- Email: [email protected]
Integration of Genomics in Primary Care
Accepted Manuscript Integration of Genomics in Primary Care Eric A. Larson, MD, Russell A. Wilke, MD, PhD PII:
S0002-9343(15)00452-0
DOI:
10.1016/j.amjmed.2015.05.011
Reference:
AJM 13005
To appear in:
The American Journal of Medicine
Received Date: 28 April 2015 Revised Date:
7 May 2015
Accepted Date: 8 May 2015
Please cite this article as: Larson EA, Wilke RA, Integration of Genomics in Primary Care, The American Journal of Medicine (2015), doi: 10.1016/j.amjmed.2015.05.011. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Integration of Genomics in Primary Care 1
Academic Associate Professor Department of Medicine University of South Dakota Sioux Falls, SD 57105 2
SC
Clinical Professor Department of Medicine University of North Dakota Fargo, ND 58102
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Eric A. Larson, MD1 and Russell A. Wilke, MD, PhD2
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Running Header: Integrating Genomics
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Abbreviations: ACMG American College of Medical Genetics ADR Adverse Drug Reaction BPA Best Practice Alert CAC Coronary Artery Calcium CPIC Clinical Pharmacogenetics Implementation Consortium CPOE Computerized Provider Order Entry EMR Electronic Medical Record GRS Genomic Risk Score PPE Potentially Preventable Events
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Keywords: Pharmacogenetics Precision medicine Personalized Medicine Biomedical Informatics Electronic Medical Record Conflict of Interest: none
Funding Source: This work was made possible through the generosity of T. Denny Sanford, whose gift of $125 million created IMAGENETICS (merging Internal Medicine and Genetics). Corresponding Author: Russell A. Wilke, MD, PhD Sanford Healthcare Medical Center 800 North Broadway Fargo, ND 58104 1
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Abstract Primary care is changing rapidly. The wide-scale expansion of electronic medical records is redefining the way we approach chronic disease management, and automated decision
RI PT
support is increasingly being leveraged to reduce risk and optimize quality. Many of these interventions are now beginning to integrate genomic data. We explore the
convergence of these two forces (expansion of clinical informatics and integration of
SC
translational genomics), and we highlight several applications where these forces are
helping our patients avoid potentially preventable events. Because gene-environment
M AN U
interactions are dynamic, the utility of gene-based decision support varies over time.
AC C
EP
TE D
Primary care providers will serve a key role as our patients navigate these changes.
2
ACCEPTED MANUSCRIPT
We are entering a new age in clinical medicine. Increasingly accurate risk prediction models and automated decision support deployed in electronic medical records (EMRs) may help providers detect disease earlier. The field of biomedical informatics is therefore
RI PT
changing the way we practice (1). The field of genomics is also changing the way we
practice. Some automated risk prediction models now include genetic covariates. Genedisease relationships allow providers to anticipate rate of progression, and gene-drug
SC
outcome relationships help identify patients at risk for undesired treatment response (2).
M AN U
The convergence of biomedical informatics and translational genomics is therefore creating an unprecedented opportunity to optimize clinical outcome. To be effective, however, this approach must be flexible over time. While genetic variability influences every facet of disease management (prevention, diagnosis, and treatment), it does so in a way that changes from year to year, throughout each patient’s lifespan. Because of their
TE D
pivotal role in maintaining continuity via longitudinal follow-up, primary care providers
EP
will be a key element in the success of our efforts to adopt translational genomics.
AC C
The Investment
A decade ago, Dr. Francis Collins charged the academic and clinical communities with the development a research agenda defining the impact of gene variation on the common complex diseases that we treat every day (3). This seminal call to arms was published to coincide with completion of the Human Genome Project, an investment of approximately $4 million. Now, a decade later, this investment has driven nearly $1 trillion in economic growth, positioning us to improve outcomes and reduce the overall burden of disease (4). 3
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Genomic deliverables are rapidly emerging within two categories: genetic predictors of disease (e.g., onset and rate of progression), and genetic predictors of treatment response (e.g., efficacy and toxicity). Strong predictors of disease (e.g., heritable thrombophilias)
RI PT
have already been used to inform clinical decision making for many years. However,
most human traits are influenced by multiple genetic factors of relatively small effect. While our ability to integrate combinations of genes into a single accurate predictive
SC
model varies by disease, changes in technology and data mining are expanding our ability to understand common complex traits (e.g., hyperlipidemias). Half a decade ago,
M AN U
Teslovich et al published an international genome wide meta-analysis identifying nearly 100 genetic loci contributing to LDL cholesterol level (5). Polygenic models of risk are therefore now being tested along with traditional Framingham risk determinants in an effort to optimize the prediction of cardiovascular disease in routine clinical care (6).
TE D
The relationship between genetic factors and treatment response is equally robust. Extending the work done with baseline LDL cholesterol level above, we and others recently published an international genome wide meta-analysis designed to elucidate the
EP
genetic predictors of drug-induced changes in LDL cholesterol level (7). In addition to
AC C
validating previously known determinants of pretreatment LDL cholesterol level (5), we identified two novel loci influencing the LDL cholesterol response to statin therapy: SORT1 and SLCO1B1. Genetic variants in SLCO1B1 alter the uptake of statin drugs by the liver, and change the concentration of these drugs within the circulation. Because SLCO1B1 gene variants are also a strong predictor of statin muscle toxicity (8, 9), many medical centers are moving this drug-gene relationship into routine clinical practice (10).
4
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Thus, one emerging principle in translational genomics is that genes impacting drug kinetics often contain clinically actionable variants (11). The importance of this principle has been underscored by the construction of the Pharmacogenetics Research Network
RI PT
(PGRN), a Herculean effort organized by Dr. Rochelle Long at the National Institute of General Medical Sciences (NIGMS) more than a decade ago (12). Since then, the U.S.
has invested more than $500 million in this network over 3 grant cycles. Deliverables like
SC
SLCO1B1 are now moving into routine care. Figure 1 illustrates a Best Practice Alert (BPA) deployed within a commonly used EMR, for patients expressing a common
M AN U
SLCO1B1 gene variant. Thus, a clear path toward implementation is beginning to emerge, particularly within the context of genes that predict treatment response (13).
TE D
Genes and Drug Response
In 2007, the U.S. Food and Drug Administration (FDA) began incorporating genetic information into drug labels (package inserts), including several “black box warnings”
EP
(14). In 2014, the FDA now requires genetic information in the labels of more than 100 drugs. In some cases, genotyping is mandatory. For most, genotyping is optional. For
AC C
clinicians, the decision whether to genotype before prescribing can be complicated. The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes gene-based dosing guidelines for drug-gene relationships known to be clinically actionable (15). At present, CPIC guidelines are available for more than two dozen drugs, and more guidelines are anticipated within the coming year [http://www.pharmgkb.org/].
5
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For the primary care provider, these guidelines will be highly desirable when they pertain to potentially preventable events (e.g., recurrent myocardial infarction). Gene-based drug dosing is therefore extremely useful when a drug’s therapeutic index is narrow (i.e., when
RI PT
small changes in blood level have a large impact on outcome). For example, clopidogrel is a pro-drug with very little antiplatelet activity. It requires bioactivation by cytochrome P450 (CYP) 2C19. Because the risk of coronary artery stent thrombosis in patients using
SC
clopidogrel is 3-fold higher in carriers of an abnormal CYP2C19 gene (16), many
M AN U
clinicians now use CYP2C19 genotype to direct antiplatelet therapy (2, 17, 18, 19). It is important to recognize that this enzyme metabolizes more than 5% of all prescription medications (including proton pump inhibitors, antidepressants, and drugs used to treat seizures). Patients with abnormal CYP2C19 genes are therefore likely to benefit from this information again, reapplying it in the context of other drugs, as their healthcare unfolds
TE D
over a lifespan. Thus, another emerging principle in translational genomics has been that clinically relevant gene variants are often pleiotropic (influencing more than one clinical outcome). Because primary care providers interact with their patients longitudinally, they
AC C
EP
are well positioned to help patients access this information as needed, decade by decade.
Genes and Disease Risk The approach above is not limited to genetic predictors of drug response. The American College of Medical Genetics (ACMG) routinely reviews genetic predictors of disease [http://www.acmg.net/], and 52 gene variants are now considered clinically actionable when identified incidentally during the process of genome scanning (20). Genomic risk 6
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scores (GRS), assessing multiple genes in combination with environmental risk factors, are also being developed for common complex traits such as diabetes and cardiovascular disease (21, 22). While debate continues as to whether genome-wide sequencing (23) or
RI PT
focused genotyping (24) is the best approach to identify patients at risk, there is no doubt that panels of gene variants will be used for risk stratification in EMRs relatively soon.
SC
Prior to 2010, only a small minority of US physicians used an EMR. In 2010, federal
legislators set an aggressive 5-year timeline for their widespread implementation. This
M AN U
expansion in the use of EMRs has not only redefined the way we practice, it has created huge observational datasets for research, some of which now emphasizes translational genomics. The National Human Genome Research Institute (NHGRI) has therefore constructed large multi-institutional networks, such as eMERGE (electronic MEdical Records and GEnomics) [http://emerge.mc.vanderbilt.edu/] and IGNITE (Implementing
TE D
GeNomics In pracTicE) [http://www.genome.gov/27554264], to quantify the potential utility of genetic predictors of clinical outcome within DNA biobanks linked to EMRs.
EP
As networks such as these harmonize their efforts to expand the use of gene variants in routine clinical care, increasingly accurate risk prediction tools are being developed for
AC C
use in the context of primary care. In the Framingham Heart Study participants, a GRS constructed from variants in 13 genes was recently shown to be associated with increased risk for incident cardiovascular events, even after adjusting for traditional risk factors (20). Because this risk score was also strongly associated with coronary artery calcium (CAC) score (O.R. = 1.18, p < 10-6) in the same study participants, decision support tools are now being developed to integrate traditional risk factors with genetic determinants, identifying patients requiring clinical intervention before their CAC is elevated. 7
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Thus, genetic predictors of clinical outcome are rapidly moving into clinical practice in the context of disease (onset and rate of progression), as well as treatment response (efficacy and toxicity). Regardless of clinical context (metabolic traits, cardiovascular
RI PT
disease, or heritable cancer syndromes), the integration of genetic data with known
clinical predictors of risk will require highly detailed functional pedigrees, as shown in
Figure 2. As EMRs evolve, patients are increasingly able to enter and update their family
SC
history (through secure portals), leading to streamlined allocation of screening resources.
M AN U
Renewed emphasis on patient-centered models of care (25) will no doubt empower our patients as we help them navigate these transitions. It is inevitable that patients will have questions about other genes, other clinical traits, and other drugs potentially influenced by their genome. Questions will arise about the implications of this information for other family members. All of these issues can be addressed by emerging models of team-based
EP
Summary
TE D
care, integrating primary care providers with nurses, pharmacists and genetic counselors.
AC C
Structured family histories (disease-specific pedigrees) entered by the patient are being integrated with genomic risk scores to reclassify disease risk and direct patients at risk toward early intervention. Automated decision support deploying high-priority gene variants are increasingly being leveraged to facilitate early diagnosis and optimize treatment response. Because the relationship between our patients’ genes and their environment changes decade by decade, primary care providers are well positioned to help them understand their genome and use this information throughout their lifespan. 8
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ACKNOWLEDGEMENTS
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The authors also thank Catherine Erickson for help with preparation of the manuscript.
9
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FIGURE LEGENDS
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Figure 1. Automated decision support deployed to reduce the frequency and severity of statin-related myopathy at a large multispecialty group practice located in the Midwestern United States (http://imagenetics.sanfordhealth.org/). [constructed by the authors, with
SC
Dr. Allison Wierda-Suttle, Dr. Megan Landsverk, Adam Stoebner, and Ryan Narlock.]
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This BPA serves the dual purpose of risk stratification and therapeutic guidance, suggesting treatment alternatives based upon genotype. To improve both workflow and patient safety, this BPA assists healthcare providers in simultaneously discontinuing a
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high risk medication while ordering a more appropriate agent with a single keystroke.
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Figure 2. Disease-specific pedigrees will soon be utilized to optimize risk stratification and assist with appropriate allocation of resources for disease prevention and early
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discovery. It is necessary that both patients and health care providers have access to these pedigrees because they are dynamic and clinical interpretation changes with time.
10
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Clinical Significance -Primary care physicians are uniquely positioned to utilize genomic information throughout patients’ lifetimes.
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-Translational genomics will enhance screening and early detection for a wide variety of diseases. -Genomic predictors of drug response will improve patient safety through Best Practice Advisories (BPAs).
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-Integration of genomic data with known clinical predictors of risk will guide shared decision making between providers and patients.
11
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REFERENCES 1. D'Avolio LW, Farwell WR, Fiore LD. Comparative effectiveness research and medical informatics. Am J Med. 2010 Dec;123(12 Suppl 1):e32-7.
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2. Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, et al. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther. 2012;92(1):87-95.
research. Nature. 2003;422:835-47.
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3. Collins FS, Green ED, Guttmacher AE, Guyer MS. A vision for the future of genomics
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4. Collins FS. Exceptional opportunities in medical science: a view from the National Institutes of Health. JAMA. 2015;313(2):131-2.
5. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature.
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2010;466:707-13.
6. Ding K, Bailey KR, Kullo IJ. Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record.
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BMC Cardiovasc Disord. 2011;11:66.
7. Postmus I, Trompet S, Deshmukh HA, Barnes MR, Li X, Warren HR, et al.
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Pharmacogenetic meta-analysis of genome-wide association studies of LDL-cholesterol response to statins. Nat Commun. 2014;5:5068.
8. Ramsey LB, Johnson SG, Caudle KE, Haidar CE, Voora D, Wilke RA, et al. The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1 and Simvastatin-Induced Myopathy. Clin Pharmacol Ther. 2014;96(4):423-28.
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9. SEARCH Collaborative Group, Link E, Parish S, Armitage J, Bowman L, Heath S, et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N Engl J Med. 2008;359(8):789-99.
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10. Denny JC, Wilke RA. Can EMRs facilitate automated gene-based drug prescribing for cardiovascular disease? Dialog Cardiovasc Med. 2012;17(4):293-300.
11. Goldstein DB, Need AC, Singh R, Sisodiya SM. Potential genetic causes of
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heterogeneity of treatment effects. Am J Med. 2007 Apr;120(4 Suppl 1):S21-5. 12. Long R. Planning for a national effort to enable and accelerate discoveries in
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pharmacogenetics: the NIH Pharmacogenetics Research Network. Clin Pharmacol Ther. 2007;81(3):450-4.
13. Wilke RA, Dolan ME. Genetics and variable drug response. JAMA. 2011;306(3):306-7. 14. Lesko LJ, Zineh I. DNA, drugs and chariots: on a decade of pharmacogenomics at the
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US FDA. Pharmacogenomics. 2010;11(4):507-12.
15. Relling MV, Klein TE. CPIC: clinical pharmacogenetics implementation consortium of the pharmacogenomics research network. Clin Pharmacol Ther. 2011;89(3):464-67.
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16. Mega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, et al. Cytochrome P450 Polymorphisms and Response to Clopidogrel. N Engl J Med. 2009;360(4):354-62.
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17. Weitzel KW, Elsey AR, Langaee TY, Burkley B, Nessl DR, Obend AO, et al. Clinical pharmacogenetics implementation: approaches, successes, and challenges. Am J Med Genet C Semin Med Genet. 2014;166C(1)56-67.
18. Scott SA, Sangkuhl K, Stein CM, Hulot JS, Mega JL, Roden DM, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin Pharmacol Ther. 2013;94(3):317-23.
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19. Scott SA, Sangkuhl K, Gardner EE, Stein CM, Hulot JS, Johnson JA, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450-2C19
32.
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(CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol Ther. 2011;90(2):328-
20. ACMG Board of Directors. ACMG policy statement: updated recommendations
sequencing. Genet Med. 2015;17(1):68-9.
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regarding analysis and reporting of secondary findings in clinical genome-scale
21. Liu S, Song Y. Building genetic scores to predict risk of complex diseases in humans: is
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it possible? Diabetes. 2010;59(11):2729-31.
22. Thanassoulis G, Peloso GM, Pencina MJ, Hoffmann U, Fox CS, Cupples LA, et al. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study. Circ Cardiovasc Genet. 2012;5(1):113-21.
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23. Dimmock D. Recommend whole genome sequencing. N Engl J Med. 2014;370(25):2444-5.
24. Burke W. Recommend sequencing of cancer genes only, if certain conditions are met. N
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Engl J Med. 2014;370(25):2443-4.
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25. Kern LM, Edwards A, Kaushal R. The patient-centered medical home, electronic health records, and quality of care. Ann Intern Med. 2014;160(11):741-9.
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S0002-9343(15)00452-0
DOI:
10.1016/j.amjmed.2015.05.011
Reference:
AJM 13005
To appear in:
The American Journal of Medicine
Received Date: 28 April 2015 Revised Date:
7 May 2015
Accepted Date: 8 May 2015
Please cite this article as: Larson EA, Wilke RA, Integration of Genomics in Primary Care, The American Journal of Medicine (2015), doi: 10.1016/j.amjmed.2015.05.011. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Integration of Genomics in Primary Care 1
Academic Associate Professor Department of Medicine University of South Dakota Sioux Falls, SD 57105 2
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Clinical Professor Department of Medicine University of North Dakota Fargo, ND 58102
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Eric A. Larson, MD1 and Russell A. Wilke, MD, PhD2
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Running Header: Integrating Genomics
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Abbreviations: ACMG American College of Medical Genetics ADR Adverse Drug Reaction BPA Best Practice Alert CAC Coronary Artery Calcium CPIC Clinical Pharmacogenetics Implementation Consortium CPOE Computerized Provider Order Entry EMR Electronic Medical Record GRS Genomic Risk Score PPE Potentially Preventable Events
AC C
EP
Keywords: Pharmacogenetics Precision medicine Personalized Medicine Biomedical Informatics Electronic Medical Record Conflict of Interest: none
Funding Source: This work was made possible through the generosity of T. Denny Sanford, whose gift of $125 million created IMAGENETICS (merging Internal Medicine and Genetics). Corresponding Author: Russell A. Wilke, MD, PhD Sanford Healthcare Medical Center 800 North Broadway Fargo, ND 58104 1
ACCEPTED MANUSCRIPT
Abstract Primary care is changing rapidly. The wide-scale expansion of electronic medical records is redefining the way we approach chronic disease management, and automated decision
RI PT
support is increasingly being leveraged to reduce risk and optimize quality. Many of these interventions are now beginning to integrate genomic data. We explore the
convergence of these two forces (expansion of clinical informatics and integration of
SC
translational genomics), and we highlight several applications where these forces are
helping our patients avoid potentially preventable events. Because gene-environment
M AN U
interactions are dynamic, the utility of gene-based decision support varies over time.
AC C
EP
TE D
Primary care providers will serve a key role as our patients navigate these changes.
2
ACCEPTED MANUSCRIPT
We are entering a new age in clinical medicine. Increasingly accurate risk prediction models and automated decision support deployed in electronic medical records (EMRs) may help providers detect disease earlier. The field of biomedical informatics is therefore
RI PT
changing the way we practice (1). The field of genomics is also changing the way we
practice. Some automated risk prediction models now include genetic covariates. Genedisease relationships allow providers to anticipate rate of progression, and gene-drug
SC
outcome relationships help identify patients at risk for undesired treatment response (2).
M AN U
The convergence of biomedical informatics and translational genomics is therefore creating an unprecedented opportunity to optimize clinical outcome. To be effective, however, this approach must be flexible over time. While genetic variability influences every facet of disease management (prevention, diagnosis, and treatment), it does so in a way that changes from year to year, throughout each patient’s lifespan. Because of their
TE D
pivotal role in maintaining continuity via longitudinal follow-up, primary care providers
EP
will be a key element in the success of our efforts to adopt translational genomics.
AC C
The Investment
A decade ago, Dr. Francis Collins charged the academic and clinical communities with the development a research agenda defining the impact of gene variation on the common complex diseases that we treat every day (3). This seminal call to arms was published to coincide with completion of the Human Genome Project, an investment of approximately $4 million. Now, a decade later, this investment has driven nearly $1 trillion in economic growth, positioning us to improve outcomes and reduce the overall burden of disease (4). 3
ACCEPTED MANUSCRIPT
Genomic deliverables are rapidly emerging within two categories: genetic predictors of disease (e.g., onset and rate of progression), and genetic predictors of treatment response (e.g., efficacy and toxicity). Strong predictors of disease (e.g., heritable thrombophilias)
RI PT
have already been used to inform clinical decision making for many years. However,
most human traits are influenced by multiple genetic factors of relatively small effect. While our ability to integrate combinations of genes into a single accurate predictive
SC
model varies by disease, changes in technology and data mining are expanding our ability to understand common complex traits (e.g., hyperlipidemias). Half a decade ago,
M AN U
Teslovich et al published an international genome wide meta-analysis identifying nearly 100 genetic loci contributing to LDL cholesterol level (5). Polygenic models of risk are therefore now being tested along with traditional Framingham risk determinants in an effort to optimize the prediction of cardiovascular disease in routine clinical care (6).
TE D
The relationship between genetic factors and treatment response is equally robust. Extending the work done with baseline LDL cholesterol level above, we and others recently published an international genome wide meta-analysis designed to elucidate the
EP
genetic predictors of drug-induced changes in LDL cholesterol level (7). In addition to
AC C
validating previously known determinants of pretreatment LDL cholesterol level (5), we identified two novel loci influencing the LDL cholesterol response to statin therapy: SORT1 and SLCO1B1. Genetic variants in SLCO1B1 alter the uptake of statin drugs by the liver, and change the concentration of these drugs within the circulation. Because SLCO1B1 gene variants are also a strong predictor of statin muscle toxicity (8, 9), many medical centers are moving this drug-gene relationship into routine clinical practice (10).
4
ACCEPTED MANUSCRIPT
Thus, one emerging principle in translational genomics is that genes impacting drug kinetics often contain clinically actionable variants (11). The importance of this principle has been underscored by the construction of the Pharmacogenetics Research Network
RI PT
(PGRN), a Herculean effort organized by Dr. Rochelle Long at the National Institute of General Medical Sciences (NIGMS) more than a decade ago (12). Since then, the U.S.
has invested more than $500 million in this network over 3 grant cycles. Deliverables like
SC
SLCO1B1 are now moving into routine care. Figure 1 illustrates a Best Practice Alert (BPA) deployed within a commonly used EMR, for patients expressing a common
M AN U
SLCO1B1 gene variant. Thus, a clear path toward implementation is beginning to emerge, particularly within the context of genes that predict treatment response (13).
TE D
Genes and Drug Response
In 2007, the U.S. Food and Drug Administration (FDA) began incorporating genetic information into drug labels (package inserts), including several “black box warnings”
EP
(14). In 2014, the FDA now requires genetic information in the labels of more than 100 drugs. In some cases, genotyping is mandatory. For most, genotyping is optional. For
AC C
clinicians, the decision whether to genotype before prescribing can be complicated. The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes gene-based dosing guidelines for drug-gene relationships known to be clinically actionable (15). At present, CPIC guidelines are available for more than two dozen drugs, and more guidelines are anticipated within the coming year [http://www.pharmgkb.org/].
5
ACCEPTED MANUSCRIPT
For the primary care provider, these guidelines will be highly desirable when they pertain to potentially preventable events (e.g., recurrent myocardial infarction). Gene-based drug dosing is therefore extremely useful when a drug’s therapeutic index is narrow (i.e., when
RI PT
small changes in blood level have a large impact on outcome). For example, clopidogrel is a pro-drug with very little antiplatelet activity. It requires bioactivation by cytochrome P450 (CYP) 2C19. Because the risk of coronary artery stent thrombosis in patients using
SC
clopidogrel is 3-fold higher in carriers of an abnormal CYP2C19 gene (16), many
M AN U
clinicians now use CYP2C19 genotype to direct antiplatelet therapy (2, 17, 18, 19). It is important to recognize that this enzyme metabolizes more than 5% of all prescription medications (including proton pump inhibitors, antidepressants, and drugs used to treat seizures). Patients with abnormal CYP2C19 genes are therefore likely to benefit from this information again, reapplying it in the context of other drugs, as their healthcare unfolds
TE D
over a lifespan. Thus, another emerging principle in translational genomics has been that clinically relevant gene variants are often pleiotropic (influencing more than one clinical outcome). Because primary care providers interact with their patients longitudinally, they
AC C
EP
are well positioned to help patients access this information as needed, decade by decade.
Genes and Disease Risk The approach above is not limited to genetic predictors of drug response. The American College of Medical Genetics (ACMG) routinely reviews genetic predictors of disease [http://www.acmg.net/], and 52 gene variants are now considered clinically actionable when identified incidentally during the process of genome scanning (20). Genomic risk 6
ACCEPTED MANUSCRIPT
scores (GRS), assessing multiple genes in combination with environmental risk factors, are also being developed for common complex traits such as diabetes and cardiovascular disease (21, 22). While debate continues as to whether genome-wide sequencing (23) or
RI PT
focused genotyping (24) is the best approach to identify patients at risk, there is no doubt that panels of gene variants will be used for risk stratification in EMRs relatively soon.
SC
Prior to 2010, only a small minority of US physicians used an EMR. In 2010, federal
legislators set an aggressive 5-year timeline for their widespread implementation. This
M AN U
expansion in the use of EMRs has not only redefined the way we practice, it has created huge observational datasets for research, some of which now emphasizes translational genomics. The National Human Genome Research Institute (NHGRI) has therefore constructed large multi-institutional networks, such as eMERGE (electronic MEdical Records and GEnomics) [http://emerge.mc.vanderbilt.edu/] and IGNITE (Implementing
TE D
GeNomics In pracTicE) [http://www.genome.gov/27554264], to quantify the potential utility of genetic predictors of clinical outcome within DNA biobanks linked to EMRs.
EP
As networks such as these harmonize their efforts to expand the use of gene variants in routine clinical care, increasingly accurate risk prediction tools are being developed for
AC C
use in the context of primary care. In the Framingham Heart Study participants, a GRS constructed from variants in 13 genes was recently shown to be associated with increased risk for incident cardiovascular events, even after adjusting for traditional risk factors (20). Because this risk score was also strongly associated with coronary artery calcium (CAC) score (O.R. = 1.18, p < 10-6) in the same study participants, decision support tools are now being developed to integrate traditional risk factors with genetic determinants, identifying patients requiring clinical intervention before their CAC is elevated. 7
ACCEPTED MANUSCRIPT
Thus, genetic predictors of clinical outcome are rapidly moving into clinical practice in the context of disease (onset and rate of progression), as well as treatment response (efficacy and toxicity). Regardless of clinical context (metabolic traits, cardiovascular
RI PT
disease, or heritable cancer syndromes), the integration of genetic data with known
clinical predictors of risk will require highly detailed functional pedigrees, as shown in
Figure 2. As EMRs evolve, patients are increasingly able to enter and update their family
SC
history (through secure portals), leading to streamlined allocation of screening resources.
M AN U
Renewed emphasis on patient-centered models of care (25) will no doubt empower our patients as we help them navigate these transitions. It is inevitable that patients will have questions about other genes, other clinical traits, and other drugs potentially influenced by their genome. Questions will arise about the implications of this information for other family members. All of these issues can be addressed by emerging models of team-based
EP
Summary
TE D
care, integrating primary care providers with nurses, pharmacists and genetic counselors.
AC C
Structured family histories (disease-specific pedigrees) entered by the patient are being integrated with genomic risk scores to reclassify disease risk and direct patients at risk toward early intervention. Automated decision support deploying high-priority gene variants are increasingly being leveraged to facilitate early diagnosis and optimize treatment response. Because the relationship between our patients’ genes and their environment changes decade by decade, primary care providers are well positioned to help them understand their genome and use this information throughout their lifespan. 8
ACCEPTED MANUSCRIPT
ACKNOWLEDGEMENTS
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The authors also thank Catherine Erickson for help with preparation of the manuscript.
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FIGURE LEGENDS
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Figure 1. Automated decision support deployed to reduce the frequency and severity of statin-related myopathy at a large multispecialty group practice located in the Midwestern United States (http://imagenetics.sanfordhealth.org/). [constructed by the authors, with
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Dr. Allison Wierda-Suttle, Dr. Megan Landsverk, Adam Stoebner, and Ryan Narlock.]
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This BPA serves the dual purpose of risk stratification and therapeutic guidance, suggesting treatment alternatives based upon genotype. To improve both workflow and patient safety, this BPA assists healthcare providers in simultaneously discontinuing a
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high risk medication while ordering a more appropriate agent with a single keystroke.
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Figure 2. Disease-specific pedigrees will soon be utilized to optimize risk stratification and assist with appropriate allocation of resources for disease prevention and early
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discovery. It is necessary that both patients and health care providers have access to these pedigrees because they are dynamic and clinical interpretation changes with time.
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Clinical Significance -Primary care physicians are uniquely positioned to utilize genomic information throughout patients’ lifetimes.
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-Translational genomics will enhance screening and early detection for a wide variety of diseases. -Genomic predictors of drug response will improve patient safety through Best Practice Advisories (BPAs).
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-Integration of genomic data with known clinical predictors of risk will guide shared decision making between providers and patients.
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