Somatic Focal Copy Number Gains of Noncoding Regions of Receptor Tyrosine Kinase Genes in Treatment-Resistant Epilepsy

Glioma genetic profiles associated with electrophysiologic hyperexcitability

BACKGROUND

Distinct genetic alterations determine glioma aggressiveness, however, the diversity of somatic mutations contributing to peritumoral hyperexcitability and seizures over the course of the disease is uncertain. This study aimed to identify tumor somatic mutation profiles associated with clinically significant hyperexcitability.

METHODS

A single center cohort of adults with WHO grades 1-4 glioma and targeted exome sequencing (n = 1716) was analyzed and cross-referenced with a validated EEG database to identify the subset of individuals who underwent continuous EEG monitoring (n = 206). Hyperexcitability was defined by the presence of lateralized periodic discharges and/or electrographic seizures. Cross-validated discriminant analysis models trained exclusively on recurrent somatic mutations were used to identify variants associated with hyperexcitability.

RESULTS

The distribution of WHO grades and tumor mutational burdens were similar between patients with and without hyperexcitability. Discriminant analysis models classified the presence or absence of EEG hyperexcitability with an overall accuracy of 70.9%, regardless of IDH1 R132H inclusion. Predictive variants included nonsense mutations in ATRX and TP53, indel mutations in RBBP8 and CREBBP, and nonsynonymous missense mutations with predicted damaging consequences in EGFR, KRAS, PIK3CA, TP53, and USP28. This profile improved estimates of hyperexcitability in a multivariate analysis controlling for age, sex, tumor location, integrated pathologic diagnosis, recurrence status, and preoperative epilepsy. Predicted somatic mutation variants were over-represented in patients with hyperexcitability compared to individuals without hyperexcitability and those who did not undergo continuous EEG.

CONCLUSION

These findings implicate diverse glioma somatic mutations in cancer genes associated with peritumoral hyperexcitability. Tumor genetic profiling may facilitate glioma-related epilepsy prognostication and management.

Unexplained Causes of Glioma-Associated Epilepsies: A Review of Theories and an Area for Research

Approximately 30% of glioma patients are able to survive beyond one year postdiagnosis. And this short time is often overshadowed by glioma-associated epilepsy. This condition severely impairs the patient's quality of life and causes great suffering. The genetic, molecular and cellular mechanisms underlying tumour development and epileptogenesis remain incompletely understood, leading to numerous unanswered questions. The various types of gliomas, namely glioblastoma, astrocytoma and oligodendroglioma, demonstrate distinct seizure susceptibility and disease progression patterns. Patterns have been identified in the presence of IDH mutations and epilepsy, with tumour location in cortical regions, particularly the frontal lobe, showing a more frequent association with seizures. Altered expression of TP53, MGMT and VIM is frequently detected in tumour cells from individuals with epilepsy associated with glioma. However, understanding the pathogenesis of these modifications poses a challenge. Moreover, hypoxic effects induced by glioma and associated with the HIF-1a factor may have a significant impact on epileptogenesis, potentially resulting in epileptiform activity within neuronal networks. We additionally hypothesise about how the tumour may affect the functioning of neuronal ion channels and contribute to disruptions in the blood-brain barrier resulting in spontaneous depolarisations.

DNA methylation as a diagnostic tool

DNA methylation of cytosines in CpG sites throughout the genome is an epigenetic mark contributing to gene expression regulation. DNA methylation patterns are specific to tissue type, conserved throughout life and reflect changes during tumorigenesis. DNA methylation recently emerged as a diagnostic tool to classify tumors based on a combination of preserved developmental and mutation induced signatures. In addition to the tumor classification, DNA methylation data can also be used to evaluate copy number variation, assess promoter methylation status of specific genes, such as MGMT or MLH1, and deconvolute the tumor microenvironment, assessing the tumor immune infiltrate as a potential biomarker for immunotherapy. Here we review the role for DNA methylation in tumor diagnosis.

Characterization and clinical relevance of PDGFRA pathway copy number variation gains across human cancers

We investigated the copy number variation (CNV) of PDGFRA pathway across all common cancer types as well as its clinical relevance. This study included a total of 10,678 patients with pan-cancerous species involving 33 types of cancers and patient information was obtained from The Cancer Genome Atlas. According to the PDGFRA pathway CNV, all samples were divided into copy number gain (CN gain) group and No CN gain group. The analysis of loss of heterozygosity (LOH) fraction, CNV burden, tumor mutation burden (TMB), and the number of immunogenic mutations were performed, as well as the correlation analysis of PDGFRA pathway CN gain with tumor-related signaling pathways and tumor-infiltrating immune cell subpopulations. The results showed that CN gain of PDGFRA pathway in the cancer patients was associated with significantly shorter overall survival. The CN gain of PDGFRA pathway was identified as a prognostic risk factor for some tumors. CN gain was accompanied by an altered percentage of LOH, CNV burden, TMB, the number of immunogenic mutations were increased and tumor-infiltrating immune cell subpopulations were less. While certain tumor-related signaling pathways, such as hypoxia, cell cycle, DNA repair, and epithelial-mesenchymal transition were more enriched in the CN gain group, quiescence, and inflammation pathways were more enriched in the No CN gain group. In conclusion, PDGFRA pathway CNV gain may be a poor prognostic factor in cancer patients.

OUP accepted manuscript

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 antiepileptic drugs for glioblastoma patients, since they were unable to stratify patients into high- or low-risk seizure groups.

METHODS

111 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 < .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 = .02, OR 5.69) and RTK II (P = .03, OR 5.01) were most predictive for preoperative seizures. During postoperative follow-up, only RTK II remained significantly associated with the development of seizures (P < .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 < .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.

Non-coding regulatory elements: Potential roles in disease and the case of epilepsy

Non-coding DNA (ncDNA) refers to the portion of the genome that does not code for proteins and accounts for the greatest physical proportion of the human genome. ncDNA includes sequences that are transcribed into RNA molecules, such as ribosomal RNAs (rRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and un-transcribed sequences that have regulatory functions, including gene promoters and enhancers. Variation in non-coding regions of the genome have an established role in human disease, with growing evidence from many areas, including several cancers, Parkinson's disease and autism. Here, we review the features and functions of the regulatory elements that are present in the non-coding genome and the role that these regions have in human disease. We then review the existing research in epilepsy and emphasise the potential value of further exploring non-coding regulatory elements in epilepsy. In addition, we outline the most widely used techniques for recognising regulatory elements throughout the genome, current methodologies for investigating variation and the main challenges associated with research in the field of non-coding DNA.

Molecular diagnostics in drug-resistant focal epilepsy define new disease entities

Structural brain lesions, including the broad range of malformations of cortical development (MCD) and glioneuronal tumors, are among the most common causes of drug-resistant focal epilepsy. Epilepsy surgery can provide a curative treatment option in respective patients. The currently available pre-surgical multi-modal diagnostic armamentarium includes high- and ultra-high resolution magnetic resonance imaging (MRI) and intracerebral EEG to identify a focal structural brain lesion as epilepsy underlying etiology. However, specificity and accuracy in diagnosing the type of lesion have proven to be limited. Moreover, the diagnostic process does not stop with the decision for surgery. The neuropathological diagnosis remains the gold standard for disease classification and patient stratification, but is particularly complex with high inter-observer variability. Here, the identification of lesion-specific mosaic variants together with epigenetic profiling of lesional brain tissue became new tools to more reliably identify disease entities. In this review, we will discuss how the paradigm shifts from histopathology toward an integrated diagnostic approach in cancer and the more recent development of the DNA methylation-based brain tumor classifier have started to influence epilepsy diagnostics. Some examples will be highlighted showing how the diagnosis and our mechanistic understanding of difficult to classify structural brain lesions associated with focal epilepsy has improved with molecular genetic data being considered in decision making.