Spatial (dis-)organisation of lipid metabolism in chronic liver disease and cancer

Non-alcoholic fatty liver disease (NAFLD) is present in around one quarter of the population in the UK. NAFLD is associated with obesity and type 2 diabetes and arises when too much fat is deposited in the liver. This can lead to more serious conditions, including formation of scar tissue (cirrhosis) and increased risk of liver cancer, which has less than 15% survival rate over 5 years. NAFLD is an increasing problem and is expected to become the number one reason patients need a liver transplant in the next 10 years. We still don't know why fat in the liver increases the risk of cirrhosis and cancer, and treatment with drugs has not been very successful so far, despite big investment.

The deposition of fat and scar tissue (fibrosis) in the liver may lead to a change in the surrounding cells' metabolic state. In addition, the liver is made of many different cell types, which can interact with each other by "cross-talk" or signalling. These cells may have different chemical compositions depending on the stage of disease. We will investigate the changes, in a range of molecules, which occur during the formation of fibrosis. We will use a special technique called mass spectrometry imaging to take molecular snapshots of healthy and scarred liver slices, in mice and humans, based on levels of biomolecules across different tissue regions and cell types. With this state-of-the-art technology, we can build a "molecular signature" across the liver to discover how normal cell metabolism is changed, leading up to the development of cirrhosis. This can help us to predict drugs that might prevent, stop or reverse the process.

Cirrhosis and NAFLD are risk factors for developing liver cancer. Cancer cells in tumours are rapidly dividing and have different metabolic needs to "normal" cells. Tumours which are more aggressive or advanced may also have a different chemical make-up to those that are less aggressive/advanced. Using our mass spectrometry imaging technology, we will make a chemical fingerprint across and between tumours, to understand how the metabolism has been rewired. We will correlate findings in tissue to blood, and link metabolic signatures in tissue to molecular subtypes of HCC, based on the presence of specific mutations and/or immune cell populations. This research has the potential to uncover molecules present in patients with cancer that could be used to generate a diagnostic test in the clinic for early detection, and to decide on the best treatment based on the molecular signature.

Finally, we will study the interaction of immune cells with liver cells, by combining information on the spatial distribution of metabolites with multiplex imaging of endogenous cell markers. This will require method development, including how best to carry out data fusion of the datasets. Overall our findings will reveal an unprecedented level of information on the disease mechanisms of liver cancer development from fatty liver disease and cirrhosis, which could also improve diagnostics and inform discovery of new drug treatments.

Non-alcoholic fatty liver disease (NAFLD) is characterised by accumulation of lipids in the liver, however a subgroup of patients will progress to more serious conditions, including nonalcoholic steatohepatitis (NASH), cirrhosis and hepatocellular carcinoma (HCC). Currently, the driver for this progression is not fully understood, particularly with respect to the metabolic adaptions in liver cells, which lead to hepatocellular injury, fibrosis and cancer. Given that NAFLD is expected to become the main cause for patients requiring a liver transplant, there is an urgent need for novel tools and approaches to better understand changing liver physiology during injury, fibrosis and cancer. Understanding cells in their morphological context is critical to understand their function. Here we will use mass spectrometry imaging and lipidomics to study spatially-resolved metabolic adaptive changes during chronic liver disease, using animal models of fibrosis and clinical samples (Aim 1). Chronic liver disease is a major risk factor for HCC. We will study the metabolic heterogeneity of tumours in a large cohort of HCC patients for improved patient stratification and link lipid profiles in tissue to blood, to form the basis for a non-invasive diagnostic test (Aim 2). Finally, we will develop a transformative strategy that combines innovative mass spectrometry experiments with multiplex imaging of proteins and sophisticated machine-learning tools to investigate modulation of immunometabolism and inter-cellular cross-talk in chronic liver disease and HCC (Aim 3). The outcome of this research will be to shed light on the complex processes governing the organisation and regulation of metabolism during liver fibrosis and carcinogenesis, identify candidate molecular markers for patient stratification and reveal metabolic vulnerabilities that can be targeted therapeutically, leading to improved patient outcome.