Classification Of Glioblastoma Research Articles

TMIC-43. BIOLOGICAL AND MOLECULAR CHARACTERIZATION OF NOVEL GLIOBLASTOMA TME SUBTYPES USING TRANSCRIPTOMIC ANALYSIS AND NETWORK MODELLING TO IDENTIFY NOVEL TARGETS OF VULNERABILITY

Abstract New precision medicine therapies are urgently required for glioblastoma (GBM). Recently, we developed a novel GBM classification system, identifying three patient clusters uniquely characterized by tumor microenvironment (TME) composition: TMELow, TMEMedium, and TMEHigh1. Our objective now is to further investigate these subtypes employing transcriptomic analysis and network modelling approaches to identify novel subtype-specific targets of vulnerability. We analyzed transcriptomic data from >600 GBM samples from publicly available and in-house datasets. All samples underwent TME subtyping. Next, we performed differential gene expression (using DESeq2, edgeR) and pathway analysis (using PROGENy). The ‘TRANSFAC’ tool 2 was used to identify potential transcription factors enriched in each TME subtype. TMELow tumours manifested highly upregulated NLGN3 (P=5.777e-5) and HES5 (P=2.233e-3) expression compared to TMEHigh. Moreover, TMEHigh compared to TMELow tumours were enriched for genes associated with tissue remodelling, and showed elevated MMP7 (P=3.051e-6), CLCL13 (P= 3.843e-4), and CXCL5 expression (P= 1.592e-6). PROGENy analysis suggested that the WNT, TRAIL, and JAK/STAT pathways were significantly enriched in TMEHigh, while the PI3K pathway was significantly upregulated in TMEMedium. Additionally, VEGF pathway activity was significantly upregulated in TMELow. TRANSFAC analysis measured by regulatory score (a measure of a transcription factor effect on gene expression) revealed that in TMELow, key transcription factors includes ETS2, NANOG, and MECP2 (regulatory score: 1.97, 1.45, and 1.4, respectively). In TMEMedium, transcription factors HIF1A, PPARA, and CDX2 were identified (regulatory score: 2.52, 2.08 and 1.96, respectively). In TMEHigh, TRANSFAC indicated activiation of transcription factors SMAD3, ETS2, and NFKB1 regulatory score: 2.56, 2.41, 2.24, respectively). Overall these findings highlight distinct molecular signatures across TME subtypes, and provide a deeper understanding of associated molecular mechanisms. Ongoing work is focused on validating key subtype specific genes and associated pathways using spatial proteomics. Prioritised targets will ultimately be interrogated in vivo, as novel subtype-specific treatments. 1. White et al. Ann Oncol. 2023;34(3):300–314. 2. Matys et al. Nucleic Acids Res. 2003;31.

Revisiting glioblastoma classification through an immunological lens: A narrative review

Glioblastoma (GBM) is characterized by a high recurrence rate, significant heterogeneity, and poor prognosis. While there has been a shift in recent years to focus on molecular phenotyping, there are limited data regarding the relationship between the immune milieu and heterogeneous molecular signatures in GBM. Given the success of immunotherapies in other cancers such as non-small-cell lung cancer and melanoma, there has been a concerted effort to correlate the immune compartment of the GBM tumor microenvironment to clinical outcomes. The aim of this narrative review is to establish the role of immunophenotyping in GBM classification. Major immune cell groups in GBM involve myeloid cells (e.g. myeloid-derived suppressor cells, tumor-associated macrophages and microglia, neutrophils, and dendritic cells), lymphocytes (e.g., T, natural killer, and B-cells), and stromal cells (e.g., fibroblasts, pericytes, and endothelial cells). Understanding the relationships between these different immune cell populations and correlating their roles with the current molecular classification scheme as described in the 2021 World Health Organization criteria may further elucidate patterns of clinical response, especially in light of recent advances in new immunotherapies.

Clinicopathological and molecular landscape of 5-year IDH-wild-type glioblastoma survivors: A multicentric retrospective study

Five-year glioblastoma (GBM) survivors (LTS) are the minority of the isocitrate dehydrogenase (IDH)-wild-type GBM patients, and their molecular fingerprint is still largely unexplored. This multicenter retrospective study analyzed a large LTS-GBM cohort from nine Italian institutions and molecularly characterized a subgroup of patients by mutation, DNA methylation (DNAm) and copy number variation (CNV) profiling, comparing it to standard survival GBM. Mutation scan allowed the identification of pathogenic variants in most cases, showing a similar mutational spectrum in both groups, and highlighted TP53 as the most commonly mutated gene in the LTS group. We confirmed DNAm as a valuable tool for GBM classification with a diagnostic refinement by using brain tumor classifier v12.5. LTS were more heterogeneous with more cases classified as diffuse pediatric high-grade glioma subtypes and having peculiar CNVs. We observed a global higher methylation in CpG islands and in gene promoters of LTS with methylation levels of distinct gene promoters correlating with prognosis.

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI.

(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L1-norm feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists.

Application of WHO CNS Fifth Edition Criteria for Glioblastoma Multiforme Diagnosis: A single center study

Abstract Introduction/Objective The fifth edition of the WHO classification of the tumors of the central nervous system (WHO CNS 5) published in 2021 introduced major changes that incorporate molecular diagnostics in CNS tumor classification along with histology and immunohistochemistry. Glioblastomas (GBM) were diagnosed based on the histologic features of diffuse and astrocytic glioma in adults, if there is microvascular proliferation and /or necrosis. Isocitrate dehydrogenase 1 (IDH) R132 H immunohistochemical stain expression and /or mutation analysis results were used to further classify GBMs as IDH mutant or IDH wild type. In WHO CNS 5 all IDH mutant diffuse astrocytic tumors are considered a single type (Astrocytoma, IDH mutant) and graded 2, 3, or 4. Glioblastoma IDH wild type is diagnosed in the setting of an IDH wild type diffuse and astrocytic glioma with microvascular proliferation or necrosis or TERT promoter mutation or EGFR gene amplification or +7/-10 chromosome copy number changes. In this study, we analyze how the change in diagnostic criterion would affect the classification of GBMs diagnosed in our institution retrospectively. Methods/Case Report The pathology database was searched for all diffuse gliomas diagnosed as GBM between 01/2019 and 12/2021. Age of the patient, gender, location of tumor, histologic features, IDH status and molecular alterations ( Onkosight Advanced solid tumor panel, 50 genes) were reviewed. Results (if a Case Study enter NA) A total of 74 adult high grade glial neoplasms were identified out of which 25 were GBMs. The age of the patients ranged from 40 to 87 years. 14 were men and 11 were females. All tumors had histologic features of GBM such as necrosis and microvascular proliferation. 24 (96%) tumors were IDH negative by immunohistochemistry and mutation analysis, and 1 (4%) was IDH strongly positive by immunohistochemistry. Molecular studies were not performed on this tumor. Conclusion 96% of GBMS diagnosed during the time period conformed to the WHO CNS 5 diagnostic criteria. The one IDH positive tumor would be reclassified as diffuse astrocytoma, IDH mutant, Grade 4. Like previous WHO classifications, WHO CNS 5 is a work in progress attempting to introduce new knowledge into the classification hoping to provide practical guidance to pathologists and neuro-oncologists and to benefit the patients with CNS tumors.

JS04.7.A ANALYSIS OF RNA CLASSIFIES NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS AND IDENTIFIES PATIENTS VULNERABLE TO TARGETED METABOLIC THERAPIES

Abstract BACKGROUND Recent computational analysis of glioblastoma (GBM) single-cell RNA-sequencing data and bulk tumors identified four stable cellular states that embody metabolic (mitochondrial and glycolytic/plurimetabolic) and developmental (neuronal and proliferative/progenitor) attributes and generated a new GBM classification. Interestingly, the mitochondrial subtype depends on oxidative phosphorylation (OXPHOS), stratifies patients with specific metabolic pharmacologic vulnerabilities. Here, we set up and optimized RNA Sequencing of frozen and FFPE samples of GBM to apply the new proposed classification to a retrospective cohort of IDH wild type GBM patients. METHODS Tumor samples from IDH wild-type (wt) GBM patients diagnosed in Pitiè-Salpétrière Hospital from 2007 and 2019 and treated by first line standard radio-chemotherapy were analyzed by 3’ mRNA NGS. RESULTS Two hundreds thirty-two newly diagnosed (232) GBM IDH patients were analyzed. Median age was 60 years old (yo) (range 31-87 yo), median KPS at diagnosis was 80, Male/female ratio 1.5, pMGMT resulted hypermethylated in 48 out of 115 tested samples (42%). RNA analysis classified patients into molecular subgroups as it follows: 60 OXPHOS+, 48 Neuronal, 55 Proliferative and 58 Glycolytic; 11 unclassifiable. A subgroup of 28 patients was analyzed by RNA Sequencing from both FFPE and frozen tumors with concordant results in 25 patients (89%). Since F3T3 gene fusions correlate with hyperexpression of mitochondrial markers, we separately analyzed a subgroup of 20 IDHwt F3T3+ GBM patients. We found that F3T3+ are most frequently OXPHOS+ (50%) but not exclusively. Survival analysis shows significant longer overall survival (OS) of OXPHOS+F3T3- patients (22.9 months) compared to OXPHOS-F3T3-(18.4 months), (p=0.045) [including Neuronal patients (OS 18.4 months), Glycolytic (18.2 m), Proliferative 18.6 m)]. PFS after first line radio-chemotherapy did not differ significantly (p=0.11) between subgroups. Among F3T3+ patients, OXPHOS+ were long survivors with a median OS of 40.1 months while Glycolytic, Neuronal and Proliferative F3T3+ patients showed a worse prognosis similar to F3T3- patients (median OS 17.3 months; p=0.01). CONCLUSIONS In conclusions the RNA expression analysis classifies GBM patients from frozen and FFPE samples in metabolic subgroups. We identified OXPHOS+ GBM patients with more indolent clinical behavior compared to Glycolytic and other subgroups and with specific vulnerabilities to metabolic manipulations, as in the case of OXPHOS+ patients that could benefit from targeted metabolic therapies. RNA sequencing of FFPE is feasible in the setting of a prospectively clinical trial, aiming to select OXPHOS+ patients among newly diagnosed GBM patients, eligible for mitochondrial inhibitors add-on to first-line standard radio-chemotherapy (Optimum trial, NCT04945148).

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

BackgroundThe histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. MethodsIn this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. ResultsThe best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. ConclusionThe developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.

Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

SummaryGlioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

Holistic Analysis of Glioblastoma Stem Cell DNA Using Nanoengineered Plasmonic Metasensor for Glioblastoma Diagnosis.

The clinical relevance of liquid biopsy for glioblastoma (GBM) remains undetermined due to practical and biological limitations such as absence of a reliable GBM-specific biomarker, trace levels in circulation due to the blood-brain-barrier, and lack of a sensitive method to detect the trace levels of biomarkers. It is hypothesized that GBM stem cell (GSC)-associated cell free DNA can function as reliable biomarker for GBM because it accounts for tumor heterogeneity and provide accurate molecular information about the cancer. An integrative methodology is used for GBM diagnosis by utilizing the sub-single molecular sensitivity of nanoengineered plasmonic metasensors for real-time genomic profiling of GSC DNA. The nanoengineered metasensors can detect the rare circulating GSC-DNA accurately from just 5µL of blood and the test can be performed in under 10 min. Analysis of clinical serum samples from GBM patients and healthy volunteers demonstrates that the technology yielded an accurate classification of GBM in an independent validation cohort (accuracy 98.3%, specificity 100%). The methodology detects GBM-signatures from the patient blood rapidly within the half-life period of cfDNA in circulation, non-invasively and amplification-free with a high diagnostic accuracy. With clinical validation, this methodology can evolve as a clinically viable diagnostic tool for fatal and hard-to-detect cancer like GBM.

DNA methylation-based age acceleration observed in IDH wild-type glioblastoma is associated with better outcome\u2014including in elderly patients

Elderly patients represent a growing proportion of individuals with glioblastoma, who however, are often excluded from clinical trials owing to poor expected prognosis. We aimed at identifying age-related molecular differences that would justify and guide distinct treatment decisions in elderly glioblastoma patients. The combined DNA methylome (450 k) of four IDH wild-type glioblastoma datasets, comprising two clinical trial cohorts, was interrogated for differences based on the patients’ age, DNA methylation (DNAm) age acceleration (DNAm age “Horvath-clock” minus patient age), DNA methylation-based tumor classification (Heidelberg), entropy, and functional methylation of DNA damage response (DDR) genes. Age dependent methylation included 19 CpGs (p-value ≤ 0.1, Bonferroni corrected), comprising a CpG located in the ELOVL2 gene that is part of a 13-gene forensic age predictor. Most of the age related CpGs (n = 16) were also associated with age acceleration that itself was associated with a large number of CpGs (n = 50,551). Over 70% age acceleration-associated CpGs (n = 36,348) overlapped with those associated with the DNA methylation based tumor classification (n = 170,759). Gene set enrichment analysis identified associated pathways, providing insights into the biology of DNAm age acceleration and respective commonalities with glioblastoma classification. Functional methylation of several DDR genes, defined as correlation of methylation with gene expression (r ≤ −0.3), was associated with age acceleration (n = 8), tumor classification (n = 12), or both (n = 4), the latter including MGMT. DNAm age acceleration was significantly associated with better outcome in both clinical trial cohorts, whereof one comprised only elderly patients. Multivariate analysis included treatment (RT, RT/TMZ→TMZ; TMZ, RT), MGMT promoter methylation status, and interaction with treatment. In conclusion, DNA methylation features of age acceleration are an integrative part of the methylation-based tumor classification (RTK I, RTK II, MES), while patient age seems hardly reflected in the glioblastoma DNA methylome. We found no molecular evidence justifying other treatments in elderly patients, not owing to frailty or co-morbidities.

Abstract 97: Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

Abstract Transcriptomic classification has been used to molecularly characterize glioblastoma (GBM) but has failed to predict survival and inform on pharmacologic vulnerability. Here, we developed a computational approach for the unbiased identification of the core biological pathways that optimally classify individual glioma cells and bulk tumors. Using single cell RNA-sequencing data from 36 high-grade gliomas, we uncovered four transcriptional states that exist along two evolutionary axes, a metabolic axis including mitochondrial and glycolytic/pluri-metabolic and a neurodevelopmental axis including proliferative/progenitor and neuronal states. The activation of the same set of biological pathways independently stratified primary GBM into four subtypes, among which the mitochondrial subgroup was associated with the most favorable clinical outcome. By integrating genomic, transcriptomic, DNA methylation, microRNA and proteomics analysis, we found that mitochondrial GBM was enriched with coherent gain-of-function of mitochondrial genes and loss-of-function alterations targeting glycolysis and alternative metabolic programs, suggesting that this subgroup may fail to produce compensatory metabolism. Mitochondrial GBM relied exclusively on oxidative phosphorylation for energy production whereas the glycolytic/pluri-metabolic subtype was sustained by concurrent activation of multiple metabolic fluxes including aerobic glycolysis, amino acid consumption and lipid synthesis and storage. Deletion of SLC45A1, a gene coding for a glucose-H+ symporter on chromosome 1p36.23, emerged as the truncal genetic alteration most significantly associated with mitochondrial GBM. Reintroduction of SLC45A1 in mitochondrial GBM cells harboring SLC45A1 gene deletion induced cytoplasmic acidification, loss of cell fitness and growth arrest. The strict dependency of mitochondrial GBM on mitochondrial respiration was associated with excessive generation of reactive oxygen species and unique sensitivity to inhibitors of oxidative phosphorylation. Collectively, this work presents a classification of GBM that informs clinical outcome and identifies patients who are more likely to benefit from therapies targeting metabolic vulnerabilities. Citation Format: Luciano Garofano, Simona Migliozzi, Young Taek Oh, Fulvio D'Angelo, Ryan D. Najac, Aram Ko, Brulinda Frangaj, Francesca Pia Caruso, Kai Yu, Jinzhou Yuan, Wenting Zhao, Anna Luisa Di Stefano, Franck Bielle, Tao Jiang, Peter Sims, Mario L. Suvà, Fuchou Tang, Xiao-Dong Su, Michele Ceccarelli, Marc Sanson, Anna Lasorella, Antonio Iavarone. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 97.

Spatial progression and molecular heterogeneity of IDH-mutant glioblastoma determined by DNA methylation-based mapping

Glioblastoma (GBM) is the most common malignant primary central nervous system (CNS) neoplasm in adults, and has an almost universally poor prognosis. Recently, an emphasis on genetic and epigenetic profiling has revealed a number of molecular features useful in the diagnostic and prognostic classification of GBM, advancing our understanding of the underlying features that make these tumors so aggressive and providing the rationale for the creation of better targeted therapeutics. One such method, DNA methylation profiling, has recently emerged as an important technique for the classification of CNS tumors, with diagnostic accuracy in some cases surpassing traditional methods. However, how DNA methylation profiles change with the course of the disease remains less understood. Here, we present a case of a 30-year-old male with primary IDH-mutant GBM with widespread recurrence and death two years later. Using unsupervised hierarchical clustering of methylation probes, we created a phylogenetic map to trace the tumor path as it spread from the initial biopsy site throughout the right hemisphere, across the corpus callosum to the contralateral hemisphere, and into the brainstem. We identified molecular divergence between the right and left hemisphere GBM samples marked by distinct copy number profile alterations, alterations in specific methylation sites, and regional loss of MGMT promoter methylation, providing a potential mechanism for treatment resistance in this case. In summary, this case both highlights the molecular diversity in GBM, and illustrates a novel use for methylation profiling in establishing a phylogenetic profile to allow for spatial mapping of tumor progression.

Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities.

The transcriptomic classification of glioblastoma (GBM) has failed to predict survival and therapeutic vulnerabilities. A computational approach for unbiased identification of core biological traits of single cells and bulk tumors uncovered four tumor cell states and GBM subtypes distributed along neurodevelopmental and metabolic axes, classified as proliferative/progenitor, neuronal, mitochondrial and glycolytic/plurimetabolic. Each subtype was enriched with biologically coherent multiomic features. Mitochondrial GBM was associated with the most favorable clinical outcome. It relied exclusively on oxidative phosphorylation for energy production, whereas the glycolytic/plurimetabolic subtype was sustained by aerobic glycolysis and amino acid and lipid metabolism. Deletion of the glucose-proton symporter SLC45A1 was the truncal alteration most significantly associated with mitochondrial GBM, and the reintroduction of SLC45A1 in mitochondrial glioma cells induced acidification and loss of fitness. Mitochondrial, but not glycolytic/plurimetabolic, GBM exhibited marked vulnerability to inhibitors of oxidative phosphorylation. The pathway-based classification of GBM informs survival and enables precision targeting of cancer metabolism.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy.

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

CBMS-03 PHOSPHORYLATION STATE OF OLIG2 REGULATES PROLIFERATION OF GLIOMA STEM CELLS

The Cancer Genome Atlas project described a robust gene expression-based molecular classification of glioblastoma with the functional and biological significance of the subclasses yet to be determined. Here, we show that a comprehensive analysis of a panel of glioma initiating cell (GIC) lines can identify a group of stem cells with high OLIGg2 expression as in Proneural-like GBM subtype. In vitro differentiation studies showed that proneural GIC lines possess the potential to differentiate into astrocytic, neuronal, and oligodendrocytic lineages, whereas mesenchymal GICs exhibited limited potential for neural lineage differentiation following retinoic acid induction. A considerable decline of OLIG2 in proneural GIC lines was observed following retinoic acid treatment. We also showed that OLIG2 is a functional marker associated with cell proliferation in Olig2-high GIC lines. In addition, OLIG2 inhibition disrupted cell-cycle control mechanism by decreasing CDK2 and CDK4 and elevating apoptosis-related molecules. Mechanistic investigations revealed molecular interactions between CDK2/CDK4 and OLIG2. Inhibition of CDK2/CDK4 activity disrupted OLIG2-CDK2/CDK4 interactions and attenuated OLIG2 protein stability. Further investigation on these mechanisms may lead to novel targeted therapy on GBMs with high OLIG2 expression.

Abstract 5175: Expression profile and role of LOXL3 in astrocytomas

Abstract Astrocytomas are the most common primary central nervous system tumors in adults. The WHO classification based on histologial characteristics according consider four malignant grades, glioblastoma (GBM) being the most malignant (grade IV). Molecular classification of GBM is based on the integration of somatic mutations, DNA methylation and specific transcript/protein expression and consider four subtypes: proneural, classic, mesenchymal and neural. Lysyl oxidase family is composed of five members (LOX, LOXL1, LOXL2, LOXL3 and LOXL4) which are enzymes responsible for catalyzing lysine-derived cross-links of collagen and elastin. The aim of the present work was to analyze the gene expression of LOXL3 in astrocytomas of different grades and different GBM molecular subtypes, the impact of LOXL3 expression level on overall survival in GBM cases of our cohort. These data were also analyzed in the TCGA database to validate our findings. LOXL3expression increased with astrocytoma malignant grades, and proved to be a prognostic factor, as GBM patients with lower LOXL3 expression presented longer survival time than those with higher expression (p<0.041). Analogous data was obtained in the TCGA database, when considering temozolamide-treated GBM patients (p=0.049). Additionally, LOXL3 expression was higher in mesenchymal GBM subtype than in proneural and classic GBM subtypes in TCGA cohort (p<0.001). To further understand LOXL3 functional role in gliomagenesis, LOXL3 was silenced with two distinct siRNA sequences in U87MG, a mesenchymal subtype of GBM cell line. LOXL3 down regulation was confirmed at gene and protein expression levels. Functional assays with silenced LOXL3 demonstrated a decrease in the cell proliferation (p<0.05), and an increase in the apoptosis associated to temozolamide treatment (p<0.05). Immunofluorescence analysis in U87MG cells showed LOXL3 colocalized with mitochondria. Interestingly, such colocalization decreased when LOXL3 was downregulated, associated to a clear morphological alteration in mitochondria shape. A rough increase in mitochondria volume was observed, suggesting fused mitochondria. In addition, such condition was associated to a 23% decrease in mitochondrial DNA copy number. Altogether, our findings suggest that LOXL3 is upregulated in GBM, with impact in prognosis and might play a role in mitochondria dynamics and/or mitophagy, modulating apoptosis. Transcriptome analysis, currently in progress, will further clarify the associated players in this signaling pathway. Citation Format: Talita de Souza Laurentinho, Roseli da Silva Soares, Suely Kazue Marie, Sueli Mieko Oba-Shinjo. Expression profile and role of LOXL3 in astrocytomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5175.

Abstract 2621: OLIG2 regulates stem cell maintenance and cell cycle in glioma stem cells

Abstract The Cancer Genome Atlas project described a robust gene expression-based molecular classification of glioblastoma with the functional and biological significance of the subclasses yet to be determined. Here, we show that a comprehensive analysis of a panel of glioma initiating cell (GIC) lines can identify a group of stem cells with high OLIGg2 expression as in Proneural-like GBM subtype. In vitro differentiation studies showed that proneural GIC lines possess the potential to differentiate into astrocytic, neuronal, and oligodendrocytic lineages, whereas mesenchymal GICs exhibited limited potential for neural lineage differentiation following retinoic acid induction. A considerable decline of OLIG2 in proneural GIC lines was observed following retinoic acid treatment. We also showed that OLIG2 is a functional marker associated with cell proliferation in Olig2-high GIC lines. In addition, OLIG2 inhibiton disrupted cell-cycle control mechanism by decreasing CDK2 and CDK4 and elevating apoptosis-related molecules. Mechanistic investigations revealed molecular interactions between CDK2/CDK4 and OLIG2. Inhibition of CDK2/CDK4 activity disrupted OLIG2-CDK2/CDK4 interactions and attenuated OLIG2 protein stability. Further investigation on these mechanisms may lead to novel targeted therapy on GBMs with high OLIG2 expression. Citation Format: Norihiko Saito, Nozomi Hirai, Kazuya Aoki, Satoshi Fujita, Haruo Nakayama, Morito Hayashi, Takatoshi Sakurai, Satoshi Iwabuchi. OLIG2 regulates stem cell maintenance and cell cycle in glioma stem cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2621.

Polysome Profiling of a Human Glioblastoma Reveals Intratumoral Heterogeneity.

Glioblastoma (GBM) is one of the most aggressive cancers, with median survival of less than 2 years. Despite of considerable advance in molecular classification of GBMs, no improvements in therapy have been described. The scenario is further complicated by tumor heterogeneity and the relationship among genetic, transcriptional and functional findings. Classically, gene expression has been evaluated by steady-state mRNA, however, this does not take translational control into consideration, which contributes considerably to the composition of the proteome. In this study, we evaluated the transcriptomic and translatomic signature of a GBM obtained from a single patient focusing in tumor heterogeneity. In a sampling of eight fragments, we investigated the translation rates, mTORC1 and ERK1/2 pathways and identified both total and polysome associated mRNAs. An increased translation rate was observed in fragments with high-grade histological features. High-grade histology was also associated with the expression of genes related to extracellular matrix (ECM) and angiogenesis, in both transcriptomes and translatomes. However, genes associated with epithelial to mesenchymal transition and stress response, were observed only in translatomes from high-grade fragments. Overall, our results demonstrate that isolation of translated mRNA can be used to identify biomarkers and reveal previously unrecognized determinants of heterogeneity in GBMs.

Role of Immunohistochemistry in the Classification of Glioblastoma and Anaplastic Astrocytoma in Kenyatta National Hospital

Role of Immunohistochemistry in the Classification of Glioblastoma and Anaplastic Astrocytoma in Kenyatta National Hospital

Correlation analysis between glioblastoma perfusion imaging and isocitrate dehydrogenase 1 mutation status

Objective To investigate the value of perfusion imaging in predicting the status of isocitrate dehydrogenase 1 (IDH1) in glioblastoma. Methods Retrospectively studied 30 patients with glioblastoma multiforme with wild type IDH1 (IDHw) and 30 patients with mutant IDH1 (IDH1m) in Department of Neurosurgery, Shanxi Provincial People′s Hospital from September 2014 to February 2016. Ktrans and Ve within the enhancing portion of each tumor were measured by using DCE-MRI data. rCBF and rCBV within the enhancing portion of each tumor were measured by using DSC-MRI. Four parameters were represented as ±s, each of the 4 parameters was compared between patients with wild type IDH1 and mutant IDH1 by using the t-test. The performance in discriminating between the two entities was evaluated by using receiver operating characteristic analysis. Results Ktrans, Ve, rCBF and rCBV inside the enhancing lesion were significantly higher in patients with wild type IDH1 than in those with mutant IDH1.There was a statistically significant difference between IDH1w group and IDH1m group of rCBF value (P<0.05). The area under the curve for Ktrans, Ve, rCBF, and rCBV inside the enhancing lesion were 0.850, 0.873, 0.739 and 0.772, respectively. The value of rCBF value of the Ktrans value Ve value of IDH1w was significantly higher than that of IDH1m(P<0.05) in the value of the Ktrans value Ve value of rCBF, the value of the AUC value in rCBV was not significantly different with the combination of the 4 parameters of the diagnostic performance (AUC=0.915). Conclusions Ktrans, Ve, rCBF and rCBV calculated from MRI are useful for predicting the IDH1 mutation status. This method is not only for the classification of brain glioblastoma diagnosis has important value, and in glioblastoma classification is of important value in the preoperative noninvasive evaluation, and it has reference significance for predicting the prognosis of patients. Key words: Glioblastoma; Perfusion imaging; Mutation; Isocitrate dehydrogenase 1