Chromosomal microarray in unexplained severe early onset epilepsy - A single centre cohort

Exome sequencing as first-tier genetic testing in infantile-onset pharmacoresistant epilepsy: diagnostic yield and treatment impact

Pharmacoresistant epilepsy presenting during infancy poses both diagnostic and therapeutic challenges. We aim to identify diagnostic yield and treatment implications of exome sequencing (ES) as first-tier genetic testing for infantile-onset pharmacoresistant epilepsy. From June 2016 to December 2020, we enrolled patients with infantile-onset (age ≤ 12 months) pharmacoresistant epilepsy. 103 unrelated patients underwent ES. Clinical characteristics and changes in management due to the molecular diagnosis were studied. 42% (43/103) had epilepsy onset within the first month of life. After ES as first-tier genetic testing, 62% (64/103) of the cases were solved. Two partially solved cases (2%; 2/103) with heterozygous variants identified in ALDH7A1 known to cause autosomal recessive pyridoxine dependent epilepsy underwent genome sequencing (GS). Two novel large deletions in ALDH7A1 were detected in both cases. ES identified 66 pathogenic and likely pathogenic single nucleotide variants (SNVs) in 27 genes. 19 variants have not been previously reported. GS identified two additional copy number variations (CNVs). The most common disease-causing genes are SCN1A (13%; 13/103) and KCNQ2 (8%; 8/103). Eight percent (8/103) of the patients had treatable disorders and specific treatments were provided resulting in seizure freedom. Pyridoxine dependent epilepsy was the most common treatable epilepsy (6%; 6/103). Furthermore, 35% (36/103) had genetic defects which guided gene-specific treatments. Altogether, the diagnostic yield is 64%. Molecular diagnoses change management in 43% of the cases. This study substantiates the use of next generation sequencing (NGS) as the first-tier genetic investigation in infantile-onset pharmacoresistant epilepsy.

Chromosomal microarray and exome sequencing in unexplained early infantile epileptic encephalopathies in a highly consanguineous population

AIM

To identify genetic causes for early infantile epileptic encephalopathies (EIEE) in Turkish children with mostly consanguineous parents.

METHODS

In a selected EIEE group (N = 59) based on results of nongenetic and initial genetic testing with unexplained etiology, 49 patients underwent array-based comparative genomic hybridization (aCGH) and 49 patients underwent whole exome sequencing (WES) including 39 with negative aCGH results and 10 with WES-only.

RESULTS

Diagnostic yield of aCGH and WES for pathogenic or likely pathogenic variants was 14.3% and 38.8%, respectively. Including de novo variants of uncertain significance linked to compatible phenotypes, increased the diagnostic yield of WES to 61.2%. Out of 38 positive variants, 18 (47.4%) were novel and 16 (42.1%) were de novo. Twenty-one (56.8%) patients had recessive variants inherited from mostly consanguineous healthy parents (85.7%). Fourteen (37.8%) of patients with diagnostic results had positive variants in established EIEE genes. Seizures started during neonatal period in 32.4% patients. Posture or movement disorders were comorbid with EIEE in 40.5% of diagnosed patients. We identified treatable metabolic disorders in 8.1% of patients and pathogenic variants in genes which support using targeted medicine in 19% of patients.

CONCLUSIONS

Detailed electro-clinical phenotyping led to expansion of some of the known phenotypes with non-neurological and neurological findings in addition to seizures, as well as suggestion of candidate genes (SEC24B, SLC16A2 and PRICKLE2) and a copy number variant (microduplication of Xp21.1p11.4). The high ratio of recessive inheritance could be important for family counseling.

Diagnostic Considerations in the Epilepsies-Testing Strategies, Test Type Advantages, and Limitations

The role of genetics in epilepsy has been recognized for a long time. Over the past decade, genome-wide technologies have identified numerous genes and variants associated with epilepsy. In the clinical setting, a myriad of genetic testing options are available, and a subset of specific genetic diagnoses have management implications. Furthermore, genetic testing can be a dynamic process. As a result, fundamental knowledge about genetics and genomics has become essential for all specialists. Here, we review current knowledge of the genetic contribution to various types of epilepsy, provide an overview of types of genetic variants, and discuss genetic testing options and their diagnostic yield. We also consider advantages and limitations of testing approaches.

Genetic Factors Underlying Sudden Infant Death Syndrome

Sudden Infant Death syndrome (SIDS) is a diagnosis of exclusion. Decades of research have made steady gains in understanding plausible mechanisms of terminal events. Current evidence suggests SIDS includes heterogeneous biological conditions, such as metabolic, cardiac, neurologic, respiratory, and infectious conditions. Here we review genetic studies that address each of these areas in SIDS cases and cohorts, providing a broad view of the genetic underpinnings of this devastating phenomenon. The current literature has established a role for monogenic genetic causes of SIDS mortality in a subset of cases. To expand upon our current knowledge of disease-causing genetic variants in SIDS cohorts and their mechanisms, future genetic studies may employ functional assessments of implicated variants, broader genetic tests, and the inclusion of parental genetic data and family history information.

Artificial intelligence for precision medicine in neurodevelopmental disorders

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.

Diagnostic implications of genetic copy number variation in epilepsy plus

OBJECTIVE

Copy number variations (CNVs) represent a significant genetic risk for several neurodevelopmental disorders including epilepsy. As knowledge increases, reanalysis of existing data is essential. Reliable estimates of the contribution of CNVs to epilepsies from sizeable populations are not available.

METHODS

We assembled a cohort of 1255 patients with preexisting array comparative genomic hybridization or single nucleotide polymorphism array based CNV data. All patients had "epilepsy plus," defined as epilepsy with comorbid features, including intellectual disability, psychiatric symptoms, and other neurological and nonneurological features. CNV classification was conducted using a systematic filtering workflow adapted to epilepsy.

RESULTS

Of 1097 patients remaining after genetic data quality control, 120 individuals (10.9%) carried at least one autosomal CNV classified as pathogenic; 19 individuals (1.7%) carried at least one autosomal CNV classified as possibly pathogenic. Eleven patients (1%) carried more than one (possibly) pathogenic CNV. We identified CNVs covering recently reported (HNRNPU) or emerging (RORB) epilepsy genes, and further delineated the phenotype associated with mutations of these genes. Additional novel epilepsy candidate genes emerge from our study. Comparing phenotypic features of pathogenic CNV carriers to those of noncarriers of pathogenic CNVs, we show that patients with nonneurological comorbidities, especially dysmorphism, were more likely to carry pathogenic CNVs (odds ratio = 4.09, confidence interval = 2.51-6.68; P = 2.34 × 10-9 ). Meta-analysis including data from published control groups showed that the presence or absence of epilepsy did not affect the detected frequency of CNVs.

SIGNIFICANCE

The use of a specifically adapted workflow enabled identification of pathogenic autosomal CNVs in 10.9% of patients with epilepsy plus, which rose to 12.7% when we also considered possibly pathogenic CNVs. Our data indicate that epilepsy with comorbid features should be considered an indication for patients to be selected for a diagnostic algorithm including CNV detection. Collaborative large-scale CNV reanalysis leads to novel declaration of pathogenicity in unexplained cases and can promote discovery of promising candidate epilepsy genes.

Rare Copy Number Variations and Predictors in Children With Intellectual Disability and Epilepsy

Introduction: The concurrence of intellectual disability/global developmental delay and epilepsy (ID/GDD-EP) is very common in the pediatric population. The etiologies for both conditions are complex and largely unknown. The predictors of significant copy number variations (CNVs) are known for the cases with ID/GDD, but unknown for those with exclusive ID/GDD-EP. Importantly, the known predictors are largely from the same ethnic group; hence, they lack replication.

Purpose: We aimed to determine and investigate the diagnostic yield of CNV tests, new causative CNVs, and the independent predictors of significant CNVs in Chinese children with unexplained ID/GDD-EP.

Materials and methods: A total of 100 pediatric patients with unexplained ID/GDD-EP and 1,000 healthy controls were recruited. The American College of Medical Genetics guideline was used to classify the CNVs. Additionally, clinical information was collected and compared between those with significant and non-significant CNVs.

Results: Twenty-eight percent of the patients had significant CNVs, 16% had variants of unknown significance, and 56% had non-significant CNVs. In total, 31 CNVs were identified in 28% (28/100) of cases: 25 pathogenic and 6 likely pathogenic. Eighteen known syndromes were diagnosed in 17 cases. Thirteen rare CNVs (8 novel and 5 reported in literature) were identified, of which three spanned dosage-sensitive genes: 19q13.2 deletion (ATP1A3), Xp11.4-p11.3 deletion (CASK), and 6q25.3-q25.3 deletion (ARID1B). By comparing clinical features in patients with significant CNVs against those with non-significant CNVs, a statistically significant association was found between the presence of significant CNVs and speech and language delay for those aged above 2 years and for those with facial malformations, microcephaly, congenital heart disease, fair skin, eye malformations, and mega cisterna magna. Multivariate logistic regression analysis allowed the identification of two independent significant CNV predictors, which are eye malformations and facial malformations.

Conclusion: Our study supports the performance of CNV tests in pediatric patients with unexplained ID/GDD-EP, as there is high diagnostic yield, which informs genetic counseling. It adds 13 rare CNVs (8 novel), which can be accountable for both conditions. Moreover, congenital eye and facial malformations are clinical markers that can aid clinicians to understand which patients can benefit from the CNV testing and which will not, thus helping patients to avoid unnecessary and expensive tests.

Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy

Early infantile epileptic encephalopathy (EIEE) is a devastating epilepsy syndrome with onset in the first months of life. Although mutations in more than 50 different genes are known to cause EIEE, current diagnostic yields with gene panel tests or whole-exome sequencing are below 60%. We applied whole-genome analysis (WGA) consisting of whole-genome sequencing and comprehensive variant discovery approaches to a cohort of 14 EIEE subjects for whom prior genetic tests had not yielded a diagnosis. We identified both de novo point and INDEL mutations and de novo structural rearrangements in known EIEE genes, as well as mutations in genes not previously associated with EIEE. The detection of a pathogenic or likely pathogenic mutation in all 14 subjects demonstrates the utility of WGA to reduce the time and costs of clinical diagnosis of EIEE. While exome sequencing may have detected 12 of the 14 causal mutations, 3 of the 12 patients received non-diagnostic exome panel tests prior to genome sequencing. Thus, given the continued decline of sequencing costs, our results support the use of WGA with comprehensive variant discovery as an efficient strategy for the clinical diagnosis of EIEE and other genetic conditions.

A population-based cost-effectiveness study of early genetic testing in severe epilepsies of infancy

OBJECTIVE

The severe epilepsies of infancy (SEI) are a devastating group of disorders that pose a major care and economic burden on society; early diagnosis is critical for optimal management. This study sought to determine the incidence and etiologies of SEI, and model the yield and cost-effectiveness of early genetic testing.

METHODS

A population-based study was undertaken of the incidence, etiologies, and cost-effectiveness of a whole exome sequencing-based gene panel (targeted WES) in infants with SEI born during 2011-2013, identified through electroencephalography (EEG) and neonatal databases. SEI was defined as seizure onset before age 18 months, frequent seizures, epileptiform EEG, and failure of ≥2 antiepileptic drugs. Medical records, investigations, MRIs, and EEGs were analyzed, and genetic testing was performed if no etiology was identified. Economic modeling was performed to determine yield and cost-effectiveness of investigation of infants with unknown etiology at epilepsy onset, incorporating targeted WES at different stages of the diagnostic pathway.

RESULTS

Of 114 infants with SEI (incidence = 54/100 000 live births/y), the etiology was determined in 76 (67%): acquired brain injuries (n = 14), focal cortical dysplasias (n = 14), other brain malformations (n = 17), channelopathies (n = 11), chromosomal (n = 9), metabolic (n = 6), and other genetic (n = 5) disorders. Modeling showed that incorporating targeted WES increased diagnostic yield compared to investigation without targeted WES (48/86 vs 39/86). Early targeted WES had lower total cost ($677 081 U.S. dollars [USD] vs $738 136 USD) than late targeted WES. A pathway with early targeted WES and limited metabolic testing yielded 7 additional diagnoses compared to investigation without targeted WES (46/86 vs 39/86), with lower total cost ($455 597 USD vs $661 103 USD), lower cost per diagnosis ($9904 USD vs $16 951 USD), and a dominant cost-effectiveness ratio.

SIGNIFICANCE

Severe epilepsies occur in 1 in 2000 infants, with the etiology identified in two-thirds, most commonly malformative. Early use of targeted WES yields more diagnoses at lower cost. Early genetic diagnosis will enable timely administration of precision medicines, once developed, with the potential to improve long-term outcome.

Epileptic Encephalopathy in Childhood: A Stepwise Approach for Identification of Underlying Genetic Causes

Epilepsy is one of the most common neurological disorders in childhood. Epilepsy associated with global developmental delay and cognitive dysfunction is defined as epileptic encephalopathy. Certain inherited metabolic disorders presenting with epileptic encephalopathy can be treated with disease specific diet, vitamin, amino acid or cofactor supplementations. In those disorders, disease specific therapy is successful to achieve good seizure control and improve long-term neurodevelopmental outcome. For this reason, intractable epilepsy with global developmental delay or history of developmental regression warrants detailed metabolic investigations for the possibility of an underlying treatable inherited metabolic disorder, which should be undertaken as first line investigations. An underlying genetic etiology in epileptic encephalopathy has been supported by recent studies such as array comparative genomic hybridization, targeted next generation sequencing panels, whole exome and whole genome sequencing. These studies report a diagnostic yield up to 70%, depending on the applied genetic testing as well as number of patients enrolled. In patients with epileptic encephalopathy, a stepwise approach for diagnostic work-up will help to diagnose treatable inherited metabolic disorders quickly. Application of detailed genetic investigations such as targeted next generation sequencing as second line and whole exome sequencing as third line testing will diagnose underlying genetic disease which will help for genetic counseling as well as guide for prenatal diagnosis. Knowledge of underlying genetic cause will provide novel insights into the pathogenesis of epileptic encephalopathy and pave the ground towards the development of targeted neuroprotective treatment strategies to improve the health outcome of children with epileptic encephalopathy.