Genetics
Most diseases have a genetic component, making them a target for genomic medicine. Some genetic variations are known to cause disease and are referred to as Mendelian disorders. Examples include cystic fibrosis
(CF) and Huntington’s disease. Overall, single-gene disorders are rare. For example, CF, the most common genetic condition in Caucasians, affects only 1 in 2,500 to 3,500 infants.
Genomic medicine will have the greatest impact on health in the United States via common complex disorders, which are caused by a combination of one or more environmental and genetic factors. Instead of “causing” the disease, genetic variations contribute to disease susceptibility. Examples include heart disease, Alzheimer’s disease, and autism.
Infectious diseases are least impacted by genetic factors because the organism causes the disease. How the body reacts to the organism, however, may have a genetic component. Tuberculosis
is known to cause symptoms in only a small percentage of people who become infected. The variability in expression of this disease in exposed individuals is believed to be genetic and is a current topic under investigation.
Prediction
The most immediate application of genomic medicine is disease prediction. Family history is currently the most useful genetic information for disease prediction. Genetics is likely to contribute more to a disease in families where it appears at an earlier age than typical, with more severity, and/or in more individuals. A person’s risk is estimated based on these factors and how closely related affected relatives are. For example, in a family where individuals develop heart disease at a young age despite a healthy diet and lifestyle, genetics is likely to have a higher impact than in one where one individual develops it in old age after a lifetime of unhealthy choices.
Once a gene is clearly established to cause or contribute to disease, it offers another tool to predict risk. This is common for Mendelian but not complex disorders because most genes are not known. Even when available, tests may not be offered immediately as a result of poor clinical validity and/or utility. An example is genetic testing for a variant of the Apoliprotein E gene that confers a risk for Alzheimer’s disease. Testing is typically not recommended because the clinical validity is low and there is no proven clinical utility. In other words, many people who test positive will not develop the disease and there is no proven strategy to prevent or delay it. However, research is quickly closing the therapeutic gap of this and many disorders, opening the doorway for risk management options, some of which are already available.
Risk Management: Screening and Prevention
The greatest promise of genomic medicine is to use risk information to identify disease early, delay disease, or, most important, prevent disease. For example, scientists can identify individuals at genetic risk for several types of cancer, including breast and colon cancer. For these individuals, screening begins earlier and is more aggressive. For those at risk for hereditary breast and ovarian cancer syndrome, a drug called Tamoxifen
has been shown to reduce the risk of breast cancer. In addition, prophylactic removal of ovaries and/or breasts has also been shown to reduce drastically the risk of cancer to these organs. While extreme, these strategies can save lives. Fortunately, research in other risk management strategies continues for these diseases and others.
Diagnosis
Genetic information can improve diagnosis in many ways and is commonly used for Mendelian disorders. For some disorders, a clinical diagnosis can be uncertain or elusive. Testing the patient for genes known to cause or contribute to the disease can aid the clinician greatly, especially when a clear diagnosis facilitates treatment. Genetic information may not always be in the form of genotype information. Expression analysis can also be useful to make a diagnosis. For example, oncologists can use expression analysis to establish a more precise diagnosis in leukemia patients. This is useful for determining prognosis and treatment. For many disorders, different genetic variations may cause or contribute to the same disorder. For example, autism in one family may be caused by different genetic factors than in another. Knowing the genetic contribution may help others in the same family obtain an earlier diagnosis, or in the future, these genetic differences may be shown to benefit from different therapies.
Treatment
One of the most touted treatment benefits of genomic medicine is pharmacogenetics, using genetic information to improve prescribing. Presently in the United States, adverse drug reactions (ADRs) are a huge health burden. The Food and Drug Administration (FDA) admits that approximately 100,000 deaths annually are attributable to ADRs; furthermore ADRs are estimated to cost more than $100 billion a year. In addition, the efficacy of a drug varies greatly among patients. Without advance insight, doctors often rely on trial and error to find the best drug for the patient. Genetic variation is believed to play a large role in both ADRs and efficacy. The FDA unanimously agreed that a certain gene variant predicts the efficacy of Tamoxifen, a drug prescribed in some women to reduce the risk of breast cancer recurrence. Having a certain variant may reduce the drug’s effectiveness and even increase the chance of a cancer recurrence. This is just one example of many to come where genetic information improves prescription practices.
Another treatment possibility is tailoring drug development to disease biology. Identifying and learning about the genes that cause or contribute to a disorder has and will continue to provide new therapeutic targets through greater understanding about the biology of the disorder. For example, enzyme replacement therapies are available for some Mendelian disorders in which the gene codes for a defective enzyme. A future application on the other end of the spectrum is a genetic variation that confers protection from the human immunodeficiency virus (HIV) in a small percentage of the population. This variant may offer solutions for new treatment strategies.
Impact
Genomic medicine will come to define the next era in medicine. Instead of a one-size-fits-all approach that prioritizes treatment over prevention, medicine will evolve to capitalize on genetic information to tailor care to the individual that prioritizes prediction and prevention of disease. Not only will this result in improved health care, but it should result in significant cost savings as well. Before this scenario can occur, however, numerous barriers must be overcome, including reimbursement struggles and educating health care providers, among others.
Key terms
adverse drug reaction
:
undesirable side effect to a medication
clinical utility
:
ability to use results to improve patient care
clinical validity
:
the likelihood a person who tests positive will develop a disorder
expression analysis
:
examining RNA to determine which genes are being transcribed
therapeutic gap
:
a situation in which there is no mechanism to improve a health outcome for those identified at risk
Bibliography
Guttmacher, Alan E., and Francis S. Collins. “Genomic Medicine: A Primer.” The New England Journal of Medicine 347 (2000): 1512–20. Print.
Innovations in Service Delivery in the Age of Genomics: Workshop Summary. Washington, DC: National Academies, 2009. Print.
Khoury, Muin J., Wylie Burke, and Elizabeth J. Thomson. Genetics and Public Health in the Twenty-First Century: Using Genetic Information to Improve Health and Prevent Disease. New York: Oxford UP, 2000. Print.
Kumar, Dhavendra, and Charis Eng. Genomic Medicine: Principles and Practice. New York: Oxford UP, 2014. Print.
McCarthy, J. J., H. L. McLeod, and G. S. Ginsburg. "Genomic Medicine: A Decade of Successes, Challenges, and Opportunities." Science Translational Medicine 5.189 (2013). Print.
Pfeffer, Ulrich. Cancer Genomics: Molecular Classification, Prognosis, and Response Prediction. New York: Springer, 2013. Print.
Simon, Richard M. Genomic Clinical Trials and Predictive Medicine. Cambridge: Cambridge UP, 2013. Print.
Suther, S., and P. Goodson. “Barriers to the Provision of Genetic Services by Primary Care Physicians: A Systematic Review of the Literature.” Genetics in Medicine 5 (2003): 70–76. Print.
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