Data-driven probabilistic modelling of clonal dynamics in hu man tissues and cancers

Cancer is one of the most deadliest diseases worldwide, and is responsible for one in eight deaths. The main causes of cancer can be attributed to genetic mutations caused by environmental (e.g. nuclear radiation, microbes and bacteria) and lifestyle factors (e.g. smoking). Other minor factors, such as inherited genetics from your parents or ancestors, could also contribute to the cause of

cancer. These genetic mutations can accumulate within cells that maintain and regenerate human tissues, and would cause abnormal cell growth, with the potential to spread to other parts of the body. In the last decade, rapid advances in next-generation genome sequencing technologies have re-shaped the way we think about this mutational process. Both cancer and tissues from

healthy individuals are now known to be a mosaic of genetic 'clones', each clone derived from a single common ancestor cell carrying a mutation similar to the way that mutation and selection govern the evolution of species. Recently, scientists have employed mathematical modelling in combination with probabilistic inference to elucidate the dynamics of clonal evolution over the

course of a human lifetime. Such work has revealed an intricate balance of cellular interactions within human tissues, which can be perturbed by certain mutations to drive the emergence of cancer. This project will develop and extend the above computational and mathematical analyses of clonal

dynamics to different tissue and cancer types, including cancers of the colon, intestine and endocrine glands. Approaches used in this project may include: 1. statistical analysis of 'big data' coming from clinical genomics; 2. stochastic modelling of clonal dynamics; 3. Bayesian inference from experimental cell lineage tracing and imaging data.