1932

Abstract

Precision medicine was conceptualized on the strength of genomic sequence analysis. High-throughput functional metrics have enhanced sequence interpretation and clinical precision. These technologies include metabolomics, magnetic resonance imaging, and I rhythm (cardiac monitoring), among others. These technologies are discussed and placed in clinical context for the medical specialties of internal medicine, pediatrics, obstetrics, and gynecology. Publications in these fields support the concept of a higher level of precision in identifying disease risk. Precise disease risk identification has the potential to enable intervention with greater specificity, resulting in disease prevention—an important goal of precision medicine.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-041316-090905
2018-01-29
2024-04-14
Loading full text...

Full text loading...

/deliver/fulltext/med/69/1/annurev-med-041316-090905.html?itemId=/content/journals/10.1146/annurev-med-041316-090905&mimeType=html&fmt=ahah

Literature Cited

  1. Bauer UE, Briss PA, Goodman RA. 1.  et al. 2014. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet 384:993745–52 [Google Scholar]
  2. Cummings BB, Marshall JL, Tukiainen T. 2.  et al. 2017. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9:386eaal5209 [Google Scholar]
  3. Lander ES, Linton LM, Birren B. 3.  et al.Intl. Hum. Genome Seq. Consort. 2001. Initial sequencing and analysis of the human genome. Nature 409:860–921 [Google Scholar]
  4. Venter JC, Adams MD, Myers EW. 4.  et al. 2001. The sequence of the human genome. Science 291:55071304–51 [Google Scholar]
  5. Bainbridge MN, Wang M, Wu Y. 5.  et al. 2011. Targeted enrichment beyond the consensus coding DNA sequence exome reveals exons with higher variant densities. Genome Biol 12:7R68 [Google Scholar]
  6. Yang Y, Muzny DM, Reid JG. 6.  et al. 2013. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N. Engl. J. Med. 369:1502–11 [Google Scholar]
  7. Telenti A, Pierce LC, Biggs WH. 7.  et al. 2016. Deep sequencing of 10,000 human genomes. PNAS 113:4211901–6 [Google Scholar]
  8. Druker BJ, Talpaz M, Resta DJ. 8.  et al. 2001. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344:1031–37 [Google Scholar]
  9. Sabatine MS, Giugliano RP, Keech AC. 9.  et al. 2017. Evolocumab and clinical outcomes in patients with cardiovascular disease. N. Engl. J. Med. 376:1713–22e [Google Scholar]
  10. Nirenberg M. 10.  1968. The genetic code Nobel Lect., Dec. 12, Nobel Found Stockholm, Sweden:
  11. 11. The 1000 Genomes Project Consortium. 2015. A global reference for human genetic variation. Nature 526:68–74 [Google Scholar]
  12. Crow JF. 12.  2000. The origins, patterns and implications of human spontaneous mutation. Nat. Rev. Genet. 1:40–47 [Google Scholar]
  13. Landrum MJ, Lee JM, Benson M. 13.  et al. 2016. ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44:D1D862–68 [Google Scholar]
  14. Smedley D, Robinson PN. 14.  2015. Phenotype-driven strategies for exome prioritization of human Mendelian disease genes. Genome Med 7:181 [Google Scholar]
  15. Caskey CT, Gonzalez-Garay ML, Pereira S, McGuire AL. 15.  2014. Adult genetic risk screening. Annu. Rev. Med. 65:1–17 [Google Scholar]
  16. Smedley D, Schubach M, Jacobsen JO. 16.  et al. 2016. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease. Am. J. Hum. Genet. 99:3595–606 [Google Scholar]
  17. Perkins BA, Caskey CT, Brar P. 17.  et al. 2017. Precision medicine screening using whole genome sequencing and advanced imaging to identify disease risk in adults. bioRxiv May 3. doi: https://doi.org/10.1101/133538 [Crossref]
  18. Sidransky E1, Nalls MA, Aasly JO. 18.  et al. 2009. Multicenter analysis of glucocerebrosidase mutations in Parkinson's disease. N. Engl. J. Med. 361:171651–61 [Google Scholar]
  19. Rousseau F1, Bonaventure J, Legeai-Mallet L. 19.  et al. 1994. Mutations in the gene encoding fibroblast growth factor receptor-3 in achondroplasia. Nature 371:6494252–54 [Google Scholar]
  20. Brown MS, Golstein JL. 20.  1985. A receptor-mediated pathway for cholesterol homeostasis Nobel Lect., Dec. 9, Nobel Found Stockholm, Sweden:
  21. Versmissen J, Oosterveer DM, Yazdanpanah M. 21.  et al. 2008. Efficacy of statins in familial hypercholesterolaemia: a long term cohort study. BMJ 337:a2423 [Google Scholar]
  22. Levy-Lahad E, Lahad A, King MC. 22.  2014. Precision medicine meets public health: population screening for BRCA1 and BRCA2. J. Natl. Cancer Inst. 107:1420 [Google Scholar]
  23. Gabai-Kapara E, Lahad A, Kaufman B. 23.  et al. 2014. Population-based screening for breast and ovarian cancer risk due to BRCA1 and BRCA2. PNAS 111:3914205–10 [Google Scholar]
  24. Forsburg SL. 24.  2001. The art and design of genetic screens: yeast. Nat. Rev. Genet. 2:659–68 [Google Scholar]
  25. Kuchenbaecker KB, McGuffog L, Barrowdale D. 25.  et al. 2017. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J. Natl. Cancer Inst. 109:7 In press. doi: 10.1093/jnci/djw302 [Google Scholar]
  26. New MI, Tong YK, Yuen T. 26.  et al. 2014. Noninvasive prenatal diagnosis of congenital adrenal hyperplasia using cell-free fetal DNA in maternal plasma. J. Clin. Endocrinol. Metab. 99:6E1022–E1030 [Google Scholar]
  27. Kalia SS, Adelman K, Bale SJ. 27.  et al. 2017. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet. Med. 19:249–55 [Google Scholar]
  28. Long T, Hicks M, Yu H-C. 28.  et al. 2017. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49:568–78 [Google Scholar]
  29. Donti TR, Cappuccio G, Hubert L. 29.  et al. 2016. Diagnosis of adenylosuccinate lyase deficiency by metabolomic profiling in plasma reveals a phenotypic spectrum. Mol. Genet. Metab. 8:61–66 [Google Scholar]
  30. Hou YC, Lavrenko V, Bartha I. 30.  et al. 2017. An interactive search engine of massive genomic and phenotypic data Presented at Am. Coll. Med. Genet. Genom. Annu. Clin. Genet. Meet., Mar. 21, Phoenix, AZ. Abstr. No. 446
  31. Guo L, Milburn MV, Ryals JAA. 31.  et al. 2015. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. PNAS 112:E4901–E4910 [Google Scholar]
  32. Miller MJ, Kennedy AD, Eckhart AD. 32.  et al. 2015. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J. Inherit. Metab. Dis. 38:61029–39 [Google Scholar]
  33. Pirmohamed M, Burnside G, Eriksson N. 33.  et al. 2013. A randomized trial of genotype-guided dosing of warfarin. N. Engl. J. Med. 369:2294–303 [Google Scholar]
  34. Nguyen CM, Mendes MAS, Ma JD. 34.  2011. Thiopurine methyltransferase (TPMT) genotyping to predict myelosuppression risk. PLOS Curr. 3:RRN1236 [Google Scholar]
  35. Cargnin S, Jommi C, Canonico PL. 35.  et al. 2014. Diagnostic accuracy of HLA-B*57:01 screening for the prediction of abacavir hypersensitivity and clinical utility of the test: a meta-analytic review. Pharmaco-genomics 15:7963–76 [Google Scholar]
  36. Thorn CF, Klein TE, Altman RB. 36.  2013. PharmGKB: the pharmacogenomics knowledge base. Methods Mol. Biol. 1015:311–20 [Google Scholar]
  37. Kerbrat A, Ferré JC, Fillatre P. 37.  et al. 2016. Acute neurologic disorder from an inhibitor of fatty acid amide hydrolase. N. Engl. J. Med. 375:181717–25 [Google Scholar]
  38. Angelo M, Bendall SC, Finck R. 38.  et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:436–42 [Google Scholar]
  39. Gawande BN, Rohloff JC, Carter JD. 39.  et al. 2016. Selection of DNA aptamers with two modified bases. PNAS 114:112898–903 [Google Scholar]
  40. Brunsing RL, Schenker-Ahmed NM, White NS. 40.  et al. 2016. Restriction spectrum imaging: an evolving imaging biomarker in prostate MRI. J. Magn. Reson. Imag. 45:2323–36 [Google Scholar]
  41. McCammack KC, Kane CJ, Parsons JK. 41.  et al. 2016. In vivo prostate cancer detection and grading using restriction spectrum imaging–MRI. Prostate Cancer Prostatic Dis 19:2168–73 [Google Scholar]
  42. Bojadzieva J, Amini B, Day SF. 42.  et al. 2017. Whole body magnetic resonance imaging (WB-MRI) and brain MRI baseline surveillance in TP53 germline mutation carriers: experience from the Li-Fraumeni Syndrome Education and Early Detection (LEAD) clinic. Fam. Cancer. In press
  43. Tomasetti C, Li L, Vogelstein B. 43.  et al. 2017. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355:63311330–34 [Google Scholar]
  44. Sternberg IA, Vela I, Scardino PT. 44.  2016. Molecular profiles of prostate cancer: to treat or not to treat. Annu. Rev. Med. 67:119–35 [Google Scholar]
  45. Megibow AJ, Baker ME, Morgan DE. 45.  2017. Management of incidental pancreatic cysts: a white paper of the ACR Incidental Findings Committee. J. Am. Coll. Radiol. 14:7911–23 [Google Scholar]
  46. Kaiser J. 46.  2016. Baby genome screening needs more time to gestate. Science 354:6311398–99 [Google Scholar]
  47. Donti TR, Cappuccio G, Hubert L. 47.  et al. 2016. Diagnosis of adenylosuccinate lyase deficiency by metabolomic profiling in plasma reveals a phenotypic spectrum. Mol. Genet. Metab. 8:61–66 [Google Scholar]
  48. Atwal PS, Donti TR, Cardon AL. 48.  et al. 2015. Aromatic L-amino acid decarboxylase deficiency diagnosed by clinical metabolomic profiling of plasma. Mol. Genet. Metab. 115:2–391–94 [Google Scholar]
  49. Lo YM, Chan KC, Sun H. 49.  et al. 2010. Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci. Transl. Med. 2:6161ra91 [Google Scholar]
  50. Hui L, Bianchi DW. 50.  2017. Noninvasive prenatal DNA testing: the vanguard of genomic medicine. Annu. Rev. Med. 68:459–72 [Google Scholar]
  51. K´ølvraa S, Singh R, Norman EA. 51.  et al. 2016. Genome-wide copy number analysis on DNA from fetal cells isolated from the blood of pregnant women. Prenat. Diagn. 36:121127–34 [Google Scholar]
  52. Edwards JG, Feldman G, Goldberg J. 52.  et al. 2015. Expanded carrier screening in reproductive medicine—points to consider: a joint statement of the American College of Medical Genetics and Genomics, American College of Obstetricians and Gynecologists, National Society of Genetic Counselors, Perinatal Quality Foundation, and Society for Maternal-Fetal Medicine. Obstet. Gynecol 125:3653–62 [Google Scholar]
  53. Pieretti M, Zhang FP, Fu YH. 53.  et al. 1991. Absence of expression of the FMR-1 gene in fragile X syndrome. Cell 66:4817–22 [Google Scholar]
  54. Fu YH1, Pizzuti A, Fenwick RG Jr. 54.  et al. 1992. An unstable triplet repeat in a gene related to myotonic muscular dystrophy. Science 255:50491256–58 [Google Scholar]
  55. Gonzalez-Garaya ML, McGuireb AL, Pereirab S, Caskey CT. 55.  2013. Personalized genomic disease risk of volunteers. PNAS 110:4216957–62 [Google Scholar]
  56. Posey JE, Rosenfeld JA, James RA. 56.  et al. 2016. Molecular diagnostic experience of whole-exome sequencing in adult patients. Genet. Med. 18:7678–85 [Google Scholar]
  57. Khera AV, Emdin CA, Drake I. 57.  et al. 2016. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375:2349–58 [Google Scholar]
  58. Abul-Husn NS, Manickam K, Jones LK. 58.  et al. 2016. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354:6319aaf7000 [Google Scholar]
  59. Wald DS, Bestwick JP, Morris JK. 59.  et al. 2016. Child-parent familial hypercholesterolemia screening in primary care. N. Engl. J. Med. 375:171628–37 [Google Scholar]
  60. Evans JP, Berg JS, Olshan AF. 60.  et al. 2013. We screen newborns, don't we? Realizing the promise of public health genomics. Genet. Med. 15:332–34 [Google Scholar]
  61. Anderson MS, Su MA. 61.  2011. AIRE and T cell development. Curr. Opin. Immunol. 23:2198–206 [Google Scholar]
  62. Ching T, Himmelstein DS, Beaulieu-Jones BK. 62.  et al. 2017. Opportunities and obstacles for deep learning in biology and medicine. bioRxiv preprint 10:1101/142760
  63. Obermeyer Z, Emanuel EJ. 63.  2016. Predicting the future—big data, machine learning, and clinical medicine. N. Engl. J. Med. 375:13 [Google Scholar]
/content/journals/10.1146/annurev-med-041316-090905
Loading
/content/journals/10.1146/annurev-med-041316-090905
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error