Precision medicine informatics

Journal of the American Medical Informatics Association

Volumn 23, Issue 4, 2016, Pages 668-670

  Frey, Lewis J ^a,b   Bernstam, Elmer V ^c   Denny, Joshua C ^d,e  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^b RALPH H JOHNSON VA MEDICAL CENTER   (United States)

^c UNIVERSITY OF TEXAS   (United States)

^d VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^e VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EDITORIAL; HEALTH CARE DELIVERY; HUMAN; LEARNING; LIFESTYLE; MEDICAL INFORMATICS; PATIENT PREFERENCE; PERSONALIZED MEDICINE; TRANSLATIONAL RESEARCH; BIOLOGY;

COMPUTATIONAL BIOLOGY; MEDICAL INFORMATICS; PRECISION MEDICINE;

EID: 84981263359     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocw053     Document Type: Editorial

References (77)

1. Translational Bioinformatics Summit 2015, American Medical Informatics Association [Internet]. https://www.amia.org/jointsummits. Accessed March 5, 2016.
2. Remarks by the president in his State of the Union address, January 20, 2015 [Internet]. https://www.whitehouse.gov/the-press-office/2015/01/20/remarkspresident-state-union-address-january-20-2015. Accessed March 5, 2016.
3. AMIA Genomics and Translational Bioinformatics Working Group (Gen-TBI, formerly the Genomics Working Group)[Internet]. https://www.amia.org/programs/ working-groups/genomics-and-translational-bioinformatics. Accessed March 5, 2016.
4. Stead WW, Kelly BJ, Kolodner RM. Achievable steps toward building a National Health Information Infrastructure in the United States. J Am Med Inform Assoc. 2005;12(2):113-120.
5. Collins FS. The case for a US prospective cohort study of genes and environment. Nature. 2004;429(6990):475-477.
6. National Research Council (US) Committee on a Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease [Internet]. Washington, DC: National Academies Press; 2011. http://www.ncbi.nlm.nih. gov/books/NBK91503/. Accessed March 5, 2016.
7. Roychowdhury S, Chinnaiyan AM. Translating genomics for precision cancer medicine. Annu Rev Genomics Hum Genet. 2014;15:395-415.
8. Frey LJ, Lenert L, Lopez-Campos G. EHR big data deep phenotyping. IMIA Yearbook. 2014;9(1):206-211.
9. Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33(5):777-780.
10. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20(1):117-121.
11. Richesson RL, Hammond WE, Nahm M, et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc. 2013;20(e2):e226-e231.
12. Ritchie MD, Denny JC, Crawford DC, et al. Robust replication of genotypephenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet. 2010;86(4):560-572.
13. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc. 2013;20(e1):e147-e154.
14. Stead WW, Starmer JM. Beyond expert based practice. In McClellan M, et al., eds. Evidence-Based Medicine and the Changing Nature of Healthcare: 2007 IOM Annual Meeting Summary. Washington, DC: National Academies Press; 2008. Institute of Medicine (US). http://www.ncbi.nlm. nih.gov/books/NBK52820/. Accessed March 5, 2016.
15. PMI Working Group. Precision Medicine Initiative-National Institutes of Health (NIH) [Internet]. http://www.nih.gov/precisionmedicine/workinggroup. htm. Accessed March 5, 2016
16. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70.
17. UK Biobank. UK Biobank: Protocol for a Large-scale Prospective Epidemiological Resource [Internet]. http://www.ukbiobank.ac.uk/wp-con tent/uploads/2011/11/UK-Biobank-Protocol.pdf. Accessed March 5, 2016.
18. Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 2010;2(57):57cm29.
19. Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011;12(6):417-428.
20. Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010;17(2):124-130.
21. Frey LJ, Sward KA, Newth CJL, et al. Virtualization of open-source secure web services to support data exchange in a pediatric critical care research network. J Am Med Inform Assoc. 2015;22(6):1271-1276. http://jamia. oxfordjournals.org/content/22/6/1271.abstract. Accessed March 5, 2016.
22. The White House. Precision Medicine Initiative: Proposed Privacy and Trust Principles [Internet]. https://www.whitehouse.gov/sites/default/files/docs/pmi_ privacy_and_trust_principles_july_2015.pdf. Accessed March 5, 2016.
23. Wallerstein N, Duran B. Community-based participatory research contributions to intervention research: the intersection of science and practice to improve health equity. Am J Public Health. 2010;100 (Suppl 1):S40-S46.
24. Jagosh J, Macaulay AC, Pluye P, et al. Uncovering the benefits of participatory research: implications of a realist review for health research and practice. Milbank Q. 2012;90(2):311-346.
25. OHRP. Federal Policy for the Protection of Human Subjects ("Common Rule") [Internet]. http://www.hhs.gov/ohrp/humansubjects/commonrule/. Accessed March 5, 2016.
26. Ashley EA, Butte AJ, Wheeler MT, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375(9725):1525-1535.
27. Chapman PB, Hauschild A, Robert C, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364(26):2507-2516.
28. Ellis PM, Coakley N, Feld R, et al. Use of the epidermal growth factor receptor inhibitors gefitinib, erlotinib, afatinib, dacomitinib, and icotinib in the treatment of non-small-cell lung cancer: a systematic review. Curr Oncol Tor Ont. 2015;22(3):e183-e215.
29. Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: a time for omic ancillary systems. JAMA. 2013;309(12):1237-1238.
30. Gaziano JM, Concato J, Brophy M, et al. Million Veterans Program: a megabiobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214-223.
31. PMI Working Group-Precision Medicine Initiative-National Institutes of Health (NIH). The Precision Medicine Initiative Cohort Program-Building a Research Foundation for 21st Century Medicine [Internet]. http://www.nih. gov/sites/default/files/research-training/initiatives/pmi/pmi-working-groupreport-20150917-2.pdf. Accessed March 5, 2016.
32. Tenenbaum J, Avillach P, Benham-Hutchins M, et al. An informatics research agenda to support precision medicine: 7 key areas. J Am Med Inform Assoc. 2016;23(4):791-795.
33. Duggan M. Cell Phone Activities 2013 [Internet]. Pew Res Cent Internet Sci Tech. http://www.pewinternet.org/2013/09/19/cell-phone-activities-2013/. Accessed March 5, 2016.
34. Smith A. U.S. Smartphone Use in 2015 [Internet]. Pew Res Cent Internet Sci Tech. http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed March 5, 2016.
35. Rappaport SM. Implications of the exposome for exposure science. J Expo Sci Environ Epidemiol. 2011;21(1):5-9.
36. Warner JL, Rioth MJ, Mandl KD, et al. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc. 2016;23(4):701-710.
37. Bainbridge MN, Wiszniewski W, Murdock DR, et al. Whole-genome sequencing for optimized patient management. Sci Transl Med. 2011;3(87):1-6.
38. Xu J, Lee HJ, Zeng J, et al. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov. J Am Med Inform Assoc. 2016;23(4):750-757.
39. Fathiamini S, Johnson AM, Zeng J, et al. Automated identification of molecular effects of drugs (AIMED). J Am Med Inform Assoc. 2016;23(4):758-765.
40. Hintzsche J, Kim J, Yadav V, et al. IMPACT: Whole-exome sequencing analysis pipeline of integrating molecular profiles with actionable therapeutics in clinical samples. J Am Med Inform Assoc. 2016;23(4):721-730.
41. Cheng F, Zhao J, Fooksa M, Zhao Z. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in human cancer genomes. J Am Med Inform Assoc. 2016;23(4):681-691.
42. Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):1-10.
43. Ahmad T, Pencina MJ, Schulte PJ, et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J Am Coll Cardiol. 2014;64(17):1765-1774.
44. Doshi-Velez F, Ge Y, Kohane I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics. 2013;peds.2013-0819.
45. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in PCSK9, Low LDL, and protection against coronary heart disease. N Engl J Med. 2006;354(12):1264-1272.
46. Denny JC, Bastarache L, Ritchie MD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31(12): 1102-1111.
47. Kho AN, Hayes MG, Rasmussen-Torvik L, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc. 2012;19(2):212-218.
48. Kho AN, Pacheco JA, Peissig PL, et al. Electronic medical records for genetic research: results of the eMERGE Consortium. Sci Transl Med. 2011;3(79):79re1.
49. Crawford DC, Crosslin DR, Tromp G, et al. eMERGEing progress in genomics-the first seven years. Front Genet. 2014;5:184.
50. Collins FS, Hudson KL, Briggs JP, Lauer MS. PCORnet: turning a dream into reality. J Am Med Inform Assoc. 2014;21(4):576-577.
51. Pulley JM, Denny JC, Peterson JF, 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
52. Halpern Y, Horng S, Choi Y, Sontag D. Electronic medical record phenotyping using the anchor & learn framework. J Am Med Inform Assoc. 2016; 23(e1):e20-e27.
53. Singhal A, Simmons M, Lu Z, et al. Text mining for precision medicine: automating disease mutation relationship extraction from biomedical literature. J Am Med Inform Assoc. 2016;23(4):766-772.
54. Rioth MJ, Thota R, Staggs D, et al. Pragmatic precision oncology: the secondary uses of clinical tumor molecular profiling. J Am Med Inform Assoc. 2016;23(4):773-776.
55. Hoffman JM, Dunnenberger HM, Hicks JK, et al. Developing knowledge resources to support precision medicine: principles from the clinical Pharmacogenetics Implementation Consortium. J Am Med Inform Assoc. 2016;23(4):796-801.
56. Torosyan Y, Hu Y, Hoffman S, et al. An in silico framework for integrating epidemiologic and genetic evidence with healthcare applications: ventilationrelated pneumothorax as a case illustration. J Am Med Inform Assoc. 2016;23(4):711-720.
57. NIH. Request for Information: NIH Precision Medicine Cohort-Strategies to Address Community Engagement and Health Disparities. 2015. http://grants.nih. gov/grants/guide/notice-files/NOT-OD-15-107.html. Accessed March 5, 2016.
58. Braveman PA, Kumanyika S, Fielding J, et al. Health disparities and health equity: the issue is justice. Am J Public Health. 2011;101(S1):S149-S155.
59. Kaufman D, Murphy J, Scott J, Hudson K. Subjects matter: a survey of public opinions about a large genetic cohort study. Genet Med. 2008;10(11):831-839.
60. Murphy J, Scott J, Kaufman D, et al. Public expectations for return of results from large-cohort genetic research. Am J Bioeth AJOB. 2008;8(11):36-43.
61. Trinidad SB, Fullerton SM, Ludman EJ, et al. Research practice and participant preferences: the growing gulf. Science. 2011;331(6015):287-288.
62. Beskow LM, Linney KN, Radtke RA, et al. Ethical challenges in genotypedriven research recruitment. Genome Res. 2010;20(6):705-709.
63. Dye, et al. Sociocultural variation in attitudes toward use of genetic information and participation in genetic research by race in the United States: implications for precision medicine. J Am Med Inform Assoc. 2016;23(4);782-786.
64. Adams SA, Petersen C. Precision medicine: opportunities, possibilities, and challenges for patients and providers. J Am Med Inform Assoc. 2016; 23(4):787-790.
65. Ni Y, Beck AF, Taylor R, et al. Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department. J Am Med Inform Assoc. 2016;23(4):671-670.
66. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov. 2013;12(8):581-594.
67. Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-381.
68. Sanseau P, Agarwal P, Barnes MR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30(4):317-320.
69. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47(8):856-860.
70. DePeralta DK, Boland GM. Melanoma: advances in targeted therapy and molecular markers. Ann Surg Oncol. 2015;22(11):3451-3458.
71. Relling MV, Gardner EE, Sandborn WJ, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing. Clin Pharmacol Ther. 2011;89(3):387-391.
72. Shuldiner AR, O'Connell JR, Bliden KP, et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA. 2009;302(8):849-857.
73. The International Warfarin Pharmacogenetics Consortium. Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data. N Engl J Med. 2009;360(8):753-764.
74. Kimmel SE, French B, Kasner SE, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med. 2013;369(24):2283-2293.
75. Eubank MH, Hyman DM, Kanakamedala AD, et al. Automated eligibility screening and monitoring for genotype driven precision oncology trials. J Am Med Inform Assoc. 2016;23(4):777-781.
76. Warner JL, Wang L, Pao W, et al. CUSTOM-SEQ: A Prototype for Oncology Rapid Learning in a Comprehensive EHR Environment. J Am Med Inform Assoc. 2016;23(4):692-700.
77. Cheng L, Schneider B, Li L. A bioinformatics approach for the precision medicine off-label drug selection among triple negative breast cancer patients. J Am Med Inform Assoc. 2016;23(4):741-749.