Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversity

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Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, life-style interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools are used in practice, they face various challenges including: (i) modest discriminatory ability due to lack of a unified model that incorporates a comprehensive set of risk-factors; (ii) inability to produce sub-type specific risk, especially considering aggressive subtypes of breast cancer and/or prophylactic endocrine therapy that is effective only for hormone receptor positive tumors; (iii) lack of data to build models for different ethnic populations; and, (iv) scant validation of models, especially in healthcare settings where models can be widely disseminated in practice. In this proposal, we will assimilate and analyze data on a large and diverse sample of women from studies participating in the NCI Cohort Consortium to develop a comprehensive tool that will predict breast cancer risk, overall and by sub-types, across major ethnic groups in the US. We further propose to prospectively validate the model in different clinical settings, including a risk-stratified screening trial. In Aim 1 we will develop a comprehensive model for predicting absolute risk of overall breast cancer for women from multiple ethnicities, incorporating information on family history; polygenic risk-scores (PRS); anthropometric, life-style and reproductive factors; hormonal biomarkers; and mammographic density. In Aim 2 we will tailor these risk models to specific breast cancer subtypes, notably estrogen receptor negative and positive cancers. In Aim 3 we will evaluate the validity of these risk prediction models in integrated health care systems, mammography registries, and an ongoing risk-based mammographic screening trial in the US. The resulting models could be used in diverse clinical settings to guide preventive therapy or risk-stratified screening programs, increasing the number of breast cancer deaths prevented while minimizing overdiagnosis and overtreatment.