The primary interest in my lab is examining the role of a key epigenetic change, altered DNA methylation, in the development, progression and drug sensitivity of leukaemia. This can be split into 3 areas:

1. Identification of novel, cancer-cell specific, targets – The optimal type of treatment for cancer would be ones that can specifically target cancer cells, while having little or no impact on normal cells and consequently low toxicity for the patient. We are using a novel bioinformatic approach, combining genome wide DNA methylation and gene expression data to identify genes that are required for the survival of cancer cells, but which are not required by normal healthy cells (known as synthetic lethal genes). Targeting the proteins produced by these synthetic lethal genes can then give us a way of specifically killing cancer cells that should have little or no impact on healthy cells. Initially we demonstrated the utility of this approach in childhood leukaemia, but have now expanded the analysis for the identification of synthetic lethal genes across multiple types of solid and haematological cancers.

2. DNA methylation changes as a cause of long term health effects in cancer survivors – Survival rates for many cancers have improved dramatically and consequently there has been a dramatic increase in the number of cancer survivors in the population. Particularly in survivors of childhood cancer, it is now clear that cancer survivors suffer from a greatly increased risk of many chronic disease and a consequent reduced life expectancy. We are investigating potential mechanism that could underlie these serious late effects that occur many years after cancer treatment have ceased. In particular, we have identified extensive epigenetic changes induced by anti-cancer treatment and are investigating the possibility that these may be key drivers of late health effects in cancer survivors.

3. Prognostic markers and predictors of drug sensitivity – Leukaemia typically exhibits very large numbers of DNA methylation changes at diagnosis. This gives us a potentially rich source of markers to allow us to predict patient prognosis or response to therapy. We are currently using genome wide DNA methylation data from diagnostic samples and also from samples after exposure to therapy to identify methylation based markers that can predict patient outcome and also to identify genes that are playing a direct role in resistance to chemotherapy