A data science-driven approach to identify synthetic lethality in cancer for new treatments

A major challenge in cancer therapeutics is to kill tumour cells without harming normal cells in the body. Traditional chemotherapy tries to do this by killing cells that are fast dividing, a characteristic hallmark of cancer cells, however as many other cells in the body are also fast dividing – such as those in the hair and the gut – chemotherapy typically results in undesirable side effects. Newer targeted therapies are designed to specifically target cancer cells, by exploiting the genetic changes that distinguish tumour cells from normal cells. One emerging and exciting concept for the development of targeted therapies is known as ‘synthetic lethality’ – whereby the function of gene X only becomes essential if gene Y is mutated. In this case, inhibiting gene X would only kill cancer cells (having mutated Y) without affecting normal cells. Research in Dr Lan Nguyen’s lab have developed preliminary bioinformatics approaches to identify these synthetic lethal X/Y pairs. This project will build on these work to identify potent synthetic lethal gene pairs for breast cancer (and others), based on which new effective targeted therapies could be developed. Candidate students will work with real-life high-throughput molecular and biomedical data available in the Nguyen lab under supervision of experienced researchers. They will utilise key data science techniques including data processing, integration, analysis and visualisation. Experience in Python, R, or MATLAB (or an equivalent language) is essential. There are also potential opportunities to continue to PhD studies.