BSU Research overview

Many diseases and medical conditions, such as cancers, dementia and rheumatic disorders, are multi-factorial and may exhibit a range of complex biological phenotypes reflecting the contribution of a multitude of genetic and environmental components. Even with rare or ultra orphan diseases, such as Gaucher, which may be the result of mutations in a single gene, varying symptomatology, outcomes and treatment responses can be displayed across patients with the disease. The complexity of diseases leads to many challenges ranging from the understanding of disease mechanisms and disease susceptibility to risk prediction and the development and application of treatments. Stratified medicine, where "homogeneous" groups of people likely to respond similarly to treatment or have similar underlying disease mechanism or outcome risk are sought based on molecular (biomarker) information, is a first step towards reducing biological variability and lies within the continuum of "patient therapy", in which empirical or "all comer" medicine sits at one end and precision medicine at the other. The fact that genetic, molecular and imaging modalities have transformed biology from an observational science to a more data and computationally intensive quantitative science has meant that the need for those who are trained in handling data, accounting for uncertainty and making inference is ever more paramount. Many of the issues and challenges confronting stratified medicine are of a statistical nature. We are undertaking a programme of research that encompasses risk stratification, prediction and validation, integrative and joint modelling of molecular and clinical data of various kinds and complexities (and their conversion into meaningful outputs that can inform health care decisions), mechanistic understanding and causality, treatment strategies and the design of innovative/purposeful clinical trials for biomarkers and dynamic treatment regimes. Brian Tom's personal page