Tran, Nhan L.

Abstract Glioblastoma (GBM) and Alzheimer’s disease (AD) are devastating, age associated disorders with no cure. Epidemiologic data has established an inverse comorbidity between AD and cancer, including GBM, with up to a 50% reduction of cancer incidence in AD patients. While the underlying biology remains unclear, myeloid cells, which are central players in both neurodegeneration and tumor immunity, may serve as a mechanistic link. We hypothesize that myeloid cells activated by pre-existing AD pathology suppress tumor growth more effectively than homeostatic microglia. In this study, age-matched APPNL-G-F/MAPT double knock-in (henceforth “AD”) and wildtype (WT) mice received orthotopic xenografts of syngeneic GBM cell lines (GL261, SB28) (n=15/sex/genotype). We observed a significant survival difference, with AD mice living an average of 20% longer than WT mice (p<0.0001). In vivo luciferase imaging revealed impaired engraftment of tumor cells in AD mice compared to WT a week post-engraftment (p<0.05). At the microscopic level, tumors in AD mice were found to have nearly 2-fold the amount of tumor-infiltrating myeloid cells (p<0.01), with the proportion of anti-tumoral “activated” myeloid cells also being greater (p<0.05). Tumors in AD mice were found to have markedly higher immune infiltrate at the tumor rim and margin (p<0.001). We also observed striking astrogliosis (GFAP+) and microgliosis (P2RY12+) in both the tumor rim (p<0.05) and margin (p<0.01) in AD animals. This robust glial response was significantly higher in AD mice bearing tumor compared to both WT mice bearing tumor and sham AD mice. Lastly, we utilized a luciferase stain to visualize tumor cells that invaded beyond the bulk tumor. We found that both the quantity (p<0.01) and invasion distance of tumor cells (p<0.05) was lower in AD mice. We demonstrate that an AD-induced inflammatory microenvironment antagonizes glioma growth, providing a foundation for novel GBM treatment strategies that potentially exploit these immune signals.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a dismal 5-year survival rate of ~6.5%. Vasogenic cerebral edema is a severe complication that occurs in the majority of GBM patients, leading to a significant increase in intracranial pressure, neurological deficits, and increased mortality rates. GBM-associated edema is almost exclusively managed by the corticosteroid dexamethasone, the use of which is associated with immunosuppression and interference with radiation therapy. While there is considerable research on edema formation, little is known about how edema resolution occurs in GBM, particularly in response to dexamethasone or other anti-edema therapies. This gap in understanding highlights the need for new approaches to treat edema effectively. The water channel aquaporin-4 (AQP4), a key player in the brain’s water homeostasis, is upregulated in GBM compared to the normal brain. To investigate the role of AQP4 in GBM-associated edema, we used the RCAS/tv-a system to generate de novo GBM with different driver mutations in Aqp4 knockout (KO) and wild-type (WT) mice. Our results indicated that Aqp4 KO tumor-bearing mice had shortened survival and increased edema content. Additionally, Aqp4 loss decreased astrocyte coverage specifically in the tumor core but not the tumor periphery. Our single cell RNA-seq data suggested that Aqp4 loss led to an astrocytic shift towards a less reactive, progenitor-like state, while also decreasing the myeloid and CD8+ T cell population in the tumor microenvironment. Interestingly, tumor-bearing mice treated with anti-VEGFA antibody, which has anti-edema properties, showed increased expression of Aqp4 and reduced vessel leakage. This suggests that AQP4 plays a critical role in mediating the effects of anti-VEGFA therapy. Overall, this study highlights the important role of AQP4 in the resolution of edema, while also revealing its additional effects on astrocyte activation and the immune microenvironment in GBM.

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC > 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

Abstract Glioma stem cells (GSCs) are key drivers of glioblastoma (GBM) progression, therapy resistance, and recurrence, making them critical therapeutic targets. While the lineage-defining transcription factor OLIG2 is critical for GSC maintenance, the molecular co-regulators and downstream pathways it engages remain poorly defined. Here, we identify a previously unrecognized, functionally significant interaction between OLIG2 and STAT5B, uncovering a novel regulatory axis in GSCs. Using proximity ligation assay (PLA), we demonstrate that OLIG2 interacts with STAT5 in human and mouse GSC models, particularly in EGFRvIII-mutant cells. We find that OLIG2 preferentially partners with STAT5B and not STAT5A. Knockdown studies in GSCs reveal that OLIG2 and STAT5B co-regulate critical oncogenes BIRC5 and NUSAP1, both of which are required for sphere formation and migration capacity of EGFRvIII positive GSCs. In orthotopic xenograft models, knockdown of either STAT5B or OLIG2 significantly prolongs survival reduces expression of stemness and invasion markers, SOX2, VIMENTIN, and NUSAP1. In contrast, STAT5A knockdown fails to recapitulate these phenotypes, underscoring the role of OLIG2-STAT5B signaling in GSCs. These findings reveal a novel OLIG2-STAT5B transcriptional circuit that plays a role in GSC stemness maintenance, invasion, and tumor growth, particularly in EGFRvIII-driven gliomas. Targeting this axis may offer a new avenue for GBM treatment.

Abstract Glioblastoma (GBM) invasion is shaped by dynamic clonal evolution within the spatial and cellular architecture of the brain. To investigate this process, we integrated multiparametric MRI, whole-exome sequencing (WES), bulk RNA-seq and single-nucleus RNA-seq (snRNA-seq) from 69 IDH-wildtype GBM patients, using spatially resolved biopsies from contrast-enhancing (CE) core tumor and non-enhancing (NE) invasive regions. Phylogenetic analysis revealed distinct clonal architectures. CE-enriched clones carried truncal mutations in conanical drivers of cell cycle and RTK signaling (TP53, PTEN, EGFR, PDGFRA/B), and private alterations in DNA repair genes (CDH11, PAX7, RAD51B). In contrast, NE-enriched clones exhibited private mutations in MAPK and Hippo pathways components (STAG2, BRAF, MAP2K3, CTNNA1), suggesting region-specific evolutionary trajectories. Furthermore, snRNAseq cell-cell communication analysis uncovered enriched ligand-receptor signaling in NE regions between malignant cells and brain-resident populations, including PTN—PTPRZ1 (myeloid), NRG-ERBB4 and SLIT-ROBO (neuronal, astrocytic, and oligodendrocytic). Bulk RNAseq deconvolution confirmed increased representation of neurons and glial cells in biopsies harboring NE-specific clones. These findings define a unique population of GBM clones localized at the infiltrative margin, marked by private MAPK/Hippo mutations and enhanced intracellular communication with the surrounding brain microenvironment. The recurrent engagement of neural-glial signaling pathways suggests that these NE-associated clones co-opt normal cellular networks to support invasion, immune evasion, and persistence following surgical resection. This study establishes a mechanistic link between spatial clonal evolution and tumor-host communication at the GBM infiltrative region and provide a framework for targeting cell-cell signaling circuits that sustain invasive tumor ecosystems and escape therapeutic treatment.

Abstract IDH-wildtype glioblastoma (GBM) is the most malignant primary adult brain tumor, with a five-year survival rate below 5%. Despite maximal treatment, including surgical resection, radiotherapy, and temozolomide (TMZ), recurrence is universal, driven by profound intratumoral heterogeneity, diffuse invasion, and therapy resistance. A hallmark of IDH-wildtype GBM is frequent alteration of epidermal growth factor receptor (EGFR), including amplification (50%), the EGFRvIII deletion variant (25%), and extracellular missense mutations such as G598V and A289V (10–15%). These missense mutations increase ligand affinity and receptor activity, yet their downstream signaling consequences and role in shaping the tumor phenotypes remain poorly defined. Recent pathway-based subclassifications identified four subtypes in IDH-wildtype GBM. Notably, neuronal (NEU) and glycolytic/plurimetabolic (GPM) subtypes predominate the invasive tumor margins. NEU programs are enriched in synaptic and neurodifferentiation signals, while GPM programs are associated with metabolic adaptation, angiogenesis, and immune evasion. Here, we show EGFR-G598V and EGFR-A289V mutations differentially regulate these invasive subtypes identities. Transcriptional analysis reveals EGFR G598V upregulates metabolic and inflammatory pathways consistent with the GPM phenotype, while EGFR A289V activates genes involved in neuronal differentiation and synaptic function, aligning with the NEU subtype. Phosphoproteomics and kinase-substrate analysis further uncovered distinct kinases to be functionally activated/inhibited in EGFR A289V-expressing cells and G598V cells. Mechanistic studies revealed EGFR A289V induces sustained EGFR phosphorylation at Y992, activating PLCγ2 via the SRC-family kinase LCK. This cascade drives calcium release, CAMK1 activation, and STAT5 phosphorylation, defining a novel EGFR A289V–LCK/PLCγ2/CAMK1/STAT5 axis. In addition, Y1068 phosphorylation is also constitutively active in EGFR A289V, resulting in increased BRAF activation. In contrast, EGFR G598V activates MAPK6 and CAMK2 inducing AKT2 activation, reinforcing GPM-associated signaling. These findings uncover mutation-specific signaling networks that drive subtype fate in invasive GBM and highlight unique pathway(s) as tractable targets for subtype-directed therapy in EGFR-mutant GBM.

Abstract High-grade gliomas represent the most common type of primary adult malignant brain tumor. IDH1/2 mutations are linked to lower-grade gliomas and better patient outcomes; however, molecular profiling has revealed additional alterations that drive aggression. CDKN2A/B loss in IDH-mutant astrocytoma is recognized as a key marker of grade 4 tumor biology, yet there remain no effective targeted therapies in aggressive gliomas. Here, we analyzed a cohort of patient-derived IDH-mutant astrocytoma with whole-exome and RNA sequencing data and identified several candidate genes and pathways concurrently regulated with CDKN2A/B loss, revealing an enhanced signature related to vascular processes. We sought to elucidate the angiogenic mechanisms and functional outcomes associated with high-grade behavior following CDKN2A/B loss in IDH-mutant astrocytoma to inform future therapeutic strategies. We used grade 4 IDH-mutant astrocytoma patient-derived xenograft (PDX) and established cell lines harboring homozygous CDKN2A/B deletion. We re-expressed the cell cycle regulators p14, p15, and p16 using a Tet-inducible lentiviral system and confirmed reduced proliferation compared to parental lines. Re-expression of p14, p15, or p16 also resulted in decreased glioma cell migration and invasion. Intriguingly, re-expressing these regulators reduced VEGF secretion. HUVEC cells cultured with conditioned media (CM) from the re-expression glioma lines decreased canonical activation of PI3K/Akt and Stat3 signaling and a diminished capacity to promote endothelial migration, invasion, and tube formation. Furthermore, in vivo Matrigel plug assays using CM from the re-expression lines showed a decreased ability to recruit endothelial cells, pericytes, and form vascular organized structures. We identified the WT1 transcription factor as an upstream regulator of angiogenesis. WT1 expression was inversely correlated with CDKN2A/B expression in patient data, and knockdown of WT1 in astrocytoma cells reduced VEGF secretion and impaired ability to promote endothelial migration. Taken together, our findings reveal a novel role for WT1 in VEGF-driven angiogenesis in IDH-mutant astrocytoma with CDKN2A/B homozygous deletion.

Of the thousands of genes and substrates identified in KRAS-mutant signaling networks in pancreatic ductal adenocarcinoma (PDAC), more than 200 are transcription factors, implying extensive and complex transcriptional regulation. However, we observed that genetic suppression of the transcription factor MYC alone was sufficient to phenocopy the effect of KRAS suppression in signaling, growth, and metabolic processes in PDAC cells. We determined the gene transcription changes caused by acute suppression of MYC function in KRAS -mutant PDAC cell lines and performed dependency map and pathway analyses on the affected gene sets. The expression of 1685 genes was increased upon suppression of MYC, and this gene set may comprise the bulk of the MYC-regulated genes essential for PDAC growth. In contrast, the 1325 genes whose expression was inhibited may comprise a compensatory response to oncogenic stress, mediated in part by the GTPase RHO. MYC-dependent transcriptional activity was largely ERK dependent, and almost one-third of ERK-regulated genes were also regulated by MYC in PDAC cells. Furthermore, chemical proteomic profiling revealed MYC-regulated protein kinases that can be targeted therapeutically. Together, these data provide a molecular portrait of MYC-dependent signaling that encompasses potentially exploitable mechanisms for treating PDAC.

Abstract Introduction: High-grade gliomas (HGG) are aggressive brain tumors with heterogeneous cell populations, including invasive regions driving disease progression and recurrence. Our previous work integrated molecular profiling with spatially matched advanced multiparametric MRI in a cohort of HGG patients with multi-regional sampling, uncovering distinct biological signatures linked to individual advanced MRI phenotypes. Yet, the relationships between MRI phenotypes—and how this potentially informs underlying biology—remains unexplored. Here, we leverage an unsupervised approach to dimensional reduction for a broad array of MRI features and compare regional transcriptomic signatures in a cohort of image-localized HGG samples. Methods: From our existing data, we selected 20 HGG patients with 73 multi-regional tumor biopsies, each including 18 advanced MRI parameter calculations. With an iterative approach, data was projected using uniform manifold approximation (UMAP) at multiple resolutions and clustered, producing high-confidence stable clusters. We used random forest modeling to assess cluster associations with previously identified signatures. Cluster-specific transcriptomic profiles were detected by DESeq2 and subsequent gene-set enrichment analysis. We further evaluated Pearson correlations and statistical fit with MRI image parameters by performing pathway analysis on genes with significant correlations by image feature, across clusters. Results: 8 clusters emerged from our analyses. Most advanced MRI features exhibited statistically significant differences across clusters, forming unique patterns of feature behavior. Notably, while these image-based clusters did not correlate well with metabolic profiles, contrast enhancement, or genomics, they did demonstrate distinct and HGG-relevant transcriptomic profiles, with shared and divergent signatures. Across clusters, we also identified several correlations between specific MRI parameters and gene pathway trends. These highlighted combined patterns of multiple MRI features, offering a spectrum of regional phenotypes imperceptible with single feature analysis. We generalize these results to an image-based decision matrix to detect biologic signatures for validation. Conclusion: Our integrative analysis of MRI mapping stratifies imaged-based patterns to non-invasively assess molecular signatures within HGG. This approach will help elucidate MRI biomarkers that may predict glioma behavior and support future individualized treatment approaches. Citation Format: Matthew W. Kenaston, Matthew Flick, Kasthuri Kannan, Michele Ceccarelli, Nhan Tran, Leland S. Hu. Integrative analysis of regional MRI phenotypes in high-grade glioma: Dimensional reduction of multi-omics imaging and molecular profiles [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 5776.