Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes

被引:9
|
作者
Evans, Perry [1 ]
Avey, Stefan [1 ]
Kong, Yong [2 ,3 ]
Krauthammer, Michael [1 ]
机构
[1] Yale Univ, Sch Med, Dept Pathol, New Haven, CT 06511 USA
[2] Yale Univ, Sch Med, Dept Mol Biophys & Biochem, New Haven, CT 06511 USA
[3] Yale Univ, Sch Med, WM Keck Fdn Biotechnol Resource Lab, New Haven, CT 06511 USA
关键词
Cancer; melanoma; sequencing; SOMATIC MUTATIONS;
D O I
10.1109/TNB.2013.2263391
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
A common goal of tumor sequencing projects is finding genes whose mutations are selected for during tumor development. This is accomplished by choosing genes that have more non-synonymous mutations than expected from an estimated background mutation frequency. While this background frequency is unknown, it can be estimated using both the observed synonymous mutation frequency and the non-synonymous to synonymous mutation ratio. The synonymous mutation frequency can be determined across all genes or in a gene-specific manner. This choice introduces an interesting trade-off. A gene-specific frequency adjusts for an underlying mutation bias, but is difficult to estimate given missing synonymous mutation counts. Using a genome-wide synonymous frequency is more robust, but is less suited for adjusting biases. Studying four evaluation criteria for identifying genes with high non-synonymous mutation burden (reflecting preferential selection of expressed genes, genes with mutations in conserved bases, genes with many protein interactions, and genes that show loss of heterozygosity), we find that the gene-specific synonymous frequency is superior in the gene expression and protein interaction tests. In conclusion, the use of the gene-specific synonymous mutation frequency is well suited for assessing a gene's non-synonymous mutation burden.
引用
收藏
页码:150 / 157
页数:8
相关论文
共 50 条
  • [21] Identification of potential colorectal cancer driver genes in focal chromosomal aberrations
    Burghel, George J.
    Lin, Wei-Yu
    Hammond, Dave
    Bury, Jonathan
    Cross, Simon S.
    Cox, Angela
    CANCER RESEARCH, 2011, 71
  • [22] Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis
    Takeda, Haruna
    CANCER SCIENCE, 2023, 114 : 1517 - 1517
  • [23] Identification of cancer driver genes based on hierarchical weak consensus model
    Li, Gaoshi
    Hu, Zhipeng
    Luo, Xinlong
    Liu, Jiafei
    Wu, Jingli
    Peng, Wei
    Zhu, Xiaoshu
    HEALTH INFORMATION SCIENCE AND SYSTEMS, 2024, 12 (01)
  • [24] The use of gene interaction networks to improve the identification of cancer driver genes
    Ramsahai, Emilie
    Walkins, Kheston
    Tripathi, Vrijesh
    John, Melford
    PEERJ, 2017, 5
  • [25] Identification of novel candidate of driver genes on chromosome 7 in colorectal cancer
    Sato, Kuniaki
    Hu, Qingjiang
    Kidogami, Shinya
    Ogawa, Yushi
    Saito, Tomoko
    Nambara, Sho
    Komatsu, Hisateru
    Hirata, Hidenari
    Sakimura, Shotaro
    Uchi, Ryutaro
    Hayashi, Naoki
    Iguchi, Tomohiro
    Eguchi, Hidetoshi
    Ito, Shuhei
    Masuda, Takaaki
    Nakagawa, Takashi
    Mimori, Koshi
    CANCER RESEARCH, 2016, 76
  • [26] Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
    Chen, Ying
    McGee, Jeremy
    Chen, Xianming
    Doman, Thompson N.
    Gong, Xueqian
    Zhang, Youyan
    Hamm, Nicole
    Ma, Xiwen
    Higgs, Richard E.
    Bhagwat, Shripad V.
    Buchanan, Sean
    Peng, Sheng-Bin
    Staschke, Kirk A.
    Yadav, Vipin
    Yue, Yong
    Kouros-Mehr, Hosein
    PLOS ONE, 2014, 9 (05):
  • [27] Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis
    Takeda, Haruna
    Jenkins, Nancy A.
    Copeland, Neal G.
    CANCER SCIENCE, 2021, 112 (06) : 2089 - 2096
  • [28] Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes
    Dong, Xiaobao
    Huang, Dandan
    Yi, Xianfu
    Zhang, Shijie
    Wang, Zhao
    Yan, Bin
    Sham, Pak Chung
    Chen, Kexin
    Li, Mulin Jun
    COMMUNICATIONS BIOLOGY, 2020, 3 (01)
  • [29] Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes
    Xiaobao Dong
    Dandan Huang
    Xianfu Yi
    Shijie Zhang
    Zhao Wang
    Bin Yan
    Pak Chung Sham
    Kexin Chen
    Mulin Jun Li
    Communications Biology, 3
  • [30] DriverMEDS: Cancer driver gene identification using mutual exclusivity from embeded features and driver mutation scoring
    Yi, Sichen
    Xie, Minzhu
    METHODS, 2025, 239 : 22 - 29