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Ji Y, Shaaban S. Interrogating Pharmacogenetics Using Next-Generation Sequencing. J Appl Lab Med 2024; 9:50-60. [PMID: 38167765 DOI: 10.1093/jalm/jfad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/18/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Pharmacogenetics or pharmacogenomics (PGx) is the study of the role of inherited or acquired sequence change in drug response. With the rapid evolution of molecular techniques, bioinformatic tools, and increased throughput of functional genomic studies, the discovery of PGx associations and clinical implementation of PGx test results have now moved beyond a handful variants in single pharmacogenes and multi-gene panels that interrogate a few pharmacogenes to whole-exome and whole-genome scales. Although some laboratories have adopted next-generation sequencing (NGS) as a testing platform for PGx and other molecular tests, most clinical laboratories that offer PGx tests still use targeted genotyping approaches. CONTENT This article discusses primarily the technical considerations for clinical laboratories to develop NGS-based PGx tests including whole-genome and whole-exome sequencing analyses and highlights the challenges and opportunities in test design, content selection, bioinformatic pipeline for PGx allele and diplotype assignment, rare variant classification, reporting, and briefly touches a few additional areas that are important for successful clinical implementation of PGx results. SUMMARY The accelerated speed of technology development associated with continuous cost reduction and enhanced ability to interrogate complex genome regions makes it inevitable for most, if not all, clinical laboratories to transition PGx testing to an NGS-based platform in the near future. It is important for laboratories and relevant professional societies to recognize both the potential and limitations of NGS-based PGx profiling, and to work together to develop a standard and consistent practice to maximize the variant or allele detection rate and utility of PGx testing.
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Affiliation(s)
- Yuan Ji
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
| | - Sherin Shaaban
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
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Bhatt M, Peshkin BN, Kazi S, Schwartz MD, Ashai N, Swain SM, Smith DM. Pharmacogenomic testing in oncology: a health system's approach to identify oncology provider perspectives. Pharmacogenomics 2023; 24:859-870. [PMID: 37942634 DOI: 10.2217/pgs-2023-0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023] Open
Abstract
Aim: Identify oncology healthcare providers' attitudes toward barriers to and use cases for pharmacogenomic (PGx) testing and implications for prescribing anticancer and supportive care medications. Materials & methods: A questionnaire was designed and disseminated to 71 practicing oncology providers across the MedStar Health System. Results: 25 of 70 (36%) eligible oncology providers were included. 88% were aware of PGx testing and 72% believed PGx can improve care. Of providers who had ordered a medication with PGx implications in the past month, interest in PGx for anticancer (90-100%) and supportive care medications (>75%) was high. Providers with previous PGx education were more likely to have ordered a test (odds ratio: 7.9; 95% CI: 1.1-56; p = 0.0394). Conclusion: Oncology provider prescribing practices and interest in PGx suggest opportunities for implementation.
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Affiliation(s)
| | - Beth N Peshkin
- Cancer Prevention & Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA
| | - Sadaf Kazi
- MedStar Health, Columbia, MD 21044, USA
- National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC 20008, USA
| | - Marc D Schwartz
- Cancer Prevention & Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA
| | - Nadia Ashai
- MedStar Health, Columbia, MD 21044, USA
- Department of Oncology, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC 20007, USA
| | - Sandra M Swain
- MedStar Health, Columbia, MD 21044, USA
- Department of Medicine, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC 20007, USA
| | - D Max Smith
- MedStar Health, Columbia, MD 21044, USA
- Cancer Prevention & Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA
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Seligson ND, Kolesar JM, Alam B, Baker L, Lamba JK, Fridley BL, Salahudeen AA, Hertz DL, Hicks JK. Integrating pharmacogenomic testing into paired germline and somatic genomic testing in patients with cancer. Pharmacogenomics 2023; 24:731-738. [PMID: 37702060 DOI: 10.2217/pgs-2023-0125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
Precision medicine has revolutionized clinical care for patients with cancer through the development of targeted therapy, identification of inherited cancer predisposition syndromes and the use of pharmacogenetics to optimize pharmacotherapy for anticancer drugs and supportive care medications. While germline (patient) and somatic (tumor) genomic testing have evolved separately, recent interest in paired germline/somatic testing has led to an increase in integrated genomic testing workflows. However, paired germline/somatic testing has generally lacked the incorporation of germline pharmacogenomics. Integrating pharmacogenomics into paired germline/somatic genomic testing would be an efficient method for increasing access to pharmacogenomic testing. In this perspective, the authors argue for the benefits of implementing a comprehensive approach integrating somatic and germline testing that is inclusive of pharmacogenomics in clinical practice.
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Affiliation(s)
- Nathan D Seligson
- Department of Pharmacotherapy & Translational Research, The University of Florida, Jacksonville, FL 32209, USA
- Center for Pharmacogenomics & Translational Research, Nemours Children's Health, Jacksonville, FL 32207, USA
| | - Jill M Kolesar
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
- Department of Pharmacy Practice & Science, University of Kentucky, Lexington, KY 40536, USA
| | - Benish Alam
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA
| | - Laura Baker
- Nemours Center for Cancer & Blood Disorders, Nemours Children's Health, Wilmington, DE 19803, USA
| | - Jatinder K Lamba
- Department of Pharmacotherapy & Translational Research, The University of Florida, Gainesville, FL 32611, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Ameen A Salahudeen
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
- Tempus Labs Inc., Chicago, IL 60654, USA
| | - Daniel L Hertz
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA
| | - J Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Shugg T, Ly RC, Osei W, Rowe EJ, Granfield CA, Lynnes TC, Medeiros EB, Hodge JC, Breman AM, Schneider BP, Sahinalp SC, Numanagić I, Salisbury BA, Bray SM, Ratcliff R, Skaar TC. Computational pharmacogenotype extraction from clinical next-generation sequencing. Front Oncol 2023; 13:1199741. [PMID: 37469403 PMCID: PMC10352904 DOI: 10.3389/fonc.2023.1199741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/22/2023] [Indexed: 07/21/2023] Open
Abstract
Background Next-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively. Methods Germline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, TPMT, and VKORC1. Results Mean WGS read depth was >30x for all variant regions except for G6PD (average read depth was 29 reads), and mean WES read depth was >30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and CYP2D6 hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within CYP2B6, CYP2C19, DPYD, SLCO1B1, and NUDT15. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with CYP2D6 copy number variation, all Aldy v4.4 diplotype calls except for one CYP3A4 diplotype call were concordant with genotyping for 161 patients in the WES cohort. Conclusion Aldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with >99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.
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Affiliation(s)
- Tyler Shugg
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Reynold C. Ly
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Wilberforce Osei
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth J. Rowe
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Caitlin A. Granfield
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Ty C. Lynnes
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth B. Medeiros
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jennelle C. Hodge
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Amy M. Breman
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Bryan P. Schneider
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - S. Cenk Sahinalp
- Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Ibrahim Numanagić
- Department of Computer Science, University of Victoria, Victoria, BC, Canada
| | | | | | | | - Todd C. Skaar
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
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Smith DM, Figg WD. Evidence Regarding Pharmacogenetics in Pain Management and Cancer. Oncologist 2023; 28:189-192. [PMID: 36718020 PMCID: PMC10020807 DOI: 10.1093/oncolo/oyac277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/15/2022] [Indexed: 02/01/2023] Open
Abstract
Patients experience interindividual variation in response to analgesics, which may be partially explained by genetics. This commentary discusses a recently published trial on COMT genotype and opioid dose requirements and describes the potential role for COMT and other genes (eg, CYP2D6) on opioid therapy and the current evidence for germline pharmacogenetics and resources for opioid pharmacogenetics.
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Affiliation(s)
- D Max Smith
- Corresponding author: D. Max Smith, PharmD, MedStar Health, 3007 Tilden Street NW, Suite 7L, Washington, DC 20008, USA.
| | - William D Figg
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Ly RC, Shugg T, Ratcliff R, Osei W, Lynnes TC, Pratt VM, Schneider BP, Radovich M, Bray SM, Salisbury BA, Parikh B, Sahinalp SC, Numanagić I, Skaar TC. Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing. J Mol Diagn 2022; 24:576-85. [PMID: 35452844 DOI: 10.1016/j.jmoldx.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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