JP6388901B2 - 診断テストを特定するための経路分析 - Google Patents
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Description
Claims (19)
- 特異的な疾患細胞株のオミクス・データに基づいて薬剤を使用して、1人の患者において病気を治療するためのマーカを判定する方法であって、
それぞれ複数の特異的な疾患細胞株のオミクス・データから導き出された複数の特異的なデータセットを格納し、それぞれのデータセットには複数の経路要素データが含まれる経路モデル・データベースを機械学習システムに情報的に連結することであって、前記複数の特異的な疾患細胞株が前記1人の患者からの細胞を含むことと、
前記機械学習システムにより、前記複数の特定的な疾患細胞株に関連付けられ、前記薬剤に反応する前記複数の特異的な疾患細胞株のうちのそれぞれの1つの感度レベルを示す感度データを受け取ることと、
前記機械学習システムにより、前記経路モデル・データベースにおいて前記複数の特異的な疾患細胞株に対応する前記複数の経路要素データを横断することにより、前記複数の特異的な疾患細胞株に関する前記感度データとの相関関係を有する発現を推測することと、
前記機械学習システムにより、前記相関関係に基づいて前記病気を治療する前記薬剤を推奨するために前記1人の患者の細胞により示される発現レベル閾値を判定することと、
を含む方法。 - 前記発現は前記複数の特異的な疾患細胞株内に存在する可能な発現から推測される、請求項1に記載の方法。
- 前記発現を推測することは、前記複数の疾患細胞株内に存在する前記可能な発現のうちのそれぞれの1つに対して、
前記複数の経路要素データにしたがって前記複数の特異的な疾患細胞株内に存在する前記1つの発現の等級を示すデータ点を生成することと、
前記機械学習システムにより、前記1つの発現の前記等級と、前記複数の特異的な疾患細胞株に関する前記感度データと、の間の相関関係を導き出すことと、
を含む、請求項2に記載の方法。 - 前記可能な発現に対応する前記導き出された相関関係から最適な相関関係を有する前記発現を推測するために機械学習を使用することをさらに含む、請求項3に記載の方法。
- 前記薬剤を用いて前記複数の特異的な疾患細胞株のサンプル疾患細胞を技師または機械によりテストすることにより前記感度データを生成することをさらに含む、請求項1に記載の方法。
- 前記1人の患者からの細胞が健康な細胞である、請求項1に記載の方法。
- 前記1人の患者からの細胞が新生物細胞である、請求項1に記載の方法。
- 前記1人の患者からの細胞が異なる組織からのものである、請求項1に記載の方法。
- 前記患者の推奨療法を含む出力データを前記機械学習システムにより生成することをさらに含む、請求項1に記載の方法。
- サンプル疾患細胞を前記患者から採取することと、
前記サンプル疾患細胞内に存在する前記発現の等級を測定し、前記推奨療法が前記発現の前記測定された等級に基づいて前記機械学習システムにより生成されることと、
をさらに含む、請求項9に記載の方法。 - 前記複数の特異的な疾患細胞株は前記薬剤に対する感度に関して互いに異なる、請求項1に記載の方法。
- 前記複数の特異的な疾患細胞株のうちの第1セットは前記薬剤を用いる治療に対して感度を有し、前記複数の特異的な疾患細胞株のうちの第2セットは前記薬剤を用いる治療に対して抵抗性を有する、請求項1に記載の方法。
- 前記オミクス・データは遺伝子コピー数データ、遺伝子突然変異データ、遺伝子メチル化データ、遺伝子発現データ、RNAスプライス情報データ、siRNAデータ、RNA翻訳データ、およびタンパク質活性データからなる群から選択された、請求項1に記載の方法。
- 前記特異的なデータセットはPARADIGMデータセットである、請求項1に記載の方法。
- 前記経路要素データは遺伝子の発現状態、タンパク質のタンパク質レベル、および/またはタンパク質のタンパク質活性である、請求項1に記載の方法。
- 特異的な疾患細胞株のオミクス・データに基づいて薬剤を使用して、1人の患者において病気を治療するためのマーカを判定するためのシステムであって、
それぞれ複数の特異的な疾患細胞株のオミクス・データから導き出され、それぞれのデータセットが複数の経路要素データを含む複数の特異的なデータセットを格納する経路モデル・データベースであって、前記複数の特異的な疾患細胞株が前記1人の患者からの細胞を含む経路モデル・データベースと、
前記経路情報モデル・データベースに情報的に連結され、且つ、 前記複数の特異的な疾患細胞株に関連付けられ、前記薬剤に反応する前記複数の特異的な疾患細胞株のうちのそれぞれの1つの感度レベルを示す感度データを受け取ること、
前記経路モデル・データベースにおいて前記複数の特異的な疾患細胞株に対応する前記複数の経路要素データを横断することにより、前記複数の特異的な疾患細胞株に関する前記感度データとの相関関係を有する発現を推測すること、および
前記相関関係に基づいて前記病気を治療する前記薬剤を推奨するために前記1人の患者の細胞により示される発現レベル閾値を判定すること、
を行うようプログラムされた、機械学習システムと、
を含むシステム。 - 前記1人の患者からの細胞が健康な細胞であるか、または前記1人の患者からの細胞が新生物細胞であるか、または前記1人の患者からの細胞が異なる組織からのものである、請求項16に記載のシステム。
- 前記機械学習システムは、前記複数の疾患細胞株内に存在する前記可能な発現のうちのそれぞれの1つに対して、
前記複数の経路要素データにしたがって前記複数の特異的な疾患細胞株内に存在する前記1つの発現の等級を示すデータ点を生成することと、
前記1つの発現の前記等級と、前記複数の特異的な疾患細胞株に関する前記感度データと、の間の相関関係を導き出すことと、
を行うことにより、前記発現を推測するようプログラムされた、請求項17に記載のシステム。 - 前記機械学習システムは、前記可能な発現に対応する前記導き出された相関関係から最適な相関関係を有する前記発現を推測するために機械学習を使用するようさらにプログラムされた、請求項18に記載のシステム。
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US11011273B2 (en) | 2013-06-28 | 2021-05-18 | Nantomics, Llc | Pathway analysis for identification of diagnostic tests |
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AU2016273897B2 (en) | 2019-01-17 |
CN110444289A (zh) | 2019-11-12 |
AU2018282245A1 (en) | 2019-01-17 |
CA2919768A1 (en) | 2014-12-31 |
CN105706097A (zh) | 2016-06-22 |
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