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

Abstract

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.

Abordagens computacionais para a descoberta de genes significativos para o câncer

“…Como decorrência da pesquisa de doutorado, vários trabalhos de pesquisa foram submetidos e publicados em conferências e revistas: 1) Um resumo expandido no Simpósio Brasileiro de Computac ¸ão Aplicada a Saúde de 2019 [Cutigi et al 2019b]; 2) Um artigo completo no Brazilian Symposium on Bioinformatics de 2019 [Cutigi et al 2019a]; 3) Um artigo completo, como segundo autor, no Simpósio Brasileiro de Computac ¸ão Aplicada a Saúde de 2020 [Ramos et al 2020]; 4) Um artigo completo na revista Journal of Bioinformatics and Computational Biology em 2020 [Cutigi et al 2020a]; 5) Um artigo completo no Brazilian Symposium on Bioinformatics de 2021 [Cutigi et al 2020b]; e 6) Um artigo completo na revista Nature Scientific Reports em 2021 [Cutigi et al 2021].…”

“…Nos últimos anos, vários métodos computacionais para identificac ¸ão de genes significativamente mutados em câncer foram foram propostos [Hou and Ma 2013, Raphael et al 2014, Cheng et al 2015, Dimitrakopoulos and Beerenwinkel 2017, Cutigi et al 2020a]. Esses métodos adotam estratégias diversas para descobrir genes drivers, como por exemplo, a análise de redes de interac ¸ão gênica.…”

Human protein–protein interaction networks: A topological comparison review

“…Researchers have utilized the analysis of PPINs and their functional modules to study complex diseases such as cancer and diabetes [5] . This has resulted in the development of computational methods aimed at improving our understanding of these diseases, including identifying driver mutations in cancer [6] . Recent studies show a wide application of PPINs, such as drug repurposing [7] , [8] , [9] , disease regulation and progression [10] , virus mechanisms [11] , and multi-omics interpretation [12] .…”

The central role of cancer driver genes in pathway networks according to persistence homology

“…While PPIN describes the whole cell interactome, pathways networks are subsets of the interactome and represent specific biological functions 6 . Several computational methods use PPIN and pathways networks topology to study cancer-associated phenomenons, such as mutual exclusivity, and to discover new driver genes [7][8][9][10] .…”