A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity

Abstract The development of cancer involves the accumulation of somatic mutations in a number of key biological pathways. Delineating the order of pathway mutations during tumorigenesis is very important to understand biological mechanisms of cancer development and to inform new therapeutic targets. A number of statistical and computational methods have been proposed for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not account for intra-tumor heterogeneity (ITH), which limits their abilities to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach for estimating the temporal order of pathway mutations by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order, wherein it focuses on the orders that are consistent with the phylogenetic structures of the tumors. A maximum likelihood approach is used to estimate the parameters and infer the order of mutations at pathway level. Analyses of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that our method is able to accurately recover the temporal order of pathway mutations in several cancer types.