New statistical models of tumour progression for studying factors of aggressive breast cancer, tumour heterogeneity and personalised screening

We will develop and use novel, biologically-motivated statistical models of tumor progression to identify factors of fast growing and spreading breast tumors, understand molecular subtypes that are associated with prognosis, elucidate mechanisms of breast cancer progression and to study the role of mammographic density and other factors in breast cancer screening.

Knowledge of tumor growth and factors of aggressive breast cancer is vital for planning and evaluating approaches to prevention.

We build on our prior statistical methods research published in leading medical statistics journals establishing the first approach to use detailed data on screening, tumor characteristics, and images, to separate out the roles of factors in screening, symptomatic detection, tumor growth and spread.

We will combine statistical, mathematical biology and image analysis methods and make use of some of the world’s most detailed population-based studies of breast cancer, which combine longitudinal register, questionnaire, image and molecular data.The collaborators involved have expertise in biostatistics, radiology, cancer epidemiology, image analysis.

Despite dramatic clinical achievements improving prognosis, some 20% of breast cancer patients still die from their disease.

Research in mechanisms underlying aggressive cancers is important and may improve approaches to prevention and have implications for cost-effectiveness of health care for cancer prevention at the population level.