Genotype-phenotype associations in 1018 individuals with SCN1A-related epilepsies.

OBJECTIVE: SCN1A variants are associated with epilepsy syndromes ranging from mild genetic epilepsy with febrile seizures plus (GEFS+) to severe Dravet syndrome (DS). Many variants are de novo, making early phenotype prediction difficult, and genotype-phenotype associations remain poorly understood. METHODS: We assessed data from a retrospective cohort of 1018 individuals with SCN1A-related epilepsies. We explored relationships between variant characteristics (position, in silico prediction scores: Combined Annotation Dependent Depletion (CADD), Rare Exome Variant Ensemble Learner (REVEL), SCN1A genetic score), seizure characteristics, and epilepsy phenotype. RESULTS: DS had earlier seizure onset than other GEFS+ phenotypes (5.3 vs. 12.0 months, p < .001). In silico variant scores were higher in DS versus GEFS+ (p < .001). Patients with missense variants in functionally important regions (conserved N-terminus, S4-S6) exhibited earlier seizure onset (6.0 vs. 7.0 months, p = .003) and were more likely to have DS (280/340); those with missense variants in nonconserved regions had later onset (10.0 vs. 7.0 months, p = .036) and were more likely to have GEFS+ (15/29, χ2 = 19.16, p < .001). A minority of protein-truncating variants were associated with GEFS+ (10/393) and more likely to be located in the proximal first and last exon coding regions than elsewhere in the gene (9.7% vs. 1.0%, p < .001). Carriers of the same missense variant exhibited less variability in age at seizure onset compared with carriers of different missense variants for both DS (1.9 vs. 2.9 months, p = .001) and GEFS+ (8.0 vs. 11.0 months, p = .043). Status epilepticus as presenting seizure type is a highly specific (95.2%) but nonsensitive (32.7%) feature of DS. SIGNIFICANCE: Understanding genotype-phenotype associations in SCN1A-related epilepsies is critical for early diagnosis and management. We demonstrate an earlier disease onset in patients with missense variants in important functional regions, the occurrence of GEFS+ truncating variants, and the value of in silico prediction scores. Status epilepticus as initial seizure type is a highly specific, but not sensitive, early feature of DS.

Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies.

BACKGROUND AND OBJECTIVES: Pathogenic variants in the neuronal sodium channel α1 subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum, including severe childhood epilepsy; Dravet syndrome, characterized by drug-resistant seizures, intellectual disability, and high mortality; and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome vs GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies. METHODS: We performed a retrospective multicenter cohort study comprising data from patients with SCN1A-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001-June 2020) including age at seizure onset and a newly developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes. RESULTS: A total of 1,018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1, and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score (133.4 [SD 78.5] vs 52.0 [SD 57.5]; p < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; p < 0.001) were each associated with Dravet syndrome vs GEFS+. A combined SCN1A genetic score and seizure onset model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC] 0.89 [95% CI 0.86-0.92]) and outperformed all other models (AUC 0.79-0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC 0.94 [95% CI 0.91-0.97]) and 2 (AUC 0.92 [95% CI 0.82-1.00]). DISCUSSION: The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/). CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a combined SCN1A genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.

Biological concepts in human sodium channel epilepsies and their relevance in clinical practice.

OBJECTIVE: Voltage-gated sodium channels (SCNs) share similar amino acid sequence, structure, and function. Genetic variants in the four human brain-expressed SCN genes SCN1A/2A/3A/8A have been associated with heterogeneous epilepsy phenotypes and neurodevelopmental disorders. To better understand the biology of seizure susceptibility in SCN-related epilepsies, our aim was to determine similarities and differences between sodium channel disorders, allowing us to develop a broader perspective on precision treatment than on an individual gene level alone. METHODS: We analyzed genotype-phenotype correlations in large SCN-patient cohorts and applied variant constraint analysis to identify severe sodium channel disease. We examined temporal patterns of human SCN expression and correlated functional data from in vitro studies with clinical phenotypes across different sodium channel disorders. RESULTS: Comparing 865 epilepsy patients (504 SCN1A, 140 SCN2A, 171 SCN8A, four SCN3A, 46 copy number variation [CNV] cases) and analysis of 114 functional studies allowed us to identify common patterns of presentation. All four epilepsy-associated SCN genes demonstrated significant constraint in both protein truncating and missense variation when compared to other SCN genes. We observed that age at seizure onset is related to SCN gene expression over time. Individuals with gain-of-function SCN2A/3A/8A missense variants or CNV duplications share similar characteristics, most frequently present with early onset epilepsy (<3 months), and demonstrate good response to sodium channel blockers (SCBs). Direct comparison of corresponding SCN variants across different SCN subtypes illustrates that the functional effects of variants in corresponding channel locations are similar; however, their clinical manifestation differs, depending on their role in different types of neurons in which they are expressed. SIGNIFICANCE: Variant function and location within one channel can serve as a surrogate for variant effects across related sodium channels. Taking a broader view on precision treatment suggests that in those patients with a suspected underlying genetic epilepsy presenting with neonatal or early onset seizures (<3 months), SCBs should be considered.

SCN1A variants from bench to bedside-improved clinical prediction from functional characterization.

Variants in the SCN1A gene are associated with a wide range of disorders including genetic epilepsy with febrile seizures plus (GEFS+), familial hemiplegic migraine (FHM), and the severe childhood epilepsy Dravet syndrome (DS). Predicting disease outcomes based on variant type remains challenging. Despite thousands of SCN1A variants being reported, only a minority has been functionally assessed. We review the functional SCN1A work performed to date, critically appraise electrophysiological measurements, compare this to in silico predictions, and relate our findings to the clinical phenotype. Our results show, regardless of the underlying phenotype, that conventional in silico software correctly predicted benign from pathogenic variants in nearly 90%, however was unable to differentiate within the disease spectrum (DS vs. GEFS+ vs. FHM). In contrast, patch-clamp data from mammalian expression systems revealed functional differences among missense variants allowing discrimination between disease severities. Those presenting with milder phenotypes retained a degree of channel function measured as residual whole-cell current, whereas those without any whole-cell current were often associated with DS (p = .024). These findings demonstrate that electrophysiological data from mammalian expression systems can serve as useful disease biomarker when evaluating SCN1A variants, particularly in view of new and emerging treatment options in DS.

The quantification and monitoring of intraoperative nociception levels in thoracic surgery: a review.

Nociception is the unconscious perception of a stimulus applied by trauma or surgery and expressed through a response of the autonomous nervous system. Local anaesthetics (LAs), opioids and other modulating agents such as ketamine are usually utilised to blunt nociception as a component during general anaesthesia (GA) and surgery. The effectiveness of these measures, however, are still difficult to quantify and monitoring of anti-nociception has been confined to assess variation of heart rate (HR) or blood pressure (BP). Recently, various monitoring concepts have been introduced to quantify nociception more systematically and on the other hand guide anti-nociceptive interventions more appropriately. This review describes the various technologies, their performance in clinical studies and provides a critical appraisal with particular application to thoracic anaesthesia and surgery and their relevance in the context of chronic pain after surgery.