Dataset Information

Identification of genetic characteristics in pediatric epilepsy with focal cortical dysplasia type 2 using deep whole-exome sequencing.

ABSTRACT:

Background

Focal cortical dysplasia type 2 (FCD2) is a malformation of cortical development that constitutes a common cause of pediatric focal epilepsy. Germline or somatic variants in the mammalian target of rapamycin (mTOR) signaling pathway genes are the pathogenesis of FCD2.

Objective

In this study, whole-exome deep sequencing was performed on dysplastic cortex from focal epilepsy in children to explore genetic characteristics in FCD2.

Methods

Resected core lesions of FCD2 were confirmed by pathology, and peripheral blood was collected from 11 patients. Deep whole-exome sequencing (>500X) was performed on derived genomic DNA, germline, or somatic variants in brain-specific genes were analyzed and identified.

Results

In 11 patients, a heterozygous likely pathogenic germline variant of DEPDC5 was identified in one case, while somatic variants were found in four brain samples. The frequencies of the somatic variant allele were 2.52%-5.12%. Somatic variants in AKT3, TSC2, and MTOR (mTOR signaling pathway genes) were found in three samples. Besides, one somatic variant was detected in MED12 which has not been reported to associate with FCD2.

Conclusion

Our study expanded the variant spectrum in the mTOR-GATOR pathway, and also detected a somatic variant in MED12 which was potentially associated with FCD 2.

SUBMITTER: Xu Y

PROVIDER: S-EPMC9747558 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Identification of genetic characteristics in pediatric epilepsy with focal cortical dysplasia type 2 using deep whole-exome sequencing.

Xu Yan Y Zhao Rui R Wang Min M Wang Xin-Hua XH Wang Yi Y Li Hao H Ma Yang-Yang YY Wu Bing-Bing BB Zhou Yuan-Feng YF

Molecular genetics & genomic medicine 20221107 12

<h4>Background</h4>Focal cortical dysplasia type 2 (FCD2) is a malformation of cortical development that constitutes a common cause of pediatric focal epilepsy. Germline or somatic variants in the mammalian target of rapamycin (mTOR) signaling pathway genes are the pathogenesis of FCD2.<h4>Objective</h4>In this study, whole-exome deep sequencing was performed on dysplastic cortex from focal epilepsy in children to explore genetic characteristics in FCD2.<h4>Methods</h4>Resected core lesions of F ...[more]

PMID: 36342087

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Project description:ObjectiveFocal cortical dysplasia (FCD) is the most common pathological cause for pediatric epilepsy, with frontal lobe epilepsy (FLE) being the most prevalent in the pediatric population. We attempted to utilize radiomic and morphological methods on MRI and PET to detect FCD in children with FLE.MethodsThirty-seven children with FLE and 20 controls were included in the primary cohort, and a five-fold cross-validation was performed. In addition, we validated the performance in an independent site of 12 FLE children. A two-stage experiments including frontal lobe and subregions were employed to detect the lesion area of FCD, incorporating the asymmetric feature between the left and right hemispheres. Specifically, for the radiomics approach, we used gray matter (GM), white matter (WM), GM and WM, and the gray-white matter boundary regions of interest to extract features. Then, we employed a Multi-Layer Perceptron classifier to achieve FCD lesion localization based on both radiomic and morphological methods.ResultsThe Multi-Layer Perceptron model based on the asymmetric feature exhibited excellent performance both in the frontal lobe and subregions. In the primary cohort and independent site, the radiomics analysis with GM and WM asymmetric features had the highest sensitivity (89.2 and 91.7%) and AUC (98.9 and 99.3%) in frontal lobe. While in the subregions, the GM asymmetric features had the highest sensitivity (85.6 and 79.7%). Furthermore, relying on the highest sensitivity of GM and WM asymmetric features in frontal lobe, when integrated with the subregions results, our approach exhibited overlaps with GM asymmetric features (55.4 and 52.4%), as well as morphological asymmetric features (54.4 and 53.8%), both in the primary cohort and at the independent site.SignificanceThis study demonstrates that a two-stage design based on the asymmetry of radiomic and morphological features can improve FCD detection. Specifically, incorporating regions of interest for GM, WM, GM, and WM, and the gray-white matter boundary significantly enhances the localization capabilities for lesion detection within the radiomics approach.

Project description:ObjectiveTissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes or treatment-related side effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilized quantitative magnetic resonance imaging (MRI), which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity.MethodsTo assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy (age range = 4-18 years) and 46 typically developing children (age range = 2-18 years). We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically defined abnormalities (MRI positive) and healthy controls, and tested whether this could classify patients without reported radiological abnormalities (MRI negative).ResultsWe uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 71.4% at 89.4% specificity on held-out MRI-negative patients.SignificanceThese findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically identified abnormalities.

Project description:Background and objectiveTo test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).MethodsWe used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity.ResultsOverall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls.DiscussionThis first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy.Classification of evidenceThis study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Dataset Information

Identification of genetic characteristics in pediatric epilepsy with focal cortical dysplasia type 2 using deep whole-exome sequencing.

Background

Objective

Methods

Results

Conclusion

Publications

Identification of genetic characteristics in pediatric epilepsy with focal cortical dysplasia type 2 using deep whole-exome sequencing.

Similar Datasets

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