Browsing by Author "Leu, Costin"
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Publication Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants(2024) Montanucci, Ludovica; Brünger, Tobias; Boßelmann, Christian; Ivaniuk, Alina; Pérez Palma, Eduardo; Lhatoo, Samden; Leu, Costin; Lal, DennisObjective: Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state-of-the-art computational tools in pathogenicity prediction of missense variants in epilepsy-associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting. Methods: We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy-associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy-associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy-associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets. Results: Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants. Significance: We show which recently developed prediction tools achieve higher performance in epilepsy-associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.Publication Identification and quantification of oligogenic loss-of-function disorders(2021) Stefanski, Arthur; Pérez, Eduardo; Mrdjen, Marko; McHugh, Megan; Leu, Costin; Lal, DennisPurpose: Monogenic disorders can present clinically heterogeneous symptoms. We hypothesized that in patients with a monogenic disorder caused by a large deletion, frequently additional loss-of-function (LOF)-intolerant genes are affected, potentially contributing to the phenotype. Methods: We investigated the LOF-intolerant gene distribution across the genome and its association with benign population and pathogenic classified deletions from individuals with presumably monogenic disorders. For people with presumably monogenic epilepsy, we compared Human Phenotype Ontology terms in people with large and small deletions. Results: We identified LOF-intolerant gene dense regions that were enriched for ClinVar and depleted for population copy number variants. Analysis of data from >143,000 individuals with a suspected monogenic disorder showed that 2.5% of haploinsufficiency disorder-associated deletions can affect at least 1 other LOF-intolerant gene. Focusing on epilepsy, we observed that 13.1% of pathogenic and likely pathogenic ClinVar deletions <3 megabase pair, covering the diagnostically most relevant genes, affected at least 1 additional LOF-intolerant gene. Those patients have potentially more complex phenotypes with increasing deletion size. Conclusion: We could systematically show that large deletions frequently affected admditional LOF-intolerant genes in addition to the established disease gene. Further research is needed to understand how additional potential disease-relevant genes influence monogenic disorders to improve clinical care and the efficacy of targeted therapies.Publication Incidence and prevalence of major epilepsy-associated brain lesions(2022) López; Javier; Smuk, Victoria; Leu, Costin; Nasr, Gaelle; Vegh, Deborah; Stefanski, Arthur; Pérez, Eduardo; Busch, Robyn; Jehi, Lara; Najm, Imad; Blümcke, Ingmar; Lal, DennisEpilepsy surgery is an effective treatment option for drug-resistant focal epilepsy patients with associated structural brain lesions. However, little epidemiological data are available regarding the number of patients with these lesions. We reviewed data regarding (1) the prevalence and incidence of epilepsy; (2) the proportion of epilepsy patients with focal epilepsy, drug-resistant epilepsy, and drug-resistant focal epilepsies; and (3) the number of epilepsy presurgical evaluations and surgical resections. We also assessed the relative proportion of brain lesions using post-surgical histopathological findings from 541 surgical patients from the Cleveland Clinic and 9,523 patients from a European multi-center cohort. Data were combined to generate surgical candidate incidence and prevalence estimates and the first lesion-specific estimates for hippocampal sclerosis (HS), low-grade epilepsy-associated brain tumors (LEAT), malformations of cortical development (MCD), glial scars, vascular malformations, and encephalitis. The most frequently diagnosed brain lesions were HS (incidence = 2.32 ± 0.26 in 100,000, prevalence = 19.40 ± 2.16 in 100,000) for adults and MCD (incidence = 1.15 ± 0.34 in 100,000, prevalence = 6.52 ± 1.89 in 100,000) for children. Our estimates can guide patient advocacy groups, clinicians, researchers, policymakers in education, development of health care strategy, resource allocation, and reimbursement schedules.Publication Polygenic score analysis identifies distinct genetic risk profiles in Alzheimer’s disease comorbidities(2025) Hernández, Carlos F.; Villaman, Camilo; Leu, Costin; Lal, Dennis; Mata, Ignacio; Klein, Andrés; Pérez-Palma, EduardoAlzheimer’s disease (AD) is usually accompanied by comorbidities such as type 2 diabetes (T2D), epilepsy, major depressive disorder (MDD), and migraine headaches (MH) that can significantly affect patient management and progression. As AD, these comorbidities have their own cumulative common genetic risk component that can be explored in a single individual through polygenic scores. Utilizing data from the UK Biobank, we investigated the correlation between polygenic scores (PGS) for these comorbidities and their actual presentation in AD patients. We show that individuals with higher PGS values showed an elevated risk of developing T2D (OR 2.1, p = 1.07 × 10−11) and epilepsy (OR 1.5, p = 0.0176). High T2D-PGS is also associated with an earlier AD onset in individuals at high genetic risk for AD (AD-PGS). In contrast, no significant genetic associations were found for MDD and MH. Our findings show distinct common genetic risk factors for T2D and epilepsy carried by AD patients that are associated with increased prevalence and earlier disease onset. These results highlight the contribution of common genetic variation to the broader clinical landscape of AD and will contribute to future tailored patient management strategies for individuals at high genetic risk.Publication Polygenic scores contribution to Parkinson's disease comorbidities(2025) Hernández, Carlos; Villaman, Camilo; Tejos, Cristian; Repetto, Gabriela; Leu, Costin; Lal, Dennis; Mata, Ignacio; Klein, Andrés; Pérez Palma, EduardoComorbidities are common in Parkinson's disease and significantly impact the disease progression and management. While polygenic scores have been widely used to assess genetic risk for complex diseases, their role in comorbidity presentation in Parkinson's disease remains unclear. This study investigates whether genetic predisposition to comorbidities, as measured by polygenic scores, differs between individuals with Parkinson's disease and the general population and explores how genetic risk influences disease onset and sex-related differences. We analysed data from 4144 individuals with Parkinson's disease and 370 480 individuals from the general population in the UK Biobank, focusing on four comorbidities with high-quality genome-wide association study data: Type 2 diabetes, major depressive disorder, migraine headaches and epilepsy. We first compared polygenic score distributions between individuals with Parkinson's disease and the general population. While our findings indicate that comorbidities and polygenic risk scores do not significantly differ between individuals with Parkinson's disease and the general population, we show an association with disease onset and sex-specific differences. Individuals with earlier disease onset (50-70 years old) had higher genetic risk for major depressive disorder (odds ratio: 2.19, P-value: 1.27 × 10⁻¹⁵) and epilepsy (odds ratio: 1.58, P-value: 0.00845). Additionally, a female participant with Parkinson's disease exhibited higher genetic risk scores for major depressive disorder (odds ratio: 1.5, P-value: 0.0119) and migraine headaches (odds ratio: 2.1, P-value: 0.0155), while a male participant displayed higher genetic risk scores for Type 2 diabetes (odds ratio: 2.7, P-value: 2.11 × 10⁻¹⁷). Comorbidity-polygenic score did not differ between people with versus without Parkinson's disease, yet within Parkinson's disease, a higher genetic burden for specific comorbidities was linked to earlier onset and sex-specific presentation, implicating common variants as modifiers of clinical heterogeneity rather than the primary disease risk. These results enhance our understanding of the genetic influences shaping the broader clinical presentation of Parkinson's disease and highlight the need for further research into the interplay between genetic risk factors, comorbidities and disease heterogeneity.Item SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities(2021) Macnee, Marie; Pérez Palma, Eduardo; Schumacher-Bass, Sarah; Dalton, Jarrod; Leu, Costin; Blankenberg, Daniel; Lal, DennisLiterature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app.Publication The genomic landscape across 474 surgically accessible epileptogenic human brain lesions(2022) López, Javier; Leu, Costin; Macnee, Marie; Khoury, Jean; Hoffmann, Lucas; Coras, Roland; Kobow, Katja; Bhattarai, Nisha; Pérez, Eduardo; Hamer, Hajo; Brandner, Sebastian; Rössler, Karl; Bien, Christian; Kalbhenn, Thilo; Pieper, Tom; Hartlieb, Till; Butler, Elizabeth; Genovese, Giulio; Becker, Kerstin; Altmüller, Janine; Niestroj, Lisa; Ferguson, Lisa; Busch, Robyn; Nürnberg, Peter; Najm, Imad; Blümcke, Ingmar; Lal, DennisUnderstanding the exact molecular mechanisms involved in the etiology of epileptogenic pathologies with or without tumor activity is essential for improving treatment of drug-resistant focal epilepsy. Here, we characterize the landscape of somatic genetic variants in resected brain specimens from 474 individuals with drug-resistant focal epilepsy using deep whole-exome sequencing (>350×) and whole-genome genotyping. Across the exome, we observe a greater number of somatic single-nucleotide variants (SNV) in low-grade epilepsy-associated tumors (LEAT; 7.92 ± 5.65 SNV) than in brain tissue from malformations of cortical development (MCD; 6.11 ± 4 SNV) or hippocampal sclerosis (HS; 5.1 ± 3.04 SNV). Tumor tissues also had the largest number of likely pathogenic variant carrying cells. LEAT had the highest proportion of samples with one or more somatic copy number variants (CNV; 24.7%), followed by MCD (5.4%) and HS (4.1%). Recurring somatic whole chromosome duplications affecting Chromosome 7 (16.8%), chromosome 5 (10.9%), and chromosome 20 (9.9%) were observed among LEAT. For germline variant-associated MCD genes such as TSC2, DEPDC5, and PTEN, germline SNV were frequently identified within large loss of heterozygosity regions, supporting the recently proposed 'second hit' disease mechanism in these genes. We detect somatic variants in twelve established lesional epilepsy genes and demonstrate exome-wide statistical support for three of these in the etiology of LEAT (e.g., BRAF) and MCD (e.g., SLC35A2 and MTOR). We also identify novel significant associations for PTPN11 with LEAT and NRAS Q61 mutated protein with a complex MCD characterized by polymicrogyria and nodular heterotopia. The variants identified in NRAS are known from cancer studies to lead to hyperactivation of NRAS, which can be targeted pharmacologically. We identify large recurrent 1q21-q44 duplication including AKT3 in association with focal cortical dysplasia type 2a with hyaline astrocytic inclusions, another rare and possibly under-recognized brain lesion. The clinical genetic analyses showed that the numbers of somatic SNV across the exome and the fraction of affected cells were positively correlated with the age at seizure onset and surgery in individuals with LEAT. In summary, our comprehensive genetic screen sheds light on the genome-scale landscape of genetic variants in epileptic brain lesions, informs the design of gene panels for clinical diagnostic screening, and guides future directions for clinical implementation of epilepsy surgery genetics.