Proponents of personalized medicine say that the incorporation of genomic data will change how patients are treated, but the Mayo Clinic's Michael Joyner and Nigel Paneth from Michigan State University argue that "the assumptions underpinning personalized medicine have largely escaped questioning," a situation they seek to rectify in an editorial appearing in the Journal of the American Medical Association.
In particular, they question how well genomics will be able to contribute to disease risk prediction, how personalized medicine will affect the cost of care, and what the public health benefit of personalized medicine will be, among other concerns.
Many of the variants linked to disease so far increase the relative risk of disease only slightly, adding little predictive power, they argue, to traditional risk prediction algorithms. They further note that the expected better adherence to lifestyle changes in the wake of patients learning their genomic risk of disease hasn't really materialized.
By almost definition, Joyner and Paneth add, personalized therapies will be expensive as they will only help a small population of patients — the cost of a new targeted cancer drug can surpass $100,000 per year. In addition, they say that the effectiveness of genome-based drugs may not be as high as hoped as two targeted cystic fibrosis drugs only improve patients' maximum forced expiratory volume by 5 percent to 10 percent. At the same time, though, they say that survival among cystic fibrosis has increased, though due to clinical management guidelines developed through pulmonary physiology and infectious disease research.
Overall, Joyner and Paneth also wonder how personalized medicine will affect public health and whether it will actually help reduce the major causes of morbidity and mortality. They note that, historically, most public health successes have targeted broad populations.
"Even though personalized medicine will be useful to better understand rare diseases and identify novel therapeutic targets for some conditions, the promise of improved risk prediction, behavior change, lower costs, and gains in public health for common diseases seem unrealistic," they add. "Proponents of personalized medicine should consider tempering their narrative of transformative change and instead communicate a more realistic set of expectations to the public."