The Cancer Genome Atlas Glioblastoma Research Articles

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells.

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

A novel prognostic risk-scoring system based on m5C methylation regulator-mediated patterns for glioma patients

N5-methylcytosine (m5C) methylation modification plays a crucial role in the epigenetic mechanisms underlying tumorigenesis, aggressiveness, and malignancy in diffuse glioma. Our study aimed to develop a novel prognostic risk-scoring system to assess the impact of m5C modification in glioma patients. Initially, we identified two distinct m5C clusters based on the expression level of m5C regulators in The Cancer Genome Atlas glioblastoma (TCGA-GBM) dataset. Differentially expressed genes (DEGs) between the two m5C cluster groups were determined. Utilizing these m5C regulation-related DEGs, we classified glioma patients into three gene cluster groups: A, B, and C. Subsequently, an m5C scoring system was developed through a univariate Cox regression model, quantifying the m5C modification patterns utilizing six DEGs associated with disease prognosis. The resulting scoring system allowed us to categorize patients into high- or low-risk groups based on their m5C scores. In test (TCGA-GBM) and validation (Chinese Glioma Genome Atlas [CGGA]-1018 and CGGA-301) datasets, glioma patients with a higher m5C score consistently exhibited shorter survival durations, fewer isocitrate dehydrogenase (IDH) mutations, less 1p/19q codeletion and higher World Health Organization (WHO) grades. Additionally, distinct immune cell infiltration characteristics were observed among different m5C cluster groups and risk groups. Our study developed a novel prognostic scoring system based on m5C modification patterns for glioma patients, complementing existing molecular classifications and providing valuable insights into prognosis for glioma patients.

DIAPH3 predicts survival of patients with MGMT-methylated glioblastoma.

Glioblastoma is one of the most aggressive primary brain tumors, with a poor outcome despite multimodal treatment. Methylation of the MGMT promoter, which predicts the response to temozolomide, is a well-established prognostic marker for glioblastoma. However, a difference in survival can still be detected within the MGMT methylated group, with some patients exhibiting a shorter survival than others, emphasizing the need for additional predictive factors. We analyzed DIAPH3 expression in glioblastoma samples from the cancer genome atlas (TCGA). We also retrospectively analyzed one hundred seventeen histological glioblastomas from patients operated on at Saint-Luc University Hospital between May 2013 and August 2019. We analyzed the DIAPH3 expression, explored the relationship between mRNA levels and Patient's survival after the surgical resection. Finally, we assessed the methylation pattern of the DIAPH3 promoter using a targeted deep bisulfite sequencing approach. We found that 36% and 1% of the TCGA glioblastoma samples exhibit copy number alterations and mutations in DIAPH3, respectively. We scrutinized the expression of DIAPH3 at single cell level and detected an overlap with MKI67 expression in glioblastoma proliferating cells, including neural progenitor-like, oligodendrocyte progenitor-like and astrocyte-like states. We quantitatively analyzed DIAPH3 expression in our cohort and uncovered a positive correlation between DIAPH3 mRNA level and patient's survival. The effect of DIAPH3 was prominent in MGMT-methylated glioblastoma. Finally, we report that the expression of DIAPH3 is at least partially regulated by the methylation of three CpG sites in the promoter region. We propose that combining the DIAPH3 expression with MGMT methylation could offer a better prediction of survival and more adapted postsurgical treatment for patients with MGMT-methylated glioblastoma.

Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BackgroundHomeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated.MethodsHOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan–Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan–Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts.ResultsHOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts.ConclusionHOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients.

A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients.

BackgroundPretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis. CUL2 expression levels are closely regulated with its copy number variations (CNVs). This study aims to develop artificial neural networks (ANNs) for pretreatment evaluation of GBM patients with inputs obtainable without intracranial surgical biopsies.MethodsPublic datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the Chinese Glioma Genome Atlas (CGGA) were used for training and testing of the ANNs. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. A ratio comparing the surface area of tumor borders versus the total volume (SvV) was derived from the DICOM-SEG conversions of segmented tumors. The edges of these borders were detected using the canny edge detector. Packages including Keras, Pytorch, and TensorFlow were tested to build the ANNs. A 4-layered ANN (8-8-8-2) with a binary output was built with optimal performance after extensive testing.ResultsThe 4-layered deep learning ANN can identify a GBM patient’s overall survival (OS) cohort with 80%–85% accuracy. The ANN requires 4 inputs, including CUL2 copy number, patients’ age at GBM diagnosis, Karnofsky Performance Scale (KPS), and SvV ratio.ConclusionQuantifiable image features can significantly improve the ability of ANNs to identify a GBM patients’ survival cohort. Features such as clinical measures, genetic data, and image data, can be integrated into a single ANN for GBM pretreatment evaluation.

Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database.

Glioblastoma (GBM) is the most common primary intracranial tumor with extremely high malignancy and poor prognosis. In order to identify the GBM prognostic biomarkers and establish a prognostic model, we analyzed the expression profile data of GBM in The Cancer Genome Atlas (TCGA) database as the experimental group. First, we identified the differentially expressed genes of different survival periods among the GBM patients. The GISTIC software and Kaplan Meier (KM) survival curve were used to analyze the copy number variation of GBM to identify the survival-associated amplified gene (SAG). We selected the intersection genes of up-regulated ones in short survival group and SAG, performed univariate Cox regression and iterative Lasso regression with them to identify the important candidate genes and establish a prognostic model. Based on the model, the prognostic score was calculated. The patients were divided into high-risk and low-risk groups according to the median prognostic score. Meanwhile ROC curve was used to evaluate the validity of the model, applying the KM survival analysis of the high-risk and low-risk groups. Multivariate Cox regression analysis was used to determine the independence of the prognostic score. All the data were verified with three external datasets: GEO GSE16011, CGGA, and Rembrandt. The results showed that differential expression analysis of different survival periods of GBM identified 426 up-regulated genes and 65 down-regulated genes in the TCGA GBM dataset. The intersection of up-regulated genes in short survival group and SAG yielded 47 genes. After the screening, the six-gene combination (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) prognostic model was finally determined. The area under ROC curve of the model in TCGA experimental group and three external validation group were all greater than 0.6, even reaching 0.912. KM analysis showed that the prognosis of the high-risk and low-risk groups was significant different (P<0.05). In the multivariate Cox regression analysis, the six-gene prognostic score was an independent factor influencing the prognosis of GBM patients (P<0.05). In summary, this study established a prognostic model of six-gene (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) for GBM. This six-gene model has good predictive ability and could be used as an independent prognostic marker for GBM patients.

Ferroptosis Suppressive Genes Correlate with Immunosuppression in Glioblastoma

Glioblastoma (GBM) is the most lethal primary tumor in the central nervous system. Ferroptosis is a type of programmed iron-dependent cell death. In the present study, we aimed to identify prognostic ferroptosis-related genes and their role in tumor immunity. We used differential and survival analysis and The Cancer Genome Atlas (TCGA) GBM RNA sequencing data. We also used systematic bioinformatic methods. Using differential and survival analysis, we found that a ferroptosis suppressor was predominant within ferroptosis-related genes in TCGA GBM RNA sequencing data. By integrating TCGA and gene expression omnibus GBM cohorts, 12 dysregulated ferroptosis suppressors were detected. Among the suppressors, CD44, heat shock protein family B (small) member 1 (HSPB1), and solute carrier family 40 member 1 (SLC40A1) were relevant to overall survival. Using systematic bioinformatic methods, we observed that ferroptosis suppressor expression correlated with immunosuppression, which could be attributed to T-cell exhaustion and cytotoxic T-lymphocyte evasion. Finally, we observed that a potential ferroptosis-inducing drug, acetaminophen, interacted with CD44, HSPB1, and SLC40A1. The ferroptosis suppressors CD44, HSPB1, and SLC40A1 were significantly associated with prognosis in GBM and correlated with immunosuppression (i.e., T-cell exhaustion and cytotoxic T-lymphocyte evasion). Acetaminophen might have an antitumor function in GBM by regulating CD44, HSPB1, and SLC40A1 to induce ferroptosis. Our results are expected to be of great significance in developing new immunotherapy strategies for GBM.

Analysis of the EGFR Amplification and CDKN2A Deletion Regulated Transcriptomic Signatures Reveals the Prognostic Significance of SPATS2L in Patients With Glioma.

Purpose: This study was conducted in order to analyze the prognostic effects of epidermal growth factor receptor (EGFR) and CDKN2A alterations and determine the prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioblastoma (GBM) or lower grade glioma (LGG).Methods: The alteration frequencies of EGFR and CDKN2A across 32 tumor types were derived from cBioPortal based on The Cancer Genome Atlas (TCGA) datasets. The Kaplan–Meier analysis was used to determine the prognostic significance of EGFR and CDKN2A alterations. EGFR and CDKN2A alterations on regulated expression signatures were identified from RNA-seq data in the TCGA GBM datasets. The prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioma was determined using the TCGA and the Chinese Glioma Genome Atlas (CGGA) datasets.Results: Compared with the other 31 tumor types, EGFR amplification and CDKN2A deletion particularly occurred in patients with GBM. GBM patients with EGFR amplification or CDKN2A deletion demonstrated poor prognosis. Statistical analysis showed the coexistence of EGFR alteration and CDKN2A deletion in GBM patients. We identified 864 genes which were commonly regulated by EGFR amplification and CDKN2A deletion, and those genes were highly expressed in brain tissues and associated with the cell cycle, EBRR2, and MAPK signaling pathways. Spermatogenesis-associated serine-rich 2-like gene (SPATS2L) was upregulated in GBM patients with EGFR amplification or CDKN2A alteration. Higher expression levels of SPATS2L were associated with worse prognosis in patients with GBM in both TCGA and CGGA datasets. Moreover, the expression levels of SPATS2L were higher in patients with a mesenchymal subtype of GBM. Statistical analysis also showed that the coexistence of EGFR alteration and CDKN2A deletion was significant in patients with LGG. SPATS2L was upregulated in LGG patients with EGFR amplification or CDKN2A alteration. Furthermore, higher expression levels of SPATS2L were associated with worse prognosis in patients with LGG in both TCGA and CGGA datasets. The expression levels of SPATS2L were higher in patients with an astrocytoma subtype of LGG. Finally, the coexistence and unfavorable prognostic effects of EGFR amplification and CDKN2A alteration were validated using the Memorial Sloan Kettering Cancer Center (MSKCC) glioma datasets.Conclusions: EGFR amplification and CDKN2A deletion of the regulated gene SPATS2L have significant prognostic effects in patients with GBM or LGG.

A genetic risk score for glioblastoma multiforme based on copy number variations

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer. Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations. We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25–4.40) higher than the average odds ratio and about 40 (95% CI 20–70) times higher than the lowest quintile. The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

NIMG-09. PREDICTING OVERALL SURVIVAL OF GLIOBLASTOMA PATIENTS ON MULTI-INSTITUTIONAL HISTOPATHOLOGY STAINED SLIDES USING DEEP LEARNING AND POPULATION-BASED NORMALIZATION

Abstract MOTIVATION Glioblastoma is the most common and aggressive adult brain tumor. Clinical histopathologic evaluation is essential for tumor classification, which according to the World Health Organization is associated with prognostic information. Accurate prediction of patient overall survival (OS) from clinical routine baseline histopathology whole slide images (WSI) using advanced computational methods, while considering variations in the staining process, could contribute to clinical decision-making and patient management optimization. METHODS We utilize The Cancer Genome Atlas glioblastoma (TCGA-GBM) collection, comprising multi-institutional hematoxylin and eosin (H&amp;E) stained frozen top-section WSI, genomic, and clinical data from 121 subjects. Data are randomly split into training (80%), validation (10%), and testing (10%) sets, while proportionally keeping the ratio of censored patients. We propose a novel deep learning algorithm to identify survival-discriminative histopathological patterns in a WSI, through feature maps, and quantitatively integrate them with gene expression and clinical data to predict patient OS. The concordance index (C-index) is used to quantify the predictive OS performance. Variations in slide staining are assessed through a novel population-based stain normalization approach, informed of glioblastoma distinct histologic sub-regions and their appearance from 509 H&amp;E stained slides with corresponding anatomical annotations from the Ivy Glioblastoma Atlas Project (IvyGAP). RESULTS C-index was equal to 0.797, 0.713, and 0.703 for the training, validation, and testing data, respectively, prior to stain normalization. Following normalization, staining variations in H&amp;E and ‘E’ gained significant improvements in IvyGAP (pWilcoxon&amp;lt; 0.01) and TCGA-GBM (pWilcoxon&amp;lt; 0.0001) data, respectively. These improvements contributed to further optimizing the C-index to 0.871, 0.777, and 0.780 for the training, validation, and testing data, respectively. CONCLUSIONS Appropriate normalization and integrative deep learning yield accurate OS prediction of glioblastoma patients through H&amp;E slides, generalizable in multi-institutional data, potentially contributing to patient stratification in clinical trials. Our computationally-identified survival-discriminative histopathological patterns can contribute in further understanding glioblastoma.

Letrozole targets the human ether-a-go-go-related gene potassium current in glioblastoma.

Aberrant expression of human ether-a-go-go-related gene (hERG) potassium channels has been implicated in the pathophysiology of glioblastoma (GBM). Letrozole has demonstrated efficacy in pre-clinical GBM models. The objective of this research was to assess the potential for hERG inhibition by letrozole to mediate efficacy in GBM. hERG currents were assessed using patch-clamp electrophysiology in an overexpression system during treatment with letrozole, exemestane or vehicle (dimethyl sulphoxide). Relative to vehicle, peak hERG tail current density was reduced when treated with 300nmol/L and 1µmol/L letrozole but not when treated with exemestane (up to 1µmol/L). Cell proliferation was assessed in cultured glioblastoma cell lines (U87 and U373) treated with letrozole, exemestane, doxazosin (hERG blocker) or vehicle. Letrozole, but not exemestane, reduced cell proliferation relative to vehicle in U87 and U373 cells. The associations between expression of hERG (KCNH2), aromatase (CYP19A1) and the oestrogen receptors (ESR1 and ESR2) and time to all-cause mortality were assessed in GBM patients within The Cancer Genome Atlas (TCGA) database. hERG expression was associated with reduced overall survival in the TCGA GBM cohort. Future work is warranted to investigate hERG expression as a potential biomarker to predict the therapeutic potential of hERG inhibitors in GBM.

Genetic and Lineage Classification of Glioma-Initiating Cells Identifies a Clinically Relevant Glioblastoma Model.

The Cancer Genome Atlas (TCGA) project described a robust gene expression-based molecular classification of glioblastoma (GBM), but the functional and biological significance of the subclasses has not been determined. The present comprehensive analysis of 25 glioma-initiating cell (GIC) lines classifies GIC lines into four subtypes (classical, mesenchymal, proneural, and neural) that are closely related to the TCGA GBM subclasses and display distinct lineage characteristics and differentiation behavior that recapitulate neural development. More importantly, the GIC subtypes exhibit distinct biological phenotypes in relation to self-renewal capacity, proliferation, invasiveness, and angiogenic potential in vitro and in vivo. In addition, the GIC subtypes exhibit divergent patterns of signaling pathway activation and deactivation of the Wnt, Notch, and TGF-β pathways. These results will improve drug discovery targeting certain genetic mutation in glioblastoma and improve the development of precision medicine.

Nrf2 and SQSTM1/p62 jointly contribute to mesenchymal transition and invasion in glioblastoma.

Accumulating evidence suggests that constitutively active Nrf2 has a pivotal role in cancer as it induces pro-survival genes that promote cancer cell proliferation and chemoresistance. The mechanisms of Nrf2 dysregulation and functions in cancer have not been fully characterized. Here, we jointly analyzed the Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) and the Cancer Genome Atlas (TCGA) multi-omics data in order to identify cancer types where Nrf2 activation is present. We found that Nrf2 is hyperactivated in a subset of glioblastoma (GBM) patients, whose tumors display a mesenchymal subtype, and uncover several different mechanisms contributing to increased Nrf2 activity. Importantly, we identified a positive feedback loop between SQSTM1/p62 and Nrf2 as a mechanism for activation of the Nrf2 pathway. We also show that autophagy and serine/threonine signaling regulates p62 mediated Keap1 degradation. Our results in glioma cell lines indicate that both Nrf2 and p62 promote proliferation, invasion and mesenchymal transition. Finally, Nrf2 activity was associated with decreased progression free survival in TCGA GBM patient samples, suggesting that treatments have limited efficacy if this transcription factor is overactivated. Overall, our findings place Nrf2 and p62 as the key components of the mesenchymal subtype network, with implications to tumorigenesis and treatment resistance. Thus, Nrf2 activation could be used as a surrogate prognostic marker in mesenchymal subtype GBMs. Furthermore, strategies aiming at either inhibiting Nrf2 or exploiting Nrf2 hyperactivity for targeted gene therapy may provide novel treatment options for this subset of GBM.

Identification of SEC61G as a Novel Prognostic Marker for Predicting Survival and Response to Therapies in Patients with Glioblastoma.

BackgroundThe survival and therapeutic outcome vary greatly among glioblastoma (GBM) patients. Treatment resistance, including resistance to temozolomide (TMZ) and radiotherapy, is a great obstacle for these therapies. In this study, we aimed to evaluate the predictive value of SEC61G on survival and therapeutic response in GBM patients.Material/MethodsSurvival analyses were performed to assess the correlation between SEC61G expression and survival of GBM patients from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. Univariate and multivariate Cox proportional hazard regression analysis was introduced to determine prognostic factors with independent impact power. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to illustrate possible biological functions of SEC61G.ResultsHigh expression of SEC61G was significantly correlated with poor prognosis in all GBM patients. High expression of SEC61G was also associated with poor outcome in those who received TMZ treatment or radiotherapy in TCGA GBM cohort. Univariate and multivariate Cox proportional hazards regression demonstrated that SEC61G was an independent prognostic factor affecting the prognosis and therapeutic outcome. The combination of age, SEC61G expression, and MGMT promoter methylation in survival analysis could provide better outcome assessment. Finally, a strong correlation between SEC61G expression and Notch pathway was observed in GSEA and GSVA, which suggested a possible mechanism that SEC61G affected survival and TMZ resistance.ConclusionsSEC61G expression may be a potential prognostic marker of poor survival, and a predictor of poor outcome to TMZ treatment and radiotherapy in GBM patients.

REST-DRD2 mechanism impacts glioblastoma stem cell-mediated tumorigenesis.

Glioblastoma (GBM) is a lethal, heterogeneous human brain tumor, with regulatory mechanisms that have yet to be fully characterized. Previous studies have indicated that the transcriptional repressor REST (repressor element-1 silencing transcription factor) regulates the oncogenic potential of GBM stem cells (GSCs) based on level of expression. However, how REST performs its regulatory role is not well understood. We examined 2 independent high REST (HR) GSC lines using genome-wide assays, biochemical validations, gene knockdown analysis, and mouse tumor models. We analyzed in-house patient tumors and patient data present in The Cancer Genome Atlas (TCGA). Genome-wide transcriptome and DNA-binding analyses suggested the dopamine receptor D2 (DRD2) gene, a dominant regulator of neurotransmitter signaling, as a direct target of REST. Biochemical analyses and mouse intracranial tumor models using knockdown of REST and double knockdown of REST and DRD2 validated this target and suggested that DRD2 is a downstream target of REST regulating tumorigenesis, at least in part, through controlling invasion and apoptosis. Further, TCGA GBM data support the presence of the REST-DRD2 axis and reveal that high REST/low DRD2 (HRLD) and low REST/high DRD2 (LRHD) tumors are specific subtypes, are molecularly different from the known GBM subtypes, and represent functional groups with distinctive patterns of enrichment of gene sets and biological pathways. The inverse HRLD/LRHD expression pattern is also seen in in-house GBM tumors. These findings suggest that REST regulates neurotransmitter signaling pathways through DRD2 in HR-GSCs to impact tumorigenesis. They further suggest that the REST-DRD2 mechanism forms distinct subtypes of GBM.

Rare but Recurrent ROS1 Fusions Resulting From Chromosome 6q22 Microdeletions are Targetable Oncogenes in Glioma.

Gliomas, a genetically heterogeneous group of primary central nervous system tumors, continue to pose a significant clinical challenge. Discovery of chromosomal rearrangements involving kinase genes has enabled precision therapy, and improved outcomes in several malignancies. Positing that similar benefit could be accomplished for patients with brain cancer, we evaluated The Cancer Genome Atlas (TCGA) glioblastoma dataset. Functional validation of the oncogenic potential and inhibitory sensitivity of discovered ROS1 fusions was performed using three independent cell-based model systems, and an in vivo murine xenograft study. In silico analysis revealed previously unreported intrachromosomal 6q22 microdeletions that generate ROS1-fusions from TCGA glioblastoma dataset. ROS1 fusions in primary glioma and ependymoma were independently corroborated from MSK-IMPACT and Foundation Medicine clinical datasets. GOPC-ROS1 is a recurrent ROS1 fusion in primary central nervous system (CNS) tumors. CEP85L-ROS1 and GOPC-ROS1 are transforming oncogenes in cells of astrocytic lineage, and amenable to pharmacologic inhibition with several ROS1 inhibitors even when occurring concurrently with other cancer hotspot aberrations frequently associated with glioblastoma. Oral monotherapy with a brain-permeable ROS1 inhibitor, lorlatinib, significantly prolonged survival in an intracranially xenografted tumor model generated from a ROS1 fusion-positive glioblastoma cell line. Our findings highlight that CNS tumors should be specifically interrogated for these rare intrachromosomal 6q22 microdeletion events that generate actionable ROS1 fusions. ROS1 fusions in primary brain cancer may be amenable for clinical intervention with kinase inhibitors, and this holds the potential of novel treatment paradigms in these treatment-refractory cancer types, particularly in glioblastoma.

A Radiosensitivity Gene Signature and PD-L1 Status Predict Clinical Outcome of Patients with Glioblastoma: Integrative Analyses of Transcriptome and Methylome

Combination of radiation therapy and immune checkpoint blockade such as programmed death-1 (PD-1) or programmed death-ligand 1 (PD-L1) blockade is being actively tested in clinical trial settings. Here, we tried to identify a subset of patients that could potentially benefit from this strategy using The Cancer Genome Atlas (TCGA) dataset for glioblastoma (GBM). Total 399 patients from TCGA GBM data set were clustered into radiosensitive (RS) versus radioresistant (RR) groups using 30 genes alleged to radiosensitivity. According to median value of CD274 mRNA expression, patients were stratified as PD-L1-High versus PD-L1-Low groups. Differentially expressed genes were identified, and enrichment analysis was performed as well. Differentially methylated regions were analyzed, and integrated into differentially expressed genes. A computational cell composition characterization tool was used for enumeration of various immune repertoire from transcriptome profile. We identified a subset of GBM, “PD-L1 high-RR group” which showed worse clinical outcome compared to the other groups. In this group, differentially expressed genes were highly enriched for immune response and mapped into activation of PI3K-AKT and MAPK signaling pathways. Integration of differentially expressed genes and differentially methylated region identified that MAP3K8 involved in T cell receptor signaling was upregulated and BA1, a factor which inhibits angiogenesis, was silenced. CIBERSORT showed that higher infiltration of immune repertoire contributed to immunosuppressive tumor microenvironment such as M2 macrophage and regulatory T cells. We validated the predictive value of a radiosensitivity gene signature and identified a subset, “PD-L1-High-RR group” which showed worse outcome following current standard therapy. The results of integrative analyses suggest that this group could potentially benefit from radiation therapy combined with PD-L1 blockade and angiogenesis inhibitor.

Overexpression of G-protein-coupled receptors 65 in glioblastoma predicts poor patient prognosis

ObjectiveG-protein-coupled receptors 65 (GPR65), identified as an acid-sensing receptor, is overexpressed in several malignancies and promote tumor development. Our aim was to investigate the expression and prognostic value of GPR65 in glioblastoma. Materials and methodsWe determined the expression of GPR65 protein using immunohistochemistry in tissue microarrays containing 11 Grade I, 107 Grade II, 47 Grade III, and 102 Grade IV gliomas and 16 normal brains. Then we evaluated its association with pathological grades, prognosis, and recurrence. The Cancer Genome Atlas (TCGA) group (N=528) was further employed to examine transcriptional level of GPR65 in glioblastoma and the correlation between GPR65 expression and clinical outcome. ResultsIn our cohort, GPR65 expression was positively related to glioma pathological grade (p<0.01) and elevated in glioblastoma (p<0.01). High expression of GPR65 was associated with significantly short overall survival (OS) (p=0.013) and progression-free survival (PFS) (p=0.029), and could be identified as an independent risk factor for OS of glioblastoma patients (Hazard Ratio [HR]=1.596, p=0.037). As an aiding evidence, increased GPR65 mRNA expression was also found in TCGA glioblastoma group (p<0.001) and its high level predicted a poor clinical outcome (OS, p=0.003; PFS, p=0.001). ConclusionOur findings suggest that GPR65 is overexpressed in glioblastoma and its high expression predicts unfavorable clinical outcome for patients. Targeting GPR65 may serve as a potential therapy for treating glioblastoma.

The RNA Binding Protein IMP2 Preserves Glioblastoma Stem Cells by Preventing let-7 Target Gene Silencing.

Cancer stem cells (CSCs) can drive tumor growth, and their maintenance may rely on post-transcriptional regulation of gene expression, including that mediated by microRNAs (miRNAs). The let-7 miRNA family has been shown to induce differentiation by silencing stem cell programs. Let-7-mediated target gene suppression is prevented by LIN28A/B, which reduce let-7 biogenesis in normal embryonic and some cancer stem cells and ensure maintenance ofstemness. Here, we find that glioblastoma stem cells (GSCs) lack LIN28 and express both let-7 and their target genes, suggesting LIN28-independent protection from let-7 silencing. Using photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP), we show that insulin-like growth factor 2 mRNA-binding protein 2 (IMP2) binds to let-7 miRNA recognition elements (MREs) and prevents let-7 target gene silencing. Our observations define the RNA-binding repertoire of IMP2 and identify a mechanism whereby it supports GSC and neural stem cell specification.

Abstract 4714: The association of glioma germline risk SNPs with mutation-based molecular subgroups

Abstract Background: Specific germline alterations within the TERT, EGFR, CCDC26, CDKN2A/B, PHLDB1, TP53 and RTEL1 regions are associated with development of glioma. While some of these germline variants are associated with all gliomas (e.g. rs2736100 in TERT), others are associated with a specific morphologic and/or molecular subtype. For example, the RTEL1 region variants are associated with primary glioblastoma and rs55705857 in CCDC26 is associated with 1p/19q co-deleted oligodendrogliomas and with IDH mutant astrocytic gliomas. We hypothesized that germline variants in these regions will be associated with other molecular subtypes. The Cancer Genome Atlas (TCGA) and other groups have described molecular subtypes of glioma based on acquired somatic mutation patterns. Herein, we grouped glioma patients into mutation-based molecular subgroups based on three molecular alterations and determined if germline polymorphisms are associated with these mutation-based molecular subtypes. Methods: Five molecular subtypes were defined based on combinations of the following molecular alterations: TERT promoter mutation (TERTmut vs. TERTwt), IDH1/2 mutation (IDH mut vs. IDH wt) and 1p/19q co-deletion (1p19qcodel vs. 1p19q noncodel). Germline single nucleotide polymorphisms (SNPs) were evaluated by custom Illumina array analysis of blood-derived DNA and 1K genome imputation. Results: One hundred ninety-nine gliomas (24 oligodendrogliomas, 59 mixed oligoastrocytomas, 41 grade 2-3 astrocytomas and 75 glioblastomas) had both SNP data and mutation data available in order to assign them into the five molecular subgroups. The prevalence of each of the molecular subtypes was as follows: TERTmut/IDHmut/1p19qcodel (20%), TERTmut/IDHmut/1p19qnoncodel (5%), TERTmut/IDHwt/1p19qnoncodel (32%), TERTwt/IDHmut/1p19qnondel (35%) and TERTwt/IDHwt/1p19qnoncodel (8%). The TERT SNP (rs2736100) was protective for TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The CCDC26 SNP (rs55705857) was strongly associated with the risk of developing TERTmut/IDHmut/1p19qcodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The TP53 SNP (rs7837822) was strongly associated with risk of developing TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. Interestingly, while one RTEL1 region SNP (rs6062297) was strongly associated with glioma risk, RTEL1 SNPs rs6010620 and rs2297440 were protective for the development of TERTmut/IDHwt/1p19qnoncodel gliomas. We are currently in the process of validating these results using the TCGA glioblastoma and low grade glioma data. Conclusions: Glioma patients can be meaningfully classified according to their acquired mutation subtype. Importantly, our results suggest that there are significant associations between germline polymorphisms and mutation-based glioma molecular subtypes. Citation Format: Jeanette E. Eckel-Passow, Thomas M. Kollmeyer, Gobinda Sarkar, Anisha Chada, Paul A. Decker, Matthew L. Kosel, Alissa A. Caron, Hugues Sicotte, Kannabiran Nandakumar, Naresh Prodduturi, Brian P. O'Neill, Daniel H. Lachance, Robert B. Jenkins. The association of glioma germline risk SNPs with mutation-based molecular subgroups. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4714. doi:10.1158/1538-7445.AM2014-4714