| Project/Area Number |
17H04340
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Obstetrics and gynecology
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| Research Institution | Tokai University |
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Principal Investigator |
MIKAMI Mikio 東海大学, 医学部, 教授 (30190606)
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| Co-Investigator(Kenkyū-buntansha) |
信田 政子 東海大学, 医学部, 講師 (10338717)
池田 仁惠 東海大学, 医学部, 講師 (20365993)
柴田 健雄 東海大学, 健康学部, 講師 (30366033)
宮澤 昌樹 東海大学, 医学部, 客員講師 (30624572)
平澤 猛 東海大学, 医学部, 准教授 (70307289)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2019: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2017: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
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| Keywords | 卵巣癌 / 血清バイオマーカー / 糖ペプチド / 質量分析 / 人工知能 / 深層学習 / リキッドバイオプシー / 卵巣癌早期診断 |
|---|
| Outline of Final Research Achievements |
Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. FS-C4BP, which was discovered by CSGSA, can be measured by Lectin and immunoblot to detect early EOC. CSGSA evaluates >10,000 glycopeptides and identifies reproducible peaks and patterns via supervised orthogonal partial least-squares discriminant modeling（OPLS-DA）. Combined CSGSA（OPLS-DA）, CA125, and HE4 had improved diagnostic performance. We also developed an AI-based CSGSA method. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let convolutional neural network (CNN) learn and distinguish between EOC and non-EOC. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic AUC of 95% was achieved. This simple and low-cost method will increase the detection of EOC. Thus, CSGSA may be a useful screening tool for detecting early stage EOC.
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| Academic Significance and Societal Importance of the Research Achievements |
卵巣癌は早期発見が難しくかつ予後も極めて悪い癌であり、新たな発想の新規診断技術導入が重要である。腫瘍マーカーは単一分子と認識され研究されてきたが、現状では卵巣癌早期診断は不可能であろう。そこで古い概念を打ち破り、究極のCombination Assayと考えられる網羅的血清糖ペプチドピークと人工知能を用いた卵巣癌早期診断を開発し、現在汎用されている卵巣癌マーカーであるCA125とHE4よりも有意に初期卵巣癌を判別できる診断法を開発した。
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