This is an application for a K08 award for Dr. Yiwey Shieh, a general internist at the University of California, San Francisco (UCSF). Dr. Shieh's long-term career goal is to become a leading clinician-investigator working at the intersection of precision medicine and cancer screening/prevention. This K08 award will provide Dr. Shieh with the support necessary to achieve the following training goals: 1) develop his understanding of breast cancer heterogeneity and the use of gene expression profiling to profile tumors; 2) deepen his familiarity with genetic association techniques; 3) learn and implement advanced techniques in prediction modeling; 4) become proficient in manipulating and analyzing high-dimensional datasets; and 5) build an independent re- search career. To help him achieve these goals, Dr. Shieh has assembled an exemplary team of mentors with complementary expertise. Dr. Elad Ziv, a leading cancer genetic epidemiologist, will serve as the primary men- tor and oversee completion of research aims and training goals. Co-mentors will include: Dr. Laura van't Veer, an expert in gene expression analysis of breast cancer; Dr. Mi-Ok Kim, an expert in biostatistical methods and study design; and Dr. Laura Esserman, a breast surgeon and leading innovator in precision oncology. The proposed work will benefit from the available expertise and rich institutional environment of UCSF, specifically the Helen Diller Family Comprehensive Cancer Center, one of the top cancer centers in the country. Dr. Shieh will leverage the above resources to address an important problem in cancer control, namely the limited ability of current breast cancer screening strategies to detect biologically aggressive cancers. These cancers tend to present symptomatically following a normal mammogram as ?interval cancers?, and often at advanced stages. Dr. Shieh's past work has focused on predicting overall breast cancer risk using clinical risk models and poly- genic risk scores (PRS) representing the combined effects of multiple single nucleotide polymorphisms (SNPs). Dr. Shieh proposes to adopt this approach to construct a risk model for interval cancers based on pre- liminary data suggesting that previously discovered SNPs may hold additional prediction for breast cancer phenotype. Given the ability of gene expression profiling to provide detailed tumor phenotypic information, Dr. Shieh proposes to identify gene expression signatures correlated with rapidly-developing (aggressive) interval cancers (Aim 1). Dr. Shieh will then use these gene expression signatures to select SNPs associated with ag- gressiveness (Aim 2). These selected SNPs will be used to construct a PRS for aggressive cancer. Finally, Dr. Shieh will test the ability of this PRS to predict interval cancers, both alone and in combination with breast den- sity and clinical risk factors (Aim 3). The proposed aims will leverage Dr. Shieh's unique access to data from clinical trials, tumor atlases, and breast cancer screening cohorts. The results will directly lead to subsequent studies on using an interval cancer risk model to prospectively inform screening and prevention, as well as studies on the role of novel biomarkers in refining risk stratification among high-risk women.

Public Health Relevance

Between 20-30% of women undergoing screening mammography develop a symptomatic breast cancer follow- ing a normal mammogram; these interval cancers are often biologically aggressive and associated with worse outcomes. The proposed research will develop a risk prediction model for interval cancers using genetic vari- ants, breast density, and clinical risk factors. This tool could enable personalized screening and prevention ap- proaches that are specifically tailored to a woman's risk of aggressive cancer, potentially decreasing morbidity and mortality related to interval cancers.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Clinical Investigator Award (CIA) (K08)
Project #
Application #
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Lim, Susan E
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Francisco
Internal Medicine/Medicine
Schools of Medicine
San Francisco
United States
Zip Code