In medicine, the advent of personalized healthcare is showing that “one size” does not necessarily “fit all.” Specifically, personalized healthcare implies an ability to use an individual’s genetic characteristics to diagnose his or her condition with more precision and finesse. With this development, physicians can select treatments that have increased chances of success and minimal possibilities of adverse reactions. However, personalized medicine does not just imply better diagnostics and therapeutics: it also underlies an ability to better predict any given individual’s susceptibility to a particular disease. Thus, it can be used to devise a comprehensive plan to avoid the disease or reduce its extent.1 The advent of personalization in healthcare has brought a preventative aspect to a field that has traditionally employed a reactive approach,2 where patients are generally treated and diagnosed after symptoms appear.

Medicine has always been personalized: treatment is tailored to individuals following examination. However, the new movement to personalize medicine takes this individualization to the next level. The initial genome sequence was reported by the International Human Genome Sequencing Consortium in 2001; now, scientists can determine information about human physiology and evolution to a detail never before possible, creating a genetics-based foundation for biomedical research.3 Genes can help determine an individual’s health, and scientists can better identify and analyze the causes of disease based on genetic polymorphisms, or variations. This scientific advancement is an integral factor in the personalized healthcare revolution. Technological developments that allow the sequencing of the human genome on a real time scale at relatively low costs have also helped to move this new era of medicine forward.2

The science behind such personalized treatment plans and prediction capabilities follows simple logic: scientists can create a guide by identifying and characterizing genomic signatures associated with particular responses to chemotherapy drugs, such as sensitivity or resistance. They can then use the aforementioned patterns to understand the molecular mechanisms that create such responses and categorize genes based on these pathways and mutations.4 Therefore, physicians can compare the genetic makeup of patients’ tumors to these libraries of information. This genetic profiling matches patients with successfully-treated individuals who have similar polymorphisms to provide effective treatment that increases the accuracy of predictions, minimizes allergic reactions, and reduces unnecessary follow-up treatments. For example, efforts are underway to create individualized cancer therapy based on molecular analysis of patients. Traditionally, prediction of cancer recurrence is based on empirical lessons learned from past cases to treat current patients, looking specifically at metrics such as tumor size, lymph node status, response to systemic treatment, and remission intervals.4 While this type of prediction has merit, it only provides generalized estimates of recurrence and survival for patients; those with no risk of cancer relapse are often put through potentially toxic chemotherapy. With the new age of personalized medicine, powerful analytical methods, such as protein profiles and dysfunctional molecular pathways, will allow physicians to predict the behavior of a patient’s tumors on a whole new level. Personalized oncologic treatment can plot the clinical course for each patient with a particular disease based on his or her own conditions rather than generalizations from a heterogeneous sample of past cases. This type of healthcare thus improves upon current medicine by creating a subset of homogeneous groups within past cases, allowing physicians to make a more accurate prediction of an individual’s response to treatment.

Additionally, personalized medicine can prevent medical maladies such as adverse drug reactions, which lead to more than two million hospitalizations and 100,000 deaths per year in the U.S. alone.5 It can also lead to safer dosing and more focused drug testing. However, this approach is hindered by the nascent nature of genomics technology and the difficulty in identifying all possible genetic variations. Particularly challenging are cases where certain drug reactions result from multiple genes working in conjunction.6 Furthermore, opponents of gene sequencing argue that harnessing too much predictive information could be frightening for the patient. For example, patients shown to possess a genetic predisposition towards a degenerative disease such as Alzheimer’s could experience serious psychological effects and depression due to a sense of fatalism; this knowledge could adversely impact their motivation to reduce risks. This possibility has been demonstrated in clinical studies regarding genetic testing for familial hypercholesterolaemia, which measures predisposition to heart disease.7 This dilemma leads back to a fundamental question of gene sequencing—how much do we really want to know about our genetic nature?

As of today, personalized medicine is starting to make its mark through some commonly available tests such as the dihydropyrimidine dehydrogenase test, which can predict if a patient will have severe, sometimes fatal, reactions to 5-fluorouracil, a common chemotherapy medicine.8 Better known are the genetic tests for BRCA1 and BRCA2 mutations that reveal an increased risk of breast cancer,9 popularized by actress Angelina Jolie’s preventative double mastectomy. With these and other such genetics-based tests, the era of personalized medicine has begun, and only time can reveal what will come next.

References

  1. Center for Personalized Genetic Medicine. http://pcpgm.partners.org/about-us/PM (accessed Oct 24, 2013).
  2. Galas, D. J.; Hood L. IBC. 2009, 1, 1-4.
  3. Venter, J. C. et al. Science 2001, 291, 1304-1351.
  4. Mansour, J. C.; Schwarz, R. E. J. Am. Coll. Surgeons 2008, 207, 250-258.
  5. Shastry, B. S. Nature 2006, 6, 16-21.
  6. CNN Health. http://www-cgi.cnn.com/HEALTH/library/CA/00078.html (accessed Oct 24, 2013).
  7. Senior, V. et al. Soc. Sci. Med. 1999, 48, 1857-1860.
  8. Salonga, D. et al. Clin. Cancer Res. 2006, 6, 1322.
  9. National Cancer Institute Fact Sheet. http://www.cancer.gov/cancertopics/factsheet/Risk/BRCA (accessed Oct 24, 2013).

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