Can Genomics Bend the Cost Curve

IN 1960, US HEALTH CARE EXPENDITURES TOTALED SLIGHTLY more than $100 per person and 5% of the gross domestic product (GDP). Now in 2012, individual health care expenditures are more than $8000 per year and the proportion of the GDP spent on health care is more than 17%. Health care costs are widely believed to contribute to the economic challenges faced by the public and private sectors of the US economy. Calls to bend the health care cost curve have become headline news. Prior approaches to slowing the growth in US health care costs have had limited success. The use of cost-benefit analyses to make coverage or clinical decisions has proven socially and politically unacceptable in the United States. Managed care temporarily slowed cost increases in health care but the public backlash against restriction of choice undermined its potential long-term effects. Similarly, the unwillingness to include economic outcomes in the current investment in comparative effectiveness research will limit its effects on health care costs. New approaches are needed. At the same time that health care costs have gained increasing attention, advances in genomics have begun to have a major influence on clinical care, particularly in oncology. Germline and somatic genetic variation is increasingly used to predict disease incidence, severity, prognosis, and response to therapy in clinical practice as well as to provide new therapeutic targets in the laboratory. Just as with infectious epidemics, this new ability to understand host factors may prove a useful approach to managing a cost epidemic, even beyond the focus on the agent (ie, the health care intervention) or the vector (ie, the health care clinician). There are 2 perspectives on the effect of genomics on health care costs. One view is that genomics is a form of new technology and it is well established that new technologies increase health care costs. For example, identification of the ERBB2 HER2/neu (formerly) gene and the development of trastuzumab has increased the cost of breast cancer care, in part because of the additional cost of ERBB2 expression testing (approximately $150), but mostly because of the cost of trastuzumab (approximately $45 000) for the 20% to 30% of women who test ERBB2-positive. The total cost of this genomic advance, with substantial clinical benefit, has been estimated at more than $750 million per year. Other examples include the implantation of defibrillators in patients who are found to carry a mutation linked to a hereditary cardiomyopathy or the use of breast magnetic resonance imaging screening for women with a BRCA mutation. From this perspective, advances in genomics will extend life but exacerbate the current growth in health care expenditures. However, genomics differs from many other health care technologies because it can also be used to identify individuals who will experience little to no benefit from an intervention, either because they are at low risk of having an adverse outcome without intervention or because they will not respond to the intervention. Reducing the use of interventions that will have little to no benefit could have an important effect on the cost curve, particularly when the intervention is common or very costly. One example is the use of gene expression profiling among women with breast cancer. A 21 gene expression panel correlates with the benefit of adjuvant chemotherapy among women with localized, estrogen receptor–positive breast cancer such that women with a low-risk score receive little to no benefit from chemotherapy. Several studies have demonstrated that use of the gene expression panel leads to lower rates of chemotherapy among women with low-risk scores. Furthermore, because the low-risk group is 2 to 3 times larger than the high-risk group, overall rates of adjuvant chemotherapy would decline by 15% to 20%. Assuming adjuvant chemotherapy incurs approximately $20 000 in health care costs per woman and 100 000 women are diagnosed with localized, estrogen receptor–positive breast cancer each year, the use of gene expression profiling in this setting has the potential to save $400 million each year. Likewise, other genomic tests help to predict the response to an expensive therapy, including the use of epidermal growth factor receptor testing to determine the use of erlotinib in advanced non–small cell lung cancer or the use of KRAS testing to determine the use of cetuximab in advanced colorectal cancer. While these examples are only a tiny fraction of health care costs, this fraction could increase if “omic” information proves useful for identifying