Economics of Genomic Testing for Women With Breast Cancer
Published Online: December 23, 2013
Robert D. Lieberthal, PhD
In women, breast cancer accounts for nearly 1 in 4 cancers. It is the most common cancer in women aside from skin cancer. It has the second-greatest mortality rate for women, exceeded only by lung cancer.1 Treatment pathways for breast cancer recurrence after primary surgical and radiation therapy are complex and are based on multiple known risk factors. Women are offered adjuvant chemotherapy if the risk of recurrence is above a certain threshold.2
While the benefit of adjuvant chemotherapy is well established in patients with early-stage breast cancer that is estrogen receptor ER) positive and lymph node negative, 65% of women diagnosed with invasive breast cancer have lymph node–negative disease; of these, 15% are expected to die or have distant metastasis in 10 years.3 Currently, novel genomic evidence about the diversity of patients is challenging scientists to develop and validate more personalized approaches to treatment.
Advances in genomic diagnostics and personalized treatment have the potential to improve health outcomes, reduce mortality, and increase quality of life for cancer patients. Genomic tests analyze the genetic profiles of patients’ tumors by generating a “recurrence score” based on a particular algorithm. The recurrence score predicts the likelihood of cancer recurrence and informs decision making.
Several types of genomic tests are available today in the United States for breast cancer, including Oncotype DX, MammaPrint, and CancerTYPE ID. Oncotype DX is a 21-gene assay test that gives patients with stage 1 or 2 ER-positive, lymph node-negative breast cancer a recurrence score between 0 and 100.4 MammaPrint is a 70-gene assay to determine the risk of cancer recurrence for ER-positive or ER-negative, lymph node–negative patients with early-stage breast cancer (stages 1 and 2).5,6 CancerTYPE ID measures a 92-gene expression taken from a tumor biopsy to determine 30 different types of tumors. A component of the test, Breast Cancer Index, can be ordered if the tumor is the suspected primary source.6 CancerTYPE ID is not as widely used as the other 2 tests in breast cancer.
ECONOMICS OF BREAST CANCER
The total cost of breast cancer includes the financial burdens for the patients, their families, and society. The National Cancer Institute estimates the national annual financial cost of breast cancer care at $13.9 billion.7 Breast cancer care represents the largest portion of all cancer care expenditures. As the healthcare system works to find ways to pay for genomic technology and healthcare costs continue to trend upward, the potential cost savings and productivity gains associated with advances in genomic diagnostics and personalized treatment that avoid unnecessary and ineffective treatments are appealing.8
Economic analysis provides a framework for assessing the value of clinical outcomes, as well as for determining how that value might differ by population.9 This literature review improves upon previously published literature reviews of genomic testing for breast cancer by focusing on the economic evidence. We are not aware of other structured reviews on this topic that utilize the economic perspective. Our key findings are that research in this area has been limited and is likely to consist primarily of modeling studies for the foreseeable future. Additional funding, better outcomes data, and regulation are identified as barriers to adoption of this new technology.
An economic evaluation prioritizes the efficient use of scarce resources. As a result, economic evaluation leads to the test that best balances the trade-offs among available alternatives rather than simply selecting the cheapest or most valid test.10 While data from randomized controlled trials are a gold standard, an economic analysis uses models to combine costs and outcomes. The combination of both types of data in a model that allows for the evaluation of the cost-effectiveness of a particular type of technology results in information that maximizes efficiency.11
This framework guided the snowball methodology, described in further detail in the eAppendix (available at www.ajmc.com). In order to appear in our literature review, a study had to cover the clinical area of breast cancer, with the scientific application of a genomic test. Further, the analysis had to include a cost-effectiveness analysis broadly defined. A costeffectiveness analysis uses 2 basic building blocks—economic cost and clinical effectiveness—to assess the value of a test, drug, or other healthcare product. Value comes from calculating the incremental improvement derived from a particular test over existing technology.12
This conceptual framework resulted in a review of 9 studies as described in the section titled Published Studies on Economic Outcomes. Due to the small number of studies available for each topic area of the review, we present studies on an individual basis rather than presenting summary results across studies.
PUBLISHED STUDIES ON ECONOMIC OUTCOMES
Several studies have been published utilizing economic modeling methods to analyze the benefits and costs of genomic testing for breast cancer. The costs of genomic testing are 2-fold: the cost of the genomic test itself and the costs associated with false-negative or false-positive results. For a complete listing of cost variables, please see the Ishikawa Fishbone Analysis13,14 depicted in the Figure. The Jefferson Population Health Continuing Professional Education Collaborative created the diagram based on the references cited in the article. Thus, the Figure is an original work based on the source material from this literature review.
The direct cost of genomic tests is clear and varies by test and payer. The evidence report commissioned by the Evaluation of Genomic Application in Practice and Prevention Working Group and the Agency for Healthcare Research and Quality identified the 3 genomic tests for women with breast cancer that are clinically available in the United States (MammaPrint, Oncotype DX, and CancerTYPE ID).2,9 The cost is generally regarded as $3460, with a range of $1960 to $4860. The cost for a particular patient depends on factors such as insurance coverage policy and regional variability.15
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