Jpn J Clin Oncol 2014;44(11)1017– 1024 doi:10.1093/jjco/hyu135 Advance Access Publication 23 September 2014
Review Article
The Potential Application of Personalized Preventive Research Minkyo Song1,2,3, Hwi-Won Lee1,2 and Daehee Kang1,2,4,* 1
Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, 2Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 3Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul and 4Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
Received May 14, 2014; accepted August 13, 2014
With increases in life expectancy, the focus has shifted to living a healthier, longer life. By concentrating on preventing diseases before occurrence, researchers aim to diminish the increasing gap in medical costs and health inequalities prevalent across many nations. Although we have entered an era of post-genomics, we are still in infancy in terms of personalized preventive research. Personalized preventive research has and will continue to improve with advancements in the use of biomarkers and risk assessment. More evidence based on well-designed epidemiologic studies is required to provide comprehensive preventive medical care based on genetic and non-genetic profile data. The realization of personalized preventive research requires building of evidence through appropriate methodology, verification of results through translational studies as well as development and application of prediction models. Key words: preventive medicine – personalized medicine – epidemiology – environmental medicine
INTRODUCTION The paradigm of health is shifting from simply ‘living a longer life’ to ‘living a healthier and longer life.’ Life expectancy has increased .10 years within the short period of 1970 – 2010 (1). However, longer life-expectancy did not guarantee a healthy-longer life. The increase in healthy life expectancy (HALE), or the average number of years that a person can expect to live in full health, is smaller in its magnitude than the increase in life expectancy (2). In fact, a 1-year increase in life expectancy is associated with a 10-month increase in HALE. The reasons for such a phenomenon can be described by slow improvements in nonfatal disease or injury in comparison with reductions in mortality, and is especially associated with chronic diseases (3,4). Chronic diseases, ranging from less lethal diseases such as hypertension and diabetes to stroke and cancer, all fit in the same category. These chronic diseases consume the affected with many inconveniences in their everyday lives as well as the accompanying medical attention.
Chronic diseases cause great loss at a national level as well as at a personal level. It directly translates into longer duration of care needed, meaning higher medical costs and poorer function of the individual. Nationally, this means rising medical costs and health inequalities, further pronounced with improvements in medicine and related fields. For instance, the average Organisation for Economic Co-operation and Development health expenditure per capita grew annually by 4% in the last decade (5). With many nations entering the aging and aged society, rise in chronic diseases is inevitable and is presumed to be a persistent problem as a national and personal agenda. Thus, the rational solution is to unravel the problem beforehand, at its causal origin, by preventing diseases before occurrence.
THE CONCEPT OF PERSONALIZED PREVENTIVE RESEARCH The field of medicine has evolved since the age of epidemics from the age of complex diseases to the current age of post-
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*For reprints and all correspondence: Daehee Kang, Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea. E-mail: [email protected]
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Figure 1. Evolution of the concept of disease and corresponding trends in medicine.
Figure 2. Three target domains of the conventional prevention pyramid.
When conducting PPR, all domains of classic preventive medicine may be considered. Primary prevention, which targets the general population who are thought to be disease free, identifies high-risk groups that are likely to develop diseases in order to prevent disease occurrence and to promote healthy lifestyles. Prevention at this level may involve behavior modification based on the person’s susceptibility to his or her risk of developing a disease. Secondary prevention refers to the state of subclinical disease where the goal is to stop or reduce the progress of disease in their earliest stages through early detection. Screening and monitoring, and targeting highrisk individuals, precisely defined by the use of information that combines genetics and other lifestyle factors before the clinical development of disease, are the purpose of secondary prevention. Tertiary prevention refers to the diagnosed state, and the goal lies in reducing further complications or recurrence of diseases (Fig. 2). Similarly, this can be achieved through the application of lifestyle modifications along with the information based on genetic susceptibility to drugs used (pharmacogenetics). The risk of disease may decrease as the mode of prevention moves downward from the apex (tertiary) to the base (primary) of the pyramid. Nevertheless, the overall benefit in the burden of public health perspective will be greater as preventive measures are applied to a larger population.
CASE STUDIES OF PPR At this stage of the post-genomic era, personalized prevention remains in its infancy. In early days, PPR improved with the
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genomics (Fig. 1) (6,7). In the age of epidemics, the control of pathogens through various means was the main treatment and prevention modality. Entering the age of complex diseases, the occurrence of various diseases was understood as multiple and complex factors related to living conditions or personal habits, and the distinction between treatment and prevention in the field of medicine became clearer. During this era, medicine was an average care empirically derived from trial and error experience of prior practice or observations. The growth of genetics research and completion of the Human Genome Project have introduced the concept of genetic susceptibility and gene – environment interaction to the age of postgenomics. Furthermore, the emphasis on efficacy and safety based on evidence, together with the increased understanding of the complex concept of genetic susceptibility, has led to the development of personalized medicine (8,9). Personalized medicine entails customized healthcare employed on people considered to be at high risk for certain diseases (10). The significance of personalized medicine is widely recognized, as governments have taken action to directly or indirectly support personalized medicine (11). For instance, long before his tenure, the US President Barack Obama has voiced support for personalized medicine by introducing the Genomics and Personalized Medicine Act in 2007 (12). According to a report published in 2009 by PricewaterhouseCoopers LLP, the market for personalized medicine in the USA is projected to grow from $232 billion to as much as $452 billion by 2015 (13). As previously noted, medical and healthcare models are rightfully evolving from simply ‘treating the ill’ to ‘aiding the healthy to maintain their good health.’ And this is where the objective of personalized preventive research (PPR) lies. Personalized prevention may be understood as a ‘stratified prevention’ conducted at a personal level (14). It aims to extend the healthy lifespan of individuals by exploring disease etiology on an individual basis as well as by providing personalized means to maintain wellness in healthy people and prevent disease occurrence in high-risk individuals by integrating the use of genetic, protein- and environment-related information (15,16). In the spectrum of individualized medicine, PPR requires that the target population is clearly defined and the factors attributable to the selected subjects’ health status are based on reliable evidence established upon information that is accurate and detailed.
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development of molecular epidemiology, which focused on biomarkers (17,18). Risk assessment has improved, and must continue to improve, with the use of biomarkers as illustrated in Fig. 3. The initial stage describes the discovery and selection of appropriate biomarkers in the areas of genomics, epigenomics, transcriptomics, metabolomics, nutrigenomics, pharmacogenomics, toxicogenomics and proteomics. In the following stage, the selected biomarkers are evaluated within the appropriate epidemiologic research settings to see whether the use of biomarkers on relevant target groups of healthy, high-risk and diagnosed individuals in primary to tertiary prevention levels are valid. In the last stage, indicators identified from target groups are applied to create relevant prediction models. THE SEOUL BREAST CANCER STUDY In the field of epidemiology, phases of biomarker discovery and validation using an epidemiologic design often occur concurrently, as in the example of the Seoul Breast Cancer Study (SeBCS). To briefly explain, the SeBCS is a case–control study with subjects recruited from 2001 to 2007, which include 4040 histologically confirmed incident breast cancer cases and 3946 non-cancer controls from 4 major teaching hospitals and community health screening programs in Seoul (19). In the SeBCS, several studies on identifying high-risk group have been conducted (Table 1), ranging from early genomic studies focused on candidate genes and single nucleotide polymorphisms (SNPs) (20 – 22) to later genomewide association studies (GWAS) (23). Also, studies have been conducted using a wide variety of biomarkers such as
methylation (24) and microRNA (25) in transcriptomics. These endeavors have led to the construction of a breast cancer risk prediction model tailored for Korean women (26). In the practice of tertiary prevention, research on disease prognosis and survival is essential, and studies on genomics (27) as well as proteomics (28,29) have been conducted using the SeBCS data. A CLINICAL APPLICATION OF GENETIC COUNSELING Eventually, genetic counseling will be one area in personalized preventive care in practice. Accompanied by much controversy, the use of genetic counseling is still considered hasty. Genetic counseling based on genetic testing has so far been established as being useful for high-penetrance hereditary diseases (30 – 32). Genetic testing provides several benefits, such as a sense of relief and fewer checkups or routine tests for those with negative results. Even people with positive results may avoid uncertain fears and be ready for informed decisions, or even have a chance to intervene to reduce risk (33). However, genetic tests simply determine whether or not possible mutations of the genes are present, and do not guarantee development of the disease. Furthermore, even if the mutations are present and the disease develops, these may go undetected throughout the person’s lifetime. Thus, caution must be taken when calculating, interpreting and counseling on the probability of developing a disease. The considerable concern in genetic counseling is that most genetic tests do not have corresponding preventive strategies. Without adequate countermeasures, there may only be potential harms. One concern is a psychological effect on the
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Figure 3. Steps in discovering biomarkers in molecular epidemiology for personalized preventive research (PPR).
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Table 1. Examples of biomarkers discovered in association with breast cancer risk from selected studies from the Seoul Breast Cancer Study Biomarkers
Indicators of high-risk group
Indicators of prognosis and survival OR (95% CI)
Ref.a
CYP19 Arg(264)C
1.5 (1.10– 2.20)
(14)
CYP2E1 c2 allele
1.9 (0.99– 3.83)
(15)
COMT, GSTM1, GSTT1
4.1 (1.40– 12.70)
(16)
rs13393577 at 2q34
1.53 (1.37–1.70)
(18)
Hyper-
FAM124B, ST6GALNAC1
FDR , 0.05
(19)
Hypo-
NAV1, PER1 AGO2 rs11786030
2.62 (1.41–4.88)
(20)
AGO2 RS2292779
2.94 (1.52–5.69)
HR (95% CI)
Ref.a
Genomics SNP
GWAS
2.1 (1.03– 4.33)
(22)
Lipocalin-2
3.17 (1.66– 6.06)
(23)
MMP-9
5.36 (2.18– 13.20)
MMP-2
2.75 (1.32– 5.73)
Transcriptomics Methylation
MicroRNA
Proteomics
(24)
FDR, false discovery rate; HR, Hazard Ratio; OR, Odds Ratio. a Indicates article reference numbers.
person, such as the fear of suffering a serious disease in the future. Genetic discrimination by employers and health insurance companies may be another problem. Although the law of the Genetic Information Nondiscrimination Act has been established in 2008, it does not warrant, at this time, genetic discrimination in all circumstances, including the use of genetic information on the provision of life insurance, disability insurance or long-term care insurance (34,35). Furthermore, the decision to receive genetic testing should be voluntary. Consequently, appropriate genetic counseling requires accurate and clinically useful information derived from welldesigned high-quality research. Counseling, similar to other clinical decision-makings, weighs the benefits and harms. Thus, agencies such as the International Agency for Research on Cancer (IARC) evaluate scientific evidence and judge carcinogenicity regarding environmental factors and cancer risk (36). However, regarding genetic factors, only limited evidence exists, and so far there are no agencies that focus on evaluating and providing evidence for genetic – disease association. Agencies such as the US Preventive Services Task Force (USPSTF) may provide with few guidelines for limited high-penetrance genetic diseases such as the recent recommendation for the primary care providers to screen women with a family history of breast, ovarian, tubal or peritoneal cancer to identify potential mutations in breast cancer susceptibility genes (37). Another example of the current status in the application of personalized prevention can be found in ‘direct-to-consumer’
(DTC) companies. DTC aims to provide individuals with information on improving health and preventing diseases based on personal genome-wide profiles (38,39). Some of the concerns regarding DTC include the lack of evidence of the relationship between genetic variations and human diseases as well as unknown, potential benefits and harms (39 – 41). In fact, in the case of 23andMe, the Food and Drug Administration’s (FDA) directive for the genetic testing company to discontinue new consumer access on personal genetic information stemmed from the regulator’s concern over misinterpretation and/or misuse of personal information beyond the uses of research and education (42,43). DTC companies allow consumers to access the information without communicating with the intermediary healthcare providers, thus the lack of professional counseling by medical doctors may also pose other problems such as inappropriate utilization of healthcare resources (44), inadequate counseling and informed consent, as well as the concerns over safety, effectiveness and risks associated with the tests with uncertain clinical utility (34). Thus, it is of utmost importance to generate and deliver the exact evidence in terms of benefit and harm to help individuals make decisions. Moreover, in terms of personalized prevention, even accurate information may yield different decisions depending on one’s personal values. Some may choose the opportunity to prevent premature cancer death and make radical choices such as mastectomy, while others may choose to take the risk and keep their breasts.
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eNOS-786C
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PREAMBLE FOR THE SUCCESSFUL IMPLEMENTATION OF PPR
high BMI in Asians when compared with Europeans, remains to be fully explained in both genetic and non-genetic terms. It challenges the concept of universal standards while reinforcing the drive for personalized research. Secondly, the observed results must be verified in terms of efficacy when applied as a preventive measure. With breast cancer, for example, researchers have identified common low penetrance alleles associated with ,1.5-fold increases in the risk through GWAS (64). Yet little is known about the relevance of these risk factors to the different molecular subtypes of breast cancer (65). Hence, there needs to be a bridging translational study to apply these research results into a clinical setting or to the general population. In such aspect, preventive trials based on results from the general population and high-risk group may offer the potential for personalized risk estimates and preventive measures (65). A number of Northern European studies, including the Finnish Diabetes Prevention Study, focused on diabetes and lifestyle interventions in a multicenter, randomized controlled trial (RCT) setting (66 – 68). In the 2002 study, the risk for type 2 diabetes increased by an odds ratio of 2.11 (95% CI 1.20 – 3.72) for subjects with the Ala12 allele compared with those with the Pro12Pro genotype (66). However, among subjects who followed an intensive, combined intervention program of diet and physical activity, those with the Ala12Ala genotype yielded improved insulin sensitivity to some extent. By implementing genetic profiles, the researchers were able to elucidate the effect of genetic variation according to the magnitude of benefits of physical activity and dietary intervention. Another example, which incorporates genetic information in preventive trials, is the Alzheimer’s Prevention Initiative, an international collaborative project focused on finding effective presymptomatic Alzheimer’s disease (AD) treatment that could reduce the symptoms or possibly prevent them completely (69). Based on the amyloid hypothesis explaining the accumulation of toxic Abeta to be the cause of the disease, the study plans to target high-risk yet cognitively normal people, who are AD-causing presenilin I [PS1] mutation carriers and apolipoprotein E (APOE) e 4 homozygotes, for intervention using amyloid-modifying treatments. Thirdly, development and application of prediction models are indispensable in all stages of personalized prevention, from screening and diagnosis to therapeutic purposes. The prediction model is a comprehensive way of interpreting and predicting the disease outcome using available information through mathematical modeling. However, there are many challenges in developing prediction models in order to apply them to target populations. The first step in the development stage is choosing the design of the study (Table 2). Researchers must consider the strengths and limitations of the study design when developing prediction models. Prediction is longitudinal in nature, thus prospective studies are often used to develop a model. However, retrospective studies such as case – control studies can also be used for their feasibility and simplicity. Choosing a well-defined target population in the process is also imperative in order to develop a valid model.
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The proper choices related to personalized preventive measures are still based on limited study results. The gap between the genomics technology and the application in practice has compelled many scientists to become aware of the need for evidenced research (45 – 47). What necessary conditions must be met for the suitable application of PPR? First and foremost, the choice of methodology in evidencebased research may provide some understanding. An appropriate study design is imperative in research. In observational epidemiology, a cohort study ranks the highest in the hierarchy of evidence; a cohort study allows a clearer explanation of the study outcome and disease etiology, as it is free from the limitations of reverse causality. Moreover, testing hypotheses for gene – environment interactions has rendered the need for larger sized cohorts to fulfill the necessary statistical power (48). Furthermore, studies at present and in the future will integrate more precise means in measuring the individual’s genetic and environmental data. Methods include next generation sequencing that measures the entire genetic data of an individual or gut microbiome-derived metagenomics that provides genetic information on a person’s microflora (49,50), as well as electronic smart devices that measure various biosignals such as physical activity and sleep (51,52), which in turn will require more statistical power. Hence, a welldesigned, large population-based genomic cohort, free from the temporal interplay of cause and outcome, is a preferable solution to PPR (53). The importance of large-scale cohort studies is being widely recognized as many nations participate in large, multi-network of data collection. In the new millennium, many large cohorts such as the UK Biobank and the China Kadoorie Biobank, each with about half a million registered subjects, have been established, and various consortia are being formed by the integration of smaller scale studies (54,55). Through these large-scale prospective cohorts, we can fill the knowledge gap between understanding the complex interplay of genes and environment as in gene – environment interactions, gene – gene interactions and environment – environment interactions in the development of diseases. Merging various cohorts into an international collaboration is one way to help overcome the aforementioned limitations of sizable design. Asia Cohort Consortium (ACC) is a product of an international collaborative project involving 50 active members and .1 million healthy cohort subjects from Pacific Rim nations (56). ACC focuses on important epidemiologic risk factors in the Asian population and has published important results including the association between total mortality and cause-specific mortality (57 – 62). The U-shaped relationship between BMI and risks of death was most prominent among East Asians by 1.5 times among subjects who had a high BMI (.35.0 kg/m2) and 2.8 times greater among subjects with a very low BMI (15.0 kg/m2) (63). The marked risk of death associated with a low BMI, rather than with a
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Table 2. Study designs for predictive modeling Study design
Strengths
Limitations
Case–control
Simple, low cost
Comparability of controls is critical
Prospective cohort
Multiple outcomes Possible repeated exposure measurements
High cost Longer follow-up period
Randomized controlled trial
Well-defined selection of subjects
Poor generalizability due to stringent in- and exclusion criteria
Furthermore, selection and coding of the variables and specification of the models are important in building the models. The majority of models developed so far have moderate discriminatory powers (70). With the evolution of genetic epidemiology, prediction models incorporating SNPs and GWAS-identified SNPs were developed. But these have shown less promising results, rendering only a moderate improvement in the model’s discriminatory power that was lower than adding the intermediate phenotypic and molecular biomarkers. For instance, Wacholder et al. (71) had gained an AUC of 0.038 from 0.580 by adding 10 published GWAS-identified SNPs to the Gail model with 4 traditional risk factors. It is thus important to incorporate multiple layers of information including genetic, environmental and clinical information to enhance the performance of the predictive ability. Moreover, validation of the developed model is also important. An internal validation of the model is also critical because overfitting is the central problem, especially when much information, including genetic, environmental and clinical factors, is to be used to construct a model. Furthermore, external validation to ensure generalizability is a critical step in verifying the applicability of the model in a different population (72). In addition to acknowledging and overcoming the aforementioned challenges, future strategies in developing predictive models should be an inclusion of a more flexible and dynamic model that reflects the changeable factors such as change in risk behaviors or epigenetic information. This may require vast amount of data as well as a more delicate and complex statistical analysis. A web-based tool in the developmental stage may be one way to overcome this problem.
CONCLUSION Progress in medicine continues today. Moreover, technological advances have also helped increase our knowledge of the molecular mechanisms involved in a host of biological activities related to normal and diseased status of human functions. However, we are still in the beginning stage of realizing personalized prevention. Many preconditions are to be met for its successful implementation, from choosing the right study
Acknowledgements This research was supported by BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347).
Conflict of interest statement None declared.
References 1. Wang H, Dwyer-Lindgren L, Lofgren KT, et al. Age-specific and sex-specific mortality in 187 countries, 1970 – 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380: 2071– 94. 2. Salomon JA, Wang H, Freeman MK, et al. Healthy life expectancy for 187 countries, 1990 – 2010: a systematic analysis for the Global Burden Disease Study 2010. Lancet 2012;380:2144–62. 3. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2095–128. 4. Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990 – 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2163–96. 5. OECD. Society at a glance 2011: OECD Social Indicators. Paris: OECD Publishing 2011. 6. Davey Smith G, Ebrahim S. Epidemiology—is it time to call it a day? Int J Epidemiol 2001;30:1–11. 7. Smith GD. The uses of ‘Uses of epidemiology’. Int J Epidemiol 2001;30:1146– 55. 8. Alemi F, Erdman H, Griva I, Evans CH. Improved statistical methods are needed to advance personalized medicine. Open Transl Med J 2009;1:16– 20. 9. Shah RR, Shah DR. Personalized medicine: is it a pharmacogenetic mirage? Br J Clin Pharmacol 2012;74:698–721. 10. Abrahams E, Ginsburg GS, Silver M. The Personalized Medicine Coalition: goals and strategies. Am J Pharmacogenomics 2005;5:345– 55. 11. Kang D. Personalized Treatment, Personalized Prevention. The Seoul Shinmun. Seoul: The Seoul Shinmun Publishing Co. 2011. 12. Lee SS, Mudaliar A. Medicine. Racing forward: the Genomics and Personalized Medicine Act. Science 2009;323:342. 13. Pricewaterhouse Coopers LLP. The New Science of Personalized Medicine: Translating the Promise into Practice. Pricewaterhouse Coopers LLP 2009. http://www.pwc.com/us/en/healthcare/publications/ personalized-medicine.jhtml (15 July 2014, date last accessed). 14. Pashayan N, Hall A, Chowdhury S, Dent T, Pharoah PDP, Burton H. Public health genomics and personalized prevention: lessons from the COGS project. J Intern Med 2013;274:451– 6. 15. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011;8:184 –7.
Downloaded from http://jjco.oxfordjournals.org/ at University of California, San Diego on January 15, 2015
Adopted and modified from ref. (72).
design, testing validity and reliability in preventive trials, to evaluating and applying using prediction models. When improved computing skills and statistics allow for a deeper understanding of precisely measured environmental factors coupled with genetic factors, preventive medicine will move toward the next level—personalized preventive medicine that caters to each individual. In epidemiologic studies, the application of tailored preventive measures is still in its nascence, and the accumulation of each study is imperative.
Jpn J Clin Oncol 2014;44(11)
39. Hogarth S, Javitt G, Melzer D. The current landscape for directto-consumer genetic testing: legal, ethical, and policy issues. Annu Rev Genomics Hum Genet 2008;9:161 –82. 40. McGuire AL, Cho MK, McGuire SE, Caulfield T. Medicine. The future of personal genomics. Science 2007;317:1687. 41. Hunter DJ, Khoury MJ, Drazen JM. Letting the genome out of the bottle—will we get our wish? N Engl J Med 2008;358:105 –7. 42. The FDA and me. Nature 2013;504:7 – 8. http://www.nature.com/ polopoly_fs/1.14289!/menu/main/topColumns/topLeftColumn/pdf/ 504007b.pdf (15 July 2014, date last accessed). 43. Kupferschmidt K. DNA testing company won’t offer health information anymore. Science: AAAS 2013. http://news.sciencemag.org/biology/2013/ 12/dna-testing-company-wont-offer-health-information-anymore (15 July 2014, date last accessed). 44. Matloff E, Caplan A. Direct to confusion: lessons learned from marketing BRCA testing. Am J Bioeth 2008;8:5 –8. 45. Phillips KA. Closing the evidence gap in the use of emerging testing technologies in clinical practice. JAMA 2008;300:2542– 4. 46. Khoury MJ. Dealing with the evidence dilemma in genomics and personalized medicine. Clin Pharmacol Ther 2010;87:635– 8. 47. Teutsch SM, Bradley LA, Palomaki GE, et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet Med 2009;11:3–14. 48. Potter JD. Toward the last cohort. Cancer Epidemiol Biomarkers Prev 2004;13:895–7. 49. Hattori M, Taylor TD. The human intestinal microbiome: a new frontier of human biology. DNA Res 2009;16:1–12. 50. Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59–65. 51. Anastasopoulou P, Tubic M, Schmidt S, Neumann R, Woll A, Hartel S. Validation and comparison of two methods to assess human energy expenditure during free-living activities. PLoS One 2014;9:e90606. 52. Alsaadi SM, McAuley JH, Hush JM, et al. Assessing sleep disturbance in low back pain: the validity of portable instruments. PLoS One 2014;9:e95824. 53. Ng PC, Murray SS, Levy S, Venter JC. An agenda for personalized medicine. Nature 2009;461:724–6. 54. Barbour V. UK Biobank: a project in search of a protocol? Lancet 2003;361:1734– 8. 55. Chen Z, Chen J, Collins R, et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol 2011;40:1652 –66. 56. Song M, Rolland B, Potter JD, Kang D. Asia Cohort Consortium: challenges for collaborative research. J Epidemiol 2012;22:287– 90. 57. Boffetta P, McLerran D, Chen Y, et al. Body mass index and diabetes in Asia: a cross-sectional pooled analysis of 900,000 individuals in the Asia cohort consortium. PLoS One 2011;6:e19930. 58. Rolland B, Smith BR, Potter JD. Coordinating centers in cancer epidemiology research: the Asia Cohort Consortium coordinating center. Cancer Epidemiol Biomarkers Prev 2011;20:2115 –9. 59. Boffetta P, Hazelton WD, Chen Y, et al. Body mass, tobacco smoking, alcohol drinking and risk of cancer of the small intestine—a pooled analysis of over 500,000 subjects in the Asia Cohort Consortium. Ann Oncol 2012;23:1894– 8. 60. Lin Y, Fu R, Grant E, et al. Association of body mass index and risk of death from pancreatic cancer in Asians: findings from the Asia Cohort Consortium. Eur J Cancer Prev 2013;22:244– 50. 61. Lee JE, McLerran DF, Rolland B, et al. Meat intake and cause-specific mortality: a pooled analysis of Asian prospective cohort studies. Am J Clin Nutr 2013;98:1032– 41. 62. Chen Y, Copeland WK, Vedanthan R, et al. Association between body mass index and cardiovascular disease mortality in east Asians and south Asians: pooled analysis of prospective data from the Asia Cohort Consortium. BMJ 2013;347:f5446. 63. Zheng W, McLerran DF, Rolland B, et al. Association between body-mass index and risk of death in more than 1 million Asians. N Engl J Med 2011;364:719– 29. 64. Maxwell KN, Nathanson KL. Common breast cancer risk variants in the post-COGS era: a comprehensive review. Breast Cancer Res 2013;15:212. 65. Couch FJ, Nathanson KL, Offit K. Two decades after BRCA: setting paradigms in personalized cancer care and prevention. Science 2014;343:1466– 70. 66. Lindi VI, Uusitupa MI, Lindstrom J, et al. Association of the Pro12Ala polymorphism in the PPAR-gamma2 gene with 3-year incidence of type
Downloaded from http://jjco.oxfordjournals.org/ at University of California, San Diego on January 15, 2015
16. Umar A, Dunn BK, Greenwald P. Future directions in cancer prevention. Nat Rev Cancer 2012;12:835–48. 17. Kulasingam V, Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol 2008;5:588 –99. 18. Honda K, Ono M, Shitashige M, et al. Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine. Jpn J Clin Oncol 2013;43:103– 9. 19. Chung S, Park SK, Sung H, et al. Association between chronological change of reproductive factors and breast cancer risk defined by hormone receptor status: results from the Seoul Breast Cancer Study. Breast Cancer Res Treat 2013;140:557–65. 20. Lee KM, Abel J, Ko Y, et al. Genetic polymorphisms of cytochrome P450 19 and 1B1, alcohol use, and breast cancer risk in Korean women. Br J Cancer 2003;88:675–8. 21. Choi JY, Abel J, Neuhaus T, et al. Role of alcohol and genetic polymorphisms of CYP2E1 and ALDH2 in breast cancer development. Pharmacogenetics 2003;13:67– 72. 22. Park SK, Yim DS, Yoon KS, et al. Combined effect of GSTM1, GSTT1, and COMT genotypes in individual breast cancer risk. Breast Cancer Res Treat 2004;88:55– 62. 23. Kim HC, Lee JY, Sung H, et al. A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study. Breast Cancer Res 2012;14:R56. 24. Li L, Lee KM, Han W, et al. Estrogen and progesterone receptor status affect genome-wide DNA methylation profile in breast cancer. Hum Mol Genet 2010;19:4273 –7. 25. Sung H, Jeon S, Lee KM, et al. Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival. BMC Cancer 2012;12:195. 26. Park B, Ma SH, Shin A, et al. Korean risk assessment model for breast cancer risk prediction. PLoS One 2013;8:e76736. 27. Choi JY, Lee KM, Noh DY, et al. Genetic polymorphisms of eNOS, hormone receptor status, and survival of breast cancer. Breast Cancer Res Treat 2006;100:213 –8. 28. Sung H, Choi JY, Lee SA, et al. The association between the preoperative serum levels of lipocalin-2 and matrix metalloproteinase-9 (MMP-9) and prognosis of breast cancer. BMC Cancer 2012;12:193. 29. Song N, Sung H, Choi JY, et al. Preoperative serum levels of matrix metalloproteinase-2 (MMP-2) and survival of breast cancer among Korean women. Cancer Epidemiol Biomarkers Prev 2012;21:1371 –80. 30. Garber JE, Offit K. Hereditary cancer predisposition syndromes. J Clin Oncol 2005;23:276– 92. 31. Lindor NM, McMaster ML, Lindor CJ, Greene MH, National Cancer Institute DoCPCO, Prevention Trials Research Group. Concise handbook of familial cancer susceptibility syndromes—second edition. J Natl Cancer Inst Monogr 2008;2008:3–93. 32. Hirasawa A, Masuda K, Akahane T, et al. Experience of risk-reducing salpingo-oophorectomy for a BRCA1 mutation carrier and establishment of a system performing a preventive surgery for hereditary breast and ovarian cancer syndrome in Japan: our challenges for the future. Jpn J Clin Oncol 2013;43:515– 9. 33. Schindler L, Kerrigan D, Kelly J, Hollen B. Understanding Cancer Series: Gene Testing. National Cancer Institute 2005. http:// www.cancer.gov/cancertopics/understandingcancer/genetesting (15 July 2014, date last accessed). 34. Robson ME, Storm CD, Weitzel J, Wollins DS, Offit K. American Society of Clinical O. American Society of Clinical Oncology policy statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol 2010;28:893–901. 35. Genetic Information Nondiscrimination Act (GINA) of 2008. National Human Genome Research Institute. http://www.genome.gov/24519851 (15 July 2014, date last accessed). 36. Richter ED, Goldsmith J. The IARC classification system: input, internal logic, output, and impact. Am J Ind Med 1991;19:385–97. 37. Nelson HD, Pappas M, Zakher B, Mitchell JP, Okinaka-Hu L, Fu R. Risk assessment, genetic counseling, and genetic testing for BRCA-related cancer in women: a systematic review to update the U.S. Preventive Services Task Force Recommendation. Ann Intern Med 2014;160:255–66. 38. Khoury MJ, Bedrosian SR, Gwinn M, Little J, Higgins JPT, Ioannidis JPA. Human genome epidemiology: the road map revisited. In: Khoury MJ, Bedrosian SR, Grwinn M, Higgins JPT, Ioannidis JPA, Little J, editors. Human Genome Epidemiology. 2nd edn. New York: Oxford University Press 2010;3– 12.
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2 diabetes and body weight change in the Finnish Diabetes Prevention Study. Diabetes 2002;51:2581 –6. 67. Kilpelainen TO, Lakka TA, Laaksonen DE, et al. Interaction of single nucleotide polymorphisms in ADRB2, ADRB3, TNF, IL6, IGF1R, LIPC, LEPR, and GHRL with physical activity on the risk of type 2 diabetes mellitus and changes in characteristics of the metabolic syndrome: the Finnish Diabetes Prevention Study. Metabolism 2008;57:428– 36. 68. Xu M, Qi Q, Liang J, et al. Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation 2013;127:1283–9.
69. Reiman EM, Langbaum JB, Fleisher AS, et al. Alzheimer’s Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J Alzheimers Dis 2011;26:321–9. 70. Pu X, Ye Y, Wu X. Development and validation of risk models and molecular diagnostics to permit personalized management of cancer. Cancer 2014;120:11– 9. 71. Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med 2010;362:986 –93. 72. Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer 2009;34.
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Review Article
The Potential Application of Personalized Preventive Research Minkyo Song1,2,3, Hwi-Won Lee1,2 and Daehee Kang1,2,4,* 1
Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, 2Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 3Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul and 4Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
Received May 14, 2014; accepted August 13, 2014
With increases in life expectancy, the focus has shifted to living a healthier, longer life. By concentrating on preventing diseases before occurrence, researchers aim to diminish the increasing gap in medical costs and health inequalities prevalent across many nations. Although we have entered an era of post-genomics, we are still in infancy in terms of personalized preventive research. Personalized preventive research has and will continue to improve with advancements in the use of biomarkers and risk assessment. More evidence based on well-designed epidemiologic studies is required to provide comprehensive preventive medical care based on genetic and non-genetic profile data. The realization of personalized preventive research requires building of evidence through appropriate methodology, verification of results through translational studies as well as development and application of prediction models. Key words: preventive medicine – personalized medicine – epidemiology – environmental medicine
INTRODUCTION The paradigm of health is shifting from simply ‘living a longer life’ to ‘living a healthier and longer life.’ Life expectancy has increased .10 years within the short period of 1970 – 2010 (1). However, longer life-expectancy did not guarantee a healthy-longer life. The increase in healthy life expectancy (HALE), or the average number of years that a person can expect to live in full health, is smaller in its magnitude than the increase in life expectancy (2). In fact, a 1-year increase in life expectancy is associated with a 10-month increase in HALE. The reasons for such a phenomenon can be described by slow improvements in nonfatal disease or injury in comparison with reductions in mortality, and is especially associated with chronic diseases (3,4). Chronic diseases, ranging from less lethal diseases such as hypertension and diabetes to stroke and cancer, all fit in the same category. These chronic diseases consume the affected with many inconveniences in their everyday lives as well as the accompanying medical attention.
Chronic diseases cause great loss at a national level as well as at a personal level. It directly translates into longer duration of care needed, meaning higher medical costs and poorer function of the individual. Nationally, this means rising medical costs and health inequalities, further pronounced with improvements in medicine and related fields. For instance, the average Organisation for Economic Co-operation and Development health expenditure per capita grew annually by 4% in the last decade (5). With many nations entering the aging and aged society, rise in chronic diseases is inevitable and is presumed to be a persistent problem as a national and personal agenda. Thus, the rational solution is to unravel the problem beforehand, at its causal origin, by preventing diseases before occurrence.
THE CONCEPT OF PERSONALIZED PREVENTIVE RESEARCH The field of medicine has evolved since the age of epidemics from the age of complex diseases to the current age of post-
# The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
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*For reprints and all correspondence: Daehee Kang, Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea. E-mail: [email protected]
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Figure 1. Evolution of the concept of disease and corresponding trends in medicine.
Figure 2. Three target domains of the conventional prevention pyramid.
When conducting PPR, all domains of classic preventive medicine may be considered. Primary prevention, which targets the general population who are thought to be disease free, identifies high-risk groups that are likely to develop diseases in order to prevent disease occurrence and to promote healthy lifestyles. Prevention at this level may involve behavior modification based on the person’s susceptibility to his or her risk of developing a disease. Secondary prevention refers to the state of subclinical disease where the goal is to stop or reduce the progress of disease in their earliest stages through early detection. Screening and monitoring, and targeting highrisk individuals, precisely defined by the use of information that combines genetics and other lifestyle factors before the clinical development of disease, are the purpose of secondary prevention. Tertiary prevention refers to the diagnosed state, and the goal lies in reducing further complications or recurrence of diseases (Fig. 2). Similarly, this can be achieved through the application of lifestyle modifications along with the information based on genetic susceptibility to drugs used (pharmacogenetics). The risk of disease may decrease as the mode of prevention moves downward from the apex (tertiary) to the base (primary) of the pyramid. Nevertheless, the overall benefit in the burden of public health perspective will be greater as preventive measures are applied to a larger population.
CASE STUDIES OF PPR At this stage of the post-genomic era, personalized prevention remains in its infancy. In early days, PPR improved with the
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genomics (Fig. 1) (6,7). In the age of epidemics, the control of pathogens through various means was the main treatment and prevention modality. Entering the age of complex diseases, the occurrence of various diseases was understood as multiple and complex factors related to living conditions or personal habits, and the distinction between treatment and prevention in the field of medicine became clearer. During this era, medicine was an average care empirically derived from trial and error experience of prior practice or observations. The growth of genetics research and completion of the Human Genome Project have introduced the concept of genetic susceptibility and gene – environment interaction to the age of postgenomics. Furthermore, the emphasis on efficacy and safety based on evidence, together with the increased understanding of the complex concept of genetic susceptibility, has led to the development of personalized medicine (8,9). Personalized medicine entails customized healthcare employed on people considered to be at high risk for certain diseases (10). The significance of personalized medicine is widely recognized, as governments have taken action to directly or indirectly support personalized medicine (11). For instance, long before his tenure, the US President Barack Obama has voiced support for personalized medicine by introducing the Genomics and Personalized Medicine Act in 2007 (12). According to a report published in 2009 by PricewaterhouseCoopers LLP, the market for personalized medicine in the USA is projected to grow from $232 billion to as much as $452 billion by 2015 (13). As previously noted, medical and healthcare models are rightfully evolving from simply ‘treating the ill’ to ‘aiding the healthy to maintain their good health.’ And this is where the objective of personalized preventive research (PPR) lies. Personalized prevention may be understood as a ‘stratified prevention’ conducted at a personal level (14). It aims to extend the healthy lifespan of individuals by exploring disease etiology on an individual basis as well as by providing personalized means to maintain wellness in healthy people and prevent disease occurrence in high-risk individuals by integrating the use of genetic, protein- and environment-related information (15,16). In the spectrum of individualized medicine, PPR requires that the target population is clearly defined and the factors attributable to the selected subjects’ health status are based on reliable evidence established upon information that is accurate and detailed.
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development of molecular epidemiology, which focused on biomarkers (17,18). Risk assessment has improved, and must continue to improve, with the use of biomarkers as illustrated in Fig. 3. The initial stage describes the discovery and selection of appropriate biomarkers in the areas of genomics, epigenomics, transcriptomics, metabolomics, nutrigenomics, pharmacogenomics, toxicogenomics and proteomics. In the following stage, the selected biomarkers are evaluated within the appropriate epidemiologic research settings to see whether the use of biomarkers on relevant target groups of healthy, high-risk and diagnosed individuals in primary to tertiary prevention levels are valid. In the last stage, indicators identified from target groups are applied to create relevant prediction models. THE SEOUL BREAST CANCER STUDY In the field of epidemiology, phases of biomarker discovery and validation using an epidemiologic design often occur concurrently, as in the example of the Seoul Breast Cancer Study (SeBCS). To briefly explain, the SeBCS is a case–control study with subjects recruited from 2001 to 2007, which include 4040 histologically confirmed incident breast cancer cases and 3946 non-cancer controls from 4 major teaching hospitals and community health screening programs in Seoul (19). In the SeBCS, several studies on identifying high-risk group have been conducted (Table 1), ranging from early genomic studies focused on candidate genes and single nucleotide polymorphisms (SNPs) (20 – 22) to later genomewide association studies (GWAS) (23). Also, studies have been conducted using a wide variety of biomarkers such as
methylation (24) and microRNA (25) in transcriptomics. These endeavors have led to the construction of a breast cancer risk prediction model tailored for Korean women (26). In the practice of tertiary prevention, research on disease prognosis and survival is essential, and studies on genomics (27) as well as proteomics (28,29) have been conducted using the SeBCS data. A CLINICAL APPLICATION OF GENETIC COUNSELING Eventually, genetic counseling will be one area in personalized preventive care in practice. Accompanied by much controversy, the use of genetic counseling is still considered hasty. Genetic counseling based on genetic testing has so far been established as being useful for high-penetrance hereditary diseases (30 – 32). Genetic testing provides several benefits, such as a sense of relief and fewer checkups or routine tests for those with negative results. Even people with positive results may avoid uncertain fears and be ready for informed decisions, or even have a chance to intervene to reduce risk (33). However, genetic tests simply determine whether or not possible mutations of the genes are present, and do not guarantee development of the disease. Furthermore, even if the mutations are present and the disease develops, these may go undetected throughout the person’s lifetime. Thus, caution must be taken when calculating, interpreting and counseling on the probability of developing a disease. The considerable concern in genetic counseling is that most genetic tests do not have corresponding preventive strategies. Without adequate countermeasures, there may only be potential harms. One concern is a psychological effect on the
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Figure 3. Steps in discovering biomarkers in molecular epidemiology for personalized preventive research (PPR).
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Table 1. Examples of biomarkers discovered in association with breast cancer risk from selected studies from the Seoul Breast Cancer Study Biomarkers
Indicators of high-risk group
Indicators of prognosis and survival OR (95% CI)
Ref.a
CYP19 Arg(264)C
1.5 (1.10– 2.20)
(14)
CYP2E1 c2 allele
1.9 (0.99– 3.83)
(15)
COMT, GSTM1, GSTT1
4.1 (1.40– 12.70)
(16)
rs13393577 at 2q34
1.53 (1.37–1.70)
(18)
Hyper-
FAM124B, ST6GALNAC1
FDR , 0.05
(19)
Hypo-
NAV1, PER1 AGO2 rs11786030
2.62 (1.41–4.88)
(20)
AGO2 RS2292779
2.94 (1.52–5.69)
HR (95% CI)
Ref.a
Genomics SNP
GWAS
2.1 (1.03– 4.33)
(22)
Lipocalin-2
3.17 (1.66– 6.06)
(23)
MMP-9
5.36 (2.18– 13.20)
MMP-2
2.75 (1.32– 5.73)
Transcriptomics Methylation
MicroRNA
Proteomics
(24)
FDR, false discovery rate; HR, Hazard Ratio; OR, Odds Ratio. a Indicates article reference numbers.
person, such as the fear of suffering a serious disease in the future. Genetic discrimination by employers and health insurance companies may be another problem. Although the law of the Genetic Information Nondiscrimination Act has been established in 2008, it does not warrant, at this time, genetic discrimination in all circumstances, including the use of genetic information on the provision of life insurance, disability insurance or long-term care insurance (34,35). Furthermore, the decision to receive genetic testing should be voluntary. Consequently, appropriate genetic counseling requires accurate and clinically useful information derived from welldesigned high-quality research. Counseling, similar to other clinical decision-makings, weighs the benefits and harms. Thus, agencies such as the International Agency for Research on Cancer (IARC) evaluate scientific evidence and judge carcinogenicity regarding environmental factors and cancer risk (36). However, regarding genetic factors, only limited evidence exists, and so far there are no agencies that focus on evaluating and providing evidence for genetic – disease association. Agencies such as the US Preventive Services Task Force (USPSTF) may provide with few guidelines for limited high-penetrance genetic diseases such as the recent recommendation for the primary care providers to screen women with a family history of breast, ovarian, tubal or peritoneal cancer to identify potential mutations in breast cancer susceptibility genes (37). Another example of the current status in the application of personalized prevention can be found in ‘direct-to-consumer’
(DTC) companies. DTC aims to provide individuals with information on improving health and preventing diseases based on personal genome-wide profiles (38,39). Some of the concerns regarding DTC include the lack of evidence of the relationship between genetic variations and human diseases as well as unknown, potential benefits and harms (39 – 41). In fact, in the case of 23andMe, the Food and Drug Administration’s (FDA) directive for the genetic testing company to discontinue new consumer access on personal genetic information stemmed from the regulator’s concern over misinterpretation and/or misuse of personal information beyond the uses of research and education (42,43). DTC companies allow consumers to access the information without communicating with the intermediary healthcare providers, thus the lack of professional counseling by medical doctors may also pose other problems such as inappropriate utilization of healthcare resources (44), inadequate counseling and informed consent, as well as the concerns over safety, effectiveness and risks associated with the tests with uncertain clinical utility (34). Thus, it is of utmost importance to generate and deliver the exact evidence in terms of benefit and harm to help individuals make decisions. Moreover, in terms of personalized prevention, even accurate information may yield different decisions depending on one’s personal values. Some may choose the opportunity to prevent premature cancer death and make radical choices such as mastectomy, while others may choose to take the risk and keep their breasts.
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eNOS-786C
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high BMI in Asians when compared with Europeans, remains to be fully explained in both genetic and non-genetic terms. It challenges the concept of universal standards while reinforcing the drive for personalized research. Secondly, the observed results must be verified in terms of efficacy when applied as a preventive measure. With breast cancer, for example, researchers have identified common low penetrance alleles associated with ,1.5-fold increases in the risk through GWAS (64). Yet little is known about the relevance of these risk factors to the different molecular subtypes of breast cancer (65). Hence, there needs to be a bridging translational study to apply these research results into a clinical setting or to the general population. In such aspect, preventive trials based on results from the general population and high-risk group may offer the potential for personalized risk estimates and preventive measures (65). A number of Northern European studies, including the Finnish Diabetes Prevention Study, focused on diabetes and lifestyle interventions in a multicenter, randomized controlled trial (RCT) setting (66 – 68). In the 2002 study, the risk for type 2 diabetes increased by an odds ratio of 2.11 (95% CI 1.20 – 3.72) for subjects with the Ala12 allele compared with those with the Pro12Pro genotype (66). However, among subjects who followed an intensive, combined intervention program of diet and physical activity, those with the Ala12Ala genotype yielded improved insulin sensitivity to some extent. By implementing genetic profiles, the researchers were able to elucidate the effect of genetic variation according to the magnitude of benefits of physical activity and dietary intervention. Another example, which incorporates genetic information in preventive trials, is the Alzheimer’s Prevention Initiative, an international collaborative project focused on finding effective presymptomatic Alzheimer’s disease (AD) treatment that could reduce the symptoms or possibly prevent them completely (69). Based on the amyloid hypothesis explaining the accumulation of toxic Abeta to be the cause of the disease, the study plans to target high-risk yet cognitively normal people, who are AD-causing presenilin I [PS1] mutation carriers and apolipoprotein E (APOE) e 4 homozygotes, for intervention using amyloid-modifying treatments. Thirdly, development and application of prediction models are indispensable in all stages of personalized prevention, from screening and diagnosis to therapeutic purposes. The prediction model is a comprehensive way of interpreting and predicting the disease outcome using available information through mathematical modeling. However, there are many challenges in developing prediction models in order to apply them to target populations. The first step in the development stage is choosing the design of the study (Table 2). Researchers must consider the strengths and limitations of the study design when developing prediction models. Prediction is longitudinal in nature, thus prospective studies are often used to develop a model. However, retrospective studies such as case – control studies can also be used for their feasibility and simplicity. Choosing a well-defined target population in the process is also imperative in order to develop a valid model.
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The proper choices related to personalized preventive measures are still based on limited study results. The gap between the genomics technology and the application in practice has compelled many scientists to become aware of the need for evidenced research (45 – 47). What necessary conditions must be met for the suitable application of PPR? First and foremost, the choice of methodology in evidencebased research may provide some understanding. An appropriate study design is imperative in research. In observational epidemiology, a cohort study ranks the highest in the hierarchy of evidence; a cohort study allows a clearer explanation of the study outcome and disease etiology, as it is free from the limitations of reverse causality. Moreover, testing hypotheses for gene – environment interactions has rendered the need for larger sized cohorts to fulfill the necessary statistical power (48). Furthermore, studies at present and in the future will integrate more precise means in measuring the individual’s genetic and environmental data. Methods include next generation sequencing that measures the entire genetic data of an individual or gut microbiome-derived metagenomics that provides genetic information on a person’s microflora (49,50), as well as electronic smart devices that measure various biosignals such as physical activity and sleep (51,52), which in turn will require more statistical power. Hence, a welldesigned, large population-based genomic cohort, free from the temporal interplay of cause and outcome, is a preferable solution to PPR (53). The importance of large-scale cohort studies is being widely recognized as many nations participate in large, multi-network of data collection. In the new millennium, many large cohorts such as the UK Biobank and the China Kadoorie Biobank, each with about half a million registered subjects, have been established, and various consortia are being formed by the integration of smaller scale studies (54,55). Through these large-scale prospective cohorts, we can fill the knowledge gap between understanding the complex interplay of genes and environment as in gene – environment interactions, gene – gene interactions and environment – environment interactions in the development of diseases. Merging various cohorts into an international collaboration is one way to help overcome the aforementioned limitations of sizable design. Asia Cohort Consortium (ACC) is a product of an international collaborative project involving 50 active members and .1 million healthy cohort subjects from Pacific Rim nations (56). ACC focuses on important epidemiologic risk factors in the Asian population and has published important results including the association between total mortality and cause-specific mortality (57 – 62). The U-shaped relationship between BMI and risks of death was most prominent among East Asians by 1.5 times among subjects who had a high BMI (.35.0 kg/m2) and 2.8 times greater among subjects with a very low BMI (15.0 kg/m2) (63). The marked risk of death associated with a low BMI, rather than with a
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Table 2. Study designs for predictive modeling Study design
Strengths
Limitations
Case–control
Simple, low cost
Comparability of controls is critical
Prospective cohort
Multiple outcomes Possible repeated exposure measurements
High cost Longer follow-up period
Randomized controlled trial
Well-defined selection of subjects
Poor generalizability due to stringent in- and exclusion criteria
Furthermore, selection and coding of the variables and specification of the models are important in building the models. The majority of models developed so far have moderate discriminatory powers (70). With the evolution of genetic epidemiology, prediction models incorporating SNPs and GWAS-identified SNPs were developed. But these have shown less promising results, rendering only a moderate improvement in the model’s discriminatory power that was lower than adding the intermediate phenotypic and molecular biomarkers. For instance, Wacholder et al. (71) had gained an AUC of 0.038 from 0.580 by adding 10 published GWAS-identified SNPs to the Gail model with 4 traditional risk factors. It is thus important to incorporate multiple layers of information including genetic, environmental and clinical information to enhance the performance of the predictive ability. Moreover, validation of the developed model is also important. An internal validation of the model is also critical because overfitting is the central problem, especially when much information, including genetic, environmental and clinical factors, is to be used to construct a model. Furthermore, external validation to ensure generalizability is a critical step in verifying the applicability of the model in a different population (72). In addition to acknowledging and overcoming the aforementioned challenges, future strategies in developing predictive models should be an inclusion of a more flexible and dynamic model that reflects the changeable factors such as change in risk behaviors or epigenetic information. This may require vast amount of data as well as a more delicate and complex statistical analysis. A web-based tool in the developmental stage may be one way to overcome this problem.
CONCLUSION Progress in medicine continues today. Moreover, technological advances have also helped increase our knowledge of the molecular mechanisms involved in a host of biological activities related to normal and diseased status of human functions. However, we are still in the beginning stage of realizing personalized prevention. Many preconditions are to be met for its successful implementation, from choosing the right study
Acknowledgements This research was supported by BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347).
Conflict of interest statement None declared.
References 1. Wang H, Dwyer-Lindgren L, Lofgren KT, et al. Age-specific and sex-specific mortality in 187 countries, 1970 – 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380: 2071– 94. 2. Salomon JA, Wang H, Freeman MK, et al. Healthy life expectancy for 187 countries, 1990 – 2010: a systematic analysis for the Global Burden Disease Study 2010. Lancet 2012;380:2144–62. 3. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2095–128. 4. Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990 – 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2163–96. 5. OECD. Society at a glance 2011: OECD Social Indicators. Paris: OECD Publishing 2011. 6. Davey Smith G, Ebrahim S. Epidemiology—is it time to call it a day? Int J Epidemiol 2001;30:1–11. 7. Smith GD. The uses of ‘Uses of epidemiology’. Int J Epidemiol 2001;30:1146– 55. 8. Alemi F, Erdman H, Griva I, Evans CH. Improved statistical methods are needed to advance personalized medicine. Open Transl Med J 2009;1:16– 20. 9. Shah RR, Shah DR. Personalized medicine: is it a pharmacogenetic mirage? Br J Clin Pharmacol 2012;74:698–721. 10. Abrahams E, Ginsburg GS, Silver M. The Personalized Medicine Coalition: goals and strategies. Am J Pharmacogenomics 2005;5:345– 55. 11. Kang D. Personalized Treatment, Personalized Prevention. The Seoul Shinmun. Seoul: The Seoul Shinmun Publishing Co. 2011. 12. Lee SS, Mudaliar A. Medicine. Racing forward: the Genomics and Personalized Medicine Act. Science 2009;323:342. 13. Pricewaterhouse Coopers LLP. The New Science of Personalized Medicine: Translating the Promise into Practice. Pricewaterhouse Coopers LLP 2009. http://www.pwc.com/us/en/healthcare/publications/ personalized-medicine.jhtml (15 July 2014, date last accessed). 14. Pashayan N, Hall A, Chowdhury S, Dent T, Pharoah PDP, Burton H. Public health genomics and personalized prevention: lessons from the COGS project. J Intern Med 2013;274:451– 6. 15. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011;8:184 –7.
Downloaded from http://jjco.oxfordjournals.org/ at University of California, San Diego on January 15, 2015
Adopted and modified from ref. (72).
design, testing validity and reliability in preventive trials, to evaluating and applying using prediction models. When improved computing skills and statistics allow for a deeper understanding of precisely measured environmental factors coupled with genetic factors, preventive medicine will move toward the next level—personalized preventive medicine that caters to each individual. In epidemiologic studies, the application of tailored preventive measures is still in its nascence, and the accumulation of each study is imperative.
Jpn J Clin Oncol 2014;44(11)
39. Hogarth S, Javitt G, Melzer D. The current landscape for directto-consumer genetic testing: legal, ethical, and policy issues. Annu Rev Genomics Hum Genet 2008;9:161 –82. 40. McGuire AL, Cho MK, McGuire SE, Caulfield T. Medicine. The future of personal genomics. Science 2007;317:1687. 41. Hunter DJ, Khoury MJ, Drazen JM. Letting the genome out of the bottle—will we get our wish? N Engl J Med 2008;358:105 –7. 42. The FDA and me. Nature 2013;504:7 – 8. http://www.nature.com/ polopoly_fs/1.14289!/menu/main/topColumns/topLeftColumn/pdf/ 504007b.pdf (15 July 2014, date last accessed). 43. Kupferschmidt K. DNA testing company won’t offer health information anymore. Science: AAAS 2013. http://news.sciencemag.org/biology/2013/ 12/dna-testing-company-wont-offer-health-information-anymore (15 July 2014, date last accessed). 44. Matloff E, Caplan A. Direct to confusion: lessons learned from marketing BRCA testing. Am J Bioeth 2008;8:5 –8. 45. Phillips KA. Closing the evidence gap in the use of emerging testing technologies in clinical practice. JAMA 2008;300:2542– 4. 46. Khoury MJ. Dealing with the evidence dilemma in genomics and personalized medicine. Clin Pharmacol Ther 2010;87:635– 8. 47. Teutsch SM, Bradley LA, Palomaki GE, et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet Med 2009;11:3–14. 48. Potter JD. Toward the last cohort. Cancer Epidemiol Biomarkers Prev 2004;13:895–7. 49. Hattori M, Taylor TD. The human intestinal microbiome: a new frontier of human biology. DNA Res 2009;16:1–12. 50. Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59–65. 51. Anastasopoulou P, Tubic M, Schmidt S, Neumann R, Woll A, Hartel S. Validation and comparison of two methods to assess human energy expenditure during free-living activities. PLoS One 2014;9:e90606. 52. Alsaadi SM, McAuley JH, Hush JM, et al. Assessing sleep disturbance in low back pain: the validity of portable instruments. PLoS One 2014;9:e95824. 53. Ng PC, Murray SS, Levy S, Venter JC. An agenda for personalized medicine. Nature 2009;461:724–6. 54. Barbour V. UK Biobank: a project in search of a protocol? Lancet 2003;361:1734– 8. 55. Chen Z, Chen J, Collins R, et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol 2011;40:1652 –66. 56. Song M, Rolland B, Potter JD, Kang D. Asia Cohort Consortium: challenges for collaborative research. J Epidemiol 2012;22:287– 90. 57. Boffetta P, McLerran D, Chen Y, et al. Body mass index and diabetes in Asia: a cross-sectional pooled analysis of 900,000 individuals in the Asia cohort consortium. PLoS One 2011;6:e19930. 58. Rolland B, Smith BR, Potter JD. Coordinating centers in cancer epidemiology research: the Asia Cohort Consortium coordinating center. Cancer Epidemiol Biomarkers Prev 2011;20:2115 –9. 59. Boffetta P, Hazelton WD, Chen Y, et al. Body mass, tobacco smoking, alcohol drinking and risk of cancer of the small intestine—a pooled analysis of over 500,000 subjects in the Asia Cohort Consortium. Ann Oncol 2012;23:1894– 8. 60. Lin Y, Fu R, Grant E, et al. Association of body mass index and risk of death from pancreatic cancer in Asians: findings from the Asia Cohort Consortium. Eur J Cancer Prev 2013;22:244– 50. 61. Lee JE, McLerran DF, Rolland B, et al. Meat intake and cause-specific mortality: a pooled analysis of Asian prospective cohort studies. Am J Clin Nutr 2013;98:1032– 41. 62. Chen Y, Copeland WK, Vedanthan R, et al. Association between body mass index and cardiovascular disease mortality in east Asians and south Asians: pooled analysis of prospective data from the Asia Cohort Consortium. BMJ 2013;347:f5446. 63. Zheng W, McLerran DF, Rolland B, et al. Association between body-mass index and risk of death in more than 1 million Asians. N Engl J Med 2011;364:719– 29. 64. Maxwell KN, Nathanson KL. Common breast cancer risk variants in the post-COGS era: a comprehensive review. Breast Cancer Res 2013;15:212. 65. Couch FJ, Nathanson KL, Offit K. Two decades after BRCA: setting paradigms in personalized cancer care and prevention. Science 2014;343:1466– 70. 66. Lindi VI, Uusitupa MI, Lindstrom J, et al. Association of the Pro12Ala polymorphism in the PPAR-gamma2 gene with 3-year incidence of type
Downloaded from http://jjco.oxfordjournals.org/ at University of California, San Diego on January 15, 2015
16. Umar A, Dunn BK, Greenwald P. Future directions in cancer prevention. Nat Rev Cancer 2012;12:835–48. 17. Kulasingam V, Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol 2008;5:588 –99. 18. Honda K, Ono M, Shitashige M, et al. Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine. Jpn J Clin Oncol 2013;43:103– 9. 19. Chung S, Park SK, Sung H, et al. Association between chronological change of reproductive factors and breast cancer risk defined by hormone receptor status: results from the Seoul Breast Cancer Study. Breast Cancer Res Treat 2013;140:557–65. 20. Lee KM, Abel J, Ko Y, et al. Genetic polymorphisms of cytochrome P450 19 and 1B1, alcohol use, and breast cancer risk in Korean women. Br J Cancer 2003;88:675–8. 21. Choi JY, Abel J, Neuhaus T, et al. Role of alcohol and genetic polymorphisms of CYP2E1 and ALDH2 in breast cancer development. Pharmacogenetics 2003;13:67– 72. 22. Park SK, Yim DS, Yoon KS, et al. Combined effect of GSTM1, GSTT1, and COMT genotypes in individual breast cancer risk. Breast Cancer Res Treat 2004;88:55– 62. 23. Kim HC, Lee JY, Sung H, et al. A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study. Breast Cancer Res 2012;14:R56. 24. Li L, Lee KM, Han W, et al. Estrogen and progesterone receptor status affect genome-wide DNA methylation profile in breast cancer. Hum Mol Genet 2010;19:4273 –7. 25. Sung H, Jeon S, Lee KM, et al. Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival. BMC Cancer 2012;12:195. 26. Park B, Ma SH, Shin A, et al. Korean risk assessment model for breast cancer risk prediction. PLoS One 2013;8:e76736. 27. Choi JY, Lee KM, Noh DY, et al. Genetic polymorphisms of eNOS, hormone receptor status, and survival of breast cancer. Breast Cancer Res Treat 2006;100:213 –8. 28. Sung H, Choi JY, Lee SA, et al. The association between the preoperative serum levels of lipocalin-2 and matrix metalloproteinase-9 (MMP-9) and prognosis of breast cancer. BMC Cancer 2012;12:193. 29. Song N, Sung H, Choi JY, et al. Preoperative serum levels of matrix metalloproteinase-2 (MMP-2) and survival of breast cancer among Korean women. Cancer Epidemiol Biomarkers Prev 2012;21:1371 –80. 30. Garber JE, Offit K. Hereditary cancer predisposition syndromes. J Clin Oncol 2005;23:276– 92. 31. Lindor NM, McMaster ML, Lindor CJ, Greene MH, National Cancer Institute DoCPCO, Prevention Trials Research Group. Concise handbook of familial cancer susceptibility syndromes—second edition. J Natl Cancer Inst Monogr 2008;2008:3–93. 32. Hirasawa A, Masuda K, Akahane T, et al. Experience of risk-reducing salpingo-oophorectomy for a BRCA1 mutation carrier and establishment of a system performing a preventive surgery for hereditary breast and ovarian cancer syndrome in Japan: our challenges for the future. Jpn J Clin Oncol 2013;43:515– 9. 33. Schindler L, Kerrigan D, Kelly J, Hollen B. Understanding Cancer Series: Gene Testing. National Cancer Institute 2005. http:// www.cancer.gov/cancertopics/understandingcancer/genetesting (15 July 2014, date last accessed). 34. Robson ME, Storm CD, Weitzel J, Wollins DS, Offit K. American Society of Clinical O. American Society of Clinical Oncology policy statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol 2010;28:893–901. 35. Genetic Information Nondiscrimination Act (GINA) of 2008. National Human Genome Research Institute. http://www.genome.gov/24519851 (15 July 2014, date last accessed). 36. Richter ED, Goldsmith J. The IARC classification system: input, internal logic, output, and impact. Am J Ind Med 1991;19:385–97. 37. Nelson HD, Pappas M, Zakher B, Mitchell JP, Okinaka-Hu L, Fu R. Risk assessment, genetic counseling, and genetic testing for BRCA-related cancer in women: a systematic review to update the U.S. Preventive Services Task Force Recommendation. Ann Intern Med 2014;160:255–66. 38. Khoury MJ, Bedrosian SR, Gwinn M, Little J, Higgins JPT, Ioannidis JPA. Human genome epidemiology: the road map revisited. In: Khoury MJ, Bedrosian SR, Grwinn M, Higgins JPT, Ioannidis JPA, Little J, editors. Human Genome Epidemiology. 2nd edn. New York: Oxford University Press 2010;3– 12.
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1024
Application of personalized preventive research
2 diabetes and body weight change in the Finnish Diabetes Prevention Study. Diabetes 2002;51:2581 –6. 67. Kilpelainen TO, Lakka TA, Laaksonen DE, et al. Interaction of single nucleotide polymorphisms in ADRB2, ADRB3, TNF, IL6, IGF1R, LIPC, LEPR, and GHRL with physical activity on the risk of type 2 diabetes mellitus and changes in characteristics of the metabolic syndrome: the Finnish Diabetes Prevention Study. Metabolism 2008;57:428– 36. 68. Xu M, Qi Q, Liang J, et al. Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation 2013;127:1283–9.
69. Reiman EM, Langbaum JB, Fleisher AS, et al. Alzheimer’s Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J Alzheimers Dis 2011;26:321–9. 70. Pu X, Ye Y, Wu X. Development and validation of risk models and molecular diagnostics to permit personalized management of cancer. Cancer 2014;120:11– 9. 71. Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med 2010;362:986 –93. 72. Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer 2009;34.
Downloaded from http://jjco.oxfordjournals.org/ at University of California, San Diego on January 15, 2015
