Integrating molecular networks with genetic variant interpretation for precision medicine

Systemic lupus erythematosus (SLE) is an autoimmune disorder with a wide range of clinical symptoms. Enormous progress has been made in the immunological and genetic understanding of SLE. However, the biology of disease heterogeneity in SLE has remained largely unexplored. Human immune profiling studies, helped by recent technological advances especially in single-cell and “omics” analyses, are now shedding light on the cellular and molecular basis of clinical symptoms and disease flares in individual patients. Peripheral blood immunophenotyping analysis with flow cytometry or mass cytometry are identifying responsible cell subsets and markers characteristic of disease heterogeneity. Transcriptome analysis is discovering molecular networks responsible for disease activity, disease subtype and future relapse. In this review, we summarize recent advances in the immune profiling analysis of SLE patients and discuss how they will be used for future precision medicine.

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Clinical genetics lacks standard definitions and protocols for the collection and use of diversity measures

10.1101/2020.04.22.20074500 ◽

2020 ◽

Author(s):

Alice B. Popejoy ◽

Kristy R. Crooks ◽

Stephanie M. Fullerton ◽

Lucia A. Hindorff ◽

Gillian W. Hooker ◽

...

Keyword(s):

Data Collection ◽

Precision Medicine ◽

Human Genetics ◽

Clinical Genetics ◽

Variant Interpretation ◽

Diversity Measures ◽

Perceived Importance ◽

Race Ethnicity ◽

Study Participants ◽

Precision Health

AbstractGenetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in either setting. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (N=448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

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Variant Re-interpretation in Survivors of Cardiac Arrest with Preserved Ejection Fraction (CASPER Registry) by Clinicians and Clinical Commercial Laboratories

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.120.003235 ◽

2021 ◽

Author(s):

Brianna Davies ◽

Kirsten Bartels ◽

Julie Hathaway ◽

Fang Xu ◽

Jason D. Roberts ◽

...

Keyword(s):

Cardiac Arrest ◽

Ejection Fraction ◽

Genetic Variant ◽

Clinical Care ◽

Variant Classification ◽

Variant Interpretation ◽

Preserved Ejection Fraction ◽

Periodic Review ◽

Clinical Genetic ◽

Over Time

Background - Following an unexplained cardiac arrest, clinical genetic testing is increasingly becoming standard of care. Periodic review of variant classification is required, as re-interpretation can change the diagnosis, prognosis, and management of patients and their relatives. Methods - This study aimed to develop and validate a standardized algorithm to facilitate clinical application of the 2015 American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) guidelines for the interpretation of genetic variants. The algorithm was applied to genetic results in the Cardiac Arrest Survivors with Preserved Ejection Fraction Registry (CASPER), to assess the rate of variant re-classification over time. Variant classifications were then compared to the classifications of two commercial laboratories to determine the rate and identify sources of variant interpretation discordance. Results - Thirty-one percent of participants (40/131) had at least one genetic variant with a clinically significant reclassification over time. Variants of uncertain significance were more likely to be downgraded (73%) to benign than upgraded to pathogenic (27%, p= 0.03). For the second part of the study, 50% (70/139) of variants had discrepant interpretations (excluding benign variants), provided by at least one team. Conclusions - Periodic review of genetic variant classification is a key component of follow-up care given rapidly changing information in the field. There is potential for clinical care gaps with discrepant variant interpretations, based in the interpretation and application of current guidelines. The development of gene and disease specific guidelines and algorithms may provide an opportunity to further standardize variant interpretation reporting in the future.

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Case Report of Novel Genetic Variant in KCNT1 Channel and Pharmacological Treatment With Quinidine. Precision Medicine in Refractory Epilepsy

Frontiers in Pharmacology ◽

10.3389/fphar.2021.648519 ◽

2021 ◽

Vol 12 ◽

Author(s):

M. C. Kravetz ◽

M. S. Viola ◽

J. Prenz ◽

M. Curi ◽

G. F. Bramuglia ◽

...

Keyword(s):

Case Report ◽

Therapeutic Drug Monitoring ◽

Precision Medicine ◽

Antiepileptic Drugs ◽

Therapeutic Range ◽

Drug Monitoring ◽

Genetic Variant ◽

Poor Response ◽

Therapeutic Drug ◽

The Individual

Case introduction: In this work we present a female infant patient with epilepsy of infancy with migrating focal seizures (EIMFS). Although many pharmacological schemes were attempted, she developed an encephalopathy with poor response to antiepileptic drugs and progressive cerebral dysfunction.Aim: To present the pharmacological response and therapeutic drug monitoring of a paediatric patient with a severe encephalopathy carrying a genetic variant in KCNT1 gene, whose identification led to include quinidine (QND) in the treatment regimen as an antiepileptic drug.Case report: Patient showed slow rhythmic activity (theta range) over left occipital areas with temporal propagation and oculo-clonic focal seizures and without tonic spasms three months after birth. At the age of 18 months showed severe impairments of motor and intellectual function with poor eye contact. When the patient was 4 years old, a genetic variant in the exon 24 of the KCNT1 gene was found. This led to the diagnosis of EIMFS. Due to antiepileptic treatment failed to control seizures, QND a KCNT1 blocker, was introduced as a therapeutic alternative besides topiramate (200 mg/day) and nitrazepam (2 mg/day). Therapeutic drug monitoring (TDM) of QND plasma levels needed to be implemented to establish individual therapeutic range and avoid toxicity. TDM for dose adjustment was performed to establish the individual therapeutic range of the patient. Seizures were under control with QND levels above 1.5 mcg/ml (65–70 mg/kg q. i.d). In addition, QND levels higher than 4.0 mcg/ml, were related to higher risk of suffering arrhythmia due to prolongation of QT segment. Despite initial intention to withdrawal topiramate completely, QND was no longer effective by itself and failed to maintain seizures control. Due to this necessary interaction between quinidine and topiramate, topiramate was stablished in a maintenance dose of 40 mg/day.Conclusion: The implementation of Precision Medicine by using tools such as Next Generation Sequencing and TDM led to diagnose and select a targeted therapy for the treatment of a KCNT1-related epilepsy in a patient presented with EIMFS in early infancy and poor response to antiepileptic drugs. QND an old antiarrhythmic drug, due to its activity as KCNT1 channel blocker, associated to topiramate resulted in seizures control. Due to high variability observed in QND levels, TDM and pharmacokinetic characterization allowed to optimize drug regimen to maintain QND concentration between the individual therapeutic range and diminish toxicity.

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Pleiotropic Variability Score: A Genome Interpretation Metric to Quantify Phenomic Associations of Genomic Variants

10.1101/2021.07.18.452819 ◽

2021 ◽

Author(s):

Shameer Khader ◽

Benjamin Glicksberg ◽

Kipp W Johnson ◽

Marcus Badgeley ◽

Joel Dudley

Keyword(s):

Genetic Variant ◽

Disease Risk ◽

Variant Interpretation ◽

Complete Understanding ◽

Disease Ontology ◽

Human Phenotype ◽

Genome Interpretation ◽

A Genome ◽

Allele Specific ◽

Score Reports

A complete understanding of phenomic space is critical for elucidating genome-phenome relationships and assessing disease risk from genome sequencing. We developed a new genome interpretation metric called Pleiotropic Variability Score (PVS) to incorporate phenomic variability into the variant interpretation. PVS uses ontologies of human diseases and medical phenotypes, namely human phenotype ontology (HPO) and disease ontology (DO), to compute the similarities of disease and clinical phenotypes associated with a genetic variant based on semantic reasoning algorithms. We tested 78 unique semantic similarity methods and integrated six robust metrics to define the pleiotropy score of SNPs. We computed PVS for 12, 541 SNPs (10, 021 SNPs mapped to DO phenotype and 8, 569 SNPs mapped to HPO phenotypes) using a repertoire of 382 HPO and 317 DO unique phenotype terms compiled from the genotype-phenotype catalog. We validated the utility of PVS by computing pleiotropy using an electronic health record-linked genomic database (BioME, n=11,210) and generated allele-specific pleiotropy. Further, we demonstrate PVS application in personalized medicine using personalized pleiotropy score reports for individuals with genomic data that could potentially aid in variant interpretation. We further developed a software framework to incorporate PVS into VCF files and consolidate pleiotropy assessment as part of genome interpretation pipelines. As the genome-phenome catalogs are growing, PVS will be a useful metric to assess genetic variation to find SNPs with highly pleiotropic effects. Additionally, genome analysts can prioritize variants with varying degrees of pleiotropy for explorative studies to understand the specific roles of SNPs and pleiotropic hubs in mediating novel phenotypes and drug development.

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