Genetics of gastro-intestinal cancers

Cancers of the gastrointestinal (GI) tract - colorectal, gastric, oesophageal, pancreatic, and hepatobiliary cancers - collectively represent one of the largest cancer burdens worldwide. Colorectal cancer alone is the third most commonly diagnosed cancer and a leading cause of cancer death, while pancreatic and oesophageal cancers, though less common, carry some of the poorest survival rates of any solid tumour (5-year survival often below 10–20%), largely because they are diagnosed late. Unlike colorectal cancer, most other GI cancers currently have no population-level screening program at all. 

Early-onset gastrointestinal cancers (EOGIC) are increasingly recognised as an urgent global health concern. For example, colorectal cancer incidence and mortality are rising sharply among adults under 50, a trend not fully explained by known lifestyle or environmental risk factors. 

These cancers are traditionally studied - and screened for - in isolation. Yet they share substantial environmental and clinical risk factors (smoking, alcohol, obesity, chronic infection or inflammation such as H. pylori and inflammatory bowel disease, and reflux/Barrett's oesophagus), and emerging evidence suggests they also share genetic risk architecture, including overlapping susceptibility loci and biological pathways (e.g. inflammation, epithelial barrier function, and metabolic regulation).

The candidate will use statistical genetics approaches (e.g. Genome-wide association study techniques), multi-omics data (DNA, gene expression, metabolomics), and clinical data to develop, validate, and apply robust genetics-based prediction models or tools for common chronic disease such as cancer. They will also use other approaches such as Mendelian randomisation to investigative putative causal factors (e.g. gut microbiome,
diet, etc) for treatment response. They will then apply the prediction models in “real world” scenarios to test their efficacy in guiding decisions for prevention, diagnosis, and treatment of common chronic diseases by healthcare providers and policy makers.  

We have large-scale genetic and clinical data sets available in the lab on common gastro-intestinal cancers. We also have access to other national and international biobanks, as well as deeply phenotyped data sets. The candidate will focus on major GI cancers (e.g. colorectal, gastric, oesophageal, pancreatic, and hepatobiliary). The candidate will use a range of statistical genetic approaches to interrogate the available genetic, clinical, transcriptomic, and other multi-omics data to determine the genes and pathways underlying these cancers and use these in prediction models to guide prevention, diagnosis, and treatment of these cancers. There is also the potential to use artificial intelligence (AI) to fine-map potential therapeutic targets.