Although genomic testing can improve the cost-effectiveness of a treatment, assessing the cost-effectiveness of genomic testing outside the context of its impact on treatment is not practical.
In recent years, progress in the treatment of patients with cancer, especially those with advanced disease, has resulted in high treatment costs, with only marginal improvements in benefit. From a societal perspective, this cost is not sustainable. Therefore, using economic data in treatment decision making is a growing field, with the promise of controlling costs without impacting quality of care. In countries with a national healthcare system, such as the United Kingdom, the cost-effectiveness of an intervention has to be within a given threshold for a drug to gain approval. This approach has not been adopted in the United States.
Another mechanism for containing cost is to limit the population of patients who are eligible for a given intervention, including drugs. The use of pharmacogenomic data to identify an eligible population is appealing, as this makes it possible to reduce the heterogeneity of patients and thus improve the effectiveness and/or reduce the side effects of a treatment. However, many pharmacogenomic tests have substantial cost, and thus the question is whether the results of a test justify its cost.
In this issue of ONCOLOGY, Goldstein and colleagues review cost-effectiveness data for genomic testing in the management of colorectal cancer. They review the role of assessment of host-related genes involved in drug metabolism, as well as the use of tumor signatures as potential markers for drug choice. Their review, although it is unique and timely, lacks information about the perspective from which these costs and effects are assessed. In other words, it is unclear whether the costs and effects are assessed from a payer, patient, or societal perspective. Identifying these perspectives is of the utmost importance, however, since depending on the perspective, interpretation of the results will be different. For example, if pharmacokinetic testing for fluorouracil (5-FU) is adopted in clinical practice, patients will need sampling at 3 and 7 hours after initiation of the 5-FU infusion; the cost of keeping patients for this period of time is significant to both the provider and the patient. This cost is not included in the modeling, and thus the final conclusion and the recommendation to adopt this approach in practice needs to be addressed.
Testing for uridine 5'-diphospho-glucuronosyltransferase 1A1 (UGT1A1) and its clinical implications in the care of patients with colorectal cancer is controversial, and so are the cost-effectiveness analyses that have been performed for this test. We agree with the authors that testing for UGT1A1 should only be done when the results will affect clinical care and that cost-effectiveness data should not change the utilization of the test.
Testing for KRAS and NRAS is linked to treatment decision making, and testing for KRAS (codons 12 and 13) has been shown to improve the cost-effectiveness of treatment with anti–epidermal growth factor receptor (EGFR) antibodies.[4,5] Therefore, widespread adoption of RAS testing, with its ability to better select the population of patients who will benefit from treatment with anti-EGFR antibodies, as well as to avoid the detrimental effects of these agents in the RAS-mutated population, will improve the value of treatment with anti-EGFR antibodies-from payer, patient, and societal perspectives.
Knowledge of microsatellite instability (MSI) status can not only guide treatment decisions in patients with stage II disease; it can also assist with providing recommendations for screening of first-degree relatives of patients with Lynch syndrome. Questions regarding the method by which MSI is tested and the population of patients who are eligible for testing are beyond the scope of this commentary. We agree with the authors that MSI testing for all patients who meet the Bethesda criteria is relevant and cost-effective. Also, MSI testing for those with stage II disease who are candidates for chemotherapy is clinically relevant, although no published cost-effectiveness analysis exists in support of this recommendation.
Gene expression profiling for patients with stage II disease is a relatively new addition to the armamentarium of genomic tests for colorectal cancer. The Oncotype DX assay has been validated as a prognostic marker; however, its results are not predictive of benefit from adjuvant chemotherapy.[7-9] Therefore, use of the results of this nonpredictive test to guide treatment decisions can only be viewed as a mechanism to relieve the anxiety of the treating oncologist who recommends against chemotherapy, and cannot be the subject of a valid cost-effectiveness analysis from any perspective!
Use of next-generation sequencing (NGS) data in the clinic is growing, although issues such as the timing of testing, interpretation of the results, and access to treatment options when the results are available are currently unresolved and remain under investigation. Although we fully agree with Dr. Goldstein and colleagues that NGS in today’s environment is not cost-effective, from a societal perspective, we have to look at NGS as an investment in the future. The challenge is indexing the findings along with other clinical information for future mining. In exchange for Medicare payment for their tests, we might ask Foundation Medicine, Caris Life Sciences, and others to develop a database for storing the results of the test linked with tumor registry data, such as those of the Surveillance, Epidemiology and End Results program, for exploration in the future.
In the end, although genomic testing can improve the cost-effectiveness of a treatment, assessing the cost-effectiveness of genomic testing outside the context of its impact on treatment is not practical. Given the emergence of more efficient methods of genomic and genetic testing and the decline in the cost of performing these tests, they will be part of everyday treatment decisions in the near future-yet data for their impact on the cost-effectiveness of available treatments will lag behind their availability and use. For now, clinicians are tasked to use these tests wisely.
Financial Disclosure:The authors have no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.
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