Female breast cancer is the fifth leading cause of death worldwide (685,000 deaths in 2020). However, breast cancer is not one entity; different subtypes have different causes and prognosis, ranging from highly fatal to fully curable.
Specific genetic, environmental and lifestyle factors increase the risk for breast cancer. It has been postulated that these risk factors together giving rise to distinct risk factor profiles increasing risk of particular subtypes of breast cancer. For example, presence of BRCA1 germline mutations predispose individuals to develop triple-negative breast tumours; risk factors such as a high body weight and use of hormones may also predispose to specific breast cancer subtypes.
B-CAST is the acronym for Breast Cancer Stratification, referring to the different subtypes of breast cancer and the need for further individualization or stratification of breast cancer prevention and treatment. In this project we aimed to better understand breast cancer risk profiles by combining genetic and lifestyle information, and linking these with prognosis. We also aimed to pioneer the development of subtype specific risk prediction models and improve an existing prognostication tool. This can enable more precise identification of women who would benefit from existing prevention and treatment strategies
Our approach:
We exploited and built upon existing resources, infrastructure and collaborations that had been established through the Breast Cancer Association Consortium (BCAC)
• We expanded the number of breast cancer patients from ~100,000 to ~226,000
(from 107 studies worldwide).
• We collected more complete and in-depth clinical and risk factor information.
• We further developed the novel STRATUS algorithm to determine breast density based on mammograms.
• We characterized over 25,000 tumours embedded in Tissue Micro Arrays using 15 different immunohistochemical markers.
• We established an image management system including transfer, harmonization, and archiving of over 600,000 images.
• We extracted DNA and RNA of 10,000 breast tumours.
• We developed a breast cancer sequencing panel (including 323 genes, and relevant single nucleotide variants for linkage and detection of copy number aberrations), which we applied to the DNA from 10,000 tumours.
Please see also:
Our output
Our impact