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http://dx.doi.org/10.3961/jpmph.2009.42.6.371

Risk Assessment and Pharmacogenetics in Molecular and Genomic Epidemiology  

Park, Sue-K. (Department of Preventive Medicine, Seoul National University College of Medicine)
Choi, Ji-Yeob (Pharmacogenomics Research Center, Inje University College of Medicine)
Publication Information
Journal of Preventive Medicine and Public Health / v.42, no.6, 2009 , pp. 371-376 More about this Journal
Abstract
In this article, we reviewed the literature on risk assessment (RA) models with and without molecular genomic markers and the current utility of the markers in the pharmacogenetic field. Epidemiological risk assessment is applied using statistical models and equations established from current scientific knowledge of risk and disease. Several papers have reported that traditional RA tools have significant limitations in decision-making in management strategies for individuals as predictions of diseases and disease progression are inaccurate. Recently, the model added information on the genetic susceptibility factors that are expected to be most responsible for differences in individual risk. On the continuum of health care, from diagnosis to treatment, pharmacogenetics has been developed based on the accumulated knowledge of human genomic variation involving drug distribution and metabolism and the target of action, which has the potential to facilitate personalized medicine that can avoid therapeutic failure and serious side effects. There are many challenges for the applicability of genomic information in a clinical setting. Current uses of genetic markers for managing drug therapy and issues in the development of a valid biomarker in pharmacogenetics are discussed.
Keywords
Risk assessment; Pharmacogenetics; Biologic markers; Genetic markers;
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1 Spitz MR, Hong WK, Amos CI, Wu X, Schabath MB, Dong Q, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst 2007; 99(9): 715-726   DOI   ScienceOn
2 Optenberg SA, Clark JY, Brawer MK, Thompson IM, Stein CR, Friedrichs P. Development of a decision-making tool to predict risk of prostate cancer: The Cancer of the Prostate Risk Index (CAPRI) test. Urology 1997; 50(5): 665-672   DOI   ScienceOn
3 Wang W, Chen S, Brune KA, Hruban RH, Parmigiani G, Klein AP. PancPRO: Risk assessment for individuals with a family history of pancreatic cancer. J Clin Oncol 2007; 25(11): 1417-1422   DOI   ScienceOn
4 Wu X, Gu J, Spitz MR. Strategies to identify pharmacogenomic biomarkers: Candidate gene, pathway-based, and genome-wide approaches. In: Innocenti F, editor. Genomics and Pharmacogenomics in Anticancer Drug Development and Clinical Response. New Jersey: Humana Press; 2009. p. 353-370
5 Quintela-Fandino M, Hitt R, Medina PP, Gamarra S, Manso L, Cortes-Funes H, et al. DNA-repair gene polymorphisms predict favorable clinical outcome among patients with advanced squamous cell carcinoma of the head and neck treated with cisplatin-based induction chemotherapy. J Clin Oncol 2006; 24(26): 4333-339   DOI   ScienceOn
6 Ulrich CM, Ambrosone CB. Molecular epidemiology designs for prognosis. In:Rebbeck TR, Ambrosone CB, Shields PG, editors. Molecular Epidemiology: Applications in Cancer and Other Human Diseases. New York: Informa Heathcare; 2008: p. 41-52
7 Imperiale TF, Wagner DR, Lin CY, Larkin GN, Rogge JD, Ransohoff DF. Using risk for advanced proximal colonic neoplasia to tailor endoscopic screening for colorectal cancer. Ann Intern Med 2003; 139(12): 959-965   DOI   PUBMED   ScienceOn
8 Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, Glas AM, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. InJ Natl Cancer st 2006; 98(17): 1183-1192   DOI   PUBMED   ScienceOn
9 Hirschhorn JN. Genomewide association studies: Illuminating biologic pathways. N Engl J Med 2009; 360(17): 1699-1701   DOI   ScienceOn
10 Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: The Framingham Study. Am J Cardiol 1976; 38(1): 46-51   DOI   ScienceOn
11 Cuzick J, Forbes J, Edwards R, Baum M, Cawthorn S, Coates A, et al. First results from the International Breast Cancer Intervention Study (IBIS-I): A randomised prevention trial. Lancet 2002; 360(9336): 817-824   DOI   PUBMED   ScienceOn
12 Relling MV, Hancock ML, Rivera GK, Sandlund JT, Ribeiro RC, Krynetski EY, et al. Mercaptopurine therapy intolerance and heterozygosity at the thiopurine Smethyltransferase gene locus. J Natl Cancer Inst 1999; 91(23): 2001-2008   DOI   PUBMED
13 Berry DA, Parmigiani G, Sanchez J, Schildkraut J, Winer E. Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. J Natl Cancer Inst 1997; 89(3): 227-238   DOI   ScienceOn
14 Meyer UA. Pharmacogenetic: five decades of therapeutic lessons from genetic diversity. Nat Rev Genet 2004; 5(9): 669-676   DOI   PUBMED   ScienceOn
15 Ziegler A, Konig IR, Thompson JR. Biostatistical aspects of genome-wide association studies. Biom J 2008; 50(1): 8-28   DOI   ScienceOn
16 Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE, Lee MT, et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med 2009; 360(8): 753-764   DOI   ScienceOn
17 Nasir K, Michos ED, Blumenthal RS, Raggi P. Detection of high-risk young adults and women by coronary calcium and National Cholesterol Education Program Panel III guidelines. J Am Coll Cardiol 2005; 46(10): 1931-1936   DOI   ScienceOn
18 Wu X, GU J. Pharmacogenetics in cancer chemotherapy. In: Rebbeck TR, Ambrosone CB, Shields PG, editors. Molecular Epidemiology: Applications in Cancer and Other Human Diseases. New York: Informa Heathcare; 2008. p.113-128
19 Colditz GA, Atwood KA, Emmons K, Monson RR, Willett WC, Trichopoulos D, et al. Harvard report on cancer prevention volume 4. Harvard Cancer Risk Index: Risk Index Working Group, Harvard Center for Cancer Prevention. Cancer Causes Control 2000; 11(6): 477-488   DOI   ScienceOn
20 Beane J, Sebastiani P, Whitfield TH, Steiling K, Dumas YM, Lenburg ME, et al. A prediction model for lung cancer diagnosis that integrates genomic and clinical features. Cancer Prev Res (Phila Pa) 2008; 1(1): 56-64   DOI   ScienceOn
21 National Heart Lung and Blood Institute. Risk Assessment Tool for Estimating Your 10-year Risk of Having a Heart Attack. Bethesda, MD: National Heart Lung and Blood Institute; 2004 [cited 2009 Oct 10]. Available from: URL:http://hp2010.nhlbihin.net/ATPiii/calcula tor.asp
22 National Cancer Institute. Breast cancer risk assessment tool. Bethesda, MD: National Cancer Institute; 2007 [cited 2009 Oct 5]. Available from: URL:http://www.cancer. gov/bcrisktool/
23 Innocenti F, Kroetz DL, Schuetz E, Dolan ME, Ramirez J, Relling M, et al. Comprehensive pharmacogenetic analysis of irinotecan neutropenia and pharmacokinetics. J Clin Oncol 2009; 27(16): 2604-2614   DOI   ScienceOn
24 Wang WY, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: Theoretical and practical concerns. Nat Rev Genet 2005; 6(2): 109-118   DOI   ScienceOn
25 Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97(18): 1837-1847   DOI   PUBMED   ScienceOn
26 Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81(24): 1879-1886   DOI   PUBMED
27 Fears TR, Guerry D 4th, Pfeiffer RM, Sagebiel RW, Elder DE, Halpern A, et al. Identifying individuals at high risk of melanoma: A practical predictor of absolute risk. J Clin Oncol 2006; 24(22): 3590-3596   DOI   ScienceOn
28 Food and Drug Administration, HHS. International Conference on Harmonisation; Guidance on E15 pharmacogenomics Definitions and Sample Coding; Availability. notice. Fed Regist 2008; 73(68): 19074-19076
29 Food and Drug Administration. Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels. Silver Spring, MD: Food and Drug Administration; 2009 [cited 2009 Oct 5]. Available from: URL:http://www.fda.gov/Drugs/ScienceResearch/Researc hAreas/Pharmacogenetics/ucm083378.htm
30 Johnson KM, Dowe DA, Brink JA. Traditional clinical risk assessment tools do not accurately predict coronary atherosclerotic plaque burden: A CT angiography study. AJR Am J Roentgenol 2009; 192(1): 235-243   DOI   ScienceOn
31 Bellcross CA, Lemke AA, Pape LS, Tess AL, Meisner LT. Evaluation of a breast/ovarian cancer genetics referral screening tool in a mammography population. Genet Med 2009. Epub 2009 Sep 11   DOI   PUBMED   ScienceOn
32 Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer: Implications for risk prediction. Cancer 1994; 73(3): 643-651   DOI   ScienceOn
33 Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, et al. SLCO1B1 variants and statin-induced myopathy: A genomewide study. N Engl J Med 2008; 359(8): 789-799   DOI   ScienceOn
34 Bach PB, Kattan MW, Thornquist MD, Kris MG, Tate RC, Barnett MJ, et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst 2003; 95(6): 470-478   DOI   PUBMED   ScienceOn
35 Akosah KO, Schaper A, Cogbill C, Schoenfeld P. Preventing myocardial infarction in the young adult in the first place: How do the National Cholesterol Education Panel III guidelines perform? J Am Coll Cardiol 2003; 41(9): 1475-1479   DOI   ScienceOn
36 Selvachandran SN, Hodder RJ, Ballal MS, Jones P, Cade D. Prediction of colorectal cancer by a patient consultation questionnaire and scoring system: A prospective study. Lancet 2002; 360(9329): 278-283   DOI   ScienceOn
37 Demchuk E, Yucesoy B, Johnson VJ, Andrew M, Weston A, Germolec DR, et al. A statistical model for assessing genetic susceptibility as a risk factor in multifactorial diseases: Lessons from occupational asthma. Environ Health Perspect 2007; 115(2): 231-234   PUBMED
38 Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst 2008; 100(14): 1037-1041   DOI   ScienceOn
39 Simon T, Verstuyft C, Mary-Krause M, Quteineh L, Drouet E, Meneveau N, et al. Genetic determinants of response to clopidogrel and cardiovascular events. N Engl J Med 2009; 360(4): 363-375   DOI   ScienceOn
40 Center for Drug Evaluation Research, Center for Biologic Evaluation Research, Center for Devices and Radiological Health. Guidance for Industry Pharmacogenomic Data Submissions. Silver Spring, MD: Food and Drug Administration; 2005 [cited 2009 Oct 5]. Available from: URL: http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm126957.pdf
41 Roses AD. Pharmacogenetics in drug discovery and development: A translational perspective. Nat Rev Drug Discov 2008; 7(10): 807-817   DOI   PUBMED   ScienceOn
42 McCanlies E, Landsittel DP, Yucesoy B, Vallyathan V, Luster ML, Sharp DS. Significance of genetic information in risk assessment and individual classification using silicosis as a case model. Ann Occup Hyg 2002; 46(4): 375-381   DOI   ScienceOn
43 National Human Genome Research Institute. A Catalog of Published Genome-Wide Association Studies. Bethesda, MD: National Human Genome Research Institute; 2009 [cited 2009 Oct 5]. Available from: URL: http://www.genome.gov/26525384
44 Mandrekar SJ, Sargent DJ. Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges. J Clin Oncol 2009; 27(24): 4027-4034   DOI   ScienceOn
45 Schulte PA, Waters M. Using molecular epidemiology in assessing exposure for risk assessment. Ann N Y Acad Sci 1999; 895: 101-111   DOI   PUBMED
46 Chung WH, Hung SI, Hong HS, Hsih MS, Yang LC, Ho HC, et al. Medical genetics: A marker for Stevens-Johnson syndrome. Nature 2004; 428(6982): 486   DOI   ScienceOn
47 Sparano JA, Paik S. Development of the 21- gene assay and its application in clinical practice and clinical trials. J Clin Oncol 2008;26(5): 721-728   DOI   ScienceOn
48 Gail MH. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst 2009; 101(13): 959-963   DOI   ScienceOn