Journal Article

A probabilistic method for leveraging functional annotations to enhance estimation of the temporal order of pathway mutations during carcinogenesis

Liu, Jinpeng, Wang, Chi, Arnold, Susanne M., Wang, Menghan, Yu, Tianxin, Villano, John L., Liu, Chunming, Chen, Li, Stromberg, Arnold J.

2019

Overview

Background Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. Although a number of statistical and computational methods have been proposed to estimate the temporal order of mutations, they do not account for the differences in the functional impacts of mutations and thus are likely to be obscured by the presence of passenger mutations that do not contribute to cancer progression. In addition, many methods infer the order of mutations at the gene level, which have limited power due to the low mutation rate in most genes. Results In this paper, we develop a P robabilistic A pproach for estimating the T emporal O rder of P athway mutations by leveraging functional A nnotations of mutations (PATOPA). PATOPA infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Simulation studies and analysis of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that PATOPA is able to accurately estimate the temporal order of pathway mutations and provides new biological insights on carcinogenesis of colorectal and lung cancers. Conclusions PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Annotations / Bioinformatics / Biomedical and Life Sciences / Cancer / Carcinogenesis / Carcinogenesis - genetics / Carcinogens / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer applications / Computer Simulation / Databases, Genetic / Development and progression / Functional annotations / Gene mutation / Gene sequencing / Genes / Genetic aspects / Genetic research / Genomes / Genomics / Health aspects / Humans / Life Sciences / Lung cancer / Methodology / Methodology Article / Methods / Microarrays / Molecular Sequence Annotation / Mutation / Mutation - genetics / Mutation Rate / Mutation rates / Neoplasms - genetics / Noise / Parameter estimation / Pathway analysis / Patients / Probabilistic methods / Probability / Reproducibility of Results / Results and data / Signal Transduction - genetics / Somatic mutations / Statistical analysis / Statistical methods / Target recognition / Temporal variations / Therapeutic applications / Time Factors / Tumors