Prediction of timing of driver mutation and distant metastasis in lung adenocarcinoma
| Project/Area Number |
18K19594
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 55:Surgery of the organs maintaining homeostasis and related fields
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| Research Institution | National Cancer Center Japan |
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Principal Investigator |
Kohno Takashi 国立研究開発法人国立がん研究センター, 研究所, 分野長 (80280783)
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| Co-Investigator(Kenkyū-buntansha) |
波江野 洋 東京大学, 大学院新領域創成科学研究科, 特任准教授 (70706754)
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| Project Period (FY) |
2018-06-29 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | がん / 遺伝子 / 治療標的 / 発がん / 癌 / ゲノム / 遺伝学 / 病理学 |
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| Outline of Final Research Achievements |
Information on genetic heterogeneity between primary and metastatic tumors in a lung cancer patient was subjected to mathematical modeling to deduce the timing of driver mutation and distant metastasis. Whole exome sequencing data of fourteen pairs of primary and metastatic lung tumors were obtained and used to identify somatic mutations shared by both tumors and those specific for the primary or metastatic lung tumors in each patient. The data coupled with factors reflecting tumor proliferation rates, which were estimated from sequential data of Computed Tomography (CT) examination, enabled the establishment of a prototype model. According to this model, the timing of driver mutation and distant metastasis was indicated to widely vary among cases. The information would be a basis for precision prevention and therapy of lung cancer and would also generate a new area of cancer study translating mathematical modeling in to the clinic.
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| Academic Significance and Societal Importance of the Research Achievements |
「実際の患者ではどのタイミングでドライバー変異が生じたのか?」の情報が本邦集団で得られたならば、がんの予防をいつから行うことが有効であるかというストラテジーに直結する。また、「実際の患者ではどのタイミングで致死的な転移が生じたのか?」の情報が得られたならば、がんの根治的外科的手術のやり方やその後の治療ストラテジーの改良に資することができる。本研究の成果は、がんの予防や外科的根治治療の精密化の根源となるデータであり、同時に、数理学的モデリングに基づくがんの予防・治療という新たな研究領域の創出につながると期待する。
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Report
(3 results)
Research Products
(8 results)
