| Project/Area Number |
21591006
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Respiratory organ internal medicine
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| Research Institution | Nippon Medical School |
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Principal Investigator |
GEMMA Akihiko 日本医科大学, 大学院・医学研究科, 教授 (20234651)
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| Co-Investigator(Kenkyū-buntansha) |
SEIKE Masahiro 日本医科大学, 医学部, 准教授 (30366687)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2011: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 肺癌薬剤感受性プロテオーム / イメージング / 抗体アレイ / EGFR / VEGFR / 分子標的治療 / 肺癌 / 感受性予測 / 網羅的解析 / シグナル情報 |
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| Research Abstract |
To identify a molecular model of sensitivity to molecular target therapy in NSCLC, we conducted a gene expression profiling study using cDNA arrays on the same set of cell lines and related the cytotoxic activity to corresponding gene expression pattern using a modified National Cancer Institute program. In addition, pathway analysis was done with Pathway Architect software. We used the genes, which were identified by gene-drug sensitivity correlation and pathway analysis, to build a support vector machine algorithm model by which sensitive cell lines were distinguished from resistant cell lines. The-gene classifier is useful in predicting drug sensitivity to HDAC inhibitors. In addition, we identified the genes influenced by HDAC inhibitor treatment and designed combination using these cross-talk The designed combination therapy with SAHA and S-1 in lung cancer may be promising due to its potential to overcome S-1 resistance via modulation of 5-FU/S-1 sensitivity-associated biomarker(TS) by HDAC inhibitor.
We identified eight genesrelated PKC inhibitor by pathway analysis of molecules having gene-drug sensitivity correlation, and used them to build a support vector machine algorithm model by which sensitive cell lines were distinguished from resistant cell lines. Pathway analysis revealed that the JAK/STAT signalling pathway was one of the main ones involved in sensitivity to enzastaurin. Simultaneous administration of enzastaurin and JAK inhibitor inhibited enzastaurin-induced cell growth-inhibitory effect. Furthermore, lentiviral-mediated JAK1-overexpressing cells were more sensitive to enzastaurin than control cells. Our results suggested that the JAK1 pathway may be used as a single predictive biomarker for enzastaurin treatment.
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