Citations

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.

Abstract Diffuse gliomas represent the most common type of primary adult malignant brain tumor historically diagnosed and graded from histologic criteria alone. Gliomas harboring isocitrate dehydrogenase (IDH) 1 or 2 mutations are associated with lower grade tumors and portend a favorable prognosis relative to IDH-wildtype glioma. However, the loss of CDKN2A/B in IDH-mutant astrocytoma confers an aggressive high grade phenotype and is the strongest implicated molecular alteration associated poor clinical survival, thus is now sufficient to define a grade 4 tumor regardless of histologic grade. However, there remains no effective therapies targeted at molecular subgroups in aggressive gliomas to date. Here, we sought to elucidate the biologic pathways and functional outcomes associated with the acquisition of high-grade behavior under loss of CDKN2A/B in IDH-mutant astrocytoma to inform future therapeutic strategies. We analyzed a cohort of patient IDH-mutant astrocytomas with RNA sequencing data and found the increased expression of regulators of embryonic nervous system development and signaling concurrent with CDKN2A/B loss. Using grade 4 IDH-mutant astrocytoma patient-derived (PDX) and cell-lines harboring CDKN2A/B homozygous deletion, we stably re-expressed p14ARF, p15INK4B, and p16INK4A using a Tet-inducible system. IDH-mutant cell lines with re-expression of p14, p15, or p16 displayed differential transcription factor activation, impaired neurosphere capability, decreased colony formation, and lower neurosphere proliferation. Intriguingly, RNA-seq data revealed CUX2, a transcription factor known to control neuronal precursor behavior, as the likely candidate regulating the differential expression profile across CDKN2A/B status. We observed increased CUX2 expression in cell lines stably re-expressing p14, p15, and p16. We validated the CUX2 signature across publicly available datasets of IDH-mutant astrocytoma where CUX2 expression negatively correlated with loss of CDKN2A/B in patient tumors. Together our work suggests CUX2 deficiency in IDH-mutant astrocytoma with CDKN2A/B deletion enables an increased neuronal stem-like phenotype in these grade 4 tumors.

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer. Standard of care surgery 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. The hypoxic tumor microenvironment is thought to drive invasion into healthy brain, and understanding cellular responses to hypoxia may provide insight into combatting recurrence. Here, we apply a graph network to integrate regionally heterogeneous data from a unique dataset of spatially-localized CE and NE biopsies from a large cohort of patients with HGG to identify gene networks regulating hypoxia response and invasion. METHODS RNA-sequencing of 1159 differentially expressed genes across 159 IDH-wt biopsies (74 patients) from NE/CE regions were represented as nodes in a Neo4j graph network. Candidate genes for network random walks occurred exclusively within NE communities detected by modularity optimization and were localized by cell-type using single-cell RNA-sequencing (scRNAseq). CSF2RA was selected as source (walk length = 5; 10,000 walks/source node). Frequently occurring genes (one-tailed test) in walks were ranked by correlation with CSF2RA expression in NE samples and analyzed by gene set enrichment analysis for biological processes (clusterProfiler). CSF2RA and hypoxia gene expression were visualized with DoHeatmap in composite NE/CE scRNAseq profiles. RESULTS CSF2RA, an immune receptor, was upregulated (logFC = 0.789) in NE samples, scRNAseq myeloid populations, and Glycolytic/Plurimetabolic glioma subtype. CSF2RA random walk genes (n=46) enriched metabolic/proliferative signatures. CSF2RA expression correlated negatively with hypoxia genes globally and regionally (NE/CE), while NE/CE scRNAseq revealed distinct cellular expression patterns of CSF2RA and hypoxia response genes. CONCLUSION CSF2RA expression may prime NE glioma cells for invasion by altering hypoxia response. The genetic network bridging CSF2RA and hypoxia response may be a targetable mechanism of tumor recurrence.

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer. Standard of care surgery 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. Recent multi-omics analyses identified predominant neuronal and immune signatures within NE tumor. Here, we apply a graph network to integrate regionally heterogeneous data from a unique dataset of spatially-localized CE and NE biopsies from a large cohort of patients with HGG. METHODS RNA-sequencing of 1159 differentially expressed genes across 159 IDH-wt biopsies (74 patients) from NE/CE regions were represented as nodes in a Neo4j network. Candidate genes for network random walks occurred exclusively within NE communities detected by modularity optimization and localized by cell-type using single-cell RNA-sequencing (scRNAseq). CSF2RA and RBFOX1 were selected as sources (walk length = 5; 10,000 walks/source node). Frequently occurring genes (one-tailed test) in walks were ranked by correlation with CSF2RA/RBFOX1 expression in NE samples and analyzed by gene set enrichment analysis for biological processes (clusterProfiler). Random walk genes were visualized (DoHeatmap) against composite NE/CE scRNAseq profiles. RESULTS CSF2RA and RBFOX1 were upregulated in NE samples (logFC = 0.789, 1.85). Upregulation of CSF2RA, an immune receptor, in the Glycolytic/Plurimetabolic and RBFOX1, a neurodevelopmental regulator, in the Neuronal glioma subtypes were observed in NE samples (ANOVA p < 0.05). CSF2RA walk genes (n=46) enriched metabolic/proliferative signatures, while RBFOX1 walk genes (n=201) showed neurodevelopmental upregulation. scRNAseq localized CSF2RA to monocytes/macrophages while RBFOX1 was expressed in distinct astrocyte/neuroepithelial populations. CONCLUSION CSF2RA and RBFOX1’s gene networks modulate immune and neurodevelopmental processes influencing glioma progression in NE tumor. Understanding the region-specific roles of these networks may offer strategies for targeting cells in the invasive glioma rim.

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer with poor prognosis. Standard of care surgery employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, typically leaving behind a non-enhancing (NE) invasive rim of tumor which invariably contributes to recurrence. Recent multi-omics analyses identified predominant neuronal and immune signatures within NE tumor, which could provide key insights for combatting recurrence. We present the novel application of graph network analyses to integrate multi-omic data from a unique dataset of multi-regional spatially-localized CE and NE biopsy samples from a large cohort of patients with HGG. MATERIAL AND METHODS 159 IDH-wildtype tumor biopsy samples from NE and CE regions of 74 glioma patients were profiled by RNA sequencing. Differentially expressed genes (DEGs) across NE and CE samples (n = 1159) were determined using the EdgeR package, and represented as nodes within a Neo4j graph network. Samples were hierarchically clustered for each gene (method = median), and edges were generated between gene nodes sharing ≥ 1 sample. Modularity optimization was used to identify communities of densely connected nodes. Genes occurring exclusively within NE communities were localized by cell type using external single-cell RNA sequencing datasets and identified as source node candidates for NE network random walks. These were performed with CSF2RA nodes as source (walk length = 5; 10,000 walks/source node). Random walks traversing samples with significantly elevated CSF2RA expression (one-tailed test) were kept for analysis. Genes occurring at high frequency (one-tailed test) within random walks were ranked by correlation with CSF2RA expression in NE samples and analyzed by gene set enrichment analysis (GSEA) for biological processes using clusterProfiler. RESULTS CSF2RA, an immune signaling receptor gene, was upregulated (logFC = 0.789) in NE samples and uniquely occurred within NE communities. We show its differential expression within the immune-enriched Glycolytic/Plurimetabolic (GPM) glioma subtype and confirm its enrichment in myeloid populations across single cell HGG datasets. NE network random walks identified 46 DEGs associated with CSF2RA. GSEA revealed upregulation of proliferative and metabolic signatures and downregulation of signaling pathways within CSF2RA’s network. CONCLUSION CSF2RA’s genetic network in NE tumor represents a potential mechanism for immunologic facilitation of glioma progression within this region. Given the expression of CSF2 ligand by glioma cells, which plays a crucial role in recruiting myeloid cells during peripheral cancer progression, modulating the expression of key genes in the CSF2RA network may be a promising approach for perturbing the crosstalk signals between immune and tumor cells in the NE region.

How the KRAS oncogene drives cancer growth remains poorly understood. Therefore, we established a systemwide portrait of KRAS- and extracellular signal–regulated kinase (ERK)–dependent gene transcription in KRAS-mutant cancer to delineate the molecular mechanisms of growth and of inhibitor resistance. Unexpectedly, our KRAS-dependent gene signature diverges substantially from the frequently cited Hallmark KRAS signaling gene signature, is driven predominantly through the ERK mitogen-activated protein kinase (MAPK) cascade, and accurately reflects KRAS- and ERK-regulated gene transcription in KRAS-mutant cancer patients. Integration with our ERK-regulated phospho- and total proteome highlights ERK deregulation of the anaphase promoting complex/cyclosome (APC/C) and other components of the cell cycle machinery as key processes that drive pancreatic ductal adenocarcinoma (PDAC) growth. Our findings elucidate mechanistically the critical role of ERK in driving KRAS-mutant tumor growth and in resistance to KRAS-ERK MAPK targeted therapies.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with few effective treatments. EGFR alterations, including expression of the truncated variant EGFRvIII, are among the most frequent genomic changes in these tumors. EGFRvIII is known to preferentially signal through STAT5 for oncogenic activation in GBM, yet targeting EGFRvIII has yielded limited clinical success to date. In this study, we employed patient-derived xenograft (PDX) models expressing EGFRvIII to determine the key points of therapeutic vulnerability within the EGFRvIII-STAT5 signaling axis in GBM. Our findings reveal that exogenous expression of paralogs STAT5A and STAT5B augments cell proliferation and that inhibition of STAT5 phosphorylation in vivo improves overall survival in combination with temozolomide (TMZ). STAT5 phosphorylation is independent of JAK1 and JAK2 signaling, instead requiring Src family kinase (SFK) activity. Saracatinib, an SFK inhibitor, attenuates phosphorylation of STAT5 and preferentially sensitizes EGFRvIII+ GBM cells to undergo apoptotic cell death relative to wild-type EGFR. Constitutively active STAT5A or STAT5B mitigates saracatinib sensitivity in EGFRvIII+ cells. In vivo, saracatinib treatment decreased survival in mice bearing EGFR WT tumors compared to the control, yet in EGFRvIII+ tumors, treatment with saracatinib in combination with TMZ preferentially improves survival.

Abstract Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.