The central concept of B-CAST is the understanding of the relationships between breast cancer risk factors, somatic characteristics of breast tumours and their prognosis, which is critical for the development of novel strategies for breast cancer prevention and treatment with reduced morbidity. This could be accomplished by helping to identify targets for preventive and therapeutic drugs; identification of women most likely to benefit from targeted prevention and early detection strategies; improved prognostication and prevention of metastasised disease; and/or the optimisation of targeted adjuvant chemotherapy.
BCAST builds on a worldwide cooperation that is unique since it comprises the largest breast cancer series with well-annotated tumour samples, including a related, comprehensive dataset on environmental factors, mammographic breast density, germline genetics, and clinical outcomes. The Breast Cancer Association Consortium (BCAC) is at the core of the proposed project.
B-CAST builds upon the knowledge already generated within BCAC, as well as on established scientific collaborations, infrastructure and resources in BCAC. By integrating and analysing data on environmental, mammographic breast density and genetic risk factors, pathology and molecular features of tumours, and clinical outcomes from the same patients, in B-CAST we will be able to: (i) gain insights into the aetiology underlying the heterogeneity of breast cancer; and (ii) understand how disease heterogeneity, combined with germline genetics, breast density, and environmental factors before and after diagnosis, influence subsequent clinical outcome.
This knowledge will then be used to identify risk factor profiles associated with clinically relevant tumour subtypes, and to inform the development and subsequent validation of risk prediction and prognostication tools. A critical objective here is to determine the added benefits of predicting subtype-specific risk, in addition to overall breast cancer risk. Subtype prediction should, for instance, identify more precisely those women that benefit from screening because of an elevated risk of tumours more likely to be screen detected; or through the identification of women more likely to benefit from preventive endocrine therapy because of increased risk for endocrine-responsive tumours. For prognostication tools, our work will add to and go beyond the tumour expression profiles used already in clinical practice for prognosis, by evaluating key markers on a much larger sample size than has previously been possible.
- To define the influence of risk factors, including reproductive history, lifestyle, mammographic breast density and germline genetic variation, on breast cancer overall and by subtypes characterized by clinical and molecular markers.
- To define the influence of risk factors and tumour subtypes on clinical prognosis.
- To develop and validate breast cancer risk and prognostication models for breast cancer, overall and by subtypes, informed by knowledge acquired under above objectives
- To implement these models into online tools for risk prediction and prognostication; and make them available in multiple countries/languages.
- To raise awareness, i.e. promote the development and integration of personalized breast cancer prevention within national public health programmes.