Our work is organized around three connected themes: early detection, precision medicine, and survivorship in breast cancer. We study how germline genetic variants and lifestyle, hormonal and environmental factors shape breast cancer subtype, prognosis, and treatment outcomes, drawing on large data resources, and combining epidemiology with genetics and bioinformatics. A particular focus is the early detection, prevention, and risk prediction of breast cancer and its recurrence, including contralateral breast cancer.
We translate these findings into prediction models and clinical decision-support tools, such as PREDICT and PredictCBC, that support shared decision-making between patients and physicians. Our overarching goals are to prevent breast cancer and its recurrence, reduce overtreatment, and improve patient outcomes.
Because this work depends on the responsible (secondary) use of human data and biological materials, we also study the Ethical, Legal, and Societal Implications (ELSI) of this use and operate an ELSI Service Desk that advises researchers, clinicians, and patients on these questions.
