JP6611844B2 - 前立腺癌のリスク及び前立腺容積を予測する方法及び装置 - Google Patents
前立腺癌のリスク及び前立腺容積を予測する方法及び装置 Download PDFInfo
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Description
を含むシステム。
ようにプログラミングされているシステム。
a)多数の男性のデータの多変数ロジスティック回帰を使用することで得られたロジスティック回帰モデルを提供すること。上記データは、上記多数の男性のうちの各男性について、前立腺癌の状態に関するデータと、前立腺癌の状態に関するデータに先行するデータであって、年齢を含むデータと、上記男性の血液サンプルから得られた血液マーカー、すなわち、総前立腺特異抗原(tPSA)、遊離型PSA(fPSA)、未変化PSA(iPSA)、及びオプションでヒトカリクレイン2(hK2)の決定とを含む。
上記ロジスティック回帰モデルは次式を用いて生成される。
b)男性の年齢を、年を単位として提供すること。
c)上記男性の血液サンプルからそれぞれ得られた上記血液マーカー、すなわち、
i)tPSA、
ii)fPSA、
iii)iPSA、
iv)オプションでhK2、
を決定すること。
d)ステップb)の上記提供された年齢と、ステップc)の上記決定された血液マーカーとを用いて、上記ロジスティック回帰モデルを使用して、上記男性の上記イベントの上記確率を、
i)使用する式を
ii)上記確率を
により取得すること。
a)多数の男性のデータの線形回帰を使用することで得られた線形回帰モデルを提供すること。
上記データは、上記多数の男性のうちの各男性について、
i)前立腺容積に関するデータと、
ii)前立腺容積に関するデータに先行するデータであって、年齢を含むデータと、
上記男性の血液サンプルから得られた血液マーカー、すなわち、総前立腺特異抗原(tPSA)、遊離型PSA(fPSA)、未変化PSA(iPSA)、及びオプションでヒトカリクレイン2(hK2)の決定と
を含む。
上記線形回帰モデルは次式を用いて生成される。
b)男性の年齢を、年を単位として提供すること。
c)上記男性の血液サンプルからそれぞれ得られた上記血液マーカー、すなわち、tPSA、fPSA、iPSA、及びオプションでhK2を決定すること。
d)ステップb)の上記提供された年齢と、ステップc)の上記決定された血液マーカーとを用いて、上記線形回帰モデルを使用して、上記男性の上記予測された前立腺容積を取得すること。
いくつかの実施形態では、
本方法は、1つ以上のカリクレインマーカー、すなわち、総前立腺特異抗原(total prostate-specific antigen :tPSA)、遊離型PSA(free PSA:fPSA)、未変化PSA(intact PSA:iPSA)、及びヒトカリクレイン2(human kallikrein 2:hK2)からの情報を包含することを含む。
任意の適切なロジスティック回帰モデルが使用されてもよく、本明細書で説明する技術はこの点で限定しない。
いくつかの実施形態では、
イベントの確率は次式(I)により決定される。
このモデルでは、総PSAに対する遊離型PSAの比が遊離型PSA項の代わりに用いられる。
このモデルでは、この予測ファクタの寄与分の増大の原因となるtPSA項にtPSAの対数が代入される。
このモデルでは、tPSA及びfPSAに関する追加の非線形項が含まれている。以下に提示する例示的な式では、tPSA項と前立腺癌のリスクとの間の直接的関係を強調するためにtPSAの2乗が使用され、また、総PSAに対する遊離型PSAの比の項とリスクとの負の関連付けを反映するために、総PSAに対する遊離型PSAの比の項の平方根が使用される。しかしながら、いくつかの実施形態では、多項式の高次の項(例えば3乗)も含まれている可能性があるということは認識されるべきである。
このモデルでは、メジアン値において単一の節点を有するように、線形スプラインが追加される。スプラインは、モデルが次式:
このモデルでは、変数の個数を減らしてモデルを簡単化するために、tPSA及びfPSAに関してのみ線形スプラインが含まれている。
このモデルにおいて、各項に関して3次スプラインが含まれている。以下で提示する例では、4つの節点を有する3次スプラインが説明される。しかしながら、代替として、5つの節点、6つの節点、7つの節点、及び8つの節点を含むが、これらに限定されない任意の適切な個数の節点を用いる3次スプラインが使用されてもよいということは認識されるべきである。スプラインは次式を用いて決定されてもよい。
このモデルでは、変数の個数を減らしてモデルを簡単化するために、tPSA及びfPSAのみに関する3次スプラインが含まれている。
このモデルでは、特定の年齢(例えば年齢65)未満又はより高い/等しい年齢を有する患者について使用するための異なる係数(β)を生成するために、データセットの2つの部分に3次スプラインが適用される。従って、このモデルでは、両方のグループの患者に同様の表現が(異なる係数値を用いて)使用される。このモデルとともに使用可能な異なる係数の例は、以下の表1に提示される。
図3に、本明細書で説明した技術及び/又はユーザ対話処理のうちの一部又はすべてが実装されてもよいコンピュータシステム300の例示的実装を示す。コンピュータシステム300は、1つ以上のプロセッサ310及び1つ以上のコンピュータ可読な非一時的記憶媒体(例えば、メモリ320及び1つ以上の不揮発性記憶メディア330)を含んでもよい。1つ以上のプロセッサ310は、メモリ320及び不揮発性記憶素子330へのデータの書き込み及びデータの読み出しを任意の適切な方法で制御してもよいが、本明細書で説明する本発明の態様はこの点で限定しない。
本明細書で説明したように、いくつかの実施形態では、システムは、前立腺癌(例えば、前立腺癌のリスク及び/又は前立腺容積)に関連付けられたイベントの確率を決定するためのアナライザと電子的に通信してロジスティック回帰モデルを評価するようにプログラミングされたプロセッサ又はコンピュータを含んでもよい。アナライザは、ロジスティック回帰モデルに入力するための血液マーカーの1つ以上の特性を決定するように適合されて構成されてもよい。いくつかの実施形態では、アナライザは微小流体サンプルアナライザであり、例えば、アナライザは、微小流体装置/カセット内で処理されたサンプルを決定するように適合されて構成されてもよい。しかしながら、他のタイプのアナライザが用いられてもよく(例えば微小ウェルELISA型分析のためのアナライザ)、本明細書で説明したシステムがこの点で限定されないことは認識されるべきである。
ld=光源をオフしたときの分析領域を通る透過光の強度
lr=基準強度(すなわち、光源を活性化したときの、空気だけがチャネルにある場合の分析開始前の、分析領域における透過光の強度)
そのようなモジュール及びプログラムは、複数の別個のコンピュータ上で実行可能である。
本発明の方法をさまざまな実施形態の形態で具体化することができ、それらの少数だけが本明細書で開示されていることは認識されるだろう。他の実施形態が存在し、本発明の精神から外れないことは、当業者には明らかであろう。従って、説明した実施形態は例示であり、限定的なものとして解釈されるべきでない。
統計モデルを使用する合計7個の別個の研究が行なわれた。研究は、増大したPSA及び2270の癌を有する合計7647人の男性を含み、5つの研究は外部検証を構成する。さらに、研究は、広範囲の臨床シナリオをカバーするように系統的に設計された。おそらくは最も重要なこととして、研究のうちの1つは自然経過アプローチを含むものだった。生検結果が代替エンドポイントであるので、重要なことは、ある男性が前立腺癌を有するか否かではなく、彼が彼の生活に影響する前立腺癌のリスクを有するか否かであり、理想的な研究は、患者から血液を取得し、その後、さらなるスクリーニングを行うことなく患者を数年にわたって追跡し、前立腺癌の結果を決定することである。我々は、十分に幸運にもそのような研究を行なうことができた(Vickers, A.J., et al., Cancer Epidemiol Biomarkers Prey, 2011. 20(2): p. 255-61)。
2. 4つのカリクレインに基づいた統計的予測モデルは、単一のトレーニング集合を用いて構築された。
3. これは、予測された癌の確率を与えるために、新規のマーカーから情報に臨床検査を統合する。
4. 合計で、パネルは、2250近くの癌を含む、診断された7500人を超える男性に適用され、5つの別個の研究が外部検証を構成した。
5. モデルは、前立腺癌に関して高い識別力を有し、標準的な予測値(総PSA、年齢、及び直腸指診)のみに基づいた統計モデルよりもずっと高いAUCを有する。
6. 前立腺生検の参照を決定する4カリクレイン統計的予測モデルの使用は、決定分析によれば、すべての男性に生検を行うことのような代替方法と比較して臨床上の結果を改善するだろう。
7. モデルは、所定範囲の異なる臨床設定値内の値を有し、先のスクリーニングを行うか否か、先の生検を行うか否か、生検を参照する前に臨床の精密検査を行うか否かを含む。
8. アーカイブされた男性の血液にモデルを適用し、スクリーニングせずに長期的に追跡することは、増大したPSAを有するが統計モデルによれば低リスクを有する男性において、後の5〜10年にわたって高悪性度の癌が進行する可能性がほとんどないことを実証した。反対に、臨床的に診断された高悪性度の癌は、モデルにより高リスクであるとされた男性において一般的だった。
Age:年を単位として年齢を入力する
tPSA:ng/mlを単位として総PSAを入力する
fPSA:ng/mlを単位として遊離型PSAを入力する
iPSA:ng/mlを単位として未変化PSAを入力する
hK2:ng/mlを単位としてhK2を入力する
tPSA≧25である場合、次式を用いる。
L=0.0733628×tPSA−1.377984
前立腺癌のリスク=exp(L)/[1+exp(L)]
Spline2_tPSA=−(162−6.4406)/(162−3)×(tPSA−3)^3+max(tPSA−6.4406,0)^3
fPSA<11.8である場合:
Spline1_fPSA=−(11.8−0.84)/(11.8−0.25)×(fPSA−0.25)^3+max(fPSA−0.84,0)^3
fPSA>11.8である場合:
Spline1_fPSA=(11.8−0.84)×(0.84−0.25)×(11.8+0.84+0.25−3×fPSA)
fPSA<11.8である場合:
Spline2_fPSA=−(11.8−1.29)/(11.8−0.25)×(fPSA−0.25)^3+max(fPSA−1.29,0)^3
fPSA>11.8である場合:
Spline2_fPSA=(11.8−1.29)×(1.29−0.25)×(11.8+1.29+0.25−3×fPSA)
次式を定義する。
x2=−3.717517×fPSA−0.6000171×Spline1_fPSA+0.275367×Spline2_fPSA
x3=3.968052×iPSA
x4=4.508231×hK2
L=−1.735529+0.0172287×Age+x1+x2+x3+x4
前立腺癌のリスク=exp(L)/[1+exp(L)]
x2=−3.460508×fPSA−0.4361686×Spline1_fPSA+0.1801519×Spline2_fPSA
x3=4.014925×iPSA
x4=3.523849×hK2
再較正は、先の陰性の生検を有する男性について使用されてもよいが、再較正は、イベントレートが(予めスクリーニングされていない)ロッテルダムコホートで観察されたイベントレート(29%)から有意に異なっている他の状況でも使用可能である。
予測された見込み=予測された癌のリスク/(1−予測された癌のリスク)
ベイズファクタ=癌の見込み/予測された見込み
y_adj=y+log(ベイズファクタ)
再較正された前立腺癌のリスク=exp(y_adj)/[1+exp(y_adj)
これは、サンプルと関連付けられた金の粒子上に無電解に銀を堆積することで、サンプルにおけるiPSA、fPSA、tPSA、及びhK2を発見する分析を行うためのカセット及びアナライザの使用を説明する予言的実施例である。図14は、この例で使用されるカセットの微小流体システム1500の概略的な例示を含む。カセットは、図7に示すカセット520を同様の形状を有するものであった。
Claims (20)
- 分析領域と、検出装置と、コンピュータとを備える分析システムであって、
上記分析領域は、内部の固体相部分に固定された複数の結合パートナーを含み、上記複数の結合パートナーは、総前立腺特異抗原(tPSA)を結合する結合パートナーと、遊離型前立腺特異抗原(fPSA)を結合する結合パートナーと、未変化前立腺特異抗原(iPSA)を結合する結合パートナーと、ヒトカリクレイン2(hK2)を結合する結合パートナーとからなるグループから選択され、
上記検出装置は、上記分析領域からのサンプルにおける分析物の存在を検出するように構成され、上記分析物は、tPSA、fPSA、iPSA、及びhK2からなるグループから選択され、
上記コンピュータは、
複数の変数のそれぞれを異なる係数値によってスケーリングして複数のスケーリングされた変数を生成し、
上記複数のスケーリングされた変数の値を合計することで、被験者から取得された前立腺組織の生検において癌が検出される確率を生成し、
上記癌が検出される確率を示す情報を出力するようにプログラミングされ、
上記複数の変数は、年齢と、上記検出装置から受信された情報に含まれる少なくとも2つの変数とを含み、上記少なくとも2つの変数は、fPSA値、iPSA値、及びtPSA値からなるグループから選択される、
分析システム。 - 上記少なくとも2つの変数はfPSA値及びtPSA値を含み、
上記コンピュータは、
fPSA値に係る第1のスプラインを定義し、
上記検出装置から受信された情報に含まれるfPSA値を用いて上記fPSA値に係る第1のスプラインを評価することにより第1のfPSA値を計算し、
tPSA値に係る第1のスプラインを定義し、
上記検出装置から受信された情報に含まれるtPSA値を用いて上記tPSA値に係る第1のスプラインを評価することにより第1のtPSA値を計算するようにさらにプログラミングされ、
上記複数のスケーリングされた変数の値は、第1の係数値によってスケーリングされた上記計算された第1のfPSA値と、第2の係数値によってスケーリングされた上記計算された第1のtPSA値とを含む、
請求項1記載の分析システム。 - 上記コンピュータは、
fPSA値に係る第2のスプラインを定義し、
上記検出装置から受信された情報に含まれるfPSA値を用いて上記fPSA値に係る第2のスプラインを評価することにより第2のfPSA値を計算し、
tPSA値に係る第2のスプラインを定義し、
上記検出装置から受信された情報に含まれるtPSA値を用いて上記tPSA値に係る第2のスプラインを評価することにより第2のtPSA値を計算するようにさらにプログラミングされ、
上記複数のスケーリングされた変数の値は、第3の係数値によってスケーリングされた上記計算された第2のfPSA値と、第4の係数値によってスケーリングされた上記計算された第2のtPSA値とをさらに含む、
請求項2記載の分析システム。 - 上記fPSA値に係る第1のスプライン、上記fPSA値に係る第2のスプライン、上記tPSA値に係る第1のスプライン、及び上記tPSA値に係る第2のスプラインのそれぞれは、3次スプラインである、
請求項3記載の分析システム。 - 上記複数のスケーリングされた変数の値を合計することで、被験者から取得された前立腺組織の生検において癌が検出される確率を生成することは、
ロジスティック回帰モデルを評価してロジット関数を決定することと、
上記ロジット関数を少なくとも部分的に基礎として、被験者から取得された前立腺組織の生検において癌が検出される確率を決定することとを含む、
請求項1記載の分析システム。 - 上記検出装置から受信された情報は、tPSA値、fPSA値、iPSA値、及びhK2値の情報を含み、
上記複数の変数は、年齢、tPSA値、fPSA値、iPSA値、及びhK2値を含む、
請求項1記載の分析システム。 - 上記癌が検出される確率を示す情報を出力することは、前立腺癌の生検が陽性になる可能性を示す情報を出力することを含む、
請求項1記載の分析システム。 - 上記検出装置は、光透過、光吸収、光散乱、光反射、視覚的技術、フォトルミネッセンス、蛍光、化学ルミネセンス、生物発光、及び電気化学ルミネセンスからなるグループから選択された光又はルミネセンス検出技術を利用する、
請求項1記載の分析システム。 - 上記検出装置は、上記結合パートナーに結合された分析物を示す化学ルミネセンス又は蛍光の放射を検出する、
請求項1記載の分析システム。 - 上記検出装置は、光信号の変動を時間の関数として検出する、
請求項1記載の分析システム。 - 上記サンプルは、全血、血清、又は血漿である、
請求項1記載の分析システム。 - 上記分析領域は、少なくとも1つの入口及び1つの出口を含む微小流体チャネルを備える、
請求項1記載の分析システム。 - 上記複数の結合パートナーは、互いに異なる第1及び第2の結合パートナーを含む、
請求項1記載の分析システム。 - 上記第1の結合パートナーは、特に、fPSA、iPSA、及びtPSAのうちの1つと結合するように適合される、
請求項13記載の分析システム。 - 上記第2の結合パートナーは、特に、fPSA、iPSA、及びtPSAのうちの他の1つと結合するように適合される、
請求項14記載の分析システム。 - 上記複数の結合パートナーは、特にhK2と結合するように適合される第3の結合パートナーを含む、
請求項15記載の分析システム。 - 上記癌が検出される確率を示す情報は、ある人物の生検の要否についての決定をガイドするために使用される、解釈可能な尺度を含む、
請求項1記載の分析システム。 - 上記癌が検出される確率を示す情報は、高悪性度の癌又は低悪性度の癌に対応する陽性の生検についての決定を含む、
請求項1記載の分析システム。 - 検出装置により、内部の固体相部分に固定された複数の結合パートナーを含む分析システムの分析領域からのサンプルにおいて、分析物の存在を検出することと、
少なくとも1つのコンピュータプロセッサによって、複数の変数のそれぞれを異なる係数値によってスケーリングして複数のスケーリングされた変数を生成することと、
少なくとも1つのコンピュータプロセッサによって、上記複数のスケーリングされた変数の値を合計することで、被験者から取得された前立腺組織の生検において癌が検出される確率を生成することと、
上記癌が検出される確率を示す情報を出力することとを含み、
上記複数の結合パートナーは、総前立腺特異抗原(tPSA)を結合する結合パートナーと、遊離型前立腺特異抗原(fPSA)を結合する結合パートナーと、未変化前立腺特異抗原(iPSA)を結合する結合パートナーと、ヒトカリクレイン2(hK2)を結合する結合パートナーとからなるグループから選択され、上記分析物は、tPSA、fPSA、iPSA、及びhK2からなるグループから選択され、
上記複数の変数は、年齢と、上記検出装置から受信された情報に含まれる少なくとも2つの変数とを含み、上記少なくとも2つの変数は、fPSA値、iPSA値、及びtPSA値からなるグループから選択される、
方法。 - 検出装置と、コンピュータによって実行されたときに所定の方法を実施する複数の命令が符号化された非一時的コンピュータ可読記憶媒体とを備える分析システムであって、
上記検出装置は、上記分析領域からのサンプルにおける分析物の存在を検出するように構成され、
上記分析領域は、内部の固体相部分に固定された複数の結合パートナーを含み、上記複数の結合パートナーは、総前立腺特異抗原(tPSA)を結合する結合パートナーと、遊離型前立腺特異抗原(fPSA)を結合する結合パートナーと、未変化前立腺特異抗原(iPSA)を結合する結合パートナーと、ヒトカリクレイン2(hK2)を結合する結合パートナーとからなるグループから選択され、
上記分析物は、tPSA、fPSA、iPSA、及びhK2からなるグループから選択され、
上記方法は、
複数の変数のそれぞれを異なる係数値によってスケーリングして複数のスケーリングされた変数を生成することと、
上記複数のスケーリングされた変数の値を合計することで、被験者から取得された前立腺組織の生検において癌が検出される確率を生成することと、
上記癌が検出される確率を示す情報を出力することとを含み、
上記複数の変数は、年齢と、上記検出装置から受信された情報に含まれる少なくとも2つの変数とを含み、上記少なくとも2つの変数は、fPSA値、iPSA値、及びtPSA値からなるグループから選択される、
分析システム。
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