Tran, Nhan L

Abstract Background Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. Methods Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. Results Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. Conclusions This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Abstract Background Glioblastoma (GBM) is driven, in part, by glioma stem cells (GSCs), which sustain therapeutic resistance and recurrence. OLIG2 is a key regulator of GSC programs but the signaling pathways that cooperate with Oligodendrocyte transcription factor 2 (OLIG2) to maintain GSC states remain incompletely defined. Although Signal transducer and activator of transcription (STAT) signaling has been implicated in GSC biology, work to date has focused primarily on STAT3, and the contribution of STAT5 remains poorly defined. Here, we investigated whether STAT5B functionally cooperates with OLIG2 in sustaining GSC phenotypes. Methods Transcriptomic analyses, proximity ligation assays and biochemical studies were utilized to define OLIG2-STAT5B interactions in patient-derived GSCs. Functional consequences of OLIG2, STAT5B, BIRC5, and NUSAP1 suppression were assessed using small interfering RNA/short hairpin RNA-knockdown, stemness, and migration assays in EGFRvIII-expressing GSCs. In vivo tumor growth studies were performed using orthotopic xenograft models. Results OLIG2 and STAT5B expression were positively correlated in GBM datasets and patient-derived models. OLIG2 physically associated with STAT5B in EGFRvIII-expressing GSCs, and loss of OLIG2 reduced STAT5 activation, supporting a functional link between these factors. OLIG2 or STAT5B suppression induced convergent transcriptional changes, including downregulation of genes associated with GSC maintenance and proliferation, notably stemness regulators BIRC5 and NUSAP1. Silencing BIRC5 or NUSAP1 impaired GSC self-renewal and migration. In orthotopic models, STAT5B knockdown prolonged survival and phenocopied OLIG2 suppression and stemness loss. Conclusions These findings identify STAT5B as a functional partner of OLIG2 in GBM and define anOLIG2-STAT5B transcriptional axis that sustains GSC maintenance and malignant behavior. Targeting this axis may represent a therapeutic strategy to disrupt treatment-refractory glioma cell states.

Background/Objectives: KRAS G12C mutations define a clinically actionable subset of solid tumors, particularly non–small cell lung cancer. Clinical responses to approved covalent inhibitors remain limited by intrinsic and acquired resistance, highlighting the need for structurally distinct inhibitor scaffolds to expand therapeutic options. The objective of this study was to identify novel covalent binders targeting the KRAS G12C switch-II pocket through large-scale in silico screening and experimental validation. Methods: More than 1.9 million small molecules from diverse commercial libraries were screened using covalent docking, followed by multi-stage refinement incorporating molecular dynamics simulations, MM/GBSA free-energy estimation, and cancer-focused QSAR modeling. Results: This integrated workflow yielded 50 prioritized compounds spanning several chemically distinct scaffold classes. These candidates displayed favorable predicted binding energetics, stable ligand-protein interactions over extended simulation timescales, and low structural similarity to clinically approved KRAS G12C inhibitors sotorasib and adagrasib. Benchmarking against these clinical agents, using identical computational parameters, yielded comparable predicted binding energies for several candidate molecules. In cellular NanoBRET target-engagement assays, selected scaffolds, including K788-7251 and AN-989/14669131, exhibited sub-micromolar engagement of KRAS G12C with minimal endothelial cytotoxicity. Conclusions: Collectively, these findings identify structurally distinct, KRAS G12C inhibitor chemotypes and provide tractable starting points for the development of next-generation targeted therapies.

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, and despite advances in treatment, immune suppression remains an obstacle to effective therapy. Effector CD4+ T cells (CD4+ Teff) are critical for antitumor immunity, but their function is often inhibited by regulatory T cells (Treg), which accumulate in lung tumors and mediate suppressive functions through multiple mechanisms. This suppression leads to tumor progression and poor patient outcomes. However, the mechanisms underlying Treg-mediated suppression are not fully understood. Herein, we identify the extracellular adenosine 5-triphosphate receptor P2RX7 as a key regulator of Treg function in lung tumors. In a murine lung cancer model induced by Lewis lung carcinoma cells, we found that P2RX7 enhanced the suppressive capacity of tumor-infiltrating Tregs, promoting tumor growth. In T cell–specific P2RX7-knockout (P2RX7-KO) mice, reduced Treg infiltration was accompanied by increased CD4+ Teff accumulation and improved tumor control. Treg-specific P2RX7-KO mice exhibited reduced tumor growth, confirming a Treg-intrinsic role of P2RX7. Suppression assays revealed that tumor-infiltrating wild-type Tregs had greater suppressive activity compared with P2RX7-KO Tregs, which failed to inhibit type 1 and follicular helper T–like responses. This was associated with increased tumor-specific IgG production by lung B cells in P2RX7-KO mice. We also observed that wild-type Tregs expressed higher levels of the immunosuppressive molecule CTLA-4 when compared with P2RX7-KO Tregs. Thus, we conclude that P2RX7 expression on Tregs is essential for their suppressive function in lung cancer and targeting P2RX7 may constitute a strategy to improve lung cancer treatment by alleviating Treg-mediated immune suppression.

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.