Exploration of tissue-specific selective pressure on tumors; integration of time-course analysis by liquid biopsy

2021 Fiscal Year Final Research Report

• Project/Area Number: 20K22918
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0903:Organ-based internal medicine and related fields
• Research Institution: The University of Tokyo
• Principal Investigator: IKUSHIMA Hiroaki 東京大学, 医学部附属病院, 届出研究員 (80719154)
• Project Period (FY): 2020-09-11 – 2022-03-31
• Keywords: 肺癌

Outline of Final Research Achievements

It has been shown in our previous studies that tumor cells are subjected to different selection pressures in each tissue in lung cancer patients with multiple metastases, resulting in different heterogeneity in each tissue, which has a significant impact on treatment responsiveness in a body as a whole. In this study, we focused on epithelial-mesenchymal transition as one of the mechanisms that confer heterogeneity to lung cancer cells. To clarify the details of epithelial-mesenchymal transition at the cellular level (i.e., not only the change in compositional allocation within a cell population), we used single-cell gene expression analysis technology to capture the intermediate stage of epithelial-mesenchymal transition. We have developed a model to predict prognosis of lung cancer patients by combining single-cell gene expression analysis with machine learning.

Free Research Field

腫瘍生物学

Academic Significance and Societal Importance of the Research Achievements

肺癌の多発転移では転移組織毎にわずかに性状の異なった細胞集団が存在し、生体全体としてみるとそれが治療応答性に大きな影響を与えていることが、本研究者らのこれまでの研究で明らかとなっている。本研究では、正常肺組織からの単一細胞遺伝子発現データを用いて、上皮系・間葉系の発現プロファイルに対する機械学習を行い、それを肺癌組織の遺伝子発現データに適応することで、各肺癌組織が上皮間葉転換のどのような段階にあるかを判定するモデルを構築した。正常肺からの単一細胞遺伝子発現データに基づく腫瘍細胞の遺伝子発現パターンの解析は、肺癌患者の予後予測に有益な情報をもたらす可能性が示唆された。