Mulé, James J.

Glioblastoma (GBM) remains one of the most lethal brain tumors, characterized by extensive immune evasion and a macrophage-dominated tumor microenvironment (TME). However, the molecular determinants governing tumor-associated macrophage (TAM) states and their immunoregulatory functions remain poorly understood. We integrated bulk- and single-cell transcriptomic datasets (TCGA, CGGA, Ivy GAP, and Brain Immune Atlas) to systematically characterize the expression, prognostic relevance, and immune contexture of the myeloid biomarker membrane-spanning 4-domain A6A, MS4A6A, in GBM. Differential expression, survival, and pathway enrichment analyses were performed. Single-cell mapping and CellChat modeling delineated MS4A6A-associated TAM subpopulations, intercellular communication networks, and ligand–receptor signaling dynamics. Spatial transcriptomic validation and pharmacogenomic modeling were conducted to assess anatomic enrichment and therapeutic vulnerabilities. High MS4A6A expression predicted unfavorable survival and correlated with increased stromal and immune infiltration. Single-cell analyses localized MS4A6A predominantly to TAMs, especially Regulatory- and Ribo-TAM states enriched for antigen presentation, T-cell regulation, and ribosomal biogenesis pathways. CellChat analysis revealed that MS4A6A-high TAMs exhibited markedly enhanced communication with CD4+ T cells and Tregs through upregulated PGE2–PTGER2/PTGER4, PECAM1–CD38, and THBS1–CD36 signaling axes, implicating MS4A6A in prostaglandin-driven immune suppression. Spatial profiling confirmed preferential localization of MS4A6A within perivascular and angiogenic niches. Pharmacogenomic prediction indicated that MS4A6A-high tumors were more sensitive to ERK, mTOR, and CDK4/6 inhibition. MS4A6A defines a macrophage-centered, immunosuppressive ecosystem in GBM, mediated by the activation of the PGE2 signaling axis. These findings position MS4A6A both as a prognostic biomarker and as a potential therapeutic node linking myeloid reprogramming to actionable pathway vulnerabilities in glioblastoma.

Background: Tumor-associated macrophages (TAMs) are essential regulators of the glioblastoma (GBM) microenvironment; their functional heterogeneity and interaction networks are not fully elucidated. We identify RNF135 as a novel TAM-enriched gene associated with immune activation and adverse prognosis in GBM. Methods: To evaluate RNF135’s expression profile, prognostic significance, and functional pathways, extensive transcriptome analyses from TCGA and CGGA cohorts were conducted. The immunological landscape and cellular origin of RNF135 were outlined using single-cell RNA-seq analyses and bulk RNA-seq immune deconvolution (MCP-counter, xCell and ssGSEA). Cell–cell communication networks between tumor cells and RNF135-positive and -negative tumor-associated macrophage subsets were mapped using CellChat. Results: RNF135 predicted a poor overall survival and was markedly upregulated in GBM tissues. Functional enrichment analyses showed that increased cytokine signaling, interferon response, and innate immune activation were characteristics of RNF135-high samples. Immune infiltration profiling showed a strong correlation between the abundance of T cells and macrophages and RNF135 expression. According to the single-cell analyses, RNF135 was primarily expressed in TAMs, specifically in proliferation, phagocytic, and transitional subtypes. RNF135-positive TAMs demonstrated significantly improved intercellular communication with aggressive tumor subtypes in comparison to RNF135-negative TAMs. This was facilitated by upregulated signaling pathways such as MHC-II, CD39, ApoE, and most notably, the CCL signaling axis. The CCL3/CCL3L3–CCR1 ligand–receptor pair was identified as a major mechanistic driver of TAM–TAM crosstalk. High RNF135 expression was also linked to greater sensitivity to Selumetinib, a selective MEK1/2 inhibitor that targets the MAPK/ERK pathway, according to drug sensitivity analysis. Conclusions: RNF135 defines a TAM phenotype in GBM that is both immunologically active and immunosuppressive. This phenotype promotes inflammatory signaling and communication between cells in the tumor microenvironment. Targeting the CCL–CCR1 axis or combining RNF135-guided immunomodulation with certain inhibitors could be a promising therapeutic strategies for GBM.

Glioblastoma (GBM) is an aggressive brain tumor with a highly immunosuppressive microenvironment that promotes tumor progression and therapy resistance. Tumor-associated macrophages (TAMs), comprising up to 50% of the tumor mass, are recruited via chemokine axes such as CCL2/CCR2, CX3CL1/CX3CR1, and CXCL12/CXCR4 and adopt an M2-like immunosuppressive phenotype, facilitating immune escape and angiogenesis. Key signaling pathways, including CSF1R, STAT3, NF-κB, PI3K/Akt, and HIF-1α, regulate TAM function, making them promising therapeutic targets. Strategies such as TAM depletion, reprogramming, and immune checkpoint blockade (PD-1/PD-L1, and CD47-SIRPα) have shown potential in preclinical models. Emerging approaches, including CAR-macrophage (CAR-M) therapy, nanotechnology-based drug delivery, and exosome-mediated modulation, offer new avenues for intervention. However, clinical translation remains challenging due to GBM’s heterogeneity and adaptive resistance mechanisms. Future research should integrate multi-omics profiling and AI-driven drug discovery to refine TAM-targeted therapies and improve patient outcomes. This review provides a comprehensive analysis of TAM-mediated immune regulation in GBM and explores evolving therapeutic strategies aimed at overcoming its treatment barriers.

Background: Glioblastoma is a highly malignant brain tumor with limited treatment options and a poor prognosis, largely driven by its complex immune microenvironment. This study aimed to identify and characterize an immune-related gene signature associated with prognosis and immune regulation in glioblastoma. Methods: We performed integrative analyses using bulk and single-cell RNA sequencing data to identify prognostically significant immune-related genes. A five-gene signature (THEMIS2, SIGLEC9, CSTA, LILRB3, and MS4A6A) was derived and its expression patterns were analyzed in association with immune cell infiltration and macrophage subtypes. Functional enrichment and pathway analyses were conducted, followed by drug sensitivity profiling to explore potential therapeutic implications. Results: The five-gene signature was significantly associated with worse survival outcomes and increased immune cell infiltration. Functional analyses revealed involvement in key immune pathways, including antigen presentation, cytokine signaling, and immune cell activation. Single-cell RNA sequencing demonstrated high expression of the signature in tumor-associated macrophages, particularly immune-suppressive and proliferation-associated subtypes. The high expression in proliferation TAMs suggests a role in promoting tumor angiogenesis and growth. Drug sensitivity analysis revealed distinct vulnerabilities between high- and low-risk groups based on signature expression. Conclusions: This Macrophage-Associated Prognostic Signature (MAPS) provides new insights into glioblastoma immunobiology and identifies potential biomarkers and therapeutic targets. It may serve as a valuable tool to guide personalized immunotherapy-based strategies for glioblastoma patients.

Abstract Background: While cancer immunotherapies have revolutionized treatment, their efficacy remains limited for most patients, highlighting tumors' capacity to evade immune detection. Epigenetic modifications, particularly DNA methylation, play a pivotal role in silencing immune-related pathways, thereby facilitating immune escape. By modulating gene expression, methylation disrupts immune activation, contributing to reduced tumor immunogenicity. This study examines the methylation landscape of co-stimulatory and immune checkpoint genes across various cancers to elucidate how these changes influence immune responses. Methods: Using The Cancer Genome Atlas (TCGA), we analyzed methylation and gene expression profiles across diverse cancer types. Data from 8,186 solid tumors and 745 adjacent normal tissues were processed via t-distributed stochastic neighbor embedding (t-SNE) with 247 probes targeting 20 immune-related genes. Principal component analysis (PCA) was applied to capture methylation variability across 8,931 tumor and normal samples. To explore the effects of methylation on immune gene expression, we analyzed data from 26 epithelial cancer cell lines treated with 5-azacitidine, a demethylating agent, using the GSE57342 dataset. Post-treatment changes in gene expression were assessed, and survival analyses from prior studies were used to explore clinical correlations. Results: We identified distinct methylation patterns in immune synapse genes, differentiating tumors from adjacent normal tissues. Co-stimulatory genes such as CD40 were hypermethylated in tumors, correlating with decreased expression, whereas immune checkpoint genes like PDL1 and HHLA2 were hypomethylated, correlating with increased expression. This inverse relationship between methylation and gene expression underscores the transcriptional regulatory role of DNA methylation. Treatment with 5-azacitidine reversed hypermethylation in co-stimulatory genes, including CD40, potentially enhancing immune recognition. Methylation levels of these genes were also associated with T cell infiltration into the tumor microenvironment and predicted better outcomes in melanoma patients, underscoring their clinical relevance. Conclusion: This study demonstrates that DNA methylation of immune synapse genes contributes to tumor immune evasion by modulating immune gene expression. Targeting these epigenetic alterations represents a promising strategy to restore tumor immunogenicity and improve immunotherapy outcomes, particularly when combined with immune checkpoint inhibitors. Reversing methylation-driven immune suppression could strengthen the immune response and enhance clinical outcomes for cancer patients. Citation Format: Imene Hamaidi, Anders Berglund, Matthew Mills, Ryan M. Putney, James J. Mulé, Sungjune Kim. DNA methylation of immune synapse genes drives tumor immune evasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4831.

Abstract The radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GESs into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process. Using HG-U133Plus2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with a reference laboratory were evaluated. RSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES: 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12% of observed range). Overall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GESs for clinical usage. Significance: The RSI and 12CK GES are two GESs that predict tumor radiation sensitivity or the presence of tertiary lymphoid structures in tumors, respectively. These GESs were assessed within the CLIA process for future clinical use. We established proficiency, reproducibility, and reliability characteristics for both signatures in a controlled setting, indicating these GESs are suitable for validation within future clinical trials.

Abstract Introduction Men with African ancestry have the highest incidence and mortality rates of prostate cancer (PCa) worldwide. Methods This study aimed to identify differentially methylated genes between tumor vs. adjacent normal and aggressive vs. indolent PCa in 121 African American patients. Epigenome‐wide DNA methylation patterns in tumor DNA were assessed using the human Illumina Methylation EPIC V1 array. Results Around 5,139 differentially methylated CpG‐sites (q < 0.01, lΔβl > 0.2) were identified when comparing normal vs. tumor, with an overall trend of hypermethylation in prostate tumors. Multiple representative differentially methylated regions (DMRs), including immune‐related genes, such as CD40 , Galectin3 , OX40L , and STING , were detected in prostate tumors when compared to adjacent normal tissues. Based on an epigenetic clock model, we observed that tumors’ total number of stem cell divisions and the stem cell division rate were significantly higher than adjacent normal tissues. Regarding PCa aggressiveness, 2,061 differentially methylated CpG‐sites (q < 0.05, lΔβl > .05) were identified when the grade group (GG)1 was compared with GG4/5. Among these 2,061 CpG sites, 155 probes were consistently significant in more than one comparison. Among these genes, several immune system genes, such as COL18A1 , S100A2 , ITGA4 , HLA‐C , and ADCYAP1 , have previously been linked to tumor progression in PCa. Conclusion Several differentially methylated genes involved in immune‐oncologic pathways associated with disease risk or aggressiveness were identified. In addition, 261 African American‐specific differentially methylated genes related to the risk of PCa were identified. These results can shedlight on potential mechanisms contributing to PCa disparities in the African American Population.