Breast cancer is the most common type of cancer found in the UK population, with 1 in 8 women diagnosed during their lifetime. As a prevalent cancer, it’s important to understand more about the potential causes and relative risks that individuals from different demographics might have.
The Cancer Epidemiology Unit (CEU) at the Nuffield Department of Population Health specialises in large-scale studies into the lifestyle and genetic risk factors of cancers such as breast cancer. In doing so, these studies can provide evidence to inform public health policies and answer outstanding questions about how cancers may arise.
The cause of breast cancer has long been researched and over the last two decades, findings from the Unit’s large prospective studies and international collaborations have helped clarify the role of many risk factors for the disease, including use of menopausal hormones and oral contraceptives, as well as factors relating to childbearing. Within the last year, an updated review of the worldwide evidence carried out in CEU showed that menopausal hormone usage increases the long term risk of breast cancer by almost twice as much as was previously thought, findings which influenced public health guidance. Other recent work found evidence to suggest that high fruit and fibre intake and physical activity may be associated with lower risks of breast cancer.
The CEU work with big data, such as its Million Women Study which contains data from 1.3 million UK women, collected since it began in 1996. The study includes 1 in 4 of all UK women born between 1935 and 1950 and remains the largest data set of its kind. The study, which aims to resurvey women every 3-5 years, continues to collect information on new potential risk factors such as working night-shifts (which in this case was shown to have no influence on breast cancer incidence).
Enhancing the quality and quantity of the Million Women Study dataset is high on the CEU’s agenda. One area of research where the study hopes to be able to contribute substantially over the next few years is in risk stratification for breast cancer. Prediction models which can be used to assess an individual’s breast cancer risk are key for planning risk-based screening approaches that are tailored to an individual, so refining their accuracy is important to ensure that interventions can be targeted appropriately. However, while existing risk prediction models look promising they need further improvement in their ability to identify those women who are most likely to get breast cancer before they can be applied at a population level. In particular, models should ideally incorporate the whole spectrum of breast cancer risk factors including genetic variation and radiological imaging data.
This will be the next stage for the Million Women Study, as Prof Gill Reeves, Head of the CEU, hopes to integrate new datasets into the study. This includes digital screening images from mammograms, and other clinical information, which could be used in combination with existing information held on participants, to allow the CEU to develop more accurate risk prediction models from the Million Women’s Study.
Prof Gill Reeves, Head of the CEU, says:
“Enriching the quality of datasets such as the Million Women Study will allow us to continue to provide reliable evidence regarding the effects of behavioural and biological factors on breast cancer risk, and help identify women who are at particularly high risk of the disease. In doing so, we can better inform public health advice, and clinical practice.”
To read more about the CEU’s work on breast cancer go to the CEU website.
About the CEU
The CEU runs the Million Women’s Study (MWS) and EPIC-Oxford (two large cohort studies). Recent grant funding from CRUK has allowed for further enhancement of the MWS so that new clinical data and other potential risk factors for cancer may be integrated.
Prof Gill Reeves, Head of the CEU, is a Professor of Statistical Epidemiology. Her main research interests are the roles of hormonal and other risk factors in the development of female cancers. She is particularly interested in risk factors and patterns of survival for molecular subtypes of breast cancer.