Hu, Leland

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.

Abstract Background Disruption of the blood–brain barrier (BBB) in high-grade brain tumors is characterized by contrast accumulation on diagnostic imaging. This window of opportunity study correlates contrast imaging features with the tumor distribution of BBB-permeable (levetiracetam) and -impermeable (cefazolin) drugs. Methods Patients with a clinical diagnosis of a high-grade brain tumor underwent MRI for surgical planning. Cefazolin and levetiracetam were administered prior to skin incision, and serial plasma and image-registered tumor samples were collected during the operation. Drug levels were measured by LC-MS/MS, tissue drug levels were corrected for residual blood, and tumor-to-plasma concentration ratios were calculated. Intraoperative microdialysis was performed in a subset of patients to measure the same two drugs. Results Tumor (n = 125) and plasma (n = 261) samples were available for analysis from 42 operative cases. Across all samples, the tumor-to-plasma ratio was significantly lower for cefazolin (marginal mean [MM]: 0.15, 95% CI: 0.11–0.19) as compared to levetiracetam (MM: 0.70, 95% CI: 0.64–0.75; P < .001). When compared between contrast-enhancing and non-enhancing regions, tumor-to-plasma ratios for cefazolin varied by 4.4-fold (0.27, 95% CI: 0.20–0.35 vs. 0.06, 95% CI: 0.04–0.08, respectively; P < .001), and varied for levetiracetam by 1.4-fold (0.88, 95% CI: 0.78–0.97 vs. 0.61, 95% CI: 0.55–0.66, respectively; P < .001). These results were confirmed with the intra-operative microdialysis and a population pharmacokinetic analysis. Conclusions This study demonstrates significant inter- and intra-tumoral heterogeneity in drug delivery for both levetiracetam and cefazolin within high-grade brain tumors that is not necessarily predicted by clinical MR imaging and may reflect tumor-induced changes in both perfusion and BBB integrity.

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.

The Quantitative Imaging Biomarkers Alliance Profile for dynamic susceptibility contrast (DSC) MRI and its longitudinal claims for the precision of a DSC MRI–derived quantitative imaging biomarker to assess the relative cerebral blood volume changes in gliomas are summarized.

Abstract BACKGROUND Standard of care surgery for high grade glioma (HGG) employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. Multi-parametric MRI (mpMRI) has recently been used to map regional biological heterogeneity of neuronal and immune signatures in HGG, and continues to offer a promising strategy for non-invasive surveillance of tumor progression. Here, we present a graph network analysis relating mpMRI to biological signatures in the NE tumor rim and show the utility of communities of imaging features for discriminating tumor phenotypes. METHODS 53 mpMRI features across 101 spatially-localized RNA-sequenced biopsy samples from 64 patients with IDH-wt HGG were represented as nodes within a Neo4j graph network. Modularity optimization was used to identify communities of densely connected nodes. Communities of nodes representing high proportions of NE samples (one-tailed test) were kept for further analysis. Gene set enrichment analysis (GSEA) on samples within selected imaging communities was analyzed for biological process enrichment using clusterProfiler. Single sample GSEA (ssGSEA) using ssGSEA2 was used to annotate neuronal and immune cell states across samples within selected communities. mpMRI feature selection within each community identified the most relevant imaging features for isolating samples classified into neuronal and immune cell states. RESULTS 11 imaging communities gathered a high proportion of NE samples. Three NE communities showed significant enrichment of neuronal signatures, while one showed significant enrichment of immune pathways. One neuronal-predominant community showed unique upregulation of 14 pathways related to neuronal migration/astrocyte projection. CONCLUSION Network imaging communities can identify clusters of NE glioma samples showing contrasting neuronal and immune signatures, including distinct upregulation of migration and projection pathways. Mapping these imaging feature communities to specific biological processes implicated in glioma progression may aid treatment planning and prognostication for glioma patients.

Abstract The intra- and inter-patient heterogeneity in high-grade glioma (HGG) continues to contribute to its poor prognosis. Clinical biopsies are often harvested from limited regions and typically are not image localized. Thus, they fail to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in tumor development. It is important to gain an understanding of these subpopulation ecologies, their spatial resolution, and interactions between them that may then be exploited for future therapeutic benefit. Further, these may differ by patient characteristics such as sex, age at diagnosis and treatment status. Using an ongoing image-localized biopsy collection protocol, we have so far evaluated the bulk transcriptomics of 202 multi-regional biopsies from 58 patients to characterize HGG heterogeneity. These samples were processed through Monocle, a reverse graph embedding algorithm that groups samples into states and orders them along developmental trajectories. Deconvolution methods were previously used to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample, which we have now overlaid on the Monocle graph. Monocle classified HGG into 4 main states along a three-pronged trajectory. These states reveal distinct population ecologies with associated enriched gene pathways. We also note significant immune pathway enrichments that differ between state and patient-reported sex. Together, these algorithms reveal a simple transcriptomic trajectory that helps us understand the development and evolution of HGG. Characterizing the in vivo diversity within and between high grade gliomas is important for understanding prognosis, stratifying future treatments and ultimately improving patient outcome.