Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

被引:5
|
作者
Cutigi, Jorge Francisco [1 ,2 ]
Evangelista, Adriane Feijo [3 ]
Simao, Adenilso [2 ]
机构
[1] Fed Inst Sao Paulo IFSP, Sao Carlos, SP, Brazil
[2] Univ Sao Paulo, Sao Carlos, SP, Brazil
[3] Barretos Canc Hosp, Barretos, SP, Brazil
关键词
Cancer bioinformatics; computational methods; driver mutations; MUTUAL EXCLUSIVITY; SOMATIC MUTATIONS; NETWORK ANALYSIS; PATHWAYS; DISCOVERY; HETEROGENEITY; ALGORITHM; PAGERANK; PATTERNS; KERNEL;
D O I
10.1142/S021972002050016X
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.
引用
收藏
页数:32
相关论文
共 50 条
  • [1] Computational Approaches to Prioritize Cancer Driver Missense Mutations
    Zhao, Feiyang
    Zheng, Lei
    Goncearenco, Alexander
    Panchenko, Anna R.
    Li, Minghui
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2018, 19 (07)
  • [2] Emerging approaches to the identification of driver mutations in lung cancer
    Hamamoto, Ryuji
    CANCER SCIENCE, 2023, 114 : 271 - 271
  • [3] Characterization of potential driver mutations involved in human breast cancer by computational approaches
    Rajendran, Barani Kumar
    Deng, Chu-Xia
    ONCOTARGET, 2017, 8 (30) : 50252 - 50272
  • [4] Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes
    Cheng, Feixiong
    Zhao, Junfei
    Zhao, Zhongming
    BRIEFINGS IN BIOINFORMATICS, 2016, 17 (04) : 642 - 656
  • [5] Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine
    Benjamin J Raphael
    Jason R Dobson
    Layla Oesper
    Fabio Vandin
    Genome Medicine, 6
  • [6] Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine
    Raphael, Benjamin J.
    Dobson, Jason R.
    Oesper, Layla
    Vandin, Fabio
    GENOME MEDICINE, 2014, 6
  • [7] Identification of Liver Cancer Driver Mutations from COSMIC Data
    Sethi, Amna Amin
    Shar, Nisar Ahmed
    INTERNATIONAL JOURNAL OF CANCER MANAGEMENT, 2023, 16 (01)
  • [8] Identification of driver mutations in gastrointestinal tract cancer
    Starr, Timothy K.
    Allaei, Raha
    Staggs, Rodney A.
    Silverstein, Kevin A.
    Dupuy, Adam J.
    Jenkins, Nancy A.
    Copeland, Neal C.
    Cormier, Robert T.
    Largaespada, David A.
    MOLECULAR CANCER THERAPEUTICS, 2007, 6 (12) : 3490S - 3491S
  • [9] Comprehensive assessment of computational algorithms in predicting cancer driver mutations
    Hu Chen
    Jun Li
    Yumeng Wang
    Patrick Kwok-Shing Ng
    Yiu Huen Tsang
    Kenna R. Shaw
    Gordon B. Mills
    Han Liang
    Genome Biology, 21
  • [10] Identification of Altered Genes in Gallbladder Cancer as Potential Driver Mutations for Diagnostic and Prognostic Purposes: A Computational Approach
    D'Afonseca, Vivian
    Arencibia, Ariel D.
    Echeverria-Vega, Alex
    Cerpa, Leslie
    Cayun, Juan P.
    Varela, Nelson M.
    Salazar, Marcela
    Quinones, Luis A.
    CANCER INFORMATICS, 2020, 19