ID 111123
Author
Kohmoto, Tomohiro Tokushima University
Shoda, Katsutoshi Tokushima University|Kyoto Prefectural University of Medicine
Hamada, Junichi Tokushima University|Kyoto Prefectural University of Medicine
Ichikawa, Daisuke Kyoto Prefectural University of Medicine
Tajima, Atsushi Tokushima University|Kanazawa University KAKEN Search Researchers
Otsuji, Eigo Kyoto Prefectural University of Medicine
Keywords
Gastric cancer
exome
somatic mutations
variant calling algorithms
Content Type
Journal Article
Description
High-throughput next-generation sequencing is a powerful tool to identify the genotypic landscapes of somatic variants and therapeutic targets in various cancers including gastric cancer, forming the basis for personalized medicine in the clinical setting. Although the advent of many computational algorithms leads to higher accuracy in somatic variant calling, no standardmethod exists due to the limitations of each method. Here, we constructed a new pipeline. We combined two different somatic variant callers with different algorithms, Strelka and VarScan 2, and evaluated performance using whole exome sequencing data obtained from 19 Japanese cases with gastric cancer (GC) ; then, we characterized these tumors based on identified driver molecular alterations. More single nucleotide variants (SNVs) and small insertions/deletions were detected by Strelka and VarScan 2, respectively. SNVs detected by both tools showed higher accuracy for estimating somatic variants compared with those detected by only one of the two tools and accurately showed the mutation signature and mutations of driver genes reported for GC. Our combinatorial pipeline may have an advantage in detection of somaticmutations in GC andmay be useful for further genomic characterization of Japanese patients with GC to improve the efficacy of GC treatments.
Journal Title
The Journal of Medical Investigation
ISSN
13496867
13431420
NCID
AA11166929
AA12022913
Publisher
Faculty of Medicine Tokushima University
Volume
64
Issue
3-4
Start Page
233
End Page
240
Sort Key
233
Published Date
2017-08
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Medical Sciences