Metabolic phenotyping of colitis associated cancers

Inflammatory bowel disease (IBD), particularly ulcerative colitis (UC), carries an increased risk of colorectal cancer. While improvements in treatment and surgical management appear to have led to rates of colitis-associated cancer (CAC) approaching the risk of sporadic colorectal cancer (sCRC) in the general population, there remain high levels of "interval cancers", cancers which are detected between scheduled colonoscopic surveillance periods. These interval cancers display a poorer prognosis than incident cancers detected in scheduled surveillance and point to a need for better understanding of factors associated with CAC development.

While there are many shared mutational events in the development of sCRC and CAC, the order in which these occur appears to be different in the inflamed colon. In addition, CAC predominantly appears to arise from regions of "flat dysplasia" without polyps, which can be difficult to identify even by experienced endoscopists. As such, surveillance strategy relies on random biopsies every 2-4 cm along the length of the colon.

In recent years, a number of bacterial species have been implicated in the development of sCRC. While it is known that there are broad patterns of change in colonic bacterial composition in the IBD population, to date relatively little is known about whether there is a composition specific to CAC, and what impact this may have on cancer development. Further, while a variety of approaches have been utilised to investigate metabolism in IBD, there remains a knowledge gap in CAC.

Utilising sequencing methods to identify bacteria specifically in biopsies and CAC specimens, our objectives are to investigate the composition and distribution of bacteria within the colon of CAC patients, how this differs to sCRC, and whether microbial profiles can be used to accurately classify samples to disease categories.

Additionally, using mass spectrometry approaches that allow us to profile the composition of these samples on a chemical level, we aim to investigate the concentration and also the spatial distribution of lipids and metabolites within CAC tissues to determine metabolic and "lipidomic" profiles, and whether these can accurately classify samples. Further, using these two datasets we aim to investigate whether there are any bacteria associated with metabolic features that may be observed within CAC via mass spectrometry.

Following this, our objective is to attempt to culture any bacteria that may be identified, and use these in conjunction with cell models of the intestine to determine whether they have the capacity to cause DNA damage and therefore potentially contribute towards cancer development, and what impact they have on the function of these cells.