JP7362241B2 - 大腸がんの検査方法 - Google Patents
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Description
(1)大腸がんの検査方法であって、被験者から採取された糞便中の細菌叢の16SリボソームRNA遺伝子の塩基配列を網羅的に解析し、得られた塩基配列のデータ群を予め得られている診断アルゴリズムによって解析し大腸がんを検出する検査方法。
(2)前記診断アルゴリズムが、進行大腸がん患者、又は早期大腸がん患者から得られた16SリボソームRNA遺伝子の塩基配列データ群を区別して求めた診断アルゴリズムであって、進行大腸がん、又は早期大腸がんを区別して解析することを特徴とする(1)記載の検査方法。
(3)前記診断アルゴリズムが、前記塩基配列のFeatureとその出現頻度を複数の機械学習アルゴリズムによって得られた結果を統合して作成した診断アルゴリズムであることを特徴とする(1)、又は(2)記載の検査方法。
(4)前記塩基配列のデータ群を大腸がん以外の疾患の診断アルゴリズムによって解析し、大腸がんと併せて診断を行うことを特徴とする(1)~(3)いずれか1項記載の検査方法。
大腸がんの場合は、進行がんであっても自覚症状のない患者も多い。また、健康診断時の検査においては、早期がん、進行がんを区別して検査することはないので、早期がん、進行がんを含めたがん患者の解析を行った。
る。その結果、培養の容易性、困難性といったバイアスをかけることなく、腸内に生息する細菌集団の配列データを高速にかつ網羅的に取得することができる。
次に、コホート1において、進行がん患者のみを抽出し、進行がん患者の診断アルゴリズムを作成した。前述のように、大腸がんにおいて、進行がんと早期がん患者の腸内細菌叢の解析を行った結果、進行がんと早期がんでは腸内細菌叢プロファイルが大きく異なっていた。コホート1の大腸がん患者は、早期がんが少ないとはいえ、63例含まれていることから、大腸がん患者のサンプルの中から、早期がん患者のサンプルを除外し進行がんのみを抽出して解析を行った。コホート1の進行がん316例、健常者815例を対象として、実施例1と同様にして診断アルゴリズムを作成し、ROC曲線を求めた(図2)。
次に早期がんを診断可能なモデルの作成を行った。2017年1月~9月に新たに早期の大腸がんサンプルと、健常者サンプルを収集した。また、併せて大腸がんの前病変であるアデノーマ(腺腫)を有する患者サンプルを集めた(以下、コホート2という。)。アデノーマのサンプルは、直径10mm以上の進行したアデノーマを有する患者の糞便サンプルを収集している。
Claims (3)
- 大腸がんの検査支援方法であって、
被験者から採取された糞便中の細菌叢の16SリボソームRNA遺伝子の塩基配列を網羅的に解析し、
得られた塩基配列のデータ群を
健常者及び大腸がん患者より得られたActinomycetales、Aeromonadales、Bacillales、Bacteroidales、Betaproteobacteriales、Bifidobacteriales、Clostridiales、Coriobacteriales、Desulfovibrionales、Enterobacteriales、Erysipelotrichales、Fusobacteriales、Lactobacillales、Mollicutes RF39、Selenomonadales、及びVerrucomicrobialesに属する細菌のFeatureとその出現頻度をディープ・ラーニングを含む複数の機械学習アルゴリズムによって予め作成されている診断アルゴリズムによって解析し、
大腸がんを検出する検査支援方法。 - 前記診断アルゴリズムが、
進行大腸がん患者、又は早期大腸がん患者から得られた16SリボソームRNA遺伝子の塩基配列データ群を区別して求めた診断アルゴリズムであって、
進行大腸がんは、Actinomycetales、Aeromonadales、Bacillales、Bacteroidales、Betaproteobacteriales、Bifidobacteriales、Clostridiales、Coriobacteriales、Desulfovibrionales、Enterobacteriales、Erysipelotrichales、Fusobacteriales、Lactobacillales、Mollicutes RF39、Selenomonadales、及びVerrucomicrobialesに属する細菌のFeatureを用い、
早期大腸がんは、Bacteroidales、Betaproteobacteriales、Bifidobacteriales、Clostridiales、Coriobacteriales、Desulfovibrionales、Lactobacillales、及びSelenomonadalesに属する細菌のFeatureを用い、
進行大腸がん、又は早期大腸がんを区別して解析することを特徴とする請求項1記載の検査支援方法。 - 前記塩基配列のデータ群を大腸がん以外の疾患の診断アルゴリズムによって解析し、
大腸がんと併せて検出することを特徴とする請求項1又は2記載の検査支援方法。
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PCT/JP2019/042980 WO2020091032A1 (ja) | 2018-11-02 | 2019-11-01 | 大腸がんの検査方法 |
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WO2018079840A1 (ja) | 2016-10-31 | 2018-05-03 | 株式会社Preferred Networks | 疾患の罹患判定装置、疾患の罹患判定方法及び疾患の罹患判定プログラム |
WO2019151515A1 (ja) | 2018-02-05 | 2019-08-08 | 国立大学法人東京工業大学 | 早期大腸癌の検出方法 |
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WO2018079840A1 (ja) | 2016-10-31 | 2018-05-03 | 株式会社Preferred Networks | 疾患の罹患判定装置、疾患の罹患判定方法及び疾患の罹患判定プログラム |
WO2019151515A1 (ja) | 2018-02-05 | 2019-08-08 | 国立大学法人東京工業大学 | 早期大腸癌の検出方法 |
Non-Patent Citations (5)
Title |
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Deep Learning with H2O,Sixth Edition,2017年,<https://h2o.ai/content/dam/h2o/en/marketing/documents/2018/01/DeepWater-BOOKLET.pdf> |
MORI G. et al.,Shifts of Faecal Microbiota During Sporadic Colorectal Carcinogenesis,SCIENTIFIC REPORTS,2018年07月09日,Vol.8, 10329,p.1-11 |
YACHIDA S. et al.,nature medicine,2019年06月,Vol. 25, p.968-976 |
ZACKULAR J. P. et al.,The Human Gut Microbiome as a Screening Tool for Colorectal Cancer,Cancer Prevention Research,2014年,Vol.7, No.11,p.1112-1121 |
メタゲノム・メタボローム解析により大腸がん発症関連細菌を特定 -便から大腸がんを早期に診断する新技術-,国立大学法人大阪大学 Press Release,p.1-9,2019年06月07日 |
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Free format text: JAPANESE INTERMEDIATE CODE: R350 |