JP7227964B2 - 黒色腫の検出方法 - Google Patents
黒色腫の検出方法 Download PDFInfo
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Description
本出願は、2017年5月25日に出願された米国仮出願第62/511,058号の利益及び優先権を主張し、その内容は、参照により本明細書に組み込まれる。
2018年5月12日に作成されたサイズが265kBの「LBIO-001_001WO SEQ LISTING.txt」という名称のテキストファイルの内容は、参照によりその全体が本明細書に組み込まれる。
本発明は、黒色腫の検出に関する。
本発明の詳細は、以下の付随する説明に記載されている。本明細書に記載されるものと類似又は同等の方法及び材料を本発明の実施又はテストに使用することができるが、例示的な方法及び材料をここで説明する。本発明の他の特徴、目的、及び利点は、明細書及び特許請求の範囲から明らかになるであろう。明細書及び特許請求の範囲において、文脈がそうでないことを明確に指示しない限り、単数形又は複数形も含む。他に定義されない限り、本明細書で使用される全ての技術用語及び科学用語は、本発明が属する技術分野の当業者によって一般に理解されるのと同じ意味を有する。本明細書で引用された全ての特許及び刊行物は、その全体が参照により本明細書に組み込まれる。
冠詞「a」及び「an」は、本開示において、冠詞の文法的対象の1つ又は複数(すなわち、少なくとも1つ)を指すために使用される。例として「要素(an element)」は、1つの要素又は複数の要素を意味する。
28個のマーカー遺伝子パネルの導出
本発明は、上記の特定の実施形態に関連して説明されたが、その多くの代替、修正、及びそれらの他の変形は、当業者には明らかであろう。そのような全ての代替、修正、及び変形は、本発明の本質及び範囲に含まれることが意図される。
Claims (20)
- 黒色腫の検出を必要とする対象の黒色腫を検出する方法であって、テストサンプルを少なくとも29個のRNAバイオマーカーの発現を検出するために特異的な複数の薬剤と接触させることにより、前記対象からのテストサンプルから前記少なくとも29個のRNAバイオマーカーの発現レベルを決定し、ここで、前記少なくとも29個のRNAバイオマーカーは、それぞれATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、YY2、及び少なくとも1つのハウスキーピング遺伝子を含み;
ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの発現レベルを少なくとも1つのハウスキーピング遺伝子に対し正規化し、それによって、ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの正規化された発現レベルを取得し;
正規化されたそれぞれの発現レベルをアルゴリズムに入力してスコアを生成し;
前記スコアを所定のカットオフ値と比較し; 及び
前記スコアが、前記所定のカットオフ値以上の場合、前記対象における黒色腫の存在を識別し、又は前記スコアが前記所定のカットオフ値未満の場合、前記対象の黒色腫の非存在を識別し、ここで、前記所定のカットオフ値は、0~100のスケールで20である、を含む方法。 - 黒色腫を有する対象の外科的切除の程度を評価する方法であって、
テストサンプルを少なくとも29個のRNAバイオマーカーの発現を検出するために特異的な複数の薬剤と接触させることにより、前記外科的切除後の対象のテストサンプルから前記少なくとも29個のRNAバイオマーカーの発現レベルを決定し、ここで、前記少なくとも29個のRNAバイオマーカーは、それぞれATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、YY2、及び少なくとも1つのハウスキーピング遺伝子を含み;
ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの前記発現レベルを前記少なくとも1つのハウスキーピング遺伝子に対し正規化し、それによって、ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの正規化された発現レベルを取得し;
正規化されたそれぞれの発現レベルをアルゴリズムに入力し、スコアを生成し;
前記スコアを所定のカットオフ値と比較し; 及び
前記正規化された発現レベルが、前記所定のカットオフ値以上の場合、前記外科的切除により黒色腫全体が除去されていないことを識別し、又は前記正規化された発現レベルが、前記所定のカットオフ値未満の場合、前記外科的切除により黒色腫全体が除去されていることを識別し、ここで、前記所定のカットオフ値は、0~100のスケールで20である、を含む方法。 - 前記方法が、前記正規化された発現レベルが、前記所定のカットオフ値以上の場合、黒色腫の再発のリスクが高いことを識別すること、又は前記正規化された発現レベルが、前記所定のカットオフ値未満の場合、黒色腫の再発のリスクが低いことを識別することをさらに含む、請求項2に記載の方法。
- 前記少なくとも1つのハウスキーピング遺伝子が、ALG9、SEPN、YWHAQ、VPS37A、PRRC2B、DOPEY2、NDUFB11、ND4、MRPL19、PSMC4、SF3A1、PUM1、ACTB、GAPD、GUSB、RPLP0、TFRC、MORF4L1、18S、PPIA、PGK1、RPL13A、B2M、YWHAZ、SDHA、HPRT1、TOX4、及びTPT1からなる群から選択される、請求項1~3の何れか1項に記載の方法。
- 前記少なくとも1つのハウスキーピング遺伝子が、TOX4及びTPT1を含む、請求項4に記載の方法。
- 前記正規化された発現レベルが、以下:
ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの発現レベルを、TOX4の発現レベルに対して正規化し、それによって、ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの第1の正規化された発現レベルを得;
ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの発現レベルを、TPT1の発現レベルに対して正規化し、それによって、ATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2のそれぞれの第2の正規化された発現レベルを得;
前記第1の正規化された発現レベルと前記第2の正規化された発現レベルとを平均して正規化された発現レベルを得る、
ことによって得られる、請求項5に記載の方法。 - 療法に対する黒色腫を有する対象の反応を決定する方法であって、第1のテストサンプルを少なくとも28個のRNAバイオマーカーの発現を検出するために特異的な複数の薬剤と接触させることにより、第1の時点で前記対象からの第1のテストサンプルから前記少なくとも28個のRNAバイオマーカーの第1の発現レベルを決定し、ここで、前記28個のRNAバイオマーカーは、それぞれATL1、ATP6V0D、C1ORF21、CFLAR、CFLAR-AS1、CHP1、DDX55、DMD、DNAJC9、ENOSF1、FANCL、HJURP、HLA-DOA、HLA-DRA、HNRNPA3P1、IL23A、IQGAP1、LOC494127、LOC646471、LOH12CR、PBXIP1、RNF5、SERTAD2、SLC35G5、SPATS2L、TDRD7、TXK、及びYY2を含み;
第2のテストサンプルを前記少なくとも28個のRNAバイオマーカーの発現を検出するために特異的な複数の薬剤と接触させることにより、第2の時点で前記対象からの第2のテストサンプルから前記少なくとも28個のRNAバイオマーカーの第2の発現レベルを決定し、ここで、前記第2の時点は、前記第1の時点の後及び前記対象への療法の実施後であり;
前記第1の発現レベルを前記第2の発現レベルと比較し; 及び
前記第2の発現レベルが、前記第1の発現レベルと比較して有意に減少した場合、前記対象が療法に反応することを識別する、
ことを含む、方法。 - 前記第1の時点が、前記対象への前記療法の実施前である、請求項7に記載の方法。
- 前記第1の時点が、前記対象への前記療法の実施後である、請求項7に記載の方法。
- 前記療法が、免疫療法を含む、請求項7~9の何れか1項に記載の方法。
- 前記療法が、標的療法を含む、請求項7~9の何れか1項に記載の方法。
- 前記標的療法が、BRAF阻害剤を含む、請求項11に記載の方法。
- (a)90%以上の感度;又は
(b)90%以上の特異性、
を有する請求項1~12の何れか1項に記載の方法。 - 前記黒色腫が、進行性である、請求項1~13の何れか1項に記載の方法。
- 前記RNAバイオマーカーの発現レベルが、前記バイオマーカーと標識されたプローブ又はプライマーとの間で複合体を形成することによって検出され、好ましくは、標識が、蛍光標識である、請求項1~14の何れか1項に記載の方法。
- RNAバイオマーカーを逆転写してcDNAを産生し、次いで産生されたcDNAの発現レベルを検出し、好ましくは、前記cDNAが、前記cDNAと標識された核酸又はプライマーとの間で複合体を検出することによって検出され、好ましくは、標識が、蛍光標識である、請求項1~15の何れか1項に記載の方法。
- 前記テストサンプルが、血液、血清、血漿、又は腫瘍組織である、請求項1~16の何れか1項に記載の方法。
- 前記所定のカットオフ値が、腫瘍性疾患の無い対象から得られた複数のリファレンスサンプルから得られたものであり、好ましくは、各リファレンスサンプルが、血液、血清、血漿、又は腫瘍組織である、請求項1~6の何れか1項に記載の方法。
- 前記黒色腫の検出を必要とする対象が、黒色腫と診断された対象、少なくとも1つの黒色腫の症状を有する対象、又は黒色腫を発症する素因又は家族歴を有する対象であり、好ましくは前記対象が、ヒトである、請求項1~18の何れか1項に記載の方法。
- 前記アルゴリズムが、eXtreme勾配ブースティング(eXtreme Gradient Boosting;XGB)、ランダムフォレスト(Random Forest;RF)、Lasso及びElastic-Net正則化一般化線形モデル(Lasso and Elastic-Net Regularized Generalized Linear Models;glmnet)、cforest、分類木及び回帰木(classification and regression tree;CART)、treebag、k近傍法(k nearest-neighbor;knn)、ニューラルネットワーク(neural network;nnet)、サポートベクターマシン-ラジアル(support vector machine-radial;SVM-radial)、サポートベクターマシン-線形(support vector machine-linear;SVM-linear)、単純ベイズ(naive bayes;NB)、ニューラルネットワークモデル学習(Neural Network Model Training;NNET)、多層パーセプトロン(multilayer perceptron ;mlp)、又はその任意の組み合わせである、請求項1~6の何れか1項に記載の方法。
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