Tumour evolution is a complex and multifaceted process that arises from the driving forces of carcinogenesis. Intra-tumour heterogeneity results in distinct cellular populations with inheritable genetic characteristics that can be observed within a single tumour. Additionally, primary tumours can seed metastases in distant parts of the body from one or several cellular sub-populations. The ability to trace the progression of a cancer, by identifying sub-populations and inferring the relationships between them from shared genetic features, has only recently become feasible. Next-generation sequencing technologies are able to provide fine-grained genomic data which can quantify the relationships between intra-tumour populations as well as distant metastases. Understanding these relationships using methods of phylogenetic reconstruction can inform the evolution of invasive or metastatic genetic changes in the evolutionary history of a cancer. This information can also assist in prognostication and prediction of cancer evolution in a clinical setting.

Several potential methods exist for deconstructing the phylogeny of cancer populations, including single-nucleotide variations (SNVs) and copy-number variation (CNVs), although there is no gold standard approach. Particularly in prostate cancer, structural variations (SVs) are commonly observed events that consist of mutational changes in the genome, consisting of insertions, deletions, duplications, translocations and/or inversions. By comparing multiple cancer samples from the same patient, distinct cellular populations and their ancestral relationships can be de-convolved and the occurrence of a particular SV within a cancer's evolution can be estimated.

We present a method that seeks to reconstruct the phylogenetic relationships of a tumour's sub-clonal cellular populations using structural variation data, detected using the Socrates[1] algorithm. We demonstrate that tumour phylogenies are able to be reconstructed with SV data alone, and that SVs can play a useful role in resolving uncertainties in particular tree branches when compared to other data, such as SNVs and CNVs.