Cancer genome sequences show that cancer is a highly heterogeneous disease, with each patient having a unique mutational profile. Reconstructing the evolutionary history of a tumour from multiregionallly-sampled data allows understanding of this heterogeneity, and is an exciting, rapidly-expanding research area. An example of this approach is identifying mutations that occurred early in the tumour’s history – truncal mutations – to enrich the mutation catalogue for potential driver mutations critical to tumour formation.
This project will focus on the extensive copy number heterogeneity of mesothelioma, and its implications for the history of the tumour, prognosis and therapy. It can be developed along bioinformatic approaches, analysing data in an evolutionary context to analyse the nature and extent of copy number variation, or along more experimental biological approaches, using new long-read sequencing technologies to characterise genomic structural rearrangements in detail and to identify potential therapeutic targets,