Ailith Ewing (Affiliate)
Evolutionary trajectories of structural variation in cancer
Research in a Nutshell
We use statistical genomics to understand how patterns of structural variants (SVs) evolve during tumourigenesis. Genome-wide patterns of SVs in tumours are a consequence of processes of mutation and repair fluctuating throughout tumour evolution. Although single nucleotide variant (SNV) patterns are well established, our understanding of the analogous patterns seen for SVs is rudimentary. Describing the dynamics of these complex patterns parsimoniously is statistically challenging and increasing volumes of whole genome sequencing (WGS) data from tumours have revealed ever more complex classes of SVs. Yet, a full account of tumour evolution depends upon the true prevalence, temporal dynamics and functional impact of these events. Meanwhile, the clinical impact of these events is poorly understood and likely to be underappreciated.
We aim to bridge the gap between germline variation and somatic structural variation observed in highly rearranged cancer types, using statistical genomics to identify novel classes of evolutionary biomarkers. Such biomarkers describe how genome-wide patterns of SVs evolve during tumourigenesis. With the increasing availability of longitudinal and multi-region sampling over tumour evolution, evolutionary biomarkers represent a new frontier in translational cancer genomics. Our research benefits from existing and newly established collaborations with world-leading cancer researchers in ovarian and colorectal cancer.
|Ailith Ewing||Group Leader|
|Stuart Brown||PhD student (jointly supervised with Colin Semple and Charlie Gourley)|
- Prof Charlie Gourley, University of Edinburgh
- Prof Ian Tomlinson, University of Edinburgh
- Prof Colin Semple, University of Edinburgh
- Dr Olga Oikonomidou, University of Edinburgh
- Dr Catalina Vallejos, University of Edinburgh
statistical genomics, structural variation, cancer genomics, germline genetics
whole genome sequencing analyses, transcriptomic analyses, electronic health records