Lafora Disease: A Case Report and Evolving Treatment Advancements.

Lafora disease is a rare genetic disorder characterized by a disruption in glycogen metabolism. It manifests as progressive myoclonus epilepsy and cognitive decline during adolescence. Pathognomonic is the presence of abnormal glycogen aggregates that, over time, produce large inclusions (Lafora bodies) in various tissues. This study aims to describe the clinical and histopathological aspects of a novel Lafora disease patient, and to provide an update on the therapeutical advancements for this disorder. A 20-year-old Libyan boy presented with generalized tonic-clonic seizures, sporadic muscular jerks, eyelid spasms, and mental impairment. Electroencephalography showed multiple discharges across both brain hemispheres. Brain magnetic resonance imaging was unremarkable. Muscle biopsy showed increased lipid content and a very mild increase of intermyofibrillar glycogen, without the polyglucosan accumulation typically observed in Lafora bodies. Despite undergoing three lines of antiepileptic treatment, the patient's condition showed minimal to no improvement. We identified the homozygous variant c.137G>A, p.(Cys46Tyr), in the EPM2B/NHLRC1 gene, confirming the diagnosis of Lafora disease. To our knowledge, the presence of lipid aggregates without Lafora bodies is atypical. Lafora disease should be considered during the differential diagnosis of progressive, myoclonic, and refractory epilepsies in both children and young adults, especially when accompanied by cognitive decline. Although there are no effective therapies yet, the development of promising new strategies prompts the need for an early and accurate diagnosis.

Case report: A novel patient presenting TRIM32-related limb-girdle muscular dystrophy.

Limb-girdle muscular dystrophy autosomal recessive 8 (LGMDR8) is a rare clinical manifestation caused by the presence of biallelic variants in the TRIM32 gene. We present the clinical, molecular, histopathological, and muscle magnetic resonance findings of a novel 63-years-old LGMDR8 patient of Italian origins, who went undiagnosed for 24 years. Clinical exome sequencing identified two TRIM32 missense variants, c.1181G > A p.(Arg394His) and c.1781G > A p.(Ser594Asp), located in the NHL1 and NHL4 structural domains, respectively, of the TRIM32 protein. We conducted a literature review of the clinical and instrumental data associated to the so far known 26 TRIM32 variants, carried biallelically by 53 LGMDR8 patients reported to date in 20 papers. Our proband's variants were previously identified only in three independent LGMDR8 patients in homozygosis, therefore our case is the first in literature to be described as compound heterozygous for such variants. Our report also provides additional data in support of their pathogenicity, since p.(Arg394His) is currently classified as a variant of uncertain significance, while p.(Ser594Asp) as likely pathogenic. Taken together, these findings might be useful to improve both the genetic counseling and the diagnostic accuracy of this rare neuromuscular condition.

Software tool for visualization of a probabilistic map of the epileptogenic zone from seizure semiologies.

Around one third of epilepsies are drug-resistant. For these patients, seizures may be reduced or cured by surgically removing the epileptogenic zone (EZ), which is the portion of the brain giving rise to seizures. If noninvasive data are not sufficiently lateralizing or localizing, the EZ may need to be localized by precise implantation of intracranial electroencephalography (iEEG) electrodes. The choice of iEEG targets is influenced by clinicians' experience and personal knowledge of the literature, which leads to substantial variations in implantation strategies across different epilepsy centers. The clinical diagnostic pathway for surgical planning could be supported and standardized by an objective tool to suggest EZ locations, based on the outcomes of retrospective clinical cases reported in the literature. We present an open-source software tool that presents clinicians with an intuitive and data-driven visualization to infer the location of the symptomatogenic zone, that may overlap with the EZ. The likely EZ is represented as a probabilistic map overlaid on the patient's images, given a list of seizure semiologies observed in that specific patient. We demonstrate a case study on retrospective data from a patient treated in our unit, who underwent resective epilepsy surgery and achieved 1-year seizure freedom after surgery. The resected brain structures identified as EZ location overlapped with the regions highlighted by our tool, demonstrating its potential utility.

Probabilistic landscape of seizure semiology localizing values.

Semiology describes the evolution of symptoms and signs during epileptic seizures and contributes to the evaluation of individuals with focal drug-resistant epilepsy for curative resection. Semiology varies in complexity from elementary sensorimotor seizures arising from primary cortex to complex behaviours and automatisms emerging from distributed cerebral networks. Detailed semiology interpreted by expert epileptologists may point towards the likely site of seizure onset, but this process is subjective. No study has captured the variances in semiological localizing values in a data-driven manner to allow objective and probabilistic determinations of implicated networks and nodes. We curated an open data set from the epilepsy literature, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, linking semiology to hierarchical brain localizations. A total of 11 230 data points were collected from 4643 patients across 309 articles, labelled using ground truths (postoperative seizure-freedom, concordance of imaging and neurophysiology, and/or invasive EEG) and a designation method that distinguished between semiologies arising from a predefined cortical region and descriptions of neuroanatomical localizations responsible for generating a particular semiology. This allowed us to mitigate temporal lobe publication bias by filtering studies that preselected patients based on prior knowledge of their seizure foci. Using this data set, we describe the probabilistic landscape of semiological localizing values as forest plots at the resolution of seven major brain regions: temporal, frontal, cingulate, parietal, occipital, insula, and hypothalamus, and five temporal subregions. We evaluated the intrinsic value of any one semiology over all other ictal manifestations. For example, epigastric auras implicated the temporal lobe with 83% probability when not accounting for the publication bias that favoured temporal lobe epilepsies. Unbiased results for a prior distribution of cortical localizations revised the prevalence of temporal lobe epilepsies from 66% to 44%. Therefore, knowledge about the presence of epigastric auras updates localization to the temporal lobe with an odds ratio (OR) of 2.4 [CI95% (1.9, 2.9); and specifically, mesial temporal structures OR: 2.8 (2.3, 2.9)], attesting the value of epigastric auras. As a further example, although head version is thought to implicate the frontal lobes, it did not add localizing value compared with the prior distribution of cortical localizations [OR: 0.9 (0.7, 1.2)]. Objectification of the localizing values of the 12 most common semiologies provides a complementary view of brain dysfunction to that of lesion-deficit mappings, as instead of linking brain regions to phenotypic-deficits, semiological phenotypes are linked back to brain sources. This work enables coupling of seizure propagation with ictal manifestations, and clinical support algorithms for localizing seizure phenotypes.

Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance.

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers. Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria. Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27). Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability. Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).

Morphological Evaluation of Tumor-Infiltrating Lymphocytes (TILs) to Investigate Invasive Breast Cancer Immunogenicity, Reveal Lymphocytic Networks and Help Relapse Prediction: A Retrospective Study.

Tumor-infiltrating lymphocytes (TILs) in breast cancer are a key representative of the tumor immune microenvironment and have been shown to provide prognostic and predictive biomarkers. The extent of lymphocytic infiltration in tumor tissues can be assessed by evaluating hematoxylin and eosin (H&E)-stained tumor sections. We investigated tissue microarrays of 31 invasive breast cancer patients, looking at quantity and topological distribution of CD3+, CD8+, CD20+, Ki67+, FoxP3+ TILs and CD3+/FoxP3+, CD8+/FoxP3+ cell ratios. We separately evaluated TILs at the invasive edge and at the center of the tumor, to find any clinical implications of tumor heterogeneity. No statistically significant difference was found in quantity and distribution of both TIL subsets and TIL ratios, by comparing patients who suffered from a local or distant recurrence of the tumor (relapse group: 13 patients) with patients not showing cancer relapse (non-relapse group: 18 patients). In the whole sample, we observed three main statistically significant positive correlations: (1) between CD3+ and CD8+ T-cells; (2) between FoxP3+ and Ki67+ lymphocyte infiltration; (3) between CD3+/FoxP3+ cell ratio (C3FR) and CD8+/FoxP3+ cell ratio (C8FR). Tumor heterogeneity and stronger positive TIL associations were found in the non-relapse group, where both CD3-CD8 and FoxP3-Ki67 inter-correlations were found to be significant at the center of the tumor, while the correlation between C3FR and C8FR was significant at the invasive edge. No correlations between TIL subsets were detected in the relapse group. Our findings suggest the existence of stronger inter-subtype lymphocytic networks in invasive breast cancer not showing recurrence. Further evaluations of clinical and topological correlations between and within TIL subsets are needed, in addition to the assessment of TIL quantification and distribution, in order to follow up on whether morphological evaluation of TILs might reveal the underlying lymphocytic functional connectivity and help relapse prediction.

Mammalian Target of Rapamycin Inhibitors and Life-Threatening Conditions in Tuberous Sclerosis Complex.

Tuberous sclerosis complex (TSC) is a multisystem disease associated with an overall reduction in life expectancy due to the possible occurrence of different life-threatening conditions. Subjects affected by TSC are, in fact, at risk of hydrocephalus secondary to the growth of subependymal giant cell astrocytomas, or of sudden unexpected death in epilepsy. Other nonneurological life-threatening conditions include abdominal bleeding owing to renal angiomyolipomas rupture, renal insufficiency due to progressive parenchymal destruction by multiple cysts, pulmonary complications due to lymphangioleiomyomatosis, and cardiac failure or arrhythmias secondary to rhabdomyomas. In the last decades, there has been a great progress in understanding the pathophysiology of TSC-related manifestations, which are mainly linked to the hyperactivation of the so-called mammalian target of rapamycin (mTOR) pathway, as a consequence of the mutation in 1 of the 2 genes TSC1 or TSC2. This led to the development of new treatment strategies for this disease. In fact, it is now available as a biologically targeted therapy with everolimus, a selective mTOR inhibitor, which has been licensed in Europe and USA for the treatment of subependymal giant cell astrocytomas and angiomyolipomas in subjects with TSC. This drug also proved to benefit other TSC-related manifestations, including pulmonary lymphangioleiomyomatosis, cardiac rhabdomyomas, and presumably epileptic seizures. mTOR inhibitors are thus proving to be a systemic therapy able to simultaneously address different and potentially life-threatening complications, giving the hope of improving life expectation in individuals with TSC.