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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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Olson JE, Ryu E, Johnson KJ, Koenig BA, Maschke KJ, Morrisette JA, Liebow M, Takahashi PY, Fredericksen ZS, Sharma RG, Anderson KS, Hathcock MA, Carnahan JA, Pathak J, Lindor NM, Beebe TJ, Thibodeau SN, Cerhan JR. The Mayo Clinic Biobank: a building block for individualized medicine. Mayo Clin Proc 2013; 88:952-62. [PMID: 24001487 PMCID: PMC4258707 DOI: 10.1016/j.mayocp.2013.06.006] [Citation(s) in RCA: 157] [Impact Index Per Article: 14.3] [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: 03/15/2013] [Revised: 05/29/2013] [Accepted: 06/03/2013] [Indexed: 01/22/2023]
Abstract
OBJECTIVE To report the design and implementation of the first 3 years of enrollment of the Mayo Clinic Biobank. PATIENTS AND METHODS Preparations for this biobank began with a 4-day Deliberative Community Engagement with local residents to obtain community input into the design and governance of the biobank. Recruitment, which began in April 2009, is ongoing, with a target goal of 50,000. Any Mayo Clinic patient who is 18 years or older, able to consent, and a US resident is eligible to participate. Each participant completes a health history questionnaire, provides a blood sample, and allows access to existing tissue specimens and all data from their Mayo Clinic electronic medical record. A community advisory board provides ongoing advice and guidance on complex decisions. RESULTS After 3 years of recruitment, 21,736 individuals have enrolled. Fifty-eight percent (12,498) of participants are female and 95% (20,541) of European ancestry. Median participant age is 62 years. Seventy-four percent (16,171) live in Minnesota, with 42% (9157) from Olmsted County, where the Mayo Clinic in Rochester, Minnesota, is located. The 5 most commonly self-reported conditions are hyperlipidemia (8979, 41%), hypertension (8174, 38%), osteoarthritis (6448, 30%), any cancer (6224, 29%), and gastroesophageal reflux disease (5669, 26%). Among patients with self-reported cancer, the 5 most common types are nonmelanoma skin cancer (2950, 14%), prostate cancer (1107, 12% in men), breast cancer (941, 4%), melanoma (692, 3%), and cervical cancer (240, 2% in women). Fifty-six percent (12,115) of participants have at least 15 years of electronic medical record history. To date, more than 60 projects and more than 69,000 samples have been approved for use. CONCLUSION The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.
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Affiliation(s)
- Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
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Olson JE, Ryu E, Johnson KJ, Maschke K, Morrisette JA, Liebow M, Takahashi PY, Sharma RG, Anderson KS, Hathcock MA, Pathak J, Lindor NM, Beebe TJ, Thibodeau SN, Cerhan JR. Abstract 2515: The Mayo Clinic Biobank - A growing resource for cancer related studies. Cancer Res 2013. [DOI: 10.1158/1538-7445.am2013-2515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
BACKGROUND: Biobanks collect and store biological samples and data from participants for use in research by multiple investigators. The Mayo Clinic Biobank was established by the Mayo Clinic Center for Individualized Medicine with a goal to support a wide array of health-related research studies at Mayo Clinic, including cancer studies. The Biobank is not focused on any particular disease, however, through annotation by questionnaires, the Mayo electronic medical record (EMR), and the Mayo Cancer Registry, it is a useful clinical research resource for cancer related studies.
METHODS: Recruitment started in April 2009. Our goal is 50,000 enrollees. Eligible subjects are adult Mayo Clinic patients, U.S residents, and able to consent. Mailed invitations were sent to patients with appointments in certain Mayo Clinic departments. Each participant completed a health history questionnaire including a wide variety of health information as well as whether they or their 1st-degree relatives had ever been diagnosed with specific cancers. For self-reported cancers, subjects were asked to provide the age they were first diagnosed. Subjects also provided a blood sample and granted research access to clinically collected specimens and data from their Mayo Clinic record, at enrollment and into the future. All research projects utilizing samples from this resource are required to return to the Biobank the data generated from their projects.
RESULTS: In the first three years, 21736 subjects consented to participate (30% response rate). Participants are 58% female, 95% White, and range in age from 18 to 97 with a median age of 62. At enrollment, 74% were residents in Minnesota, 6% in Iowa, 4% in Wisconsin, and 15% in the other states. Twelve percent reported being a current smoker, while 59% were never smokers. The most common self-reported cancers at baseline were non-melanoma skin cancer (n=2950), prostate cancer (n=1107), breast cancer (n=941), melanoma (n=692), colorectal cancer (n=296), cervical cancer (n=240), sarcoma (n=233), urinary/bladder (n=211), lymphoma (n=187), thyroid cancer (n=186), endometrial cancer (n=170), kidney cancer (n=163), lung cancer (n=145). Excluding non-melanoma skin cancer, 16188 (74%) (6516 male, 9672 female) self-reported being cancer free at baseline. Data will be presented on the frequency of incident cancers diagnosed since enrollment in the Biobank. To date, the Biobank has served as a source of controls for 17 cancer related studies on genetic susceptibility, including studies of the breast, colon, myeloma, brain, lung, pancreas, endometrium, and kidneys.
CONCLUSIONS: The Mayo Clinic Biobank has quickly been established as a valuable resource for cancer researchers at Mayo Clinic - primarily as a source of controls for genetic studies. As the Biobank continues to grow in sample size and length of follow-up, it will also serve as a cohort study of cancer incidence embedded within an EMR of a large health organization.
Citation Format: Janet E. Olson, Euijung Ryu, Kiley J. Johnson, Karen Maschke, Jody A. Morrisette, Mark Liebow, Paul Y. Takahashi, Ruchi G. Sharma, Kari S. Anderson, Matthew A. Hathcock, Jyotishman Pathak, Noralane M. Lindor, Timothy J. Beebe, Stephen N. Thibodeau, James R. Cerhan. The Mayo Clinic Biobank - A growing resource for cancer related studies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2515. doi:10.1158/1538-7445.AM2013-2515
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Affiliation(s)
| | - Euijung Ryu
- 1Mayo Clinic College of Medicine, Rochester, MN
| | | | | | | | - Mark Liebow
- 1Mayo Clinic College of Medicine, Rochester, MN
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