Although the incidence of breast cancer is steadily increasing, mortality rates are decreasing. This means that the majority of women with breast cancer now survive, making it even more important to tailor therapy appropriately, reducing toxicity and long-term side-effects. Some patients still succumb despite early intervention, and improved stratification approaches and more diverse treatment options are required.
This presentation will outline the benefits of a collaborative, inter-disciplinary approach to address key challenges in breast cancer, in particular in relation to the personalisation of therapy. A working case model will be provided of a virtual Collaborative Cancer Research Centre, BREAST-PREDICT (www.breastpredict.com), which tackles clinical needs with respect to the development of more individualised therapies for breast cancer. The BREAST-PREDICT model incorporates the diverse skill sets of several disciplines, including biological, mathematical, computational, pharmaco-epidemiological and systems medicine approaches, in order to determine how to treat individual breast cancer patients according to the particular characteristics of their own cancer.
BREAST-PREDICT is consolidating existing breast cancer biobank and data resources, and making these available for research projects. Pharmaco-epidemiology approaches are being employed to determine the effects of previous drug exposures on outcome, and multi-omic analyses are being performed on longitudinal samples from primary and metastatic tumours, in order to map the molecular evolution of breast cancer. The group is also leveraging these resources in order to identify new therapeutic targets and combinatorial treatment strategies for specific subtypes, and to develop new tools for improved prediction of patient outcome and response to treatment. The presentation will outline the benefit of this approach and how it can be used to fast-track progress towards personalised therapy in breast cancer.