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Zhou Y, Lauschke VM. Next-generation sequencing in pharmacogenomics - fit for clinical decision support? Expert Rev Clin Pharmacol 2024; 17:213-223. [PMID: 38247431 DOI: 10.1080/17512433.2024.2307418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
INTRODUCTION The technological advances of sequencing methods during the past 20 years have fuelled the generation of large amounts of sequencing data that comprise common variations, as well as millions of rare and personal variants that would not be identified by conventional genotyping. While comprehensive sequencing is technically feasible, its clinical utility for guiding personalized treatment decisions remains controversial. AREAS COVERED We discuss the opportunities and challenges of comprehensive sequencing compared to targeted genotyping for pharmacogenomic applications. Current pharmacogenomic sequencing panels are heterogeneous and clinical actionability of the included genes is not a major focus. We provide a current overview and critical discussion of how current studies utilize sequencing data either retrospectively from biobanks, databases or repurposed diagnostic sequencing, or prospectively using pharmacogenomic sequencing. EXPERT OPINION While sequencing-based pharmacogenomics has provided important insights into genetic variations underlying the safety and efficacy of a multitude pharmacological treatments, important hurdles for the clinical implementation of pharmacogenomic sequencing remain. We identify gaps in the interpretation of pharmacogenetic variants, technical challenges pertaining to complex loci and variant phasing, as well as unclear cost-effectiveness and incomplete reimbursement. It is critical to address these challenges in order to realize the promising prospects of pharmacogenomic sequencing.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
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2
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Turner AJ, Nofziger C, Ramey BE, Ly RC, Bousman CA, Agúndez JAG, Sangkuhl K, Whirl-Carrillo M, Vanoni S, Dunnenberger HM, Ruano G, Kennedy MA, Phillips MS, Hachad H, Klein TE, Moyer AM, Gaedigk A. PharmVar Tutorial on CYP2D6 Structural Variation Testing and Recommendations on Reporting. Clin Pharmacol Ther 2023; 114:1220-1237. [PMID: 37669183 PMCID: PMC10840842 DOI: 10.1002/cpt.3044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/23/2023] [Indexed: 09/07/2023]
Abstract
The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus and a comprehensive summary of structural variation. CYP2D6 contributes to the metabolism of numerous drugs and, thus, genetic variation in its gene impacts drug efficacy and safety. To accurately predict a patient's CYP2D6 phenotype, testing must include structural variants including gene deletions, duplications, hybrid genes, and combinations thereof. This tutorial offers a comprehensive overview of CYP2D6 structural variation, terms, and definitions, a review of methods suitable for their detection and characterization, and practical examples to address the lack of standards to describe CYP2D6 structural variants or any other pharmacogene. This PharmVar tutorial offers practical guidance on how to detect the many, often complex, structural variants, as well as recommends terms and definitions for clinical and research reporting. Uniform reporting is not only essential for electronic health record-keeping but also for accurate translation of a patient's genotype into phenotype which is typically utilized to guide drug therapy.
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Affiliation(s)
- Amy J Turner
- Department of Pediatrics, Children’s Research Institute, The Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- RPRD Diagnostics LLC, Wauwatosa, Wisconsin, USA
| | | | | | - Reynold C Ly
- Department of Medical and Molecular Genetics, Division of Diagnostic Genomics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Chad A Bousman
- Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada
| | - José AG Agúndez
- University of Extremadura, Cáceres, Spain
- Institute of Molecular Pathology Biomarkers, Cáceres, Spain
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | | | | | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University Health System, Evanston, Illinois, USA
| | - Gualberto Ruano
- Institute of Living, Hartford Hospital (Hartford CT) and Department of Psychiatry, University of Connecticut School of Medicine (Farmington CT), USA
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | | | - Houda Hachad
- Houda Hachad, Department of Clinical Operations, AccessDx Laboratories, Houston, Texas, USA
| | - Teri E Klein
- Departments of Biomedical Data Science and Medicine (BMIR), Stanford University, Stanford, California, USA
| | - Ann M Moyer
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrea Gaedigk
- Children’s Mercy Research Institute (CMRI), Kansas City, Missouri, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
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Huebner T, Steffens M, Scholl C. Current status of the analytical validation of next generation sequencing applications for pharmacogenetic profiling. Mol Biol Rep 2023; 50:9587-9599. [PMID: 37787843 PMCID: PMC10635985 DOI: 10.1007/s11033-023-08748-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/08/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Analytical validity is a prerequisite to use a next generation sequencing (NGS)-based application as an in vitro diagnostic test or a companion diagnostic in clinical practice. Currently, in the United States and the European Union, the intended use of such NGS-based tests does not refer to guided drug therapy on the basis of pharmacogenetic profiling of drug metabolizing enzymes, although the value of pharmacogenetic testing has been reported. However, in research, a large variety of NGS-based tests are used and have been confirmed to be at least comparable to array-based testing. METHODS AND RESULTS A systematic evaluation was performed screening and assessing published literature on analytical validation of NGS applications for pharmacogenetic profiling of CYP2C9, CYP2C19, CYP2D6, VKORC1 and/or UGT1A1. Although NGS applications are also increasingly used for implementation assessments in clinical practice, we show in the present systematic literature evaluation that published information on the current status of analytical validation of NGS applications targeting drug metabolizing enzymes is scarce. Furthermore, a comprehensive performance evaluation of whole exome and whole genome sequencing with the intended use for pharmacogenetic profiling has not been published so far. CONCLUSIONS A standard in reporting on analytical validation of NGS-based tests is not in place yet. Therefore, many relevant performance criteria are not addressed in published literature. For an appropriate analytical validation of an NGS-based qualitative test for pharmacogenetic profiling at least accuracy, precision, limit of detection and specificity should be addressed to facilitate the implementation of such tests in clinical use.
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Affiliation(s)
- Tatjana Huebner
- Research Division, Federal Institute for Drugs and Medical Devices (BfArM), Kurt-Georg-Kiesinger-Allee 3, Bonn, 53175, Germany.
| | - Michael Steffens
- Research Division, Federal Institute for Drugs and Medical Devices (BfArM), Kurt-Georg-Kiesinger-Allee 3, Bonn, 53175, Germany
| | - Catharina Scholl
- Research Division, Federal Institute for Drugs and Medical Devices (BfArM), Kurt-Georg-Kiesinger-Allee 3, Bonn, 53175, Germany
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Manson LEN, Delwig SJ, Drabbels JJM, Touw DJ, De Vries APJ, Roelen DL, Guchelaar HJ. Repurposing HLA genotype data of renal transplant patients to prevent severe drug hypersensitivity reactions. Front Genet 2023; 14:1289015. [PMID: 37908589 PMCID: PMC10613976 DOI: 10.3389/fgene.2023.1289015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/03/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction: Specific alleles in human leukocyte antigens (HLAs) are associated with an increased risk of developing drug hypersensitivity reactions induced by abacavir, allopurinol, carbamazepine, oxcarbazepine, phenytoin, lamotrigine, or flucloxacillin. Transplant patients are genotyped for HLA as a routine practice to match a potential donor to a recipient. This study aims to investigate the feasibility and potential impact of repurposing these HLA genotype data from kidney transplant patients to prevent drug hypersensitivity reactions. Methods: A cohort of 1347 kidney transplant recipients has been genotyped in the Leiden University Medical Center (LUMC) using next-generation sequencing (NGS). The risk alleles HLA-A*31:01, HLA-B*15:02, HLA-B*15:11, HLA-B*57:01, and HLA-B*58:01 were retrieved from the NGS data. Medical history, medication use, and allergic reactions were obtained from the patient's medical records. Carrier frequencies found were compared to a LUMC blood donor population. Results: A total of 13.1% of transplant cohort patients carried at least one of the five HLA risk alleles and therefore had an increased risk of drug-induced hypersensitivity for specific drugs. HLA-A*31:01, HLA-B*15:02, HLA-B*57:01, and HLA-B*58:01 were found in carrier frequencies of 4.61%, 1.19%, 4.46%, and 3.35% respectively. No HLA-B*15:11 carrier was found. In total nine HLA-B*57:01 carriers received flucloxacillin and seven HLA-B*58:01 carriers within our cohort received allopurinol. Discussion: Our study shows that repurposing HLA genotype data from transplantation patients for the assignment of HLA risk alleles associated with drug hypersensitivity is feasible. The use of these data by physicians while prescribing drugs or by the pharmacist when dispensing drugs holds the potential to prevent drug hypersensitivity reactions. The utility of this method was highlighted by 13.1% of the transplant cohort patients carrying an actionable HLA allele.
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Affiliation(s)
- Lisanne E. N. Manson
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Sander J. Delwig
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Jos J. M. Drabbels
- Department of Immunohematology, Leiden University Medical Center, Leiden, Netherlands
| | - Daan J. Touw
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Aiko P. J. De Vries
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands
| | - Dave L. Roelen
- Department of Immunohematology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
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Tremmel R, Pirmann S, Zhou Y, Lauschke VM. Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance. Br J Clin Pharmacol 2023. [PMID: 37759374 DOI: 10.1111/bcp.15915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.
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Affiliation(s)
- Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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Graansma LJ, Zhai Q, Busscher L, Menafra R, van den Berg RR, Kloet SL, van der Lee M. From gene to dose: Long-read sequencing and *-allele tools to refine phenotype predictions of CYP2C19. Front Pharmacol 2023; 14:1076574. [PMID: 36937863 PMCID: PMC10014917 DOI: 10.3389/fphar.2023.1076574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/31/2023] [Indexed: 03/06/2023] Open
Abstract
Background: Inter-individual differences in drug response based on genetic variations can lead to drug toxicity and treatment inefficacy. A large part of this variability is caused by genetic variants in pharmacogenes. Unfortunately, the Single Nucleotide Variant arrays currently used in clinical pharmacogenomic (PGx) testing are unable to detect all genetic variability in these genes. Long-read sequencing, on the other hand, has been shown to be able to resolve complex (pharmaco) genes. In this study we aimed to assess the value of long-read sequencing for research and clinical PGx focusing on the important and highly polymorphic CYP2C19 gene. Methods and Results: With a capture-based long-read sequencing panel we were able to characterize the entire region and assign variants to their allele of origin (phasing), resulting in the identification of 813 unique variants in 37 samples. To assess the clinical utility of this data we have compared the performance of three different *-allele tools (Aldy, PharmCat and PharmaKU) which are specifically designed to assign haplotypes to pharmacogenes based on all input variants. Conclusion: We conclude that long-read sequencing can improve our ability to characterize the CYP2C19 locus, help to identify novel haplotypes and that *-allele tools are a useful asset in phenotype prediction. Ultimately, this approach could help to better predict an individual's drug response and improve therapy outcomes. However, the added value in clinical PGx might currently be limited.
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Affiliation(s)
- Lonneke J. Graansma
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Qinglian Zhai
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Loes Busscher
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Roberta Menafra
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Redmar R. van den Berg
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Susan L. Kloet
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Maaike van der Lee
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- *Correspondence: Maaike van der Lee,
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7
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Tafazoli A, van der Lee M, Swen JJ, Zeller A, Wawrusiewicz-Kurylonek N, Mei H, Vorderman RHP, Konopko K, Zankiewicz A, Miltyk W. Development of an extensive workflow for comprehensive clinical pharmacogenomic profiling: lessons from a pilot study on 100 whole exome sequencing data. THE PHARMACOGENOMICS JOURNAL 2022; 22:276-283. [PMID: 35963939 PMCID: PMC9674517 DOI: 10.1038/s41397-022-00286-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
This pilot study is aimed at implementing an approach for comprehensive clinical pharmacogenomics (PGx) profiling. Fifty patients with cardiovascular diseases and 50 healthy individuals underwent whole-exome sequencing. Data on 1800 PGx genes were extracted and analyzed through deep filtration separately. Theoretical drug induced phenoconversion was assessed for the patients, using sequence2script. In total, 4539 rare variants (including 115 damaging non-synonymous) were identified. Four publicly available PGx bioinformatics algorithms to assign PGx haplotypes were applied to nine selected very important pharmacogenes (VIP) and revealed a 45-70% concordance rate. To ensure availability of the results at point-of-care, actionable variants were stored in a web-hosted database and PGx-cards were developed for quick access and handed to the study subjects. While a comprehensive clinical PGx profile could be successfully extracted from WES data, available tools to interpret these data demonstrated inconsistencies that complicate clinical application.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Bialystok, Poland
- Clinical Research Centre, University Hospital of Bialystok, 15-276, Bialystok, Poland
| | - Maaike van der Lee
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Anna Zeller
- Clinical Research Centre, University Hospital of Bialystok, 15-276, Bialystok, Poland
| | | | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Ruben H P Vorderman
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Krzysztof Konopko
- Department of Photonics, Electronics, and Lighting Technology, Faculty of Electrical Engineering, Bialystok University of Technology, 15-351, Bialystok, Poland
| | - Andrzej Zankiewicz
- Department of Photonics, Electronics, and Lighting Technology, Faculty of Electrical Engineering, Bialystok University of Technology, 15-351, Bialystok, Poland
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Bialystok, Poland.
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Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing. J Mol Diagn 2022; 24:576-585. [PMID: 35452844 DOI: 10.1016/j.jmoldx.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/31/2022] [Accepted: 03/04/2022] [Indexed: 12/24/2022] Open
Abstract
Germline whole exome sequencing from molecular tumor boards has the potential to be repurposed to support clinical pharmacogenomics. However, accurately calling pharmacogenomics-relevant genotypes from exome sequencing data remains challenging. Accordingly, this study assessed the analytical validity of the computational tool, Aldy, in calling pharmacogenomics-relevant genotypes from exome sequencing data for 13 major pharmacogenes. Germline DNA from whole blood was obtained for 164 subjects seen at an institutional molecular solid tumor board. All subjects had whole exome sequencing from Ashion Analytics and panel-based genotyping from an institutional pharmacogenomics laboratory. Aldy version 3.3 was operationalized on the LifeOmic Precision Health Cloud with copy number fixed to two copies per gene. Aldy results were compared with those from genotyping for 56 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, and TPMT. Read depth was >100× for all variants except CYP3A4∗22. For 75 subjects in the validation cohort, all 3393 Aldy variant calls were concordant with genotyping. Aldy calls for 736 diplotypes containing alleles assessed by both platforms were also concordant. Aldy identified additional star alleles not covered by targeted genotyping for 139 diplotypes. Aldy accurately called variants and diplotypes for 13 major pharmacogenes, except for CYP2D6 variants involving copy number variations, thus allowing repurposing of whole exome sequencing to support clinical pharmacogenomics.
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Lanillos J, Carcajona M, Maietta P, Alvarez S, Rodriguez-Antona C. Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data. NPJ Genom Med 2022; 7:12. [PMID: 35181665 PMCID: PMC8857256 DOI: 10.1038/s41525-022-00283-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 01/21/2022] [Indexed: 11/22/2022] Open
Abstract
Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.
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Affiliation(s)
- Javier Lanillos
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Madrid, Spain
| | | | | | | | - Cristina Rodriguez-Antona
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Madrid, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain.
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10
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Shugg T, Ly RC, Rowe EJ, Philips S, Hyder MA, Radovich M, Rosenman MB, Pratt VM, Callaghan JT, Desta Z, Schneider BP, Skaar TC. Clinical Opportunities for Germline Pharmacogenetics and Management of Drug-Drug Interactions in Patients With Advanced Solid Cancers. JCO Precis Oncol 2022; 6:e2100312. [PMID: 35201852 PMCID: PMC9848543 DOI: 10.1200/po.21.00312] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/26/2021] [Accepted: 01/26/2022] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Precision medicine approaches, including germline pharmacogenetics (PGx) and management of drug-drug interactions (DDIs), are likely to benefit patients with advanced cancer who are frequently prescribed multiple concomitant medications to treat cancer and associated conditions. Our objective was to assess the potential opportunities for PGx and DDI management within a cohort of adults with advanced cancer. METHODS Medication data were collected from the electronic health records for 481 subjects since their first cancer diagnosis. All subjects were genotyped for variants with clinically actionable recommendations in Clinical Pharmacogenetics Implementation Consortium guidelines for 13 pharmacogenes. DDIs were defined as concomitant prescription of strong inhibitors or inducers with sensitive substrates of the same drug-metabolizing enzyme and were assessed for six major cytochrome P450 (CYP) enzymes. RESULTS Approximately 60% of subjects were prescribed at least one medication with Clinical Pharmacogenetics Implementation Consortium recommendations, and approximately 14% of subjects had an instance for actionable PGx, defined as a prescription for a drug in a subject with an actionable genotype. The overall subject-level prevalence of DDIs and serious DDIs were 50.3% and 34.8%, respectively. Serious DDIs were most common for CYP3A, CYP2D6, and CYP2C19, occurring in 24.9%, 16.8%, and 11.7% of subjects, respectively. When assessing PGx and DDIs together, approximately 40% of subjects had at least one opportunity for a precision medicine-based intervention and approximately 98% of subjects had an actionable phenotype for at least one CYP enzyme. CONCLUSION Our findings demonstrate numerous clinical opportunities for germline PGx and DDI management in adults with advanced cancer.
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Affiliation(s)
- Tyler Shugg
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Reynold C. Ly
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Elizabeth J. Rowe
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Santosh Philips
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Mustafa A. Hyder
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Milan Radovich
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Marc B. Rosenman
- Ann & Robert H. Lurie Children's Hospital of Chicago and Institute of Public Health, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Victoria M. Pratt
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - John T. Callaghan
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, INPreprint version available on MedRXiv, https://www.medrxiv.org/content/10.1101/2021.08.23.21262496v1.full-text
| | - Zeruesenay Desta
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Bryan P. Schneider
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Todd C. Skaar
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
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Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle. THE PHARMACOGENOMICS JOURNAL 2022; 22:75-81. [PMID: 34741133 PMCID: PMC8794781 DOI: 10.1038/s41397-021-00259-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The use of pharmacogenomics in clinical practice is becoming standard of care. However, due to the complex genetic makeup of pharmacogenes, not all genetic variation is currently accounted for. Here, we show the utility of long-read sequencing to resolve complex pharmacogenes by analyzing a well-characterised sample. This data consists of long reads that were processed to resolve phased haploblocks. 73% of pharmacogenes were fully covered in one phased haploblock, including 9/15 genes that are 100% complex. Variant calling accuracy in the pharmacogenes was high, with 99.8% recall and 100% precision for SNVs and 98.7% precision and 98.0% recall for Indels. For the majority of gene-drug interactions in the DPWG and CPIC guidelines, the associated genes could be fully resolved (62% and 63% respectively). Together, these findings suggest that long-read sequencing data offers promising opportunities in elucidating complex pharmacogenes and haplotype phasing while maintaining accurate variant calling.
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Barker CIS, Groeneweg G, Maitland-van der Zee AH, Rieder MJ, Hawcutt DB, Hubbard TJ, Swen JJ, Carleton BC. Pharmacogenomic testing in paediatrics: clinical implementation strategies. Br J Clin Pharmacol 2021; 88:4297-4310. [PMID: 34907575 PMCID: PMC9544158 DOI: 10.1111/bcp.15181] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/22/2021] [Accepted: 11/09/2021] [Indexed: 11/27/2022] Open
Abstract
Pharmacogenomics (PGx) relates to the study of genetic factors determining variability in drug response. Implementing PGx testing in paediatric patients can enhance drug safety, helping to improve drug efficacy or reduce the risk of toxicity. Despite its clinical relevance, the implementation of PGx testing in paediatric practice to date has been variable and limited. As with most paediatric pharmacological studies, there are well‐recognised barriers to obtaining high‐quality PGx evidence, particularly when patient numbers may be small, and off‐label or unlicensed prescribing remains widespread. Furthermore, trials enrolling small numbers of children can rarely, in isolation, provide sufficient PGx evidence to change clinical practice, so extrapolation from larger PGx studies in adult patients, where scientifically sound, is essential. This review paper discusses the relevance of PGx to paediatrics and considers implementation strategies from a child health perspective. Examples are provided from Canada, the Netherlands and the UK, with consideration of the different healthcare systems and their distinct approaches to implementation, followed by future recommendations based on these cumulative experiences. Improving the evidence base demonstrating the clinical utility and cost‐effectiveness of paediatric PGx testing will be critical to drive implementation forwards. International, interdisciplinary collaborations will enhance paediatric data collation, interpretation and evidence curation, while also supporting dedicated paediatric PGx educational initiatives. PGx consortia and paediatric clinical research networks will continue to play a central role in the streamlined development of effective PGx implementation strategies to help optimise paediatric pharmacotherapy.
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Affiliation(s)
- Charlotte I S Barker
- Department of Medical & Molecular Genetics, King's College London, London, UK.,Department of Clinical Genetics, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Gabriella Groeneweg
- Division of Translational Therapeutics, Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.,Pharmaceutical Outcomes Programme, BC Children's Hospital, Vancouver, British Columbia, Canada
| | - Anke H Maitland-van der Zee
- Respiratory Medicine/Pediatric Respiratory Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Michael J Rieder
- Departments of Paediatrics, Physiology and Pharmacology and Medicine, Western University, London, Ontario, Canada.,Molecular Medicine Group, Robarts Research Institute, London, Ontario, Canada
| | - Daniel B Hawcutt
- Department of Women's and Children's Health, University of Liverpool, Liverpool, UK.,NIHR Clinical Research Facility, Alder Hey Children's Hospital, Liverpool, UK
| | - Tim J Hubbard
- Department of Medical & Molecular Genetics, King's College London, London, UK.,Genomics England, London, UK
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden, The Netherlands
| | - Bruce C Carleton
- Division of Translational Therapeutics, Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.,Pharmaceutical Outcomes Programme, BC Children's Hospital, Vancouver, British Columbia, Canada.,BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
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Piriyapongsa J, Sukritha C, Kaewprommal P, Intarat C, Triparn K, Phornsiricharoenphant K, Chaosrikul C, Shaw PJ, Chantratita W, Mahasirimongkol S, Tongsima S. PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation. J Pers Med 2021; 11:1230. [PMID: 34834582 PMCID: PMC8618518 DOI: 10.3390/jpm11111230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/17/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
The increasing availability of next generation sequencing (NGS) for personal genomics could promote pharmacogenomics (PGx) discovery and application. However, current tools for analysis and interpretation of pharmacogenomic variants from NGS data are inadequate, as none offer comprehensive analytic functions in a simple, web-based platform. In addition, no tools exist to analyze human leukocyte antigen (HLA) genes for determining potential risks of immune-mediated adverse drug reaction (IM-ADR). We describe PharmVIP, a web-based PGx tool, for one-stop comprehensive analysis and interpretation of genome-wide variants obtained from NGS platforms. PharmVIP comprises three main interpretation modules covering analyses of pharmacogenes involved in pharmacokinetics, pharmacodynamics and IM-ADR. The Guideline module provides Clinical Pharmacogenetics Implementation Consortium (CPIC) drug guideline recommendations based on the translation of genotypic data in genes having guidelines. The HLA module reports HLA genotypes, potential adverse drug reactions, and the relevant drug guidelines. The Pharmacogenes module is employed for prioritizing variants according to variant effect on gene function. Detailed, customizable reports are provided as exportable files and as an interactive web version. PharmVIP is a new integrated NGS workflow for the PGx community to facilitate discovery and clinical application.
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Affiliation(s)
- Jittima Piriyapongsa
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chanathip Sukritha
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Pavita Kaewprommal
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chalermpong Intarat
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Kwankom Triparn
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Krittin Phornsiricharoenphant
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chadapohn Chaosrikul
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Philip J. Shaw
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand;
| | - Wasun Chantratita
- Center for Medical Genomics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Phayathai, Bangkok 10400, Thailand;
| | - Surakameth Mahasirimongkol
- Division of Genomic Medicine and Innovation Support, Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand;
| | - Sissades Tongsima
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
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14
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Warmerdam R, Lanting P, Deelen P, Franke L. Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores. Bioinformatics 2021; 38:1059-1066. [PMID: 34792549 PMCID: PMC8796367 DOI: 10.1093/bioinformatics/btab783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/07/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Identifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores. RESULTS In the Lifelines population-based biobank, we calculated polygenic scores (PGSs) for 25 traits for 32 786 participants. We then applied Idéfix to compare the actual phenotypes to PGSs, and to use the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Subsequent simulations present Idéfix's potential in varying datasets with more powerful PGSs. This suggests its performance will likely improve when more highly powered GWASs for commonly measured traits will become available. Idéfix can be used to identify a set of high-quality participants for whom it is very unlikely that they reflect sample mix-ups, and for these participants we can use genetic data for clinical purposes, such as pharmacogenetic profiles. For instance, in Lifelines, we can select 34.4% of participants, reducing the sample mix-up rate from 0.15% to 0.01%. AVAILABILITYAND IMPLEMENTATION Idéfix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix. The individual-level data that support the findings were obtained from the Lifelines biobank under project application number ov16_0365. Data is made available upon reasonable request submitted to the LifeLines Research office (research@lifelines.nl, https://www.lifelines.nl/researcher/how-to-apply/apply-here). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Robert Warmerdam
- Department of Genetics, University Medical Center Groningen, University of Groningen, 9700AB Groningen, The Netherlands
| | - Pauline Lanting
- Department of Genetics, University Medical Center Groningen, University of Groningen, 9700AB Groningen, The Netherlands
| | | | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, 9700AB Groningen, The Netherlands,Department of Genetics, University Medical Center Utrecht, 3508GA Utrecht, The Netherlands
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15
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Tafazoli A, Guchelaar HJ, Miltyk W, Kretowski AJ, Swen JJ. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021; 12:693453. [PMID: 34512329 PMCID: PMC8424415 DOI: 10.3389/fphar.2021.693453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Adam J. Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jesse J. Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
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16
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Tong H, Phan NVT, Nguyen TT, Nguyen DV, Vo NS, Le L. Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:61-75. [PMID: 33469342 PMCID: PMC7812041 DOI: 10.2147/pgpm.s290781] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/26/2020] [Indexed: 12/27/2022]
Abstract
Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.
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Affiliation(s)
- Hang Tong
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Nga V T Phan
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Thanh T Nguyen
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Dinh V Nguyen
- Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital, Hanoi, Vietnam.,College of Health Sciences, VinUniversity, Hanoi, Vietnam
| | - Nam S Vo
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Ly Le
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam.,Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
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17
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McInnes G, Lavertu A, Sangkuhl K, Klein TE, Whirl-Carrillo M, Altman RB. Pharmacogenetics at Scale: An Analysis of the UK Biobank. Clin Pharmacol Ther 2020; 109:1528-1537. [PMID: 33237584 DOI: 10.1002/cpt.2122] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/22/2020] [Indexed: 01/06/2023]
Abstract
Pharmacogenetics (PGx) studies the influence of genetic variation on drug response. Clinically actionable associations inform guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), but the broad impact of genetic variation on entire populations is not well understood. We analyzed PGx allele and phenotype frequencies for 487,409 participants in the UK Biobank, the largest PGx study to date. For 14 CPIC pharmacogenes known to influence human drug response, we find that 99.5% of individuals may have an atypical response to at least 1 drug; on average they may have an atypical response to 10.3 drugs. Nearly 24% of participants have been prescribed a drug for which they are predicted to have an atypical response. Non-European populations carry a greater frequency of variants that are predicted to be functionally deleterious; many of these are not captured by current PGx allele definitions. Strategies for detecting and interpreting rare variation will be critical for enabling broad application of pharmacogenetics.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informfatics Training Program, Stanford University, Stanford, California, USA
| | - Adam Lavertu
- Biomedical Informfatics Training Program, Stanford University, Stanford, California, USA
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Department of Medicine, Stanford University, Stanford, California, USA
| | | | - Russ B Altman
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Departments of Bioengineering, Genetics, and Medicine, Stanford University, Stanford, California, USA
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18
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Technologies for Pharmacogenomics: A Review. Genes (Basel) 2020; 11:genes11121456. [PMID: 33291630 PMCID: PMC7761897 DOI: 10.3390/genes11121456] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 11/30/2020] [Accepted: 12/02/2020] [Indexed: 12/11/2022] Open
Abstract
The continuous development of new genotyping technologies requires awareness of their potential advantages and limitations concerning utility for pharmacogenomics (PGx). In this review, we provide an overview of technologies that can be applied in PGx research and clinical practice. Most commonly used are single nucleotide variant (SNV) panels which contain a pre-selected panel of genetic variants. SNV panels offer a short turnaround time and straightforward interpretation, making them suitable for clinical practice. However, they are limited in their ability to assess rare and structural variants. Next-generation sequencing (NGS) and long-read sequencing are promising technologies for the field of PGx research. Both NGS and long-read sequencing often provide more data and more options with regard to deciphering structural and rare variants compared to SNV panels-in particular, in regard to the number of variants that can be identified, as well as the option for haplotype phasing. Nonetheless, while useful for research, not all sequencing data can be applied to clinical practice yet. Ultimately, selecting the right technology is not a matter of fact but a matter of choosing the right technique for the right problem.
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Update on next generation sequencing of pharmacokinetics-related genes: Development of the PKseq panel, a platform for amplicon sequencing of drug-metabolizing enzyme and drug transporter genes. Drug Metab Pharmacokinet 2020; 37:100370. [PMID: 33508759 DOI: 10.1016/j.dmpk.2020.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/01/2020] [Accepted: 11/18/2020] [Indexed: 02/08/2023]
Abstract
Genetic variation in pharmacokinetics (PK)-related genes encoding drug metabolizing enzymes or drug transporters is one of the most practical pharmacogenetic biomarkers for the prediction or explanation of an individual's response to drugs. Many pharmacogenomic variations are identified using targeted, whole-exome, and whole-genome sequencing, and the number of known novel variations and alleles in PK-related genes is increasing. The high homology of sequences among PK-related genes is suspected to lead to potential read misalignment and genotyping errors when short-read sequencing was performed. Therefore, highly efficient and accurate next generation sequencing (NGS) platforms for the sequencing of PK-related genes are needed. We have developed PKseq, a targeted sequencing panel based on multiplex PCR, which targets the coding regions of 37 drug transporters, 30 cytochrome P450 isoforms, 10 UDP-glucuronosyltransferases, 5 flavin-containing monooxygenases, 4 glutathione S-transferases, 4 sulfotransferases, and 10 other genes. In this review, we describe the current NGS platforms for the sequencing of PK-related genes. The NGS platforms, including the PKseq panel, will be useful not only for the identification of all the variants of PK-related genes associated with adverse drug reactions and drug efficacy, but also for clinical sequencing to achieve pharmacogenomics-based stratified medicine.
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