101
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Lauschke VM, Zhou Y, Ingelman-Sundberg M. Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity. Pharmacol Ther 2019; 197:122-152. [PMID: 30677473 PMCID: PMC6527860 DOI: 10.1016/j.pharmthera.2019.01.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.
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Affiliation(s)
- Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Magnus Ingelman-Sundberg
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
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102
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Schwarz UI, Gulilat M, Kim RB. The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics. Cold Spring Harb Perspect Med 2019; 9:cshperspect.a033027. [PMID: 29844222 DOI: 10.1101/cshperspect.a033027] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Inherited genetic variations in pharmacogenetic loci are widely acknowledged as important determinants of phenotypic differences in drug response, and may be actionable in the clinic. However, recent studies suggest that a considerable number of novel rare variants in pharmacogenes likely contribute to a still unexplained fraction of the observed interindividual variability. Next-generation sequencing (NGS) represents a rapid, relatively inexpensive, large-scale DNA sequencing technology with potential relevance as a comprehensive pharmacogenetic genotyping platform to identify genetic variation related to drug therapy. However, many obstacles remain before the clinical use of NGS-based test results, including technical challenges, functional interpretation, and strict requirements for diagnostic tests. Advanced computational analyses, high-throughput screening methodologies, and generation of shared resources with cell-based and clinical information will facilitate the integration of NGS data into candidate genotyping approaches, likely enhancing future drug phenotype predictions in patients.
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Affiliation(s)
- Ute I Schwarz
- Division of Clinical Pharmacology, Department of Medicine, Western University, London, Ontario N6A 5A5, Canada.,Department of Physiology and Pharmacology, Western University, London, Ontario N6A 5A5, Canada
| | - Markus Gulilat
- Department of Physiology and Pharmacology, Western University, London, Ontario N6A 5A5, Canada
| | - Richard B Kim
- Division of Clinical Pharmacology, Department of Medicine, Western University, London, Ontario N6A 5A5, Canada.,Department of Physiology and Pharmacology, Western University, London, Ontario N6A 5A5, Canada
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103
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Klein K, Tremmel R, Winter S, Fehr S, Battke F, Scheurenbrand T, Schaeffeler E, Biskup S, Schwab M, Zanger UM. A New Panel-Based Next-Generation Sequencing Method for ADME Genes Reveals Novel Associations of Common and Rare Variants With Expression in a Human Liver Cohort. Front Genet 2019; 10:7. [PMID: 30766545 PMCID: PMC6365429 DOI: 10.3389/fgene.2019.00007] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/09/2019] [Indexed: 01/10/2023] Open
Abstract
We developed a panel-based NGS pipeline for comprehensive analysis of 340 genes involved in absorption, distribution, metabolism and excretion (ADME) of drugs, other xenobiotics, and endogenous substances. The 340 genes comprised phase I and II enzymes, drug transporters and regulator/modifier genes within their entire coding regions, adjacent intron regions and 5' and 3'UTR regions, resulting in a total panel size of 1,382 kbp. We applied the ADME NGS panel to sequence genomic DNA from 150 Caucasian liver donors with available comprehensive gene expression data. This revealed an average read-depth of 343 (range 27-811), while 99% of the 340 genes were covered on average at least 100-fold. Direct comparison of variant annotation with 363 available genotypes determined independently by other methods revealed an overall accuracy of >99%. Of 15,727 SNV and small INDEL variants, 12,022 had a minor allele frequency (MAF) below 2%, including 8,937 singletons. In total we found 7,273 novel variants. Functional predictions were computed for coding variants (n = 4,017) by three algorithms (Polyphen 2, Provean, and SIFT), resulting in 1,466 variants (36.5%) concordantly predicted to be damaging, while 1,019 variants (25.4%) were predicted to be tolerable. In agreement with other studies we found that less common variants were enriched for deleterious variants. Cis-eQTL analysis of variants with (MAF ≥ 2%) revealed significant associations for 90 variants in 31 genes after Bonferroni correction, most of which were located in non-coding regions. For less common variants (MAF < 2%), we applied the SKAT-O test and identified significant associations to gene expression for ADH1C and GSTO1. Moreover, our data allow comparison of functional predictions with additional phenotypic data to prioritize variants for further analysis.
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Affiliation(s)
- Kathrin Klein
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
| | - Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
| | - Sarah Fehr
- CeGaT GmbH, Tübingen, Germany
- Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Florian Battke
- CeGaT GmbH, Tübingen, Germany
- Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Tim Scheurenbrand
- CeGaT GmbH, Tübingen, Germany
- Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
| | - Saskia Biskup
- CeGaT GmbH, Tübingen, Germany
- Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
- Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany
- Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Ulrich M. Zanger
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- Medical School, University of Tübingen, Tübingen, Germany
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104
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Lauschke VM, Ingelman-Sundberg M. Prediction of drug response and adverse drug reactions: From twin studies to Next Generation Sequencing. Eur J Pharm Sci 2019; 130:65-77. [PMID: 30684656 DOI: 10.1016/j.ejps.2019.01.024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 01/12/2023]
Abstract
Understanding and predicting inter-individual differences related to the success of drug therapy is of tremendous importance, both during drug development and for clinical applications. Importantly, while seminal twin studies indicate that the majority of inter-individual differences in drug disposition are driven by hereditary factors, common genetic polymorphisms explain only less than half of this genetically encoded variability. Recent progress in Next Generation Sequencing (NGS) technologies has for the first time allowed to comprehensively map the genetic landscape of human pharmacogenes. Importantly, these projects have unveiled vast numbers of rare genetic variants, which are estimated to contribute substantially to the missing heritability of drug metabolism phenotypes. However, functional interpretation of these rare variants remains challenging and constitutes one of the important frontiers of contemporary pharmacogenomics. Furthermore, NGS technologies face challenges in the interrogation of genes residing in complex genomic regions, such as CYP2D6 and HLA genes. We here provide an update of the implementation of pharmacogenomic variations in the clinical setting and present emerging strategies that facilitate the translation of NGS data into clinically useful information. Importantly, we anticipate that these developments will soon result in a paradigm shift of pre-emptive genotyping away from the interrogation to candidate variants and towards the comprehensive profiling of an individuals genotype, thus allowing for a true individualization of patient drug treatment regimens.
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Affiliation(s)
- Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Magnus Ingelman-Sundberg
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
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105
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Haga SB, Kantor A. Horizon Scan Of Clinical Laboratories Offering Pharmacogenetic Testing. Health Aff (Millwood) 2019; 37:717-723. [PMID: 29733708 DOI: 10.1377/hlthaff.2017.1564] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Pharmacogenetic (PGx) testing involves the analysis of genes known to affect response to medications. The field has been projected as a leading application of personalized or precision medicine, but the use of PGx tests has been stymied, in part, by the lack of clinical evidence of utility and reported low provider awareness. Another factor is the availability of testing. The range and types of PGx tests available have not been assessed to date. In the period September 2017-January 2018 we analyzed the numbers and types of PGx tests offered by clinical testing laboratories in the US. Of the 111 such labs that we identified, we confirmed that 76 offered PGx testing services. Of these, 31 offered only tests for single genes; 30 offered only tests for multiple genes; and 15 offered both types of tests. Collectively, 45 laboratories offered 114 multigene panel tests covering 295 genes. The majority of these tests did not have any clinical guidelines. PGx tests vary in type and makeup, which presents challenges in appropriate test evaluation and selection for providers, insurers, health systems, and patients alike.
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Affiliation(s)
- Susanne B Haga
- Susanne B. Haga ( ) is an associate professor of medicine at the Duke University School of Medicine, in Durham, North Carolina
| | - Ariel Kantor
- Ariel Kantor is an undergraduate research assistant at Duke University
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106
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Sivadas A, Scaria V. Population-scale genomics-Enabling precision public health. ADVANCES IN GENETICS 2018; 103:119-161. [PMID: 30904093 DOI: 10.1016/bs.adgen.2018.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.
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Affiliation(s)
- Ambily Sivadas
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
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107
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Qiao W, Martis S, Mendiratta G, Shi L, Botton MR, Yang Y, Gaedigk A, Vijzelaar R, Edelmann L, Kornreich R, Desnick RJ, Scott SA. Integrated CYP2D6 interrogation for multiethnic copy number and tandem allele detection. Pharmacogenomics 2018; 20:9-20. [PMID: 30730286 DOI: 10.2217/pgs-2018-0135] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
AIM To comprehensively interrogate CYP2D6 by integrating genotyping, copy number analysis and novel strategies to identify CYP2D6*36 and characterize CYP2D6 duplications. METHODS Genotyping of 16 CYP2D6 alleles, multiplex ligation-dependent probe amplification (MLPA) and CYP2D6*36 and duplication allele-specific genotyping were performed on 427 African-American, Asian, Caucasian, Hispanic, and Ashkenazi Jewish individuals. RESULTS A novel PCR strategy determined that almost half of all CYP2D6*10 (100C>T) alleles are actually *36 (isolated or in tandem with *10) and all identified duplication alleles were characterized. Integrated results from all testing platforms enabled the refinement of genotype frequencies across all studied populations. CONCLUSION The polymorphic CYP2D6 gene requires comprehensive interrogation to characterize allelic variation across ethnicities, which was enabled in this study by integrating multiplexed genotyping, MLPA copy number analysis, novel PCR strategies and duplication allele-specific genotyping.
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Affiliation(s)
- Wanqiong Qiao
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Suparna Martis
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Geetu Mendiratta
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Lisong Shi
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Mariana R Botton
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Yao Yang
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, MO 64108, USA
| | - Raymon Vijzelaar
- MRC-Holland, Willem Schoutenstraat 6, Amsterdam, The Netherlands
| | - Lisa Edelmann
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Ruth Kornreich
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Robert J Desnick
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Stuart A Scott
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
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108
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Zhou Y, Fujikura K, Mkrtchian S, Lauschke VM. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front Pharmacol 2018; 9:1437. [PMID: 30564131 PMCID: PMC6288784 DOI: 10.3389/fphar.2018.01437] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 11/20/2018] [Indexed: 12/21/2022] Open
Abstract
Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.
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Affiliation(s)
- Yitian Zhou
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Kohei Fujikura
- Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Souren Mkrtchian
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M. Lauschke
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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109
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Tasa T, Krebs K, Kals M, Mägi R, Lauschke VM, Haller T, Puurand T, Remm M, Esko T, Metspalu A, Vilo J, Milani L. Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records. Eur J Hum Genet 2018; 27:442-454. [PMID: 30420678 PMCID: PMC6460570 DOI: 10.1038/s41431-018-0300-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 10/15/2018] [Accepted: 10/25/2018] [Indexed: 11/23/2022] Open
Abstract
Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel loss-of-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery.
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Affiliation(s)
- Tõnis Tasa
- Institute of Computer Science, University of Tartu, Tartu, 50409, Estonia.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Kristi Krebs
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Mart Kals
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, Stockholm, 171 77, Sweden
| | - Toomas Haller
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Tarmo Puurand
- Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia
| | - Maido Remm
- Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Jaak Vilo
- Institute of Computer Science, University of Tartu, Tartu, 50409, Estonia
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia. .,Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, 751 44, Sweden.
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110
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Campuzano O, Sanchez-Molero O, Fernandez A, Mademont-Soler I, Coll M, Perez-Serra A, Mates J, Del Olmo B, Pico F, Nogue-Navarro L, Sarquella-Brugada G, Iglesias A, Cesar S, Carro E, Borondo JC, Brugada J, Castellà J, Medallo J, Brugada R. Sudden Arrhythmic Death During Exercise: A Post-Mortem Genetic Analysis. Sports Med 2018; 47:2101-2115. [PMID: 28255936 DOI: 10.1007/s40279-017-0705-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Sudden cardiac death is a natural and unexpected death that occurs within 1 h of the first symptom. Most sudden cardiac deaths occur during exercise, mostly as a result of myocardial infarction. After autopsy, some cases, especially in the young, are diagnosed as cardiomyopathies or remain without a conclusive cause of death. In both situations, genetic alterations may explain the arrhythmia. OBJECTIVE Our aim was to identify a genetic predisposition to sudden cardiac death in a cohort of post-mortem cases of individuals who died during exercise, with a structurally normal heart, and were classified as arrhythmogenic death. METHODS We analyzed a cohort of 52 post-mortem samples from individuals <50 years old who had a negative autopsy. Next-generation sequencing technology was used to screen genes associated with sudden cardiac death. RESULTS Our cohort showed a male prevalence (12:1). Half of the deaths occurred in individuals 41-50 years of age. Running was the most common exercise activity during the fatal event, accounting for 46.15% of cases. Genetic analysis identified 83 rare variants in 37 samples (71.15% of all samples). Of all rare variants, 36.14% were classified as deleterious, being present in 53.84% of all cases. CONCLUSIONS A comprehensive analysis of sudden cardiac death-related genes in individuals who died suddenly while exercising enabled the identification of potentially causative variants. However, many genetic variants remain of indeterminate significance, thus further work is needed before clinical translation. Nonetheless, comprehensive genetic analysis of individuals who died during exercise enables the detection of potentially causative variants and helps to identify at-risk relatives.
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Affiliation(s)
- Oscar Campuzano
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain.,Medical Science Department, School of Medicine, University of Girona, Girona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Olallo Sanchez-Molero
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Anna Fernandez
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Irene Mademont-Soler
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Monica Coll
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Alexandra Perez-Serra
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Jesus Mates
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Bernat Del Olmo
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Ferran Pico
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain
| | - Laia Nogue-Navarro
- Medical Science Department, School of Medicine, University of Girona, Girona, Spain
| | | | - Anna Iglesias
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Sergi Cesar
- Arrhythmias Unit, Hospital Sant Joan de Déu, University of Barcelona, Barcelona, Spain
| | - Esther Carro
- Arrhythmias Unit, Hospital Sant Joan de Déu, University of Barcelona, Barcelona, Spain
| | - Juan Carlos Borondo
- Histopathology Unit, Instituto Nacional Toxicología y Ciencias Forenses (INTCF), Barcelona, Spain
| | - Josep Brugada
- Arrhythmias Unit, Hospital Sant Joan de Déu, University of Barcelona, Barcelona, Spain
| | - Josep Castellà
- Forensic Pathology Service, Institut de Medicina Legal i Ciències Forenses de Catalunya (IMLCFC), Barcelona, Spain
| | - Jordi Medallo
- Forensic Pathology Service, Institut de Medicina Legal i Ciències Forenses de Catalunya (IMLCFC), Barcelona, Spain
| | - Ramon Brugada
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), University of Girona, C/Dr Castany s/n, Parc Hospitalari Martí i Julià (M-2), Salt, 17190, Girona, Spain. .,Medical Science Department, School of Medicine, University of Girona, Girona, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. .,Cardiology Service, Hospital Josep Trueta, Girona, Spain.
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111
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Zhang B, Lauschke VM. Genetic variability and population diversity of the human SLCO (OATP) transporter family. Pharmacol Res 2018; 139:550-559. [PMID: 30359687 DOI: 10.1016/j.phrs.2018.10.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 09/14/2018] [Accepted: 10/17/2018] [Indexed: 01/12/2023]
Abstract
Organic anion transporting polypeptides (OATP) encoded by the SLCO gene family constitute clinically important transporters involved in the disposition of endogenous compounds and many commonly prescribed drugs, including statins, methotrexate and antihypertensive medications. Common genetic polymorphisms in SLCO genes are known to affect OATP function and modulate efficacy and safety of OATP substrates. However, current frequency data of these variants and haplotypes is generally based on few rather heterogenous populations of relatively small sample size. Furthermore, the genetic variability beyond these selected pharmacogenetic biomarkers has not been systematically analyzed. Here, we provide a global consolidated map of SLCO variability by leveraging fully compatible Next Generation Sequencing data from 138,632 unrelated individuals across seven major human populations. Overall, we find 9811 exonic single nucleotide variants and 155 copy number variations of which 99.3% were rare with frequencies <1%. Using orthogonal computational functionality predictors optimized for pharmacogenetic assessments, we find that four out of five individuals carry at least one deleterious variant in an SLCO transporter gene and rare variants contribute 23% to the genetically encoded functional variability. Moreover, 74.9% of all variants were found to be population-specific with important consequences for population-specific genotyping strategies and precision public health approaches. Combined, our analyses provide the most comprehensive data set of SLCO variability published to date and incentivize the integration of comprehensive NGS-based genotyping into personalized predictions of OATP substrate disposition.
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Affiliation(s)
- Boyao Zhang
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
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112
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Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions. Genet Med 2018; 21:1345-1354. [PMID: 30327539 PMCID: PMC6752278 DOI: 10.1038/s41436-018-0337-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 10/02/2018] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations. METHODS We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. RESULTS Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants. CONCLUSION We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.
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113
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Affiliation(s)
- David R Adams
- From the Office of the Clinical Director, National Human Genome Research Institute, and the Undiagnosed Diseases Program, National Institutes of Health, Bethesda, MD (D.R.A.); and the Department of Molecular and Human Genetics, Baylor College of Medicine, and Baylor Genetics - both in Houston (C.M.E.)
| | - Christine M Eng
- From the Office of the Clinical Director, National Human Genome Research Institute, and the Undiagnosed Diseases Program, National Institutes of Health, Bethesda, MD (D.R.A.); and the Department of Molecular and Human Genetics, Baylor College of Medicine, and Baylor Genetics - both in Houston (C.M.E.)
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114
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Zhou Y, Mkrtchian S, Kumondai M, Hiratsuka M, Lauschke VM. An optimized prediction framework to assess the functional impact of pharmacogenetic variants. THE PHARMACOGENOMICS JOURNAL 2018; 19:115-126. [PMID: 30206299 PMCID: PMC6462826 DOI: 10.1038/s41397-018-0044-2] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 06/27/2018] [Accepted: 08/10/2018] [Indexed: 01/25/2023]
Abstract
Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1–50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Souren Mkrtchian
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Masaki Kumondai
- Laboratory of Pharmacotherapy of Life-Style Related Diseases, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Masahiro Hiratsuka
- Laboratory of Pharmacotherapy of Life-Style Related Diseases, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden.
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115
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Ingelman-Sundberg M, Lauschke VM. Human liver spheroids in chemically defined conditions for studies of gene–drug, drug–drug and disease–drug interactions. Pharmacogenomics 2018; 19:1133-1138. [DOI: 10.2217/pgs-2018-0096] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Recent phenotypically and functionally relevant human hepatic in vitro systems combine the ability to preserve interindividual molecular differences between patients’ livers in culture with the accessibility and high-throughput compatibility of in vitro assays. These features facilitate studies of specific genetic polymorphisms by using cells from donors with defined variants of interest or by selective gene knock-down experiments. Furthermore, these models constitute promising tools to evaluate drug–drug interactions as well as the effects of liver diseases on drug pharmacokinetics in co-cultures of hepatocytes and non-parenchymal cells. In the near future, we anticipate that these tools will be of high relevance for predicting the in vivo kinetics, toxicity and drug–drug interactions of drug candidates already during preclinical development.
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Affiliation(s)
- Magnus Ingelman-Sundberg
- Department of Physiology & Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology & Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
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116
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Butkiewicz M, Blue EE, Leung YY, Jian X, Marcora E, Renton AE, Kuzma A, Wang LS, Koboldt DC, Haines JL, Bush WS. Functional annotation of genomic variants in studies of late-onset Alzheimer's disease. Bioinformatics 2018; 34:2724-2731. [PMID: 29590295 PMCID: PMC6084586 DOI: 10.1093/bioinformatics/bty177] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 03/17/2018] [Accepted: 03/23/2018] [Indexed: 01/01/2023] Open
Abstract
Motivation Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied. Results In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%). Availability and implementation Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mariusz Butkiewicz
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Elizabeth E Blue
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xueqiu Jian
- Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center, Houston, TX, USA
| | - Edoardo Marcora
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan E Renton
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amanda Kuzma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
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117
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Klein TE, Ritchie MD. PharmCAT: A Pharmacogenomics Clinical Annotation Tool. Clin Pharmacol Ther 2018; 104:19-22. [PMID: 29194583 PMCID: PMC5984125 DOI: 10.1002/cpt.928] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 10/20/2017] [Accepted: 10/28/2017] [Indexed: 12/11/2022]
Abstract
Implementation of genomic medicine into clinical care continues to increase in prevalence in medical centers worldwide. As defined by the National Human Genome Research Institute, "Genomic medicine is an emerging medical discipline that involves using genomic information about an individual as part of their clinical care.…" The genomic information utilized falls broadly into two categories: 1) highly penetrant genetic disorders and 2) pharmacogenomics. Herein, we focus on pharmacogenomics, although the Pharmacogenomics Clinical Annotation Tool (PharmCAT) tool could be extended to include other types of genetic variation.
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Affiliation(s)
- Teri E. Klein
- Department of Biomedical Data ScienceStanford UniversityPalo AltoCaliforniaUSA
| | - Marylyn D. Ritchie
- Biomedical and Translational Informatics InstituteGeisingerDanvillePennsylvaniaUSA
- Department of GeneticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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118
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Yee SW, Brackman DJ, Ennis EA, Sugiyama Y, Kamdem LK, Blanchard R, Galetin A, Zhang L, Giacomini KM. Influence of Transporter Polymorphisms on Drug Disposition and Response: A Perspective From the International Transporter Consortium. Clin Pharmacol Ther 2018; 104:803-817. [PMID: 29679469 DOI: 10.1002/cpt.1098] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/10/2018] [Accepted: 04/11/2018] [Indexed: 12/21/2022]
Abstract
Advances in genomic technologies have led to a wealth of information identifying genetic polymorphisms in membrane transporters, specifically how these polymorphisms affect drug disposition and response. This review describes the current perspective of the International Transporter Consortium (ITC) on clinically important polymorphisms in membrane transporters. ITC suggests that, in addition to previously recommended polymorphisms in ABCG2 (BCRP) and SLCO1B1 (OATP1B1), polymorphisms in the emerging transporter, SLC22A1 (OCT1), be considered during drug development. Collectively, polymorphisms in these transporters are important determinants of interindividual differences in the levels, toxicities, and response to many drugs.
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Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Deanna J Brackman
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Elizabeth A Ennis
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Yuichi Sugiyama
- Sugiyama Laboratory, RIKEN Innovation Center, Research Cluster for Innovation, RIKEN, Yokohama, Japan
| | - Landry K Kamdem
- Department of Pharmaceutical Sciences, Harding University College of Pharmacy, Searcy, Arkansas, USA
| | | | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, UK
| | - Lei Zhang
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, California, USA.,Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
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119
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Ingelman-Sundberg M, Mkrtchian S, Zhou Y, Lauschke VM. Integrating rare genetic variants into pharmacogenetic drug response predictions. Hum Genomics 2018; 12:26. [PMID: 29793534 PMCID: PMC5968569 DOI: 10.1186/s40246-018-0157-3] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 05/08/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response. RESULTS To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes. CONCLUSIONS Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment.
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Affiliation(s)
- Magnus Ingelman-Sundberg
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Souren Mkrtchian
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77, Stockholm, Sweden.
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120
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Lavertu A, McInnes G, Daneshjou R, Whirl-Carrillo M, Klein TE, Altman RB. Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum Mol Genet 2018; 27:R72-R78. [PMID: 29635477 PMCID: PMC5946941 DOI: 10.1093/hmg/ddy116] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 03/27/2018] [Accepted: 03/28/2018] [Indexed: 02/06/2023] Open
Abstract
The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.
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Affiliation(s)
- Adam Lavertu
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA
| | - Greg McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA
| | - Roxana Daneshjou
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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121
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Roden DM, Van Driest SL, Mosley JD, Wells QS, Robinson JR, Denny JC, Peterson JF. Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome. Clin Pharmacol Ther 2018; 103:787-794. [PMID: 29377064 PMCID: PMC6134843 DOI: 10.1002/cpt.1035] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/08/2018] [Accepted: 01/22/2018] [Indexed: 12/13/2022]
Abstract
The development of new knowledge around the genetic determinants of variable drug action has naturally raised the question of how this new knowledge can be used to improve the outcome of drug therapy. Two broad approaches have been taken: a point-of-care approach in which genotyping for specific variant(s) is undertaken at the time of drug prescription, and a preemptive approach in which multiple genetic variants are typed in an individual patient and the information archived for later use when a drug with a "pharmacogenetic story" is prescribed. This review addresses the current state of implementation, the rationale for these approaches, and barriers that must be overcome. Benefits to pharmacogenetic testing are only now being defined and will be discussed.
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Affiliation(s)
- Dan M. Roden
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
- Department of Pharmacology, Vanderbilt University Medical Center Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center Nashville, TN
| | - Sara L. Van Driest
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
- Department of Pediatrics, Vanderbilt University Medical Center Nashville, TN
| | - Jonathan D. Mosley
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center Nashville, TN
| | - Quinn S. Wells
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
| | - Jamie R. Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center Nashville, TN
- Department of Surgery, Vanderbilt University Medical Center Nashville, TN
| | - Joshua C. Denny
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center Nashville, TN
| | - Josh F. Peterson
- Department of Medicine, Vanderbilt University Medical Center Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center Nashville, TN
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122
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Mills RA, Eichmeyer JN, Williams LM, Muskett JA, Schmidlen TJ, Maloney KA, Lemke AA. Patient Care Situations Benefiting from Pharmacogenomic Testing. CURRENT GENETIC MEDICINE REPORTS 2018. [DOI: 10.1007/s40142-018-0136-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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123
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Swen JJ, Nijenhuis M, van Rhenen M, de Boer-Veger NJ, Buunk AM, Houwink EJF, Mulder H, Rongen GA, van Schaik RHN, van der Weide J, Wilffert B, Deneer VHM, Guchelaar HJ. Pharmacogenetic Information in Clinical Guidelines: The European Perspective. Clin Pharmacol Ther 2018; 103:795-801. [PMID: 29460273 DOI: 10.1002/cpt.1049] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/19/2018] [Accepted: 02/14/2018] [Indexed: 12/13/2022]
Abstract
Surveys among pharmacists and physicians show that these healthcare professionals have successfully adopted the concept of pharmacogenomics (PGx).1-3 In addition, patients are willing to consent to participate in PGx implementation studies.4 However, the surveys also show that healthcare professionals do not frequently order or recommend a PGx test.1,2 Among others, a frequently perceived hurdle for clinical uptake of PGx is the availability of guidelines translating PGx test results into clinical actions for individual patients.5,6.
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Affiliation(s)
- Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Marga Nijenhuis
- Royal Dutch Pharmacists Association (KNMP), The Hague, The Netherlands
| | - Mandy van Rhenen
- Royal Dutch Pharmacists Association (KNMP), The Hague, The Netherlands
| | | | | | - Elisa J F Houwink
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Center, Leiden, The Netherlands
| | - Hans Mulder
- Department of Clinical Pharmacy, Wilhelmina Hospital, Assen, The Netherlands
| | - Gerard A Rongen
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands.,Department of Pharmacology and Toxicology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jan van der Weide
- Department of Clinical Chemistry, St. Jansdal Hospital, Harderwijk, The Netherlands
| | - Bob Wilffert
- Department of PharmacoTherapy, Epidemiology & Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen and Department of Clinical Pharmacy & Pharmacology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Vera H M Deneer
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, Leiden, The Netherlands
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Volpi S, Bult CJ, Chisholm RL, Deverka PA, Ginsburg GS, Jacob HJ, Kasapi M, McLeod HL, Roden DM, Williams MS, Green ED, Rodriguez LL, Aronson S, Cavallari LH, Denny JC, Dressler LG, Johnson JA, Klein TE, Leeder JS, Piquette-Miller M, Perera M, Rasmussen-Torvik LJ, Rehm HL, Ritchie MD, Skaar TC, Wagle N, Weinshilboum R, Weitzel KW, Wildin R, Wilson J, Manolio TA, Relling MV. Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects. Clin Pharmacol Ther 2018; 103:778-786. [PMID: 29460415 DOI: 10.1002/cpt.1048] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/31/2018] [Accepted: 02/14/2018] [Indexed: 12/29/2022]
Abstract
Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.
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Affiliation(s)
- Simona Volpi
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Carol J Bult
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Rex L Chisholm
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | | | - Geoffrey S Ginsburg
- Duke Center for Applied Genomic and Precision Medicine, Duke University, Durham, North Carolina, USA
| | - Howard J Jacob
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | - Melpomeni Kasapi
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Howard L McLeod
- DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center, Tampa, Florida, USA
| | - Dan M Roden
- Department of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA
| | - Eric D Green
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura Lyman Rodriguez
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, Florida, USA
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Lynn G Dressler
- Mission Health, Personalized Medicine Program, Asheville, North Carolina, USA
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, Florida, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - J Steven Leeder
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Hospital, Kansas City, Missouri, USA
| | | | - Minoli Perera
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Heidi L Rehm
- Department of Pathology, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Todd C Skaar
- Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Nikhil Wagle
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics and Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristin W Weitzel
- Department of Pharmacotherapy & Translational Research, University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Robert Wildin
- Departments of Pathology and Laboratory Medicine, and Pediatrics, University of Vermont Medical Center, Burlington, Vermont, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mary V Relling
- Pharmaceutical Sciences Department, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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Manolio TA, Hutter CM, Avigan M, Cibotti R, Davis RL, Denny JC, Grenade LL, Wheatley LM, Carrington MN, Chantratita W, Chung WH, Dalton AD, Hung SI, Lee MTM, Leeder JS, Lertora JJL, Mahasirimongkol S, McLeod HL, Mockenhaupt M, Pacanowski M, Phillips EJ, Pinheiro S, Pirmohamed M, Sung C, Suwankesawong W, Trepanier L, Tumminia SJ, Veenstra D, Yuliwulandari R, Shear NH. Research Directions in Genetic Predispositions to Stevens-Johnson Syndrome / Toxic Epidermal Necrolysis. Clin Pharmacol Ther 2018; 103:390-394. [PMID: 29105735 PMCID: PMC5805563 DOI: 10.1002/cpt.890] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 08/28/2017] [Accepted: 09/20/2017] [Indexed: 12/11/2022]
Abstract
Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) is one of the most devastating of adverse drug reactions (ADRs) and was, until recently, essentially unpredictable. With the discovery of several risk alleles for drug-induced SJS/TEN and the demonstration of effectiveness of screening in reducing incidence, the stage is set for implementation of preventive strategies in populations at risk. Yet much remains to be learned about this potentially fatal complication of commonly used drugs.
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Affiliation(s)
- Teri A Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Carolyn M Hutter
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Mark Avigan
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ricardo Cibotti
- Division of Skin and Rheumatic Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, Maryland, USA
| | - Robert L Davis
- Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Lois La Grenade
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Lisa M Wheatley
- Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, USA
| | - Mary N Carrington
- Cancer and Inflammation Program, Laboratory of Experimental Immunology, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA and Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Wasun Chantratita
- Medical Genomic Center, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Wen-Hung Chung
- Department of Dermatology, Drug Hypersensitivity Clinical and Research Center, Chang Gung Memorial Hospitals, Taipei, Linkou, and Keelung, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Andrea D Dalton
- Stevens-Johnson Syndrome Foundation, Westminster, Colorado, USA
| | - Shuen-Iu Hung
- Institute and Department of Pharmacology, National Yang-Ming University, Taipei, Taiwan
| | - Ming Ta Michael Lee
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - J Steven Leeder
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Juan J L Lertora
- Clinical Pharmacology Program, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Surakameth Mahasirimongkol
- Medical Genetics Center, Medical Life Sciences Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | - Howard L McLeod
- DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center, Tampa, Florida, USA
| | - Maja Mockenhaupt
- Dokumentationszentrum schwerer Hautreaktionen (dZh), Department of Dermatology, Medical Center and Medical Faculty - University of Freiburg, Freiburg, Germany
| | - Michael Pacanowski
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Elizabeth J Phillips
- Department of Medicine, Pharmacology, Oates Institute for Experimental Therapeutics, Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, Tennessee, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
| | - Simone Pinheiro
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Munir Pirmohamed
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Cynthia Sung
- Duke-National University of Singapore Medical School, Singapore
| | - Wimon Suwankesawong
- Health Product Vigilance Center, Thai Food and Drug Administration, Nonthaburi, Thailand
| | - Lauren Trepanier
- School of Veterinary Medicine, University of Wisconsin Madison, Madison, Wisconsin, USA
| | - Santa J Tumminia
- Office of the Director, National Eye Institute, Bethesda, Maryland, USA
| | - David Veenstra
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | | | - Neil H Shear
- Department of Medicine (Dermatology and Clinical Pharmacology and Toxicology), University of Toronto, Toronto, Canada
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126
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Wright GEB, Carleton B, Hayden MR, Ross CJD. The global spectrum of protein-coding pharmacogenomic diversity. THE PHARMACOGENOMICS JOURNAL 2018; 18:187-195. [PMID: 27779249 PMCID: PMC5817389 DOI: 10.1038/tpj.2016.77] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 06/22/2016] [Accepted: 08/25/2016] [Indexed: 12/23/2022]
Abstract
Differences in response to medications have a strong genetic component. By leveraging publically available data, the spectrum of such genomic variation can be investigated extensively. Pharmacogenomic variation was extracted from the 1000 Genomes Project Phase 3 data (2504 individuals, 26 global populations). A total of 12 084 genetic variants were found in 120 pharmacogenes, with the majority (90.0%) classified as rare variants (global minor allele frequency <0.5%), with 52.9% being singletons. Common variation clustered individuals into continental super-populations and 23 pharmacogenes contained highly differentiated variants (FST>0.5) for one or more super-population comparison. A median of three clinical variants (PharmGKB level 1A/B) was found per individual, and 55.4% of individuals carried loss-of-function variants, varying by super-population (East Asian 60.9%>African 60.1%>South Asian 60.3%>European 49.3%>Admixed 39.2%). Genome sequencing can therefore identify clinical pharmacogenomic variation, and future studies need to consider rare variation to understand the spectrum of genetic diversity contributing to drug response.
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Affiliation(s)
- G E B Wright
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
| | - B Carleton
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
- Division of Translational Therapeutics, Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada
| | - M R Hayden
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
| | - C J D Ross
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada
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127
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CYP2B6*6 or Not CYP2B6*6-That Remains a Question for Precision Medicine and Ketamine! Anesthesiology 2017; 125:1085-1087. [PMID: 27763886 DOI: 10.1097/aln.0000000000001399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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128
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Schärfe CPI, Tremmel R, Schwab M, Kohlbacher O, Marks DS. Genetic variation in human drug-related genes. Genome Med 2017; 9:117. [PMID: 29273096 PMCID: PMC5740940 DOI: 10.1186/s13073-017-0502-5] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 11/24/2017] [Indexed: 12/17/2022] Open
Abstract
Background Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. Methods Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations. Results We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents. Conclusions This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Charlotta Pauline Irmgard Schärfe
- Department of Systems Biology, Harvard Medical School, Boston, 02115, Massachusetts, USA.,Center for Bioinformatics, University of Tübingen, 72076, Tübingen, Germany.,pplied Bioinformatics, Department of Computer Science, 72076, Tübingen, Germany
| | - Roman Tremmel
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376, Stuttgart, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376, Stuttgart, Germany.,Department of Clinical Pharmacology, University Hospital Tübingen, 72076, Tübingen, Germany.,Department of Pharmacy and Biochemistry, University of Tübingen, 72076, Tübingen, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Oliver Kohlbacher
- Center for Bioinformatics, University of Tübingen, 72076, Tübingen, Germany. .,pplied Bioinformatics, Department of Computer Science, 72076, Tübingen, Germany. .,Quantitative Biology Center, 72076, Tübingen, Germany. .,Faculty of Medicine, University of Tübingen, 72076, Tübingen, Germany. .,Biomolecular Interactions, Max Planck Institute for Developmental Biology, 72076, Tübingen, Germany.
| | - Debora Susan Marks
- Department of Systems Biology, Harvard Medical School, Boston, 02115, Massachusetts, USA.
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Hall MA, Wallace J, Lucas A, Kim D, Basile AO, Verma SS, McCarty CA, Brilliant MH, Peissig PL, Kitchner TE, Verma A, Pendergrass SA, Dudek SM, Moore JH, Ritchie MD. PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies. Nat Commun 2017; 8:1167. [PMID: 29079728 PMCID: PMC5660079 DOI: 10.1038/s41467-017-00802-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Accepted: 07/28/2017] [Indexed: 12/22/2022] Open
Abstract
Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits. Centralized infrastructure to support analyses involving complexity beyond genome-wide association studies is broadly needed. Here, Ritchie and colleagues develop PLATO, a software tool to process and integrate various methods for this task.
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Affiliation(s)
- Molly A Hall
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John Wallace
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA
| | - Anastasia Lucas
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA
| | - Dokyoon Kim
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA
| | - Anna O Basile
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Shefali S Verma
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA.,Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, 16802, USA
| | | | | | - Peggy L Peissig
- Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA
| | | | - Anurag Verma
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA.,Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA
| | - Scott M Dudek
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, 17821, USA. .,Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, 16802, USA.
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130
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Keeling NJ, Rosenthal MM, West-Strum D, Patel AS, Haidar CE, Hoffman JM. Preemptive pharmacogenetic testing: exploring the knowledge and perspectives of US payers. Genet Med 2017; 21:1224-1232. [PMID: 31048813 PMCID: PMC5920773 DOI: 10.1038/gim.2017.181] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 09/14/2017] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Preemptive pharmacogenetic testing aims to optimize medication use by having genetic information at the point of prescribing. Payers’ decisions influence implementation of this technology. We investigated U.S. payers’ knowledge, awareness, and perspectives on preemptive pharmacogenetic testing. METHODS A qualitative study was conducted using semi-structured interviews. Participants were screened for eligibility through an online survey. A blended inductive and deductive approach was used to analyze the transcripts. Two authors conducted an iterative reading process to code and categorize the data. RESULTS Medical or pharmacy directors from 14 payer organizations covering 122 million U.S. lives were interviewed. Three concept domains and ten dimensions were developed. Key findings include: clinical utility concerns and limited exposure to preemptive germline testing, continued preference for outcomes from randomized controlled trials, interest in guideline development, importance of demonstrating an impact on clinical decision making, concerns of downstream costs and benefit predictability, and the impact of public stakeholders such as the FDA and CMS. CONCLUSION Both barriers and potential facilitators exist to developing cohesive reimbursement policy for pharmacogenetics, and there are unique challenges for the preemptive testing model. Prospective outcome studies, more precisely defining target populations, and predictive economic models are important considerations for future research.
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Affiliation(s)
- Nicholas J Keeling
- Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Oxford, Mississippi, USA.,Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Meagen M Rosenthal
- Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Oxford, Mississippi, USA
| | - Donna West-Strum
- Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Oxford, Mississippi, USA
| | - Amit S Patel
- Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Oxford, Mississippi, USA.,Medical Marketing Economics, Oxford, Mississippi, USA
| | - Cyrine E Haidar
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - James M Hoffman
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
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131
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Moyer AM, Caraballo PJ. The challenges of implementing pharmacogenomic testing in the clinic. Expert Rev Pharmacoecon Outcomes Res 2017; 17:567-577. [PMID: 28949250 DOI: 10.1080/14737167.2017.1385395] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Pharmacogenomic testing has the potential to greatly benefit patients by enabling personalization of medication management, ensuring better efficacy and decreasing the risk of side effects. However, to fully realize the potential of pharmacogenomic testing, there are several important issues that must be addressed. Areas covered: In this expert review we discuss current challenges impacting the implementation of pharmacogenomic testing in the clinical practice. We emphasize issues related to testing methods, reporting of the results, test selection, clinical interpretation of the results, cost-effectiveness, and the long-term use of pharmacogenomic results in clinical practice. We identify opportunities and future directions to facilitate clinical implementation. Expert commentary: Several key elements are necessary to optimally integrate pharmacogenomic testing into clinical practice. Collaborative efforts among laboratories are needed to improve standardization of testing and reporting of the results. Clinicians need educational opportunities to improve understanding of which test to order and how to interpret the results. The electronic health records and other clinical systems need to improve their storage of the pharmacogenomics test results and interoperability to facilitate the use of clinically actionable results to improve patient care.
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Affiliation(s)
- Ann M Moyer
- a Department of Laboratory Medicine and Pathology , Mayo Clinic , Rochester , MN , USA
| | - Pedro J Caraballo
- b Department of Medicine , Mayo Clinic , Rochester , MN , USA.,c Center for Translational Informatics and Knowledge Management, Mayo Clinic , Rochester , MN , USA
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132
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Martin-Sanchez FJ, Aguiar-Pulido V, Lopez-Campos GH, Peek N, Sacchi L. Secondary Use and Analysis of Big Data Collected for Patient Care. Yearb Med Inform 2017; 26:28-37. [PMID: 28480474 PMCID: PMC6239231 DOI: 10.15265/iy-2017-008] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.
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Affiliation(s)
- F. J. Martin-Sanchez
- Weill Cornell Medicine, Department of Healthcare Policy and Research, Division of Health Informatics, New York, USA
| | - V. Aguiar-Pulido
- Weill Cornell Medicine, Brain and Mind Research Institute, New York, USA
| | - G. H. Lopez-Campos
- The University of Melbourne, Health & Biomedical Informatics Centre, Melbourne, Australia
| | - N. Peek
- MRC Health e-Research Centre, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - L. Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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Manson LE, van der Wouden CH, Swen JJ, Guchelaar HJ. The Ubiquitous Pharmacogenomics consortium: making effective treatment optimization accessible to every European citizen. Pharmacogenomics 2017; 18:1041-1045. [PMID: 28685652 DOI: 10.2217/pgs-2017-0093] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Lisanne En Manson
- Department of Clinical Pharmacy & Toxicology, Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Cathelijne H van der Wouden
- Department of Clinical Pharmacy & Toxicology, Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden Network for Personalised Therapeutics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
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Ipe J, Swart M, Burgess KS, Skaar TC. High-Throughput Assays to Assess the Functional Impact of Genetic Variants: A Road Towards Genomic-Driven Medicine. Clin Transl Sci 2017; 10:67-77. [PMID: 28213901 PMCID: PMC5355973 DOI: 10.1111/cts.12440] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 01/03/2017] [Indexed: 01/08/2023] Open
Affiliation(s)
- J Ipe
- Indiana University School of MedicineDepartment of MedicineDivision of Clinical PharmacologyIndianapolisIndianaUSA
| | - M Swart
- Indiana University School of MedicineDepartment of MedicineDivision of Clinical PharmacologyIndianapolisIndianaUSA
| | - KS Burgess
- Indiana University School of MedicineDepartment of MedicineDivision of Clinical PharmacologyIndianapolisIndianaUSA
- Indiana University School of MedicineDepartment of Pharmacology and ToxicologyIndianapolisIndianaUSA
| | - TC Skaar
- Indiana University School of MedicineDepartment of MedicineDivision of Clinical PharmacologyIndianapolisIndianaUSA
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135
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Leppig KA, Thiese HA, Carrel D, Crosslin DR, Dorschner MO, Gordon AS, Hartzler A, Ralston J, Scrol A, Larson EB, Jarvik GP. Building a family network from genetic testing. Mol Genet Genomic Med 2016; 5:122-129. [PMID: 28361098 PMCID: PMC5370219 DOI: 10.1002/mgg3.259] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 10/12/2016] [Accepted: 10/19/2016] [Indexed: 12/29/2022] Open
Abstract
Background Genetic testing has multigenerational and familial repercussions. However, the “trickle‐down effect” of providing genetic counseling and testing to family members at risk after an initial identification of a pathogenic variant in a medically actionable gene has been poorly understood. Methods Three probands were identified during the pharmacogenetics research phase of eMERGEII (electronic MEdical Record and Genomics, phase II) to have variants in genes associated with autosomal dominant adult‐onset disorders determined to be actionable by the American College of Medical Genetics (ACMG). Two of the three probands had variants that were classified as pathogenic and the third proband had a variant ultimately classified of uncertain significance, but of concern due to the proband's own phenotype. All probands had additional family members at risk for inheriting the variant. Two of the three probands had family members who received their medical care from the same health care system, Group Health Cooperative (GHC). It was recommended that the proband contact their family members at risk to be referred to genetic counseling for consideration of genetic testing. Results The two probands with pathogenic variants contacted some of their family members at risk. Individuals contacted included children and adult grandchildren, particularly if they received their medical care at GHC. To the best of our knowledge, siblings and more distant relatives at risk were not informed by the proband of their genetic risk. Conclusions Establishing a family network is essential to disseminate knowledge of genetic risk. These three initial cases describe our experience of contacting eMERGE participants with identified variants, providing the probands with appropriate genetic counseling and care coordination, and recommendations for contacting family members at risk. Greater challenges were observed for coordinating genetics care for family members and extending the family network to include other relatives at risk.
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Affiliation(s)
- Kathleen A Leppig
- Genetic ServicesGroup Health CooperativeSeattleWA98112USA; Department of PathologyUniversity of WashingtonSeattleWA98195USA
| | - Heidi A Thiese
- Genetic Services Group Health Cooperative Seattle WA 98112 USA
| | - David Carrel
- Group Health Research Institute Group Health Cooperative Seattle WA 98101 USA
| | - David R Crosslin
- Department of Biomedical Informatics and Medical Education University of Washington Seattle WA 98195 USA
| | | | - Adam S Gordon
- Department of Medicine (Medical Genetics) and Genomic Sciences University of Washington Seattle WA 98195 USA
| | - Andrea Hartzler
- Group Health Research Institute Group Health Cooperative Seattle WA 98101 USA
| | - James Ralston
- Group Health Research Institute Group Health Cooperative Seattle WA 98101 USA
| | - Aaron Scrol
- Group Health Research Institute Group Health Cooperative Seattle WA 98101 USA
| | - Eric B Larson
- Group Health Research Institute Group Health Cooperative Seattle WA 98101 USA
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics) and Genomic Sciences University of Washington Seattle WA 98195 USA
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136
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Han SM, Park J, Lee JH, Lee SS, Kim H, Han H, Kim Y, Yi S, Cho JY, Jang IJ, Lee MG. Targeted Next-Generation Sequencing for Comprehensive Genetic Profiling of Pharmacogenes. Clin Pharmacol Ther 2016; 101:396-405. [PMID: 27727443 DOI: 10.1002/cpt.532] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 09/26/2016] [Accepted: 09/27/2016] [Indexed: 12/12/2022]
Abstract
Phenotypic differences in drug responses have been associated with known pharmacogenomic loci, but many remain to be characterized. Therefore, we developed next-generation sequencing (NGS) panels to enable broad and unbiased inspection of genes that are involved in pharmacokinetics (PKs) and pharmacodynamics (PDs). These panels feature repetitively optimized probes to capture up to 114 PK/PD-related genes with high coverage (99.6%) and accuracy (99.9%). Sequencing of a Korean cohort (n = 376) with the panels enabled profiling of actionable variants as well as rare variants of unknown functional consequences. Notably, variants that occurred at low frequency were enriched with likely protein-damaging variants and previously unreported variants. Furthermore, in vitro evaluation of four pharmacogenes, including cytochrome P450 2C19 (CYP2C19), confirmed that many of these rare variants have considerable functional impact. The present study suggests that targeted NGS panels are readily applicable platforms to facilitate comprehensive profiling of pharmacogenes, including common but also rare variants that warrant screening for personalized medicine.
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Affiliation(s)
- S M Han
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea
| | - J Park
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea
| | - J H Lee
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Seoul, Korea
| | - S S Lee
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
| | - H Kim
- Celemics Inc, Seoul, Korea
| | - H Han
- Celemics Inc, Seoul, Korea
| | - Y Kim
- Celemics Inc, Seoul, Korea
| | - S Yi
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - J-Y Cho
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - I-J Jang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - M G Lee
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea
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Samwald M, Xu H, Blagec K, Empey PE, Malone DC, Ahmed SM, Ryan P, Hofer S, Boyce RD. Incidence of Exposure of Patients in the United States to Multiple Drugs for Which Pharmacogenomic Guidelines Are Available. PLoS One 2016; 11:e0164972. [PMID: 27764192 PMCID: PMC5072717 DOI: 10.1371/journal.pone.0164972] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 10/04/2016] [Indexed: 01/12/2023] Open
Abstract
Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009-2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40-64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies.
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Affiliation(s)
- Matthias Samwald
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- * E-mail:
| | - Hong Xu
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Kathrin Blagec
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Philip E. Empey
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Daniel C. Malone
- College of Pharmacy, University of Arizona, Tucson, Arizona, United States of America
| | - Seid Mussa Ahmed
- Department of Pharmacy, College of public health and medical sciences, Jimma University, Jimma, Ethiopia
| | - Patrick Ryan
- Janssen Research and Development, Titusville, New Jersey, United States of America
- Observational Health Data Sciences and Informatics, New York, New York, United States of America
| | - Sebastian Hofer
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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Dawes M, Aloise MN, Ang JS, Cullis P, Dawes D, Fraser R, Liknaitzky G, Paterson A, Stanley P, Suarez-Gonzalez A, Katzov-Eckert H. Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study. CMAJ Open 2016; 4:E528-E534. [PMID: 27730116 PMCID: PMC5047800 DOI: 10.9778/cmajo.20150070] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. METHODS We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. RESULTS Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months. INTERPRETATION Physicians and pharmacists can collect saliva samples of sufficient quantity and quality for DNA extraction, purification and genotyping. A clinical decision support system with integrated data from pharmacogenetic tests may enable personalized prescribing within primary care. Trial registration: ClinicalTrials.gov, NCT02383290.
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Affiliation(s)
- Martin Dawes
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Martin N Aloise
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - J Sidney Ang
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Pieter Cullis
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Diana Dawes
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Robert Fraser
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Gideon Liknaitzky
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Andrea Paterson
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Paul Stanley
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Adriana Suarez-Gonzalez
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Hagit Katzov-Eckert
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
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KIMBERLY ROBERTP. Prospecting for Precision: Promises for Personalized Medicine. J Rheumatol 2016; 43:999-1000. [DOI: 10.3899/jrheum.160424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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