Challenges and considerations in implementing clinical pharmacogenomics

被引:0
|
作者
Kanegusuku, Anastasia Gant [1 ]
Chan, Clarence W. [2 ]
Thoburn, Christopher A. [3 ]
van Wijk, Xander M. R. [4 ]
Tang, Nga Yeung [3 ,5 ]
机构
[1] Loyola Univ Chicago, Dept Pathol & Lab Med, Chicago, IL USA
[2] Univ Chicago, Dept Pathol, Chicago, IL USA
[3] Corewell Hlth, Dept Pathol & Lab Med, 601 W 13 Mile Rd, Royal Oak, MI 48073 USA
[4] Univ Calif San Francisco, Dept Lab Med, San Francisco, CA USA
[5] Oakland Univ, William Beaumont Sch Med, Dept Pathol & Lab Med, Auburn Hills, MI 48326 USA
关键词
Pharmacogenomic (PGx); clinical implementation; harmonization; preemptive testing; COST-EFFECTIVENESS; CYP2D6; GENE; ASSAY PANEL; VALIDATION; MODEL; POLYMORPHISM; METABOLISM; DISCOVERY; VARIANTS; THERAPY;
D O I
10.21037/jlpm-24-36
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
This article reviews pharmacogenomic (PGx) testing from the clinical laboratorian's perspective. First, the status of several large PGx programs around the world is highlighted, and challenges for clinical implementation [e.g., potential lack of knowledge among physicians, results reporting in the electronic health system (EHS) yet to be standardized], including those for the pediatric populations (e.g., lack of reproducible clinical trials, metabolic and hemostasis systems that differ from adults) are covered. Thereafter, technical and clinical considerations are discussed, from the selection of variants to validation, data analysis, and results reporting. Although several manufacturers offer ready-to-use pre-selected panels, these may not be suitable for specific target populations (e.g., patients needing oncology drugs), which may require a custom-designed panel. Some helpful resources for variant selection are described. There are various methodologies to interrogate variants, and it is important to choose a method which suits a PGx program best. For example, next-generation sequencing (NGS) may be more suitable for variant discovery, while a polymerase chain reaction (PCR)-based array has higher throughput. Key technical details for analysis of OpenArray data are highlighted, particularly the review of discrimination plots and how to handle variants which were not detected during validation. For the data analysis of NGS results, useful alignment tools and variant calling algorithms or software are introduced. This is followed by a high-level description of the key steps for performing validation in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Finally, for results reporting, which could also be a challenge for PGx testing implementation, some good practices in PGx data reporting are highlighted.
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页数:12
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