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Ji Y, Skierka JM, Blommel JH, Moore BE, VanCuyk DL, Bruflat JK, Peterson LM, Veldhuizen TL, Fadra N, Peterson SE, Lagerstedt SA, Train LJ, Baudhuin LM, Klee EW, Ferber MJ, Bielinski SJ, Caraballo PJ, Weinshilboum RM, Black JL. Preemptive Pharmacogenomic Testing for Precision Medicine: A Comprehensive Analysis of Five Actionable Pharmacogenomic Genes Using Next-Generation DNA Sequencing and a Customized CYP2D6 Genotyping Cascade. J Mol Diagn 2016; 18:438-445. [PMID: 26947514 DOI: 10.1016/j.jmoldx.2016.01.003] [Citation(s) in RCA: 138] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 12/24/2015] [Accepted: 01/11/2016] [Indexed: 01/08/2023] Open
Abstract
Significant barriers, such as lack of professional guidelines, specialized training for interpretation of pharmacogenomics (PGx) data, and insufficient evidence to support clinical utility, prevent preemptive PGx testing from being widely clinically implemented. The current study, as a pilot project for the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment Protocol, was designed to evaluate the impact of preemptive PGx and to optimize the workflow in the clinic setting. We used an 84-gene next-generation sequencing panel that included SLCO1B1, CYP2C19, CYP2C9, and VKORC1 together with a custom-designed CYP2D6 testing cascade to genotype the 1013 subjects in laboratories approved by the Clinical Laboratory Improvement Act. Actionable PGx variants were placed in patient's electronic medical records where integrated clinical decision support rules alert providers when a relevant medication is ordered. The fraction of this cohort carrying actionable PGx variant(s) in individual genes ranged from 30% (SLCO1B1) to 79% (CYP2D6). When considering all five genes together, 99% of the subjects carried an actionable PGx variant(s) in at least one gene. Our study provides evidence in favor of preemptive PGx testing by identifying the risk of a variant being present in the population we studied.
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Affiliation(s)
- Yuan Ji
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Jennifer M Skierka
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Joseph H Blommel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Brenda E Moore
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Douglas L VanCuyk
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Jamie K Bruflat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Lisa M Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | - Numrah Fadra
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Sandra E Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Susan A Lagerstedt
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Laura J Train
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Linnea M Baudhuin
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Eric W Klee
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota; Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota
| | - Matthew J Ferber
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota; Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Pedro J Caraballo
- Department of General Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Richard M Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - John L Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
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Bielinski SJ, Olson JE, Pathak J, Weinshilboum RM, Wang L, Lyke KJ, Ryu E, Targonski PV, Van Norstrand MD, Hathcock MA, Takahashi PY, McCormick JB, Johnson KJ, Maschke KJ, Rohrer Vitek CR, Ellingson MS, Wieben ED, Farrugia G, Morrisette JA, Kruckeberg KJ, Bruflat JK, Peterson LM, Blommel JH, Skierka JM, Ferber MJ, Black JL, Baudhuin LM, Klee EW, Ross JL, Veldhuizen TL, Schultz CG, Caraballo PJ, Freimuth RR, Chute CG, Kullo IJ. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol. Mayo Clin Proc 2014; 89:25-33. [PMID: 24388019 PMCID: PMC3932754 DOI: 10.1016/j.mayocp.2013.10.021] [Citation(s) in RCA: 222] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 10/16/2013] [Accepted: 10/23/2013] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.
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Affiliation(s)
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | - Richard M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN; Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - Kelly J Lyke
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Paul V Targonski
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | - Jennifer B McCormick
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN; Center for Individualized Medicine, Mayo Clinic, Rochester, MN; Division of General Internal Medicine, Mayo Clinic, Rochester, MN
| | - Kiley J Johnson
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | - Eric D Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN
| | - Gianrico Farrugia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN; Division of Gastroenterology, Mayo Clinic, Rochester, MN
| | | | - Keri J Kruckeberg
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Jamie K Bruflat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Lisa M Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Joseph H Blommel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Jennifer M Skierka
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Matthew J Ferber
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - John L Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Linnea M Baudhuin
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Eric W Klee
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Jason L Ross
- Department of Information Technology, Mayo Clinic, Rochester, MN
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