Utility of Genetic Testing in Paediatric Epilepsy: Experience from a Low- Middle- Income Country

Utility of next generation sequencing in paediatric neurological disorders: experience from South Africa

Next generation sequencing (NGS)-based tests have become routine first-line investigative modalities in paediatric neurology clinics in many high-income countries (HICs). Studies from these countries show that these tests are both cost-effective and reliable in diagnosing many complex childhood neurological diseases. However, NGS-based testing in low-and middle-income countries (LMICs) is limited due to affordability constraints. The primary objective of this study was to evaluate the diagnostic yield and impact of targeted gene panel sequencing in a selected paediatric cohort attending a tertiary paediatric neurology clinic in the Western Cape Province of South Africa. This retrospective study included 124 consecutive paediatric patients with neurological disease, aged 6 weeks to 17 years, referred for NGS-based multi-gene panel testing over a 41-month period. Twenty-four different disease group-specific panels were utilized. A caregiver experience questionnaire was administered when a pathogenic variant was identified. The overall study diagnostic yield (DY) was 45% (56/124 patients). The diagnostic yield in this study is similar to previously reported paediatric cohorts in HICs. The high yields for neuromuscular disorders (52%) and early epileptic encephalopathies (41%) suggest that NGS-based panels may be more cost-effective as first-line testing in well-defined phenotypes. The latter finding argues for early inclusion of all children with developmental epileptic encephalopathies (DEE), as early diagnosis leads to better treatment and avoidance of unnecessary investigations.

Genetic and phenotypic landscape of pediatric-onset epilepsy in 142 Indian families: Counseling and therapeutic implications

The application of genomic technologies has led to unraveling of the complex genetic landscape of disorders of epilepsy, gaining insights into their underlying disease mechanisms, aiding precision medicine, and providing informed genetic counseling. We herein present the phenotypic and genotypic insights from 142 Indian families with epilepsy with or without comorbidities. Based on the electroclinical findings, epilepsy syndrome diagnosis could be made in 44% (63/142) of the families adopting the latest proposal for the classification by the ILAE task force (2022). Of these, 95% (60/63) of the families exhibited syndromes with developmental epileptic encephalopathy or progressive neurological deterioration. A definitive molecular diagnosis was achieved in 74 of 142 (52%) families. Infantile-onset epilepsy was noted in 81% of these families (61/74). Fifty-five monogenic, four chromosomal, and one imprinting disorder were identified in 74 families. The genetic variants included 65 (96%) single-nucleotide variants/small insertion-deletions, 1 (2%) copy-number variant, and 1 (2%) triplet-repeat expansion in 53 epilepsy-associated genes causing monogenic disorders. Of these, 35 (52%) variants were novel. Therapeutic implications were noted in 51% of families (38/74) with definitive diagnosis. Forty-one out of 66 families with monogenic disorders exhibited autosomal recessive and inherited autosomal dominant disorders with high risk of recurrence.

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history

BACKGROUND

Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy.

RESULTS

In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients.

CONCLUSION

Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

Yield and Utility of Routine Epilepsy Panel Genetic Testing Among Young Patients With Seizures

Objective: We examined the yield of routine epilepsy panel genetic testing in pediatric patients. Methods: We retrospectively reviewed epilepsy genetic panel results routinely performed in the hospital or clinic on patients <8 years old from July 2021 to July 2023. We evaluated demographics, family history, seizure type, severity, and frequency, development, tone and movement abnormalities, dysmorphism, and electroencephalography (EEG) or magnetic resonance imaging (MRI) results as predictors of results. Results: 65 patients were included with mean age 4.5 years. Sixty percent of patients were male; 11 patients had pathogenic variants (16.9%), 7 were carriers for autosomal recessive conditions (10.8%), 36 had variants of uncertain significance (55.4%), and 11 tested negative (16.9%). Pathogenic variants and variants of uncertain significance were unassociated with demographics, clinical features, imaging, or family history. Conclusion: Variants identified have potential implications for treatment (SCN1), comorbidity screening (TSC1), reproduction (ATAD1, PSAT1, and CLN8), and prognostication (FOXG1). Patients not routinely screened for a genetic cause of epilepsy by our standard practices had clinically relevant results.

Navigating the Intersection of Technology and Depression Precision Medicine

This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.