2016 Fiscal Year Final Research Report
Lung adenocarcinoma classification by big data analysis
Project/Area Number |
25871068
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Tumor diagnostics
Human pathology
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Research Institution | Japanese Foundation for Cancer Research |
Principal Investigator |
Fujiwara Takeshi 公益財団法人がん研究会, がん研究所 がんゲノム研究部, 研究員 (00552712)
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Research Collaborator |
Yuichi Ishikawa 公益財団法人がん研究会, がん研究所 病理部, 部長
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Project Period (FY) |
2013-04-01 – 2017-03-31
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Keywords | 肺がん / ゲノム解析 / 遺伝子病理診断学 |
Outline of Final Research Achievements |
Seven additional independent web-based datasets of lung adenocarcinoma were examined. The expression level of the ASCL1 geneset was used to define the neuroendocrine character based on the method we previously reported. Subtyping was performed by consensus clustering with nonnegative matrix factorization. Correlation of overall survival was analyzed with the Kaplan-Meier method. The neuroendocrine subtype was identified from each of seven independent cohorts. Among them, three datasets showed statistically significant association with patient survival (p < 0.05). Somatic mutations identified in the neuroendocrine subtype with the TCGA data were common ones such as TP53, STK11 and KRAS. Transcriptomic profiling partially reproduced the neuroendocrine subtype in lung adenocarcinoma samples derived from the independent datasets.
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Free Research Field |
ゲノムインフォマティクス
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