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Clinical Genomicist in the Future of Medical Practice

Chapter

Abstract

Medical subspecialties commonly arise from either primarily academic origins or because of practical need. A subspecialty often coalesces around academic thought leaders enunciating a cogent intellectual construct, regardless of whether there is a demand for such consultants in the practice setting. Alternatively, subspecialization emerges as a natural consequence of the complexity of medical knowledge and the practical need for division of labor in day-to-day clinical practice. An example of a subspecialty that still remains largely academic is clinical pharmacology. By contrast, physical impossibility for the two essential functions in the operating room to reside in the same person – surgery and anesthesia – accelerated the emergence of anesthesiology as a clinical discipline. In reality, the distinction between an academic and a practical subspecialty is artificial since many subdisciplines start as academic societies and only later emerge as bona fide clinical specialties. This branch point occurs when the intellectual complexity of the science or the difficulty of diagnostic or therapeutic procedures requires special training and where mastery of special procedures/techniques has significant impact on patient care. For example, in the early part of the twentieth century, a well-trained general surgeon commonly did most invasive operations which would include abdominal and orthopedic surgery. A century later, orthopedics is a separate department from general surgery with a highly differentiated training and accreditation program. Similarly, it can be argued that renal dialysis differentiated nephrology into a subspecialty essential to the operation of a health-care system. In so many cases, from gastroenterology to invasive cardiology and to neuroradiology, it was the availability of innovative and powerful technologies that spawned new clinical subspecialties.

Keywords

Clinical Genetic Physician Expert Clinical Discipline Medical Subspecialty Clinical Genomic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

The author thanks Dr. Pauline Ng for her contributions to this chapter.

References

  1. 1.
    Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, Glas AM, Bogaerts J, Cardoso F, Piccart-Gebhart MJ, Rutgers ET, Van’t Veer LJ (2009) TRANSBIG consortium. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1-3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat 116(2):295–302PubMedCrossRefGoogle Scholar
  2. 2.
    Kim C, Paik S (2010) Gene-expression-based prognostic assays for breast cancer. Nat Rev Clin Oncol 7(6):340–347PubMedCrossRefGoogle Scholar
  3. 3.
    Boone PM, Bacino CA, Shaw CA, Eng PA, Hixson PM, Pursley AN, Kang SH, Yang Y, Wiszniewska J, Nowakowska BA, del Gaudio D, Xia Z, Simpson-Patel G, Immken LL, Gibson JB, Tsai AC, Bowers JA, Reimschisel TE, Schaaf CP, Potocki L, Scaglia F, Gambin T, Sykulski M, Bartnik M, Derwinska K, Wisniowiecka-Kowalnik B, Lalani SR, Probst FJ, Bi W, Beaudet AL, Patel A, Lupski JR, Cheung SW, Stankiewicz P (2010) Detection of clinically relevant exonic copy-number changes by array CGH. Hum Mutat 31(12):1326–1342PubMedCrossRefGoogle Scholar
  4. 4.
    Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D et al (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466(7304):368–372PubMedCrossRefGoogle Scholar
  5. 5.
    Poulikakos PI, Rosen N (2011) Mutant BRAF melanomas – dependence and resistance. Cancer Cell 19(1):11–15PubMedCrossRefGoogle Scholar
  6. 6.
    Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, Fujiwara S, Watanabe H, Kurashina K, Hatanaka H, Bando M, Ohno S, Ishikawa Y, Aburatani H, Niki T, Sohara Y, Sugiyama Y, Mano H (2007) Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 448(7153):561–566PubMedCrossRefGoogle Scholar
  7. 7.
    Pao W, Chmielecki J (2010) Rational, biologically based treatment of EGFR-mutant non-small-cell lung cancer. Nat Rev Cancer 10(11):760–774PubMedCrossRefGoogle Scholar
  8. 8.
    Dennis Lo Y, Chiu RW (2011) Plasma nucleic acid analysis by massively parallel sequencing: pathological insights and diagnostic implications. J Pathol 225(3):318–323PubMedCrossRefGoogle Scholar
  9. 9.
    McCarthy MI (2010) Genomics, type 2 diabetes, and obesity. N Engl J Med 363(24): 2339–2350PubMedCrossRefGoogle Scholar
  10. 10.
    Pharoah PD, Antoniou AC, Easton DF, Ponder BA (2008) Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 358(26):2796–2803PubMedCrossRefGoogle Scholar
  11. 11.
    Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI (2011) Human nutrition, the gut microbiome and the immune system. Nature 474(7351):327–336PubMedCrossRefGoogle Scholar
  12. 12.
    Heijtz RD, Wang S, Anuar F, Qian Y, Björkholm B, Samuelsson A, Hibberd ML, Forssberg H, Pettersson S (2011) Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci USA 108(7):3047–3052CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.The Jackson LaboratoryBar HarborUSA

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