations) that may influence their immunobiology. Here, we compared the immune contexture of de novo and recurrent GBM (rGBM) using high-dimensional cytometry and multiplex immunohistochemistry. Although myeloid and T cells were similarly abundant in de novo and rGBM, their spatial organization within tumors differed and was linked to outcomes. In rGBM, T cells were enriched and activated in perivascular regions and clustered with activated macrophages and fewer regulatory T cells. Moreover, higher expression of phosphorylated STAT1 by T cells in these regions at recurrence was associated with a favorable prognosis. Together, our data identify differences in the immunobiology of de novo and rGBM and identify perivascular T cells as potential therapeutic targets.

Introduction

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and is associated with a poor prognosis (1). Over the past 15 years, treatment options for GBM have shown little advancement. Standard-of-care treatment is surgical resection with adjuvant chemoradiotherapy followed by maintenance chemotherapy with temozolomide, an alkylating agent. However, with this approach, median overall survival is only 16.9 months and unfortunately, relapse is expected for nearly all patients (2).

Therapeutic resistance in GBM has been associated with tumor evolution. Genomic alterations are known to accumulate in response to temozolomide such that recurrent GBM (rGBM) displays a hypermutated phenotype compared to the initial tumor at diagnosis (de novo) (3,4). The quality of tumor-specific mutant peptides (neoantigens) present in rGBM along with T lymphocyte infiltrates identifies a subset of patients with a more favorable outcome (5). These findings raise the possibility that rGBM and de novo GBM differ immunologically, with potential implications for immunotherapy.

Immunotherapy has become a standard-of-care treatment for many solid cancers, yet therapeutic targeting of the immune system has not produced significant clinical benefit for most patients with GBM (6). This treatment resistance has been attributed to the ability of GBM to orchestrate an immunosuppressive tumor microenvironment and to a lack of immunogenic neoantigens expressed by tumor cells that have a low mutational burden in de novo GBM (7). Overall, GBM is defined as an immunologically “cold” tumor characterized by a paucity of tumor-infiltrating CD3⁺ T cells but a strong presence of tumor-associated macrophages (8,9). The abundance of tumor-associated macrophages correlates with worse survival (8,9).

Several studies suggest that immune checkpoint blockade with anti–PD-1 as well as vaccines might trigger productive T-cell responses in some patients with GBM (10–13). These studies have been conducted in patients with rGBM and thus, the contribution of standard-of-care treatment to any immunological alterations detected remain undefined. Thus, there is a strong need to examine the immune contexture of both de novo and rGBM to understand the immune response that occurs during GBM evolution.

Here, we performed deep cytometric profiling of tumor-infiltrating immune cells using mass cytometry and multiplex immunohistochemistry (mIHC) in de novo and rGBM surgical specimens to define the composition and distribution of immune-cell subsets detected in GBM tumor tissues. We found similar proportions of myeloid and T cells in de novo and rGBM, but differences in subsets across tumors, and substantial intratumoral heterogeneity in the spatial distribution of immune infiltrates. We identified increased numbers of CD3⁺pSTAT1⁺ cells in perivascular regions as associated with a longer median time from relapse to death. Collectively, our findings indicate that although both de novo and rGBM display potential targets for immunotherapy, rGBM might have a better potential for response to immunotherapies.

Methods

Patient and sample characteristics

Tumor samples were obtained from patients with GBM after written informed consent and de-identified. Peripheral blood mononuclear cells (PBMC) from healthy volunteers were obtained from the Human Immunology Core at the University of Pennsylvania, Studies were conducted in accordance with the 1996 Declaration of Helsinki and approved by the University of Pennsylvania’s Institutional Review Board. For analysis by cytometry by time of flight (CyTOF), tumor samples were obtained from 27 patients (14 with de novo GBM and 13 with rGBM) after confirmation of a GBM diagnosis by a board-certified neuropathologist. Whole tumor was processed by mechanical and enzymatic dissociation using a gentleMACS™ Octo Dissociator in combination with the Brain Tumor Dissociation Kit (Miltenyi Biotec, cat no. 130–095-942) and then filtered through a 75mm strainer to generate a single-cell suspension. Samples were frozen in liquid nitrogen until further use. Characteristics of patients included in the CyTOF analysis are listed in Supplemental Table S1. For mIHC analyses, 17 patients who had previously had surgery for de novo and then rGBM performed at the University of Pennsylvania were identified through a medical records search. Unstained slides were cut at 5 μm thickness from formalin-fixed paraffin-embedded (FFPE) blocks for analysis. Characteristics of patients included in the mIHC analyses are listed in Supplemental Table S2 and the number of regions analyzed are detailed in Supplemental Table S3.

CyTOF

Thawed single-cell suspensions of tumor or healthy volunteer PBMC were assessed using CyTOF as previously described (14). Briefly, antibody panels (lymphoid, myeloid, and T cell) were designed to simultaneously measure the expression of molecules related to cell lineage, differentiation state, and function (Supplemental Table S4). Cells (3x10⁶) were incubated with cisplatin for 1 minute, then stained at room temperature for 30 minutes with metal-labeled antibodies to detect surface proteins. Cells were then permeabilized using Fixation/Permeabilization solution (eBioscience, Thermo Fisher, cat no. 00–5123-43) at room temperature for 30 minutes and then stained at room temperature for 60 minutes with metal-labeled antibodies to detect intracellular proteins. Cells were washed in permeabilization buffer and resuspended in PBS-1.6% paraformaldehyde before acquisition by CyTOF (Helios, Fluidigm). CD45⁺ cells were identified as Iridium⁺Beads⁻Cisplatin⁻CD45⁺. Monocytes were identified as CD3⁻CD14⁺. B cells were identified as CD3⁻CD19⁺. NK cells were identified as CD3⁻CD56⁺. Mucosal associated invariant T (MAIT) cells were identified as CD3⁺CD161⁺TCRV7.2⁺. T cell receptor gamma/delta (TCRgd) cells were identified as CD3⁺TCRgd⁺. Regulatory T (Treg) cells were identified as CD3⁺CD4⁺CD25⁺CD127⁻. Follicular helper T (TFH) cells were identified as CD3⁺CD4⁺CXCR5⁺PD-1⁺. Natural killer T (NKT) cells were identified as CD3⁺CD161⁺TCRV24⁺. Analysis was performed using FlowJo v10 (TreeStar), as well as R v3.5.1 and RStudio v1.1.383. R scripts and other resources are available online at https://github.com/wherrylab/Cytof_analysis_calanio.

Microscopic analysis

FFPE tumor specimens were sectioned onto positively charged glass microscope slides. Automated mIHC was performed on a Ventana Discovery Ultra automated slide staining system (Roche). Slides were deparaffinized at 70°C for 24 minutes. Following deparaffinization, heat-induced epitope retrieval was performed using an EDTA-based preconditioning solution (Ventana Medical Systems, cat no. 950–500) for 64 minutes at 100°C. Slides were incubated with the appropriate primary and secondary antibodies prior to chromogenic signal detection using the regents and antibodies listed in Supplemental Table S5. Tissues were counterstained with hematoxylin (Ventana Medical Systems, cat no. 760–2021) and bluing reagent (Ventana Medical Systems, cat no. 760–2037). Whole slide scans of stained tissues were acquired at 40X using an Aperio CS2 scanner (Leica Biosystems).

mIHC image analysis

Scanned brightfield images were digitally quantified using custom image analysis algorithms. All algorithms were created using Visiopharm Integrator System (VIS) software (Version 2019.07). A board-certified neuropathologist identified and annotated areas of dense and infiltrative tumor from hematoxylin- and eosin-stained slides. Annotations were then transcribed onto scanned multiplex images. Areas of necrosis, non-neoplastic brain, predominantly reactive/treatment-related changes, and artifactual staining were excluded from analysis. Custom image analysis algorithms were applied to quantify the number of CD3⁺, CD3⁺Ki-67⁺, CD8⁺, Foxp3⁺, CD68⁺, CD163⁺, CD163⁺Ki-67⁺, EGFR⁺p53⁺, HLA-DR⁺, HLA-DR⁺CD68⁺, and Ki-67⁺ cells within the neuropathologist-annotated areas of GBM. Thresholds were similarly applied across all samples to reduce variability. Each Visiopharm algorithm was reviewed to ensure reproducibility and to minimize error and variability with cell segmentation and with detection of cytoplasmic and nuclear features. Absolute cell counts were normalized to the total area of the annotations and reported as densities (cells per mm²).

To visualize the quantity and distribution of CD8⁺ lymphoid aggregates in paired de novo and rGBM specimens, heat maps based on the density of CD8⁺ cells present within a 100μm drawing radius were generated on annotated tissues. Standardized minimum and maximum thresholds were applied during heatmap generation to allow for comparisons among specimens.

Separately, to facilitate the unbiased selection of T-cell high and T-cell low regions, individual regions-of-interest (ROIs) were delineated around areas of low and high density identified from the heat map. ROI sizes were determined by overall areas of identified tumor. These ROIs were carried over to annotate the identical T-cell low and T-cell high regions on serial sections of tissues that were stained for additional immune markers. All quantitative measurements were normalized to the area of the ROIs. To quantify the number of T-cell high loci per case, 1,035 by 1,035 μm square grids were superimposed upon CD8 density heatmaps. The number of grids containing CD8-enriched hotspots was quantified and expressed as a percentage of the total number of grids.

Statistical analyses

For group comparisons and correlation analyses, testing was performed using PRISM 8.4 (GraphPad Software). Normality of distributions was assessed using D’Agostino–Pearson omnibus normality test and variance between groups of data was determined using the F-test. For non-normal data, non-parametric Mann–Whitney U-tests were used for unpaired analyses. Descriptive statistics included mean, median, standard deviation and range for continuous variables and frequency and proportion for categorical variables. For survival discrimination, unsupervised k-means clustering (k=2) was used to stratify patients into two groups based on overall survival, via the kmeans command in the stats suite of commands on R (version 3.5.3). 10 random starts and 1,000 maximum iterations were used as parameters. K-means clustering demonstrated successful convergence, with stratification of patients into two distinct survival groups minimizing within-cluster variance (between-cluster sum of squares / total sum of squares = 0.701). Cluster analysis was also re-performed with kernel density estimation using the pdfCluster package on R, which determines the optimal number of clusters and partitions variables via nonparametric density estimation. The same survival group assignments were reached through kernel density estimation.

Analyses correlating gene expression and patient survival used the maximal selected rank statistics package (maxstat on R) according to prior studies (9). This methodology was used to select the optimal cutoff for dichotomizing a quantitative predictor, such as gene expression, into high and low groups, when analyzed in association with a quantitative response, such as survival. Subsequently, Kaplan-Meier curves were generated for high- and low-expression groups and differences in survival were quantified via the maximally selected rank test and independent t-tests. Statistical significance was defined as P<0.05.

Data availability statement

The data generated in this study are available within the article and its supplementary data files or from the corresponding authors upon reasonable request.

Results

Evaluation of immune contexture in de novo and recurrent human GBM

To examine the immune infiltrate of human GBM, we performed deep single-cell immune profiling of tumors using mass CyTOF. We evaluated human GBM surgical specimens collected on unmatched patients with de novo (n=14) or rGBM (n=13) (Figure 1A, Supplemental Table S1). All rGBM patients had received standard-of-care therapy, comprised of concurrent radio- and chemotherapy, followed by maintenance chemotherapy with temozolomide. Three rGBM patients (010, 012, and 065) received experimental treatment on a clinical trial after completing standard-of-care therapy (Supplementary Table S1).

Figure 1. Immune composition of de novo and rGBM.

A. Schematic of the patient cohorts included in the CyTOF analysis. B. CyTOF t-SNE plots of CD45⁺ infiltrating cells in de novo and recurrent tumors assessed together in one experiment using FlowSOM analysis. Left, t-SNE plot shows overlay of myeloid cells (red, defined as CD14⁺CD64⁺CD15⁺), T cells (blue, defined as CD3⁺) and other cells (pink). Right, color gradient plots show expression of CD64, CD11b, CD3 and CD14. C. Percentage of CD3⁺ T cells detected in de novo and rGBM tumors. D. Percentage of CD14⁺ myeloid cells detected in de novo and recurrent GBM tumors. E. CyTOF t-SNE plots of de novo and recurrent tumors from one experiment using FlowSOM analysis. Left, t-SNE plot gated on CD45⁺ myeloid cells showing subsets including Myeloid I (red), Myeloid II (blue) and Myeloid III (pink). On the right, color gradient plots show expression of CD163, HLA-DR, CD80 and CD86. F. Percentage of all myeloid cells expressing CD163, HLA-DR, CD80, CD86 detected in de novo and rGBM tumors. G. CD45⁺ myeloid-cell composition in de novo and rGBM tumors. H. Percentage of CD8⁺ (left) and CD4⁺ (right) T cells detected in de novo and rGBM. I. Heatmap showing relative expression of phenotypic markers by PD-1⁺CD8⁺ T cells in de novo (red) or rGBM (pink) tumor samples. Statistical significance calculated using two-tailed Mann-Whitney test; shown is mean with error bars indicating SD; *, P<0.05; unpaired two-tailed t-test.

We used three CyTOF antibody panels (myeloid, lymphoid, and T cell) to measure the expression of up to 40 molecules by tumor-infiltrating immune cells (Supplemental Table S4). All samples were analyzed using a T-cell panel, but due to insufficient material from some samples, analysis with either a myeloid or lymphoid panel was limited to only a subset of patients (de novo, n=5 and rGBM, n=5 for myeloid panel; de novo, n=10 and rGBM, n=9 for lymphoid panel). Using this approach, we found that the CD45⁺ leukocyte infiltrate seen in GBM tissues consisted primarily of myeloid cells (~60% of CD45⁺ hematopoietic cells) and T cells (~20%) (Figure 1B–D). Proportions of myeloid cells and T cells were similar in de novo and rGBM (Figure 1B–D, Supplemental Table S6). Other cells detected included B cells (~6%), NK cells (~4%), MAIT cells (~1%), TCRgd cells (~1%), and rare NKT cells (~0.05%) (Supplemental Figure S1A).

A more detailed analysis of CD45⁺ myeloid cells identified three major subsets. The Myeloid I subset was the most abundant and expressed CD64, CD11b, HLA-DR, CD80, and low levels of CD14 but lacked expression of CD68 and CD163 (Figure 1E and Supplemental Figure S1B). As such, Myeloid I resembled tissue-resident microglial-derived tumor-associated macrophages (TAM) (15,16). The Myeloid II subset expressed CD68, HLA-DR, CD1c, CD14, CD15, CD163, CD40, and CD73, but lacked expression of costimulatory molecules including CD80 and CD86. As such, Myeloid II is consistent with a mixture of type-1 myeloid dendritic cells (cDC1) and CD163^hi monocyte-derived TAM (15,16). Finally, the Myeloid III subset expressed CD68, CD24, CD14, CD80, and low CD163 but lacked expression of HLA-DR (Figure 1E and Supplemental Figure S1B). As such, Myeloid III resembled CD163^low monocyte-derived TAM (15,16). Overall, the proportion of Myeloid I, II and III subsets varied among patients and between de novo and rGBM tumors, with a tendency for more monocyte-derived (Myeloid II and III) and less tissue-resident (Myeloid I) TAM in the recurrent cases (Figure 1E). The overall proportions of cells expressing CD163, HLA-DR, CD80, and CD86 were largely equivalent between de novo and rGBM tumors except for a slight increase in the proportion of CD80-expressing myeloid cells detected in de novo tumors (p=0.01, Figure 1F). The latter may relate to the increased abundance of CD80-expressing cells in the Myeloid I subset seen in the de novo setting (Figure 1G). As such, although myeloid cells were equally abundant in de novo and rGBM tumors, their subset composition appeared to differ with increased abundance of monocyte-derived macrophages in rGBM tumors.

Given that T cells are key mediators of antitumor activity in response to immunotherapy, we next examined T-cell composition in GBM tumors. Overall, the frequency of CD8⁺ T cells was similar for de novo and rGBM. However, de novo tumors contained a higher frequency of CD4⁺ T cells (~35% versus 20% in rGBM) (Figure 1H). CD4⁺ T cells for de novo and recurrent tumors were comprised of similar proportions of CD25⁺CD127⁻ Treg cells (~15%) (Supplemental Figure S1C) and CXCR5⁺PD-1⁺ TFH cells (~15%) (Supplemental Figure S1D), indicating that the increased frequency of CD4⁺ T cells may relate to an increase in conventional CD4⁺ T cells in de novo tumors. The majority of conventional CD8⁺ T cells expressed PD-1 (~80%), in both de novo and rGBM tumors (Supplemental Figure S2A & S2B). PD-1 expressing CD8⁺ T cells also co-expressed multiple co-inhibitory immune checkpoints such as TIM-3, LAG-3, and CTLA4, consistent with a substantial proportion of exhausted CD8⁺ T cells (TEX) infiltrating human GBM (Figure 1I) (17). In both de novo and rGBM, ~60% of CD8⁺ T cells also expressed both PD-1 and CD39, a combination associated with TEX cells and previously implicated as identifying tumor-reactive T cells in other settings (18) (Supplemental Figure S2C & S2D).

In settings of chronic infection or cancer in mice, populations of progenitor TEX cells (CXCR5⁺CD69^+/–), transitional or intermediate TEX cells (CXCR5⁻CD69⁻), and terminal TEX cells (CXCR5⁻CD69⁺) have been defined with distinct functional properties (19–23). For example, progenitor TEX cells have a key role in response to immune checkpoint blockade (24–26). In addition, progenitor TEX cells contain the highest frequency of IFNg and TNF producers and are the subset of TEX cells that undergoes proliferative expansion in response to PD-1 pathway blockade (19). In contrast, intermediate TEX cells contain more migratory cells that are in cell cycle and are derived from progenitor TEX cells. Intermediate TEX cells eventually transition to terminal TEX cells that display features of tissue residency (19,20,23). Based on this biology, we evaluated expression of CXCR5 and CD69 on PD-1⁺CD8⁺ T cells in GBM tumors. In both de novo and rGBM, approximately 60–75% of PD-1⁺CD8⁺ T cells expressed CXCR5 with about 50% expressing CD69. In addition, most CD69⁺CD8⁺ T cells co-expressed CXCR5 with 10–15% lacking CXCR5 (Supplemental Figure S3A & S3B). This pattern is suggestive of a predominantly progenitor TEX–cell phenotype detected in GBM tumors (19,27). Thus, our data show that CD8⁺ T-cell infiltrates in GBM are likely to be tumor-reactive based on high PD-1 and CD39 co-expression (18) and exist predominantly in a progenitor TEX cell state.

Perivascular immune communities distinguish de novo and rGBM

Although myeloid and T cells appeared to be similarly abundant phenotypically between de novo and rGBM, we hypothesized that their spatial distribution may be altered. To explore this possibility, we performed mIHC on 17 paired de novo and rGBM tumor tissues (Supplemental Table S2). Here, we focused our analyses on ROIs containing tumor, an approach that contrasts with our CyTOF studies, which examined cells present throughout the entire tumor tissue specimen. Despite this difference, mIHC revealed a similar overall content of tumor-infiltrating CD8⁺ T cells in the paired de novo and rGBM samples (Figure 2A). CD8⁺ T cells were detected throughout the tumor microenvironment, but the density of CD8⁺ T cells was greatest in perivascular regions for both de novo and rGBM (Figure 2B), consistent with previous findings (28). Visual examination of CD8 staining showed qualitative differences in the distribution of CD8⁺ T cells between paired de novo and rGBM samples (Figure 2C). This difference was due to an increased number of perivascular regions associated with CD8⁺ T cells seen in rGBM samples compared to paired de novo samples (Figure 2D) and appeared to be independent of sex as a biological variable (Supplemental Figure S4).

Figure 2. Perivascular T cell–enriched regions differentiate de novo and rGBM.

A. Whole tumor quantification of CD8⁺ T cells by mIHC in de novo and rGBM. B. Representative images of the vascular region in GBM stained for hematoxylin- and eosin (top) and CD8, Foxp3 and CD68 (bottom). C. Representative heatmaps of CD8 expression in paired de novo and recurrent GBM tumors from three patients. D. Paired analysis showing percent tumor regions enriched for CD8⁺ cells among de novo and rGBM tumors. E. Schematic overview showing image analysis workflow to detect T-cell aggregates within tumor regions. F. Representative images showing CD3⁺ T cells and CD68⁺ macrophages detected among T-cell low (left) and T-cell high (right) regions in rGBM. For G, H, J, and L, multiple regions defined as T-cell high and T-cell low were identified for each de novo and rGBM sample analyzed (Supplemental Table S3). G. Quantification of CD3⁺Ki-67⁺ cells and pSTAT1⁺ cells in T-cell low and T-cell high regions in rGBM. H. Quantification of HLA-DR⁺CD68⁺ and CD68⁺pSTAT1⁺ myeloid subsets in T-cell low and T-cell high regions in rGBM. I. Images showing CD8⁺ (yellow) and FOXP3⁺ (purple) cells detected in T-cell high regions in de novo (left) and recurrent (right) GBM. J. Quantification of FOXP3⁺ cells (left) and ratio of CD8⁺ to FOXP3⁺ cells in T-cell high regions of de novo and rGBM. K. Images showing CD3⁺ (yellow), GzmB⁺ (purple), pSTAT1⁺ (brown), and CD68⁺ (teal) cells in T-cell high regions of de novo and rGBM tumors. L. Quantification of total pSTAT1 expression in T-cell high regions in de novo and rGBM tumors. Scale bars, 60 mm. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; error bars indicate SD; statistical significance calculated using two-tailed Mann-Whitney test (A, F, G, I, K) or paired Wilcoxon test (C).

Next, we applied a series of mIHC panels and computer-assisted image analysis algorithms to define the immune microenvironment of perivascular T cell–enriched regions present in the setting of rGBM (Figure 2E, Supplemental Figure S5). Heatmaps displaying the density of CD8⁺ cells in tumor regions (Figure 2C) were used to define CD8⁺ T-cell low (“T-cell low”) and CD8⁺ T-cell high (“T-cell high”) regions (Supplemental Table S7). This approach identified proliferating T cells, as defined by Ki67 expression, mainly in T-cell high regions in rGBM (Figure 2F, G). In addition, pSTAT1 expression, a marker of interferon signaling, was frequently associated with T-cell high regions, although some rGBM samples lacked pSTAT1 expression and not all T-cell high regions associated with pSTAT1 (Figure 2F, G). Similarly, the myeloid compartment in T-cell high regions was enriched in HLA-DR⁺ and pSTAT1⁺ myeloid cells (Figure 2F, H). In contrast, the density of CD68⁺ macrophages that lacked HLA-DR was similar between T-cell low and T-cell high regions in rGBM samples (Supplemental Figure S6).

We next asked whether immune features present within T-cell high regions differed between de novo and rGBM. Perivascular T-cell high regions in rGBM displayed a lower density of FOXP3⁺ Treg cells and a significantly higher ratio of CD8⁺ T cells to FOXP3⁺ Treg cells compared to T-cell high regions in de novo GBM (Figure 2I, J). Moreover, some perivascular T-cell high regions in rGBM also showed a higher expression of pSTAT1⁺ cells compared to perivascular T-cell high regions in de novo GBM, although this comparison did not meet statistical significance (P = 0.08) (Figure 2K, L). Collectively, these data suggest that rGBM displays features of an immunologically active tumor that are distinct from the de novo tumor, particularly in the perivascular regions where immune cells may accumulate.

Immunological features associated with perivascular regions correlate with clinical outcomes

A key question is whether intra- or peri-tumoral immune features have any relationship to disease outcomes. Thus, we next examined the relationship between immune features present in T-cell high regions in rGBM and survival outcomes. Patients were dichotomized into either short-term survivors (STS) or long-term survivors (LTS) using an unsupervised k-means clustering algorithm based on the median overall survival from time of diagnosis (Figure 3A). The median overall survival in the STS group was 11.5 months and in the LTS group was 27.5 months (P<0.0001). In the LTS group, T-cell high regions contained significantly fewer cells expressing Ki-67 (Figure 3B, C). To investigate whether this Ki-67 difference reflected changes in tumor-cell proliferation, we excluded proliferating T cells (CD3⁺Ki-67⁺) and macrophages (CD163⁺Ki-67⁺) from the total population of Ki-67⁺ cells detected in T-cell high regions. Consistent with prior work (29), the residual CD3⁻CD163⁻Ki-67⁺ population, presumably composed primarily of tumor cells, showed the same Ki-67 relationship with survival, with the LTS cohort exhibiting lower numbers of Ki-67⁺ tumor cells in T-cell high regions (Figure 3C). T-cell high regions in LTS patients also were enriched in CD68⁺ and HLA-DR⁺CD68⁺ macrophages (Figure 3D & 3E). Of note, LTS patients displayed a significantly higher density of pSTAT1⁺ cells but the density of CD3⁺ cells co-expressing pSTAT1 did not reach significance (Figure 3F & 3G). Taken together, these data suggest that the immunologic microenvironment of T-cell high regions in rGBM may harbor prognostic value for overall survival.

Figure 3. Immune contexture of T-cell high tumor regions in rGBM associates with patient outcomes.

A. Kaplan-Meier plot of overall survival for short-term survivors (STS, N=7) and long-term survivors (LTS, N=9) with GBM. Cohorts were determined by k-means clustering and the median overall survival of the STS group was 11.5 months compared to 27.5 months in the LTS group (P<0.0001). B. Representative images showing CD3 (purple), Ki-67 (yellow), and CD163 (teal) expression in T-cell high regions in rGBM tumors for the STS and LTS cohorts. C. Quantification of total Ki-67⁺ (left) and CD3⁻CD163⁻Ki-67⁺ (right) expression in T-cell high regions for the STS and LTS cohorts. D. Representative images of EGFR+p53 (brown), CD68 (purple), HLA-DR (teal) expression in T-cell high regions in recurrent tumors for the STS and LTS cohorts. E. Quantification of CD68⁺ (left) and CD68⁺HLA-DR⁺ (right) expression in T-cell high regions of recurrent tumors for the STS and LTS cohorts. F. Representative images of CD3 (yellow), GzmB (purple), pSTAT1 (brown), and CD68 (teal) expression in T-cell high regions in rGBM for the STS and LTS cohorts. G. Quantification of total pSTAT1⁺ (left) and CD3⁺pStat1⁺ (right) expression in T-cell high regions in the rGBM for the STS and LTS cohorts. Scale bars, 60 mm. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; error bars indicate SD; statistical significance calculated using Log-Rank (Mantel-Cox) test (A) or two-tailed Mann-Whitney test (C, E, G).

To further examine the relationship between spatial immune microenvironmental features and disease outcome, we evaluated immunological features defined by the mIHC analyses and their association with clinical outcomes in rGBM. To do this, we stratified patients with rGBM (Supplemental Table S2) into two groups using the maximal selected rank statistics approach based on all expression features individually. We then tested their association with survival using log-rank tests and corrected for multiple testing. The CD3⁺pSTAT1⁺ binary classification (low vs high) identified 6 patients with high expression and 8 patients with low expression. Although no difference in overall survival was observed based on this classification, patients in the CD3⁺pSTAT1⁺ high group had a longer median time from relapse to death (high, 10 months; low, 4 months; p=0.0223, Figure 4). This extended time to relapse was confirmed in a Cox proportional-hazards model including age, sex, Ki67, MGMT, EGFR, TP53, and extent of resection at de novo status as predictor variables (for CD3⁺pSTAT1⁺ p=0.0223). These data suggest that the T-cell compartment, especially in the perivascular regions of the tumor, in some patients with rGBM may be polarized toward an antitumor activated phenotype with clinical implications.

Figure 4. CD3⁺pSTAT1⁺ cells associate with increased survival at time of GBM recurrence.

Tumors from patients with rGBM were segregated using maximal selected rank statistics based on CD3⁺pSTAT1⁺ and classified as low (<12 cells/mm²) or high (≥22 cells/mm²) expression. A. Representative images showing CD3 (yellow), GzmB (purple), pSTAT1 (brown), and CD68 (teal) in rGBM tumors classified as low or high CD3⁺pSTAT1⁺ expression. Scale bars, 60mm. B. Quantification of CD3⁺pSTAT1⁺ cells detected in rGBM tumors classified as low or high expression. C. Kaplan-Meier plot showing time from recurrence to death for patients with rGBM tumors displaying high or low expression of CD3⁺pSTAT1⁺ cells. ***, P<0.001; error bars indicate SD; statistical significance calculated using two-tailed Mann-Whitney test.

Discussion

In this study, we examined contextual and spatial differences in the immune landscape of de novo and rGBM tumors and identified immunological features with potential clinical implications. Using mass cytometry, myeloid cells and T cells were found to be similarly present in de novo and rGBM. Myeloid cells were the most abundant cells in the tumor microenvironment and were accompanied by infiltrating TEX cells, consistent with prior studies (30–32). This finding is also consistent with recent work comparing the microenvironmental landscape in glioma and brain metastases (15,16). In these studies, the myeloid cell composition of gliomas was shown to be markedly heterogeneous and was composed of tissue-resident and infiltrating leukocytes. Our study is consistent with these reports and provides new insights into variations in the leukocyte infiltrate present in de novo and rGBM. For example, we identified differences in the composition of myeloid subsets detected in de novo and rGBM samples. Although we were not able to definitively distinguish between tissue-resident and monocyte-derived macrophages in our studies (15,16), rGBM tended to have an increased frequency of myeloid cells that resembled monocyte-derived macrophages. Prior studies have shown that a high infiltration of monocyte-derived macrophages associates with poor survival in GBM patients (15). In contrast, de novo GBM tended to have an increased frequency of myeloid cells that resembled tissue-resident macrophages. It is possible that these differences reflect response to standard-of-care therapy (33). Alternatively, they may indicate cancer cell–intrinsic differences between de novo and rGBM tumors that differentially shape the recruitment and in situ differentiation of monocyte-derived macrophages.

Differences in T-cell subsets were also observed between de novo and rGBM. With deep profiling, TEX cells were detected in both de novo and rGBM, with most of these cells expressing CXCR5, as well as potential therapeutic targets such as PD-1, LAG-3 and CTLA-4. Notably, these TEX cells expressed CD39 together with PD-1, suggesting that they may be tumor reactive (18). Using mIHC, we then further tested if the spatial distribution of infiltrating immune cells differed between de novo and rGBM. T-cell high perivascular regions were more abundant in rGBM compared to de novo GBM and differentially displayed markers of immune activation (pSTAT1) and cellular proliferation (Ki67). Increased numbers of CD3⁺pSTAT1⁺ cells in perivascular regions associated with a longer median time from relapse to death. Taken together, our findings identify the spatial distribution of T cells rather than their abundance as a potential key immunological determinant that associates with the evolution and pathogenesis of GBM.

Immune infiltration is a recognized determinant of outcomes in cancer. In de novo GBM, myeloid-cell infiltrates associate with an unfavorable prognosis (8,9). In contrast, variable correlations with survival have been reported for T-cell infiltrates (9,34). However, less is known about the immunological features of rGBM, where tumors are known to have acquired molecular alterations, including increased alternative-splicing events, that may influence tumor immunogenicity (35). Here, our studies identified distinct spatial organization and activation characteristics of immune cells in de novo versus rGBM tumors. Specifically, T cell–enriched perivascular regions were more abundant in rGBM compared to de novo GBM. Perivascular spaces have been recently identified as lymphopoietic niches at the central nervous system border (36,37). It is also known that the perivascular space is important for maintaining the self-renewal and stemness of cancer cells (38,39). Our findings suggest that enrichment of activated T cells in perivascular regions may be a determinant of longer survival in patients with rGBM. This observation raises the possibility that tumor-reactive T cells in perivascular regions may control the fate of glioma stem cells. Consistent with this idea, cytotoxic T cells engineered to recognize tumor antigens (e.g. HER2 and NKG2D ligands) or redirected against CD133, a marker of stem cells, have shown antitumor activity against glioma stem cells (40–42). However, it remains unclear whether endogenous tumor-reactive T cells are present in perivascular regions and capable of eliminating glioma stem cells in patients. In contrast, prior studies have shown that glioma stem cells can suppress T-cell proliferation and activation (43). Nonetheless, manipulating the perivascular niche may be a strategy to leverage the antitumor potential of infiltrating leukocytes (44).

Our findings of abundant T cell–enriched perivascular regions in rGBM may indicate their potential role in response to therapeutic interventions. In line with this idea, we found that T-cell activation in the perivascular region associated with increased time from relapse to death. Specifically, a high density of CD3⁺ T cells expressing pSTAT1 was found suggesting recent signaling from interferons or other inflammatory mediators. Interferons can have antitumor activity but may also support immune evasion by triggering the upregulation of immune checkpoint molecules (45). In GBM, interferon signaling has been previously associated with a poor prognosis (46). However, our data differ from these analyses given that we focused on pSTAT1 expression in T cell–enriched regions of GBM. It will be important in subsequent analyses to better understand the signaling programs that distinguish T cell–enriched regions defined by high and low pSTAT1 expression.

Perivascular regions in GBM are associated with monocyte-derived macrophages, which portend a poor outcome (15). TAMs in these regions support tumor growth by producing soluble factors that sustain glioma stemness (47). However, we found that an increased density of CD68⁺ myeloid cells expressing HLA-DR associated with increased survival. These changes seen in the activation status of both myeloid cells and T cells may reflect antitumor immune activity and/or treatment-induced inflammation. In addition, we found that not all perivascular regions were infiltrated by T cells in rGBM, and the degree of infiltration was variable between patients, thereby illustrating both intra- and inter-patient heterogeneity in the immune response to GBM. Analyses of intercellular communications and the phenotypical state of cells in perivascular regions after various treatment regimens are warranted.

Our studies were conducted in a small cohort of patients with de novo and rGBM and thus, may not be representative of all subsets of GBM. Our analyses also assessed only a small cross-sectional tumor region of GBM for each patient raising the possibility of sampling bias due to the prominent intratumoral heterogeneity that is characteristic of GBM. In addition, our studies addressed defined timepoints, namely surgical resection for de novo and recurrent GBM, and thus, did not address the dynamics of GBM pathogenesis. In this regard, diagnostic imaging strategies that incorporate tissue biomarkers may enable a whole lesion assessment and permit non-invasive longitudinal analyses.

Immunotherapy has yet to reproducibly demonstrate activity in GBM. For example, monotherapy with pembrolizumab has shown minimal benefit so far (48). However, recent work suggests that combining immune checkpoint blockade (e.g. anti-PD-1) with tumor vaccines might trigger productive T-cell responses in some patients with GBM (10–13). These studies have all been conducted in patients with rGBM and whether targeting de novo tumors would be more impactful is unclear. Our findings revealed abundant T cell–enriched perivascular regions in rGBM and show that immune activation associates with delayed disease progression. Thus, we propose that rGBM may have a greater potential for response to immunotherapies.

In summary, our study identifies the perivascular space as a potential immune determinant of GBM biology. Specifically, increased T-cell infiltration into perivascular regions was seen in rGBM compared to de novo GBM and T-cell activation in perivascular regions correlated with improved outcomes. Thus, the perivascular space in GBM may be a key immunological target with therapeutic implications.

Synopsis.

Single-cell profiling using mass cytometry and multiplex immunohistochemistry reveals distinct intratumoral immune landscapes in de novo and recurrent glioblastoma, and an increase in activated perivascular T cells at recurrence is shown to associate with improved clinical outcome.