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

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Abstract

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 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. Conclusions: PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.

Original languageEnglish
Article number620
JournalBMC Bioinformatics
Volume20
Issue number1
DOIs
StatePublished - Dec 2 2019

Bibliographical note

Funding Information:
This work was supported by National Institutes of Health (NIH) [R21CA205778, UL1TR001998, P20GM103436-15 and the Cloud Credits Model Pilot, a component of the NIH Big Data to Knowledge (BD2K) program], the Kentucky Lung Cancer Research Program [PO2 415 1400004000, PO2 415 1600001032], and the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center [P30 CA177558]. None of the funding bodies played any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Publisher Copyright:
© 2019 The Author(s).

Keywords

  • Carcinogenesis
  • Functional annotations
  • Pathway analysis
  • Somatic mutations

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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