Cost-effectiveness analyses of population-based multi-gene testing for the prevention of breast and ovarian cancer in the United States.

Abstract

10596 Background: The current method of testing for BRCA gene mutations, which is based on family history, often fails to identify many carriers. With the advancement of next-generation sequencing technologies, which have significantly lowered the cost of genetic testing and sequencing, population-based testing has been proposed. We evaluated the cost-effectiveness of population-based multi-gene testing as a means of preventing breast and ovarian cancer. Methods: We developed a microsimulation model to assess the cost-effectiveness of multi-gene testing (BRCA1/BRCA2/PALB2) for all women aged 30-35 years compared to the current standard of care that is family history-based. We selected these genes for analyses as they are most prevalent high penetrance genes. We assumed a test uptake rate of 70%. Carriers of pathogenic variants who were not affected by cancer were offered interventions, such as MRI/mammography, chemoprevention, or risk-reducing mastectomy and risk-reducing salpingo-ophorectomy (RRSO), to reduce the risk of breast and ovarian cancer. We incorporated excess coronary heart disease (CHD) deaths from premenopausal RRSO. Our main outcome measure was the incremental cost-effectiveness ratio (ICER)(i.e., cost per-quality-adjusted life year (QALY) gained), with the commonly used societal willingness to pay of $100,000/QALY in the US as the C-E threshold. We ran 500 simulations on 1,000,000 women, employing a lifetime time horizon and payer perspective, and adjusted the cost to 2022 US dollar. We also used probabilistic sensitivity analyses to evaluate model uncertainty. Results: In the base case, population-based multi-gene testing is more cost-effective compared to family-history based testing, with an ICER of $41,306/QALY (95%CI $35,630-$48,441/QALY). This new testing method is able to prevent an additional 1,369 cases of breast cancer and 807 cases of ovarian cancer, but it will also result in 8 excess heart-disease deaths per million women. The probabilistic sensitivity analyses show that the probability that population-based multi-gene testing is cost-effective is 100% for all the simulations. When the cost of the test exceeds $1,100 population-based multi-gene testing becomes economically inefficient (ICER $10,0886/QALY). Conclusions: Population-based multi-gene testing is a more cost-effective option for the prevention of breast cancer and ovarian cancer when compared to current family-history-based testing methods.