Quantifying the evolutionary dynamics of human cancers

The fundamental evolutionary parameters that define cancer evolution, such as the mutation rate per cell division and selective advantage conferred by each mutation, remain poorly characterised. Here I will discuss how these parameters can be derived from routinely-available cancer genome sequencing data, via statistical inference of mathematical population genetics models of clonal evolution. We measure that positively selected mutations can cause fitness increases as large as 50%, and also explore the dynamics of negatively-selected mutations (neoantigens) in a growing tumour. These quantitative measurements of cancer evolution enable mechanistic forecasting of the future evolution of a tumour.