Summary
Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. 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 mutation order during cancer progression. However, the errors 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. Using simulation, we show that our method outperforms existing methods for identifying mutation order in most cases, especially when the number of cells is large. Our method also provides a means to quantify the uncertainty in the inferred mutation order along a fixed phylogeny. We apply our method to empirical data from colorectal and prostate cancer patients.
Competing Interest Statement
The authors have declared no competing interest.
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