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

Authors
  • Wang, Menghan1
  • Yu, Tianxin2
  • Liu, Jinpeng3
  • Chen, Li3, 4
  • Stromberg, Arnold J.1
  • Villano, John L.3, 5
  • Arnold, Susanne M.3, 5
  • Liu, Chunming3, 6
  • Wang, Chi3, 4
  • 1 Department of Statistics, University of Kentucky, Lexington, USA , Lexington (United States)
  • 2 Department of Molecular & Cellular Biology, Roswell Park Comprehensive Cancer Center, Buffalo, USA , Buffalo (United States)
  • 3 Markey Cancer Center, University of Kentucky, Lexington, USA , Lexington (United States)
  • 4 Department of Biostatistics, University of Kentucky, Lexington, USA , Lexington (United States)
  • 5 Department of Internal Medicine, University of Kentucky, Lexington, USA , Lexington (United States)
  • 6 Department of Molecular & Cellular Biochemistry, University of Kentucky, Lexington, USA , Lexington (United States)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Dec 02, 2019
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12859-019-3218-2
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundCancer 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.ResultsIn this paper, we develop a Probabilistic Approach for estimating the Temporal Order of Pathway mutations by leveraging functional Annotations 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.ConclusionsPATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.

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