The expression of the genes within our cells is strictly controlled according to the type of tissue and stage of development. For example, expression of genes whose products are needed at early stages of the development of embryos but not in adult cells will be switched on and off only at the right time. There are many mechanisms involved in regulating the switching on or off of gene expression and we have recently uncovered a new set of players in these processes-the variant U1 snRNPs. These novel complexes contain the nucleic acid, RNA together with proteins and there are indications that mis-regulation of the components of these complexes is associated with some neurodegenerative disease, including Spinal Muscular Atrophy (SMA) and some cancers, including Wilms' Tumour. Using the latest experimental techniques, we aim to fully characterise the composition of these complexes and identify which genes they regulate. This is a critical step towards understanding the exact roles these complexes play in human cells and how mis-regulation of the RNA or proteins could cause disease. The long-term goal of our studies is to develop therapies to combat the diseases associated with mis-regulation of these complexes.

Every year, over 360,000 people in the UK are diagnosed with cancer and around 160,000 die as a result of the disease. Cancer costs the NHS over £5 billion annually, while the loss of human productivity due to cancer in the UK is estimated to be £18 billion a year. Above all, cancer impacts patients and their families in ways that are beyond measure. This makes cancer one of the most pressing societal challenges of this century. Cancer is a disease of the genome. Certain changes that are acquired over the course of life in the genomes of healthy cells in the human body (somatic genomic changes) dysregulate the fine balance between cell death and proliferation. These somatic genomic aberrations are the cornerstone of malignant cellular transformation. Targeting somatic genomic changes is fundamental to the practice of precision cancer medicine. We understand that common exposures and cancer risk factors such as ultraviolet light and smoking accelerate the acquisition of these changes. However, little is actually known about how everyday exogeneous and endogenous factors such as diet, obesity, and insulin resistance relate to, and likely drive, carcinogenic changes in the somatic genome. This is because it is difficult to measure lifelong trajectories of the factors retrospectively at cancer diagnosis and expensive to measure them prospectively in large numbers of individuals until some of them develop cancer. Such one-time "snapshot" measures, even where feasible, are prone to bias and confounding. Specific inherited or germline genetic variants have been found to be robustly associated with these exposures or factors. Since genetic variants are allocated at random at conception and fixed thereafter, they are less affected by bias and confounding. The factor-associated variants provide remarkable proxies for the lifetime levels of these factors even in patients in whom the factor itself has not been measured. These variants collected into polygenic scores serve as instruments in Mendelian randomisation (MR) studies that evaluate association between the germline genetically-inferred levels of the factor and a disease outcome. MR studies of cancer have so far been limited to an appraisal of the relationship between putative risk factors and cancer risk. The crucial conceptual advances being proposed here are the application of an MR-like approach to identify somatic/tumour molecular changes that operate within the cancer and are associated with factors such as obesity and the illumination of the role of the identified tumour molecular changes in driving cancer progression and response to cancer drugs. This novel shift in the conventional MR paradigm is challenging to accomplish but has dramatic potential for translational clinical impact. First, by testing for association between a comprehensive range of potentially modifiable everyday exposures and specific somatic genetic mechanisms on the pathway to cancer, the proposed research will generate a rich catalogue of precise molecular targets for further preventive intervention. The availability of a target would mean that such intervention could go beyond policies aimed at influencing behaviour and take the form of primary chemoprevention for high-risk populations. Second, these molecular targets with a clear and well-reasoned link to common exposures may serve as biomarkers for early detection and in the diagnostic or prognostic classification of cancer. Third, untangling the complex interplay between extrinsic/intrinsic exposures and the somatic genome and establishing the sequence of events from exposure to pre-malignancy to cancer may inform strategies for rational anti-tumour therapeutic development. An exhaustive set of tumour molecular changes will be evaluated but a particular focus will be on mutational signatures and anti-tumour immune cell infiltrate signatures, given that these may determine response to chemotherapy, and targeted and immuno-oncology treatments.

A sedentary lifestyle, poor diet, smoking, and genetic and other health factors are major contributors to coronary heart disease (CHD). Despite recent medical advances that have lowered the number of deaths compared to the past decades, CHD still remains the number 1 disease in mortality in the UK (73,000 deaths per year) with a tremendous economic burden: estimates put the cost to UK's economy at £6.7 billion per year. The overriding goal of this project is to take advantage of multimodal information within cardiac magnetic resonance images to improve their analysis and facilitate the diagnosis and improve treatment of CHD. Magnetic Resonance Imaging (MRI) as an imaging diagnostic tool is uniquely positioned to help as it is non-invasive and does not use radiation. A typical cardiac protocol relies on several MR imaging sequences to provide images of different contrast, termed as modalities hereafter, to assess disease progression and status. As a result of this range of acquisitions, hundreds of multidimensional multimodal images are generated in a single patient exam leading to severe data overload. Therefore, robust and automated analyses algorithms would help alleviate the clinical reading burden. Several algorithms have been proposed to segment and register the myocardium in the most commonly used modalities by considering them independently. However, the problem remains difficult and performance is not yet adequate. Currently, the analysis of cardiac imaging data still remains a manual, time consuming, and expensive process typically performed by clinical experts. As a result, despite the huge amount of data generated, not only in a clinical but also in a research setting, only a fraction is being analysed robustly, due to the vast amount of time required for the analysis of this data. This proposal aims to address the above shortcomings by proposing mechanisms that take advantage of the shared information that exists across modalities to enable the joint analysis of cardiac imaging data and thus make a significant leap in how we approach their analysis. We propose new multimodal machine learning driven mechanisms to learn image features (i.e. how local image information is represented for an algorithm to use) that do not change between imaging modalities whilst preserving shared anatomical information. We will then use the learned features in multimodal patch-based myocardial segmentation and inter-modality non-linear registration (i.e. the non-linear registration between two images coming from different cardiac MR sequences) thus enabling us to relate images of the same patient across different modalities. To maximise impact, we will develop an inter-modality cardiac registration plugin for a commercial clinical package that is also offered as an open source variant for academic purposes. We expect that when our complete framework is integrated into clinical tools and becomes widely available it can radically change current clinical reading workflow and decision-making. It will permit the propagation of annotations across multimodal images of a patient exam effortlessly and seamlessly, thus significantly reducing reading time and permitting the analysis of cardiac data on a larger scale.