STING activation induces cytotoxic and immune responses in meningiomas via inflammatory cell death pathways

Domain: neuroscience
Published: 2026-02-12
Editorial outcome: minor_revision

Abstract
Meningiomas are common tumors of the central nervous system that are typically treated with surgery or radiation, but lack established systemic therapies. Activation of the stimulator of interferon genes pathway with an agonist such as 8803 can trigger anti-tumor immune responses. Using integrated molecular approaches, here we show that this pathway is targetable in both neoplastic and immune populations within the meningioma microenvironment. Meningioma tumor cells exhibit promoter hypomethylation and increased chromatin accessibility of the STING genomic locus, associated with robust expression of this gene. Treatment of diverse patient meningiomas ex vivo with 8803 induces direct tumor cytotoxicity through inflammatory cell death pathways, including induction of gasdermin D membrane pore formation. Release of necrotic tumor debris triggered by 8803 activates macrophages and upregulates matrix metalloproteinase production, facilitating degradation of extra-cellular collagen. Injection of preclinical meningiomas with 8803 induces survival benefits, including in an immunocompetent orthotopic setting, through remodeling of the tumor microenvironment, immune infiltration, and downregulation of tumor-mediated immune suppression, thereby nominating 8803 for treatment consideration in meningiomas.

Introduction
Meningiomas are common tumors of the central nervous system (CNS) that arise from the fibrous covering of the brain and spinal cord1. There are three grades of meningiomas designated by the World Health Organization (WHO), which are associated with an increasing likelihood of recurrence. While meningiomas may be treated effectively with surgery and radiotherapy in some cases, a subset of patients will be resistant to these approaches, and the tumor will recur with progressive morbidity or mortality. Transcriptional studies using single-cell sequencing and spatial techniques have revealed a surprising degree of heterogeneity within meningiomas, particularly among high-grade lesions that are more likely to be treatment-resistant2–6. Additional molecular approaches, including genetic, transcriptional, and epigenetic profiling, have further identified key biomarkers that predict the risk of recurrence or aggressive behavior2,4,7–12. Integrated analyses of these modalities have consequently led to the emergence of prognostic molecular subgroups associated with distinctive copy number changes, transcriptional signatures, and DNA methylation profiles, with varying levels of immunologic infiltration2,4,11,13. Although there is a growing appreciation for the role of the immune system in brain tumor prognosis and clinical decision-making, limited work to date has focused on immune cells in meningiomas. Additional insights into meningioma immunology could spur therapeutic advances and potentially reduce the morbidity of invasive surgical approaches that are ineffective in some patients.

Prior efforts to characterize meningioma immunology have utilized bulk tissue datasets, flow cytometry, or scRNA sequencing approaches to describe cellular constituents and phenotypes within the tumor microenvironment (TME)2,3,14. Deconvolution of bulk methylation has characterized broad immunological clusters and high-level cell states, such as naïve or memory, resting or activated, and macrophage polarization status across meningioma grades, and suggested broad immune suppression associated with myeloid cell enrichment, and a low frequency of T cells. Two recent clinical trials evaluated immune checkpoint inhibitors as monotherapy in recurrent meningiomas. Although treatment was well tolerated, only a subset of patients derived a therapeutic benefit15,16 indicating that the reprogramming of the TME will be essential for the efficacy of immune-based therapies.

An immune modulatory strategy that might leverage a myeloid-dominated microenvironment is the activation of the STING pathway. Activation of STING via cGAS-mediated recognition of double-stranded DNA results in phosphorylation and nuclear localization of IRF3, culminating in the production of type I interferons (IFN) and induction of local immune responses17. Agonists of the STING pathway, such as the potent dinucleotide analog 880318, are being investigated pre-clinically in the indication of gliomas to induce pro-inflammatory immune responses19–23. As STING expression is limited in neoplastic cell populations of gliomas and other CNS tumors, the mechanism of action for STING agonists has mainly focused on immune-mediated anti-cancer activities. To date, the direct effects of STING agonists on solid cancer cells have not been fully clarified. STING activation has been shown to induce apoptosis24,25, necroptosis26,27, and autophagy28 in leukemia cells; however, cell death pathways have not been well evaluated in solid cancers.

In this study, we utilize integrated transcriptional, epigenetic, and spatial multiplex immunofluorescent approaches to identify the widespread expression of STING in the meningioma TME, including in neoplastic and infiltrating immune cell populations. The STING agonist 8803 induced direct cytotoxicity of ex vivo resected meningiomas, independent of CD45+ populations, in a process that depended in part on programmed necrotic cell death pathways (necroptosis, pyroptosis, ferroptosis) and reactive oxygen species production. Membrane pore formation via GSDMD oligomerization was induced in meningioma cells after 8803 treatment, thereby allowing release of damage-associated molecular patterns (DAMPs) that synergistically activate local immune populations and result in upregulation of collagen-degrading genes. Efficacy of STING targeting was confirmed across multiple preclinical meningioma models, providing a compelling justification for a clinical trial of 8803 in surgically refractory meningiomas.

Results
Single-cell and spatial analyses identify collagen-mediated immunosuppression as a predominant feature of the meningioma microenvironment
To characterize the immunobiology of meningiomas and to identify immune therapeutic targets, single-cell RNA-seq (scRNA-seq) datasets with adjacent dura samples2,3 were analyzed. These data encompassed twenty-two patients and a total of 30,206 immune cells (Fig. S1). Lineage marker analysis revealed fifteen immune (CD45+) populations (Fig. 1A), with distinctive profiles between meningioma and dura. For example, phagocytic (phago) tumor-associated macrophages (TAMs) were significantly enriched in meningiomas relative to the adjacent dura (Fig. 1B, C, gold color; Fig. S2) and exhibited expression of CD163 and HLA-DR (Fig. 1D), as previously reported29. These cells localize to meningioma stromal niche regions and express immunosuppressive proteins such as HAVCR2 (TIM3) and LAIR-1 (Fig. 2A), but rarely CD47 (Fig. S3). Because the deposition of collagen is an early embryonic step during the development of the meninges30 and regulates the immunological microenvironment in other cancers31,32, additional focused analysis of collagen family members using scRNA-seq and spatial multiplex immunofluorescence was performed. COL1A1, COL3A1, COL4A1, COL5A1/A2, and COL6A1/A2 were the most expressed collagens in meningioma cells, and these were found to colocalize in concentric laminar patterns across meningioma grades (Fig. 2B, left; Fig. S4A, B). These regions also contained abundant LAIR-1 expression (Fig. 2B, right), consistent with the role of collagens as ligands for this inhibitory immune receptor33. Adjacent normal brain did not express COL1A, COL4A, or COL6A (Fig. S4C), and infiltration of macrophages was not identified in the brain or adjacent dura (Fig. S4C, D). An additional enriched immune population in meningiomas were heat-shock protein (HSP) TAMs (Fig. 1B, C), suggesting a particular affinity of macrophage populations to the meningioma microenvironment. HSP proteins are intracellular DAMP molecules released from damaged or dying cells that induce inflammatory responses.

In contrast, terminally exhausted T cells (T.Tex; identified from markers highlighted in Fig. 1A) and NK cells were more frequently observed in meningioma-adjacent dura (Fig. 1B, C), as previously reported34,35. Consistent with these observations, myeloid-associated chemokines such as CCL8 and CCL4L2 were upregulated in meningiomas (Fig. 2C, blue), while the dura exhibited higher T cell-associated chemokines (red). T and NK immune effector cells could potentially exert cytotoxic functions based on the expression of GZMA, PRF1, and NKG7; however, they also display markers of exhaustion including TIGIT, LAG3, and PD-1 (Fig. 1D). Three NK states were identified in the meningioma microenvironment from scRNA-seq data based on established gene expression markers for resting, activated, and exhausted phenotypes (Fig. 2D). Exhausted NK cells, based on the co-expression of markers of activation (such as granzyme family members) and inhibition (KLRD1), were more frequent in the meningioma relative to the dura (Fig. 1). This population also exhibited TGF-β-mediated pathway activation (JUND and SMAD7), which is typically not reversible with TGF-β antagonists36. Cumulatively, these data indicate that meningiomas are a myeloid-enriched disease, with suppression of cytotoxic immune functions (including NK and T cells) and collagen-mediated immune suppression in tumor tissue relative to the adjacent dura. Given these findings, therapeutic strategies that drive pro-inflammatory immune responses into the meningioma hold the greatest promise for treatment efficacy.

Epigenetic activation drives STING expression across meningioma subgroups and cell types
Signaling pathway analysis of immune cells indicated that MHC, galectin, CypA, Annexin, CCL, CLEC, TNF, Lck, CD86, and IFN-β were upregulated across multiple populations in the meningioma microenvironment (Fig. S5A). Notably, these genes have been previously associated with the STING pathway37–43, suggesting a possible immune therapeutic target for meningiomas. To further investigate the expression of STING in meningiomas, the complete set of cells from the meningioma TME was annotated, including myeloid, T/NK, endothelial, fibroblasts, pericytes, meningioma, and extra-cellular matrix (ECM) expression-associated meningioma (ECM meningioma) cells (Fig. 3A). Across these populations the expression of STING (TMEM173) was highest in meningioma cells and endothelium (Fig. 3B), while other immune targets such as CD47 were minimally expressed (Fig. S3) or were only expressed on the ECM meningioma cells (such as CD274/PD-L1 and CD276/B7-H3) (Fig. 3B). Spatial multiplex immunofluorescent staining revealed that STING expression was high in both high-grade and benign meningiomas, but not brain or dura (Fig. S5B, C). More specifically, STING was identified in SSTR2+ meningioma cells, CD163+ macrophages, and CD31+ endothelial cells (Fig. 3C), indicating that this pathway may be targetable across multiple cell types within the meningioma TME. Downstream canonical STING activity was heterogeneous within STING+ tumor cells and macrophages (as denoted by p-IRF3 expression; Fig. 3C), indicating potential for further pathway activation using chemical agonists.

Though therapeutic STING targeting is under active investigation in gliomas, this protein exhibits minimal expression in neoplastic populations of these and other types of CNS tumors20. As our data suggests that meningiomas are an exception to this pattern, epigenetic profiling was performed to understand regulatory differences that may drive STING expression. Pseudo-bulk analysis of snATAC-seq data in meningioma (n=4 patients; 29,100 cells) and glioma (n=2 patients; 4850 cells) cells was performed to produce lineage-specific ATAC tracks. Separate clustering of cancer-lineage-specific immune populations revealed consistent chromatin accessibility in both tumor types. By contrast, meningioma neoplastic cells exhibited chromatin accessibility on par with myeloid immune lineages, while glioblastoma tumor cells had negligible open chromatin in the STING promoter (Fig. 3D, blue highlight). Analysis of bulk methylation patterns between tumor types indicated that the STING promoter region was hypomethylated in meningioma (average β=0.10, n=1412 patients) but mostly hypermethylated in glioblastoma (average β=0.72, n=170 patients) (Fig. 2D, center). Across a subset of meningioma samples with paired RNA-seq data (n=584 patients), methylation of the promoter site cg16983159 exhibited the strongest correlation with STING expression (rho=−0.54, p<0.001; Fig. 3E), consistent with associations in glioma previously reported20. We did not observe significant differences in STING expression in meningiomas across prognostic clinical variables, including WHO grade, tumor location, methylation subgroup, and previous resection, further suggesting that STING is consistently present across the broad spectrum of meningioma subtypes (Figure. S5D). Collectively, these data indicate that the cancer lineage-specific nature of STING chromatin accessibility, alongside differences in promoter methylation levels, is associated with STING expression in meningioma tumor cells.

STING activation induces direct meningioma cytotoxicity through ROS-induced programmed necrotic cell death
The effects of STING activation were evaluated on ex vivo patient meningiomas (n=9 patients) collected directly from the operating room (Fig. 4A; Figure. S6; Table S1). Treatment of these samples (which included the entire meningioma microenvironment) with the STING agonist 8803 induced progressive cell death over 72 hours, relative to untreated matched controls (p<0.01; Fig. 4B). To clarify if 8803 was causing a direct cytotoxic effect on meningioma neoplastic cells, an additional set of ex vivo human meningioma samples (n=3 patients) were dissociated into single cell suspensions, and the CD45+ immune population was removed using immunoaffinity columns before the initiation of treatment. In the absence of immune cells, 8803 again induced meningioma cell death at 72 hours (p<0.01), confirming that the effects of this drug were not dependent on co-culture with immune cells (Fig. 4C).

To clarify the underlying mechanism of 8803-induced meningioma cell death, this ex vivo assay was repeated in tandem with a panel of cell death pathway inhibitors, aiming to understand which pathways were essential for full 8803 efficacy. Inhibitors of necroptosis, pyroptosis, and ferroptosis partially reversed 8803-induced meningioma cytotoxicity (Fig. 4D), suggesting that these mechanisms play a role in inducing cell death after STING activation. These pathways, collectively categorized as programmed necrotic cell death44, can be activated through the induction of ROS45–47, and STING has been shown to regulate the transcriptional program that controls the generation of ROS48. Bulk RNA-sequencing of patient meningioma samples after treatment with 8803 confirmed upregulation of gene signatures associated with autophagy, necroptosis, and pyroptosis (Fig. 4E). RNA-seq also revealed that caspase family genes including CASP1, 4, 7, 8, and 10 were upregulated after treatment with 8803, as well as increased expression of genes that are essential for the necrotic cell death pathway pyroptosis (Fig. 4F).

Proteolytic maturation of the pore-forming protein Gasdermin D (GSDMD) by caspase-1 is necessary for triggering pyroptosis49, and results in oligomerization of the N-terminal of this gene (Fig. 5A). Since caspase-1 was upregulated in meningioma cells treated with 8803, uncleaved and oligomerized GSDMD were quantified by western blot across meningiomas treated with 8803 (n=5 patients) (Fig. 5B, C). There was a small but insignificant difference in total GSDMD in these conditions; however, the ratio of cleaved to total GSDMD was significantly increased upon 8803 treatment (p=0.031). At the transcriptional level, there was a concordant increase in GSDMD expression with 8803 treatment based on bulk RNA-seq data (fold change = 4.64, p<0.001). As previously observed in pyroptosis50, increases in cleaved GSDMD lead to membrane pore formation, cell death, and leakage of cellular content, including DAMP molecules, further promoting cytotoxicity and possibly immune activation. Indeed, scanning electron microscopy of 8803 treated meningioma cells demonstrated stepwise pore formation in the plasma membrane over 48 hours that ultimately resulted in cell lysis (Figs. 5D; S7A–D).

Pyroptosis (in addition to necroptosis and ferroptosis) can be activated through the induction of ROS45–47, and STING has been shown to regulate the transcriptional program that controls the generation of ROS48. In meningiomas, 8803 mediated cell death was partially blocked with the ROS inhibitors N-acetylcysteine (NAC) and GSH ethyl ester (GSHee) (Fig. 5E), indicating ROS production contributes to STING cytotoxicity. As ROS release can be associated with mitochondrial membrane permeability, tunneling electron microscopy was used to examine meningioma cells after administration of 8803. Over 48 hours, there was diffuse blunting of mitochondrial cristae and compromise of the double membrane in these organelles relative to untreated samples (Fig. 5F; Fig. S7E).

The STING agonist 8803 exerts a therapeutic effect in preclinical meningioma models
We next evaluated the therapeutic efficacy of 8803 in several murine meningioma models. Nude mice were implanted with IOMM-LEE meningioma cells bilaterally in both flanks but were only treated on the right flank with 8803 on day 10 post-tumor implantation (Fig. 6A). Western blot analysis confirmed expression of STING in meningioma cell lines (IOMM-LEE, CH157, MGS1), but as expected, not in glioblastoma cell line controls (0827 and GBM12) (Fig. 6B, C). The treated meningioma showed decreased volume relative to the untreated contralateral tumor and bilaterally untreated mice (P<0.01) (Fig. 6D, E), indicating that there were no systemic anti-cancer therapeutic effects of 8803 with direct intratumoral injection. To evaluate the effects of 8803 at a later time point, the flank experiment was repeated but mice were treated on day 17, rather than day 10. Tumors in the untreated group continued to grow, while those that received 5 µg of 8803 remained stable (P<0.01) (Fig. 6F).

Next, two orthotopic models were evaluated based on the response to the STING agonist in the flank model. J:NU mice bearing intracranial IOMM-Lee or CH157 meningiomas, which also express STING (Fig. 6B), were treated with the STING agonist 8803. The mice were randomized to be treated with either PBS control or 8803 (5 µg/mouse) injected directly into the tumor starting on day 5 (Fig. 7A). 8803 increased median survival in the orthotopic IOMM-LEE model (8803 median survival (MS): 34 days vs untreated MS: 24.5 days; P=0.0125) (Fig. 7B). Long-term durable survival was observed in the CH157 bearing mice, with four mice treated with 8803 surviving past 150 days (8803 MS: 45 days vs untreated MS: 16 days; P<0.0001) (Fig. 7C). Because Nude mice do not express mature T cells, these data would suggest that the mechanism of action is not fully dependent on T cell-mediated immune responses and that 8803 may be exerting direct cytotoxicity in these in vivo models.

STING activation triggers local inflammation and reshapes the meningioma microenvironment
The therapeutic activity of 8803 was next evaluated in an immunocompetent, syngeneic, murine mouse model51, via orthotopic implantation of MGS1 cells in FVB/N background mice. A dose of 5 µg of 8803 prolonged survival in treated mice (8803 MS: undefined vs untreated MS: 39 days; P<0.001) (Fig. 8A). On day 48, the experiment was terminated secondary to meningioma tumor growth outside of the skull in some animals. At this point, the extent of the intracranial burden was analyzed. 8803-treated mice had minimal tumor growth intracranially, whereas all control mice had extensive growth (Fig. 8B), including within the brain (Fig. 8C). Additionally, we observed inflammatory cells present in mice treated with 8803, while those treated with PBS alone exhibited possible brain invasion. During the therapeutic window of treatment with 8803 (on day 28), a parallel cohort of mice was terminated and perfused, and the meningioma and the adjacent brain were dissected for single-cell analysis (2 samples per condition; total of 81,170 cells). Within the CD45+ immune compartment, 8803 induced a marked expansion of macrophages, NK, and lymphoid immune cell populations (Figs. 8D, E, S8A–C, and Table S2). Adjacent microglia shifted from a homeostatic to an early activation and interferon-stimulated gene (ISG) signature state. Across immune lineages, 8803 increased the presence of immune effector responses within the meningioma (Fig. 8F). These findings are consistent with the known functions of STING agonists, including activation of macrophages, NK cells, and cytotoxic T cells.

Unbiased gene expression profiling demonstrated 8803 markedly upregulated IFN, perforin, and MMP but down-regulated immune suppressive Havcr2 (i.e., TIM3) and Lair1 (Fig. 9A). When the murine meningioma tumor cells treated with 8803 were analyzed, after alignment with human gene signatures (Fig. S8A), gene expression profiling was notable for the induction of caspases, reactive oxygen species, STING, and GSMD, but a reduction of collagen (Fig. 9B, C). These data were consistent with human meningioma specimens treated with 8803 (Figs. 4F, S9).

Meningioma lysis triggers macrophage activation and ECM breakdown through the TLR and STING pathways
The immune modulatory effects of STING agonists have been extensively described; however, the synergistic effects of tumor lysis in this setting are not well understood. Since 8803 mediates direct tumor cytotoxicity in meningiomas, DAMP molecules may trigger inflammatory responses through pattern recognition receptors such as toll-like receptors (TLR). To evaluate the effects of meningioma DAMPs on macrophage activation, we devised a strategy whereby isolated macrophages from FvB WT, MyD88-/-, or STING-deficient Tmem173gt background mice were treated with control media or lysed MGS1 cells. This approach circumvented the direct effects of 8803 on macrophages (meningioma cells were lysed using a mechanical technique), while the use of wild-type, TLR-deficient (MyD88-/-) and STING-deficient (Tmem173gt) macrophages permitted independent assessment of these components in macrophage activation. Across a Luminex panel of cytokines and chemokines, VEGF, and MMP-3 demonstrated marked physiologically relevant and consistent increases in macrophages when exposed to the MGS1 lysate (Figs. 9D; S10, S11). MMP-3 activates MMP-9, which degrades collagen52–57 that forms a substantial component of the meningioma stroma (Fig. 2B). MMP-3 relies on the TLR pathway for activation in the setting of DAMPs, since production was markedly attenuated in the MyD88-/- deficient macrophages (Fig. S10). As such, when a STING agonist such as 8803 induces tumor cell death, immune responses are potentiated through the TLR pathway.

To ascertain if the TLR and STING pathways are working in synergy through DAMP-related and 8803-mediated processes in macrophages, an additional Luminex assay was performed using wild-type (FVB) macrophages treated with media (control), lysed MGS1 meningioma cells, 8803, or the combination (Fig. S11). Interaction effects in linear regression models revealed that MMP-3, VEGF, and TWEAK were primarily driven by exposure to MGS1 lysate, while CCL2 and CXCL10 were due to the 8803 treatment. Notably, TNF-α, IL-6, and CXCL9 exhibited manifold effects from the combination of 8803 and MGS1 lysate, suggesting synergy between these exposures. These results indicate that the treatment of meningiomas with 8803 induces inflammatory cytokines via combined mechanisms, including direct action of this agonist and release of DAMPs from necrotic meningioma cells to activate macrophages.

As described previously58, human meningiomas exert significant mass effect through the presence of collagen, including COL6A (Figs. 2B; S4). Although 8803 may induce tumor cell death, the persistence of collagen could be a confounder for symptomatic treatment responses. As transcriptional results from human and mouse meningiomas suggested decreased collagen production and increased MMP production in response to 8803, and production of MMP-3 was also seen in macrophages via Luminex, we performed multiplex immunofluorescent analysis to interrogate collagen in murine MGS1 meningiomas after orthotopic treatment. Control-treated intracranial tumors demonstrated marked COL6A1/A2 expression throughout the tumor (Fig. 9E, left), similar to human meningiomas. In contrast, 8803-treated meningiomas had virtually no COL6A1/A2 expression (Fig. 9E, right) among all mice investigated (n=3 mice for each condition; Fig. S12). These data indicate that standard MRI imaging could be used as a therapeutic endpoint in a clinical trial of 8803 for meningiomas. Collectively, our results demonstrate that STING agonist 8803 exhibits complementary therapeutic mechanisms in meningiomas that include direct cytotoxicity, immune activation, and degradation of the tumor stroma (Fig. 9F).

Methods
Ethics Statement
The Northwestern University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and all animal care followed institutional guidelines. For flank models, the tumor was used with a Hamilton syringe, with treatment initiated as designated in the schemas. Tumors were measured with calipers in two directions. Flank tumor volumes were calculated using (LxW^2)/2. The total size of 3.0 cm in diameter of all tumors combined in mice was not exceeded per IACUC guidelines. Animals bearing tumors that interfered with normal functions, such as eating, ambulating, and eliminating waste, were considered humane endpoints. Rodents with functional impairments were immediately euthanized. Ulcerated tumors were considered an automatic endpoint. For orthotopic models, animals were when they became moribund which was defined as >20% weight loss, neurologic symptoms, or evidence of pain/distress as per Northwestern IACUC.

Study approval and cohort collection
These research studies comply with all relevant ethical regulations. Collection and analysis of samples for this study were conducted under protocols STU00211542 and STU00214485, approved by the Northwestern University Institutional Review Board (IRB). Samples that underwent molecular profiling and/or sequential multiplex were collected by the Nervous System Tumor Bank (NSTB) under an additional IRB-approved protocol. Animal studies were approved by the Northwestern Institutional Animal Care and Use Committee (IACUC). Due to specimen availability, not all assays could be run across all ex vivo samples. Human meningioma samples were selected based on the availability of tissue for characterization, and both male and female subjects were included. For all mice studies, both sexes were included.

Joint single-nucleus RNA-seq and ATAC-seq profiling of meningiomas
Nuclei were isolated from previously frozen meningioma specimens (n=4) using the 10x Chromium Nuclei Isolation Kit with a RNase Inhibitor (PN-1000494) according to the manufacturer’s protocol. Samples were selected for profiling based on RNA integrity number (RIN) values >7 on bulk RNA isolation. After isolation, the nuclei were analyzed using the Nexcelom Cellometer Auto2000 with the AOPI fluorescent staining method, followed by tagging of open chromatin regions with Tn5 transposase. Each sample was then processed on an independent well of the Chromium Next GEM Chip J for GEM generation and molecule barcoding. Both RNA and ATAC-seq libraries were constructed using Chromium Next GEM Single Cell Multiome ATAC+Gene expression kit (10X Genomics, PN-1000281). After library construction, RNA-seq reads were captured by paired barcodes with unique molecular identifiers and ATAC-seq reads labeled with 10x Genomics cell barcodes. Sequencing was performed on the Illumina platform Novaseq X Plus at the Northwestern University NUSeq sequencing facility core, with a targeted sequencing depth of 20,000 and 25,000 reads per sample acquired for RNA and ATAC profiling, respectively. A total of 10,000 nuclei were targeted for each sample, with 25,000 scATAC-seq and 20,000 scRNA-seq reads in each nucleus.

Analysis of single-cell RNA-seq and ATAC-seq data
10x multiome data was demultiplexed using the Cellranger-ARC pipeline with the mkfastq command used to perform raw base calling of both RNA and ATAC reads in each nucleus. Subsequent alignment, quantification, and peak calling were performed using the count command on each fastq file. The data were loaded in R using the Seurat software package. Cells were filtered based on the following criteria: <2.5% mitochondrial DNA, RNA count range of 500 to 25000, and ATAC count range of 1000 to 25,000. RNA assays for cells were subject to SCTransform for normalization/feature selection and PCA for dimensionality reduction (Choudhary & Satija, 2022). ATAC assays for cells were subject to TF-IDF for normalization, FindTopFeatures for feature selection, and RunSVD for dimensionality reduction. Reduced dimensions for ATAC and RNA data were then normalized for batch effects using Harmony83. The UMAP algorithm was used to visualize both RNA and ATAC data separately. The Weighted Nearest Neighbors algorithm was used to integrate RNA and ATAC assays for multimodal clustering. Cell clusters were annotated using the ScType R package with a combination of meningioma cell type markers from previous work (Choudhury, 2022) and canonical non tumor lineages. Separately, these analyses were performed on glioma samples from publicly available data (GSE230389).

Additional single-cell RNA-seq data were extracted from publicly available repositories2,3 and processed according to prior descriptions2. Briefly, the Seurat R Package using the scRNA-seq Seurat10x genomic workflow was used for all analyses unless noted otherwise84. Cells were filtered using a percent mitochondrial DNA threshold of 20% and a UMI range of 500 to 10,000. Cells were then subject to Log Normalize, Scale Data, and PCA functions. The FindClusters and FindMarkers functions were utilized for clustering and marker identification, and non-linear dimensional reduction techniques were applied to visual data in UMAP plot format. The Harmony algorithm was used to regress batch effects83. Tumor cells were identified using CONICSmat85 to determine copy-number loss of chromosome 22q using repetitions=100 and postProb=0.75. Cells with postProb <0.15 were considered tumor and > 08.5 as normal. Clusters with more than 80% normal cells were considered non-meningioma clusters. The fibroblast cluster had an intermediate proportion of cells with a loss of 22q and is likely a mixture of both normal and tumor cells. Non-tumor cell clusters were annotated using three methods to produce robust cell assignments: 1) comparison against known cell markers; 2) examination of DEGs against the Human Protein Atlas; and 3) ScType R package, an automated cell assignment algorithm. MiloR package was utilized for differential abundance testing using standard parameters and protocol86. QC and harmonization data are shown in Fig. S1.

Bulk DNA-methylation profiling and analysis
DNA was extracted from 508 meningiomas resected at Northwestern Memorial Hospital, followed by profiling on the Illumina Infinium platform (MethylationEPIC BeadChip v1 or v2). IDAT format files for each sample were imported into R (v 4.3.1) using the SeSAMe R package (Bioconductor v3.18)87. Probes were normalized within samples using the standard SeSAMe preprocessing pipeline, including normal-exponential out-of-band background correction, nonlinear dye bias correction, and out-of-band array hybridization masking to produce methylated and unmethylated signals. Probes located on the X and Y chromosomes were filtered out. Samples were merged into a single dataset by selecting for probes present on all array types (EPIC v1 or EPIC v2), totaling 705,536 probes. Unmethylated and methylated signals were then normalized for array type by applying linear modeling to the log2-transformed intensity values via the limma R package88. β-values were calculated from normalized methylated and unmethylated signals using an offset of 100 as recommended by Illumina. Methylation within the STING genomic regions was determined by selecting probes within 10KB of the STING transcription start site. Copy Number Variant (CNV) profiles were derived from methylation signals using the cnSegmentation() function from SeSAMe. Comparison glioblastoma methylation data (n=170 patients) was sourced from the TCGA database and processed using the same methods89. Publicly available meningioma methylation data was also incorporated with the institutional cohort, also processed using the same methods (n=904 patients)2,7,11.

Bulk RNA sequencing analysis
Expression of STING and other genes in meningiomas was quantified after harmonization of publicly available RNA-seq datasets for a total of 584 meningiomas with paired methylation profiles2,11,13. Following RNA purification, all samples were sequenced on the Illumina platform, using single- or paired-end sequencing, with read lengths of 50 bp and 150 bp. Raw FASTQ files were downloaded from all studies, and reads were processed using the standard ENCODE pipeline with initial adapted trimming using TrimGalore! (v.0.6.1) with the paired parameter. Subsequently, trimmed reads were mapped to genome (GRCh38) using STAR (v.2.7.5) and quantified using RSEM (v.1.3.1)90,91. Tximport was used to import RSEM counts into R (v.4.3.1), where data were initially processed into log counts using the edgeR pipeline92,93. Briefly, counts were first batch corrected for institution of origin and single/paired-end sequencing using ComBat-seq, and genes were filtered for low expression using standard edgeR parameters94. Then, counts were converted into counts per million (cpm) and then log2 transformed. Final preprocessing was applied to normalize effective library sizes (calcNormFactors() from edgeR) and transform data for linear modeling purposes (voom() from limma)95. Comparison of glioblastoma expression data with paired methylation data referenced in “Bulk DNA-methylation profiling and analysis” (n=170 patients) was sourced from the TCGA database and processed using the same methods. An additional 4 samples of ex vivo, patient-derived meningioma cell cultures were also processed for RNA-seq, with each sample either being an untreated control or treated with 8803. Differential expression was performed on this data using limma-voom. Single-sample gene set enrichment analysis (ssGSEA) was performed using the GSVA package96.

Multiplex fluorescence staining
Multiplex profiling of paraffin-embedded meningioma slides (4 um thick) was performed using a sequential immunofluorescence (seqIF™) panel that included the following unconjugated antibodies: CD31 (endothelial cells; Abcam, clone EPR17259, cat#ab225883), GFAP (astrocytes in adjacent brain; Sigma, clone GA5, cat#MAB360), SSTR2 (meningioma tumor cells; Abcam, clone UMB1, cat#ab134152), CD163 (macrophage scavenger receptor; Abcam, clone EPR19518, cat#ab182422), STING (Cell signaling, clone D2P2F, cat#13647), and p-IRF3 (downstream activation of the STING pathway; Cell signaling, clone 4D4G, cat#4947). All antibodies were validated using conventional immunohistochemistry and/or immunofluorescence (IF) staining, in conjunction with the corresponding fluorophore and the spectral 4’,6-diamidino-2-pheynlindole (DAPI; ThermoFisher Scientific) counterstain. For optimal concentration and the best signal/noise ratio, all antibodies were tested at three different dilutions, starting with the manufacturer-recommended dilution (MRD), then MRD/2 and MRD/4. Secondary Alexa Fluor 555 (ThermoFisher Scientific) and Alexa Fluor 647 (ThermoFisher Scientific) were used at 1/200 and 1/400 dilutions, respectively. The optimizations and full runs of the multiplex panel were executed using the seqIF™ methodology integrated into the Lunaphore COMET™ platform (characterization 2 and 3 protocols, and seqIF™ protocols, respectively)97. The staining is performed using automated cycles of 2 antibodies at a time, followed by imaging and elution, in which no sample manipulation is required. All reagents were diluted in Multistaining Buffer (BU06, Lunaphore Technologies). The elution step lasted 2 minutes for each cycle and was performed with Elution Buffer (BU07-L, Lunaphore Technologies) at 37°C. Quenching lasted for 30 sec and was performed with Quenching Buffer (BU08-L, Lunaphore Technologies). Staining incubation times lasted four minutes for all primary antibodies and two minutes for secondary antibodies. Imaging was performed in Imaging Buffer (BU09, Lunaphore Technologies) with an integrated epifluorescent microscope at 20x magnification. Image registration was performed immediately after concluding the staining and imaging procedures by COMET™ Control Software. Slides were automatically scanned using the Lunaphore COMET™ following the manual’s instructions with a fluorescent high-power field scan after each staining cycle (20x magnification microscope). The microscope captures the fluorescent signals (DAPI, TRITC, and Cy5) separately at the corresponding fluorophore wavelength, with preset exposure times. Then, these captures are co-registered in one multilayer image (OME.TIFF) without disrupting the unique fluorescent spectral signature of the markers. Markers were analyzed and pseudo-colored using the HORIZON Viewer from Lunaphore.

Ex vivo meningioma cell viability assay
Patient-derived meningiomas (n=9 patients) were collected and processed into single-cell suspensions within an hour after resection. The selected samples encompassed a range of WHO grades, tumor locations, and DNA methylation subgroups. Tumor tissue was dissociated using collagenase (20 µl/ml; Roche) and DNase (1 µl/ml; Roche) in RPMI 1640 medium (Roswell Park Memorial Institute; Corning) supplemented with 2% fetal bovine serum (FBS). The cells were washed and then plated as 5 x 105 viable cells/well in triplicate. The cells were incubated for 24–72 h at 37°C in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with GlutaMAX™ (Gibco) and 10% FBS before being treated with 8803 (10 µM). Tumor cell viability was measured using the MTT colorimetric assay kit (CellTiter 96® Aqueous One Solution Cell Proliferation Assay; Promega) at 24, 48, and 72 hours after treatment. Treated-group values were normalized to the control-group values set at 100% of the maximal signal. Differences between treated and untreated at the same time point were analyzed using 2-way ANOVA analysis.

Immune cell removal of ex vivo meningioma cytotoxicity assays
Patient-derived WHO grade I meningiomas (n=3 patients) were collected and processed into single-cell suspensions within an hour after resection. Tumor tissue was dissociated using collagenase (20 µl/ml; Roche) and DNase (1 µl/ml; Roche) in RPMI 1640 medium (Roswell Park Memorial Institute; Corning) supplemented with 2% fetal bovine serum (FBS). CD45+ and CD45- populations were isolated using CD45 selection columns (Miltenyi). The cell populations were washed, tagged with either green (CD45+ population) or red (CD45- population) cell tracker dyes (Thermofisher), and then plated separately or in combination (1:1 ratio) at 2-5 x 105 cells/well in triplicate. The cells were incubated for 2 hours at 37°C in DMEM (Dulbecco’s Modified Eagle Medium) supplemented with GlutaMAX™ (Gibco) and 10% FBS and then treated with 8803 (10 µM). After 48 hours, the cells were collected and stained for flow cytometry.

Cell death pathway inhibition
The role of cell death pathways in mediating 8803 efficacy was evaluated via pharmacologic inhibition. Freshly resected meningiomas (n=3 patients) underwent single-cell dissociation and were plated as described above. For each condition, tumor cells were plated in triplicate and then incubated for 24 hours to ensure baseline viability. Six to twelve hours before 8803 administration, an inhibitor from each of the following groups was added to the appropriate wells: apoptosis (pan-caspase Z-VAD-FMK inhibitor; 20 µM; Promega), necrosis (Necrostatin-1; 10 µM; Selleckchem), ferroptosis (Ferrostatin 1; 5 µM; MedChemExpress), pyroptosis (Disulfiram; 20 µM; Selleckchem), and autophagic cell death (3-Methyladenine; 5 mM; MedChemExpress). Cells were then treated with 8803, and viability was measured at 48–72 hours post-treatment via flow cytometry analysis. Differences between groups were analyzed using 2-way ANOVA analysis.

Antioxidant assay
Meningioma cells were cultured in DMEM/F-12 media (Thermo Fisher, cat#10565-018) supplemented with 10% FBS (Cytiva, cat#SH3007103). A total of 5,000 cells per well were seeded in triplicate in 96-well plates. Glutathione-ethyl-ester (GSHee, Cayman, cat#14953) and N-acetylcysteine (NAC, Thermo Scientific, cat#160280250) were prepared as a 10 mM stock solution in PBS, filtered through a 0.22 µm PVDF membrane (Millipore Sigma, cat#SH3007103), and added to fresh media at the desired final concentrations. Cells were treated with 8803 at 10 ug in combination with each antioxidant. Cells were maintained in 100 µL of media and treated for 48 hours. Cell viability was assessed using the CellTiter-Glo® Luminescent Cell Viability Assay (Promega) according to the manufacturer’s protocol. CellTiter-Glo® reagent (3 mL) was diluted in 9 mL of DPBS, and 100 µL of this solution was added to each well on top of culture media. Plates were shaken on an orbital shaker for 2 minutes and incubated at room temperature for 10 minutes. Luminescence was measured using a Synergy 2 Microplate Reader (BioTek), and cell viability was calculated by normalizing to the luminescence values of untreated control wells.

Meningioma cell lines
The IOMM-LEE (catalog number CRL-3370)98 human meningioma cell line was obtained from the American Type Culture Collection and underwent pathogen and authentication testing by short-tandem repeat analysis before use in experiments. CH157-MN was obtained from Dr Craig Horbinski99. Both cell lines were propagated as monolayers in a complete medium consisting of Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F-12, Gibco), 10% FBS (Cytiva; Marlborough, MA), and sodium pyruvate (Gibco). Half of the medium in each culture was replaced with fresh medium every 3–4 days. Culture passages were performed using Trypsin-EDTA (0.05%, Gibco, #25300-054). The murine MGS1 cell line was obtained from Dr. Michel Kalamarides (AP-HP Pitié-Salpêtrière University) and Dr. Jacky Yeung (Yale University) and was verified to be pathogen-free before implantation. This meningioma cell line was propagated in DMEM supplemented with 10% fetal bovine serum, 1% penicillin/streptomycin, and 1% L-glutamine, as previously reported14,51. All cells were incubated at 37°C in a humidified atmosphere containing 95% O2 and 5% CO2.

Western blot analysis of STING expression
CH157, IOMM-LEE, MGS1, GBM12, and 0827 cell pellets stored at -80°C were thawed on ice, resuspended in cell lysis buffer (RIPA with 1X Halt protease inhibitor) and sonicated with a QSonica Q800R3 sonicator at 50% amplitude, 20 seconds on, 20 seconds off for a total of 40 seconds. Cell lysates were cleared of insoluble material by centrifugation and protein was quantified using a Bradford assay. The sample (25 ug/lane) was loaded onto a 4-12% Bis-Tris polyacrylamide gel (Cat# NP0323), run, and then transferred onto a 0.2 µm PVDF membrane. Membranes were incubated overnight with anti-STING (Cell Signaling, Cat# 50494, 1:1000), anti-GSDMD (Cell Signaling, Cat# 97558, 1:1000), or anti-GAPDH (Cell Signaling, Cat# 5174, 1:1000) primary antibodies, followed by incubation with an anti-rabbit HRP-conjugated secondary antibody (Cell Signaling, Cat# 7074, 1:10,000). Chemiluminescent imaging of blots was performed using the ChemiDoc MP imaging system (BioRad; Hercules, CA).

Flow cytometry analysis
Primary meningioma tissue isolated from the operating room was enzymatically dissociated, and CD45+ cells were isolated using CD45 magnetic beads (Miltenyi). The CD45+ fraction was labeled with CellTracker Green (Fisher), and the CD45- population was labeled with CellTracker Red (Fisher). Cells were then plated either alone or in combination and treated with 8803 for 48 hours, with or without cell death inhibitors. On the day of the assay, cells were lifted, washed with 1X PBS, and stained (1:1000) with eBioscience™ Fixable Viability Dye eFluor™ 780 for 30 minutes on ice. After subsequent washes, cells were resuspended in FACS buffer (PBS+2% FBS). Samples were acquired using a FACSymphony A5-Laser Analyzer (Fig. S13), and data were analyzed using FlowJo software.

Meningioma in vivo models
J:NU (strain 007850) and FVB/NJ (strain 001800) 6-8 weeks old mice were purchased from Jackson Laboratories (Bar Harbor, ME) for in vivo therapeutic evaluations of 8803. Male and female mice were used. All mice were housed under standard barrier conditions, which included filtered air and sterilized food, water, bedding, and cages and racks. Housing conditions included a 12-hour dark/12 light cycle, 18-23°C ambient temperature and 40-60% humidity. The Northwestern University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and all animal care followed institutional guidelines. For flank models, mice were bilaterally implanted with 3x106 IOMM-LEE cells. 8803 (5 ug) was delivered subcutaneously into the tumor using a Hamilton syringe, with treatment initiated as designated in the schemas. Tumors were measured with calipers in two directions. Flank tumor volumes were calculated using (LxW^2)/2. A total size of 3.0 cm in diameter of all tumors combined in mice was not exceeded per IACUC guidelines. Animals bearing tumors that interfered with normal functions, such as eating, ambulating, and eliminating waste, were considered humane endpoints. Rodents with functional impairments were immediately euthanized. Ulcerated tumors were considered an automatic endpoint. For orthotopic models, CH157 or IOMM-Lee cells (1.0 × 105 cells) were implanted under the skull in the right frontal subdural region, approximately 2 mm posterior to the bregma and 1 mm to the right of midline, and to a depth of 3 mm from the bone surface. 8803 treatment (5 ug) was delivered either through a previously implanted guide cannula or using the same stereotactic coordinates for the tumor implantation. Treatment initiation and schedule are designated in the schemas. For the immune-competent murine meningioma model, the tumorigenic dose for MGS cells was 2.5x105 in a total volume of 2.5 µl. Typically, five days after implantation, mice were randomized to either the 8803-treatment group or vehicle control. Animals were euthanized by CO2 asphyxiation and general anesthesia when they became moribund (>20% weight loss, neurologic symptoms, or evidence of pain/distress), and survival time was recorded. Survival analyses were performed using GraphPad Prism 9 software.

Sc-RNA sequencing of 8803 treated meningiomas
Six mice were utilized for sc-RNA experiments for each condition (treated; control). MGS1-bearing FVB/N mice were treated with 8803 on day 21 and euthanized on day 28. Tumors were grossly dissected and digested using collagenase (20 µl/ml; Roche) and DNase (1 µl/ml; Roche) in RPMI 1640 medium (Roswell Park Memorial Institute; Corning) supplemented with 2% fetal bovine serum (FBS). The CD45+ population was isolated using Percoll. Three mice were pooled in preparation for each sequencing run and spiked 1:1 with CD45- before sequencing to obtain sufficient immune cells for profiling. Sequencing was performed by the Northwestern University NUSeq sequencing facility core, with a targeted sequencing depth of 20,000 reads per sample acquired for RNA, and a total of 20,000 cells were targeted for each sample. Cell Ranger was used to align subsequent reads to mm10. Similar to the human meningioma data, the Seurat R Package using the scRNA-seq Seurat10x genomic workflow was used for all analyses84. SoupX was used to remove ambient RNA, and scDblFinder was used to remove doublets. Following this, cells were filtered using a percent mitochondrial DNA threshold of 10% and a UMI range of 100 to 4000. Cells were then subject to Log Normalize, Scale Data, and PCA functions. Murine tumor cells were identified through reference mapping to human meningioma cells. Briefly, human orthology of murine genes was called from Orthology.eg.db, following which Seurat function of FindTransferAnchors and TransferData was used to map murine query data onto human meningioma samples. Immune cells were subsequently subclustered, and the FindClusters and FindMarkers functions were utilized for clustering and marker identification. Non-linear dimensional reduction techniques were applied to visual data in UMAP plot format. The Harmony algorithm was used to regress batch effects83. The MiloR package was utilized for differential abundance testing using standard parameters and protocol. DEGs were calculated using pseudo-bulk analysis using the DESeq2 package between 8803 vs control.

Murine macrophage culture and treatment
Bone marrow-derived macrophages were isolated from male and female mice from three strains of mice: FVB, MyD88, and Tmem173gt. Mice were euthanized, after which both hindlimbs were collected and cleaned of muscle and connective tissue to expose the femur and tibia. One end of each bone was cut, and the bones were placed cut-side down in a 0.6 mL microcentrifuge tube with a hole punctured at the bottom. This tube was placed within a 1.5 mL collection tube and centrifuged at 5000 x g for 4 minutes to extract bone marrow. Isolated marrow was resuspended in Ammonium-Chloride-Potassium (ACK) lysis buffer and incubated for 3 minutes to lyse red blood cells. Lysis was quenched with media, and cells were counted and pelleted by centrifugation. The cell pellet was resuspended in complete media containing 40 ng/mL Macrophage Colony-Stimulating Factor (M-CSF). Two million macrophages were seeded per well in a 6-well plate and maintained in culture for one week, with fresh media added every three days. Simultaneously, MGS1 cells were seeded at 2x106 cells per 10 cm dish and cultured in parallel for preparation of the macrophage treatments. After culture, supernatant was collected and centrifuged at 2600 x g for 5 minutes to remove cell debris. For lysate preparation, MGS1 cells were harvested by trypsinization and resuspended at 2x106 cells/mL in media. Cells were transferred to 0.5 mL sonication tubes and lysed using a Q800R3 Sonicator (QSonica, 3 cycles at 50% amplitude, 20 seconds ON, 20 seconds OFF, at 4°C). The resulting lysate was centrifuged at 2600 x g for 5 minutes to clear debris. Macrophages were treated with either MGS1-conditioned media or cell lysate for 48 hours. Following treatment, macrophage supernatants were collected and centrifuged to remove residual debris. Cleared supernatants were stored at –80°C until analysis using a Luminex Multiplex Assay.

Luminex multiplex assay
A multiplex Luminex assay (R&D Systems, cat# LXSAMSM) was performed according to the manufacturer’s instructions. Macrophage culture supernatants were diluted 1:2 in calibrator diluent (75 µL sample + 75 µL diluent) before analysis. Briefly, 50 µL of each diluted sample was incubated with a premixed magnetic microparticle cocktail in a 96-well plate for 2 hours at room temperature on a shaker (800 rpm), protected from light. Following three washes, biotinylated detection antibodies and streptavidin-PE were sequentially added, followed by incubation and washing. Plates were read on a Luminex® 100/200™ analyzer, and the median fluorescence intensity (MFI) was recorded for each analyte. MFI readings were converted to analyte concentrations using a 5-parameter logistic (5-PL) standard curve. Final values were adjusted based on the dilution.

Electron microscopy
To obtain Transmission Electron Microscopy (TEM) data, cultured meningioma cells were fixed in glutaraldehyde (2.5%) and paraformaldehyde (2%) in a cacodylate buffer (0.1M) overnight at 4°C. After fixation in 1% osmium tetroxide and 3% uranyl acetate, meningioma cells were dehydrated using ethanol, then embedded in Epon resin and polymerized for 48 hours at 60°C. Ultrathin sections were cut using an Ultracut UC7 Ultramicrotome (Leica Microsystems, Germany) and contrasted with 3% uranyl acetate and Reynolds’s lead citrate. Imaging of the samples was performed on a FEI Tecnai Spirit G2 transmission electron microscope (FEI Company, Hillsboro, OR) operated at 80 kV. An Eagle 4k HR 200 kV CCD camera was used for image capture. For Scanning Electron Microscopy (SEM), the medium was removed after cell incubation, and fixation was performed overnight at 4°C in glutaraldehyde solution (2%). After rinsing of the cells three times in 0.1M sodium cacodylate buffer, incubation was performed in osmium tetroxide (1%) for 2 h. Specimens were subsequently dehydrated in a series of ethanol washes (50% to 100%), critical-point-dried with carbon dioxide (Samdri-790, Tousimis, Rockville, USA), mounted on a carbon tape with an aluminum stud, and coated with 20 nm gold in a EM ACE600 Sputter coater (Leica, Germany). Finally, images were captured under a scanning electron microscope (JCM-6000PLUS, JEOL, Japan).

Statistical analysis
Human patient sample size was based on feasibility of obtaining post-operative surgical resections specimens within a two-year time window. No data was excluded, and replications were confirmatory. Statistical significance was evaluated in pairwise comparisons with control values using a One-way and Two-way ANOVA test after confirmation of normal distributions with GraphPad Prism 8.0 and R (v 4.3.1). All samples were quantified as means + SEM, and P<0.05 was considered significant. Interaction effects between conditions in the Luminex assay were modeled using linear regression in R. For survival endpoints in the preclinical studies, PASS 2021 indicates that a sample size of 10 mice/group achieves 80% power to detect a log hazard ratio of −0.69 (i.e., hazard ratio 0.5) at a 0.025 significance level using the Cox proportional hazard model. Mice were randomized into experimental treatment arms. The endpoint of death did not require blinding.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "1. General statement\nThe manuscript presents STING induction as a promising therapeutic strategy for meningiomas,\nsupported by a plethora of both ex vivo and in vivo functional and mechanistic data. These\nfunctional findings are compelling and well-supported. However, the accompanying multi-omic\ncharacterization appears underdeveloped, both in terms of analytical depth and sample size.\nMoreover, it often feels disconnected from the central narrative. To enhance clarity and impact, I\nrecommend bringing the functional data to the forefront and moderating conclusions drawn from\nthe limited single-cell datasets.\n2. Figure Legends and Data Clarity\nAcross the manuscript, the figure legends lack sufficient methodological detail and often focus on\ninterpretation rather than explanation. This makes it difficult to assess the analysis pipelines and\nunderstand how results were derived. I recommend revising figure legends to clearly state: data\nsources, sample sizes, statistical tests.\n3. Statistical Considerations\nKey single-cell analyses, including comparisons of cell-type frequencies and gene expression\n(e.g., Figures 1C, 1G, 2F-H), should be aggregated and statistically evaluated at the level of\nbiological replicates (patients or mice) rather than individual cells. Many of the results are\npresented without appropriate statistical testing. Of particular concern are the single-cell studies\nin Figure 5, which are based on only two replicates (n=2) and are therefore significantly\nunderpowered. Conclusions from these analyses should be toned down to reflect their exploratory\nnature.\n4. Data Availability and Reproducibility\nWhile the authors reference accession numbers for external datasets, they should also provide\nannotated and integrated data objects (e.g., Seurat objects) to facilitate reproducibility and allow\nthe community to explore the integrated analyses directly.\nFigure 1: This figure begins with a reanalysis of previously published single-cell transcriptomic\ndata, but its connection to the rest of the manuscript remains unclear. The added value beyond\nthe original studies is unclear, as STING appears to a promising targeting strategy, while its\nbaseline activity seems not being correlated with any clinical outcomes. If this analysis does not\ninform subsequent findings, the authors should consider citing the sources instead of reproducing\nit.\nThe integration of multiple published datasets lacks critical validation steps. Basic quality control\nmetrics and integration diagnostics (e.g., batch effect correction, patient-level summaries) are not\nshown.\nWhere possible, summary statistics at the patient level and appropriate statistical tests should be\nprovided.\nFigure 3: The color coding in this figure is not explained and should be clarified.\n• Can the authors address the observed inter-patient heterogeneity for blocking apoptosis (Fig.\n3D)? What distinguishes responders from non-responders to STING agonists in this context?\nFigure 4: The data show convincingly that early administration of STING agonists yields a strong\nresponse, using multiple controls. However, at later time point, comparable controls are not\nincluded, weakening this conclusion. A direct comparison between treated and vehicle groups at\nlate time points would strengthen the argument considerably.\nFigure 5: As noted before, the single-cell analysis from this figure is underpowered (n=2). Final\ninterpretations should be made at the biological replicate level. Either more mice should be\nanalyzed, or the conclusions must be framed as exploratory.\n• Panel 5D: Please include split UMAP plots showing treated vs. control groups separately.\n• Panel 5F: Provide mean cell-type frequencies per murine replicate.\n• Panels 5G–5H: The figure legends do not specify the source or statistical treatment of these\ndata. If these plots are derived from the same single-cell dataset, statistical analysis must be\nperformed at the sample level, not the individual cell level. If other sources were used, please\nclarify explicitly.\n• The transcriptional downregulation of collagens and upregulation of MMPs following STING\nagonist treatment is intriguing. What is the proposed mechanistic basis for these changes?\nAdditional discussion or referencing of supporting literature would be helpful.\nOverall, the manuscript is built on a strong foundation of functional data, and STING agonism\nappears to be a promising therapeutic approach in meningiomas. However, to improve scientific\nrigor and clarity, the authors should consider to emphasize functional results as the central\nnarrative, revise the multi-omic analysis to reflect its exploratory nature and add statistical rigor,\nparticularly for the partly underpowered single-cell data.", "reviewer_2": "In this manuscript, Youngblood et al. describe the expression and therapeutic targeting of the\nSTING pathway in meningiomas. Using integrated transcriptional, epigenetic, and spatial\napproaches, the authors show broad expression of STING across multiple cell types in\nmeningiomas, linking promoter hypomethylation and open chromatin accessibility to elevated\nSTING expression. Importantly, they demonstrate direct tumor cell cytotoxicity mediated by\nprogrammed necrotic cell death pathways (pyroptosis, necroptosis, ferroptosis) upon exposure to\nthe STING agonist 8803, independent of immune cell activity. This cytotoxicity involves ROS\ninduction, caspase activation, and gasdermin D-mediated membrane disruption, leading to\nmacrophage activation and extracellular matrix remodeling, especially collagen degradation, thus\nreshaping the immunosuppressive tumor microenvironment. These observations are\ncomplemented by robust in vivo data across several murine models, showing clear therapeutic\nefficacy of the agonist with significant tumor volume reduction, immune infiltration, and survival\nbenefits. Overall, the study presents a comprehensive preclinical evaluation of STING agonism\nin meningiomas and proposes a unique therapeutic angle leveraging direct tumor cell cytotoxicity\nin addition to classical immune activation. Having said that, there are multiple key weaknesses\nand concerns regarding the novelty discussed below with suggested approaches to address\nthem.\nNovelty of the Therapeutic Approach: While the study demonstrates robust preclinical efficacy of\nSTING agonism, the manuscript does not adequately address previous clinical setbacks with\nSTING agonists despite compelling pre-clinical results. The authors need to articulate clearly why\nmeningiomas represent a unique or particularly suitable context for STING activation relative to\nother solid tumors, particularly given multiple failed clinical trials with STING agonists. One\nplausible hypothesis, partially hinted at but insufficiently developed, is the relative chromosomal\nstability of meningiomas and potentially lower basal STING pathway activation at baseline. The\nabsence of pre-treatment chronic STING activation suggests that the pathway is primed to\nrespond to an agonist through a pro-inflammatory signal that would yield tumor control.\nDemonstrating that meningiomas uniquely harbor STING expression without chronic activation\nwould strengthen their claim to novelty.\nPro-tumorigenic Roles of STING: The manuscript overlooks established evidence that STING\nactivation also can promote metastasis, immune suppression, and tumor growth. Given that\nSTING agonism is a double-edged sword, the authors must explicitly reconcile their findings with\nthe literature describing STING as potentially pro-tumorigenic under chronic conditions.\nClarification of acute versus chronic STING activation states in meningiomas would substantially\nenhance the manuscript’s conceptual rigor. This might also bear on the clinical development path\nof STING agonists and how frequently it should be dosed in patients to maximize its efficacious\neffect while minimizing the side effects.\nImmune Independence and Direct Cytotoxicity: A key strength of this study is the demonstration\nof STING agonist-induced direct cytotoxicity in tumor cells independently of immune cells, which\nis a significant departure from classical STING agonist applications. However, the authors need\nto provide clearer mechanistic evidence distinguishing the direct cytotoxic pathways from\nimmune-mediated mechanisms.\nAlso why meningioma is an important question that should be addressed in this case, in light of\nthe lack of clear clinical efficacy of STING agonists to date.\nTumor Microenvironment Remodeling: The proposed mechanism whereby STING agonism\nremodels the collagen-rich, immunosuppressive microenvironment via DAMP release and\nmacrophage activation is intriguing. However, the authors need more direct evidence linking the\nobserved collagen reduction with therapeutic efficacy and macrophage activation. Specifically,\ngenetic or pharmacological blockade of collagen degradation pathways (e.g., inhibition of matrix\nmetalloproteinases) to determine the importance of this remodeling step would add considerable\nstrength. Finally, and critically the authors need to rule in or rule out STING dependent type I\ninterferon signaling in this process.\nClinical Translation and Endpoint Considerations: The manuscript makes strong translational\nclaims about the therapeutic potential of STING agonist 8803 for meningiomas, even suggesting\nradiographic tumor volume reduction as a clinical endpoint. It is unclear however if this means\ntumor eradication and whether this would translate into reduced recurrence in the clinical setting.\nAdditional discussion of how these endpoints translate to clinically meaningful outcomes such as\nsymptom relief, quality of life, or progression-free survival would provide valuable context for the\nclinical viability of this approach.\nThe manuscript could improve by clearer definitions and consistency in the descriptions of\nimmune cell populations, particularly macrophage subsets. Figures and legends should explicitly\nclarify experimental conditions (e.g., dosages of agonist/inhibitors, exact animal numbers per\ngroup) to improve reproducibility.\nIn conclusion, the manuscript is a strong preclinical contribution with exciting therapeutic\nimplications. However, it requires critical refinement in addressing novelty concerns and\nmechanistic clarity to ensure the significance and uniqueness of STING agonism in meningiomas\nrelative to existing literature and clinical experience.", "reviewer_3": "This study has systematically investigated the effect of STING expression and agonism in\nmeningioma cells and their TME. Differences predicted by sc seq were validated by spectral\nImmunofluorescence staining. The investigators identify that STING is expressed in meningioma\nand these tumor cells were sensitive to sting agonist, with increased gene expression of pathways\nrelated to associated with autophagy, necroptosis, and proptosis. Thus treatment of mice bearing\nsubcutaneous and orthotopic meningiomas with STING agonist 8803 showed anti-tumor efficacy.\nThe study is well controlled, and the findings are significant, and support future translation of\ntreating meningioma patients with STING agonist. The study while being translationally very\nsignificant stops short of mechanistically uncovering new pathways which advance our\nunderstanding of the role of STING pathway in meningioma and TME.\nFigure 1: Move fig 1D to 1B. most highly differentially expressed genes/cluster that are used to\nassign cluster identity should be shown with Figure 1A, at the time of description of the clusters.\nLine 171: Markers for T.Tex and NK are not shown.\nFigure 1G: not clear if DEG genes are from bulk sequencing tumor Vs dura, or from select cell\nclusters in 1A. please clarify\nLegend of S4A says these pathways are up in sting high tumors, however the results does not\nsay that. Is this analysis from sc seq shown in fig 1? How were sting hi Vs sting low tumors\ndefined?\nLine 213: The authors state” Pseudo-bulk snATAC-seq data were used to analyze the cellular\npopulations in meningioma (n = 4; 29,100 cells) and glioma (n = 2; 4,850 cells), which revealed\nconsistent chromatin accessibility in the immune cell lineages between tumor types.” For pseudo\nbulk the single cells in each cluster are likely bulked together, so this was likely an expected result\nsince clustering cannot be performed again.\nLine 262 states: CASP1, 4, 7, 8, and 10 were 262 upregulated after treatment with 8803, but data\nin Fig 3F looks reduced after 8802 treatments.\nLine 305: the authors state: Nude mice only have an intact innate immune system; these data\nindicate that the mechanism of action is not reliant on T cell-mediated immune responses.\nSince these mice don’t express mature T cells, I think it is hard to make a statement about T cell\ninvolvement either way. This appears to be extrapolation of data. The authors can only state that\nthe impact of T cells was not investigated.", "reviewer_4": "The authors have sufficiently addressed the comments raised by both Reviewer #1 and Reviewer #3. The methodological\nconcerns have been handled appropriately through clear quality control, batch management, and additional analyses.\nThe figure legends point readers to the relevant methods and supporting analyses. While the sample imbalancement between\nconditions could be a potential confounding factor, the authors’ use of MiloR, which is widely applied to mitigate cell-number\nimbalancement, appears appropriate and yielded robust and reliable results. In addition, the number of mice per condition in\nFigure 5 seems adequate to support the conclusions, particularly given the consistency with the human data.\nIt would nevertheless be interesting to identify which macrophage subsets differ between the control and 8803. Moreover, a\nrecent single-cell study of matched primary and recurrent meningiomas demonstrated enrichments of one of the collagen\nfamily members, COL6A3, in recurrent tumors. It would be interesting to know whether COL6A3 enrichment is also observed\nin the current cohort. Lastly, to improve the reproducibility of cell-type annotation, I recommend providing a comprehensive\nmarker table used for cell-type assignments. While not mandatory, the current color scale gradient, where high gene\nexpression is shown in blue and low expression in red, can be confusing as it differs from conventional use. This likely\ncontributed to Reviewer #3’s concern regarding Figure 3F."}, "author_response": "We thank the reviewers for their thoughtful comments and suggestions. To address these, the\nfollowing new experimental data has been generated to support the findings of this manuscript:\n Longitudinal scanning electron microscopy of meningiomas treated with 8803\ndemonstrating membrane pore formation that compromises cellular integrity (new Fig. 3J;\nFig. S7), thereby confirming direct cytotoxic effects on the meningioma cell that do not\ninvolve the immune system.\n Transmission electron microscopy showing that 8803 induces mitochondrial cristae\ncollapse and compromise of the double membrane (new Fig. 3L; Fig. S7), providing\nadditional data regarding the mechanism for direct cell death.\n In vivo treatment of meningioma at a later time (day 17) that compares control versus 8803\n(new Fig. 4F).\nReviewer #1:\n1. General statement\nThe manuscript presents STING induction as a promising therapeutic strategy for meningiomas,\nsupported by a plethora of both ex vivo and in vivo functional and mechanistic data. These\nfunctional findings are compelling and well-supported. However, the accompanying multi-omic\ncharacterization appears underdeveloped, both in terms of analytical depth and sample size.\nMoreover, it often feels disconnected from the central narrative. To enhance clarity and impact, I\nrecommend bringing the functional data to the forefront and moderating conclusions drawn from\nthe limited single-cell datasets.\nRevision: The multi-omic characterization depth has been increased throughout the manuscript.\nThe sample size was incorrectly described and has been corrected. The narrative has been\nrevised throughout the manuscript to increase the central narrative, and additional functional data\nhas been added.\n2. Figure Legends and Data Clarity\nAcross the manuscript, the figure legends lack sufficient methodological detail and often focus on\ninterpretation rather than explanation. This makes it difficult to assess the analysis pipelines and\nunderstand how results were derived. I recommend revising figure legends to clearly state: data\nsources, sample sizes, statistical tests.\nRevision: To improve clarity and reproducibility, these details for each analysis have been\nprovided in Figure Legends 1A, 1C, 1D, 1E, 1F, 1G, 1H, 2A, 3G, 5D, 5E, 5G, and 5H). \nFurthermore, the specific R functions utilized in the creation of the data are displayed in each\npanel. This information is also cross-referenced in the corresponding methods sections.\n3. Statistical Considerations\nKey single-cell analyses, including comparisons of cell-type frequencies and gene expression\n(e.g., Figures 1C, 1G, 2F-H), should be aggregated and statistically evaluated at the level of\nbiological replicates (patients or mice) rather than individual cells. Many of the results are\npresented without appropriate statistical testing. Of particular concern are the single-cell studies\nin Figure 5, which are based on only two replicates (n=2) and are therefore significantly\nunderpowered. Conclusions from these analyses should be toned down to reflect their exploratory\nnature.\nResponse and Revisions: Statistical comparisons performed in panel 1C were created using\nMiloR differential abundance testing, which is performed at the level of biological replicates. The\npoints displayed represent “neighborhoods,” not individual cells. In the legend we clarify:\n“Differential abundance testing was conducted on batch corrected sample level clusters using\nMiloR standard workflow including: buildgraph, makeNhoods, countcells, calcNhoodDistance,\nand testNhoods. Visualization was performed using plotDAbeeswarm. Neighborhoods are\ncolored if p-value < 0.05 otherwise shown in grey. Immune cell populations with fewer than 5\nsignificant neighborhoods were removed from the strip plot. Data was also analyzed using t-tests\n(with multiple testing correction) and results are further plotted in Fig. S2.” In Figure S2, we display\nthis data as box plots where each point represents a single patient having performed multiple\ncorrections.\nIn panel 1G, we agree that this should have been done at sample-size, although we initially\nconducted an exploratory analysis hence using Wilcoxon Rank Test through FindMarkers. We\nhave provided a new sample-level pseudobulk volcano plot using DESeq2. The main conclusions\nremain the same. Additionally, the following clarification was made in the legend: “Volcano plot\nshowing select chemokines enriched in the dura (left side) and meningioma tissues (right side)\nbased on scRNA-seq of immune clusters shown in panel A. The annotated chemokines correlate\nwith the elevated myeloid (blue text) and lymphoid (red text) populations in the meningioma and\ndura, respectively. Differentially expressed genes were calculated through pseudobulk analysis\nusing DESeq2 analysis on immune cells grouped to compare between dura and meningioma.\nSince the manuscript did not contain a submitted Fig. 2F-H we are presuming that the\nreviewer is referring to Fig. 5F-H. The n represented the number of sequencing runs rather than\nthe number of mice (n=12). Figure Legend 5D has been corrected: “UMAP plot of scRNA-seq\ndata of 42,150 immune cells (CD45+). N = 6 mice per condition (untreated and 8803). Three mice\nwere pooled for each sequencing run.” Since the data represents n = 2 sequencing run and for\nenhance data clarity, we have provided DEGs using both Wilcoxon Rank Test through\nFindMarkers and DESeq2 sample level. In Fig. 5G we clarified: “Differentially expressed genes\nwere calculated using Seurat FindMarkers using Wilcoxon Rank Test between 8803 vs control\nfrom immune cells. Data was also analyzed using DESeq2 and presented in Fig. S8D.” In Fig.\n5H, the following details are provided: “Differentially expressed genes were calculated using\nSeurat FindMarkers using Wilcoxon Rank Test between 8803 and control from meningioma tumor\ncells. Data was also analyzed using DESeq2 and presented in Fig. S8E.”\n4. Data Availability and Reproducibility\nWhile the authors reference accession numbers for external datasets, they should also provide\nannotated and integrated data objects (e.g., Seurat objects) to facilitate reproducibility and allow\nthe community to explore the integrated analyses directly.\nResponse: These files were submitted/uploaded on 10/3/2025.\nFigure 1: This figure begins with a reanalysis of previously published single-cell transcriptomic\ndata, but its connection to the rest of the manuscript remains unclear. The added value beyond\nthe original studies is unclear, as STING appears to a promising targeting strategy, while its\nbaseline activity seems not being correlated with any clinical outcomes. If this analysis does not\ninform subsequent findings, the authors should consider citing the sources instead of reproducing\nit.\nResponse and Revision: Although prior published studies have characterized the immune\ninfiltration of meningiomas, the analysis conducted in Fig. 1 demonstrates for the first time that\ncollagen-mediated immune suppression, including LAIR-1, predominates within the meningioma\nmicroenvironment which has not been previously described. These data lay the groundwork for\nFig. 5 in which 8803 is shown to reverse this dominant immune suppressive mechanism. To help\nclarify this point, the header for the Results section has been modified: “Single-cell and spatial\nanalyses identify collagen-mediated immune suppression as a predominant feature of the\nmeningioma microenvironment.”\nThe integration of multiple published datasets lacks critical validation steps. Basic quality control\nmetrics and integration diagnostics (e.g., batch effect correction, patient-level summaries) are not\nshown.\nRevision: The new Fig. S1 includes batch effect correction, nFeature and nCount violin plots, and\npatient-level summaries.\nWhere possible, summary statistics at the patient level and appropriate statistical tests should be\nprovided.\nResponse and Revision: Differential abundance at the patient level in form of box plots is\nprovided in Fig. S2 corresponding to MiloR plot shown in Fig 1B and C. Otherwise, all analyses\nincluded have been performed on the patient/mouse level.\nFigure 3: The color coding in this figure is not explained and should be clarified.\nRevision: We have clarified this in the legend: “Volcano plot showing select chemokines enriched\nin the dura (left side) and meningioma tissues (right side) based on scRNA-seq of immune clusters\nshown in panel A. The annotated chemokines correlate with the elevated myeloid (blue text) and\nlymphoid (red text) populations in the meningioma and dura, respectively. Differentially expressed\ngenes were calculated through pseudobulk analysis using DESeq2 analysis on immune cells\ngrouped to compare between dura and meningioma.”\n• Can the authors address the observed inter-patient heterogeneity for blocking apoptosis (Fig.\n3D)? What distinguishes responders from non-responders to STING agonists in this context?\nResponse and revision: Response biomarkers for immune therapeutics are a significant\nchallenge for the entire field of oncology, even for well-established therapeutics like anti-PD-1.\nGiven the complexity of orchestrating multiple steps of immune activation and cytolytic activity, it\nis unlikely something simple will be predictive of therapeutic response to 8803. As an example of\nthis type of challenge, one of the few FDA-approved biomarkers for immune checkpoint inhibition,\ntumor mutation burden is not predictive of therapeutic response across cancer lineages\n(PMID: 33736924). Factors that were analyzed to distinguish responders versus non-responders\nfor 8803 included STING promoter methylation, global methylation (every CpG), and methylation\nsubgroup as a linear regression against percent viability. None correlated with response. As such,\nresponse to 8803 will almost certainly be multi-factorial: “Because meningiomas generally\ndemonstrate chromosomal stability and low basal levels of STING activation, the STING\npathway may be primed to respond to an agonist, thereby yielding tumor control. Most likely these\nresponses are based on a variety of factors including epigenetic regulation of the STING\npromoter, STING isoforms and mutations, immune composition of the TME, and other regulatory\nfactors that are now being elucidated. To investigate the therapeutic potential of this pathway in\nmeningiomas, we used the agonist 8803, a 2’,3’-thiophosphate CDN analog that we previously\ndesigned, which shows superior and robust activation of the STING pathway based on\nbenchmarking to ADU-S100. While therapeutic resistance to STING agonists can be acquired\nthrough mutations and alterations of STING isoforms that render them insensitive to stimulation,\n8803 can still generate immune activation in this scenario.” Further biomarker response\nclarification studies will be conducted in parallel with the Phase I clinical trial.\nFigure 4: The data show convincingly that early administration of STING agonists yields a strong\n a strong\nresponse, using multiple controls. However, at later time point, comparable controls are not\nincluded, weakening this conclusion. A direct comparison between treated and vehicle groups at\nlate time points would strengthen the argument considerably.\nRevision: A direct comparison of 8803 and PBS treated controls at a later point (day 17) is\nprovided in Fig. 4F. This day was selected since it represents the last feasible time point with\nenough time to measure both control and 8803-treated tumors before becoming necrotic with skin\nbreakdown which requires the experiment to be terminated.\nFigure 5: As noted before, the single-cell analysis from this figure is underpowered (n=2). Final\ninterpretations should be made at the biological replicate level. Either more mice should be\nanalyzed, or the conclusions must be framed as exploratory.\nResponse and revision: The n represented the number of sequencing runs rather than the\nnumber of mice (n=12). Figure Legend 5D has been corrected: “UMAP plot of scRNA-seq data of 42,150 immune cells (CD45+). N = 6 mice per condition (untreated and 8803). Three mice\nwere pooled for each sequencing run.” Since the data represents n = 2 sequencing run and for\nenhance data clarity, we have provided DEGs using both Wilcoxon Rank Test through\nFindMarkers and DESeq2 sample level. In Fig. 5G we clarified: “Differentially expressed genes\nwere calculated using Seurat FindMarkers using Wilcoxon Rank Test between 8803 vs control\nfrom immune cells. Data was also analyzed using DESeq2 and presented in Fig. S8D.” In Fig.\n5H, the following details are provided: “Differentially expressed genes were calculated using\nSeurat FindMarkers using Wilcoxon Rank Test between 8803 and control from meningioma tumor\ncells. Data was also analyzed using DESeq2 and presented in Fig. S8E.”\n• Panel 5D: Please include split UMAP plots showing treated vs. control groups separately.\nRevision: A split batch-corrected UMAP plot is provided in Fig. S8B.\n• Panel 5F: Provide mean cell-type frequencies per murine replicate.\nResponse and Revision: This information is included in Fig. S8C; however, due to the\nsequencing run size of 2 per condition as the reviewer mentioned above, MiloR differential\nabundance testing was used to make conclusions regarding changes in the immune populations\nfollowing 8803 administration.\n• Panels 5G–5H: The figure legends do not specify the source or statistical treatment of these\ndata. If these plots are derived from the same single-cell dataset, statistical analysis must be\nperformed at the sample level, not the individual cell level. If other sources were used, please\nclarify explicitly.\nRevision: The following clarifications in the Legends were provided: “(G) Volcano plot of DEGs\nfrom immune cells showing that cytotoxic genes such as Prf1 and Ifng and matrix\nmetalloproteinases are highly upregulated, whereas the immune suppressive molecules like TIM3\nand LAIR are decreased following 8803 treatment. Differentially expressed genes were calculated\nusing Seurat FindMarkers using Wilcoxon Rank Test between 8803 and control from immune\ncells. Data was also analyzed using DESeq2 and presented in Fig. S8D. (H) Volcano plot of DEGs\nfrom meningioma cells showing a decrease in several notable collagens and an increase in\nSTING (Tmem173), GSDMD, and cell death caspases. Differentially expressed genes were\ncalculated using Seurat FindMarkers using Wilcoxon Rank Test between 8803 vs control from\nmeningioma tumor cells. Data was also analyzed using DESeq2 and presented in Fig. S8E.”\n• The transcriptional downregulation of collagens and upregulation of MMPs following STING\nagonist treatment is intriguing. What is the proposed mechanistic basis for these changes?\nAdditional discussion or referencing of supporting literature would be helpful.\nResponse and Revision: This has not been described previously in the literature. In the\nDiscussion, we provided the mechanistic basis for this finding based on the generated data.\n“Notably, some cytokines like MMP were specifically induced by DAMPs, while others were more\nstrongly activated by 8803 directly, thus demonstrating the involvement of both the TLR and\nSTING pathways in triggering inflammation in the setting of direct tumor cytotoxicity via 8803. The\nimmunocompetent MGS1 models only grows in the FVB/N background. As such, implantation of\nMGS1 into backgrounds that have genetically modified alterations in collagen or\nmetalloproteinases will not support tumor growth. To overcome this limitation, we used\nmacrophages from TLR and STING KO backgrounds that were treated with 8803 and/or\nmeningioma lysate. The data indicates that collagen degrading MMP-3 is induced with the MGS1\nlysate, which is abrogated in the MyD88 KO macrophages indicating that this effect requires the\nTLR pathway. 8803 alone does not trigger MMP-3 indicating meningioma cell death facilitates\ncollagen degradation.”\nOverall, the manuscript is built on a strong foundation of functional data, and STING agonism\nappears to be a promising therapeutic approach in meningiomas. However, to improve scientific\nrigor and clarity, the authors should consider to emphasize functional results as the central\nnarrative, revise the multi-omic analysis to reflect its exploratory nature and add statistical rigor,\nparticularly for the partly underpowered single-cell data.\nReviewer #2:\nRevision: In the Discussion, we clarify: “The first clinical trial of a STING agonist for solid cancer\nused the ADU-S100 compound, which was injected directly into a solid tumor in patients with\nmetastatic disease. Although well tolerated, with the injected lesions showing either stability or\nregression, there were only a few patients with partial responses. Other STING agonists such as\nBI 1703880, MK-2118, E7766, and IMSA101 have also been developed and evaluated in Phase\nI clinical trials, but the signals of response have remained modest. In contrast to these previously\ninvestigated indications, meningioma is usually a local, solitary disease. Because meningiomas\ngenerally demonstrate chromosomal stability and low basal levels of STING activation, the STING\npathway may be primed to respond to an agonist, thereby yielding tumor control. Most likely these\nresponses are based on a variety of factors including epigenetic regulation of the STING\npromoter, STING isoforms and mutations, immune composition of the TME, and other regulatory\nfactors that are now being elucidated. To investigate the therapeutic potential of this pathway in\nmeningiomas, we used the agonist 8803, a 2’,3’-thiophosphate CDN analog that we previously\ndesigned, which shows superior and robust activation of the STING pathway based on\nbenchmarking to ADU-S100. While therapeutic resistance to STING agonists can be acquired\nthrough mutations and alterations of STING isoforms that render them insensitive to stimulation,\n8803 can still generate immune activation in this scenario. As such, the combination of 8803 in\nmeningiomas represents a unique therapeutic opportunity that holds promise relative to previous\nclinical indications.”\nRevision: In the Discussion, we clarify: “Though a previous study suggested that long-term\nSTING induction may promote tumor growth, acute administration of 8803 in an\nimmunocompetent setting is expected to be safe and efficacious. Indeed, STING agonists have\nnot shown tumor-promoting effects across a variety of preclinical models, nor has this been\nreported in clinical trials. Pharmacological modulation using low pulsed dosing with STING\nagonists is critical for boosting T cell and antigen-presenting responses without inducing cell\ncycle-limiting and immunosuppressive feedback due to the effects of excess IFN signaling and\nhigh levels of neutrophil activation. Nonetheless, radiographic imaging of meningiomas in clinical\nstudies using 8803 should more frequently assess for the possibility of tumor promoting effects\nalthough this would be unlikely given the intermittent nature of therapeutic delivery.”\nRevision: New longitudinal scanning electron microscopy of only meningioma cells treated with\n8803 demonstrate that the mechanism of direct cytolytic death is through the generation of\nmembrane pores compromising cellular integrity (new Fig. 3J; Fig. S7). Transmission electron\nmicroscopy shows that 8803 directly induces mitochondrial cristae collapse and compromise of\nthe double membrane in meningioma cells (new Fig. 3L; Fig. S7). In the Discussion, we have\nincluded the following clarification: “8803-induced STING activation triggers Gasdermin D, which\ndamages both the membranes of the cell and mitochondria. This permeabilization leads to\nreduced mitochondrial membrane potential, increased ROS, and the release of mitochondrial\nDNA. The mitochondrial DNA leakage into the cytoplasm of the cells triggers additional STING\nactivation through cGAS, thereby amplifying the response. Mitochondrial damage and the\nproduction of ROS are essential for mediating other types of cell death, including necroptosis,\npyroptosis, and ferroptosis.”\nRevision: In the Discussion, we clarify: “The first clinical trial of a STING agonist for solid cancer\nused the ADU-S100 compound, which was injected directly into a solid tumor in patients with\nmetastatic disease. Although well tolerated, with the injected lesions showing either stability or\nregression, there were only a few patients with partial responses. Other STING agonists such as\nBI 1703880, MK-2118, E7766, and IMSA101 have also been developed and evaluated in Phase\nI clinical trials, but the signals of response have remained modest. In contrast to these previously\ninvestigated indications, meningioma is usually a local, solitary disease. Because meningiomas\ngenerally demonstrate chromosomal stability and low basal levels of STING activation, the STING\npathway may be primed to respond to an agonist, thereby yielding tumor control. Most likely these\nresponses are based on a variety of factors including epigenetic regulation of the STING\npromoter, STING isoforms and mutations, immune composition of the TME, and other regulatory\nfactors that are now being elucidated. To investigate the therapeutic potential of this pathway in\nmeningiomas, we used the agonist 8803, a 2’,3’-thiophosphate CDN analog that we previously\ndesigned, which shows superior and robust activation of the STING pathway based on\nbenchmarking to ADU-S100. While therapeutic resistance to STING agonists can be acquired\nthrough mutations and alterations of STING isoforms that render them insensitive to stimulation,\n8803 can still generate immune activation in this scenario. As such, the combination of 8803 and\nmeningiomas represents a unique therapeutic opportunity that holds promise relative to previous\nclinical indications.”\nResponse and Revision: In the Discussion: “The immunocompetent MGS1 models only grows\nin the FVB/N background. As such, implantation of MGS1 into backgrounds that have genetically\nmodified alterations in collagen or metalloproteinases will not support tumor growth. To overcome\nthis limitation, we used macrophages from TLR and STING KO backgrounds that were treated\nwith 8803 and/or meningioma lysate. The data indicates that collagen degrading MMP-3 is\ninduced with the MGS1 lysate (Fig. 5), which is abrogated in the MyD88 KO macrophages (Fig.\nS10, Fig. S11) indicating that this effect requires the TLR pathway. 8803 alone does not trigger\nMMP-3 (Fig. S11) indicating that meningioma cell death facilitates collagen degradation.\nResponse: These points are well taken, and we agree with the reviewer that they would need to\nbe addressed in the context of a clinical trial. The pre-IND meeting with the FDA has been\ncompleted and the final IND enabling studies including CNS toxicity studies will be finished by the\nend of the year. The clinical trial endpoints that are being considered include longitudinal\nvolumetric reduction or alterations of the growth curve on standard MRI imaging and neurological\nsymptom alleviation. Exploratory endpoints that are being considered include decreased signal in\n68Ga-DOTATE PET post 8803 treatment to evaluate for decreased tumor signal and/or increased\nSTING pathway activation engagement using [18F] FLT PET imaging (PMID: 34480004). We have\ntouched upon this in a general way in the Discussion since these endpoints have not yet been\nfinalized although many of the logistics have been worked out.\nRevisions and Response: To improve clarity and reproducibility, these details for each analysis\nhave been provided in the figure legends (1A, 1C, 1D, 1E, 1F, 1G, 1H, 2A, 3D, 5D, 5E, 5G, and\n5H). Furthermore, the specific R functions utilized in the creation of the data is displayed in each\npanel. This information is also cross-referenced in the corresponding methods sections\nReviewer #3:\nResponse and Revision: We believe there was lack of clarity in our figure. Fig. 1A shows the\nhighly differentially expressed genes that were used to assign cluster identity. This is also included\nin the Supplementary Materials as a full table with all DEGs. Fig. 1D shows select immune\nfunctional markers to interpret immunological functions in the context of meningioma and not used\nto assign lineage/cluster identifies. This has been clarified in the legend: “(A) Fifteen distinct\nimmune populations (CD45 positive) were identified using single-cell RNA-sequencing data from\nmeningioma (n = 13) and adjacent dura tissues (n = 9) based on the listed markers. Differentially\nexpressed genes (DEGs) were found using standard parameters in Seurat FindAllMarkers (which\nuse default non-parametric Wilcoxon rank sum test on all clusters), and examples are shown in\neach cluster box. 30,206 immune cells were included. (D) Gene expression dot plot of selected\nfunctional markers among the general immune populations shown in panel A. 30,206 total\nimmune cells were used for analysis. Bubble size corresponds to the percent of cells expressing\neach marker; colors indicate average expression. Phagocytic TAMs have high expression of M2-\nlike markers such as CD163, CD204, and CD206.”\nResponse and Revision: The markers are shown in Fig. 1A. and this has been clarified in the\ntext as such: “In contrast, terminally exhausted T cells (T.Tex; identified from markers highlighted\nin Fig. 1A) and NK cells were more frequently observed in meningioma-adjacent dura (Fig. 1B-\nC).”\nResponse and Revision: This has been clarified in the legend: (G) Volcano plot showing select\nchemokines enriched in the dura (left side) and meningioma tissues (right side) based on scRNA-\nseq of immune clusters shown in panel A. The annotated chemokines correlate with the elevated\nmyeloid (blue text) and lymphoid (red text) populations in the meningioma and dura, respectively.\nDifferentially expressed genes were calculated through pseudobulk analysis using DESeq2\nanalysis on immune cells grouped to compare between dura and meningioma.”\nResponse and Revision: The top related genes in this analysis are associated with the STING\npathway. We did not designate STINGhi and low tumors but have provided further clarification in\nthe legend.\nRevision: In the Results, we clarify: “Pseudo-bulk analysis snATAC-seq data in meningioma (n =\n4; 29,100 cells) and glioma (n = 2; 4,850 cells) cells was performed to produce lineage specific\nATAC tracks. Separate clustering of cancer-lineage specific immune populations revealed\nconsistent chromatin accessibility in both tumor types.”\nRevision: Per the scale bar shown (bottom left), red values are low and blue values are high. The\ncolor designations for low and high values were harmonized throughout the entire manuscript with\nred representing down/decreased values as shown in Fig. 1D,1G, 1H, 2D, 3F, 5G, and 5H.\nRevision: This statement has been amended as follows: “Because Nude mice do not express\nmature T cells, these data would suggest that the mechanism of action is not fully dependent on\nT cell-mediated immune responses and that 8803 may be exerting direct cytotoxicity in these in\nvivo models.”\nReviewer Response Letter\nRevision: We have clarified that the tumor-associated macrophage (TAM) subtypes listed in the\nUMAP in the original Fig. 5D (new Fig. 7D) are macrophages. 8803 increased the infiltration of all\nthree of the macrophage subtypes (ISG, Tissue Repair, and Phago) as shown in Fig. 5E and F\n(new Fig. 7E and F). The main text and figure legend have been adjusted to better reflect this\nenrichment of macrophage subtypes.\nResponse: As was noted in Supplementary Fig. S4, COL6A3 showed low expression within the\nincluded publicly available meningioma sequencing. These data were mostly confined to newly\ndiagnosed meningioma, and as such, we are unable to analyze COL6A3 expression by\nrecurrence status. We would direct the reader to PUBMED ID: 40593590\nResponse and Revision: The cell type markers directly referenced in Fig. 1A for the human\nscRNA. Table S2 has been provided with the cell type annotation for murine scRNA.\nResponse: To keep consistency and avoid confusion with the rest of the panels throughout the\nentire manuscript where blue is high/upregulated and red is low/downregulated, we respectfully\ndecline to change this specific panel."}]

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