One of the greatest challenges in cancer medicine is understanding and predicting the trajectory of individual cancers. This leads to both over and under-treatment of cancer. We apply evolutionary principles to this question by studying the forces that shape cancer evolution and use this knowledge to improve patient outcomes.

As a multi-disciplinary group of cancer geneticists, computational biologists and clinician scientists we are using methods from evolutionary biology to understand the variable natural history of individual cancers, and most critically the emergence of metastases and drug resistance.

We evaluate and model the main forces that shape cancer evolution: mutations, natural selection and therapy. Mutations are genetic changes in the cancer which fuel evolution. Selection is a process by which some mutations will be favoured because they improve the survival of the cancer population they occur in, so they are perpetuated. Mutations that are detrimental are selected against. Selection is determined not just by the nature of the mutation but also by the cells directly surrounding tumours, called the tumour microenvironment (TME).

We are focusing on two cancers: renal cell carcinoma (RCC), which is the most common type of kidney tumour, and melanoma, the most aggressive type of skin cancer. RCC and melanoma exhibit a wide spectrum of clinical behaviour, some are indolent, others extremely aggressive and we want to know how this relates to their evolutionary trajectory. Ultimately, by understanding the forces that drive tumours down more aggressive paths, we can 're-route' them towards a better outcome. 

We work with a wide range of clinical and pharmaceutical partners and our work is underpinned by large-scale translational studies including TRACERx Renal and HOLST-F. These studies are unique in their nature and have already provided us some unprecedented insights into cancer evolution:

We also work with large-scale datasets generated by the 100K Genome Project. The main methods we utilise in the lab include genomic data, whole genome, exome, panel, single cell and RNA sequencing, as well as multiplex immunohistochemistry,  genome editing and patient-derived models