With this Career Development Award, I aim to develop independence in cancer prevention research through consistent and focused mentorship, didactic coursework and seminars, and advanced skill training. While I already have a solid foundation in predictive modeling and advanced statistical and epidemiological methods, I need intensive training in the fields of population genetics, decision analytic modeling, economic evaluation, and comparative effectiveness research to further my career in cancer prevention. This award will provide me with the training, support, mentorship, and research experience that I require. I will be mentored by leading experts in the fields of comparative effectiveness research (Dr. Yong-fang Kuo), economic evaluation (Dr. Ya- Chen Tina Shih), and women's health and cancer prevention (Dr. Abbey B. Berenson). My K07 project will explore whether population-based genetic testing in young women (20?40 years) is advisable, feasible, and cost-effective for the prevention of breast and ovarian cancer. In the 1990s, the discovery of BRCA1 and BRCA2 mutations in women susceptible to breast and ovarian cancer opened up the opportunity for individualized cancer prevention care. Since then, numerous genetic variants have been linked to high cancer susceptibility. Over half of women with BRCA mutations will develop breast or ovarian cancer during their lifetimes, but BRCA-related cancers are preventable if mutation carriers are identified and treated before cancer onset. BRCA testing based on family history and ancestry has been recommended by the US Preventive Services Task Force (USPSTF) for cancer prevention. However, BRCA testing currently fails to identify most mutation carriers before they are diagnosed with breast or ovarian cancer; only 10% of unaffected BRCA mutation carriers have been identified in the US. We propose to study current genetic testing practices and conduct an economic evaluation of population-based screening using simulation modeling.
The specific aims of this proposal are to 1) assess current practice patterns of genetic testing and the comparative effectiveness of current risk management strategies among mutation carriers in the US, 2) quantify the health impact of genetic testing and risk-based cancer preventive care on breast and ovarian cancer outcomes by creating a Markov simulation model, and 3) conduct an economic evaluation of population-based genetic testing in young women. This research project will provide an excellent vehicle for me to develop expertise in comparative effectiveness research, decision modeling, and economic evaluation in cancer preventive research. These advanced skills and the preliminary data I collect will ensure a successful bridge to submission of competitive R01 applications to conduct economic evaluations of new screening, diagnostic, and risk management strategies in multiple types of cancer. The proposed research will help policy- makers, health care organizations, and providers make more informed decisions to promote efficient utilization of cancer preventive care, equitable distribution of care, and overall population health.

Public Health Relevance

Rapid advances in genetic sequencing technology promise to greatly reduce the cost of genetic testing, thus providing a unique opportunity for the wide use of genetic testing for cancer risk in young women, followed by preventive care for those at high risk. This project will address whether population-based genetic testing in young women is advisable, feasible, and cost-effective for the prevention of breast and ovarian cancer. By applying rigorous scientific investigation and effectively translating and disseminating results to key stakeholders, we expect to inform policy decision-makers, health care organizations, and providers and advise them on future clinical guidelines.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Academic/Teacher Award (ATA) (K07)
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Subcommittee I - Transistion to Independence (NCI)
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Bian, Yansong
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University of Texas Med Br Galveston
Obstetrics & Gynecology
Schools of Medicine
United States
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