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

Simple Summary: Cancer arises through the accumulation of somatic mutations in key biological pathways. This paper aims to develop a probabilistic approach to delineate the temporal order of mutations during cancer development based on mutation profile data from a cohort of patients. A unique feature of our method is that it incorporates intra-tumor heterogeneity (ITH) information, which refers to the heterogeneous cell populations within a tumor and characterizes the evolutionary history of the tumor. We showed that by integrating ITH, pathways, and functional annotation information, our method yielded high accuracy in inferring the temporal order of pathway mutations during carcinogenesis. The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced 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 take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method's ability to recover the temporal order of pathway mutations in several cancer types. [ABSTRACT FROM AUTHOR]