Drug‐resistant epilepsy classified by a phenotyping algorithm associates with
            <i>NTRK2</i>

Almoguera, Berta; McGinnis, Emily; Abrams, Debra; Vazquez, Lyam; Cederquist, Anna; Sleiman, Patrick; Dlugos, Dennis J.; Hákonarson, Hákon; Cagan, Andrew; Connolly, John J.; Gainer, Vivian S.; Garifallou, James; Kaminski, Courtney; Lee, Yvonne; Mafra, Fernanda; Mentch, Frank; Pellegrino, Renata; Qiu, Haijun; Snyder, James W.; Tian, Lifeng; Manzi, Shanon; Holm, Ingrid A.; Karlson, Elizabeth W.

doi:10.1111/ane.13115

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…3,18 For each of the 3 phenotypes, we created a list of clinical concepts (seizure diagnosis, medication, brain MRI, etc) that were likely associated with the diagnosis based on expert input (ZG). 19 Each concept was approximated with data elements available in the electronic health record extract. We then developed, iterated, and evaluated multiple computable phenotypes for each condition.…”

Section: Methodsmentioning

confidence: 99%

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

Pan

Weiner

et al. 2021

J Child Neurol

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Introduction: Computable phenotypes allow identification of well-defined patient cohorts from electronic health record data. Little is known about the accuracy of diagnostic codes for important clinical concepts in pediatric epilepsy, such as (1) risk factors like neonatal hypoxic-ischemic encephalopathy; (2) clinical concepts like treatment resistance; (3) and syndromes like juvenile myoclonic epilepsy. We developed and evaluated the performance of computable phenotypes for these examples using electronic health record data at one center. Methods: We identified gold standard cohorts for neonatal hypoxic-ischemic encephalopathy, pediatric treatment-resistant epilepsy, and juvenile myoclonic epilepsy via existing registries and review of clinical notes. From the electronic health record, we extracted diagnostic and procedure codes for all children with a diagnosis of epilepsy and seizures. We used these codes to develop computable phenotypes and evaluated by sensitivity, positive predictive value, and the F-measure. Results: For neonatal hypoxic-ischemic encephalopathy, the best-performing computable phenotype (HIE ICD-9 /10 and [brain magnetic resonance imaging (MRI) or electroencephalography (EEG) within 120 days of life] and absence of commonly miscoded conditions) had high sensitivity (95.7%, 95% confidence interval [CI] 85-99), positive predictive value (100%, 95% CI 95-100), and F measure (0.98). For treatment-resistant epilepsy, the best-performing computable phenotype (3 or more antiseizure medicines in the last 2 years or treatment-resistant ICD-10) had a sensitivity of 86.9% (95% CI 79-93), positive predictive value of 69.6% (95% CI 60-79), and F-measure of 0.77. For juvenile myoclonic epilepsy, the best performing computable phenotype (JME ICD-10) had poor sensitivity (52%, 95% CI 43-60) but high positive predictive value (90.4%, 95% CI 81-96); the F measure was 0.66. Conclusion: The variable accuracy of our computable phenotypes (hypoxic-ischemic encephalopathy high, treatment resistance medium, and juvenile myoclonic epilepsy low) demonstrates the heterogeneity of success using administrative data to identify cohorts important for pediatric epilepsy research.

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Section: Methodsmentioning

confidence: 99%

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

Pan

Weiner

et al. 2021

J Child Neurol

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“…Notably, while an NTRK2 T720I patient responded favorably to VPA treatment, patients harboring Y434C, the second variant characterized in this study, did not exhibit the same favorable response (Hamdan et al, 2017). Interestingly, NTRK2 has been linked to drug-resistant epilepsy (Almoguera et al, 2019). However, the drug-resistance may be allele specific, and these differences could potentially be explained by the differing functional consequences of each variant.…”

Section: Discussionmentioning

confidence: 69%

“…NTRK2 variant Y434C exhibited an upregulation of BDNF/TRKB signaling. Increased activation of TRKB signaling has already been suggested to promote epilepsy and increased NTRK2 is associated with drug-resistant epilepsy (Almoguera et al, 2019;Heinrich et al, 2011;Liu, Kotloski, & McNamara, 2014). In cells overexpressing Y434C, constitutive ligand-independent TRKB signaling with associated increased MAPK activity was observed.…”

Section: Discussionmentioning

confidence: 88%

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Functional characterization and potential therapeutic avenues for variants in the NTRK2 gene causing developmental and epileptic encephalopathies

Long

Crouse

Kesterson

et al. 2021

American J of Med Genetics Pt B

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Variants within the Neurotrophic Tyrosine Kinase Receptor Type 2 (NTRK2) gene have been discovered to play a role in developmental and epileptic encephalopathies, a group of debilitating conditions for which little is known about cause or treatment.Here, we determine the functional consequences of two variants: p.Tyr434Cys (Y434C) (located in the transmembrane domain) and p.Thr720Ile (T720I) (located in the catalytic domain). Wild-type and variant cDNAs were constructed and transfected into HEK293 cells. In cell culture, variant Y434C exhibited ligand-independent activation of tropomyosin-related kinase B (TRKB) signaling with an associated abnormal response to brain-derived neurotrophic factor (BDNF) stimulation and increased levels of phosphorylated extracellular signal-regulated kinase (ERK) and ETS like-1 protein (ELK1) activity. Expression of variant T720I resulted in decreased TRKB signaling with reduced mTor activity as determined by decreased levels of phosphorylated S6. With the deleterious mechanisms characterized, we utilized mediKanren (a novel artificial intelligence tool) to identify therapeutics to compensate for the pathological effects. Downregulation of TRKB through inhibition with mediKanren-predicted compound 1NM-PP1 led to decreased MEK activity.Upregulation of TRKB signaling by mediKanren-predicted valproic acid led to subsequent increase of mTor activity. Overall, our results provide further characterization of the pathogenicity of these two variants in the NTRK2 gene. Indeed, Y434C is the first patient-specific NTRK2 variant with demonstrated hypermorphic activity. Furthermore, we observed that variants Y434C and T720I result in distinct functional consequences that require distinct therapeutic strategies. These data suggest the possibility that unique mutations within different regions of the NTRK2 gene results in separate clinical presentations, representing distinct genetic disorders requiring unique therapeutics.

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“…A study from the Electronic Medical Records and Genomics initiative used an algorithm to identify children with drug-resistant epilepsy (excluding structural causes and syndromic epilepsies) from routinely collected data. 42 They compared results from a drug transporter gene panel (82 genes) from 96 children with drug-resistant epilepsy, 343 children with drugresponsive epilepsy, and 896 controls. They found an association between NTRK2 variants and drug-resistant epilepsy.…”

Section: Comparison To Other Studiesmentioning

confidence: 99%

Genetic influences on epilepsy outcomes: A whole‐exome sequencing and health care records data linkage study

Fonferko‐Shadrach,

Lacey,

Strafford

et al. 2023

Epilepsia

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ObjectiveThis study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes.MethodsWe linked whole‐exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)‐derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom.ResultsWe linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty‐six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure‐free. There was no significant difference between the outcome groups in terms of the exome‐wide and gene‐based burden of rare and deleterious genetic variants.SignificanceWe successfully uploaded, annotated, and linked genetic sequence data and NLP‐derived seizure frequency data to anonymized health care records in this proof‐of‐concept study. We did not detect a genetic influence on real‐world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

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Drug‐resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

Cited by 7 publications

References 33 publications

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

Functional characterization and potential therapeutic avenues for variants in the NTRK2 gene causing developmental and epileptic encephalopathies

Genetic influences on epilepsy outcomes: A whole‐exome sequencing and health care records data linkage study

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