Skip to main content
SpringerLink
Log in

Summary and Future Applications of Precision Medicine in Pulmonary, Critical Care, and Sleep Medicine

  • Chapter
  • First Online:
Precision in Pulmonary, Critical Care, and Sleep Medicine

Part of the book series: Respiratory Medicine ((RM))

  • 773 Accesses

Abstract

Advances in our understanding of disease pathogenesis coupled with the availability of well-curated cohorts and high-throughput methods have facilitated the search for omics-based and imaging biomarkers. Initial discoveries of molecular underpinnings of disease endotypes have led to the development of novel biomarker candidates and drug targets, establishing an approach that is becoming widely adopted in pulmonary, critical care, and sleep medicine. As the development of specific precision medicine strategies continues, their widespread implementation will require inclusion of diverse individuals in research and clinical studies, consideration of cost-effectiveness of novel interventions, and education of healthcare providers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nogee LM. Genetic causes of surfactant protein abnormalities. Curr Opin Pediatr. 2019;31(3):330–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tsui JL, Estrada OA, Deng Z, et al. Analysis of pulmonary features and treatment approaches in the COPA syndrome. ERJ Open Res [Internet]. 2018;4(2). Available from: https://doi.org/10.1183/23120541.00017-2018.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Trapnell BC, Nakata K, Bonella F, et al. Pulmonary alveolar proteinosis. Nat Rev Dis Primers. 2019;5(1):16.

    Article  PubMed  Google Scholar 

  4. Gupta N, Vassallo R, Wikenheiser-Brokamp KA, McCormack FX. Diffuse cystic lung disease. Part I. Am J Respir Crit Care Med. 2015;191(12):1354–66.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Torres-Durán M, Lopez-Campos JL, Barrecheguren M, et al. Alpha-1 antitrypsin deficiency: outstanding questions and future directions. Orphanet J Rare Dis. 2018;13(1):114.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schwentner R, Kolenová A, Jug G, et al. Longitudinal assessment of peripheral blood BRAFV600E levels in patients with Langerhans cell histiocytosis [Internet]. Pediatr Res. 2019;85(6):856–64. Available from: https://doi.org/10.1038/s41390-018-0238-y.

    Article  PubMed  Google Scholar 

  7. Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Might M, Wilsey M. The shifting model in clinical diagnostics: how next-generation sequencing and families are altering the way rare diseases are discovered, studied, and treated. Genet Med. 2014;16(10):736–7.

    Article  CAS  PubMed  Google Scholar 

  9. Raghu G, Remy-Jardin M, Myers JL, et al. Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline [Internet]. Am J Respir Crit Care Med. 2018;198(5):e44–68. Available from: https://doi.org/10.1164/rccm.201807-1255st.

    Article  PubMed  Google Scholar 

  10. Nogee LM, Dunbar AE, Wert SE, Askin F, Hamvas A, Whitsett JA. A mutation in the surfactant protein C gene associated with familial interstitial lung disease [Internet]. N Engl J Med. 2001;344(8):573–9. Available from: https://doi.org/10.1056/nejm200102223440805.

    Article  CAS  Google Scholar 

  11. Kropski JA, Pritchett JM, Zoz DF, et al. Extensive phenotyping of individuals at risk for familial interstitial pneumonia reveals clues to the pathogenesis of interstitial lung disease. Am J Respir Crit Care Med. 2015;191(4):417–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang Y, Kuan PJ, Xing C, et al. Genetic defects in surfactant protein A2 are associated with pulmonary fibrosis and lung cancer. Am J Hum Genet. 2009;84(1):52–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Borie R, Tabèze L, Thabut G, et al. Prevalence and characteristics of TERT and TERC mutations in suspected genetic pulmonary fibrosis. Eur Respir J. 2016;48(6):1721–31.

    Article  CAS  PubMed  Google Scholar 

  14. Seibold MA, Wise AL, Speer MC, et al. A common MUC5B promoter polymorphism and pulmonary fibrosis. N Engl J Med. 2011;364(16):1503–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Roy MG, Livraghi-Butrico A, Fletcher AA, et al. Muc5b is required for airway defence. Nature. 2014;505(7483):412–6.

    Article  CAS  PubMed  Google Scholar 

  16. Mathai SK, Yang IV, Schwarz MI, Schwartz DA. Incorporating genetics into the identification and treatment of idiopathic pulmonary fibrosis. BMC Med. 2015;13:191.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Sakornsakolpat P, Prokopenko D, Lamontagne M, et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat Genet. 2019;51(3):494–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. American Thoracic Society, European Respiratory Society. American Thoracic Society/European Respiratory Society statement: standards for the diagnosis and management of individuals with alpha-1 antitrypsin deficiency. Am J Respir Crit Care Med. 2003;168(7):818–900.

    Article  Google Scholar 

  19. Moffatt MF, Gut IG, Demenais F, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363(13):1211–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Torgerson DG, Ampleford EJ, Chiu GY, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Demenais F, Margaritte-Jeannin P, Barnes KC, et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nat Genet. 2018;50(1):42–53.

    Article  CAS  PubMed  Google Scholar 

  22. Miller M, Rosenthal P, Beppu A, et al. ORMDL3 transgenic mice have increased airway remodeling and airway responsiveness characteristic of asthma. J Immunol. 2014;192(8):3475–87.

    Article  CAS  PubMed  Google Scholar 

  23. Das S, Miller M, Beppu AK, et al. GSDMB induces an asthma phenotype characterized by increased airway responsiveness and remodeling without lung inflammation. Proc Natl Acad Sci U S A. 2016;113(46):13132–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Panganiban RA, Sun M, Dahlin A, et al. A functional splice variant associated with decreased asthma risk abolishes the ability of gasdermin B to induce epithelial cell pyroptosis. J Allergy Clin Immunol. 2018;142(5):1469–78.e2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Alberg AJ, Samet JM. Epidemiology of lung cancer. Chest. 2003;123(1 Suppl):21S–49S.

    Article  PubMed  Google Scholar 

  26. Molina JR, Yang P, Cassivi SD, Schild SE, Adjei AA. Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc. 2008;83(5):584–94.

    Article  PubMed  Google Scholar 

  27. Ai X, Guo X, Wang J, et al. Targeted therapies for advanced non-small cell lung cancer. Oncotarget. 2018;9(101):37589–607.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mayekar MK, Bivona TG. Current landscape of targeted therapy in lung cancer. Clin Pharmacol Ther. 2017;102(5):757–64.

    Article  PubMed  Google Scholar 

  29. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685–96.

    Article  CAS  PubMed  Google Scholar 

  30. Pleasance ED, Stephens PJ, O’Meara S, et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature. 2010;463(7278):184–90.

    Article  CAS  PubMed  Google Scholar 

  31. Byers LA, Rudin CM. Small cell lung cancer: where do we go from here? Cancer. 2015;121(5):664–72.

    Article  CAS  PubMed  Google Scholar 

  32. Nielsen AO, Jensen CS, Arredouani MS, Dahl R, Dahl M. Variants of the ADRB2 gene in COPD: systematic review and meta-analyses of disease risk and treatment response. COPD. 2017;14(4):451–60.

    Article  PubMed  Google Scholar 

  33. Slob EMA, Vijverberg SJH, Palmer CNA, et al. Pharmacogenetics of inhaled long-acting beta2-agonists in asthma: a systematic review. Pediatr Allergy Immunol. 2018;29(7):705–14.

    Article  PubMed  Google Scholar 

  34. Himes BE, Jiang X, Hu R, et al. Genome-wide association analysis in asthma subjects identifies SPATS2L as a novel bronchodilator response gene. PLoS Genet. 2012;8(7):e1002824.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tantisira KG, Lasky-Su J, Harada M, et al. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma [Internet]. N Engl J Med. 2011;365(13):1173–83. Available from: https://doi.org/10.1056/nejmoa0911353.

    Article  CAS  Google Scholar 

  36. Mosteller M, Hosking L, Murphy K, et al. No evidence of large genetic effects on steroid response in asthma patients. J Allergy Clin Immunol. 2017;139(3):797–803.e7.

    Article  CAS  PubMed  Google Scholar 

  37. Schlaberg R, Queen K, Simmon K, et al. Viral pathogen detection by metagenomics and pan-viral group polymerase chain reaction in children with pneumonia lacking identifiable etiology. J Infect Dis. 2017;215(9):1407–15.

    Article  CAS  PubMed  Google Scholar 

  38. Charalampous T, Kay GL, Richardson H, et al. Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection. Nat Biotechnol. 2019;37(7):783–92.

    Article  CAS  PubMed  Google Scholar 

  39. Blair JMA, Webber MA, Baylay AJ, Ogbolu DO, Piddock LJV. Molecular mechanisms of antibiotic resistance [Internet]. Nat Rev Microbiol. 2015;13(1):42–51. Available from: https://doi.org/10.1038/nrmicro3380.

    Article  PubMed  CAS  Google Scholar 

  40. Vedel-Krogh S, Nielsen SF, Lange P, Vestbo J, Nordestgaard BG. Blood eosinophils and exacerbations in chronic obstructive pulmonary disease. The Copenhagen general population study. Am J Respir Crit Care Med. 2016;193(9):965–74.

    Article  PubMed  Google Scholar 

  41. Bafadhel M, Greening NJ, Harvey-Dunstan TC, et al. Blood eosinophils and outcomes in severe hospitalized exacerbations of COPD [Internet]. Chest. 2016;150(2):320–8. Available from: https://doi.org/10.1016/j.chest.2016.01.026.

    Article  PubMed  Google Scholar 

  42. Pavord ID, Korn S, Howarth P, et al. Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial. Lancet. 2012;380(9842):651–9.

    Article  CAS  PubMed  Google Scholar 

  43. Guilleminault L, Ouksel H, Belleguic C, et al. Personalised medicine in asthma: from curative to preventive medicine. Eur Respir Rev [Internet]. 2017;26(143). Available from: https://doi.org/10.1183/16000617.0010-2016.

    Article  PubMed  Google Scholar 

  44. Berry A, Busse WW. Biomarkers in asthmatic patients: has their time come to direct treatment? J Allergy Clin Immunol. 2016;137(5):1317–24.

    Article  PubMed  Google Scholar 

  45. Miller BE, Tal-Singer R, Rennard SI, et al. Plasma fibrinogen qualification as a drug development tool in chronic obstructive pulmonary disease. Perspective of the chronic obstructive pulmonary disease biomarker qualification consortium [Internet]. Am J Respir Crit Care Med. 2016;193(6):607–13. Available from: https://doi.org/10.1164/rccm.201509-1722pp.

    Article  CAS  PubMed  Google Scholar 

  46. Ley B, Brown KK, Collard HR. Molecular biomarkers in idiopathic pulmonary fibrosis. Am J Physiol Lung Cell Mol Physiol. 2014;307(9):L681–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Takahashi H, Fujishima T, Koba H, et al. Serum surfactant proteins A and D as prognostic factors in idiopathic pulmonary fibrosis and their relationship to disease extent. Am J Respir Crit Care Med. 2000;162(3 Pt 1):1109–14.

    Article  CAS  PubMed  Google Scholar 

  48. Nathan N, Giraud V, Picard C, et al. Germline SFTPA1 mutation in familial idiopathic interstitial pneumonia and lung cancer. Hum Mol Genet. 2016;25(8):1457–67.

    Article  CAS  PubMed  Google Scholar 

  49. Naikawadi RP, Disayabutr S, Mallavia B, et al. Telomere dysfunction in alveolar epithelial cells causes lung remodeling and fibrosis. JCI Insight. 2016;1(14):e86704.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Armanios MY, Chen JJ-L, Cogan JD, et al. Telomerase mutations in families with idiopathic pulmonary fibrosis. N Engl J Med. 2007;356(13):1317–26.

    Article  CAS  PubMed  Google Scholar 

  51. Tsakiri KD, Cronkhite JT, Kuan PJ, et al. Adult-onset pulmonary fibrosis caused by mutations in telomerase. Proc Natl Acad Sci U S A. 2007;104(18):7552–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rosas IO, Richards TJ, Konishi K, et al. MMP1 and MMP7 as potential peripheral blood biomarkers in idiopathic pulmonary fibrosis. PLoS Med. 2008;5(4):e93.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Bauer Y, White ES, de Bernard S, et al. MMP-7 is a predictive biomarker of disease progression in patients with idiopathic pulmonary fibrosis. ERJ Open Res [Internet]. 2017;3(1). Available from: https://doi.org/10.1183/23120541.00074-2016.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Tzouvelekis A, Herazo-Maya JD, Slade M, et al. Validation of the prognostic value of MMP-7 in idiopathic pulmonary fibrosis [Internet]. Respirology. 2017;22(3):486–93. Available from: https://doi.org/10.1111/resp.12920.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Richards TJ, Kaminski N, Baribaud F, et al. Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2012;185(1):67–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. White ES, Xia M, Murray S, et al. Plasma surfactant protein-D, matrix metalloproteinase-7, and osteopontin index distinguishes idiopathic pulmonary fibrosis from other idiopathic interstitial pneumonias [Internet]. Am J Respir Crit Care Med. 2016;194(10):1242–51. Available from: https://doi.org/10.1164/rccm.201505-0862oc.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. O’Connor JPB, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–86.

    Article  PubMed  CAS  Google Scholar 

  58. Washko GR, Parraga G. COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging. Eur Respir J [Internet]. 2018;52(5). Available from: https://doi.org/10.1183/13993003.01570-2018.

    Article  CAS  Google Scholar 

  59. Sack CS, Doney BC, Podolanczuk AJ, et al. Occupational exposures and subclinical interstitial lung disease. The MESA (multi-ethnic study of atherosclerosis) air and lung studies. Am J Respir Crit Care Med. 2017;196(8):1031–9.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Washko GR, Hunninghake GM, Fernandez IE, et al. Lung volumes and emphysema in smokers with interstitial lung abnormalities. N Engl J Med. 2011;364(10):897–906.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Dawes TJW, de Marvao A, Shi W, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283(2):381–90.

    Article  PubMed  Google Scholar 

  62. Siddiqui SH, Guasconi A, Vestbo J, et al. Blood eosinophils: a biomarker of response to extrafine beclomethasone/formoterol in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2015;192(4):523–5.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Pavord ID, Lettis S, Locantore N, et al. Blood eosinophils and inhaled corticosteroid/long-acting β-2 agonist efficacy in COPD. Thorax. 2016;71(2):118–25.

    Article  PubMed  Google Scholar 

  64. Pascoe S, Locantore N, Dransfield MT, Barnes NC, Pavord ID. Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials. Lancet Respir Med. 2015;3(6):435–42.

    Article  CAS  PubMed  Google Scholar 

  65. Scicluna BP, van Vught LA, Zwinderman AH, et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med. 2017;5(10):816–26.

    Article  PubMed  Google Scholar 

  66. Antcliffe DB, Burnham KL, Al-Beidh F, et al. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH randomized trial. Am J Respir Crit Care Med. 2019;199(8):980–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611–20.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Famous KR, Delucchi K, Ware LB, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am J Respir Crit Care Med. 2017;195(3):331–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med. 2018;6(9):691–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sinha P, Delucchi KL, Thompson BT, et al. Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study. Intensive Care Med. 2018;44(11):1859–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hemnes AR, Trammell AW, Archer SL, et al. Peripheral blood signature of vasodilator-responsive pulmonary arterial hypertension. Circulation. 2015;131(4):401–9; discussion 409.

    Article  CAS  PubMed  Google Scholar 

  72. Zinchuk AV, Jeon S, Koo BB, et al. Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea. Thorax. 2018;73(5):472–80.

    Article  PubMed  Google Scholar 

  73. Malafeev A, Laptev D, Bauer S, et al. Automatic human sleep stage scoring using deep neural networks. Front Neurosci. 2018;12:781.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK biobank participants. Sci Rep. 2018;8(1):7961.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Himes BE, Weitzman ER. Innovations in health information technologies for chronic pulmonary diseases. Respir Res. 2016;17:38.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. research2guidance. mHealth economics 2017 report: status and trends in digital health | R2G [Internet]. [cited 2019 Jul 16]. Available from: https://research2guidance.com/product/mhealth-economics-2017-current-status-and-future-trends-in-mobile-health/.

  77. Wu AC, Carpenter JF, Himes BE. Mobile health applications for asthma. J Allergy Clin Immunol Pract. 2015;3(3):446–8.e1–16.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Chan Y-FY, Wang P, Rogers L, et al. The asthma mobile health study, a large-scale clinical observational study using ResearchKit. Nat Biotechnol. 2017;35(4):354–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Rock Health. Healthcare consumers in a digital transition | Rock Health [Internet]. [cited 2019 Jul 8]. Available from: https://rockhealth.com/reports/healthcare-consumers-in-a-digital-transition/.

  80. Alexeeff SE, Roy A, Shan J, et al. High-resolution mapping of traffic related air pollution with Google street view cars and incidence of cardiovascular events within neighborhoods in Oakland, CA. Environ Health. 2018;17(1):38.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The exposome: molecules to populations. Annu Rev Pharmacol Toxicol. 2019;59:107–27.

    Article  CAS  PubMed  Google Scholar 

  82. Agache I, Miller R, Gern JE, et al. Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma: a Practall document. Allergy. 2019;74(3):449–63.

    Article  PubMed  Google Scholar 

  83. Lynch TJ, Bell DW, Sordella R, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39.

    Article  CAS  PubMed  Google Scholar 

  84. Kwak EL, Bang Y-J, Camidge DR, et al. Anaplastic lymphoma kinase inhibition in non–small-cell lung cancer. N Engl J Med. 2010;363(18):1693–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Politi K, Herbst RS. Lung cancer in the era of precision medicine. Clin Cancer Res. 2015;21(10):2213–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ramsey BW, Davies J, McElvaney NG, et al. A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med. 2011;365(18):1663–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Taylor-Cousar JL, Munck A, McKone EF, et al. Tezacaftor-ivacaftor in patients with cystic fibrosis homozygous for Phe508del. N Engl J Med. 2017;377(21):2013–23.

    Article  CAS  PubMed  Google Scholar 

  88. Rowe SM, Daines C, Ringshausen FC, et al. Tezacaftor-ivacaftor in residual-function heterozygotes with cystic fibrosis. N Engl J Med. 2017;377(21):2024–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Agbor-Enoh S, Wang Y, Tunc I, et al. Donor-derived cell-free DNA predicts allograft failure and mortality after lung transplantation. EBioMedicine. 2019;40:541–53.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Subira C, de Haro C, Magrans R, Fernández R, Blanch L. Minimizing asynchronies in mechanical ventilation: current and future trends. Respir Care. 2018;63(4):464–78.

    Article  PubMed  Google Scholar 

  91. Lilly CM, Cody S, Zhao H, et al. Hospital mortality, length of stay, and preventable complications among critically ill patients before and after tele-ICU reengineering of critical care processes. JAMA. 2011;305(21):2175–83.

    Article  CAS  PubMed  Google Scholar 

  92. Babb S, Malarcher A, Schauer G, Asman K, Jamal A. Quitting smoking among adults - United States, 2000-2015. MMWR Morb Mortal Wkly Rep. 2017;65(52):1457–64.

    Article  PubMed  Google Scholar 

  93. Salloum NC, Buchalter ELF, Chanani S, et al. From genes to treatments: a systematic review of the pharmacogenetics in smoking cessation. Pharmacogenomics. 2018;19(10):861–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Chenoweth MJ, Tyndale RF. Pharmacogenetic optimization of smoking cessation treatment. Trends Pharmacol Sci. 2017;38(1):55–66.

    Article  CAS  PubMed  Google Scholar 

  95. National Heart, Lung, and Blood Institute (NHLBI). Trans-omics for precision medicine (TOPMed) Program [Internet]. [cited 2019 Jul 14]. Available from: https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program.

  96. Guglielmi G. Facing up to injustice in genome science [Internet]. Nature. 2019;568(7752):290–3. Available from: https://doi.org/10.1038/d41586-019-01166-x.

    Article  CAS  PubMed  Google Scholar 

  97. Gurdasani D, Barroso I, Zeggini E, Sandhu MS. Genomics of disease risk in globally diverse populations. Nat Rev Genet [Internet]. 2019. Available from: https://doi.org/10.1038/s41576-019-0144-0.

    Article  CAS  PubMed  Google Scholar 

  98. Starita LM, Ahituv N, Dunham MJ, et al. Variant interpretation: functional assays to the rescue. Am J Hum Genet. 2017;101(3):315–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wu AC, Kiley JP, Noel PJ, et al. Current status and future opportunities in lung precision medicine research with a focus on biomarkers. An American thoracic society/national heart, lung, and blood institute research statement. Am J Respir Crit Care Med. 2018;198(12):e116–36.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Kim H, Ellis AK, Fischer D, et al. Asthma biomarkers in the age of biologics. Allergy Asthma Clin Immunol. 2017;13:48.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pulmonary, Critical Care and Sleep Medicine Section, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA

    Jose L. Gomez

  2. Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA

    Naftali Kaminski

  3. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Blanca E. Himes

Authors
  1. Jose L. Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naftali Kaminski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Blanca E. Himes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Blanca E. Himes .

Editor information

Editors and Affiliations

  1. Assistant Professor Pulmonary, Critical Care and Sleep Medicine Section, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA

    Jose L. Gomez

  2. Assistant Professor of Informatics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Blanca E. Himes

  3. Boehringer-Ingelheim Endowed, Professor of Internal Medicine, Chief of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA

    Naftali Kaminski

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gomez, J.L., Kaminski, N., Himes, B.E. (2020). Summary and Future Applications of Precision Medicine in Pulmonary, Critical Care, and Sleep Medicine. In: Gomez, J., Himes, B., Kaminski, N. (eds) Precision in Pulmonary, Critical Care, and Sleep Medicine. Respiratory Medicine. Humana, Cham. https://doi.org/10.1007/978-3-030-31507-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31507-8_28

  • Published:

  • Publisher Name: Humana, Cham

  • Print ISBN: 978-3-030-31506-1

  • Online ISBN: 978-3-030-31507-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation