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Golnari P, Prantzalos K, Hood V, Meskis MA, Isom LL, Wilcox K, Parent JM, Lal D, Lhatoo SD, Goodkin HP, Wirrell EC, Knupp KG, Patel M, Loeb JA, Sullivan JE, Harte-Hargrove L, Fureman BE, Buchhalter J, Sahoo SS. Ontology accelerates few-shot learning capability of large language model: A study in extraction of drug efficacy in a rare pediatric epilepsy. Int J Med Inform 2025; 201:105942. [PMID: 40311258 DOI: 10.1016/j.ijmedinf.2025.105942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2024] [Revised: 03/11/2025] [Accepted: 04/20/2025] [Indexed: 05/03/2025]
Abstract
OBJECTIVE Dravet Syndrome (DS) is a developmental and epileptic encephalopathy that is characterized by severe, prolonged motor seizures and high resistance to multiple antiseizure medications (ASMs) with multiple comorbidities. Evaluating the efficacy of new drugs in DS preclinical models and mapping them to human phenotypes of DS through analysis of published literature is an important goal for improving outcomes in this rare pediatric epilepsy. MATERIALS AND METHODS Large language models (LLM) have demonstrated great promise in parsing published literature; however, the performance of LLMs falls short in medical applications. In this study, we investigate the effectiveness of domain ontology developed by human experts to optimize LLMs for medical text processing in a rare disease. Utilizing a benchmark dataset that describes the efficacy of 17 ASMs tested in preclinical models and DS patients, we define a new ontology-augmented phased in-context learning (PCL) approach to process 4935 full-text DS articles. We expand this analysis to 7 new drugs that demonstrate efficacy in reducing seizures to identify gaps in current knowledge for designing new experimental studies for drug discovery in DS. RESULTS Few-shot or in-context learning is a foundational capability of LLMs and the few-shot learning capability of the Gemini 1.0 Pro version LLM dramatically increases when we augment prompts with the DS epilepsy ontology. The DS epilepsy ontology is the largest epilepsy and seizure ontology in clinical use that was developed by DS basic scientists and clinical neurologists. The ontology-augmented PCL prompt achieves 100% accuracy in reproducing the benchmark drug efficacy dataset for 17 ASMs with only two examples for in-context learning. CONCLUSION The new ontology-augmented PCL approach significantly accelerates the few-shot learning capabilities of the Gemini LLM, thereby reducing the number of required examples and time needed to optimize LLMs for medical applications.
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Affiliation(s)
- Pedram Golnari
- Department of Population and Quantitative Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Katrina Prantzalos
- Department of Population and Quantitative Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Veronica Hood
- Dravet Syndrome Foundation, Inc, PO Box 3026, Cherry Hill, NJ 08034, USA
| | - Mary Anne Meskis
- Dravet Syndrome Foundation, Inc, PO Box 3026, Cherry Hill, NJ 08034, USA
| | - Lori L Isom
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI 48109-5632, USA
| | - Karen Wilcox
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA
| | - Jack M Parent
- Department of Neurology and Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Dennis Lal
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Center for Neurogenetics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T., Cambridge, MA 02142, USA
| | - Samden D Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Howard P Goodkin
- Department of Neurology and Pediatrics, University of Virginia, Charlottesville VA 22903, USA
| | - Elaine C Wirrell
- Divisions of Child & Adolescent Neurology and Epilepsy, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kelly G Knupp
- Department of Pediatrics and Neurology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Manisha Patel
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO 80045, USA
| | - Jeffrey A Loeb
- Department of Neurology and Rehabilitation, University of Illinois Chicago, Chicago, IL 60612, USA
| | - Joseph E Sullivan
- Departments of Neurology and Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Brandy E Fureman
- Epilepsy Foundation, 3540 Crain Highway, Suite 675, Bowie, MD 20716, USA
| | - Jeffrey Buchhalter
- Department of Pediatrics, University of Calgary School of Medicine, Calgary AB T2N 4N1, Canada
| | - Satya S Sahoo
- Department of Population and Quantitative Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
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Magielski JH, Cohen S, Kaufman MC, Parthasarathy S, Xian J, Brimble E, Fitter N, Furia F, Gardella E, Møller RS, Helbig I, McKee JL. Deciphering the Natural History of SCN8A-Related Disorders. Neurology 2025; 104:e213533. [PMID: 40228184 PMCID: PMC11998016 DOI: 10.1212/wnl.0000000000213533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2024] [Accepted: 02/18/2025] [Indexed: 04/16/2025] Open
Abstract
BACKGROUND AND OBJECTIVES SCN8A-related disorders encompass a range of neurodevelopmental and epilepsy phenotypes. However, despite representing one of the most common epilepsy-associated channelopathies, its longitudinal phenotypes remain largely uncharacterized. METHODS In this study, we harmonized electronic medical record data from 82 individuals with SCN8A-related disorders to reconstruct the natural history of the disorder in comparison with a cohort of 2,833 individuals with known or presumed genetic epilepsies. RESULTS Compared with the cohort of other known or presumed genetic epilepsies, those with SCN8A-related disorders (mean age = 8.3 years, 52% female) had >10-fold odds of bilateral tonic-clonic seizures as early as at 1 year (p = 1.70 × 10-14, OR 10.56, CI 5.85-18.90). Individuals carrying gain-of-function (GOF) SCN8A variants had particularly high seizure risk at 6 months (p = 0.007/pthreshold = 4.25 × 10-4, OR 4.71, CI 1.36-21.25) and an increased risk of global developmental delay as early as at 3 months (p = 0.002/pthreshold = 4.72 × 10-5, OR 5.67, CI 1.74-20.23) when compared with the broader SCN8A cohort. Individuals with loss-of-function variants were more likely to have atypical absence seizures, most prominently at 4.25 years (p = 0.013/pthreshold = 7.08 × 10-4, OR 32.71, CI 1.44-2,193.51). Compared with the broader SCN8A cohort, individuals with the recurrent p.Arg850Gln variant were more likely to have infantile spasms at 6 months and those with variants at the p.Arg1872Trp/Gln/Leu hotspot were more likely to have neonatal seizures. Individuals with the recurrent p.Gly1475Arg variant were more likely to have active epilepsy after 5 years of age. In later childhood, focal seizures were more prominent in individuals with the recurrent p.Arg1617Gln variant while generalized-onset seizures were more prominent with the p.Asn1877Ser variant. We also established the effectiveness of sodium channel blockers in managing SCN8A epilepsy in individuals carrying GOF variants and those whose variants have not been functionally characterized, suggesting that many unstudied SCN8A variants may have GOF mechanisms. DISCUSSION SCN8A-related disorders distinguish themselves from other genetic epilepsies by the frequent bilateral tonic-clonic seizures in infancy, prominent early epileptic and developmental features in GOF variant carriers, and unique seizure phenotypes in those with recurrent variants. Our study provides a longitudinal perspective on SCN8A-related disorders, paving the way for future precision medicine approaches.
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Affiliation(s)
- Jan H Magielski
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
| | - Stacey Cohen
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
| | - Michael C Kaufman
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
| | - Shridhar Parthasarathy
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
- Johns Hopkins School of Medicine, Baltimore, MD
| | | | | | - Francesca Furia
- Department of Epilepsy Genetics and Personalized Treatment, The Danish Epilepsy Center, Dianalund, Denmark
- Institute for Regional Health Services, University of Southern Denmark, Odense; and
| | - Elena Gardella
- Department of Epilepsy Genetics and Personalized Treatment, The Danish Epilepsy Center, Dianalund, Denmark
- Institute for Regional Health Services, University of Southern Denmark, Odense; and
| | - Rikke Steensbjerre Møller
- Department of Epilepsy Genetics and Personalized Treatment, The Danish Epilepsy Center, Dianalund, Denmark
- Institute for Regional Health Services, University of Southern Denmark, Odense; and
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Jillian L McKee
- Division of Neurology, Children's Hospital of Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Philadelphia
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3
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Beniczky S, Trinka E, Wirrell E, Abdulla F, Al Baradie R, Alonso Vanegas M, Auvin S, Singh MB, Blumenfeld H, Bogacz Fressola A, Caraballo R, Carreno M, Cendes F, Charway A, Cook M, Craiu D, Ezeala-Adikaibe B, Frauscher B, French J, Gule MV, Higurashi N, Ikeda A, Jansen FE, Jobst B, Kahane P, Kishk N, Khoo CS, Vinayan KP, Lagae L, Lim KS, Lizcano A, McGonigal A, Perez-Gosiengfiao KT, Ryvlin P, Specchio N, Sperling MR, Stefan H, Tatum W, Tripathi M, Yacubian EM, Wiebe S, Wilmshurst J, Zhou D, Cross JH. Updated classification of epileptic seizures: Position paper of the International League Against Epilepsy. Epilepsia 2025. [PMID: 40264351 DOI: 10.1111/epi.18338] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 02/13/2025] [Accepted: 02/14/2025] [Indexed: 04/24/2025]
Abstract
The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic-clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource-limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.
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Affiliation(s)
- Sándor Beniczky
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
| | - Eugen Trinka
- Department of Neurology, Center for Cognitive Neuroscience, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Neuroscience Institute, Center for Cognitive Neuroscience, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Medical Informatics, and Technology, Institute of Public Health, Medical Decision Making, and Health Technology Assessment, University for Health Sciences, Hall in Tyrol, Austria
| | - Elaine Wirrell
- Division of Child and Adolescent Neurology and Epilepsy, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Fatema Abdulla
- Department of Clinical Neurosciences, Salmaniya Medical Complex, Government Hospitals, Manama, Kingdom of Bahrain
| | | | | | - Stéphane Auvin
- Pediatric Neurology Department, CRMR Epilepsies Rares, APHP, Robert Debré University Hospital, Paris, France
- INSERM NeuroDiderot, Université Paris Cité, Paris, France
- Institut Universitaire de France, Paris, France
| | - Mamta Bhushan Singh
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Hal Blumenfeld
- Department of Neurology, Neuroscience, and Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | | | - Mar Carreno
- Clinical Institute of Neurosciences, University Hospital Clinic of Barcelona, Barcelona, Spain
- Epilepsy Unit, Neurology Department, University Hospital Clinic of Barcelona, Barcelona, Spain
| | - Fernando Cendes
- Department of Neurology, School of Medical Sciences, University of Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, São Paulo, Brazil
| | | | - Mark Cook
- Faculties of Engineering and Information Technology, and Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Dana Craiu
- Pediatric Neurology Discipline, Neuroscience Department, "Carol Davila" University of Medicine, Bucharest, Romania
- Center of Expertise of Rare Pediatric Neurological Disorders Al Obregia Clinical Hospital, Bucharest, Romania
| | - Birinus Ezeala-Adikaibe
- Department of Medicine, University of Nigeria Teaching Hospital, Ituku/Ozalla, Enugu, Nigeria
- Neurology Unit, Memfys Hospital, Enugu, Nigeria
| | - Birgit Frauscher
- Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
| | - Jacqueline French
- Department of Neurology, New York University, New York, New York, USA
| | - M V Gule
- Division of Neurology, Department of Medicine, Neuroscience Institute and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
- Centre for Global Epilepsy, University of Oxford, Oxford, UK
| | - Norimichi Higurashi
- Musashi-Kosugi Pediatrics & Epilepsy Clinic and Department of Pediatrics, Jikei University School of Medicine, Minato-ku, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders, and Physiology, Kyoto University Graduate School of Medicine Shogoin, Kyoto, Japan
| | - Floor E Jansen
- Department of Child Neurology, Brain Center, University Medical Center, Utrecht, the Netherlands
| | - Barbara Jobst
- Department of Neurology, Dartmouth-Hitchcock Health, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
| | - Philippe Kahane
- Neurology Department, CHU Grenoble Alpes, Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Nirmeen Kishk
- Department of Neurology, School of Medicine, Kasralainy Hospital, Cairo University, Cairo, Egypt
| | - Ching Soong Khoo
- Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Neurology Unit, Department of Medicine, Hospital Canselor Tuanku Muhriz, Kuala Lumpur, Malaysia
- Centre for Global Epilepsy, Wolfson College, University of Oxford, Oxford, UK
| | | | - Lieven Lagae
- Pediatric Neurology, University of Leuven, Leuven, Belgium
| | - Kheng-Seang Lim
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Angelica Lizcano
- Department of Clinical Neurophysiology and Epilepsy Clinic, Neurocentro and Coneuro, Pereira, Colombia
- Laboratory of Neuroimmunology, Medcare, Pereira, Colombia
| | - Aileen McGonigal
- Centre for Neurosciences, Mater Hospital Brisbane, University of Queensland, Brisbane, Queensland, Australia
| | - Katerina Tanya Perez-Gosiengfiao
- Department of Neurosciences, University of the Philippines-Philippine General Hospital, Manila, Philippines
- Department of Neurosciences, Makati Medical Center, Makati, Philippines
- Institute for Neurological Sciences, St. Lukes Medical Center Global City, Taguig, Philippines
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and Université de Lausanne, Lausanne, Switzerland
| | - Nicola Specchio
- Neurology, Epilepsy, and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy and University Hospitals KU Leuven, Leuven, Belgium
| | - Michael R Sperling
- Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Hermann Stefan
- Department of Neurology-Biomagnetism, University Hospital, Erlangen, Germany
| | - William Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Elza Márcia Yacubian
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Samuel Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Jo Wilmshurst
- Department of Pediatric Neurology, Red Cross War Memorial Children's Hospital, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - J Helen Cross
- NIHR BRC Great Ormond Street Institute of Child Health, Great Ormond Street Hospital & Young Epilepsy, University College London, London, UK
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4
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Ganesan S, Ruggiero SM, Parthasarathy S, Galer PD, Lewis-Smith D, McSalley I, Cohen SR, Lusk L, Prentice AJ, McKee JL, Pendziwiat M, Smith L, Weber Y, Mefford HC, Poduri A, Helbig I. Phenotypic analysis of 11,125 trio exomes in neurodevelopmental disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.11.642649. [PMID: 40161685 PMCID: PMC11952407 DOI: 10.1101/2025.03.11.642649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Genomic sequencing is widely used to identify causative genetic changes in neurodevelopmental disorders, such as autism, intellectual disability, and epilepsy. Most neurodevelopmental disorders also present with diverse clinical features, and delineating the interaction between causative genetic changes and phenotypic features is a key prerequisite for developing personalized therapies. However, assessing clinical features at a scale that parallels genomic sequencing remains challenging. Here, we standardize phenotypic information across 11,125 patient-parent trios with exome sequencing data using biomedical ontologies, analyzing 674,767 phenotypic terms. We find that individuals with de novo variants in 69 out of 261 neurodevelopmental genes exhibit statistically significant clinical similarities with distinct phenotypic fingerprints. We also observe that phenotypic relatedness follows a gradient, spanning from highly similar to dissimilar phenotypes, with intra-gene similarities suggesting clinically distinct subgroups for seven neurodevelopmental genes. For most genetic etiologies, only a small subset of highly phenotypically similar individuals carried de novo variants in the same gene, highlighting the heterogeneous and complex clinical landscape of neurodevelopmental disorders. Our study provides a large-scale overview of the dynamic relationship between genotypes and phenotypes in neurodevelopmental disorders, underscoring how the inherent complexity of these conditions can be deciphered through approaches that integrate genomic and phenotypic data.
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Affiliation(s)
- Shiva Ganesan
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Sarah M. Ruggiero
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Shridhar Parthasarathy
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Peter D. Galer
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA, United States
| | - David Lewis-Smith
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
- FutureNeuro, the Research Ireland Centre for Translational Brain Science; RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Ian McSalley
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Stacey R. Cohen
- Division of Translational Medicine and Human Genetics, The Hospital of the University of Pennsylvania, Philadelphia, PA, United States
- Genetic Diagnostic Laboratory, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, United States
| | - Laina Lusk
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Anna J. Prentice
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Jillian L. McKee
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Manuela Pendziwiat
- Institute of Clinical Molecular Biology , Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Lacey Smith
- Epilepsy Genetics Program, Department of Neurology, Boston Children’s Hospital, Boston, MA, United States
| | - Yvonne Weber
- Department of Epileptology and Neurology, RWTH University of Aachen, 52074 Aachen, Germany
| | - Heather C. Mefford
- Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Annapurna Poduri
- National Institute of Neurological Disorders and Stroke
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Boston, MA, United States
- Epilepsy Genetics Program, Department of Neurology, Boston Children’s Hospital - Harvard Medical School, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Boston, MA, United States
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Deptartment of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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5
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Galer PD, Parthasarathy S, Xian J, McKee JL, Ruggiero SM, Ganesan S, Kaufman MC, Cohen SR, Haag S, Chen C, Ojemann WKS, Kim D, Wilmarth O, Vaidiswaran P, Sederman C, Ellis CA, Gonzalez AK, Boßelmann CM, Lal D, Sederman R, Lewis-Smith D, Litt B, Helbig I. Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals. Genet Med 2024; 26:101211. [PMID: 39011766 PMCID: PMC11656408 DOI: 10.1016/j.gim.2024.101211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
PURPOSE An early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records. METHODS We extracted 89 million time-stamped standardized clinical annotations using Natural Language Processing from 4,572,783 clinical notes from 32,112 individuals with childhood epilepsy, including 1925 individuals with known or presumed genetic epilepsies. We applied these features to train random forest models to predict SCN1A-related disorders and any genetic diagnosis. RESULTS We identified 47,774 age-dependent associations of clinical features with genetic etiologies a median of 3.6 years before molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6 to 9 months increased the likelihood of a later molecular diagnosis 5-fold (P < .0001, 95% CI = 3.55-7.42). A later diagnosis of SCN1A-related disorders (area under the curve [AUC] = 0.91) or an overall positive genetic diagnosis (AUC = 0.82) could be reliably predicted using random forest models. CONCLUSION Clinical features predictive of genetic epilepsies precede molecular diagnoses by up to several years in conditions with known precision treatments. An earlier diagnosis facilitated by automated electronic medical records analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.
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Affiliation(s)
- Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA
| | - Shridhar Parthasarathy
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Jillian L McKee
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sarah M Ruggiero
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Michael C Kaufman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Stacey R Cohen
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Scott Haag
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | | | - William K S Ojemann
- University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA
| | | | - Olivia Wilmarth
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Priya Vaidiswaran
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Casey Sederman
- Department of Human Genetics, University of Utah, Salt Lake City, UT; Utah Center for Genetic Discovery, School of Medicine, University of Utah, Salt Lake City, UT
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Alexander K Gonzalez
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Christian M Boßelmann
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH; Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
| | | | - David Lewis-Smith
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK; Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK; FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Brian Litt
- University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
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6
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Magielski J, McSalley I, Parthasarathy S, McKee J, Ganesan S, Helbig I. Advances in big data and omics: Paving the way for discovery in childhood epilepsies. Curr Probl Pediatr Adolesc Health Care 2024; 54:101634. [PMID: 38825428 DOI: 10.1016/j.cppeds.2024.101634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The insights gained from big data and omics approaches have transformed the field of childhood genetic epilepsy. With an increasing number of individuals receiving genetic testing for seizures, we are provided with an opportunity to identify clinically relevant subgroups and extract meaningful observations from this large-scale clinical data. However, the volume of data from electronic medical records and omics (e.g., genomics, transcriptomics) is so vast that standardized methods, such as the Human Phenotype Ontology, are necessary for reliable and comprehensive characterization. Here, we explore the integration of clinical and omics data, highlighting how these approaches pave the way for discovery in childhood epilepsies.
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Affiliation(s)
- Jan Magielski
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA
| | - Ian McSalley
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA
| | - Shridhar Parthasarathy
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jillian McKee
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA; School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19014, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19146, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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7
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Montanucci L, Lewis-Smith D, Collins RL, Niestroj LM, Parthasarathy S, Xian J, Ganesan S, Macnee M, Brünger T, Thomas RH, Talkowski M, Helbig I, Leu C, Lal D. Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals. Nat Commun 2023; 14:4392. [PMID: 37474567 PMCID: PMC10359300 DOI: 10.1038/s41467-023-39539-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 06/16/2023] [Indexed: 07/22/2023] Open
Abstract
Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice.
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Affiliation(s)
- Ludovica Montanucci
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.) and Harvard, Cambridge, USA
| | | | - Shridhar Parthasarathy
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Julie Xian
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Marie Macnee
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Tobias Brünger
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Michael Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.) and Harvard, Cambridge, USA
| | - Ingo Helbig
- The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, UK.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T, Cambridge, MA, USA.
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, US.
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.) and Harvard, Cambridge, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T, Cambridge, MA, USA.
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, US.
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8
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Henry OJ, Stödberg T, Båtelson S, Rasi C, Stranneheim H, Wedell A. Individualised human phenotype ontology gene panels improve clinical whole exome and genome sequencing analytical efficacy in a cohort of developmental and epileptic encephalopathies. Mol Genet Genomic Med 2023; 11:e2167. [PMID: 36967109 PMCID: PMC10337286 DOI: 10.1002/mgg3.2167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 02/21/2023] [Accepted: 03/01/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype-based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy. METHODS We have tested a standardised phenotyping method termed 'Phenomodels' for integrating deep-phenotyping information with our in-house developed clinical whole exome/genome sequencing analytical pipeline. Phenomodels includes a user-friendly epilepsy phenotyping template and an objective measure for selecting which template terms to include in individualised Human Phenotype Ontology (HPO) gene panels. In a pilot study of 38 previously solved cases of developmental and epileptic encephalopathies, we compared the sensitivity and specificity of the individualised HPO gene panels with the clinical epilepsy gene panel. RESULTS The Phenomodels template showed high sensitivity for capturing relevant phenotypic information, where 37/38 individuals' HPO gene panels included the causative gene. The HPO gene panels also had far fewer variants to assess than the epilepsy gene panel. CONCLUSION We have demonstrated a viable approach for incorporating standardised phenotype information into clinical genomic analyses, which may enable more efficient analysis.
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Affiliation(s)
- Olivia J. Henry
- Department of Molecular Medicine and SurgeryKarolinska InstitutetStockholmSweden
| | - Tommy Stödberg
- Department of Women's and Children's HealthKarolinska InstitutetStockholmSweden
- Department of Pediatric NeurologyKarolinska University HospitalStockholmSweden
| | - Sofia Båtelson
- Department of Pediatric NeurologyKarolinska University HospitalStockholmSweden
| | - Chiara Rasi
- Science for Life Laboratory, Department of Microbiology, Tumour and Cell BiologyKarolinska InstitutetStockholmSweden
| | - Henrik Stranneheim
- Department of Molecular Medicine and SurgeryKarolinska InstitutetStockholmSweden
- Science for Life Laboratory, Department of Microbiology, Tumour and Cell BiologyKarolinska InstitutetStockholmSweden
- Centre for Inherited Metabolic DiseasesKarolinska University HospitalStockholmSweden
| | - Anna Wedell
- Department of Molecular Medicine and SurgeryKarolinska InstitutetStockholmSweden
- Centre for Inherited Metabolic DiseasesKarolinska University HospitalStockholmSweden
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9
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Daniali M, Galer PD, Lewis-Smith D, Parthasarathy S, Kim E, Salvucci DD, Miller JM, Haag S, Helbig I. Enriching representation learning using 53 million patient notes through human phenotype ontology embedding. Artif Intell Med 2023; 139:102523. [PMID: 37100502 PMCID: PMC10782859 DOI: 10.1016/j.artmed.2023.102523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.
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Affiliation(s)
- Maryam Daniali
- Department of Computer Science, Drexel University, Philadelphia, PA, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Peter D Galer
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - David Lewis-Smith
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK; Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Shridhar Parthasarathy
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Edward Kim
- Department of Computer Science, Drexel University, Philadelphia, PA, USA
| | - Dario D Salvucci
- Department of Computer Science, Drexel University, Philadelphia, PA, USA
| | - Jeffrey M Miller
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Scott Haag
- Department of Computer Science, Drexel University, Philadelphia, PA, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ingo Helbig
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
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10
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McKee JL, Kaufman MC, Gonzalez AK, Fitzgerald MP, Massey SL, Fung F, Kessler SK, Witzman S, Abend NS, Helbig I. Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study. Lancet Digit Health 2023; 5:e217-e226. [PMID: 36963911 PMCID: PMC10065843 DOI: 10.1016/s2589-7500(23)00004-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/09/2022] [Accepted: 01/06/2023] [Indexed: 03/26/2023]
Abstract
BACKGROUND Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates. METHODS In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days. FINDINGS We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83-94) for all neonates and 97% (88-99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91-100) for all neonates and 100% (100-100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84-96) in the overall cohort and 97% (80-99) in neonates with hypoxic ischaemic encephalopathy. INTERPRETATION Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use. FUNDING Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.
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Affiliation(s)
- Jillian L McKee
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael C Kaufman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Alexander K Gonzalez
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mark P Fitzgerald
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shavonne L Massey
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - France Fung
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sudha K Kessler
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie Witzman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nicholas S Abend
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Anesthesia and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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11
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Lewis-Smith D, Parthasarathy S, Xian J, Kaufman MC, Ganesan S, Galer PD, Thomas RH, Helbig I. Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery. Hum Mutat 2022; 43:1642-1658. [PMID: 35460582 PMCID: PMC9560951 DOI: 10.1002/humu.24389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/23/2022] [Accepted: 04/21/2022] [Indexed: 11/09/2022]
Abstract
Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.
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Affiliation(s)
- David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
- Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shridhar Parthasarathy
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Julie Xian
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Michael C. Kaufman
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Peter D. Galer
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
- Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
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12
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Krey I, Platzer K, Esterhuizen A, Berkovic SF, Helbig I, Hildebrand MS, Lerche H, Lowenstein D, Møller RS, Poduri A, Sadleir L, Sisodiya SM, Weckhuysen S, Wilmshurst JM, Weber Y, Lemke JR. Current practice in diagnostic genetic testing of the epilepsies. Epileptic Disord 2022; 24:765-786. [PMID: 35830287 PMCID: PMC10752379 DOI: 10.1684/epd.2022.1448] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/10/2022] [Indexed: 01/19/2023]
Abstract
Epilepsy genetics is a rapidly developing field, in which novel disease-associated genes, novel mechanisms associated with epilepsy, and precision medicine approaches are continuously being identified. In the past decade, advances in genomic knowledge and analysis platforms have begun to make clinical genetic testing accessible for, in principle, people of all ages with epilepsy. For this reason, the Genetics Commission of the International League Against Epilepsy (ILAE) presents this update on clinical genetic testing practice, including current techniques, indications, yield of genetic testing, recommendations for pre- and post-test counseling, and follow-up after genetic testing is completed. We acknowledge that the resources vary across different settings but highlight that genetic diagnostic testing for epilepsy should be prioritized when the likelihood of an informative finding is high. Results of genetic testing, in particular the identification of causative genetic variants, are likely to improve individual care. We emphasize the importance of genetic testing for individuals with epilepsy as we enter the era of precision therapy.
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Affiliation(s)
- Ilona Krey
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Alina Esterhuizen
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- National Health Laboratory Service, Groote Schuur Hospital, Cape Town, South Africa
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine, University of Melbourne (Austin Health), Heidelberg, VIC, Australia
| | - Ingo Helbig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
- Department of Neuropediatrics, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Building C, Arnold-Heller-Straße 3, 24105 Kiel, Germany
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, 19104 USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104 USA
| | - Michael S. Hildebrand
- Epilepsy Research Centre, Department of Medicine, The University of Melbourne, Austin Health, Heidelberg and Murdoch Children’s Research Institute, Royal Children’s Hospital, Victoria, Australia
| | - Holger Lerche
- Department of Epileptology and Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Daniel Lowenstein
- Department of Neurology, University of California, San Francisco, USA
| | - Rikke S. Møller
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Centre, Dianalund, Denmark
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
| | - Annapurna Poduri
- Epilepsy Genetics Program, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lynette Sadleir
- Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology London, UK and Chalfont Centre for Epilepsy, Buckinghamshire, UK
| | - Sarah Weckhuysen
- Center for Molecular Neurology, VIB-University of Antwerp, VIB, Antwerp, Belgium; Department of Neurology, University Hospital Antwerp, Antwerp, Belgium
| | - Jo M. Wilmshurst
- Department of Paediatric Neurology, Paediatric and Child Health, Red Cross War Memorial Children’s Hospital, Neuroscience Institute, University of Cape Town, South Africa
| | - Yvonne Weber
- Department of Epileptology and Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
- Department of Epileptology and Neurology, University of Aachen, Germany
| | - Johannes R. Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
- Center for Rare Diseases, University of Leipzig Medical Center, Leipzig, Germany
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13
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Knowles JK, Helbig I, Metcalf CS, Lubbers LS, Isom LL, Demarest S, Goldberg EM, George AL, Lerche H, Weckhuysen S, Whittemore V, Berkovic SF, Lowenstein DH. Precision medicine for genetic epilepsy on the horizon: Recent advances, present challenges, and suggestions for continued progress. Epilepsia 2022; 63:2461-2475. [PMID: 35716052 PMCID: PMC9561034 DOI: 10.1111/epi.17332] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 01/18/2023]
Abstract
The genetic basis of many epilepsies is increasingly understood, giving rise to the possibility of precision treatments tailored to specific genetic etiologies. Despite this, current medical therapy for most epilepsies remains imprecise, aimed primarily at empirical seizure reduction rather than targeting specific disease processes. Intellectual and technological leaps in diagnosis over the past 10 years have not yet translated to routine changes in clinical practice. However, the epilepsy community is poised to make impressive gains in precision therapy, with continued innovation in gene discovery, diagnostic ability, and bioinformatics; increased access to genetic testing and counseling; fuller understanding of natural histories; agility and rigor in preclinical research, including strategic use of emerging model systems; and engagement of an evolving group of stakeholders (including patient advocates, governmental resources, and clinicians and scientists in academia and industry). In each of these areas, we highlight notable examples of recent progress, new or persistent challenges, and future directions. The future of precision medicine for genetic epilepsy looks bright if key opportunities on the horizon can be pursued with strategic and coordinated effort.
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Affiliation(s)
- Juliet K. Knowles
- Department of Neurology, Division of Child Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- Department of Neuropediatrics, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Cameron S. Metcalf
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA
| | - Laura S. Lubbers
- Citizens United for Research in Epilepsy, Chicago, Illinois, USA
| | - Lori L. Isom
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Scott Demarest
- Department of Pediatrics and Neurology, University of Colorado, School of Medicine, Aurora, Colorado, USA
| | - Ethan M. Goldberg
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alfred L. George
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Sarah Weckhuysen
- Division of Neurology, University Hospital Antwerp, Antwerp, Belgium
- Applied and Translational Neurogenomics Group, Vlaams Instituut voor Biotechnologie Center for Molecular Neurology, Antwerp, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Vicky Whittemore
- Division of Neuroscience, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, Maryland, USA
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine, Austin Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Daniel H. Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
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14
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Dhombres F, Morgan P, Chaudhari BP, Filges I, Sparks TN, Lapunzina P, Roscioli T, Agarwal U, Aggarwal S, Beneteau C, Cacheiro P, Carmody LC, Collardeau‐Frachon S, Dempsey EA, Dufke A, Duyzend MH, el Ghosh M, Giordano JL, Glad R, Grinfelde I, Iliescu DG, Ladewig MS, Munoz‐Torres MC, Pollazzon M, Radio FC, Rodo C, Silva RG, Smedley D, Sundaramurthi JC, Toro S, Valenzuela I, Vasilevsky NA, Wapner RJ, Zemet R, Haendel MA, Robinson PN. Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2022; 190:231-242. [PMID: 35872606 PMCID: PMC9588534 DOI: 10.1002/ajmg.c.31989] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/01/2022] [Indexed: 01/07/2023]
Abstract
Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne University, GRC26, INSERM, Limics, Armand Trousseau Hospital, Fetal Medicine Department, APHPParisFrance
| | - Patricia Morgan
- American College of Medical Genetics and Genomics, Newborn Screening Translational Research NetworkBethesdaMarylandUSA
| | - Bimal P. Chaudhari
- Institute for Genomic MedicineNationwide Children's HospitalColumbusOhioUSA
| | - Isabel Filges
- University Hospital Basel and University of Basel, Medical GeneticsBaselSwitzerland
| | - Teresa N. Sparks
- Department of Obstetrics, Gynecology, & Reproductive SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Pablo Lapunzina
- CIBERER and Hospital Universitario La Paz, INGEMM‐Institute of Medical and Molecular GeneticsMadridSpain
| | - Tony Roscioli
- Neuroscience Research Australia (NeuRA), University of New South WalesSydneyNew South WalesAustralia
| | - Umber Agarwal
- Department of Maternal and Fetal MedicineLiverpool Women's NHS Foundation TrustLiverpoolUK
| | - Shagun Aggarwal
- Department of Medical GeneticsNizam's Institute of Medical SciencesHyderabadTelanganaIndia
| | - Claire Beneteau
- Service de Génétique Médicale, UF 9321 de Fœtopathologie et Génétique, CHU de NantesNantesFrance
| | - Pilar Cacheiro
- William Harvey Research InstituteQueen Mary University of LondonLondonUK
| | - Leigh C. Carmody
- Department of Genomic MedicineThe Jackson LaboratoryFarmingtonConnecticutUSA
| | | | - Esther A. Dempsey
- St George's University of London, Molecular and Clinical Sciences Research InstituteLondonUK
| | - Andreas Dufke
- University of Tübingen, Institute of Medical Genetics and Applied GenomicsTübingenGermany
| | | | | | - Jessica L. Giordano
- Department of Obstetrics and GynecologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Ragnhild Glad
- Department of Obstetrics and GynecologyUniversity Hospital of North NorwayTromsøNorway
| | - Ieva Grinfelde
- Department of Medical Genetics and Prenatal diagnosisChildren's University HospitalRigaLatvia
| | - Dominic G. Iliescu
- Department of Obstetrics and GynecologyUniversity of Medicine and Pharmacy CraiovaCraiovaDoljRomania
| | - Markus S. Ladewig
- Department of OphthalmologyKlinikum SaarbrückenSaarbrückenSaarlandGermany
| | - Monica C. Munoz‐Torres
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Marzia Pollazzon
- Azienda USL‐IRCCS di Reggio EmiliaMedical Genetics UnitReggio EmiliaItaly
| | | | - Carlota Rodo
- Vall d'Hebron Hospital Campus, Maternal & Fetal MedicineBarcelonaSpain
| | - Raquel Gouveia Silva
- Hospital Santa Maria, Serviço de Genética, Departamento de PediatriaHospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte, Centro Académico de Medicina de LisboaLisboaPortugal
| | - Damian Smedley
- William Harvey Research InstituteQueen Mary University of LondonLondonUK
| | | | - Sabrina Toro
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Irene Valenzuela
- Hospital Vall d'Hebron, Clinical and Molecular Genetics AreaBarcelonaSpain
| | - Nicole A. Vasilevsky
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Ronald J. Wapner
- Department of Obstetrics and GynecologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Roni Zemet
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Melissa A Haendel
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Peter N. Robinson
- Department of Genomic MedicineThe Jackson LaboratoryFarmingtonConnecticutUSA
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15
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Sánchez-Lijarcio O, Yubero D, Leal F, Couce ML, Luis GGS, López-Laso E, García-Cazorla À, Pías-Peleteiro L, de Azua Brea B, Ibáñez-Micó S, Martínez GM, Schifferli MT, Enriquez SW, Ugarte M, Artuch R, Pérez B. The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1. Clin Genet 2022; 102:40-55. [PMID: 35388452 PMCID: PMC9325084 DOI: 10.1111/cge.14138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/27/2022]
Abstract
Glucose transporter 1 deficiency syndrome (GLUT1DS) is a neurometabolic disorder caused by haploinsufficiency of the GLUT1 glucose transporter (encoded by SLC2A1) leading to defective glucose transport across the blood–brain barrier. This work describes the genetic analysis of 56 patients with clinical or biochemical GLUT1DS hallmarks. 55.4% of these patients had a pathogenic variant of SLC2A1, and 23.2% had a variant in one of 13 different genes. No pathogenic variant was identified for the remaining patients. Expression analysis of SLC2A1 indicated a reduction in SLC2A1 mRNA in patients with pathogenic variants of this gene, as well as in one patient with a pathogenic variant in SLC9A6, and in three for whom no candidate variant was identified. Thus, the clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1.
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Affiliation(s)
- Obdulia Sánchez-Lijarcio
- Centro de Diagnóstico de Enfermedades Moleculares, Center of Molecular Biology Severo Ochoa (CBMSO), Autonomous University of Madrid, CIBERER, IdiPAZ, Madrid, Spain
| | - Delia Yubero
- Sant Joan de Déu Research Institute, CIBERER, Barcelona, Spain
| | - Fátima Leal
- Centro de Diagnóstico de Enfermedades Moleculares, Center of Molecular Biology Severo Ochoa (CBMSO), Autonomous University of Madrid, CIBERER, IdiPAZ, Madrid, Spain
| | - María L Couce
- Unit for the Diagnosis and Treatment of Congenital Metabolic Diseases, Clinical University Hospital of Santiago de Compostela, Health Research Institute of Santiago de Compostela, University of Santiago de Compostela, CIBERER, MetabERN, Santiago de Compostela, Spain
| | | | - Eduardo López-Laso
- Paediatric Neurology Unit, Department of Paediatrics, University Hospital Reina Sofía, Maimónides Institute of Biomedical Investigation of Cordoba (IMIBIC) and CIBERER, Córdoba, Spain
| | | | | | | | - Salvador Ibáñez-Micó
- Neuropaediatrics Unit, Department of Pediatrics, Virgen de la Arrixaca University Hospital, Murcia, Spain
| | | | | | - Scarlet Witting Enriquez
- Child Neurology Service, Clinical Hospital San Borja Arriarán, University of Chile, Santiago, Chile
| | - Magdalena Ugarte
- Centro de Diagnóstico de Enfermedades Moleculares, Center of Molecular Biology Severo Ochoa (CBMSO), Autonomous University of Madrid, CIBERER, IdiPAZ, Madrid, Spain
| | - Rafael Artuch
- Sant Joan de Déu Research Institute, CIBERER, Barcelona, Spain
| | - Belén Pérez
- Centro de Diagnóstico de Enfermedades Moleculares, Center of Molecular Biology Severo Ochoa (CBMSO), Autonomous University of Madrid, CIBERER, IdiPAZ, Madrid, Spain
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16
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Benke TA, Park K, Krey I, Camp CR, Song R, Ramsey AJ, Yuan H, Traynelis SF, Lemke J. Clinical and therapeutic significance of genetic variation in the GRIN gene family encoding NMDARs. Neuropharmacology 2021; 199:108805. [PMID: 34560056 PMCID: PMC8525401 DOI: 10.1016/j.neuropharm.2021.108805] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/18/2021] [Accepted: 09/20/2021] [Indexed: 02/03/2023]
Abstract
Considerable genetic variation of N-methyl-d-aspartate receptors (NMDARs) has recently become apparent, with many hundreds of de novo variants identified through widely available clinical genetic testing. Individuals with GRIN variants present with neurological conditions such as epilepsy, autism, intellectual disability (ID), movement disorders, schizophrenia and behavioral disorders. Determination of the functional consequence of genetic variation for NMDARs should lead to precision therapeutics. Furthermore, genetic animal models harboring human variants have the potential to reveal mechanisms that are shared among different neurological conditions, providing strategies that may allow treatment of individuals who are refractory to therapy. Preclinical studies in animal models and small open label trials in humans support this idea. However, additional functional data for variants and animal models corresponding to multiple individuals with the same genotype are needed to validate this approach and to lead to thoughtfully designed, randomized, placebo-controlled clinical trials, which could provide data in order to determine safety and efficacy of potential precision therapeutics.
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Affiliation(s)
- Tim A Benke
- Departments of Pediatrics, Pharmacology, Neurology, and Otolaryngology, University of Colorado, School of Medicine and Children's Hospital Colorado, United States.
| | - Kristen Park
- Departments of Pediatrics and Neurology, University of Colorado School of Medicine and Children's Hospital Colorado, United States
| | - Ilona Krey
- Institute of Human Genetics, Leipzig Medical Center, Leipzig, Germany
| | - Chad R Camp
- Department of Pharmacology and Chemical Biology and the Center for Functional Evaluation of Rare Variants, Emory University School of Medicine, Atlanta, GA, United States
| | - Rui Song
- Department of Pharmacology and Chemical Biology and the Center for Functional Evaluation of Rare Variants, Emory University School of Medicine, Atlanta, GA, United States
| | - Amy J Ramsey
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Hongjie Yuan
- Department of Pharmacology and Chemical Biology and the Center for Functional Evaluation of Rare Variants, Emory University School of Medicine, Atlanta, GA, United States
| | - Stephen F Traynelis
- Department of Pharmacology and Chemical Biology and the Center for Functional Evaluation of Rare Variants, Emory University School of Medicine, Atlanta, GA, United States
| | - Johannes Lemke
- Institute of Human Genetics, Leipzig Medical Center, Leipzig, Germany
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17
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Boßelmann CM. Seizures, semiology, and syndromes: A narrative review. Seizure 2021; 92:230-233. [PMID: 34607271 DOI: 10.1016/j.seizure.2021.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022] Open
Abstract
Clinical seizure signs continue to be of central importance to guide diagnosis, classification, treatment and prognosis. Some basic principles guide history-taking and observation in clinical epileptology. The information contained within subjective seizure descriptions can be framed within standardized vocabulary and a classification of ictal signs, seizure types, and the integrated framework of epilepsy syndromes. As illustrative examples, we discuss the historical origins and current research context of Dravet syndrome and Janz syndrome, two genetic epilepsy syndromes. In candidates for epilepsy surgery, ictal signs aid us in identifying the symptomatogenic zone and hence delineating the ictal onset zone. Here, historical reports from Victor Horsley and Hughlings Jackson provide valuable perspective on clinical reasoning. Lastly, the information contained within clinical signs and syndromes presents an indispensable data source in future efforts of large-scale genotype-phenotype correlations and machine learning methods.
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Affiliation(s)
- Christian Malte Boßelmann
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Tuebingen, Germany.
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