| Project/Area Number |
19K17174
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Sapporo Medical University |
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Principal Investigator |
Kitagawa Mio 札幌医科大学, 医学部, 助教 (30608922)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2021: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 肺癌 / 放射線治療 / リキッドバイオプシー |
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| Outline of Research at the Start |
近年、放射線治療と併用する機会が増加している免疫チェックポイント阻害剤の治療効果のモニタリングや、放射線肺障害の発現予測において、リンパ球のRNA発現変化やリキッドバイオプシーを応用することで、肺がんに対する個別化放射線治療の実現を目指すため、
①放射線治療前後の血清中のエクソソーム内mRNA/miRNA発現を解析し、
②DNA損傷修復に関わるタンパクや免疫チェックポイントに関わるタンパク発現との関連
性を検討し、放射線治療効果や放射線性肺障害を予測することを目的とする。
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| Outline of Final Research Achievements |
The aim of this study was to predict individualized radiotherapy response and radiation lung injury in patients with stage III unresectable non-small cell lung cancer treated with chemoradiotherapy and immunotherapy. 10 ml of peripheral blood was drawn in 10 patients before the start of treatment, at the end of chemoradiotherapy and 2 weeks after the first immunotherapy administration. PBMC fractions were isolated and RNA TCR sequences were analyzed to confirm changes over time.
The results showed that the mean observation period from the start of treatment exceeded 15 months, with 6 cases of CR and 4 cases of PD in the chemoradiotherapy plus immunotherapy group, and that the change in TCR lepatore over time tended to be greater in the CR cases than in the PD cases.
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| Academic Significance and Societal Importance of the Research Achievements |
切除不能Ⅲ期肺癌の化学放射線療法および免疫療法を行った症例において、化学放射線療法前後および免疫療法前後でのTCRレパトアを調べることで、放射線治療の効果予測に利用できる可能性を示した。今後症例数を増やして、より高い精度での治療効果を予測する方法の確立を目指す。
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