A Phylogenetic Approach to Inferring the Order in Which Mutations Arise during Cancer Progression

Although the role of evolutionary processes in cancer progression is widely accepted, increasing attention is being given to the mechanisms by which variation in these processes across patients and across cancer types can lead to differences in clinical outcomes. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing provides a unique opportunity to examine the effect that mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing complicate this task. We propose a new method for inferring the order in which somatic mutations arise within a tumor using noisy single-cell sequencing data that incorporates the errors that arise from the data collection process. Through analyses of simulations and of empirical data from cancer patients, we show that our method outperforms existing methods for identifying mutation order and leads to new insights about the evolutionary trajectories of cancer. Most importantly, our method is the first to provide probabilistic information to quantify the uncertainty of the inferred mutation order.