Abstract

The evolutionary dynamics of subclones during cancer growth remain largely undetermined as the longitudinal measurement of tumour subclones is impractical. I will describe how the underlying patterns of subclone evolution can be extracted by fitting multiscale mathematical models of cancer evolution to genomic data (n=238 tumours).  Our analysis reveals widespread effectively-neutral evolution across cancer types, due to the suppression of selective effects by tumour growth. When subclonal selection does occur, large subclones experience enormous selective advantages (>20%).  Overall, we demonstrate how population genetics theory, expressed using mathematical modelling, can be used to extract hidden temporal information about cancer evolutionary dynamics from the cancer genome.