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Tron C, Bouvet R, Verdier MC, Lamoureux F, Hennart B, Dubourg C, Bellissant E, Galibert MD. A Robust and Fast/Multiplex Pharmacogenetics Assay to Simultaneously Analyze 17 Clinically Relevant Genetic Polymorphisms in CYP3A4, CYP3A5, CYP1A2, CYP2C9, CYP2C19, CYP2D6, ABCB1, and VKORC1 Genes. Pharmaceuticals (Basel) 2022; 15:ph15050637. [PMID: 35631462 PMCID: PMC9145594 DOI: 10.3390/ph15050637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/16/2022] Open
Abstract
In the field of pharmacogenetics, the trend is to analyze a panel of several actionable genetic polymorphisms. It may require the use of high-throughput sequencing which demands expensive reagents/instruments and specific skills to interpret results. As an alternative, the aim of this work was to validate an easy, fast, and inexpensive multiplex pharmacogenetics assay to simultaneously genotype a panel of 17 clinically actionable variants involved in drug pharmacokinetics/pharmacodynamics. We designed primers to perform a multiplex PCR assay using a single mix. Primers were labeled by two fluorescent dye markers to discriminate alleles, while the size of the PCR fragments analyzed by electrophoresis allowed identifying amplicon. Polymorphisms of interest were CYP3A4*22, CYP3A5*3, CYP1A2*1F, CYP2C9*2-*3, CYP2C19*2-*3-*17, VKORC1-1639G > A, ABCB1 rs1045642-rs1128503-rs2229109-rs2032582, and CYP2D6*3-*4-*6-*9. The assay was repeatable and a minimum quantity of 10 ng of DNA/ sample was needed to obtain accurate results. The method was applied to a validation cohort of 121 samples and genotyping results were consistent with those obtained with reference methods. The assay was fast and cost-effective with results being available within one working-day. This robust assay can easily be implemented in laboratories as an alternative to cumbersome simplex assays or expensive multiplex approaches. Together it should widespread access to pharmacogenetics in clinical routine practice.
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Affiliation(s)
- Camille Tron
- Pharmacology Department, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)-UMR_S 1085, Univ Rennes, F-35000 Rennes, France; (M.-C.V.); (E.B.)
- Correspondence: ; Tel.: +33-2-99-28-42-80
| | - Régis Bouvet
- Department of Molecular Genetics and Genomics, Rennes Hospital University, F-35000 Rennes, France; (R.B.); (C.D.); (M.-D.G.)
| | - Marie-Clémence Verdier
- Pharmacology Department, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)-UMR_S 1085, Univ Rennes, F-35000 Rennes, France; (M.-C.V.); (E.B.)
| | | | - Benjamin Hennart
- CHU Lille, Service de Toxicologie et Génopathies, F-59000 Lille, France;
| | - Christèle Dubourg
- Department of Molecular Genetics and Genomics, Rennes Hospital University, F-35000 Rennes, France; (R.B.); (C.D.); (M.-D.G.)
- CNRS, IGDR (Institut de Génétique et Développement de Rennes)-UMR 6290, Univ Rennes, F-35000 Rennes, France
| | - Eric Bellissant
- Pharmacology Department, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)-UMR_S 1085, Univ Rennes, F-35000 Rennes, France; (M.-C.V.); (E.B.)
| | - Marie-Dominique Galibert
- Department of Molecular Genetics and Genomics, Rennes Hospital University, F-35000 Rennes, France; (R.B.); (C.D.); (M.-D.G.)
- CNRS, IGDR (Institut de Génétique et Développement de Rennes)-UMR 6290, Univ Rennes, F-35000 Rennes, France
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Bouvet R, Verdier MC, El Baroudi Y, Galibert MD, David V, Schutz S, Tron C. PharmFrag: An Easy and Fast Multiplex Pharmacogenetics Assay to Simultaneously Analyze 9 Genetic Polymorphisms Involved in Response Variability of Anticancer Drugs. Int J Mol Sci 2020; 21:ijms21249650. [PMID: 33348915 PMCID: PMC7766892 DOI: 10.3390/ijms21249650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 11/16/2022] Open
Abstract
Regarding several cytotoxic agents, it was evidenced that genetic polymorphisms in genes encoding enzymes involved in their metabolism are associated with higher risk of toxicity. Genotyping these genes before treatment is a valuable strategy to prevent side effects and to predict individual response to drug therapy. This pharmacogenetic approach is recommended for chemotherapies such as thiopurines (azathioprine, 6-mercaptopurine, thioguanine), irinotecan, and fluoropyrimidines (capecitabine and 5-fluorouracil). In this study, we aimed at developing and validating a fast, cost-effective, and easily implementable multiplex genotyping method suitable for analyzing a panel of nine variants involved in the pharmacogenetics of widely prescribed anticancer drugs. We designed a multiplex-specific PCR assay where fragments were labeled by two different fluorescent dye markers (HEX/FAM) identifiable by fragment analysis. These two labels were used to discriminate bi-allelic variants, while the size of the fragment allowed the identification of a particular polymorphism location. Variants of interest were TPMT (rs1800462, rs1142345, rs1800460), NUDT15 (rs116855232), DPYD (rs55886062, rs3918290, rs67376798, rs75017182), and UGT1A1 (rs8175347). The assay was repeatable, and genotypes could be determined when DNA sample amounts ranged from 25 to 100 ng. Primers and dye remained stable in a ready-to-use mixture solution after five freeze–thaw cycles. Accuracy was evidenced by the consistency of 187 genotyping results obtained with our multiplex assay and a reference method. The developed method is fast and cost-effective in simultaneously identifying nine variants involved in the pharmacological response of anticancer drugs. This assay can be easily implemented in laboratories for widespread access to pharmacogenetics in clinical practice.
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Affiliation(s)
- Régis Bouvet
- Department of Molecular Genetics and Genomics, Rennes University Hospital, 35000 Rennes, France; (R.B.); (Y.E.B.); (M.-D.G.); (V.D.); (S.S.)
| | - Marie-Clémence Verdier
- IRSET (Institut de Recherche en Santé, Environnement et Travail), University of Rennes, CHU Rennes, EHESP, UMR_S 1085, 35000 Rennes, France;
- Inserm, Centre D’investigation Clinique 1414, Rennes University Hospital, 35000 Rennes, France
- Pharmacology Department, Rennes University Hospital, 35000 Rennes, France
| | - Yahya El Baroudi
- Department of Molecular Genetics and Genomics, Rennes University Hospital, 35000 Rennes, France; (R.B.); (Y.E.B.); (M.-D.G.); (V.D.); (S.S.)
| | - Marie-Dominique Galibert
- Department of Molecular Genetics and Genomics, Rennes University Hospital, 35000 Rennes, France; (R.B.); (Y.E.B.); (M.-D.G.); (V.D.); (S.S.)
| | - Véronique David
- Department of Molecular Genetics and Genomics, Rennes University Hospital, 35000 Rennes, France; (R.B.); (Y.E.B.); (M.-D.G.); (V.D.); (S.S.)
| | - Sacha Schutz
- Department of Molecular Genetics and Genomics, Rennes University Hospital, 35000 Rennes, France; (R.B.); (Y.E.B.); (M.-D.G.); (V.D.); (S.S.)
- Genetic Laboratory Department, Brest University Hospital, 29200 Brest, France
| | - Camille Tron
- IRSET (Institut de Recherche en Santé, Environnement et Travail), University of Rennes, CHU Rennes, EHESP, UMR_S 1085, 35000 Rennes, France;
- Inserm, Centre D’investigation Clinique 1414, Rennes University Hospital, 35000 Rennes, France
- Pharmacology Department, Rennes University Hospital, 35000 Rennes, France
- Correspondence: ; Tel.: +33-(0)-299-284280; Fax: +33-(0)-299-284184
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Krier JB, Kalia SS, Green RC. Genomic sequencing in clinical practice: applications, challenges, and opportunities. DIALOGUES IN CLINICAL NEUROSCIENCE 2017. [PMID: 27757064 PMCID: PMC5067147 DOI: 10.31887/dcns.2016.18.3/jkrier] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The development of massively parallel sequencing (or next-generation sequencing) has facilitated a rapid implementation of genomic sequencing in clinical medicine. Genomic sequencing (GS) is now an essential tool for evaluating rare disorders, identifying therapeutic targets in neoplasms, and screening for prenatal aneuploidy. Emerging applications, such as GS for preconception carrier screening and predisposition screening in healthy individuals, are being explored in research settings and utilized by members of the public eager to incorporate genomic information into their health management. The rapid pace of adoption has created challenges for all stakeholders in clinical GS, from standardizing variant interpretation approaches in clinical molecular laboratories to ensuring that nongeneticist clinicians are prepared for new types of clinical information. Clinical GS faces a pivotal moment, as the vast potential of new quantities and types of data enable further clinical innovation and complicated implementation questions continue to be resolved.
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Affiliation(s)
- Joel B Krier
- Genomes2People Research Program, Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA
| | | | - Robert C Green
- Genomes2People Research Program, Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA; Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Larsen JB, Rasmussen JB. Pharmacogenetic testing revisited: 5' nuclease real-time polymerase chain reaction test panels for genotyping CYP2D6 and CYP2C19. Pharmgenomics Pers Med 2017; 10:115-128. [PMID: 28458572 PMCID: PMC5403119 DOI: 10.2147/pgpm.s131580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Due to their involvement in the metabolization of commonly prescribed psychopharmaceutical drugs, the cytochrome oxidase genes CYP2D6 and CYP2C19 are extensive targets for pharmacogenetic testing. The existence of common allelic variants allows the prediction of a metabolic phenotype based on a genotype result, hereby supplying a clinical tool for optimizing prescription and minimizing adverse effects. In this study, we present the development of two 5' nuclease real-time polymerase chain reaction (PCR) test panels, capable of detecting eight of the most clinically relevant alleles of the CYP2D6 gene (*2, *3, *4, *6, *9, *10, 17, *41) and the three most common nonfunctional alleles of CYP2C19 (*2, *3, *4). The assays have been thoroughly validated using a large collection of reference samples, by parallel testing and by DNA sequencing. The reanalysis of reference samples provided the calculation of the frequency of the CYP2D6*4K allele in a population, not previously reported. Furthermore, original test results from CYP2D6*41, generated based on the presence of the 2850T and the lack of the -1584G single-nucleotide polymorphism (SNP), were compared with genotyping based on the current acknowledged founder SNP 2988G of this allele. These results indicate that up to 17.7% of the patients originally tested as carriers of the CYP2D6*41 allele may have had an incorrect phenotypic result assigned. The two 5' nuclease real-time PCR test panels have subsequently been optimized for use in the clinical laboratory, using a standard real-time PCR instrument and software.
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Gupta PD. Pharmacogenetics, pharmacogenomics and ayurgenomics for personalized medicine: a paradigm shift. Indian J Pharm Sci 2015; 77:135-41. [PMID: 26009644 PMCID: PMC4442460 DOI: 10.4103/0250-474x.156543] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 10/26/2014] [Accepted: 03/06/2015] [Indexed: 01/11/2023] Open
Abstract
The value of health care can be increased tremendously through individualized medicine. With the promise of individualized medicine, healthcare professionals will be able to better predict disease risk, prevent development of disease and manage treatments more efficiently thereby allowing people to be healthier and active longer. The developments in the area of pharmacogenetics/pharmacogenomics can help the physicians achieve the target of personalized medicine. Personalized medicine will come to mean not just the right drug for the right individual, but the right drug for the specific disease affecting a specific individual. The use of personalized medicine will make clinical trials more efficient by lowering the costs that would arise due to adverse drug effects and prescription of drugs that have been proven ineffective in certain genotypes. The genotypic experiments have laid valuable insights into genetic underpinnings of diseases. However it is being realized that identification of sub-groups within normal controls corresponding to contrasting disease susceptibility could lead to more effective discovery of predictive markers for diseases. However there are no modern methods available to look at the inter-individual differences within ethnically matched healthy populations. Ayurveda, an exquisitely elaborate system of predictive medicine which has been practiced for over 3500 years in India, can help in bridging this gap. In contrast to the contemporary system of medicine, the therapeutic regimen in Ayurveda is implicated on tridoshas and prakriti. According to this system, every individual is born with his or her own basic constitution, which to a great extent regulates inter-individual variability in susceptibility to diseases and response to external environment, diet and drugs. Thus the researchers in India have demonstrated that integration of this stratified approach of Ayurveda into genomics i.e. Ayurgenomics could complement personalized medicine.
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Affiliation(s)
- Pooja D Gupta
- The Foundation for Medical Research, 84-A, RG Thadani Marg, Worli, Mumbai-400 018, India
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Pabinger S, Rödiger S, Kriegner A, Vierlinger K, Weinhäusel A. A survey of tools for the analysis of quantitative PCR (qPCR) data. BIOMOLECULAR DETECTION AND QUANTIFICATION 2014; 1:23-33. [PMID: 27920994 PMCID: PMC5129434 DOI: 10.1016/j.bdq.2014.08.002] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 08/26/2014] [Accepted: 08/26/2014] [Indexed: 01/12/2023]
Abstract
Real-time quantitative polymerase-chain-reaction (qPCR) is a standard technique in most laboratories used for various applications in basic research. Analysis of qPCR data is a crucial part of the entire experiment, which has led to the development of a plethora of methods. The released tools either cover specific parts of the workflow or provide complete analysis solutions. Here, we surveyed 27 open-access software packages and tools for the analysis of qPCR data. The survey includes 8 Microsoft Windows, 5 web-based, 9 R-based and 5 tools from other platforms. Reviewed packages and tools support the analysis of different qPCR applications, such as RNA quantification, DNA methylation, genotyping, identification of copy number variations, and digital PCR. We report an overview of the functionality, features and specific requirements of the individual software tools, such as data exchange formats, availability of a graphical user interface, included procedures for graphical data presentation, and offered statistical methods. In addition, we provide an overview about quantification strategies, and report various applications of qPCR. Our comprehensive survey showed that most tools use their own file format and only a fraction of the currently existing tools support the standardized data exchange format RDML. To allow a more streamlined and comparable analysis of qPCR data, more vendors and tools need to adapt the standardized format to encourage the exchange of data between instrument software, analysis tools, and researchers.
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Affiliation(s)
- Stephan Pabinger
- Health & Environment Department, Molecular Diagnostics, AIT - Austrian Institute of Technology, Muthgasse 11, 1190 Vienna, Austria
| | - Stefan Rödiger
- Faculty of Natural Sciences, InnoProfile Group "Image-based Assays", Brandenburg University of Technology Cottbus - Senftenberg, Großenhainer Straße 57, 01968 Senftenberg, Germany
| | - Albert Kriegner
- Health & Environment Department, Molecular Diagnostics, AIT - Austrian Institute of Technology, Muthgasse 11, 1190 Vienna, Austria
| | - Klemens Vierlinger
- Health & Environment Department, Molecular Diagnostics, AIT - Austrian Institute of Technology, Muthgasse 11, 1190 Vienna, Austria
| | - Andreas Weinhäusel
- Health & Environment Department, Molecular Diagnostics, AIT - Austrian Institute of Technology, Muthgasse 11, 1190 Vienna, Austria
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Krier JB, Green RC. Management of incidental findings in clinical genomic sequencing. ACTA ACUST UNITED AC 2013; Chapter 9:Unit9.23. [PMID: 23595601 DOI: 10.1002/0471142905.hg0923s77] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Genomic sequencing is becoming accurate, fast, and inexpensive, and is rapidly being incorporated into clinical practice. Incidental findings, which result in large numbers from genomic sequencing, are a potential barrier to the utility of this new technology due to their high prevalence and the lack of evidence or guidelines available to guide their clinical interpretation. This unit reviews the definition, classification, and management of incidental findings from genomic sequencing. The unit focuses on the clinical aspects of handling incidental findings, with an emphasis on the key role of clinical context in defining incidental findings and determining their clinical relevance and utility.
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Affiliation(s)
- Joel B Krier
- Harvard Medical School Genetics Training Program, Boston, MA, USA
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