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Economics of Genomic Testing for Women With Breast Cancer
Robert D. Lieberthal, PhD
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Economics of Genomic Testing for Women With Breast Cancer

Robert D. Lieberthal, PhD
All the economic studies of genomic tests for breast cancer rely on modeling rather than randomized controlled trials or other direct trial data.
We included 5 cost-effectiveness studies of Oncotype DX, all of which concluded that treatment guided by genomic testing is cost-effective compared with conventional treatment selected according to guidelines. In 1 study, Lyman and colleagues26 examined the incremental cost-effectiveness ratio associated with 3 treatment strategies: tamoxifen alone, chemotherapy followed by tamoxifen, and recurrence score–guided treatment. They found that genomic testing is costeffective relative to the other strategies.26 In another study, Klang and colleagues18 applied recurrence score–guided recommendations to patients of a managed care organization in Israel. They found that treatment pathways were changed from the initial recommendation (according to traditional prognostic pathways) in 40% of patients and that the incremental cost-effectiveness ratio for using Oncotype DX was $10,770 per QALY.18 That is well below the international norms of willingness to pay used in Israel and the threshold of $50,000 per QALY gained commonly used in the United States context (Table).27

One study included in our comparison discussed cost-effectiveness analyses of a 70-gene expression-profiling test (MammaPrint). This study, performed in 2005, found that MammaPrint had lower costs and worse outcomes, in terms of lower life expectancy and QALYs, than the National Institutes of Health clinical guidelines for treatment of breast cancer. Specifically, the study found that use of the test “resulted in an absolute 5% decrease in the proportion of cases of distant recurrence prevented, 0.21 fewer QALYs, and a cost savings of $2882.”15 A more recent study performed in 2010, also in our comparison, found that MammaPrint had a favorable  incremental cost-effectiveness ratio of $7000 per QALY and $10,000 per life-year saved compared with Adjuvant! Online software  guidance.7 The contradictory findings from these 2 studies are primarily due to the control group being used—National Institutes of  Health guidelines in one case and decision software in the other case. The findings may also be related to the timing of these  studies, one of which was published 5 years later than the other.

Two international studies included in our comparison conducted generic cost-effectiveness analyses of genomic testing rather than selecting a specific marketed product. One European study used a different type of genomic test (189-gene expression profiling) not usually available in the US market and applied cost-minimization analysis.28 They found that compared with conventional adjuvant chemotherapy, genomic testing is cost-effective only if the test cost is less than €2090 ($2720).28 A Brazilian study focused on the financial impact of using genomic testing (21-gene expression profiling) by taking into account the medical costs and excluding  utility or QALY measures.19 Bacchi and colleagues19 conducted a Web-based survey among medical oncologists in Brazil and  compared the costs associated with individual decisions for treating hypothetical patients with the costs associated with 21-gene  expression assay–guided decisions. They found that for a hypothetical cohort of 100 patients with access to the test, $79,400  would be saved in direct medical costs.

Perspectives Represented in Our Review

In our economic analyses we compared, payer, provider, and societal perspectives. A total of 3 studies examined the societal perspective, 4 described the payer perspective, and 2 studies used a provider perspective (Table 1).

Those studies that used the societal perspective included relevant costs and outcomes that were associated with breast cancer in their models, as well as cost utility. Including cost utility proved extremely useful.

Studies using the payer perspective compared the actual cost per QALY with the acceptable threshold in order to determine the cost-effectiveness of the assay.21 Out-of-pocket patient costs and indirect medical costs are not included in the payer perspective.


Data Versus Modeling Methods

The studies reviewed are largely based on data from clinical trials. They all rely on economic modeling methods to generate cost predictions or projections. A variety of modeling methods were used, most commonly Markov modeling. Cost-effectiveness analysis and budget impact analysis were also used.


These economic studies took into account the opportunistic cost of having unnecessary chemotherapy for a certain subpopulation and humanistic measures such as quality of life. The majority of studies provided monetary thresholds for each product in the market, which helped enable economic decision making about whether to use genomic testing.


Economic modeling has significant limitations compared with clinical trials. Because these studies are usually extrapolated from  clinical trial data, the same prognostic accuracy may not apply in a real-world setting. The Evaluation of Genomic Applications in Practice and Prevention Working Group identified 2 additional concerns. One is that details and characteristics about certain tests  are not published. Additionally, “concerns about the parameter estimates, lack of sensitivity analyses to assess sources of bias,  and changes in the National Comprehensive Cancer Network (NCCN) guidelines reduce the conīŦdence and relevance of one of  these studies.”2 We agree that comparative effectiveness analysis in this area is challenging because the outcome of value is more  difficult to measure than clinical end points and because technology is evolving so rapidly. To improve the economic evidence base for these tests, we believe that there should be increased funding for economic evaluation and that the results of any testing should be made more transparent.

Future Research and the Case for the Economic Benefit of Genomic Testing

There is a great need for additional translational research, including both clinical and outcomes research.29,30 There is also an opportunity to develop tests that can inform treatment  decisions for subpopulations for whom current tests are not applicable, such  as patients diagnosed with triple-negative breast cancer or those with ER-negative status. There may also be interest in comparative effectiveness research results from head-to-head trials comparing genomic tests and treatment outcomes.

Genomic medicine requires additional evidence about the value of these tests to reach its full potential.31 A value calculation requires evidence of both clinical utility and economic effectiveness.32 Several articles discuss the barriers genomic medicine faces  with regard to making the case for value. Experts recommend additional funding for trials oftranslational research, specifically in  phases 2 through 4, additional evidence of clinical utility,33 more outcomes data, and improved regulation of genomic test  production.34,35



Genomic tests for breast cancer are relatively new. In the United States, regulation is the responsibility of the US Food and Drug  Administration (FDA), which regulates genomic tests as devices. The FDA has indicated a strong preference for co-approval of  genomic tests and the treatments that they test for.36 It has issued significant guidance on genomic technologies that are subject  to FDA approval. However, “the overwhelming majority of genetic tests are not currently subject to FDA scrutiny.”37 The international regulatory perspective is generally more focused on a combination of effectiveness and cost-effectiveness compared with the US  erspective, which is focused solely on effectiveness. For that reason, additional cost-effectiveness studies may help with the  diffusion of genomic testing outside the United States.


Reimbursement for genomic testing depends upon the type of insurance, if any, that the patient has. Oncotype DX is widely utilized  and reimbursed, and is often covered by Medicare. Trosman and colleagues38 studied the approaches used by private payers to  develop their coverage strategy for Oncotype DX and found that all payers prioritized clinical evidence as the most important  decision factor, also taking into account medical society recommendations. The majority of payers were not concerned that  Oncotype DX has not received FDA approval.

Future Study Designs

The economic analysis studies reviewed in this article make a case for the cost-effectiveness of genomic testing for breast cancer  treatment decision making. While the modeling studies we reviewed do support the value proposition for genomic testing for breast  ancer, more must be done to show the projected benefits that could be realized in realworld settings. Trials centered around the  ctual use of such tests in the breast cancer population are necessary to bolster the evidence base.33

Policy Implications

The cooperation of multiple stakeholders will be essential if genomic medicine is to reach its full potential. The barrier to progress for  enomic medicine is the lack of empirical evidence for the clinical utility and value of genomic testing.29,33 The literature appears  to support the need for additional studies to evaluate the value of genomic testing for breast cancer.38 The lack of  ranslational research is mentioned repeatedly as an obstacle to establishing the clinical utility and outcomes evidence base needed  to inform regulatory, coverage, and reimbursement decisions.30 Clear standards and processes for oversight and regulation of genomic testing are also lacking.39-41 Currently, policy makers must evaluate these promising new technologies without full information.  dditional economic evaluations can serve to reduce the regulatory uncertainty regarding a disease that affects many women, their families, and their communities.

Author Affiliation: Jefferson School of Population Health, Thomas Jefferson University, Philadelphia, PA.

Funding Source: This article was written as part of an education grant funded by Genomic Health.

Author Disclosures: The author report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Analysis and interpretation of data; critical revision of the manuscript for important intellectual content; and supervision.

Address correspondence to: Robert D. Lieberthal, PhD, Assistant Professor,  Jefferson School of Population Health, Thomas Jefferson University, 901 Walnut St, 10th Floor, Philadelphia, PA 19107. E-mail:
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