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.
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 and additional studies from healthcare settings, clinical trials and mammographic screening registries to develop and validate a comprehensive tool that will predict breast cancer risk, overall and by sub-types, across major ethnic groups in the US.
