NDLI: Identification and Characterization of Cancer Mutations in Japanese Lung Adenocarcinoma without Sequencing of Normal Tissue Counterparts

Content Provider	PubMed Central
Author	Suzuki, Ayako Mimaki, Sachiyo Yamane, Yuki Kawase, Akikazu Matsushima, Koutatsu Suzuki, Makito Goto, Koichi Sugano, Sumio Esumi, Hiroyasu Suzuki, Yutaka Katsuya, Tsuchihara
Editor	Sun, H. Sunny
Copyright Year	2013
Abstract	We analyzed whole-exome sequencing data from 97 Japanese lung adenocarcinoma patients and identified several putative cancer-related genes and pathways. Particularly, we observed that cancer-related mutation patterns were significantly different between different ethnic groups. As previously reported, mutations in the EGFR gene were characteristic to Japanese, while those in the KRAS gene were more frequent in Caucasians. Furthermore, during the course of this analysis, we found that cancer-specific somatic mutations can be detected without sequencing normal tissue counterparts. 64% of the germline variants could be excluded using a total of 217 external Japanese exome datasets. We also show that a similar approach may be used for other three ethnic groups, although the discriminative power depends on the ethnic group. We demonstrate that the ATM gene and the PAPPA2 gene could be identified as cancer prognosis related genes. By bypassing the sequencing of normal tissue counterparts, this approach provides a useful means of not only reducing the time and cost of sequencing but also analyzing archive samples, for which normal tissue counterparts are not available.
Related Links	http://dx.doi.org/10.1371/journal.pone.0073484
Starting Page	73484
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	9
Volume Number	8
Language	English
Publisher	Public Library of Science
Publisher Date	2013-09-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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