Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes.
Journal
Genetics in medicine : official journal of the American College of Medical Genetics
ISSN: 1530-0366
Titre abrégé: Genet Med
Pays: United States
ID NLM: 9815831
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
received:
20
05
2020
accepted:
29
10
2020
revised:
29
10
2020
pubmed:
28
1
2021
medline:
4
6
2021
entrez:
27
1
2021
Statut:
ppublish
Résumé
Electronic health records are gaining popularity to detect and propose interdisciplinary treatments for patients with similar medical histories, diagnoses, and outcomes. These files are compiled by different nonexperts and expert clinicians. Data mining in these unstructured data is a transposable and sustainable methodology to search for patients presenting a high similitude of clinical features. Exome and targeted next-generation sequencing bioinformatics analyses were performed at the Imagine Institute. Similarity Index (SI), an algorithm based on a vector space model (VSM) that exploits concepts extracted from clinical narrative reports was used to identify patients with highly similar clinical features. Here we describe a case of "automated diagnosis" indicated by Dr. Warehouse, a biomedical data warehouse oriented toward clinical narrative reports, developed at Necker Children's Hospital using around 500,000 patients' records. Through the use of this warehouse, we were able to match and identify two patients sharing very specific clinical neonatal and childhood features harboring the same de novo variant in KCNA2. This innovative application of database clustering clinical features could advance identification of patients with rare and common genetic conditions and detect with high accuracy the natural history of patients harboring similar genetic pathogenic variants.
Identifiants
pubmed: 33500571
doi: 10.1038/s41436-020-01039-z
pii: S1098-3600(21)01432-5
pmc: PMC8105164
doi:
Substances chimiques
KCNA2 protein, human
0
Kv1.2 Potassium Channel
0
Types de publication
Case Reports
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
968-971Références
Jannot, A. S. et al. The Georges Pompidou University Hospital Clinical Data Warehouse: a 8-years follow-up experience. Int. J. Med. Inform. 102, 21–28 (2017).
doi: 10.1016/j.ijmedinf.2017.02.006
Garcelon, N. et al. Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse. J. Am. Med. Informatics Assoc. 80, 52–63 (2017).
Hardies, K., Weckhuysen, S., De Jonghe, P. & Suls, A. Lessons learned from gene identification studies in Mendelian epilepsy disorders. Eur. J. Hum. Genet. 24, 961–967 (2016).
doi: 10.1038/ejhg.2015.251
McTague, A. et al. The genetic landscape of the epileptic encephalopathies of infancy and childhood. Lancet. Neurol. 5, 304–316 (2016).
doi: 10.1016/S1474-4422(15)00250-1
Barcia, G. et al. Epilepsy with migrating focal seizures. Neurol. Genet. 5, e363 (2019).
doi: 10.1212/NXG.0000000000000363
Pena, S. D. J. & Coimbra, R. L. M. Ataxia and myoclonic epilepsy due to a heterozygous new mutation in KCNA2: proposal for a new channelopathy. Clin. Genet. 87, e1–e3 (2015).
doi: 10.1111/cge.12542
Syrbe, S. et al. De novo loss-or gain-of-function mutations in KCNA2 cause epileptic encephalopathy. Nat. Genet. 47, 393–399 (2015).
doi: 10.1038/ng.3239
Masnada, S. et al. Clinical spectrum and genotype–phenotype associations of KCNA2-related encephalopathies. Brain. 140, 2337–2354 (2017).
doi: 10.1093/brain/awx184
Garcelon, N. et al. A clinician friendly data warehouse oriented toward narrative reports: Dr Warehouse. J. Biomed. Inform. 80, 52–63 (2018).
doi: 10.1016/j.jbi.2018.02.019
Hundallah, K., Alenizi, A., Alhashem, A. & Tabarki, B. Severe early-onset epileptic encephalopathy due to mutations in the KCNA2 gene: expansion of the genotypic and phenotypic spectrum. Eur. J. Paediatr. Neurol. 20, 657–660 (2016).
doi: 10.1016/j.ejpn.2016.03.011
Lindberg, D. A., Humphreys, B. L. & McCray, A. T. The Unified Medical Language System. Methods Inf. Med. 32, 281–291 (1993).
doi: 10.1055/s-0038-1634945
Garcelon, N. et al. Next generation phenotyping using narrative reports in a rare disease clinical data warehouse. Orphanet. J. Rare Dis. 13, 85 (2018).
doi: 10.1186/s13023-018-0830-6
Garcelon, N. et al. Finding patients using similarity measures in a rare diseases-oriented clinical data warehouse: Dr. Warehouse and the needle in the needle stack. J. Biomed. Inform. 73, 51–61 (2017).
doi: 10.1016/j.jbi.2017.07.016
Brew, H. M. et al. Seizures and reduced life span in mice lacking the potassium channel subunit Kv1.2, but hypoexcitability and enlarged Kv1 currents in auditory neurons. J. Neurophysiol. 98, 1501–1525 (2007).
doi: 10.1152/jn.00640.2006
Xie, G. et al. A new Kv1.2 channelopathy underlying cerebellar ataxia. J. Biol. Chem. 285, 32160–32173 (2010).
doi: 10.1074/jbc.M110.153676
Srdanović, S. et al. Transient knock-down of kcna2 reduces sleep in larval zebrafish. Behav. Brain Res. 326, 13–21 (2017).
doi: 10.1016/j.bbr.2017.02.026
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).
doi: 10.1038/gim.2015.30