FiMO: Inferring the Temporal Order of Mutations on Clonal Phylogeny under Finite-sites Models

Determining the temporal order of somatic mutations that drives cancer progression is essential for understanding the intra-tumor heterogeneity (ITH) and designing personalized therapy. Recently emerged single-cell DNA sequencing (SCS) technologies provide high-resolution datasets suitable for elucidating the temporal order of mutation. However, this task is challenged by technical artifacts associated with single-cell sequencing. While computational methods have been developed to account for these errors, these methods rely on using infinite sites assumption which gets violated in human cancers due to deletion, loss of heterozygosity and parallel mutations. Here, we propose a novel method FiMO, which employs a Bayesian frameowrk for inferring the temporal order of somatic mutations from noisy SCS mutational profiles under tumor evolutionary models that account for mutation recurrence and losses. Using synthetic datasets generated under a wide variety of settings, we show that FiMO outperforms the state-of-the-art methods in inferring the temporal order of mutations. We also applied FiMO on two experimental colon cancer datasets for inferring the temporal order of somatic mutations and quantifying their posterior probability. FiMO is publicly available at https://github.com/aveshag/FiMO.