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Identification of Local Clusters of Mutation Hotspots in Cancer-Related Genes and Their Biological Relevance

Authors
 Je-Keun Rhee  ;  Jinseon Yoo  ;  Kyu Ryung Kim  ;  Jeeyoon Kim  ;  Yong-Jae Lee  ;  Byoung Chul Cho  ;  Tae-Min Kim 
Citation
 IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, Vol.16(5) : 1656-1662, 2019-09 
Journal Title
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN
 1545-5963 
Issue Date
2019-09
MeSH
Algorithms ; Amino Acid Sequence / genetics ; Cluster Analysis ; Computational Biology / methods* ; Databases, Genetic ; Humans ; Mutation / genetics* ; Neoplasms / genetics*
Keywords
Amino acids ; Cancer ; Clustering algorithms ; Tumors ; Genomics ; Databases ; Proteins ; Bioinformatics ; computational biology ; genetics ; oncology ; clustering methods
Abstract
Mutation hotspots are either solitary amino acid residues or stretches of amino acids that show elevated mutation frequency in cancer-related genes, but their prevalence and biological relevance are not completely understood. Here, we developed a Smith-Waterman algorithm-based mutation hotspot discovery method, MutClustSW, to identify mutation hotspots of either single or clustered amino acid residues. We identified 181 missense mutation hotspots from COSMIC and TCGA mutation databases. In addition to 77 single amino acid residue hotspots (42.5 percent) including well-known mutation hotspots such as IDH1 (p.R132) and BRAF (p.V600), we identified 104 mutation hotspots (57.5 percent) as clusters or stretches of multiple amino acids, and the hotspots on MUC2, EPPK1, KMT2C, and TP53 were larger than 50 amino acids. Twelve of 27 nonsense mutation hotspots (44.4 percent) were observed in four cancer-related genes, TP53, ARID1A, CDKN2A, and PTEN, suggesting that truncating mutations on some tumor suppressor genes are not randomly distributed as previously assumed. We also show that hotspot mutations have higher mutation allele frequency than non-hotspots, and the hotspot information can be used to prioritize the cancer drivers. Together, the proposed algorithm and the mutation hotspot information can serve as valuable resources in the selection of functional driver mutations and associated genes.
Full Text
https://ieeexplore.ieee.org/document/8318935
DOI
10.1109/TCBB.2018.2813375
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Cho, Byoung Chul(조병철) ORCID logo https://orcid.org/0000-0002-5562-270X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/189151
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