| Project/Area Number |
15390368
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General surgery
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| Research Institution | HOKKAIDO UNIVERSITY |
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Principal Investigator |
TADA Mitsuhiro Hokkaido Univ., Inst.Genet.Med., Asso.Prof., 遺伝子病制御研究所, 助教授 (10241316)
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| Co-Investigator(Kenkyū-buntansha) |
KONDO Satoshi Hokkaido Univ., Grad.Sch.Med., Prof., 大学院・医学研究科, 教授 (30215454)
MORIUCHI Tetsuya Hokkaido Univ., Inst.Genet.Med., Prof., 遺伝子病制御研究所, 教授 (20174394)
HAMADA Jun-ichi Hokkaido Univ., Inst.Genet.Med., Asso.Prof., 遺伝子病制御研究所, 助教授 (50192703)
加藤 紘之 北海道大学, 大学院・医学研究科, 教授 (80002369)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥12,400,000 (Direct Cost: ¥12,400,000)
Fiscal Year 2004: ¥5,800,000 (Direct Cost: ¥5,800,000)
Fiscal Year 2003: ¥6,600,000 (Direct Cost: ¥6,600,000)
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| Keywords | cDNA array data / expression profile / human cancer / malignant character / personalized medicine |
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| Research Abstract |
This project aimed to establish a predictive method of cDNA array-based prediction of malignant characters of cancers for personalized treatment for patients. We collected frozen cancer tissue samples surgically reseated in 31 hospitals in Hokkaido prefecture and analyzed the mRNA expression profiles with cDNA array analysis. By using of a novel computer algorithm which efficiently select features (gene expressions) that were significantly correlated with malignant characters of cancers, we explored functional significance of the gene expressions and established an effective method of personalization.
1)We could collect more than 5,800 cases of cancer samples and the pertinent clinicopathological data.
2)Among them, 600 cases were analyzed with cDNA array.
3)We developed a new method of feature subset selection utilizing k-nearest neighbor as an assessment function, a method of ensemble machine learning combining probabilistic neural networks.
4)By applying this method, we could effectively predict lymph node metastasis of colorectal cancers, lung cancers and esophageal cancers, short term survival of patients with pancreatic cancers, long-term survival of gastric caner patients, and perineural invasion of bile-duct cancers, histological malignancy of breast cancers and liver cancers. These have been reported in academic meetings and published in international journals.
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