2021 Fiscal Year Final Research Report
Development of a methodology to non-invasively analyze cancer invasion by using a three-dimensional in vitro model consisting of oral cancer cells and cancer-associated fibroblasts.
| Project/Area Number |
20K18663
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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 57060:Surgical dentistry-related
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| Research Institution | Niigata University |
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Principal Investigator |
Saito Yuko (原夕子) 新潟大学, 医歯学総合病院, 専任助教 (80827676)
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| Project Period (FY) |
2020-04-01 – 2022-03-31
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| Keywords | がん関連線維芽細胞 / 口腔がん / 浸潤 / OCT（光干渉断層撮影） / 非侵襲解析 |
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| Outline of Final Research Achievements |
In this study, we manufactured 3D in vitro oral cancer models consisting of OSCC cells and the underlying collagen gel (stromal layer) in which either CAFs or NOFs are repopulated, according to our platform technology of a tissue-engineered oral mucosa fabrication. Subsequently, the OCT imaging technology was applied to those models. As a result, we were able to obtain clear OCT images of the 3D in vitro oral cancer models because the stromal layer was distinct from the overlying cancer cells. The interface between the cancer cells and the stroma was also distinguishable. Therefore, it was successful to conduct non-invasive evaluation of the invasion of OSCC cells into the underlying stromal layer over time when comparing with the conventional histologic examinations stained with HE. Specifically, the invasion was more severe in the model using CAF than NOF. This suggested the OCT imaging is a useful tool to non-invasively evaluate oral cancer cell invasion in our 3D models over time.
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| Free Research Field |
口腔外科
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| Academic Significance and Societal Importance of the Research Achievements |
口腔がんの治療成績の向上のため、新たな治療戦略の開発は喫緊の課題である。癌の悪性度を左右する浸潤能は癌細胞自身が有する遺伝的、生物学的因子だけでなく、癌微小環境に存在するCAFの影響を受けると考えられ、癌微小環境における相互作用に注目することは新規治療ターゲットの開発に有用である。申請者のラボで確立した、口腔がん3次元モデルはCAFをターゲットした癌微小環境に対する研究ツールとして有用であると考えているが、これにOCTを適用したところ、画像解析評価により経時的な癌浸潤像の観察が可能で、OCTの利用可能性が証明された。OCT画像に加え、深層学習法を適用することで、さらに定量的評価も可能となる。
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