Genome-wide expression profiling of glioblastoma using a large combined cohort

miRNA-194-3p represses NF-κB in gliomas to attenuate iPSC genes and proneural to mesenchymal transition

Severe tumor heterogeneity drives the aggressive and treatment refractory nature of glioblastomas (GBMs). While limiting GBM heterogeneity offers promising therapeutic potential, the underlying mechanisms that regulate GBM plasticity remain poorly understood. We utilized 14 patient-derived and four commercially available cell lines to uncover miR-194-3p as a key epigenetic determinant of stemness and transcriptional subtype in GBM. We demonstrate that miR-194-3p degrades TAB2, an important mediator of NF-κB activity, decreasing NF-κB transcriptional activity. The loss in NF-κB activity following miR-194-3p overexpression or TAB2 silencing decreased expression of induced pluripotent stem cell (iPSC) genes, inhibited the oncogenic IL-6/STAT3 signaling axis, suppressed the mesenchymal transcriptional subtype in relation to the proneural subtype, and induced differentiation from the glioma stem cell (GSC) to monolayer (ML) phenotype. miR-194-3p/TAB2/NF-κB signaling axis acts as an epigenetic switch that regulates GBM plasticity and targeting this signaling axis represents a potential strategy to limit transcriptional heterogeneity in GBMs.

Viral Particle-Mediated SAMHD1 Depletion Sensitizes Refractory Glioblastoma to DNA-Damaging Therapeutics by Impairing Homologous Recombination

The current standard-of-care treatment for glioblastoma includes DNA damaging agents, γ-irradiation (IR) and temozolomide (TMZ). These treatments fail frequently and there is limited alternative strategy. Therefore, identifying a new therapeutic target is urgently needed to develop a strategy that improves the efficacy of the existing treatments. Here, we report that tumor samples from GBM patients express a high level of SAMHD1, emphasizing SAMHD1's importance. The depletion of SAMHD1 using virus-like particles containing Vpx, VLP(+Vpx), sensitized two independent GBM cell lines (LN-229 and U-87) to veliparib, a well-established PARP inhibitor, and slowed cell growth in a dose-dependent manner. In the mouse GBM xenograft model, Vpx-mediated SAMHD1 depletion reduced tumor growth and SAMHD1 knockout (KO) improved survival. In combination with IR or TMZ, SAMHD1 KO and exposure to 50% growth inhibitory dose (gID50) of VLP(+Vpx) displayed a synergistic effect, resulting in impaired HR, and improved LN-229 cells' sensitivity to TMZ and IR. In conclusion, our finding demonstrates that SAMHD1 promotes GBM resistance to treatment, and it is a plausible therapeutic target to improve the efficacy of TMZ and IR in GBM. Furthermore, we show that Vpx could be a potential therapeutic tool that can be utilized to deplete SAMHD1 in GBM.

Evaluation of the role of KPNA2 mutations in breast cancer prognosis using bioinformatics datasets

Breast cancer, comprising of several sub-phenotypes, is a leading cause of female cancer-related mortality in the UK and accounts for 15% of all cancer cases. Chemoresistant sub phenotypes of breast cancer remain a particular challenge. However, the rapidly-growing availability of clinical datasets, presents the scope to underpin a data-driven precision medicine-based approach exploring new targets for diagnostic and therapeutic interventions.We report the application of a bioinformatics-based approach probing the expression and prognostic role of Karyopherin-2 alpha (KPNA2) in breast cancer prognosis. Aberrant KPNA2 overexpression is directly correlated with aggressive tumour phenotypes and poor patient survival outcomes. We examined the existing clinical data available on a range of commonly occurring mutations of KPNA2 and their correlation with patient survival.Our analysis of clinical gene expression datasets show that KPNA2 is frequently amplified in breast cancer, with differences in expression levels observed as a function of patient age and clinicopathologic parameters. We also found that aberrant KPNA2 overexpression is directly correlated with poor patient prognosis, warranting further investigation of KPNA2 as an actionable target for patient stratification or the design of novel chemotherapy agents.In the era of big data, the wealth of datasets available in the public domain can be used to underpin proof of concept studies evaluating the biomolecular pathways implicated in chemotherapy resistance in breast cancer.

Chitinase 3-Like 2

OBJECTIVES

We aimed to evaluate the expression pattern of chitinase 3-like 2 (CHI3L2) in the tumor core and peritumoral brain zone (PBZ) of newly diagnosed glioblastoma (GBM) in recurrent tumors and its association with patient prognosis.

METHODS

The study was conducted on three sample sets derived from different patient cohorts. Messenger RNA (mRNA) expression of CHI3L2 in the tumor core and PBZ (n = 34) compared with control (n = 20) tissues was studied by quantitative polymerase chain reaction in sample set 1. Sample set 2 included 19 paired, primary-recurrent GBM tissues. Sample set 3 comprised 82 GBM tissues of patients with treatment and follow-up information. Immunohistochemistry (IHC) was performed on all three sample sets.

RESULTS

mRNA expression of CHI3L2 was significantly higher in the tumor core and PBZ compared with control (P < .0001). By IHC, CHI3L2 showed strong cytoplasmic staining in tumor cells. Recurrent tumors had a higher expression of CHI3L2 compared with primary tumors (P = .007). Survival analysis showed CHI3L2 expression was associated with shorter overall survival (P = .034) and progression-free survival (P = .010), which was in line with The Cancer Genome Atlas cohort (P = .043).

CONCLUSIONS

High expression of CHI3L2 in the tumor core and PBZ, as well as its association with tumor recurrence and poor patient prognosis, suggests it might be contributing to tumor spread and recurrence.

In Vitro and In Vivo Enhancement of Temozolomide Effect in Human Glioblastoma by Non-Invasive Application of Cold Atmospheric Plasma

Glioblastoma (GBM) is one of the most aggressive forms of adult brain cancers and is highly resistant to treatment, with a median survival of 12-18 months after diagnosis. The poor survival is due to its infiltrative pattern of invasion into the normal brain parenchyma, the diffuse nature of its growth, and its ability to quickly grow, spread, and relapse. Temozolomide is a well-known FDA-approved alkylating chemotherapy agent used for the treatment of high-grade malignant gliomas, and it has been shown to improve overall survival. However, in most cases, the tumor relapses. In recent years, CAP has been used as an emerging technology for cancer therapy. The purpose of this study was to implement a combination therapy of CAP and TMZ to enhance the effect of TMZ and apparently sensitize GBMs. In vitro evaluations in TMZ-sensitive and resistant GBM cell lines established a CAP chemotherapy enhancement and potential sensitization effect across various ranges of CAP jet application. This was further supported with in vivo findings demonstrating that a single CAP jet applied non-invasively through the skull potentially sensitizes GBM to subsequent treatment with TMZ. Gene functional enrichment analysis further demonstrated that co-treatment with CAP and TMZ resulted in a downregulation of cell cycle pathway genes. These observations indicate that CAP can be potentially useful in sensitizing GBM to chemotherapy and for the treatment of glioblastoma as a non-invasive translational therapy.

A Genome-Wide Profiling of Glioma Patients with an IDH1 Mutation Using the Catalogue of Somatic Mutations in Cancer Database

Simple Summary

Glioma patients that present a somatic mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a significantly better prognosis and overall survival than patients with the wild-type genotype. An IDH1 mutation is hypothesized to occur early during cellular transformation and leads to further genetic instability. A genome-wide profiling of glioma patients in the Catalogue of Somatic Mutations in Cancer (COSMIC) database was performed to classify the genetic differences in IDH1-mutant versus IDH1-wildtype patients. This classification will aid in a better understanding of how this specific mutation influences the genetic make-up of glioma and the resulting prognosis. Key differences in co-mutation and gene expression levels were identified that correlate with an improved prognosis.

Abstract

Gliomas are differentiated into two major disease subtypes, astrocytoma or oligodendroglioma, which are then characterized as either IDH (isocitrate dehydrogenase)-wild type or IDH-mutant due to the dramatic differences in prognosis and overall survival. Here, we investigated the genetic background of IDH1-mutant gliomas using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. In astrocytoma patients, we found that IDH1 is often co-mutated with TP53, ATRX, AMBRA1, PREX1, and NOTCH1, but not CHEK2, EGFR, PTEN, or the zinc finger transcription factor ZNF429. The majority of the mutations observed in these genes were further confirmed to be either drivers or pathogenic by the Cancer-Related Analysis of Variants Toolkit (CRAVAT). Gene expression analysis showed down-regulation of DRG2 and MSN expression, both of which promote cell proliferation and invasion. There was also significant over-expression of genes such as NDRG3 and KCNB1 in IDH1-mutant astrocytoma patients. We conclude that IDH1-mutant glioma is characterized by significant genetic changes that could contribute to a better prognosis in glioma patients.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Simple Summary

Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics.

Abstract

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

Development of a gene expression-based prognostic signature for IDH wild-type glioblastoma

BACKGROUND

We aimed to develop a gene expression-based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations.

METHODS

Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as "ATE"), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net-selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts.

RESULTS

NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status.

CONCLUSIONS

The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

SOX1 Is a Backup Gene for Brain Neurons and Glioma Stem Cell Protection and Proliferation

Failed neuroprotection leads to the initiation of several diseases. SOX1 plays many roles in embryogenesis, oncogenesis, and male sex determination, and can promote glioma stem cell proliferation, invasion, and migration due to its high expression in glioblastoma cells. The functional versatility of the SOX1 gene in malignancy, epilepsy, and Parkinson's disease, as well as its adverse effects on dopaminergic neurons, makes it an interesting research focus. Hence, we collate the most important discoveries relating to the neuroprotective effects of SOX1 in brain cancer and propose hypothesis worthy of SOX1's role in the survival of senescent neuronal cells, its roles in fibroblast cell proliferation, and cell fat for neuroprotection, and the discharge of electrical impulses for homeostasis. Increase in electrical impulses transmitted by senescent cells affects the synthesis of neurotransmitters, which will modify the brain cell metabolism and microenvironment.

Genome-Wide Identification and Analysis of the Methylation of lncRNAs and Prognostic Implications in the Glioma

Glioma is characterized by rapid cell proliferation and extensive infiltration among brain tissues, but the molecular pathology has been still poorly understood. Previous studies found that DNA methylation modifications play a key role in contributing to the pathogenesis of glioma. On the other hand, long noncoding RNAs (lncRNAs) has been discovered to be associated with some key tumorigenic processes of glioma. Moreover, genomic methylation can influence expression and functions of lncRNAs, which contributes to the pathogenesis of many complex diseases. However, to date, no systematic study has been performed to detect the methylation of lncRNAs and its influences in glioma on a genome-wide scale. Here, we selected the methylation data, clinical information, expression of lncRNAs, and DNA methylation regulatory proteins of 537 glioma patients from TCGA and TANRIC databases. Then, we performed a differential analysis of lncRNA expression and methylated regions between low-grade glioma (LGG) and glioblastoma multiform (GBM) subjects, respectively. Next, we further identified and verified potential key lncRNAs contributing the pathogenesis of glioma involved in methylation modifications by an annotation and correlation analysis, respectively. In total, 18 such lncRNAs were identified, and 7 of them have been demonstrated to be functionally linked to the pathogenesis of glioma by previous studies. Finally, by the univariate Cox regression, LASSO regression, clinical correlation, and survival analysis, we found that all these 18 lncRNAs are high-risk factors for clinical prognosis of glioma. In summary, this study provided a strategy to explore the influence of lncRNA methylation on glioma, and our findings will be benefit to improve understanding of its pathogenesis.

Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma

For decades, it has been known that gliomas follow a non-random spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the localization patterns of gliomas could provide clues to the origins of these types of tumours, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumour frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells of origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signalling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

Expression of the Androgen Receptor Governs Radiation Resistance in a Subset of Glioblastomas Vulnerable to Antiandrogen Therapy

New approaches are needed to overcome intrinsic therapy resistance in glioblastoma (GBM). Because GBMs exhibit sexual dimorphism and are reported to express steroid hormone receptors, we reasoned that signaling through the androgen receptor (AR) could mediate therapy resistance in GBM, much as it does in AR-positive prostate and breast cancers. We found that nearly half of GBM cell lines, patient-derived xenografts (PDX), and human tumors expressed AR at the transcript and protein level-with expression levels overlapping those of primary prostate cancer. Analysis of gene expression datasets also revealed that AR expression is higher in GBM patient samples than normal brain tissue. Multiple clinical-grade antiandrogens slowed the growth of and radiosensitized AR-positive GBM cell lines and PDXs in vitro and in vivo Antiandrogens blocked the ability of AR-positive GBM PDXs to engage adaptive transcriptional programs following radiation and slowed the repair of radiation-induced DNA damage. These results suggest that combining blood-brain barrier permeable antiandrogens with radiation may have promise for patients with AR-positive GBMs.

RSK4: a new prognostic factor in glioma

Glioma is the most common and fatal brain tumour and has a poor prognosis. Ribosomal S6 protein kinase 4 (RSK4) has been found to be involved in multiple tumour types; however, the role of RSK4 in gliomas and its clinical relevance remain unclear. In the present study, RSK4 expression was found to be significantly increased in glioma tissues compared with matched adjunct non-noncancerous tissues. Moreover, the expression of RSK4 was significantly higher in high-grade (III and IV) glioma tissues than in low-grade (I and II) glioma tissues. The data showed that the expression of RSK4 was significantly correlated with WHO grade, three-year survival rate and five-year survival rate. Kaplan-Meier analyses showed that patients with high RSK4 expression had poor overall survival. In addition, multivariate Cox regression analysis showed that RSK4 might be an independent prognostic factor in glioma patients. Collectively, these results suggest that RSK4 may be a new prognostic factor in glioma patients, and RSK4 is expected to be a potential biomarker and a potential target for glioma therapy.

A Glioblastoma Genomics Primer for Clinicians

New discoveries in Glioblastoma (GBM) biology have been made using genomics data. Genomic markers are routinely integrated into clinical neurosurgical practice. In this manuscript, we review the fundamentals of genomics such as the differences between first, second, and third generation sequencing technology. We also review the impact of single cell genomics in understanding the complex heterogenous GBM microenvironment. Finally, we will discuss advances in epigenetics that have lent insights into treatment resistance. The integration of genomics into neuro-oncology clinical practice is routine and will continue to expand with the expansion of precision of medicine. We provide a primer for clinicians.

ZW10 interacting kinetochore protein may serve as a prognostic biomarker for human breast cancer: An integrated bioinformatics analysis

ZW10 interacting kinetochore protein (ZWINT) is an essential component for the mitotic spindle checkpoint and has been reported to be upregulated in numerous types of human cancer. Nonetheless, its role in breast cancer (BC) remains unclear. Herein, it was demonstrated that the expression of ZWINT was significantly higher in BC than in normal breast tissues, on the basis of integrated analysis of bioinformatics studies, cancer database analyses and immunohistochemical detection. Elevated ZWINT levels were associated with a number of clinicopathological characteristics in patients with BC. These characteristics include: i) Positive human epidermal growth factor receptor 2 expression; ii) triple-negative BC; iii) younger age; iv) basal-like subtype; and v) greater Scarff-Bloom-Richardson grades. Additionally, prognostic analysis indicated that shorter relapse-free survival, overall survival and metastatic relapse-free survival may be associated with high ZWINT expression. A total of 16 pathways associated with high ZWINT expression, including Myc targets V1/2, DNA repair and mitotic spindle pathways, were identified using Gene Set Enrichment Analysis. In addition, a positive correlation between cyclin-dependent kinase 1 (CDK1) and ZWINT mRNA expression was identified by co-expression analysis. The present study suggested that ZWINT may serve as an effective prognostic biomarker for BC. In addition, ZWINT may be implicated in the CDK1-mediated initiation and progression of BC. However, further research is required to understand the role of ZWINT in BC.