Abstract
Tumours evolve through time and space. To infer these evolutionary dynamics for DNA sequencing data, many subclonal reconstruction techniques have been developed and applied to large datasets. Surprisingly, though, there has been no systematic evaluation of these methods, in part due to the complexity of the mathematical and biological questions and the difficulties in creating gold-standards. To fill this gap, we systematically elucidated key algorithmic problems in subclonal reconstruction, and developed mathematically valid quantitative metrics for evaluating them. We then developed approaches to simulate realistic tumour genomes that harbour all known mutation types and processes. Finally, we benchmarked a set of 500 subclonal reconstructions, creating a key resource, and quantified the impact of sequencing read-depth and somatic variant detection strategies on the accuracy of specific subclonal reconstruction approaches. Inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis, and this work sets standards for evaluating its accuracy.
Footnotes
↵# These authors jointly directed the work
