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BACKGROUND: The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long-term survival of more than three years after diagnosis. We aimed to elucidate the poorly characterized molecular aberrations in glioblastomas of longterm survivors. METHODS: We performed genome- and/or transcriptome-wide molecular profiling of primary tumor samples from 94 glioblastoma patients of the German Glioma Network, including 28 longterm survivors with >36 months overall survival (OS), 20 short-term survivors with

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy., National Institutes of Health (U.S.) (U24 CA180922)

Significance Despite measurable advances in cancer treatment, patients with glioblastoma multiforme (GBM) typically survive only 12–14 mo because of tumor recurrence. Tumor hypoxia has been associated with chemoresistance. We hypothesized that the pronounced repression of microRNA-218 ( miR-218 ) observed in samples from patients with highly hypoxic and necrotic GBM contributes to this recurrent phenotype. We demonstrate here that mice harboring intracranial tumors with increased miR-218 expression exhibit significantly reduced tumor burden and increased survival when challenged with the chemotherapeutic agent temozolomide. Moreover, low miR-218 levels increase the expression of multiple components of the receptor tyrosine kinase signaling pathway, which promote the activation of hypoxia-inducible factor. The identification of the molecular bases for miR-218 -mediated chemoresistance should promote the development of targeted therapies.

The prevalence of brain tumors in males is common but unexplained. While sex differences in disease are typically mediated through acute sex hormone actions, sex-specific differences in brain tumor rates are comparable at all ages, suggesting that factors other than sex hormones underlie this discrepancy. We found that mesenchymal glioblastoma (Mes-GBM) affects more males as the result of cell-intrinsic sexual dimorphism in astrocyte transformation. We used astrocytes from neurofibromin-deficient (Nf1(-/-)) mice expressing a dominant-negative form of the tumor suppressor p53 (DNp53) and treated them with EGF as a Mes-GBM model. Male Mes-GBM astrocytes exhibited greater growth and colony formation compared with female Mes-GBM astrocytes. Moreover, male Mes-GBM astrocytes underwent greater tumorigenesis in vivo, regardless of recipient mouse sex. Male Mes-GBM astrocytes exhibited greater inactivation of the tumor suppressor RB, higher proliferation rates, and greater induction of a clonogenic, stem-like cell population compared with female Mes-GBM astrocytes. Furthermore, complete inactivation of RB and p53 in Mes-GBM astrocytes resulted in equivalent male and female tumorigenic transformation, indicating that intrinsic differences in RB activation are responsible for the predominance of tumorigenic transformation in male astrocytes. Together, these results indicate that cell-intrinsic sex differences in RB regulation and stem-like cell function may underlie the predominance of GBM in males.

Summary Background The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients. Aims The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM. Results For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores. Conclusions The signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.

Purpose: Positive patient outcomes with the use of aggressive concurrent chemoradiotherapy have been shown for classical and mesenchymal glioblastoma multiformae (GBM) subtypes, while not having the same positive effect for patients with neural and proneural subtypes. Quantitative MR Imaging features from multimodal MRI have been recently explored for GBM subtype classification with some promising results. We have integrated 3D textural and moment invariant features to investigate if these will strengthen the association between glioblastoma MR image features and genomic markers. Methods: 147 subjects with a complete set of T1, T1C (post-contrast T1), T2, and FLAIR imaging modalities without significant imaging artifacts acquired prior to surgery and with a known genomic subtype (Classical: 30, Mesenchymal:47, Neural:25, and Proneural:45) were chosen from TCIA database for GBM. The subject data were preprocessed and tumor and edema were contoured for the entire volume. Features were extracted from the segmented multi-modal data. These 3D features include volume, surface area, surface roughness, moment invariants, intensity statistics, and texture features. Feature selection and classification was performed using random forest. Features were extracted from the four modalities and difference fields of T1C-T1 and FLAIR-T2. Results: Random forest out-of-bag classification error of 23.91% was achieved in the classification of mesenchymal group from the proneural group using the top twenty-eight selected features. These imaging features were primarily texture features on T1C but also include a moment invariant of T1, and ROI roughness statistics of the tumor and of the edema ROIs. These features show that tumor heterogeneity, gross shape, as well the tumor and edema border smoothness distinguish the molecular subtypes. Conclusion: Using heterogeneous MRI data from the TCIA GBM database we have shown a 76% correct classification rate for GBM genomic subtypes with the same treatment protocol. Imaging features may lead to preoperative classification for improved treatment planning.

SummaryThe Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.

Glioblastoma is a lethal brain tumor that exhibits heterogeneity and resistance to therapy. Our understanding of tumor homeostasis is limited by a lack of genetic tools to selectively identify tumor states and fate transitions. Here, we use glioblastoma subtype signatures to construct synthetic genetic tracing cassettes and investigate tumor heterogeneity at cellular and molecular levels, in vitro and in vivo. Through synthetic locus control regions, we demonstrate that proneural glioblastoma is a hardwired identity, whereas mesenchymal glioblastoma is an adaptive and metastable cell state driven by proinflammatory and differentiation cues and DNA damage, but not hypoxia. Importantly, we discovered that innate immune cells divert glioblastoma cells to a proneural-to-mesenchymal transition that confers therapeutic resistance. Our synthetic genetic tracing methodology is simple, scalable, and widely applicable to study homeostasis in development and diseases. In glioblastoma, the method causally links distinct (micro)environmental, genetic, and pharmacologic perturbations and mesenchymal commitment. Significance: Glioblastoma is heterogeneous and incurable. Here, we designed synthetic reporters to reflect the transcriptional output of tumor cell states and signaling pathways' activity. This method is generally applicable to study homeostasis in normal tissues and diseases. In glioblastoma, synthetic genetic tracing causally connects cellular and molecular heterogeneity to therapeutic responses. This article is highlighted in the In This Issue feature, p. 521

Glioblastomas are characterized by transcriptionally distinct subtypes, but despite possible clinical relevance, their regulation remains poorly understood. The commonly used molecular classification systems for GBM all identify a subtype with high expression of mesenchymal marker transcripts, strongly associated with invasive growth. We used a comprehensive data-driven network modeling technique (augmented sparse inverse covariance selection, aSICS) to define separate genomic, epigenetic, and transcriptional regulators of glioblastoma subtypes. Our model identified Annexin A2 (ANXA2) as a novel methylation-controlled positive regulator of the mesenchymal subtype. Subsequent evaluation in two independent cohorts established ANXA2 expression as a prognostic factor that is dependent on ANXA2 promoter methylation. ANXA2 knockdown in primary glioblastoma stem cell-like cultures suppressed known mesenchymal master regulators, and abrogated cell proliferation and invasion. Our results place ANXA2 at the apex of a regulatory cascade that determines glioblastoma mesenchymal transformation and validate aSICS as a general methodology to uncover regulators of cancer subtypes.

Intratumoral heterogeneity is a characteristic of glioblastomas that contain an intermixture of cell populations displaying different glioblastoma subtype gene expression signatures. Proportions of these populations change during tumor evolution, but the occurrence and regulation of glioblastoma subtype transition is not well described. To identify regulators of glioblastoma subtypes we utilized a combination of in vitro experiments and in silico analyses, using experimentally generated as well as publicly available data. Through this combined approach SOX2 was identified to confer a proneural glioblastoma subtype gene expression signature. SFRP2 was subsequently identified as aSOX2-antagonist, able to induce a mesenchymal glioblastoma subtype signature. A subset of patient glioblastoma samples with highSFRP2and lowSOX2expression was particularly enriched with mesenchymal subtype samples. Phenotypically, SFRP2 decreased tumor sphere formation, stemness as assessed by limiting dilution assay, and overall cell proliferation but increased cell motility, whereas SOX2 induced the opposite effects. Furthermore, an SFRP2/non-canonical-WNT/KLF4/PDGFR/phospho-AKT/SOX2 signaling axis was found to be involved in the mesenchymal transition. Analysis of human tumor tissue spatial gene expression patterns showed distinct expression ofSFRP2- and SOX2-correlated genes in vascular and cellular areas, respectively. Finally, conditioned media from SFRP2 overexpressing cells increased CD206 on macrophages. Together, these findings present SFRP2 as a SOX2-antagonist with the capacity to induce a mesenchymal subtype transition in glioma cells located in vascular tumor areas, highlighting its role in glioblastoma tumor evolution and intratumoral heterogeneity.

Background Glioblastoma (GBM) are highly heterogeneous on the cellular and molecular basis. It has been proposed that glutamine metabolism of primary cells established from human tumors discriminates aggressive mesenchymal GBM subtype to other subtypes. Methods To study glutamine metabolism in vivo, we used a human orthotopic mouse model for GBM. Tumors evolving from the implanted primary GBM cells expressing different molecular signatures were analyzed using mass spectrometry for their metabolite pools and enrichment in carbon 13 (13C) after 13C-glutamine infusion. Results Our results showed that mesenchymal GBM tumors displayed increased glutamine uptake and utilization compared to both control brain tissue and other GBM subtypes. Furthermore, both glutamine synthetase and transglutaminase-2 were expressed accordingly to GBM metabolic phenotypes. Conclusion Thus, our results outline the specific enhanced glutamine flux in vivo of the aggressive mesenchymal GBM subtype.

Glioblastoma (GBM) still remains an incurable disease being radiotherapy (RT) the mainstay treatment. Glioblastoma intra-tumoral heterogeneity and Glioblastoma-Initiating Cells (GICs) challenge the design of effective therapies. We investigated GICs and non-GICs response to RT in a paired in-vitro model and addressed molecular programs activated in GICs after RT. Established GICs heterogeneously expressed several GICs markers and displayed a mesenchymal signature. Upon fractionated RT, GICs reported higher radioresistance compared to non-GICs and showed lower α- and β-values, according to the Linear Quadratic Model interpretation of the survival curves. Moreover, a significant correlation was observed between GICs radiosensitivity and patient disease-free survival. Transcriptome analysis of GICs after acquisition of a radioresistant phenotype reported significant activation of Proneural-to-Mesenchymal transition (PMT) and pro-inflammatory pathways, being STAT3 and IL6 the major players. Our findings support a leading role of mesenchymal GICs in defining patient response to RT and provide the grounds for targeted therapies based on the blockade of inflammatory pathways to overcome GBM radioresistance.