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A distinct microRNA expression profile is associated with ?[11C]-methyl-L-tryptophan (AMT) PET uptake in epileptogenic cortical tubers resected from patients with tuberous sclerosis complex.

ABSTRACT: Tuberous sclerosis complex (TSC) is characterized by hamartomatous lesions in various organs and arises due to mutations in the TSC1 or TSC2 genes. TSC mutations lead to a range of neurological manifestations including epilepsy, cognitive impairment, autism spectrum disorders (ASD), and brain lesions that include cortical tubers. There is evidence that seizures arise at or near cortical tubers, but it is unknown why some tubers are epileptogenic while others are not. We have previously reported increased tryptophan metabolism measured with ?[11C]-methyl-l-tryptophan (AMT) positron emission tomography (PET) in epileptogenic tubers in approximately two-thirds of patients with tuberous sclerosis and intractable epilepsy. However, the underlying mechanisms leading to seizure onset in TSC remain poorly characterized. MicroRNAs are enriched in the brain and play important roles in neurodevelopment and brain function. Recent reports have shown aberrant microRNA expression in epilepsy and TSC. In this study, we performed microRNA expression profiling in brain specimens obtained from TSC patients undergoing epilepsy surgery for intractable epilepsy. Typically, in these resections several non-seizure onset tubers are resected together with the seizure-onset tubers because of their proximity. We directly compared seizure onset tubers, with and without increased tryptophan metabolism measured with PET, and non-onset tubers to assess the role of microRNAs in epileptogenesis associated with these lesions. Whether a particular tuber was epileptogenic or non-epileptogenic was determined with intracranial electrocorticography, and tryptophan metabolism was measured with AMT PET. We identified a set of five microRNAs (miR-142-3p, 142-5p, 223-3p, 200b-3p and 32-5p) that collectively distinguish among the three primary groups of tubers: non-onset/AMT-cold (NC), onset/AMT-cold (OC), and onset/AMT-hot (OH). These microRNAs were significantly upregulated in OH tubers compared to the other two groups, and microRNA expression was most significantly associated with AMT-PET uptake. The microRNAs target a group of genes enriched for synaptic signaling and epilepsy risk, including SLC12A5, SYT1, GRIN2A, GRIN2B, KCNB1, SCN2A, TSC1, and MEF2C. We confirmed the interaction between miR-32-5p and SLC12A5 using a luciferase reporter assay. Our findings provide a new avenue for subsequent mechanistic studies of tuber epileptogenesis in TSC.

SUBMITTER: Bagla S

PROVIDER: S-EPMC6070303 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A distinct microRNA expression profile is associated with α[<sup>11</sup>C]-methyl-L-tryptophan (AMT) PET uptake in epileptogenic cortical tubers resected from patients with tuberous sclerosis complex.

Bagla Shruti S Cukovic Daniela D Asano Eishi E Sood Sandeep S Luat Aimee A Chugani Harry T HT Chugani Diane C DC Dombkowski Alan A AA

Neurobiology of disease 20171007 Pt A

Tuberous sclerosis complex (TSC) is characterized by hamartomatous lesions in various organs and arises due to mutations in the TSC1 or TSC2 genes. TSC mutations lead to a range of neurological manifestations including epilepsy, cognitive impairment, autism spectrum disorders (ASD), and brain lesions that include cortical tubers. There is evidence that seizures arise at or near cortical tubers, but it is unknown why some tubers are epileptogenic while others are not. We have previously reported ...[more]

PMID: 28993242

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Project description:BackgroundMore than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images.MethodsForty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis.ResultsPET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70-0.83) than the LDA (AUC = 0.7506; 95% CI 0.68-0.82) and ANN models (AUC = 0.7425; 95% CI 0.67-0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71-0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71-0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74-0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75-0.87).ConclusionsThe 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.

Project description:PurposeIn PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers.MethodsIn 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework.ResultsIn the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1-87.8] for identification of suspicious uptake sites, 77% (CI: 70.0-83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage.ConclusionThe evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.

Project description:BackgroundPET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age-specific PET template. This study aims to develop a pediatric age-specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy.MethodsWe recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6-10 years (n = 17) and 11-18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template).ResultsSpatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%).ConclusionThe PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT.Trial registrationNCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162.

Dataset Information

A distinct microRNA expression profile is associated with ?[11C]-methyl-L-tryptophan (AMT) PET uptake in epileptogenic cortical tubers resected from patients with tuberous sclerosis complex.

Publications

A distinct microRNA expression profile is associated with α[<sup>11</sup>C]-methyl-L-tryptophan (AMT) PET uptake in epileptogenic cortical tubers resected from patients with tuberous sclerosis complex.

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