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DNA Methylation subclass Receptor Tyrosine Kinase II (RTK II) is predictive for seizure development in glioblastoma patients

ABSTRACT: Background: Seizures can present at any time before or after the diagnosis of a glioma. Roughly, 25-30 % of glioblastoma (GBM) patients initially present with seizures, and an additional 30 % develop seizures during the course of the disease. Early studies failed to show an effect of general administration of anti-epileptic drugs for glioblastoma patients, since they were unable to stratify patients into high- or low- risk seizure groups. Methods: 112 patients, who underwent surgery for a GBM, were included. Genome- wide DNA methylation profiling was performed, before methylation subclasses and copy number changes inferred from methylation data were correlated with clinical characteristics. Independently, global gene expression was analyzed in GBM methylation subclasses from TCGA datasets (n=68). Results: Receptor tyrosine Kinase (RTK) II GBM showed a significantly higher incidence of seizures than RTK I and mesenchymal (MES) GBM (p<0.01). Accordingly, RNA expression datasets revealed an upregulation of genes involved in neurotransmitter synapses and vesicle transport in RTK II glioblastomas. In a multivariate analysis, temporal location (p=0.01, OR 6.35) and RTK II (p=0.03, OR 5.56) were most predictive for preoperative seizures. During postoperative follow-up, only RTK II remained significantly associated with the development of seizures (p<0.01, OR 8.23). Consequently, the need for antiepileptic medication and its increase due to treatment failure was highly associated with the RTK II methylation subclass (p<0.01). Conclusion: Our study shows a strong correlation of RTK II glioblastomas with preoperative and long-term seizures. These results underline the benefit of molecular glioblastoma profiling with important implications for postoperative seizure control.

ORGANISM(S): Homo sapiens

PROVIDER: GSE188547 | GEO | 2021/11/13

REPOSITORIES: GEO

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Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.

Project description:This SuperSeries is composed of the SubSeries listed below. BACKGROUND: Gliomas evade current therapies through primary and acquired resistance and the effect of temozolomide is mainly restricted to the subgroup of methylguanin-O6-methyltransferase promoter (MGMT) hypermethylated tumors. Further resistance markers and pathways against chemotherapy and radiotherapy are largely unknown and would help for better stratification. METHODS: The diagnostic cohorts involved clinical data and methylation profiles of the NOA-08 (n = 104, elderly glioblastoma) and the EORTC 26101 (n = 297, glioblastoma) studies and 398 patients with glioblastoma from the Heidelberg center. Twenty-eight glioblastoma CpGs from 17 DNA damage response (DDR) genes that negatively correlate with expression derived from a 450 DDR gene list as well as telomerase reverse transcriptase (TERT) promoter mutations were investigated for outcome and interaction with therapy and classifier assignments. RESULTS: CpG methylation in IDH wildtype tumors had the highest association with the mesenchymal (MES) and receptor tyrosine kinase (RTK) I glioblastoma subgroup. MES tumors have lower tumor purity and differ in copy number variations on chromosomes 7 and 10 from the RTK I and II subtypes. CpG hypomethylation of DDR genes (TP73, CCND3, CSNK1E, EXO1, CUL4A and PRPF19) correlated with worse patient survival in particular in MGMT unmethylated tumors. TERT promoter mutation is most frequent in RTK I and II subtypes and associated with worse survival in primary glioblastoma. Primary glioma cells show methylation pattern that resemble RTK I and II glioma and differ from long term established cell lines. Knock-down of selected resistance genes PRPF19 and TERT increase sensitivity to temozolomide treatment in vitro. CONCLUSION: Methylation of DDR genes and TERT promoter mutations are associated with tumor prognosis, dependent on the methylation cluster and MGMT promoter methylation status in high-grade glioma.

Project description:Nicotine, acting through the neuronal nicotinic acetylcholine receptors (nAChR), can induce seizures in mice. We aimed to study brain transcriptional response to seizure and to identify genes whose expression is altered after nicotine-induced seizures. Whole brains of untreated mice were compared to brains one hour after seizure activity, using Affymetrix U74Av2 microaarays. Experimental groups included wild-type mice and both nicotine-induced seizures sensitive and resistant nAChR mutant mice. Each genotype group received different nicotine doses to generate seizures. This approach allowed the identification of significantly changed genes whose expression was dependent on seizure activity, nicotine administration or both, but not on the type of nAChR subunit mutation or the amount of nicotine injected. Significant expression changes were detected in 62 genes (p < 0.05, FDR correction). Among them, GO functional annotation analysis determined that the most significantly over-represented categories were of genes encoding MAP kinase phosphatases, regulators of transcription and nucleosome assembly proteins. In-silico bioinformatic analysis of the promoter regions of the 62 changed genes detected the significant enrichments of 16 transcription regulatory elements (TREs), creating a network of transcriptional regulatory responses to seizures. The TREs for ATF and SRF were most significantly enriched, supporting their association with seizure activity. Our data suggest that nicotine-induced seizure in mice is a useful model to study seizure activity and its global brain transcriptional response. The differentially expressed genes detected here can help understand the molecular mechanisms underlying seizures in animal models, and may also serve as candidate genes to study epilepsy in humans. Experiment Overall Design: Whole brain expression profiles were determined in two experimental groups of mice, sixteen mice that were not treated with nicotine and twelve mice one hour after experiencing nicotine-induced seizure. The untreated group included six wild-type mice, five alpha7+/T and five beta4-/- mice. The group of mice that underwent nicotine-induced seizures included three wild-types, five alpha7+/T and four beta4-/- mice. Different doses of nicotine were injected intraperitoneally (i.p.) to each genotype group of mice in order to achieve a similar seizure score in all three genotypes.

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DNA Methylation subclass Receptor Tyrosine Kinase II (RTK II) is predictive for seizure development in glioblastoma patients

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