Clinically significant changes in genes and variants associated with epilepsy over time: implications for re-analysis.

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

Early cost-utility analysis of genetically guided therapy for patients with drug-resistant epilepsy.

OBJECTIVE: Existing gene panels were developed to understand the etiology of epilepsy, and further benefits will arise from an effective pharmacogenomics panel for personalizing therapy and achieving seizure control. Our study assessed the cost-effectiveness of a pharmacogenomics panel for patients with drug-resistant epilepsy, compared with usual care. METHODS: A cost-utility analysis was employed using a discrete event simulation model. The microsimulation model aggregated the costs and benefits of genetically guided treatment versus usual care for 5000 simulated patients. The 10-year model combined data from various sources including genomic databases on prevalence of variants, population-level pharmaceutical claims on antiseizure medications, published long-term therapy retention rates, patient-level cost data, and systematic reviews. Incremental cost per quality-adjusted life-year (QALY) gained was computed. Deterministic and probabilistic sensitivity analyses were undertaken to address uncertainty in model parameters. RESULTS: The mean cost of the genetically guided treatment option was AU$98 199 compared with AU$95 386 for usual care. Corresponding mean QALYs were 4.67 compared with 4.28 for genetically guided and usual care strategies, respectively. The incremental cost per QALY gained was AU$7381. In probabilistic sensitivity analyses, the incremental cost per QALY gained was AU$6321 (95% uncertainty interval = AU$3604-AU$9621), with a 100% likelihood of being cost-effective in the Australian health care system. The most influential drivers of the findings were the monthly health care costs associated with reduced seizures, costs when seizures continued, and the quality-of-life estimates under genetically guided and usual care strategies. SIGNIFICANCE: This early economic evaluation of a pharmacogenomics panel to guide treatment for drug-resistant epilepsy could potentially be cost-effective in the Australian health care system. Clinical trial evidence is necessary to confirm these findings.

A Multi-Disciplinary Team Approach to Genomic Testing for Drug-Resistant Epilepsy Patients-The GENIE Study.

BACKGROUND: The genomic era has led to enormous progress in clinical care and a multi-disciplinary team (MDT) approach is imperative for integration of genomics into epilepsy patient care. METHODS: The MDT approach involved patient selection, genomic testing choice, variant discussions and return of results. Genomics analysis included cytogenomic testing and whole exome sequencing (WES). Neurologist surveys were undertaken at baseline and after genomic testing to determine if genomic diagnoses would alter their management, and if there was a change in confidence in genomic testing and neurologist perceptions of the MDT approach. RESULTS: The total diagnostic yield from all genomic testing was 17% (11/66), with four diagnoses from cytogenomic analyses. All chromosomal microarray (CMA) diagnoses were in patients seen by adult neurologists. Diagnostic yield for WES was 11% (7/62). The most common gene with pathogenic variants was DCX, reported in three patients, of which two were mosaic. The genomic diagnosis impacted management in 82% (9/11). There was increased confidence with integrating genomics into clinical care (Pearson chi square = 83, p = 0.004) and qualitative comments were highly supportive of the MDT approach. CONCLUSIONS: We demonstrated diagnostic yield from genomic testing, and the impact on management in a cohort with drug-resistant epilepsy. The MDT approach increased confidence in genomic testing and neurologists valued the input from this approach. The utility of CMA was demonstrated in epilepsy patients seen by adult neurologists as was the importance of considering mosaicism for previously undiagnosed patients.

Deciphering the role of epigenetics in self-limited epilepsy with centrotemporal spikes.

OBJECTIVE: The aetiology of self-limited epilepsy with centro-temporal spikes (SECTS) remains controversial and a strong genetic basis has long been presumed. The discordant monozygotic twin (MZ) model controls for shared genetic and environmental factors, enabling focus on the potential role of the non-shared environment. METHODS: DNA methylation data was acquired from DNA extracted from three discordant MZ twin pairs, from both new born blood spots before epilepsy onset, and blood samples taken after epilepsy onset. An epigenome-wide analysis was performed, using the Illumina Infinium EPIC array. Differentially methylated regions (DMR) were identified using the bumphunter package in R. Comparative analyses were undertaken at the two different time points as well as a combined analysis independent of time. RESULTS: Many of the top DMR-associated genes have previously been described in neurodevelopmental disorders. The LYPD8 gene was associated with a top-ranked DMR both at birth and across the two time points. CONCLUSION: We have demonstrated the novel utility of the longitudinal, discordant MZ twin model, to facilitate a deeper appreciation of the complex neurobiology of SECTS. The genetic architecture of SECTS is complex and is likely to involve an interplay between genes and environment, in part mediated by epigenetics.

Evidence for type-specific DNA methylation patterns in epilepsy: a discordant monozygotic twin approach.

Aim: Epilepsy is a common neurological disorder characterized by recurrent seizures. We performed epigenetic analyses between and within 15 monozygotic (MZ) twin pairs discordant for focal or generalized epilepsy. Methods: DNA methylation analysis was performed using Illumina Infinium MethylationEPIC arrays, in blood and buccal samples. Results: Differentially methylated regions between epilepsy types associated with PM20D1 and GFPT2 genes in both tissues. Within MZ discordant twin pairs, differentially methylated regions associated with OTX1 and ARID5B genes for generalized epilepsy and TTC39C and DLX5 genes for focal epilepsy. Conclusion: This is the first epigenome-wide association study, utilizing the discordant MZ co-twin model, to deepen our understanding of the neurobiology of epilepsy.