The Cancer Genome Atlas Glioblastoma Multiforme Research Articles

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan–Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, for T2w, and .992 ± 2.63 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, 2.49 ± 6.89 ×10-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-2}$$\\end{document}, 40.51 ± 0.22, and 0.993 ± 3.40 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document} for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient’s outcome prediction with multi-contrast images.

Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Abstract 3170: Human retroviral sequences transcription in glioma/glioblastoma sample

Abstract In glioblastoma patients, expansion of glioma initiating cells (GICs), a discrete population of cells demonstrating stem-like phenotype, is often associated with drug resistance development and ultimately cancer recurrence. Therefore a better understanding of the molecular and cellular mechanisms underlying the origin of GICs is critical to develop new therapies better targeting glioblastoma. Human endogenous retroviral sequences (HERVs), present in approximately 500,000 copies and comprising nearly 8% of the human genome, may serve as an abundant genetic material equipping GIC with phenotypic plasticity needed to develop stem-like phenotype. Recent epigenomic and transcriptomic data show HERV activation during early human development. Moreover, increased HERV transcription is observed in OCT4/SOX2/KLF4/c-MYC induced pluripotent cells. Finally, activation of HERV loci transcription is also commonly observed in different tumor types. Using publicly available The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) RNA-seq data collection, we have quantified transcripts for all 13,208 HERV loci annotated in the RepBase database. To improve the accuracy of quantifying the HERV transcriptomic landscape, we reassigned already mapped fragments to the most probable transcript using the Telescope software. Our comprehensive approach allowed us to identify the set of HERV loci uniquely dysregulated in the glioblastoma compared to normal brain tissue. In addition, we compared 540 low-grade glial tumors from TCGA-LGG project (classified as grade II and III gliomas) with 165 glioblastoma samples from TCGA-GBM (classified as grade IV gliomas) using the same bioinformatics pipeline. Comprehensive analysis of transcript levels allowed us to identify HERV assemblage differentially expressed between these two patient cohorts. The correlation between HERV and ORF transcript levels show strong coregulation of HERVs expression with genes involved in establishing/maintaining embryonic/neural/glial stem cells phenotype. To confirm our observations we have up- or downregulated HERV loci combining guide RNA array targeting robustly approximately 90% of HERV LTR5Hs elements with a vector expressing CRISPR-based transcriptional activator or repressor. Downregulation of HERV transcription with this system was associated with the inhibition of clonogenic growth in cell culture. In summary, this study sets the stage for identifying HERV loci that could serve as potential GIC biomarkers, as well as the molecular targets for GIC elimination. Citation Format: Adam J. Lassak, Monika Rak, Melody C. Baddoo, Hui-Yi Lin, Krzysztof Reiss. Human retroviral sequences transcription in glioma/glioblastoma sample [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3170.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.

BackgroundGlioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA‐seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.MethodsWe studied single‐cell RNA‐seq dataset and The Cancer Genome Atlas‐glioblastoma multiforme (TCGA‐GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.ResultsWe identified 2128 DEGs from the single‐cell RNA‐seq dataset and 6461 DEGs from the bulk RNA‐seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low‐risk group showed better OS than those in the high‐risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.ConclusionWe constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA‐seq dataset and a bulk RNA‐seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.

Simple SummaryIn 2016, the World Health Organization (WHO) recommended the incorporation of molecular parameters, in addition to histology, for an optimal definition of the central nervous system (CNS) tumors. Gliomas, being among the most common types of CNS tumors, have distinct clinical outcomes and treatment strategies based on different tumor grades, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion statuses. This paper uses radiomics models to noninvasively predict the glioma subtype with clinical Magnetic Resonance Imaging (MRI) images. Different settings in the radiomics pipeline were investigated to achieve optimal performance, together with a better understanding of the exact role of each setting in the model performance. The characteristics of the radiomic features that best distinguish the glioma subtypes were also analyzed. This paper not only provides a radiomics pipeline which works well for predicting the glioma subtype, but it also contributes to the radiomics model development and interpretability.Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

Analysis of Gene Co-Expression Network to Identify the Role of CD8 + T Cell Infiltration-Related Biomarkers in High-Grade Glioma.

BackgroundHigh-grade glioma is a type of heterogeneous lethal brain tumor most common in adults. At present, immune checkpoint inhibitors (ICIs) are being considered for first-line therapeutics for malignant GBM. Nonetheless, molecular markers for malignant GBM are unavailable at present. As a result, it is important to explore molecular markers related to immunity for GBM.Materials and MethodsThe present study adopted a deconvolution algorithm for quantifying immunocyte composition and measuring gene expression, and used weighted gene co-expression network analysis (WGCNA) to analyze GBM expression data obtained from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) databases. Thereafter, key CD8+ T cell infiltration-related genes and modules were identified, and database analysis was conducted to verify the therapeutic and immune features of the selected genes.ResultsFrom this study, CD8+ T cell-related modules were identified. By using consistent clustering analysis, two panels of genes (red and green) with the highest correlation with CD8+ T cells infiltration were used to construct high-, low-expression groups, silent and/or mixed group of T cell infiltrations. In the high and low CD8+ T cell infiltration groups, a total of 535 differential genes were obtained, of which ten genes (RPS5, RPS6, FAU, RPS19, RPS23, RPS15A, RPS29, RPS14, RPS16, RPS27A) were identified through protein–protein interactions and co-expression network analysis. Post Cox regression and Kaplan–Meier (K-M) survival analysis, RPS5, RPS6, and RPS16 were selected as candidate prognostic biomarkers related to CD8+ T cells.ConclusionThe three associated genes RPS5, RPS6, and RPS16 were markedly related to degree of T cell infiltration and immune-related activated. We identified their potential biomarkers and therapeutic targets associated with the extent of CD8+ T cell infiltration in GBM.

FBXW7 induces apoptosis in glioblastoma cells by regulating HDAC7.

Glioblastoma is an aggressive type of brain cancer with an extremely poor prognosis. Additionally, the F-box WD repeat-containing protein 7 (FBXW7) is a component of the ubiquitin-proteasome system that has been widely implicated in human cancers. In this study, we investigated the role and mechanism of FBXW7 in glioblastoma. FBXW7 expression was analyzed in normal and glioblastoma tissue samples using The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) database. Then, quantitative reverse transcription-polymerase chain reaction (RT-PCR) was used to examine mRNA expression, whereas, western blot analysis was conducted to determine protein levels of the samples. Furthermore, cell apoptosis was assessed using the Annexin V staining method, followed by flow cytometry analysis. Immunoprecipitation (IP) assay was conducted as well to test protein-protein interactions. Lastly, protein expression in tissues was examined by conducting immunohistochemistry (IHC). Results showed that the glioblastoma tissue samples displayed an FBXW7 downregulation compared with normal tissues. In vitro, the overexpression of FBXW7 in glioblastoma cells induced apoptosis, whereas, its knockdown displayed the opposite effect. Mechanistically, FBXW7 interacted with HDAC7 to promote HDAC7 ubiquitination, however, the overexpression of HDAC7 in glioblastoma cells blocked FBXW7-induced apoptosis. Finally, FBXW7 and HDAC7 displayed an inverse correlation in glioblastoma tissues in vivo. Therefore, our data demonstrated an important function of FBXW7 in promoting glioblastoma apoptosis by interacting with HDAC7 and promoting HDAC7 ubiquitination.

Highly Expressed CYBRD1 Associated with Glioma Recurrence Regulates the Immune Response of Glioma Cells to Interferon.

Invasiveness, resistance to treatment, and recurrence of gliomas are significant hurdles to successful treatment regimens. Data sets from Gene Expression Omnibus (GEO), CGGA-RNAseq, and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) were analyzed, and an increased expression of Cytochrome B Reductase 1 (CYBRD1) was identified and could be associated with aggravated clinical outcomes. Gene ontology (GO) enrichment analysis indicated that CYBRD1 co-expressed genes are enriched during an immune response. CYBRD1 overexpression in glioma cell lines is enhanced, whereas CYBRD1 silencing attenuated the aggressiveness of glioma cells. In IFN-α-treated glioma cells, IFN-α suppressed the viability and migratory ability and invasive ability of glioma cells, whereas CYBRD1 overexpression attenuated the antitumor effects of IFN-α. CYBRD1 could potentially serve as a biomarker for glioma recurrence. CYBRD1 overexpression enhances glioma cell aggressiveness and attenuates glioma cell response to IFN-α.

Abstract 2427: Identification of transcription factors activated in glioblastoma multiforme

Abstract Introduction Glioblastoma multiforme (GBM) is the most common intrinsic brain tumor in adults and is universally fatal. Our strategy to study the molecular mechanisms leading to GBM is to investigate the role of transcription factors (TFs) that are differentially expressed between normal and GBM. Previous studies have shown that ZFX is a key TF that shows increased expression in GBM. However, the role of ZFX, and the identity of TFs that may work with ZFX, in driving GBM carcinogenesis is not yet understood. Materials and methods: ChIP-seq was performed to determine ZFX binding sites in the U87 GBM cancer cell line. RNA-seq datasets generated from GBM tissues by The Cancer Genome Atlas (TCGA) were analyzed to identify TFs showing increased expression in GBM. Publicly available H3K27ac chromatin immunoprecipitation (ChIP-seq) and Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) datasets generated from GBM tissues were analyzed to identify active enhancers and TFs that bind to GBM enhancers. Results: We found that a majority of ZFX binding sites in GBM are located at promoters, with ~1000 binding sites being at non-promoter regions. An overlap analysis of ZFX binding sites in multiple cancer cell types showed that a high percentage of ZFX-binding sites at promoter regions are shared across cell types whereas hundreds of GBM-specific binding sites are located at non-promoter regions. Using TCGA GBM RNA-seq datasets, we next identified differentially expressed TFs. For example, we found that MTY1L and NEUROD2, which have been reported to be tumor suppressors, are downregulated while HOX family TFs are upregulated in GBM tissues, as compared to normal tissues. We also identified thousands of enhancers specifically active in GBM, as compared to normal cells, using H3K27ac ChIP-seq and ATAC-seq datasets. TF motif analysis of the identified enhancers revealed that motifs of Basic Leucine Zipper Domain (bZIP), CAAT box-binding transcription factor (CTF), homeobox (HOX), signal transducer and activator of transcription (STAT), and helix-turn-helix (HTH) family were enriched. Conclusion: By integrating transcriptomic and epigenomic datasets, we identified and characterized TFs activated in GBM. We are currently investigating how ZFX cooperates with the TFs we identified as bound to the GBM-specific enhancers. These studies will provide insight into the molecular mechanisms underlying GBM. Citation Format: Zexun Wu, Shannon Schreiner, Peggy J. Farnham, Suhn K. Rhie. Identification of transcription factors activated in glioblastoma multiforme [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 2427.

UBE2D3 Activates SHP-2 Ubiquitination to Promote Glycolysis and Proliferation of Glioma via Regulating STAT3 Signaling Pathway.

Glioma is a primary brain cancer with high malignancy and morbidity. Current management for glioma cannot reach optimal remission. Therefore, it is necessary to find novel targets for glioma treatment. Ubiquitin-conjugating enzyme E2 D3 (UBE2D3) is involved in the pathogenesis of various kinds of cancer. However, its role in glioma remains unclear. Our study aims to explore the function and underlying mechanism of UBE2D3 in the development of glioma. By analysis with The Cancer Genome Atlas-Glioblastoma multiforme (TCGA-GBM) dataset, we found that UBE2D3 was highly expressed in glioma and it is positive correlation with glycolysis, apoptosis, and STAT3 pathway. Then, we explore the effects of UBE2D3 knockdown in the biological functions of glioma cell lines. Cell proliferation and apoptosis were estimated by cell counting kit-8 assay and flow cytometry. Extracellular acidification rate and oxygen consumption rate were estimated to determine the level of cell glycolysis. Xenograft experiments were performed to identify in vivo function of UBE2D3. The results showed that the inhibition of UBE2D3 could suppress the proliferation, glycolysis, and STAT3 phosphorylation of GBM both in vitro and in vivo. UBE2D3 could interact with SHP-2 and promoted its ubiquitination, which elevated the activation of STAT3 pathway. Overexpressed SHP-2 could reverse the effect of UBE2D3 and they shared contrary expression patterns in glioma and normal brain tissues. In summary, our study revealed that UBE2D3 could promote the ubiquitination of SHP-2, which activated STAT3 pathway and promoted glioma proliferation as well as glycolysis. UBE2D3 could be a potential target for glioma treatment.

An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification

Molecular marker-based glioblastoma (GBM) subclassification is emerging as a key factor in personalized GBM treatment planning. Multiple genetic alterations, including methylation status and mutations, have been proposed in GBM subclassification. RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable. Machine learning (ML) algorithms have been reported as an applicable method that can consistently subgroup GBM. In this study, we systematically studied the applicability of the commonly used ML algorithms based on The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset and cross-validated in the Chinese Glioma Genome Atlas (CGGA) dataset. ML algorithms studied include Binomial and multinomial Logistic Regression, Linear discriminant analysis, Decision trees, K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Gradient Boosting, Voting Ensemble, Multi-Layer Perceptron. RNA-Seq data of 44 biomarkers were passed through the algorithms for performance evaluation. We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.

The lncRNA RP3-439F8.1 promotes GBM cell proliferation and progression by sponging miR-139-5p to upregulate NR5A2

Nuclear Receptor Subfamily 5 Group A Member 2 (NR5A2, LRH-1) is an oncogene in a wide range of cancer types. Bioinformatics analysis on glioblastoma multiforme (GBM) tumors has revealed that the miR-139-5p-NR5A2 axis may be putatively regulated by the long non-coding RNA (lncRNA) RP3-439F8.1. This led us to hypothesize the existence of a RP3-439F8.1-miR-139-5p-NR5A2 regulatory axis in GBM cells. Gene expression analysis was performed in GBM tumor samples and normal controls from our hospital, the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) cohort, and the Gene Expression Omnibus (GEO) database (GSE7696). Cell proliferation, apoptosis, Matrigel Transwell, colony formation, and cell cycle assays were performed in T98 G and U251 cells in vitro. An orthotopic U251 xenograft murine model was employed to test the effects of RP3-439F8.1 knockdown in vivo. NR5A2 was upregulated in the three independent GBM tumor cohorts. In vitro, NR5A2 overexpression enhanced GBM cell proliferation, colony formation, invasiveness, and G0-G1 cell cycle phase shift via co-activating β-catenin/TCF4 signaling, with no apparent effect upon apoptosis. In contrast, RP3-439F8.1 knockdown produced the opposite effects. RP3-439F8.1 knockdown reduced tumor progression in vivo, increasing overall survival in model mice. Further in vitro experiments revealed that RP3-439F8.1 acts as a competing endogenous RNA (ceRNA) to regulate NR5A2 by sponging the microRNA miR-139-5p. These findings were clinically validated by a positive correlation between RP3-439F8.1 and NR5A2 and a negative correlation between RP3-439F8.1 and miR-139-5p in GBM tumors. Our study supports a tumorigenic role for RP3-439F8.1 in GBM through the RP3-439F8.1/miR-139-5p/NR5A2 axis.

Four specific biomarkers associated with the progression of glioblastoma multiforme in older adults identified using weighted gene co-expression network analysis

ABSTRACT Glioblastoma multiforme (GBM) is the most common primary intracranial malignancy in adults. Owing to individual tolerance and tumor heterogeneity, the therapy methods for young adults do not apply to older adults. The present study aimed to identify specific biomarkers for GBM in older adults using weighted gene co-expression network analysis (WGCNA). Gene expression profiles of older adults with GBM were downloaded from The Cancer Genome Atlas (TCGA) and set as a discovery cohort to construct WGCNA. Core genes of clinically significant modules were used to perform functional enrichment, protein-protein interaction, and Pearson correlation analyses. Gene expression profiles of young in TCGA and older GBM patients from our research group were set as verification cohorts for hub gene expression and diagnostic value. Four significant gene modules associated clinically with older adults with GBM were identified, whereas 251 genes were core genes with module membership>0.8 and gene significance>0.2. Ermin (ERMN), myelin-associated oligodendrocyte basic protein (MOBP), proteolipid protein 1 (PLP1), and oligodendrocytic myelin paranodal and inner loop protein (OPALIN) genes had significant relationships with the Karnofsky score (KPS) in older GBM patients. ERMN, MOBP, PLP1, and OPALIN had no relationship with KPS in young GBM patients. These genes were upregulated in GBM tissues from older patients with low but not high KPS and had high diagnostic value. In conclusion, ERMN, MOBP, PLP1, and OPALIN may serve as specific biomarkers for the progression of GBM in older adults.

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.

ILK silencing inhibits migration and invasion of more invasive glioblastoma cells by downregulating ROCK1 and Fascin-1.

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor and it is associated with poor survival. Integrin-linked kinase (ILK) is a serine/threonine protein pseudo-kinase that binds to the cytoplasmic domains of β1 and β3 integrins and has been previously shown to promote invasion and metastasis in many cancer types, including GBM. However, little is known regarding the exact molecular mechanism implicating ILK in GBM aggressiveness. In this study, we used two brain cell lines, the non-invasive neuroglioma H4 cells, and the highly invasive glioblastoma A172 cells, which express ILK in much higher levels than H4. We studied the effect of ILK silencing on the metastatic behavior of glioblastoma cells in vitro and elucidate the underlying molecular mechanism. We showed that siRNA-mediated silencing of ILK inhibits cell migration and invasion of the highly invasive A172 cells while it does not affect the migratory and invasive capacity of H4 cells. These data were also supported by respective changes in the expression of Rho-associated kinase 1 (ROCK1), fascin actin-bundling protein 1 (FSCN1), and matrix metalloproteinase 13 (MMP13), which are known to regulate cell migration and invasion. Our findings were further corroborated by analyzing the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. We conclude that ILK promotes glioblastoma cell invasion through activation of ROCK1 and FSCN1 in vitro, providing a more exact molecular mechanism for its action.

Analysis of single nucleotide variants of HFE gene and association to survival in The Cancer Genome Atlas GBM data.

Human hemochromatosis protein (HFE) is involved in iron metabolism. Two major HFE polymorphisms, H63D and C282Y, have been associated with an increased risk of cancers. Previously, we reported decreased gender effects in overall survival based on H63D or C282Y HFE polymorphisms patients with glioblastoma multiforme (GBM). However, the effect of other single nucleotide variation (SNV) in the HFE gene on the cancer development and progression has not been systematically studied. To expand our finding in a larger sample, and to identify other HFE SNV, we analyzed the frequency of somatic SNV in HFE gene and its relationship to survival in GBM patients using The Cancer Genome Atlas (TCGA) GBM (Caucasian only) database. We found 9 SNVs with increased frequency in blood normal of TCGA GBM patients compared to the 1000Genome. Among 9 SNVs, 7 SNVs were located in the intron and 2 SNVs (i.e., H63D, C282Y) in the exon of HFE gene. The statistical analysis demonstrated that blood normal samples of TCGA GBM have more H63D (p = 0.0002, 95% Confidence interval (CI): 0.2119–0.3223) or C282Y (p = 0.0129, 95% CI: 0.0474–0.1159) HFE polymorphisms than 1000Genome. The Kaplan-Meier survival curve for the 264 GBM samples revealed no difference between wild type (WT) HFE and H63D, and WT HFE and C282Y GBM patients. In addition, there was no difference in the survival of male/female GBM patients based on HFE genotype. There was no correlation between HFE expression and survival. In conclusion, the current results suggest that somatic HFE polymorphisms do not impact GBM patients’ survival in the TCGA data set of GBM.

An integrative characterization of recurrent molecular aberrations in glioblastoma genomes

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults. Decades of investigations and the recent effort of the Cancer Genome Atlas (TCGA) project have mapped many molecular alterations in GBM cells. Alterations on DNAs may dysregulate gene expressions and drive malignancy of tumors. It is thus important to uncover causal and statistical dependency between ‘effector’ molecular aberrations and ‘target’ gene expressions in GBMs. A rich collection of prior studies attempted to combine copy number variation (CNV) and mRNA expression data. However, systematic methods to integrate multiple types of cancer genomic data—gene mutations, single nucleotide polymorphisms, CNVs, DNA methylations, mRNA and microRNA expressions and clinical information—are relatively scarce. We proposed an algorithm to build ‘association modules’ linking effector molecular aberrations and target gene expressions and applied the module-finding algorithm to the integrated TCGA GBM data sets. The inferred association modules were validated by six tests using external information and datasets of central nervous system tumors: (i) indication of prognostic effects among patients; (ii) coherence of target gene expressions; (iii) retention of effector–target associations in external data sets; (iv) recurrence of effector molecular aberrations in GBM; (v) functional enrichment of target genes; and (vi) co-citations between effectors and targets. Modules associated with well-known molecular aberrations of GBM—such as chromosome 7 amplifications, chromosome 10 deletions, EGFR and NF1 mutations—passed the majority of the validation tests. Furthermore, several modules associated with less well-reported molecular aberrations—such as chromosome 11 CNVs, CD40, PLXNB1 and GSTM1 methylations, and mir-21 expressions—were also validated by external information. In particular, modules constituting trans-acting effects with chromosome 11 CNVs and cis-acting effects with chromosome 10 CNVs manifested strong negative and positive associations with survival times in brain tumors. By aligning the information of association modules with the established GBM subclasses based on transcription or methylation levels, we found each subclass possessed multiple concurrent molecular aberrations. Furthermore, the joint molecular characteristics derived from 16 association modules had prognostic power not explained away by the strong biomarker of CpG island methylator phenotypes. Functional and survival analyses indicated that immune/inflammatory responses and epithelial-mesenchymal transitions were among the most important determining processes of prognosis. Finally, we demonstrated that certain molecular aberrations uniquely recurred in GBM but were relatively rare in non-GBM glioma cells. These results justify the utility of an integrative analysis on cancer genomes and provide testable characterizations of driver aberration events in GBM.

Unique genome-wide map of TCF4 and STAT3 targets using ChIP-seq reveals their association with new molecular subtypes of glioblastoma

Aberrant activation of beta-catenin/TCF4 and STAT3 signaling in glioblastoma multiforme (GBM) has been reported. However, the molecular mechanisms related to this process are still poorly understood. Genome-wide screening of the binding characteristics of the transcription factors TCF4 and STAT3 in GBM cells was performed by chromatin immunoprecipitation sequencing (ChIP-seq) assay. Hierarchical clustering was used to analyze the association of TCF4 and STAT3 coregulated genes with The Cancer Genome Atlas (TCGA) GBM subtypes (classical, mesenchymal, neural, and proneural). New molecular classification of GBM was proposed and validated in Western and Asian populations. We identified 1250 overlapping putative target genes that were coregulated by TCF4 and STAT3. Further, the coregulated genes had the potential to guide TCGA GBM subtypes. Finally, we proposed a new molecular classification of GBM into 2 subtypes (proneural-like and mesenchymal-like) and showed that the new classification could be applied to both Western and Asian populations. In addition, the GBM response to temozolomide therapy differed depending on its subtype; mesenchymal-like GBM benefited, while there was no benefit for proneural-like GBM. This is the first comprehensive study to combine a ChIP-seq assay of TCF4 and STAT3 and data mining of patient cohorts to derive molecular subtypes of GBM.

Abstract 3968: Exploring cancer datasets in the integrative genomics viewer (IGV)

Abstract Cancer genome characterization studies, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), are producing a flood of diverse data including whole-genome sequencing scans, expression profiles, high-resolution SNP and copy number data, and epigenetic profiles. These diverse datasets are enabling researchers to study the cancer genome in unprecedented detail. However, their sheer size and diversity presents significant challenges to downstream interpretation. While much progress has been made in automated analysis, human review enabled by intuitive and responsive visualizations remains an essential component of data analysis. The Integrative Genomics Viewer (IGV) was developed to address this need by providing researchers with an intuitive, user-friendly, interactive visualization tool for exploration of diverse genomic datasets. IGV has extensive support for both next-generation and micro-array based platforms, and for integration of these data types with clinical and phenotypic data. It provides an intuitive interface similar to online mapping tools such as Google Maps, enabling smooth zooming and panning at all resolution scales, from whole genome to base pairs. Data can be annotated, filtered, grouped, and sorted in a variety of ways. This ability to dynamically and flexibly integrate multiple different datasets, and view them at any scale, allows investigators to elucidate complex biological relationships that are not otherwise readily apparent. In this presentation we describe the IGV, with emphasis on recent features focused on data integration and interpretation, developed in close collaboration with cancer researchers. Specifically (1) a multi-locus pathway view which supports simultaneous viewing of data in multiple genomic regions defined by pathways or gene sets, (2) integration with external tools such as Mutation Assessor and PolyPhen, to provide views and data to asses the functional significance of somatic events, and (3) a flexible, interactive charting capability to enable detailed exploration of inter-dependencies between data types at a specific locus or set of loci. These features will be illustrated in the context of use cases drawn from the TCGA glioblastoma multiforme and ovarian datasets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3968. doi:1538-7445.AM2012-3968

Abstract 5561: Radiogenomic analysis indicates MR images are potentially predictive of EGFR mutation status in glioblastoma multiforme

Abstract Objective: To predict mutations of key genes in glioblastoma multiforme (GBM) from MR image features. Methods: We obtained mutational and MR image data from 35 patients in the Cancer Genome Atlas (TCGA) GBM database. T1-weighted axial images pre and post gadolinium contrast MRI were processed as follows: a board certified neuro-radiologist traced a region of interest (ROI) around the enhanced part of the largest lesion in the T1 post-contrast MRI and confirmed by comparing it with the T1 pre-contrast image. This ROI was then used to compute features that characterized the intensity of the enhanced lesion, the sharpness of lesion boundaries and the boundary shape. We used the resulting data set to build a linear regression model with regularization to predict the presence of a mutation in terms of image features. We focused on predicting mutations in EGFR because several drugs target it and because EGFR mutations are prevalent in the TCGA GBM data. We evaluated the performance of predicting EGFR mutations from MR imaging data using 5-fold cross validation (5F-CV) and the area under the ROC Curve (AUC). The optimal ROC operating point was defined as the point with the maximal sum of sensitivity and specificity when the cost of false positives and false negatives are considered equivalent. Results: We found that computationally-derived MR image features can predict the presence of EGFR mutations with an AUC of 0.80. The optimal operating point had a sensitivity and specificity of 83% and 93% respectively. The top ranked features in the image-based EGFR-predictor model suggest that EGFR-mutated tumors have blurrier edges than EGFR-wild-type tumors. Conclusion: Our preliminary radiogenomic analysis of GBM suggests MR images may be used to non-invasively determine the mutation status of EGFR, an important drug target in glioblastoma. The ability to determine important genomic aberrations based on image data suggests a increasingly important role for imaging in personalized medicine and warrants further investigation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5561. doi:1538-7445.AM2012-5561