Objective: Dravet syndrome is a severe developmental and epileptic encephalopathy (DEE) most often caused by de novo pathogenic variants in SCN1A.Individuals with Dravet syndrome rarely achieve seizure control and have significantly elevated risk for sudden unexplained death in epilepsy (SUDEP).Heterozygous deletion of Scn1a in mice (Scn1a +/− ) recapitulates several core phenotypes, including temperature-dependent and spontaneous seizures, SUDEP, and behavioral abnormalities. Furthermore, Scn1a +/− mice exhibit a similar clinical response to standard anticonvulsants. Cholesterol 24-hydroxlase (CH24H) is a brain-specific enzyme responsible for cholesterol catabolism. Recent research has indicated the therapeutic potential of CH24H inhibition for diseases associated with neural excitation, including seizures.Methods:In this study, the novel compound soticlestat, a CH24H inhibitor, was administered to Scn1a +/− mice to investigate its ability to improve Dravet-like phenotypes in this preclinical model. Results: Soticlestat treatment reduced seizure burden, protected against hyperthermia-induced seizures, and completely prevented SUDEP in Scn1a +/− mice. Video-electroencephalography (EEG) analysis confirmed the ability of soticlestat to reduce occurrence of electroclinical seizures.Significance: This study demonstrates that soticlestat-mediated inhibition of CH24H provides therapeutic benefit for the treatment of Dravet syndrome in mice and has the potential for treatment of DEEs.
Pathogenic variants in epilepsy genes result in a spectrum of clinical severity. One source of phenotypic heterogeneity is modifier genes that affect expressivity of a primary pathogenic variant. Mouse epilepsy models also display varying degrees of clinical severity on different genetic backgrounds. Mice with heterozygous deletion of Scn1a (Scn1a+/−) model Dravet syndrome, a severe epilepsy most often caused by SCN1A haploinsufficiency. Scn1a+/− mice recapitulate features of Dravet syndrome, including spontaneous seizures, sudden death, and cognitive/behavioral deficits. Scn1a+/− mice maintained on the 129S6/SvEvTac (129) strain have normal lifespan and no spontaneous seizures. In contrast, admixture with C57BL/6J (B6) results in epilepsy and premature lethality. We previously mapped Dravet Survival Modifier loci (Dsm1-Dsm5) responsible for strain-dependent differences in survival. Gabra2, encoding the GABAA α2 subunit, was nominated as a candidate modifier at Dsm1. Direct measurement of GABAA receptors found lower abundance of α2-containing receptors in hippocampal synapses of B6 mice relative to 129. We also identified a B6-specific single nucleotide deletion within Gabra2 that lowers mRNA and protein by nearly 50%. Repair of this deletion reestablished normal levels of Gabra2 expression. In this study, we used B6 mice with a repaired Gabra2 allele to evaluate Gabra2 as a genetic modifier of severity in Scn1a+/− mice. Gabra2 repair restored transcript and protein expression, increased abundance of α2-containing GABAA receptors in hippocampal synapses, and rescued epilepsy phenotypes of Scn1a+/− mice. These findings validate Gabra2 as a genetic modifier of Dravet syndrome, and support enhancing function of α2-containing GABAA receptors as treatment strategy for Dravet syndrome.
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
Pathogenic variants in SCN1A result in a spectrum of phenotypes ranging from mild febrile seizures to Dravet syndrome, a severe infant-onset epileptic encephalopathy. Individuals with Dravet syndrome have developmental delays, elevated risk for sudden unexpected death in epilepsy (SUDEP), and have multiple seizure types that are often refractory to treatment. Although most Dravet syndrome variants arise de novo, there are cases where an SCN1A variant was inherited from mildly affected parents, as well as some individuals with de novo loss-of-function or truncation mutations that presented with milder phenotypes. This suggests that disease severity is influenced by other factors that modify expressivity of the primary mutation, which likely includes genetic modifiers. Consistent with this, the Scn1a+/− mouse model of Dravet syndrome exhibits strain-dependent variable phenotype severity. Scn1a+/− mice on the 129S6/SvEvTac (129) strain have no overt phenotype and a normal lifespan, while [C57BL/6Jx129]F1.Scn1a+/− mice have severe epilepsy with high rates of premature death. Low resolution genetic mapping identified several Dravet syndrome modifier (Dsm) loci responsible for the strain-dependent difference in survival of Scn1a+/− mice. To confirm the Dsm5 locus and refine its position, we generated interval-specific congenic strains carrying 129-derived chromosome 11 alleles on the C57BL/6J strain and localized Dsm5 to a 5.9 Mb minimal region. We then performed candidate gene analysis in the modifier region. Consideration of brain-expressed genes with expression or coding sequence differences between strains along with gene function suggested numerous strong candidates, including several protein coding genes and two miRNAs that may regulate Scn1a transcript.
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