JP2023152669A - 診断および治療モニタリングのためのバイオマーカーとしての糖ペプチドの同定および使用 - Google Patents
診断および治療モニタリングのためのバイオマーカーとしての糖ペプチドの同定および使用 Download PDFInfo
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Abstract
Description
対象から単離された複数の生物試料のそれぞれにおいて、1つ以上のプロテアーゼを用いてグリコシル化タンパク質を断片化することであって、グリコシル化ペプチド断片を生成する、ことと、
液体クロマトグラフィーおよび質量分析法(LC-MS)でグリコシル化ペプチド断片を定量することであって、定量結果を提供する、ことと、
分類の予測に役立つグリコシル化ペプチド断片を選択するために、機械学習法で対象の分類とともに定量結果を分析することと、
グリコシル化ペプチド断片の同一性を決定することと、を含む。
対象から単離された複数の生物試料のそれぞれにおいて、1つ以上のプロテアーゼを用いてグリコシル化タンパク質を断片化することであって、グリコシル化ペプチド断片を生成する、ことと、
液体クロマトグラフィーおよび質量分析法(LC-MS)でグリコシル化ペプチド断片を定量することであって、定量結果を提供する、ことと、
分類の予測に役立つグリコシル化ペプチド断片を選択するために、機械学習法で対象の分類とともに定量結果を分析することと、
グリコシル化ペプチド断片の同一性を決定することと、を含み、機械学習アプローチは、深層学習、ニューラルネットワーク、線形判別分析、二次判別分析、サポートベクターマシン、ランダムフォレスト、最近傍、またはそれらの組み合わせである。別の実施形態では、機械学習アプローチは、深層学習、ニューラルネットワーク、またはそれらの組み合わせである。
本明細書で使用される場合、以下の単語および語句は、それらが使用される文脈がそうでないことを示す場合を除いて、一般に、以下に示される意味を有することを意図している。
本開示は、いくつかの実施形態では、グリコプロテオミクス、高度なLC/MS機器を使用したバイオマーカー発見、標的発見、および検証のためのグリコプロテオミクスに関する。本開示は、機械学習法を利用して分子データを処理する。分析は、ゲノムデータ、プロテオミクス、メタボリクス、リピドミクスデータ、またはそれらの組み合わせを様々な疾患の新しいバイオマーカーを発見する際に利用することをさらに含む。本開示の方法の一般的な概略図を図1に示す。
本開示のバイオマーカー発見方法は、標的化アプローチおよび/または非標的化アプローチの両方を使用する。この方法は通常、3つの異なるフェーズ、つまり、発見フェーズ、事前検証フェーズ、および検証フェーズで構成される。
標的化アプローチは、対象の生物学的試料中の既知のグリコフォームで既知の糖タンパク質を特定および監視することを含む。様々な疾患についてFDAが承認した糖タンパク質バイオマーカーが知られており、それらは本開示の方法を使用して監視され、対象の分類を特定する。通常、バイオマーカーのグリコシル化の変化は腫瘍特異的であり、疾患または疾患のステージの有望なリスクを特定するのに役立つ。標的化アプローチでは、データ収集が実行される前の研究の開始時に確立された生物学的重要性が化学的に特徴付けられ、生物学的に注釈が付けられている、既知の糖タンパク質とそのグリコフォームに焦点を合わせる。定量化は、内部標準および化学的標準標品を使用して実行される。
次いで、このようにして発見段階で特定された候補バイオマーカーは、疾患または病態を有する多数の対象および疾患または病態を有しないそれらの適合対照から得られた生体試料の独立した試験セットで試験され、候補バイオマーカーの性能が決定される。選択されたバイオマーカー、そのランキング、および発見フェーズで開発されたモデルのパラメータ推定はすべてモデリングの一部であり、この独立した事前検証フェーズで試験される。候補バイオマーカーのシグナルによって、診断試験は生体試料を疾患のあるグループと疾患のないグループとの2つのグループに分類する。次いで、陽性適中率、陰性適中率、特異性および感度に基づいて、試験の有用性が評価される。また、診断性能は受信者動作特性(ROC)曲線を使用して評価され、どのバイオマーカーまたは複数のバイオマーカーの組み合わせが疾患または病態の統計的に優れた診断試験であるかを試験する。検証に成功した個々のバイオマーカーは、複合マーカーのパネルを形成するためにその後に組み込むために検査される。複合マーカーは、重み付き多変数ロジスティック回帰または他の分類アルゴリズムによって構築される。
事前検証フェーズで保持された候補バイオマーカーは、その後、多数の対象からの独立した盲検化された生体試料を使用した独立した検証を通じて検証される。このフェーズの目的は、選択したバイオマーカーの診断精度を評価することである。
対象から単離された複数の生物試料のそれぞれにおいて、1つ以上のプロテアーゼを用いてグリコシル化タンパク質を断片化することであって、グリコシル化ペプチド断片を生成する、ことと、
液体クロマトグラフィーおよび質量分析法(LC-MS)でグリコシル化ペプチド断片を定量することであって、定量結果を提供する、ことと、
分類の予測に役立つグリコシル化ペプチド断片を選択するために、機械学習法で対象の分類とともに定量結果を分析することと、
グリコシル化ペプチド断片の同一性を決定することと、を含む。
一実施形態では、本開示は、質量分析計を使用することによりグリコシル化ペプチド断片を定量化することを含む、本明細書に記載の方法を提供する。一実施形態では、本方法は、「多重反応モニタリング(MRM)」と呼ばれる技術を使用する。多くの場合、この手法は液体クロマトグラフィー(LC/MRM-MS)と組み合わされ、1回のLC/MRM-MS分析で数百のグリコシル化ペプチド断片(およびその親タンパク質)を定量できる。本開示の高度な質量分析技術は、効果的なイオン源、より高い分解能、より速い分離、およびより高いダイナミックレンジを備えた検出器を提供し、標的化測定の利点を保持する幅広い非標的化測定を可能にする。
本開示は、生体試料からのグリコシル化ペプチド断片の定量に基づく方法を提供する。いくつかの実施形態では、生体試料は過去に収集された1つ以上の臨床試料であり、したがって、新しいバイオマーカーの同定に付さなければならないリソースおよび時間を削減する。いくつかの実施形態では、生物学的試料は、1~50年以上のスパンにわたって生じた1つ以上の過去の研究からのものである。いくつかの実施形態では、研究は、様々な他の臨床パラメータと、対象の年齢、身長、体重、民族性、病歴などの既知の情報を伴う。そのような追加情報は、対象を疾患または病態に関連付けるのに役立つ。いくつかの実施形態では、生体試料は、対象から前向きに収集された1つ以上の臨床試料である。
本開示の方法は、対象の生体試料からのグリコシル化ペプチド断片を分析することにより検出され得るいずれかの疾患または病態に適用可能である。いくつかの実施形態では、疾患または病態はがんである。他の実施形態では、がんは、急性リンパ性白血病(ALL)、急性骨髄性白血病(AML)、副腎皮質がん、肛門がん、膀胱がん、血液がん、骨がん、脳腫瘍、乳がん、女性生殖器系のがん、がん男性生殖器系、中枢神経系リンパ腫、子宮頸がん、小児横紋筋肉腫、小児肉腫、慢性リンパ性白血病(CLL)、慢性骨髄性白血病(CML)、結腸および直腸がん、結腸がん、子宮内膜がん、子宮内膜肉腫、食道がん、眼がん、胆嚢がん、胃がん、消化管がん、有毛細胞白血病、頭頸部がん、肝細胞がん、ホジキン病、下咽頭がん、カポジ肉腫、腎臓がん、喉頭がん、白血病、肝臓がん、肺がん、悪性腫瘍線維性組織球腫、悪性胸腺腫、黒色腫、中皮腫、多発性骨髄腫、骨髄腫、鼻腔および副鼻腔がん、鼻咽頭がん、神経系がん、神経芽細胞腫、非ホジキンリンパ腫、口腔がん、口腔咽頭がん、骨肉腫、卵巣がん、膵臓がん、副甲状腺がん、陰茎がん、咽頭がん、下垂体腫瘍、形質細胞新生物、原発性CNSリンパ腫、前立腺がん、直腸がん、呼吸器系、網膜芽細胞腫、唾液腺がん、皮膚がん、小腸がん、軟部組織肉腫、胃がん、精巣がん、甲状腺がん、尿路がん、子宮肉腫、膣がん、血管系、ウォルデンストロムのマクログロブリン血症、ウィルムス腫瘍などである。別の実施形態では、がんは乳がん、子宮頸がんまたは卵巣がんである。
生体試料は、数千人の対象から取得される。これらの対象は、これまでに発見されていないマーカーのディープマイニングと検証を目的としたデジタル化に使用される。いくつかの実施形態では、生体試料は腫瘍試料または血液試料である。これらはLC/MS機器を使用してデジタル化され、膨大な量のデータが生成され、深層機械学習分析を受けて様々な疾患の新しい標的を発見する。いくつかの実施形態では、疾患はがんまたは自己免疫疾患である。
対象から単離された複数の生物試料のそれぞれにおいて、1つ以上のプロテアーゼを用いてグリコシル化タンパク質を断片化することであって、グリコシル化ペプチド断片を生成する、ことと、
液体クロマトグラフィーおよび質量分析法(LC-MS)でグリコシル化ペプチド断片を定量することであって、定量結果を提供する、ことと、
分類の予測に役立つグリコシル化ペプチド断片を選択するために、機械学習法で対象の分類とともに定量結果を分析することと、
グリコシル化ペプチド断片の同一性を決定することと、を含み、ここで、機械学習アプローチは、深層学習、ニューラルネットワーク、線形判別分析、二次判別分析、サポートベクターマシン、ランダムフォレスト、最近傍、またはそれらの組み合わせである。いくつかの実施形態では、機械学習アプローチは、深層学習、ニューラルネットワーク、またはそれらの組み合わせである。分析は、ゲノムデータ、プロテオミクス、メタボリクス、リピドミクスデータ、またはそれらの組み合わせをさらに含む。図1は、グライコミクス、LC/MS、および機械学習の統合を示す概略図であり、さらにプロテオミクス、ゲノム、リピドミクス、およびメタボリクスと組み合わせて、様々な疾患のバイオマーカーを同定する。
バイオマーカー発見の一般的な方法
標的化アプローチでは、まず目的の糖タンパク質を生体試料で特定し、次にLC/MSを使用して修飾部位、修飾の性質、修飾のアイデンティティ、各修飾の相対存在量を分析して、ペプチド断片の同一性とペプチド断片の定量を行った。このアプローチでは、三連四重極(QQQ)質量分析計を使用して、グリコシル化されたペプチド断片を定量し、対象の分類との関係について分析する。
乳がんの有望なバイオマーカーとしてのIgG糖ペプチドの定量化
がんの様々な段階にある乳がん患者と、年齢が一致する対照の血漿試料を、IgG1、IgG0、およびIgG2糖ペプチドについて分析し、それらの比率の変化を比較した。具体的には、Tisステージの20の試料、EC1ステージの50の試料、EC2ステージの試料、EC3ステージの25の試料、EC4ステージの9の試料、および73の年齢が一致した対照試料を、QQQ質量分析計でMRM定量分析にかけた。図2の定量的結果からからわかるように、特定のIgG1糖ペプチドのレベルを対照と比較して評価したところ、特定のIgG1糖ペプチドのレベルはこの実験で研究された乳がんのすべての段階での対照と比較して減少した。例えば、A1~A11と名付けられたIgG1糖ペプチドをモニターしたところ、糖ペプチドA1およびA2のレベルは対照と比較して上昇し、一方、糖ペプチドA8、A9およびA10のレベルはこの実験で研究された乳がんのすべての段階での対照と比較して減少した。したがって、糖ペプチドA1、A2、A8、A9およびA10は、乳がんの有望なバイオマーカーである。
PSCおよびPBCの有望なバイオマーカーとしてのIgG糖ペプチドの定量化
原発性硬化性胆管炎(PSC)を有する患者、原発性胆汁性肝硬変(PBC)を有する患者からの血漿試料、および健常ドナーからの血漿試料をIgG1およびIgG2糖ペプチドについて分析し、グリコペピド比の変化を比較した。具体的には、100のPBC血漿試料、76のPSC血漿試料、49の健常ドナーからの血漿試料を、QQQ質量分析計でMRM定量分析にかけた。図3の定量結果からわかるように、特定のIgG1糖ペプチドは健常ドナーと比較して上昇したが、特定のIgG1糖ペプチドはPBCとPSCの患者の血漿試料の対照と比較して減少した。例えば、糖ペプチドAはPBCとPSCの患者の健常ドナーと比較して上昇したが、糖ペプチドH、IとJはPBCとPSCの患者の血漿試料の健常ドナーと比較して減少した。したがって、糖ペプチドA、H、I、およびJは、PBCおよびPSCの有望なバイオマーカーである。
Claims (20)
- 有望なバイオマーカーとしてグリコシル化ペプチド断片を同定する方法であって、
対象から単離された複数の生体試料のそれぞれにおいて、1つ以上のプロテアーゼを用いてグリコシル化タンパク質を断片化することであって、グリコシル化ペプチド断片を生成する、ことと、
液体クロマトグラフィーおよび質量分析法(LC-MS)で前記グリコシル化ペプチド断片を定量することであって、定量結果を提供する、ことと、
機械学習法で前記定量結果を前記対象の分類と共に分析することであって、前記分類を予測するために有用なグリコシル化ペプチド断片を選択する、ことと、
グリコシル化ペプチド断片の同一性を決定することと、を含む、方法。 - 前記対象が、疾患または病態を有する対象と、前記疾患または病態を有しない対象とを含む、請求項1に記載の方法。
- 前記対象が、疾患の治療を受けている対象と、前記疾患を有するが治療を受けていない対象とを含む、請求項1または2に記載の方法。
- 前記疾患ががんまたは自己免疫疾患である、請求項2または3に記載の方法。
- 前記疾患が、乳がん、子宮頸がんまたは卵巣がんから選択されるがんである、請求項4に記載の方法。
- 前記疾患が、HIV、原発性硬化性胆管炎、原発性胆汁性肝硬変または乾癬から選択される自己免疫疾患である、請求項5に記載の方法。
- 前記グリコシル化ペプチド断片がN-グリコシル化されている、請求項1~6のいずれか1項に記載の方法。
- 前記グリコシル化ペプチド断片がO-グリコシル化されている、請求項1~7のいずれか1項に記載の方法。
- 前記グリコシル化タンパク質が、アルファ-1-酸性糖タンパク質、アルファ-1-アンチトリプシン、アルファ-1B-糖タンパク質、アルファ-2-HS-糖タンパク質、アルファ-2-マクログロブリン、アンチトロンビンIII、アポリポタンパク質B-100、アポリポタンパク質D、アポリポタンパク質F、ベータ-2-糖タンパク質1、セルロプラスミン、フェチュイン、フィブリノゲン、免疫グロブリン(Ig)A、IgG、IgM、ハプトグロビン、ヘモペキシン、ヒスチジンリッチ糖タンパク質、キニノゲン-1、セロトランスフェリン、トランスフェリン、ビトロネクチン、亜鉛-アルファ-2-糖タンパク質のうちの1つ以上である、請求項1~8のいずれか1項に記載の方法。
- 前記グリコシル化タンパク質が、アルファ-1-酸糖タンパク質、免疫グロブリン(Ig)A、IgGまたはIgMのうちの1つ以上である、請求項9に記載の方法。
- 前記グリコシル化ペプチド断片が5~50アミノ酸残基の平均長を有する、請求項1~10のいずれか1項に記載の方法。
- 前記1つ以上のプロテアーゼが少なくとも2つのプロテアーゼを含む、請求項1~11のいずれか1項に記載の方法。
- 質量分析法が多重反応モニタリング質量分析法(MRM-MS)を含む、請求項1~12のいずれか1項に記載の方法。
- 前記生体試料が、体組織、唾液、涙、喀痰、脊髄液、尿、滑液、全血、血清または血漿である、請求項1~13のいずれか1項に記載の方法。
- 前記生体試料が全血、血清または血漿である、請求項14に記載の方法。
- 前記対象が哺乳動物である、請求項1~15のいずれか1項に記載の方法。
- 前記対象がヒトである、請求項16に記載の方法。
- 前記機械学習アプローチが、深層学習、ニューラルネットワーク、線形判別分析、二次判別分析、サポートベクターマシン、ランダムフォレスト、最近傍またはそれらの組み合わせである、請求項1~17のいずれか1項に記載の方法。
- 前記機械学習アプローチが、深層学習、ニューラルネットワーク、またはそれらの組み合わせである、請求項1~18のいずれか1項に記載の方法。
- 前記分析が、ゲノムデータ、プロテオミクス、メタボリクス、リピドミクスデータ、またはそれらの組み合わせをさらに含む、請求項1~19のいずれか1項に記載の方法。
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