JP6675716B2 - Prognostic expression formula and prognosis estimation method for lung adenocarcinoma using immune factors as indices - Google Patents

Prognostic expression formula and prognosis estimation method for lung adenocarcinoma using immune factors as indices Download PDF

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JP6675716B2
JP6675716B2 JP2017517920A JP2017517920A JP6675716B2 JP 6675716 B2 JP6675716 B2 JP 6675716B2 JP 2017517920 A JP2017517920 A JP 2017517920A JP 2017517920 A JP2017517920 A JP 2017517920A JP 6675716 B2 JP6675716 B2 JP 6675716B2
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lung adenocarcinoma
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祥弘 大植
祥弘 大植
三喜男 岡
三喜男 岡
浩史 黒瀬
浩史 黒瀬
睿一 中山
睿一 中山
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer

Description

本発明は、原発性肺腺癌（以下、肺腺癌）の予後を予測する為の演算式の作成方法及びそれを用いた肺腺癌の予後推定方法に関する。より詳細には、肺腺癌の予後と関連する複数の因子の腫瘍組織における発現プロファイルと予後との関連性に基づいて予後演算式を作成する方法、及び該演算式を用いて肺腺癌の予後を推定する方法に関する。   The present invention relates to a method for preparing an arithmetic expression for predicting the prognosis of primary lung adenocarcinoma (hereinafter, lung adenocarcinoma) and a method for estimating prognosis of lung adenocarcinoma using the same. More specifically, a method of creating a prognostic formula based on the relationship between the prognosis and the expression profile in tumor tissue of a plurality of factors associated with prognosis of lung adenocarcinoma, and a method for lung adenocarcinoma using the formula It relates to a method for estimating prognosis.

本邦の癌罹患率は年々増加しており、現在死亡原因の第１位となっている。癌の標準的治療法に加えて、新たな治療法の開発が急務である。近年悪性黒色腫を先導に肺がんでも抗体免疫療法への道が切り開かれるようになったが、肺がんでの免疫監視機構は不明である。
腫瘍局所における免疫監視機構の検討には従来、腫瘍側の免疫抑制分子やがん抗原の発現と宿主側の免疫細胞の浸潤を検討する２つの方法があった。肺がんにおける腫瘍側の免疫抑制分子としてＰＤ−Ｌ１／ＰＤ−１経路と予後との関連を検討する研究が多くなされてきた（特許文献１、非特許文献３）。それ以外の免疫抑制分子やがん抗原の発現パターンと頻度および予後との関連については、例えば特許文献２や非特許文献２に測定対象となる癌関連蛋白質としてＸＡＧＥ１が、非特許文献１にはGalectin-9が肺癌の術後再発の予後因子となり得ることが記載されている。一方、宿主側の因子と予後については、種々の免疫細胞浸潤の多寡と予後との関連を検討するのみであった。しかしながら、腫瘍側、宿主側それぞれの複数の因子を組み合わせての検討は行われていない。The incidence of cancer in Japan is increasing year by year and is currently the leading cause of death. In addition to standard treatments for cancer, the development of new treatments is urgent. In recent years, the path to antibody immunotherapy for lung cancer has been paved by malignant melanoma, but the immune surveillance mechanism in lung cancer is unknown.
Conventionally, there have been two methods for examining the immune surveillance mechanism in the local area of a tumor, in which the expression of immunosuppressive molecules and cancer antigens on the tumor side and the infiltration of immune cells on the host side are examined. Many studies have been conducted to examine the relationship between the PD-L1 / PD-1 pathway as a tumor-side immunosuppressive molecule in lung cancer and prognosis (Patent Document 1, Non-Patent Document 3). Regarding the relationship between the expression pattern of other immunosuppressive molecules and cancer antigens and the frequency and prognosis, for example, Patent Document 2 and Non-Patent Document 2 disclose XAGE1 as a cancer-related protein to be measured, and Non-Patent Document 1 It has been described that Galectin-9 may be a prognostic factor for postoperative recurrence of lung cancer. On the other hand, with regard to host factors and prognosis, only the relationship between the degree of various infiltration of immune cells and the prognosis was examined. However, no study has been conducted in which a plurality of factors on the tumor side and the host side are combined.

特表２０１２−５０３９８４号公報JP-T-2012-503984 特表２０１２−５１５３３４号公報JP 2012-515334 A

尾崎良智ほか：日本癌学会学術総会記事，Vol. 65th page 645 (2006.08.28)Yoshitomo Ozaki et al .: Article of the Japanese Society for Cancer Research, Vol. 65th page 645 (2006.08.28) Ohue, Y., et al.,: Clin. Cancer Res., 20, 5052-5063 (2014)Ohue, Y., et al.,: Clin. Cancer Res., 20, 5052-5063 (2014) 川村直ほか：月刊臨床免疫・アレルギー科，56，1-6（2011）Kawamura Nao et al .: Clinical Immunology and Allergy Monthly, 56, 1-6 (2011)

本発明は、より簡易且つ迅速に、肺腺癌の予後を推定する方法、及び該方法を実施するための演算式を作成する方法を提供することを課題とする。   An object of the present invention is to provide a method for estimating the prognosis of lung adenocarcinoma more easily and quickly, and a method for creating an arithmetic expression for implementing the method.

本発明者らは上記課題に鑑み、肺腺癌において複数の免疫因子を検討することによって、肺腺癌の予後の推定を試みた。結果、本発明者らは、肺癌では腫瘍側の免疫抑制因子としてＰＤ−Ｌ１（Programmed death-ligand 1）とＧａｌ−９（galectin-9）が重要であり、更に、肺腺癌において、免疫原性を有するＸＡＧＥ１発現とＰＤ−Ｌ１、Ｇａｌ−９分子の発現パターンは予後に関与することを見出した。これらの因子は単独ではなく、ＸＡＧＥ１発現の有無、ＰＤ−Ｌ１発現の強度、Ｇａｌ−９発現の強度を組み合わせることによってのみ予後を判定できる。
また、本発明者らは、宿主側の因子としてＣＤ４及びＣＤ８ Ｔ細胞浸潤の多寡は予後に関与し、Ｔ細胞浸潤の程度、ＸＡＧＥ１発現の有無、ＰＤ−Ｌ１発現の強度、Ｇａｌ−９発現の強度を組み合わせることによって肺腺癌患者の予後をさらに明確に判別できることを見出した。
これらの知見に基づいて本発明者らは更に検討を進め、本発明を完成するに至った。In view of the above problems, the present inventors have attempted to estimate the prognosis of lung adenocarcinoma by examining multiple immune factors in lung adenocarcinoma. As a result, the present inventors have found that PD-L1 (Programmed death-ligand 1) and Gal-9 (galectin-9) are important as immunosuppressive factors on the tumor side in lung cancer. It was found that the expression of XAGE1 having sex and the expression patterns of PD-L1 and Gal-9 molecules are involved in prognosis. These factors are not used alone, and the prognosis can be determined only by combining the presence or absence of XAGE1 expression, the intensity of PD-L1 expression, and the intensity of Gal-9 expression.
In addition, the present inventors have determined that the degree of infiltration of CD4 and CD8 T cells as factors on the host side is involved in prognosis, the degree of T cell infiltration, the presence or absence of XAGE1 expression, the intensity of PD-L1 expression, the intensity of Gal-9 expression. It has been found that the prognosis of lung adenocarcinoma patients can be more clearly determined by combining the intensities.
The present inventors further studied based on these findings, and completed the present invention.

即ち、本発明は以下に関する。
［１］複数の肺腺癌患者の各検体について、
免疫染色法によりＰＤ−Ｌ１、Ｇａｌ−９およびＸＡＧＥ１の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、
第１スコアと第２スコアと第３データを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法。
［２］複数の肺腺癌患者の各検体について、
免疫染色法によりＰＤ−Ｌ１、Ｇａｌ−９、ＸＡＧＥ１、ＣＤ４およびＣＤ８の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データと、Ｔ細胞の浸潤の割合に基づき特定される第４スコアを二値化した第４データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、第１スコアと第２スコアと第３データと第４スコアとを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法。
［３］上記［１］に記載の方法により求められた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データを代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法。
［４］上記［２］に記載の方法により求められた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データと第４スコアを代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法。
［５］上記［３］又は［４］に記載の肺腺癌の予後推定方法を用いることを特徴とする、予後不良な早期肺癌患者を選別し、予後改善に必要な治療を実施するためのコンパニオン診断方法。
［６］上記［３］又は［４］に記載の肺腺癌の予後推定方法を用いることを特徴とする、肺腺癌治療方法の効果判定方法。
［７］被験者が早期肺癌患者であり、肺腺癌の予後推定方法が当該患者における術後の再発率を判定するものである、上記［３］又は［４］に記載の肺腺癌の予後推定方法。That is, the present invention relates to the following.
[1] For each specimen of a plurality of lung adenocarcinoma patients,
The expression of PD-L1, Gal-9 and XAGE1 was detected by immunostaining,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Lung adenocarcinoma patients are divided into a good prognosis group and a poor prognosis group based on the second data obtained by binarizing the second score, and the third data binarized based on the presence or absence of XAGE1,
A method for creating a prognosis calculation formula, wherein a calculation formula for classifying into a good prognosis group and a bad prognosis group is obtained by multivariate analysis using the first score, the second score, and the third data as explanatory variables.
[2] For each specimen of a plurality of lung adenocarcinoma patients,
Detecting the expression of PD-L1, Gal-9, XAGE1, CD4 and CD8 by immunostaining,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Second data obtained by binarizing the second score, third data obtained by binarizing based on the presence or absence of XAGE1, and fourth data obtained by binarizing the fourth score specified based on the percentage of infiltration of T cells. Based on the 4 data, the lung adenocarcinoma patients were classified into a good prognosis group and a bad prognosis group, and a group with a good prognosis was determined by multivariate analysis using the first score, the second score, the third data, and the fourth score as explanatory variables. A method for creating a prognostic formula, wherein a formula for classifying a bad group is obtained.
[3] Substituting the first and second scores and the third data detected from the subject's sample into the arithmetic expression obtained by the method according to [1], and estimating the prognosis of the subject by calculation. Method for estimating prognosis of lung adenocarcinoma.
[4] Substituting the first and second scores, the third data and the fourth data detected from the subject's sample into the arithmetic expression obtained by the method according to the above [2] to determine the prognosis of the subject. A prognosis estimation method for lung adenocarcinoma estimated by calculation.
[5] A method for selecting an early lung cancer patient with a poor prognosis, which uses the method for estimating prognosis of lung adenocarcinoma according to the above [3] or [4], and performing a treatment necessary for improving the prognosis. Companion diagnostic method.
[6] A method for determining the effect of a method for treating lung adenocarcinoma, comprising using the method for estimating prognosis of lung adenocarcinoma according to [3] or [4].
[7] The prognosis of lung adenocarcinoma according to the above [3] or [4], wherein the subject is an early-stage lung cancer patient, and the method for estimating prognosis of lung adenocarcinoma determines the postoperative recurrence rate in the patient. Estimation method.

本発明によれば、肺腺癌において免疫因子を検討することによって予後の推定が可能となるシステムが提供される。実臨床においては、臨床病期及び病理学的病期によって治療法が選択されるが、それらの病期とは別に免疫因子を指標とした予後判定システムを用いることで、臨床的な早期がんであっても、積極的な集学的治療が必要な患者を選択できる。このような患者選択は、新たな創薬や治療法の開発の基盤となる。また、本発明は、肺腺癌と免疫因子との関連性の解明に有用であるが、免疫因子への重み付けは、肺腺癌におけるがん免疫療法など新薬開発への重要な指標となる。また、本発明は、気管支鏡検体やＣＴガイド下肺生検による検体といった微小な腫瘍組織を検討するだけで予後を判定できる。従って、病期の全身検索ができないような患者において、微小な臨床検体を採取することで予後判定が可能であり、その後の治療方針の決定に貢献することができる。   According to the present invention, there is provided a system capable of estimating a prognosis by examining immune factors in lung adenocarcinoma. In the actual clinical setting, treatment is selected depending on the clinical stage and pathological stage, but by using a prognostic system using immune factors as an index separately from those stages, clinical early cancer Even so, patients who need active multidisciplinary treatment can be selected. Such patient selection is the basis for the development of new drug discovery and treatments. In addition, the present invention is useful for elucidating the relationship between lung adenocarcinoma and immune factors, but the weighting of immune factors is an important index for the development of new drugs such as cancer immunotherapy for lung adenocarcinoma. In addition, the present invention can determine the prognosis only by examining a small tumor tissue such as a bronchoscope specimen or a specimen obtained by CT-guided lung biopsy. Therefore, prognosis can be determined by collecting a small clinical specimen in a patient whose whole body cannot be searched for the disease stage, which can contribute to the determination of a subsequent treatment policy.

示した細胞株についての細胞表面（Surface）および細胞質（Intracellular）におけるPD-L1、Galectin-9発現のフローサイトメトリー解析の結果を示す図である。灰色はアイソタイプ抗体での結果を示す。It is a figure which shows the result of flow cytometry analysis of PD-L1 and Galectin-9 expression on the cell surface (Surface) and cytoplasm (Intracellular) about the shown cell line. Gray indicates results with isotype antibodies. 肺癌細胞株におけるIFN-γ処理によるPD-L1、Gal-9の発現の変化（A-B）、分子標的薬（Afatinib）処理の有りまたは無しでOU-LC-SK細胞およびPC-9細胞から放出されるGal-9濃度（C）、がん患者の血清または胸水中のGal-9濃度（D）、化学療法中の経過の患者血清中のGal-9濃度（E）を示す。D, Eにおいて、Gal-9濃度は5倍希釈の濃度を示す。Changes in expression of PD-L1 and Gal-9 by IFN-γ treatment (AB) in lung cancer cell lines, and release from OU-LC-SK cells and PC-9 cells with or without treatment with a molecular targeted drug (Afatinib) (C), the concentration of Gal-9 in serum or pleural effusion of cancer patients (D), and the concentration of Gal-9 in serum of patients during the course of chemotherapy (E). In D and E, the Gal-9 concentration indicates a concentration of 5-fold dilution. がん局所に浸潤するT細胞（TIL）の表面での免疫抑制分子の発現をフローサイトメトリー法により解析した結果（A-B）、TILにおけるPD-1、Tim-3の発現と、CD27発現、Ki-67発現、Annexin V発現との関係（C）、XAGE1特異的CD8クローンにおけるPD-1、Tim-3発現（D）、XAGE1特異的CD8クローンにGal-9を示した濃度で加えた時のアポトーシスの程度（E）、更に抗Gal-9を加えることによるアポトーシス阻害効果（F）を示す。Analysis of the expression of immunosuppressive molecules on the surface of T cells (TIL) invading local cancer by flow cytometry (AB). The expression of PD-1 and Tim-3 in TIL, CD27 expression, Ki -67 expression, relationship to Annexin V expression (C), PD-1 and Tim-3 expression in XAGE1-specific CD8 clones (D), when Gal-9 was added to XAGE1-specific CD8 clones at the indicated concentration The degree of apoptosis (E) and the effect of inhibiting apoptosis by adding anti-Gal-9 (F) are shown. 肺癌組織におけるPD-L1、Galectin-9、XAGE1についての免疫染色像を示す図である。It is a figure which shows the immunostaining image about PD-L1, Galectin-9, and XAGE1 in a lung cancer tissue. 肺癌組織におけるPD-L1、Gal-9、XAGE1の発現と予後との関係を示す図である。各グラフ中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。FIG. 4 is a diagram showing the relationship between the expression of PD-L1, Gal-9, and XAGE1 in lung cancer tissues and prognosis. In each graph, the horizontal axis represents the time (month) after the operation, and the vertical axis represents the overall survival rate (%). 宿主側の因子（CD4、CD8、FoxP3）の免疫染色像（A）、その発現強度のスコア付けと頻度（B）、およびCD4陽性T細胞の割合と検体数との関係のプロットの結果より二値化する基準（C）を示す図である。From the results of immunostaining of host factors (CD4, CD8, FoxP3) (A), scoring and frequency of expression intensity (B), and plots of the relationship between the proportion of CD4-positive T cells and the number of samples It is a figure which shows the reference | standard (C) which makes a value. 腫瘍局所浸潤Ｔ細胞の多寡およびPD-L1、Galectin-9またはXAGE1の発現の多寡と予後との関係を示す図である。各グラフ中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。FIG. 3 is a graph showing the relationship between the amount of tumor local infiltrating T cells and the amount of PD-L1, Galectin-9 or XAGE1 expression and prognosis. In each graph, the horizontal axis represents the time (month) after the operation, and the vertical axis represents the overall survival rate (%). 原発性肺腺癌における腫瘍側の3因子（PD-L1、Gal-9、XAGE1）の発現に基づく予後関数の導出（A-C）、上記3因子に加えて宿主因子であるT細胞浸潤の多寡に基づく予後関数の導出（D-F）を示す図である。A、B、D、Eにおいて、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。Derivation of a prognostic function based on the expression of three tumor-side factors (PD-L1, Gal-9, XAGE1) in primary lung adenocarcinoma (AC). It is a figure which shows derivation (DF) of a prognosis function based on it. In A, B, D, and E, the horizontal axis indicates the time (month) after the operation, and the vertical axis indicates the overall survival rate (%). 病期分類ＩＡ期に属する早期原発性肺腺癌における腫瘍側３因子（ＰＤ−Ｌ1、Ｇａｌ−９、ＸＡＧＥ１）の発現及び宿主因子であるＴ細胞浸潤の多寡に基づく、予後関数の導出を示す図である。各図中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。FIG. 2 shows the derivation of a prognostic function based on the expression of three tumor-side factors (PD-L1, Gal-9, and XAGE1) in early primary lung adenocarcinoma belonging to stage IA and the degree of infiltration of T cells as a host factor. FIG. In each figure, the horizontal axis indicates the time (months) after the operation, and the vertical axis indicates the overall survival rate (%). 判別関数［数５］を用いて予測した肺腺癌患者１２０名全員、病期分類Ｉ期に属する６２名及び病期分類ＩＩ-ＩＩＩＡに属する５８名の患者の実際の予後を示す図である。各図中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。It is a figure which shows the actual prognosis of all the 120 patients with lung adenocarcinoma predicted using the discriminant function [Equation 5], 62 patients belonging to stage I and 58 patients belonging to stage II-IIIA. . In each figure, the horizontal axis indicates the time (months) after the operation, and the vertical axis indicates the overall survival rate (%). 判別関数［数６］を用いて予測した肺腺癌患者１２０名全員の実際の予後を示す図である。図中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。It is a figure which shows the actual prognosis of all the 120 lung adenocarcinoma patients predicted using the discriminant function [Formula 6]. In the figure, the horizontal axis indicates the time (month) after the operation, and the vertical axis indicates the overall survival rate (%). 検証コホート研究による、判別関数［数５］を用いて予測した病期分類Ｉに属する患者６８名の実際の予後を示す図である。左図中、横軸は手術後の時間（月）、縦軸は全生存率（%）を示す。右図中、横軸は手術後の時間（月）、縦軸は無病生存率（%）を示す。It is a figure by the verification cohort study which shows the actual prognosis of 68 patients who belong to the stage classification I predicted using the discriminant function [Equation 5]. In the left figure, the horizontal axis indicates the time (months) after the operation, and the vertical axis indicates the overall survival rate (%). In the right figure, the horizontal axis indicates the time (month) after the operation, and the vertical axis indicates the disease-free survival rate (%).

本明細書中、「検体」は、演算式作成の為に用いられる複数の肺腺癌患者由来の各検体、及び予後推定の対象となる被験者由来の検体を意味する。「検体」としては、肺腺癌由来の腫瘍組織を含むものであれば、その採取方法は特に制限されず、手術による切除検体、気管支鏡検体及び肺生検による検体等が挙げられる。「検体」は、任意の哺乳動物（例えば、ヒト、マウス、ラット、サル、イヌ、ネコ、ウシ、ウマ、ブタ、ヒツジ、ヤギ、ウサギ、ハムスター等）に由来してもよく、より好ましくはヒト、マウス、ラット、サル、イヌ、ネコ等、特に好ましくはヒト由来である。検体の形態は、用いる発現解析手段に応じて様々であってよい。例えば、免疫組織化学染色法を用いる場合、検体としてはミクロトーム等で薄く切片化された組織切片を用いても、ホールマウントされた組織又は個体を用いても良いが、好ましくは、組織切片が用いられる。組織切片は、凍結組織を切片化し、パラフィンまたは樹脂で包埋された切片であってもよい。また、手術による切除検体、気管支鏡検体及び肺生検による検体を、凍結またはパラフィン包埋せずに直接、フローサイトメトリー解析、マスサイトメトリー解析、遺伝子検査などに供してもよい。   In the present specification, “sample” means each sample derived from a plurality of lung adenocarcinoma patients used for creating the arithmetic expression and a sample derived from a subject whose prognosis is to be estimated. The “specimen” is not particularly limited as long as it contains a tumor tissue derived from lung adenocarcinoma, and examples include a surgically excised specimen, a bronchoscopy specimen, and a lung biopsy specimen. The “specimen” may be derived from any mammal (eg, human, mouse, rat, monkey, dog, cat, cow, horse, pig, sheep, goat, rabbit, hamster, etc.), and more preferably human , Mice, rats, monkeys, dogs, cats and the like, particularly preferably human. The form of the sample may vary depending on the expression analysis means used. For example, when the immunohistochemical staining method is used, the specimen may be a tissue section thinly sectioned with a microtome or the like, or a whole-mounted tissue or individual may be used, but preferably, a tissue section is used. Can be The tissue section may be a section in which frozen tissue is sectioned and embedded in paraffin or resin. In addition, a surgically resected specimen, a bronchoscope specimen, and a lung biopsy specimen may be directly subjected to flow cytometry analysis, mass cytometry analysis, genetic testing, and the like without being frozen or embedded in paraffin.

本明細書中、「被験者」は、予後の推定が求められる肺腺癌患者である。被験者は、通常、本発明に従う予後演算式の作成に用いた検体が由来する動物種と同種の動物である。従って、被験者は、任意の哺乳動物（例えば、ヒト、マウス、ラット、サル、イヌ、ネコ、ウシ、ウマ、ブタ、ヒツジ、ヤギ、ウサギ、ハムスター等）であってよく、より好ましくはヒト、マウス、ラット、サル、イヌ、ネコ等、特に好ましくはヒトである。   In the present specification, the “subject” is a lung adenocarcinoma patient for which estimation of prognosis is required. The subject is usually an animal of the same species as the species from which the specimen used to create the prognostic expression according to the present invention is derived. Thus, the subject may be any mammal (eg, human, mouse, rat, monkey, dog, cat, cow, horse, pig, sheep, goat, rabbit, hamster, etc.), more preferably human, mouse , Rats, monkeys, dogs, cats and the like, particularly preferably humans.

一般的に、癌患者の予後とは、ある時点、例えば癌診断時または治療時（手術時等）、から見て、癌の将来の状態についての予測を意味し、癌の病状進行・再発・転移のリスク（可能性）が高いときは予後不良、癌の病状進行・再発・転移のリスク（可能性）が高くないときは予後良好という。予測の指標については特定されないが、例えば全生存率（overall survival; OS）が好適である。ここで、「全生存率」とは、一般に、観察症例のうち、特定の観察開始時点から特定の期間経過の後に生存していることが確認できる割合を意味する。   In general, the prognosis of a cancer patient means prediction of the future state of the cancer at a certain point in time, for example, at the time of cancer diagnosis or treatment (at the time of surgery, etc.). The prognosis is poor when the risk (possibility) of metastasis is high, and the prognosis is good when the risks (possibility) of progression, recurrence and metastasis of cancer are not high. Although the index of the prediction is not specified, for example, overall survival (OS) is preferable. Here, the “overall survival rate” generally means a ratio of observed cases that can be confirmed to be alive after a specific period of time has elapsed from a specific observation start time.

本発明において発現の測定の対象とされるＰＤ−Ｌ１（Programmed death-ligand 1）は、cluster of differentiation 274（ＣＤ２７４）遺伝子によりコードされ、ＣＤ２７４またはＢ７ホモログ１（Ｂ７−Ｈ１）としても知られる公知の膜貫通タンパク質である。ＰＤ−Ｌ１は腫瘍側の免疫抑制分子であり、活性化Ｔ細胞、Ｂ細胞などに発現するその受容体であるＰＤ−１との結合により、免疫抑制において重要な役割を果たすと考えられている。種々の動物種についてＰＤ−Ｌ１のアミノ酸配列が公知であり、例えば、ヒトＰＤ−Ｌ１の配列はRefSeq No. NP_001254635として入手可能である。   In the present invention, PD-L1 (Programmed death-ligand 1) to be measured for expression is encoded by the cluster of differentiation 274 (CD274) gene, and is also known as CD274 or B7 homolog 1 (B7-H1). Is a transmembrane protein. PD-L1 is an immunosuppressive molecule on the tumor side, and is thought to play an important role in immunosuppression by binding to PD-1 which is a receptor expressed on activated T cells, B cells and the like. . The amino acid sequence of PD-L1 is known for various animal species, for example, the sequence of human PD-L1 is available as RefSeq No. NP_001254635.

本発明において発現の測定の対象とされるＧａｌ−９（Galectin-9）は、ＬＧＡＬＳ９遺伝子によりコードされ、ＬＧＡＬＳ９またはGalactoside-binding, soluble, 9としても知られる公知の分子であり、活性化T細胞のアポトーシス誘導、好酸球遊走、癌細胞のアポトーシス誘導、細胞接着などの多岐に渡る活性が報告されている。また、Ｇａｌ−９は腫瘍側の免疫抑制分子であり、Ｔｈ１細胞などの表面抗原であるＴｉｍ−３のリガンドの一つとして免疫抑制において重要な役割を果たすと考えらえている。種々の動物種についてＧａｌ−９のアミノ酸配列が公知であり、例えば、ヒトＧａｌ−９の配列はRefSeq No. NP_002299.2として入手可能である。   Gal-9 (Galectin-9) whose expression is to be measured in the present invention is a known molecule encoded by the LGALS9 gene, also known as LGALS9 or Galactoside-binding, soluble, 9; A wide variety of activities have been reported, including apoptosis induction, eosinophil migration, induction of cancer cell apoptosis, and cell adhesion. Gal-9 is a tumor-side immunosuppressive molecule, and is considered to play an important role in immunosuppression as one of the ligands of Tim-3, which is a surface antigen such as Th1 cells. The amino acid sequence of Gal-9 is known for various animal species, for example, the sequence of human Gal-9 is available as RefSeq No. NP_002299.2.

本発明において発現の測定の対象とされるＸＡＧＥ１（X Antigen Family, Member 1）は、ＸＡＧＥ１遺伝子によりコードされ、Cancer/Testis antigen 12.1（CT12.1）としても知られる公知のがん精巣抗原である。これまでにＸＡＧＥ１Ａ−Ｅの５種類の遺伝子が同定され、その関連タンパク質はＧＡＧＥＤ２であり、ＧＡＧＥＤ２ａとＧＡＧＥＤ２ｄの二種類のアイソフォームが存在することが知られている。また、これまでにＸＡＧＥ−１ａ，ｂ，ｃ，ｄの４種類の選択的スプライスバリアントが同定されている（Sato et al., Cancer Immunity, vol. 7, page 5 (2007)）。本発明において発現を測定するＸＡＧＥ１はいずれのアイソフォームであってもよく、またいずれの選択的スプライスバリアントによりコードされるタンパク質であってもよいが、好ましくはＧＡＧＥＤ２ａ（ＸＡＧＥ−１ｂタンパク質）である。種々の動物種についてＸＡＧＥ１のアミノ酸配列が公知であり、例えば、ヒトＧＡＧＥＤ２ａの配列はRefSeq No. NM_001097594.2として、ヒトＧＡＧＥＤ２ｄの配列はRefSeq No. NM_001097596.2として入手可能である。   XAGE1 (X Antigen Family, Member 1) whose expression is to be measured in the present invention is a known cancer testis antigen that is encoded by the XAGE1 gene and is also known as Cancer / Testis antigen 12.1 (CT12.1). . Up to now, five types of XAGE1A-E genes have been identified, the related protein of which is GAGED2, and it is known that there are two types of isoforms, GAGED2a and GAGED2d. In addition, four alternative splice variants of XAGE-1a, b, c, and d have been identified so far (Sato et al., Cancer Immunity, vol. 7, page 5 (2007)). XAGE1 whose expression is measured in the present invention may be any isoform, and may be a protein encoded by any alternative splice variant, but is preferably GAGED2a (XAGE-1b protein). The amino acid sequence of XAGE1 is known for various animal species. For example, the sequence of human GAGED2a is available as RefSeq No. NM_001097594.2, and the sequence of human GAGED2d is available as RefSeq No. NM_001097596.2.

本発明において、「Ｔ細胞」は、腫瘍免疫にかかわる宿主側の因子としての免疫担当細胞であって、ＣＤ４陽性Ｔ細胞及びＣＤ８陽性Ｔ細胞を意味する。また、ＣＤ４陽性Ｔ細胞及びＣＤ８陽性Ｔ細胞をあわせてＣＤ３陽性Ｔ細胞としてもよい。   In the present invention, “T cell” is an immunocompetent cell as a host-side factor involved in tumor immunity, and means CD4-positive T cell and CD8-positive T cell. In addition, CD4 positive T cells and CD8 positive T cells may be combined into CD3 positive T cells.

本発明において、ＰＤ−Ｌ１、Ｇａｌ−９、ＸＡＧＥ１、ＣＤ４およびＣＤ８（以下、これらを総称して「本発明の因子」ともいう。）の発現の検出は、これらのタンパク質を対象として測定してもよいし、これらのタンパク質をコードする遺伝子の転写産物を対象として測定してもよい。
転写産物を対象とする場合、常法に従い、ＲＮＡを生体試料から単離することができる。ＲＮＡを抽出するための一般的な方法が、当技術分野において周知されており、Ausubel et al.,Current Protocols of Molecular Biology,John Wiley and Sons (1997)など、分子生物学の標準的な教科書に開示されている。具体的には、Ｑｉａｇｅｎなどの製造業者から入手した精製キット、緩衝液セット、およびプロテアーゼを用いて、製造業者の指示に従ってＲＮＡ単離を行うことができる。
転写産物を対象とするマーカー遺伝子の発現レベルの測定方法としては、特に限定されるものではないが、ノーザンブロッティングおよびインサイチュハイブリダイゼーション（Parker & Barnes,Methods in Molecular Biology 106:247-283(1999)）；ＲＮａｓｅプロテクション・アッセイ法（Hod,Biotechniques 13:852-854(1992)）；逆転写ポリメラーゼ連鎖反応法（ＲＴ−ＰＣＲ）（Weis et al.,Trends in Genetics 8:263-264(1992)）；リアルタイム定量的ＲＴ−ＰＣＲ（Held et al.,Genome Research 6:986-994(1996)）；ならびにマイクロアレイ解析法などがある。マイクロアレイ解析法は、製造業者の指示に従って、Ａｆｆｙｍｅｔｒｉｘ ＧｅｎｅＣｈｉｐ技術、Ａｇｉｌｅｎｔ Ｔｅｃｈｎｏｌｏｇｉｅｓのマイクロアレイ技術またはＩｎｃｙｔｅのマイクロアレイ技術を用いるなどして、市販されている装置によって実施することができる。In the present invention, the detection of the expression of PD-L1, Gal-9, XAGE1, CD4 and CD8 (hereinafter collectively referred to as “the factor of the present invention”) is performed by measuring these proteins as targets. Alternatively, the measurement may be performed on transcripts of genes encoding these proteins.
When a transcript is targeted, RNA can be isolated from a biological sample according to a conventional method. General methods for extracting RNA are well known in the art and are described in standard textbooks of molecular biology, such as Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). It has been disclosed. Specifically, RNA isolation can be performed using a purification kit, buffer set, and protease obtained from a manufacturer such as Qiagen, according to the manufacturer's instructions.
The method for measuring the expression level of the marker gene targeting the transcript is not particularly limited, but Northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106: 247-283 (1999)) RNase protection assay (Hod, Biotechniques 13: 852-854 (1992)); reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8: 263-264 (1992)); Real-time quantitative RT-PCR (Held et al., Genome Research 6: 986-994 (1996)); and microarray analysis. Microarray analysis can be performed with commercially available equipment, such as using Affymetrix GeneChip technology, Agilent Technologies microarray technology, or Incyte microarray technology, according to the manufacturer's instructions.

タンパク質を検出の対象とする場合の測定手段は特に限定されないが、好ましくは免疫染色法を用いることができる。免疫染色法を用いた具体的手段としては、免疫組織化学染色（ＩＨＣ）法、フローサイトメトリー解析、マスサイトメトリー解析などが挙げられる。用いる抗体は、各因子を検出可能な抗体である限り特に限定されるものではなく、ポリクローナル抗体、モノクローナル抗体、キメラ抗体、一本鎖抗体、またはＦａｂフラグメントやＦａｂ発現ライブラリーによって生成されるフラグメントなどのように抗原結合性を有する前記抗体の一部が包含される。本発明の因子のそれぞれについて、市販の抗体を利用することができる。また、各因子の抗体は、各因子またはその一部を抗原として用い、自体公知の方法によって調製することができる。   The measurement means when a protein is to be detected is not particularly limited, but preferably an immunostaining method can be used. Specific means using the immunostaining method include immunohistochemical staining (IHC), flow cytometry analysis, mass cytometry analysis, and the like. The antibody to be used is not particularly limited as long as it is an antibody capable of detecting each factor, such as a polyclonal antibody, a monoclonal antibody, a chimeric antibody, a single-chain antibody, or a Fab fragment or a fragment produced by a Fab expression library. And a part of the antibody having an antigen-binding property as described above. For each of the factors of the present invention, commercially available antibodies can be utilized. In addition, antibodies for each factor can be prepared by a method known per se using each factor or a part thereof as an antigen.

免疫組織化学染色は、本発明の因子のそれぞれについて独立に行うことができる。本発明の因子のそれぞれの免疫染色は、自体公知の方法に従って行うことができ（例えば、改訂四版 渡辺・中根 酵素抗体法、発行：学際企画株式会社、編集：名倉宏、長村義之、堤寛、ISBN4-906514-73-X C304を参照のこと）、酵素抗体法（Enzyme labeled antibody method）および蛍光抗体法等が好適に用いられる。   Immunohistochemical staining can be performed independently for each of the factors of the invention. The immunostaining of each of the factors of the present invention can be performed according to a method known per se (for example, Watanabe and Nakane Enzyme Antibody Method, 4th Edition, published by Gakkai Planning Co., Ltd., editors: Hiroshi Nakura, Yoshiyuki Nagamura, Tsutsumi Hiroshi, see ISBN4-906514-73-X C304), the enzyme antibody method (Enzyme labeled antibody method), the fluorescent antibody method and the like are preferably used.

好ましい実施態様として、免疫染色の感度、検出手段などの観点から、酵素抗体法による免疫染色法が挙げられる。酵素抗体法は、酵素で標識した抗体により、本発明の因子を検出する。標識を直接行う場合と、二次抗体を用いて間接的に行う場合がある。検出感度を上げるために種々の改良がなされ、ビオチン−ストレプトアビジンを用いたＡＢＣ法、チラミド（タイラマイド）を用いたチラミド法（ＴＳＡ法、Tyramide Signal Amplification）などの改良法で、非常に高い感度を実現できる。   A preferred embodiment includes an immunostaining method using an enzyme antibody method from the viewpoints of immunostaining sensitivity, detection means, and the like. In the enzyme antibody method, the factor of the present invention is detected by an antibody labeled with an enzyme. Labeling may be performed directly or indirectly using a secondary antibody. Various improvements have been made to increase the detection sensitivity, and very high sensitivity has been achieved by improved methods such as the ABC method using biotin-streptavidin and the tyramide method (TSA method, Tyramide Signal Amplification) using tyramide (Tylamide). realizable.

標識酵素は、ペルオキシダーゼ（ＨＲＰ、horseradish peroxidase）およびアルカリホスファターゼ（ＡＰ、alkaline phosphatase）が汎用されており、本発明においても好適に使用することができる。酵素抗体法に適した抗体試薬および発色キット等が市販されており、当該試薬およびキットに添付された製造業者のプロトコールに従って、免疫染色を行うことができる。   As the labeling enzyme, peroxidase (HRP, horseradish peroxidase) and alkaline phosphatase (AP, alkaline phosphatase) are widely used, and can be suitably used in the present invention. Antibody reagents and color development kits suitable for the enzyme antibody method are commercially available, and immunostaining can be performed according to the manufacturer's protocol attached to the reagents and kits.

一般的なプロトコールとして、必要により脱パラフィンまたは固定処理した細胞標本を準備し、必要により前処理（例えば、生体色素の脱色または除去処理、ブロッキング処理）または抗原賦活化（温浴、マイクロウェーブ処理、オートクレーブ処理、プロテアーゼ処理等）し、一次抗体と反応させ（例えば、４℃〜室温で０．５〜２４時間）、反応終了後細胞標本を洗浄し、二次抗体と反応させ（例えば、４℃〜室温で５〜１２０分）、反応終了後細胞標本を洗浄する。次いで、ジアミノベンジジン（3, 3-diaminobenzidine:ＤＡＢ）などの発色基質を添加し、発色させる。顕微鏡での形態観察のために、さらにヘマトキシリン染色等の対比染色を行ってもよい。   As a general protocol, if necessary, prepare a cell specimen that has been deparaffinized or fixed, and if necessary, perform pretreatment (for example, decolorization or removal of a living pigment, blocking treatment) or antigen activation (warm bath, microwave treatment, autoclave) Treatment, protease treatment, etc.), and react with the primary antibody (for example, at 4 ° C. to room temperature for 0.5 to 24 hours). After the reaction, the cell specimen is washed and reacted with the secondary antibody (for example, at 4 ° C. After completion of the reaction, the cell specimen is washed. Next, a color-developing substrate such as diaminobenzidine (3,3-diaminobenzidine: DAB) is added to develop color. For morphological observation with a microscope, counterstaining such as hematoxylin staining may be further performed.

免疫染色された試料を、撮影装置を搭載した光学顕微鏡または蛍光顕微鏡（蛍光染色の場合）などを用いて撮像し、デジタル画像としてコンピュータに保存する。デジタル画像は二次元データであっても三次元データであってもよいが、本発明においては二次元データで十分に解析可能である。解析を行う前に、フィルタリングなどの画像処理を行ってもよい。   The immunostained sample is imaged using an optical microscope or a fluorescent microscope (in the case of fluorescent staining) equipped with an imaging device and stored as a digital image in a computer. The digital image may be two-dimensional data or three-dimensional data, but in the present invention, two-dimensional data can be sufficiently analyzed. Before performing the analysis, image processing such as filtering may be performed.

免疫染色された細胞の画像解析は、使用する顕微鏡に搭載されている画像解析用ソフトウエア、その他の市販のソフトウエアを用いて行うことができる。市販ソフトウエアの一例として、ScanScope（登録商標、Aperio社）が挙げられる。また、画像解析は、各検体の全ての細胞の画像データを処理することにより行うことができる。本発明の因子の免疫染色強度の分類（後述）は、免疫組織染色の病理専門医による標準的な判定基準に従うことができる。   The image analysis of the immunostained cells can be performed using image analysis software mounted on the microscope to be used or other commercially available software. One example of commercially available software is ScanScope (registered trademark, Aperio). The image analysis can be performed by processing image data of all cells of each specimen. Classification of the immunostaining intensity of the factor of the present invention (described later) can be performed according to standard criteria by a pathologist of immunohistological staining.

一方、フローサイトメトリーまたはマスサイトメトリーにより本発明の因子の発現を検出する場合、採取した肺腺癌組織から細胞浮遊液または細胞懸濁液を調製し、それを解析に用いることができる。細胞浮遊液または細胞懸濁液の調製は当業者に周知の手法を用いて行うことができ、例えば、機械的破砕、またはトリプシン、コラゲナーゼ、エラスターゼ、プロナーゼなどのプロテアーゼやヒアルロニダーゼ処理により細胞が分散した細胞浮遊液または細胞懸濁液を取得することができる。フローサイトメトリー解析およびマスサイトメトリー解析は、市販の装置（例えば、ＦＡＣＳＣａｎｔｏＴＭＩＩフローサイトメーター、ＣｙＴＯＦ２など）を用いて、製造者のマニュアルに従って実施することができる。On the other hand, when the expression of the factor of the present invention is detected by flow cytometry or mass cytometry, a cell suspension or cell suspension can be prepared from the collected lung adenocarcinoma tissue and used for analysis. Preparation of cell suspensions or cell suspensions can be performed using techniques well known to those skilled in the art, for example, cells are dispersed by mechanical disruption or by treatment with proteases such as trypsin, collagenase, elastase, and pronase or hyaluronidase. A cell suspension or cell suspension can be obtained. Flow cytometry analysis and mass cytometry analysis can be performed using a commercially available device (eg, FACSCanto ™ II flow cytometer, CyTOF2, etc.) according to the manufacturer's manual.

以下、本発明の肺腺癌の予後演算式作成方法について詳細に述べる。
［予後演算式作成方法］
本発明の予後演算式作成方法の一態様は以下の通りである。
複数の肺腺癌患者の各検体について、
ＰＤ−Ｌ１、Ｇａｌ−９およびＸＡＧＥ１の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、
第１スコアと第２スコアと第３データを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法（以下、本発明の予後演算式作成方法１とも称する）。Hereinafter, the method of preparing a prognosis calculation formula for lung adenocarcinoma of the present invention will be described in detail.
[Prognostic formula creation method]
One embodiment of the prognostic expression creation method of the present invention is as follows.
For each specimen of multiple lung adenocarcinoma patients,
Detecting the expression of PD-L1, Gal-9 and XAGE1,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Lung adenocarcinoma patients are divided into a good prognosis group and a poor prognosis group based on the second data obtained by binarizing the second score, and the third data binarized based on the presence or absence of XAGE1,
A prognostic expression creating method (hereinafter referred to as “the present invention”), wherein an arithmetic expression that divides into a good prognosis group and a poor prognosis group is obtained by multivariate analysis using the first score, the second score, and the third data as explanatory variables. Prognostic expression formula creation method 1).

上記複数の検体の個数は、本発明に従う予後演算式の作成を可能とする限り特に限定されないが、例えば５０以上、好ましくは１００以上である。   The number of the plurality of specimens is not particularly limited as long as the prognostic expression according to the present invention can be created, but is, for example, 50 or more, preferably 100 or more.

ここで、第１スコアは、腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される。各検体について同様に実施される限り、第１スコアを特定する方法は特に限定されないが、一例として以下の方法によって算出することができる。
（算出法）
上述の方法に従うＰＤ−Ｌ１の発現の検出の結果に基づいて、ＰＤ−Ｌ１の発現強度（Ｉ:Intensity）を下記の基準に基づいて評価する。
Ｉ；検出されない０：弱い１：中程度２：顕著３
更に、該検体において強度Ｉの細胞が全有核細胞に占める割合を決定し、分布範囲（Ｄ:Distribution）を以下の通りに定義する。
Ｄ；０％の場合０：１〜５０％の場合１：５１〜１００％の場合２
発現強度Ｉと分布範囲Ｄとの積（Ｄ×Ｉ）を計算し、それをＩＨＣスコアとする。
上記ＩＨＣスコアを細胞膜及び細胞質に対してそれぞれ求め、細胞膜もしくは細胞質の少なくとも一方で３以上のＩＨＣスコアであれば、その検体は陽性とする。Here, the first score is specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue. The method of specifying the first score is not particularly limited as long as the method is similarly performed for each sample, but can be calculated by the following method as an example.
(Calculation method)
Based on the result of detection of PD-L1 expression according to the above-described method, PD-L1 expression intensity (I: Intensity) is evaluated based on the following criteria.
I; not detected 0: weak 1: moderate 2: remarkable 3
Furthermore, the ratio of cells of intensity I to all nucleated cells in the specimen is determined, and the distribution range (D) is defined as follows.
D; 0%, 0: 1 to 50%, 1:51 to 100%, 2
The product (D × I) of the expression intensity I and the distribution range D is calculated, and the result is used as the IHC score.
The above-mentioned IHC score is obtained for each of the cell membrane and the cytoplasm. If at least one of the cell membrane and the cytoplasm has an IHC score of 3 or more, the specimen is regarded as positive.

第２スコアは、腫瘍組織におけるＧａｌ−９の分布範囲と発現強度に基づき特定される。各検体について同様に実施される限り、第２スコアを特定する方法は特に限定されないが、一例として以下の方法によって算出することができる。
（算出法）
上述の方法に従うＧａｌ−９の発現の検出の結果に基づいて、Ｇａｌ−９の発現強度（Ｉ:Intensity）を下記の基準に基づいて評価する。
Ｉ；検出されない０：弱い１：中程度２：顕著３
更に、該検体において強度Ｉの細胞が全有核細胞に占める割合を決定し、分布範囲（Ｄ:Distribution）を以下の通りに定義する。
Ｄ；０％の場合０：１〜５０％の場合１：５１〜１００％の場合２
発現強度Ｉと分布範囲Ｄとの積（Ｄ×Ｉ）を計算し、それをＩＨＣスコアとする。
上記ＩＨＣスコアを細胞膜及び細胞質に対してそれぞれ求め、細胞膜もしくは細胞質の少なくとも一方で３以上のＩＨＣスコアであれば、その検体は陽性とする。The second score is specified based on the distribution range and expression intensity of Gal-9 in the tumor tissue. The method of specifying the second score is not particularly limited as long as the method is similarly performed for each sample, but can be calculated by the following method as an example.
(Calculation method)
Based on the results of detecting the expression of Gal-9 according to the above-described method, the expression intensity (I: Intensity) of Gal-9 is evaluated based on the following criteria.
I; not detected 0: weak 1: moderate 2: remarkable 3
Furthermore, the ratio of cells of intensity I to all nucleated cells in the specimen is determined, and the distribution range (D) is defined as follows.
D; 0%, 0: 1 to 50%, 1:51 to 100%, 2
The product (D × I) of the expression intensity I and the distribution range D is calculated, and the result is used as the IHC score.
The above-mentioned IHC score is obtained for each of the cell membrane and the cytoplasm. If at least one of the cell membrane and the cytoplasm has an IHC score of 3 or more, the specimen is regarded as positive.

第３データは、ＸＡＧＥ１の発現の有無に基づき、陽性又は陰性として二値化することで得られる。各検体について同様に実施される限り、第３データを取得する方法は特に限定されないが、一例として以下の方法によって算出することができる。
（算出法）
上述の方法に従うＸＡＧＥ１の発現の検出の結果に基づいて、ＸＡＧＥ１陽性細胞の存否を計数する。解析した全有核細胞中、ＸＡＧＥ１陽性細胞が１％以上の場合に陽性、１％未満の場合に陰性として二値化したものを第３データとする。The third data is obtained by binarizing as positive or negative based on the presence or absence of XAGE1 expression. The method of obtaining the third data is not particularly limited as long as the method is similarly performed for each sample, but the third data can be calculated by the following method as an example.
(Calculation method)
The presence or absence of XAGE1 positive cells is counted based on the result of detection of XAGE1 expression according to the above-described method. Of all nucleated cells analyzed, binarized as XAGE1-positive cells as 1% or more as positive when 1% or less and less than 1% as negative as third data.

続いて、上記で得られた各データに基づいて、各肺腺癌患者を予後の良いグループと悪いグループにそれぞれ区分する。予後の判定は全生存率を用いて行うことができる。
具体的には、後述の実施例にも記載の通り、例えば、
（ａ１）第１データが陽性かつ第２データが陰性かつ第３データが陰性の検体、
（ａ２）第１データが陽性かつ第２データが陽性かつ第３データが陰性の検体、及び、
（ａ３）第１データが陰性かつ第２データが陰性かつ第３データが陰性の検体
を予後の良いグループに区分し、それ以外の検体、即ち、
（ｂ１）第１データが陽性かつ第２データが陰性かつ第３データが陽性の検体、
（ｂ２）第１データが陰性かつ第２データが陽性かつ第３データが陰性の検体、
（ｂ３）第１データが陰性かつ第２データが陽性かつ第３データが陽性の検体、
（ｂ４）第１データが陽性かつ第２データが陽性かつ第３データが陽性の検体、及び、
（ｂ５）第１データが陰性かつ第２データが陰性かつ第３データが陽性の検体
を予後の悪いグループに区分することができる。Subsequently, each lung adenocarcinoma patient is classified into a good prognosis group and a bad prognosis group based on each data obtained above. Prognosis can be determined using the overall survival rate.
Specifically, as described in Examples below, for example,
(A1) a sample in which the first data is positive, the second data is negative, and the third data is negative,
(A2) a sample whose first data is positive, whose second data is positive and whose third data is negative, and
(A3) A sample in which the first data is negative, the second data is negative, and the third data is negative is classified into a group having a good prognosis, and the other samples, that is,
(B1) a sample whose first data is positive, whose second data is negative and whose third data is positive,
(B2) a sample in which the first data is negative, the second data is positive and the third data is negative,
(B3) a sample in which the first data is negative, the second data is positive, and the third data is positive;
(B4) a sample whose first data is positive, whose second data is positive and whose third data is positive, and
(B5) A sample in which the first data is negative, the second data is negative, and the third data is positive can be classified into a group having a poor prognosis.

続いて、第１スコアと第２スコアと第３データを説明変数とし、多変量解析することにより、予後の良いグループと悪いグループに区分する為の演算式、即ち予後演算式(以下、単に「演算式」という場合がある。)を求める。
多変量解析する方法としては、特に限定されないが、好ましくは判別分析が用いられる。判別分析は、２群以上の母集団から抽出した標本データを得て、どの母集団に属するか不明のサンプルデータがある場合に、このデータがどの母集団に属するかを調べる方法であり、本発明においてはＰＤ−Ｌ１、Ｇａｌ−９及びＸＡＧＥ１発現に関する数値からなる多変量データを説明変数として用いる。このような判別分析の手法は公知であり、最も代表的な判別分析に線形判別関数による判別があるが、それに限定されない。また、市販の統計解析ソフトウエアを用いて行うことができる。
かくして得られた演算式は以下で表すことができる。Subsequently, the first score, the second score, and the third data are used as explanatory variables, and a multivariate analysis is performed to obtain an arithmetic expression for classifying the group into a good prognosis group and a poor prognosis group, that is, a prognosis operation expression (hereinafter simply referred to as “ Calculation formula).
The method of multivariate analysis is not particularly limited, but preferably, discriminant analysis is used. Discriminant analysis is a method of obtaining sample data extracted from two or more groups of population, and examining which population this data belongs to when there is sample data that does not belong to which population. In the present invention, multivariate data consisting of numerical values relating to PD-L1, Gal-9 and XAGE1 expression are used as explanatory variables. Such a discriminant analysis technique is known, and the most representative discriminant analysis includes discrimination by a linear discriminant function, but is not limited thereto. The analysis can be performed using commercially available statistical analysis software.
The arithmetic expression thus obtained can be expressed as follows.

ａ０、ａ１、ａ２、及びａ３の値は、演算式を作成するために用いる肺腺癌患者の検体の数によっても異なる。本発明の方法で作成された予後演算式の一例として、後述の実施例において作成された以下の演算式が挙げられる。The values of a 0 , a 1 , a 2 , and a 3 also vary depending on the number of specimens of a lung adenocarcinoma patient used to create the arithmetic expression. As an example of the prognosis calculation formula created by the method of the present invention, the following calculation formula created in the embodiment described later is given.

説明変数である第１スコアの係数が正であることから、ＰＤ−Ｌ１は予後良好因子であり、説明変数である第２スコア及び第３データの係数が負であることから、Ｇａｌ−９及びＸＡＧＥ１は予後不良因子であることがわかる。   Since the coefficient of the first score, which is an explanatory variable, is positive, PD-L1 is a favorable prognostic factor, and the coefficients of the second score and the third data, which are explanatory variables, are negative. It can be seen that XAGE1 is a poor prognostic factor.

本発明の予後演算式作成方法の別の一態様は以下の通りである。
複数の肺腺癌患者の各検体について、
ＰＤ−Ｌ１、Ｇａｌ−９、ＸＡＧＥ１、ＣＤ４およびＣＤ８の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データと、Ｔ細胞の浸潤の割合に基づき特定される第４スコアを二値化した第４データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、第１スコアと第２スコアと第３データと第４スコアとを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法（以下、本発明の予後演算式作成方法２とも称する）。
第１スコア及びそれを二値化した第１データ、第２スコア及びそれを二値化した第２データ、並びに第３データは、上記「本発明の予後演算式作成方法１」と同様にして取得する。また、用いる検体の個数、およびその好ましい態様についても上記「本発明の予後演算式作成方法１」と同様である。Another embodiment of the prognostic expression creation method of the present invention is as follows.
For each specimen of multiple lung adenocarcinoma patients,
Detecting the expression of PD-L1, Gal-9, XAGE1, CD4 and CD8,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Second data obtained by binarizing the second score, third data obtained by binarizing based on the presence or absence of XAGE1, and fourth data obtained by binarizing the fourth score specified based on the percentage of infiltration of T cells. Based on the 4 data, the lung adenocarcinoma patients were classified into a good prognosis group and a bad prognosis group, and a group with a good prognosis was determined by multivariate analysis using the first score, the second score, the third data, and the fourth score as explanatory variables. A prognostic calculation formula creation method characterized by calculating a calculation formula for classifying a bad group (hereinafter, also referred to as a prognosis calculation formula creation method 2 of the present invention).
The first score and the first data obtained by binarizing the first score, the second score and the second data obtained by binarizing the second score, and the third data are obtained in the same manner as in the above-described “Prognostic expression creation method 1 of the present invention”. get. Further, the number of specimens to be used and the preferred embodiment thereof are also the same as in the above-mentioned “Prognostic expression creation method 1 of the present invention”.

第４スコアは、腫瘍組織へのＴ細胞の浸潤の割合に基づき特定される。各検体について同様に実施される限り、第４スコアを同定する方法は特に限定されないが、一例として以下の方法によって算出することができる。
（算出法）
ＣＤ４陽性細胞の割合およびＣＤ８陽性細胞の割合をそれぞれ求め、これらの割合の和を腫瘍組織へのＴ細胞の浸潤の割合とする。
具体的には、上述の方法に従うＣＤ４の発現の検出の結果に基づいて、各細胞についてＣＤ４の発現強度を以下の通りに分類する。
発現強度；検出されない０；弱い１：中程度２：顕著３
発現強度２以上の細胞をＣＤ４陽性細胞と判定する。
用いた複数の検体のそれぞれについて、全有核細胞に占める上記ＣＤ４陽性細胞の割合を決定する。
同様に、上述の方法に従うＣＤ８の発現の検出の結果に基づいて、各細胞についてＣＤ８の発現強度を以下の通りに分類する。
発現強度；検出されない０；弱い１：中程度２：顕著３
発現強度２以上の細胞をＣＤ８陽性細胞と判定する。
用いた複数の検体のそれぞれについて、全有核細胞に占める上記ＣＤ８陽性細胞の割合を決定する。
上記に従って得られたＣＤ４陽性細胞の割合とＣＤ８陽性細胞の割合との和を第４スコアとする。横軸を検体数、縦軸を第４スコアとしてその結果をプロットし、それを三次関数で近似する。該近似曲線の変曲点をカットオフ値として、カットオフ値以上の陽性細胞数頻度の検体を陽性検体、それ未満の陽性細胞数頻度の検体を陰性検体として二値化したものを第４データとする。上記カットオフ値の一例を後述の実施例（表４）に示す。A fourth score is identified based on the percentage of T cell infiltration into tumor tissue. The method of identifying the fourth score is not particularly limited as long as the method is similarly performed for each sample, but can be calculated by the following method as an example.
(Calculation method)
The ratio of CD4 positive cells and the ratio of CD8 positive cells are determined, and the sum of these ratios is defined as the ratio of infiltration of T cells into tumor tissue.
Specifically, the expression intensity of CD4 for each cell is classified as follows based on the result of detection of the expression of CD4 according to the method described above.
Expression intensity; not detected 0; weak 1: moderate 2: remarkable 3
Cells with an expression intensity of 2 or more are determined as CD4-positive cells.
For each of the plurality of specimens used, the ratio of the CD4 positive cells to the total nucleated cells is determined.
Similarly, the expression intensity of CD8 for each cell is classified as follows based on the result of detection of the expression of CD8 according to the method described above.
Expression intensity; not detected 0; weak 1: moderate 2: remarkable 3
Cells with an expression intensity of 2 or more are determined as CD8 positive cells.
For each of the plurality of specimens used, the ratio of the CD8-positive cells to the total nucleated cells is determined.
The sum of the ratio of CD4-positive cells and the ratio of CD8-positive cells obtained as described above is defined as a fourth score. The results are plotted with the horizontal axis representing the number of samples and the vertical axis representing the fourth score, which is approximated by a cubic function. Using the inflection point of the approximate curve as a cutoff value, binarizing a sample having a frequency of positive cells equal to or greater than the cutoff value as a positive sample and a sample having a frequency of positive cells less than the cutoff value as a negative sample as fourth data And An example of the cutoff value is shown in an example (Table 4) described later.

続いて、上記で得られた各データに基づいて、各肺腺癌患者を予後の良いグループと悪いグループにそれぞれ区分する。予後の判定は全生存率を用いて行うことができる。
具体的には、後述の実施例にも記載の通り、例えば、
（Ａ１）第１データが陽性かつ第２データが陰性かつ第３データが陰性の検体かつ第４データが陽性の検体、
（Ａ２）第１データが陽性かつ第２データが陽性かつ第３データが陰性の検体かつ第４データが陽性の検体、及び、
（Ａ３）第１データが陰性かつ第２データが陰性かつ第３データが陰性の検体かつ第４データが陽性の検体
を予後の良いグループに区分し、それ以外の検体、即ち、
（Ｂ１）第１データが陽性かつ第２データが陰性かつ第３データが陰性の検体かつ第４データが陰性の検体、
（Ｂ２）第１データが陽性かつ第２データが陽性かつ第３データが陰性の検体かつ第４データが陰性の検体、
（Ｂ３）第１データが陰性かつ第２データが陰性かつ第３データが陰性の検体かつ第４データが陰性の検体、
（Ｂ４）第１データが陽性かつ第２データが陰性かつ第３データが陽性の検体かつ第４データが陽性若しくは陰性の検体、
（Ｂ５）第１データが陰性かつ第２データが陽性かつ第３データが陰性の検体かつ第４データが陽性若しくは陰性の検体、
（Ｂ６）第１データが陰性かつ第２データが陽性かつ第３データが陽性の検体かつ第４データが陽性若しくは陰性の検体、
（Ｂ７）第１データが陽性かつ第２データが陽性かつ第３データが陽性の検体かつ第４データが陽性若しくは陰性の検体、及び、
（Ｂ８）第１データが陰性かつ第２データが陰性かつ第３データが陽性の検体かつ第４データが陽性若しくは陰性の検体
を予後の悪いグループに区分することができる。Subsequently, each lung adenocarcinoma patient is classified into a good prognosis group and a bad prognosis group based on each data obtained above. Prognosis can be determined using the overall survival rate.
Specifically, as described in Examples below, for example,
(A1) a sample in which the first data is positive, the second data is negative, the third data is negative, and the fourth data is positive,
(A2) a sample in which the first data is positive, the second data is positive, the third data is negative, and the fourth data is positive, and
(A3) A sample in which the first data is negative, the second data is negative, the third data is negative, and the fourth data is positive are classified into good prognostic groups, and the other samples, that is,
(B1) a sample in which the first data is positive, the second data is negative, the third data is negative, and the fourth data is negative,
(B2) a sample in which the first data is positive, the second data is positive, the third data is negative, and the fourth data is negative,
(B3) a sample in which the first data is negative, the second data is negative, the third data is negative, and the fourth data is negative;
(B4) a sample in which the first data is positive, the second data is negative, the third data is positive, and the fourth data is positive or negative,
(B5) a sample in which the first data is negative, the second data is positive, the third data is negative, and the fourth data is positive or negative,
(B6) a sample in which the first data is negative, the second data is positive, the third data is positive, and the fourth data is positive or negative;
(B7) a sample in which the first data is positive, the second data is positive, the third data is positive, and the fourth data is positive or negative, and
(B8) A sample in which the first data is negative, the second data is negative and the third data is positive, and a sample in which the fourth data is positive or negative can be classified into a group having a poor prognosis.

続いて、第１スコアと第２スコアと第３データと第４スコアを説明変数とし、多変量解析することにより、予後の良いグループと悪いグループに区分する為の演算式、即ち予後演算式を求める。
多変量解析する方法としては、特に限定されないが、好ましくは判別分析が用いられる。本発明においてはＰＤ−Ｌ１、Ｇａｌ−９及びＸＡＧＥ１発現並びにＴ細胞の浸潤の割合に関する数値からなる多変量データを説明変数として用いる。このような判別分析の手法は公知であり、最も代表的な判別分析に線形判別関数による判別があるが、それに限定されない。また、市販の統計解析ソフトウエアを用いて行うことができる。
かくして得られた演算式は以下で表すことができる。Subsequently, by using the first score, the second score, the third data, and the fourth score as explanatory variables, and performing multivariate analysis, an arithmetic expression for classifying into a good prognosis group and a poor prognosis group, that is, a prognosis arithmetic expression Ask.
The method of multivariate analysis is not particularly limited, but preferably, discriminant analysis is used. In the present invention, multivariate data consisting of numerical values relating to PD-L1, Gal-9 and XAGE1 expression and the percentage of T cell infiltration are used as explanatory variables. Such a discriminant analysis technique is known, and the most representative discriminant analysis includes discrimination by a linear discriminant function, but is not limited thereto. The analysis can be performed using commercially available statistical analysis software.
The arithmetic expression thus obtained can be expressed as follows.

ａ０、ａ１、ａ２、ａ３及びａ４の値は、演算式を作成するために用いる肺腺癌患者の検体の数によっても異なる。本発明の方法で作成された予後演算式の一例として、後述の実施例において作成された以下の演算式が挙げられる。The values of a 0 , a 1 , a 2 , a 3, and a 4 also vary depending on the number of specimens of a lung adenocarcinoma patient used to create an arithmetic expression. As an example of the prognosis calculation formula created by the method of the present invention, the following calculation formula created in the embodiment described later is given.

説明変数である第１スコア及び第４スコアの係数が正であることから、ＰＤ−Ｌ１及び腫瘍組織へのＴ細胞の浸潤は予後良好因子であり、説明変数である第２スコア及び第３データの係数が負であることから、Ｇａｌ−９及びＸＡＧＥ１は予後不良因子であることがわかる。   Since the coefficients of the first score and the fourth score, which are explanatory variables, are positive, infiltration of T cells into PD-L1 and tumor tissue is a favorable prognostic factor, and the second score and the third data, which are explanatory variables, Is negative, indicating that Gal-9 and XAGE1 are poor prognostic factors.

本発明は、上記した本発明の予後演算式作成方法によって作成された演算式を用いて、肺腺癌の予後を推定する方法を提供する。以下、本発明の肺腺癌の予後推定方法について詳細に述べる。
［予後推定方法］
本発明の肺腺癌の予後推定方法の一態様は以下の通りである。
本発明の予後演算式作成方法１を用いて得られた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データを前記演算式に代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法（以下、本発明の予後推定方法１とも称する）。
ここで、演算式の作成は上述した通りである。
第１・第２スコア及び第３データを被験者の検体から検出する方法は演算式を作成する際に実施した方法と同じあれば、特に限定されない。かくして得られた第１・第２スコア及び第３データをあらかじめ求められた演算式に代入して前記被験者の予後を推定する。演算式に代入して得られた値（以下、予後スコア１とも称する）が０よりも大きい場合には予後良好と推定し、０よりも小さい場合に予後不良と推定することができる。
本発明の予後演算式作成方法の別の一態様は以下の通りである。
本発明の予後演算式作成方法２を用いて得られた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データと第４スコアを前記演算式に代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法（以下、本発明の予後推定方法２とも称する）。
ここで、演算式の作成は上述した通りである。
第１・第２スコアと第３データと第４スコアを被験者の検体から検出する方法は演算式を作成する際に実施した方法と同じあれば、特に限定されない。かくして得られた第１・第２スコアと第３データと第４スコアをあらかじめ求められた演算式に代入して前記被験者の予後を推定する。演算式に代入して得られた値（以下、予後スコア２とも称する）が０よりも大きい場合には予後良好と推定し、０よりも小さい場合に予後不良と推定することができる。
なお、第４スコアとして、ＣＤ４陽性Ｔ細胞及びＣＤ８陽性Ｔ細胞をあわせたＣＤ３陽性Ｔ細胞の浸潤率を用いてもよく、カットオフ値を用いることなく決定したＣＤ４陽性Ｔ細胞及びＣＤ８陽性Ｔ細胞の浸潤率の和、或いは、ＣＤ３陽性Ｔ細胞の浸潤率をそのまま用いてもよい。The present invention provides a method for estimating the prognosis of lung adenocarcinoma using an arithmetic expression created by the above-described prognostic expression creating method of the present invention. Hereinafter, the prognosis estimation method for lung adenocarcinoma of the present invention will be described in detail.
[Prognostic estimation method]
One embodiment of the method for estimating prognosis of lung adenocarcinoma of the present invention is as follows.
The first and second scores and the third data detected from the subject's sample are substituted into the above-mentioned arithmetic expression for the arithmetic expression obtained by using the prognostic arithmetic expression creating method 1 of the present invention, and the prognosis of the subject is obtained. A prognosis estimation method for lung adenocarcinoma estimated by calculation (hereinafter, also referred to as a prognosis estimation method 1 of the present invention).
Here, the creation of the arithmetic expression is as described above.
The method for detecting the first and second scores and the third data from the sample of the subject is not particularly limited as long as it is the same as the method used when creating the arithmetic expression. The prognosis of the subject is estimated by substituting the first and second scores and the third data obtained in this way into a previously calculated arithmetic expression. When the value obtained by substituting into the arithmetic expression (hereinafter also referred to as prognostic score 1) is larger than 0, it can be estimated that the prognosis is good, and when it is smaller than 0, it can be estimated that the prognosis is poor.
Another embodiment of the prognostic expression creation method of the present invention is as follows.
The first and second scores, third data, and fourth score detected from the subject's sample are substituted into the arithmetic expression obtained by using the prognostic arithmetic expression creating method 2 of the present invention. A prognosis estimation method for lung adenocarcinoma in which the prognosis of a subject is estimated by calculation (hereinafter, also referred to as a prognosis estimation method 2 of the present invention).
Here, the creation of the arithmetic expression is as described above.
The method for detecting the first and second scores, the third data, and the fourth score from the sample of the subject is not particularly limited as long as it is the same as the method used when creating the arithmetic expression. The prognosis of the subject is estimated by substituting the first and second scores, the third data, and the fourth score obtained in this way into a previously obtained arithmetic expression. When the value (hereinafter also referred to as prognosis score 2) obtained by substituting into the arithmetic expression is larger than 0, the prognosis is estimated to be good, and when it is smaller than 0, the prognosis is poor.
As the fourth score, the infiltration rate of CD3 + T cells combined with CD4 + T cells and CD8 + T cells may be used, and CD4 + T cells and CD8 + T cells determined without using a cutoff value may be used. Or the infiltration rate of CD3-positive T cells may be used as it is.

本発明の肺腺癌の予後推定方法は、従来の病期分類ではＩＡ期と判定される早期癌、或いは、ＩＡ、ＩＢ、ＩＩＡ、ＩＩＢ、又はＩＩＩＡ期と判定される早期癌を含む局所進行期（以下、「局所進行期」という。）にある肺癌患者の中から、実際は術後の予後が不良な患者を識別することを可能とするものである。もちろん、本発明の肺腺癌の予後推定方法は、ＩＩＩＢ期やＩＶ期の肺癌患者の予後推定のためにも用いることができる。従来、病期分類ＩＡ期においては、手術単独で５年生存率９０％前後である為、本邦では術後追加治療は行われておらず、早期或いは局所進行期の肺癌においては予後の良否の判別が困難だったため、各々の従来の判定方法による病期に対応した治療のみが実施されており、予後不良患者の発生を阻止しえなかった。本発明の予後推定方法は、従来の病期分類では不可能であった早期或いは局所進行期にある肺腺癌患者から予後不良患者の判別や早期肺腺癌患者の再発率を判定することができるので、当該予後不良患者に対して、従来適用されなかった術後追加治療、さらには、従来適用が不可能だった集学的治療や個別化医療の早期からの適用機会を提供することができ、患者の予後の悪さの改善に寄与するものである。このように、本発明の予後推定方法は、その病期が局所進行期、とりわけ早期の予後不良患者群に対して新たな臨床試験の場を提供することができるので、当該治療方法を適用できる予後不良な肺腺癌患者を選別するためのコンパニオン診断方法として有用である。   The method for estimating the prognosis of lung adenocarcinoma according to the present invention comprises a method for estimating local progression including early stage cancer determined to be IA stage by conventional staging or early stage cancer determined to be IA, IB, IIA, IIB or IIIA stage. The present invention makes it possible to identify a patient with a poor prognosis after surgery from lung cancer patients in the stage (hereinafter referred to as “locally advanced stage”). Of course, the method for estimating prognosis of lung adenocarcinoma of the present invention can also be used for estimating the prognosis of lung cancer patients in stage IIIB or stage IV. Conventionally, in the stage IA stage, the 5-year survival rate of surgery alone is around 90%, so no additional postoperative treatment has been performed in Japan, and the prognosis of early or locally advanced lung cancer is poor. Because of the difficulty in discrimination, only the treatment corresponding to the stage according to each conventional judgment method was performed, and the occurrence of patients with poor prognosis could not be prevented. The prognosis estimation method of the present invention can discriminate patients with poor prognosis from early stage or locally advanced stage lung adenocarcinoma patients who cannot be performed by conventional staging or determine the recurrence rate of early lung adenocarcinoma patients. Therefore, it is possible to provide postoperative additional treatments that were not previously applied to patients with poor prognosis, as well as the opportunity to apply multidisciplinary treatments and personalized medicines that were not previously possible. And contribute to improving the poor prognosis of patients. As described above, the prognosis estimation method of the present invention can provide a new clinical trial site for a patient group whose stage is locally advanced stage, particularly an early poor prognosis, so that the treatment method can be applied. It is useful as a companion diagnostic method for selecting lung adenocarcinoma patients with poor prognosis.

また、本発明の肺腺癌の予後推定方法は、上述のように、従来の病期分類からは予測できない肺癌の予後の悪さを推定できることから、従来の病期分類に基づき決定される治療方法の適用が当該肺癌に対し有効かどうかの判断、すなわち、肺癌患者に対する治療方法の効果判定方法として用いることができる。この効果判定方法は、肺腺癌の治療方法であれば、その方法の如何によらず、対象とする病変部位が原発巣であっても転移部位であってもよく、術後に再発した場合でも適用が可能であるが、判別関数を求めるために用いる識別因子の性状を勘案すると、免疫抑制系経路に影響を与える治療方法に対する適用が有用であり、とりわけ、ＰＤ−Ｌ１の発現、Ｇａｌ−９の発現、ＸＡＧＥ１の発現及びＴ細胞浸潤の何れか１又は２以上に影響を与える治療方法の効果判定に有利に利用することができる。さらに、本発明の予後推定方法は、肺癌の術後再発率の予測方法として有用であり、なかでも早期肺腺癌の再発率の予測方法として有用であり、特に早期肺腺癌の術後５年を超える長期の再発率の予測方法として有用である。   In addition, as described above, the method for estimating the prognosis of lung adenocarcinoma of the present invention can estimate the poor prognosis of lung cancer that cannot be predicted from the conventional staging, and thus the treatment method determined based on the conventional staging. Can be used as a method for judging whether or not the application is effective for the lung cancer, that is, a method for judging the effect of a treatment method on a lung cancer patient. This effect determination method may be a method of treating lung adenocarcinoma, regardless of the method, whether the target lesion site may be a primary lesion or a metastatic site, and if the disease has recurred after surgery However, taking into account the properties of the discriminating factors used to determine the discriminant function, it is useful to apply to therapeutic methods that affect the immunosuppressive pathway, and in particular, PD-L1 expression, Gal- 9 and XAGE1 expression and T cell infiltration. Further, the prognosis estimation method of the present invention is useful as a method for predicting the postoperative recurrence rate of lung cancer, and particularly useful as a method for predicting the recurrence rate of early lung adenocarcinoma. It is useful as a method for predicting long-term recurrence rates over a year.

以下に実施例を挙げて本発明をより具体的に説明するが、これらは単なる例示であって本発明を何ら限定するものではない。   Hereinafter, the present invention will be described more specifically with reference to examples, but these are merely examples and do not limit the present invention in any way.

試験例１
肺癌細胞株における免疫抑制分子のPD-L1、Gal-9の発現について調べた。用いた材料及び方法を以下に示す。
（材料）
使用したフローサイトメトリー抗体：
・PD-L1 (clone 29E.2A3; BioLegend, San Diego, CA, USA)
・Gal-9 (clone 9M1-3; BioLegend, San Diego, CA, USA)

測定機器：
フローサイトメトリー：FACS CantoII（BD社）

使用した細胞株、肺癌組織：
肺腺癌（A549、PC-9、HLC-1、II-18、OU-LC-ON、OU-LC-SK）
肺扁平上皮癌（Sq-1、EBC-1、RERF-LC-AI）

（方法）
フローサイトメトリー法による肺癌細胞株におけるPD-L1、Gal-9の発現解析：
細胞表面
1. 肺癌細胞株を10％FCS/RPMIにてフラスコ内で数日間培養。
2. EDTA液にて、培養細胞をフラスコより剥がしFACS buffer（1％FCS/0.1％ azide/PBS)で洗浄。
3. 細胞を1ｘ105個使用し、PD-L1 (clone 29E.2A3)、Gal-9 (clone 9M1-3)とそれぞれのアイソタイプコントロール抗体を使用し、添付文書（企業data sheet）の抗体量を使用し、On-ice 20分染色。
4. FACS bufferで洗浄した後、FACS CantoIIで計測。

細胞内染色
1. 肺癌細胞株を10％FCS/RPMIにてフラスコ内で数日間培養。
2. EDTA液にて、培養細胞をフラスコより剥がしFACS buffer（1％FCS/0.1％ azide/PBS)で洗浄。
3. 細胞を1ｘ105個使用し、BD Cytofix/CytopermTM Fixation/Permeabilization Solution Kitで、細胞に穴を開ける。
4. PD-L1 (clone 29E.2A3)、Gal-9 (clone 9M1-3 )とそれぞれのアイソタイプコントロール抗体を使用し、添付文書（企業data sheet）の抗体量を使用し、On-ice 20分染色。
5. FACS bufferで洗浄した後、FACS CantoIIで計測。

（結果）
結果を図１に示す。
結果、多くの肺癌細胞株において、PD-L1及びGal-9分子が強く発現していることが確認された。 Test example 1
The expression of the immunosuppressive molecules PD-L1 and Gal-9 in lung cancer cell lines was examined. The materials and methods used are shown below.
(material)
Flow cytometry antibodies used:
・ PD-L1 (clone 29E.2A3; BioLegend, San Diego, CA, USA)
・ Gal-9 (clone 9M1-3; BioLegend, San Diego, CA, USA)

measuring equipment:
Flow cytometry: FACS CantoII (BD)

Cell lines and lung cancer tissues used:
Lung adenocarcinoma (A549, PC-9, HLC-1, II-18, OU-LC-ON, OU-LC-SK)
Squamous cell carcinoma of the lung (Sq-1, EBC-1, RERF-LC-AI)

(Method)
Expression analysis of PD-L1 and Gal-9 in lung cancer cell lines by flow cytometry:
Cell surface
1. Lung cancer cell line cultured in flask with 10% FCS / RPMI for several days.
2. Remove the cultured cells from the flask with EDTA solution and wash with FACS buffer (1% FCS / 0.1% azide / PBS).
3. Using 1 × 10 5 cells, use PD-L1 (clone 29E.2A3), Gal-9 (clone 9M1-3) and their isotype control antibodies, and determine the amount of antibody in the package insert (company data sheet). Use and stain on-ice for 20 minutes.
4. After washing with FACS buffer, measure with FACS CantoII.

Intracellular staining
1. Lung cancer cell line cultured in flask with 10% FCS / RPMI for several days.
2. Remove the cultured cells from the flask with EDTA solution and wash with FACS buffer (1% FCS / 0.1% azide / PBS).
3. Using 1 × 10 5 cells, make holes in the cells using the BD Cytofix / Cytoperm ™ Fixation / Permeabilization Solution Kit.
4. Using PD-L1 (clone 29E.2A3) and Gal-9 (clone 9M1-3) and their respective isotype control antibodies, use the amount of antibody on the package insert (company data sheet), and on-ice for 20 minutes staining.
5. After washing with FACS buffer, measure with FACS CantoII.

(result)
The results are shown in FIG.
As a result, it was confirmed that PD-L1 and Gal-9 molecules were strongly expressed in many lung cancer cell lines.

試験例２
肺癌細胞株におけるPD-L1及びGal-9分子の発現について更なる解析を行った。
種々の肺腺癌細胞株（A549、PC-9、HLC-1、II-18、OU-LC-ON、OU-LC-SK）をIFN-γ処理し、処理の前後での細胞表面、細胞内におけるPD-L1及びGal-9の発現の変化を調べた。発現解析は試験例１と同様にしてフローサイトメトリー法により行った。
II-18細胞での発現解析の結果を図２Ａに、試験した細胞株におけるIFN-γ処理による発現の変化を図２Ｂに示す。結果、PD-L1はIFN-γ処理により細胞表面での発現が増加するが、Gal-9の発現は変化しなかった。 Test example 2
Further analysis was performed on the expression of PD-L1 and Gal-9 molecules in a lung cancer cell line.
Various lung adenocarcinoma cell lines (A549, PC-9, HLC-1, II-18, OU-LC-ON, OU-LC-SK) were treated with IFN-γ, and cell surfaces and cells before and after treatment were treated. Changes in the expression of PD-L1 and Gal-9 in the cells were examined. Expression analysis was performed by flow cytometry in the same manner as in Test Example 1.
FIG. 2A shows the results of expression analysis in II-18 cells, and FIG. 2B shows changes in expression due to IFN-γ treatment in the tested cell lines. As a result, expression of PD-L1 on the cell surface was increased by IFN-γ treatment, but expression of Gal-9 was not changed.

次に、分子標的薬が奏功する肺癌細胞株PC-9と奏功しないOU-LC-SKを用いて、分子標的薬処理後の培養上清中のGal-9濃度を測定した。分子標的薬処理は、10 nMのAfatinibと共に細胞を96時間培養することにより行った。
結果、分子標的薬で腫瘍が崩壊するとGal-9が上清中に放出されることが明らかになった（図２Ｃ）。分子標的薬により腫瘍が一部崩壊してもGal-9という免疫を強く抑制する物質が放出される。従って、このことは、分子標的薬と、Gal-9とTim-3を標的とした抗体医薬との併用療法が重要であることを意味する。
実際、がん患者の胸水（癌局所に類似）ではGal-9濃度が高く（図２Ｄ）、また化学療法中の経過でもGal-9が血液中から検出される患者がいることが確認された（図２Ｅ；10症例中3人で標準偏差以上の差が認められた）。Next, the concentration of Gal-9 in the culture supernatant after the molecular targeted drug treatment was measured using the lung cancer cell line PC-9 that responded to the molecular targeted drug and OU-LC-SK that did not respond. The molecular target drug treatment was performed by culturing the cells for 96 hours with 10 nM Afatinib.
As a result, it was revealed that Gal-9 was released into the supernatant when the tumor was disrupted by the molecular targeting drug (FIG. 2C). Even if the tumor is partially destroyed by molecular targeted drugs, Gal-9, a substance that strongly suppresses immunity, is released. Therefore, this means that a combination therapy of a molecular targeting drug and an antibody drug targeting Gal-9 and Tim-3 is important.
In fact, it was confirmed that Gal-9 concentration was high in pleural effusion (similar to local cancer) of cancer patients (Fig. 2D), and that Gal-9 was detected in blood even during the course of chemotherapy. (FIG. 2E; difference of more than standard deviation was recognized in 3 out of 10 cases).

試験例３
がん局所に浸潤するＴ細胞（TIL）の表面での免疫抑制分子（PD-1、TIM-3、BTLA、LAG-3）の発現解析を行った。解析は、以下の抗体を用いて試験例１に記載の方法と同様にしてフローサイトメトリー法により行った。
・PD-1（Anti-CD279-PE/Cy7 clone EH12.2H7, Biolegend）
・TIM-3（Anti-Tim3-APC clone F38-2E2, eBioscience）
・BTLA（Anti-CD272-PE clone MH26, Biolegend）
・LAG-3（Anti-CD223-FITC clone 17B4, Enzo）
結果、CD4陽性細胞、CD8陽性細胞のいずれにおいても、PD-L1の受容体であるPD-1、Gal-9の受容体であるTim-3が高発現しており（図３Ａ−Ｂ）、肺腺癌における免疫抑制分子経路として、PD-1/PD-L1及びTim-3/Gal-9経路が重要であることが示された。 Test example 3
We analyzed the expression of immunosuppressive molecules (PD-1, TIM-3, BTLA, LAG-3) on the surface of T cells (TIL) infiltrating local cancer. Analysis was performed by flow cytometry using the following antibodies in the same manner as described in Test Example 1.
・ PD-1 (Anti-CD279-PE / Cy7 clone EH12.2H7, Biolegend)
・ TIM-3 (Anti-Tim3-APC clone F38-2E2, eBioscience)
・ BTLA (Anti-CD272-PE clone MH26, Biolegend)
・ LAG-3 (Anti-CD223-FITC clone 17B4, Enzo)
As a result, PD-1 which is a receptor for PD-L1 and Tim-3 which is a receptor for Gal-9 are highly expressed in both CD4 positive cells and CD8 positive cells (FIGS. 3A-B). The PD-1 / PD-L1 and Tim-3 / Gal-9 pathways have been shown to be important as immunosuppressive molecular pathways in lung adenocarcinoma.

更に、がん局所の細胞におけるPD-1及びTim-3の発現と、分化度（CD27）、増殖能（Ki-67）、アポトーシス（Annexin V）との関係を調べた。
結果、Tim-3+ PD-1+ T細胞は細胞疲弊しているとの報告がこれまで多かったが、そうではなく、CD27は維持され（最終分化すると低下する為）、Ki-67は高く、Annexin Vは高値であった（図３Ｃ）。これは、T細胞増殖能が亢進していると同時に細胞死も高頻度に誘導されていることを反映している。がん治療のためにはこれらの活性化した細胞を維持することが重要である。これまで報告されているのは、PD-1抗体、PD-L1抗体での臨床試験であり、それによりアポトーシスを防げるのはPD-1+ Tim-3-の細胞集団が主であり、Tim-3+細胞はアポトーシスを免れない。その為、Gal-9とTim-3を標的とする抗体医薬が重要であることが示唆される。Furthermore, the relationship between the expression of PD-1 and Tim-3 in cancer-localized cells and the degree of differentiation (CD27), proliferation ability (Ki-67), and apoptosis (Annexin V) was examined.
As a result, there have been many reports that Tim-3 + PD-1 + T cells are exhausted, but this is not the case. CD27 is maintained (because it decreases after terminal differentiation), and Ki-67 is high. And Annexin V were high (FIG. 3C). This reflects that T cell proliferation ability is enhanced and cell death is induced at a high frequency. It is important to maintain these activated cells for cancer treatment. It has been reported so far in clinical trials with PD-1 and PD-L1 antibodies, which can prevent apoptosis mainly by the PD-1 + Tim-3- cell population and Tim- 3+ cells cannot escape apoptosis. Therefore, it is suggested that antibody drugs targeting Gal-9 and Tim-3 are important.

次に、がん抗原XAGE1に対する特異的なCD8+ T細胞を樹立した。細胞表面のPD-1、TIM-3発現をフローサイトメトリー法により調べたところ、Tim-3が高発現していることが分かった（図３Ｄ）。
そのT細胞（1 x 104細胞）に、図に示した濃度でGal-9を加えて8時間培養したところ、細胞はアポトーシスした（図３Ｅ）。また、そのT細胞（1 x 104細胞）を用いて、Gal-9（300 pg/ml）に対し、抗Gal-9抗体（1 μg/ml）を加えて12時間培養したところ、アポトーシスが阻害された（図３Ｆ）。Next, CD8 + T cells specific for the cancer antigen XAGE1 were established. Examination of PD-1 and TIM-3 expression on the cell surface by flow cytometry revealed that Tim-3 was highly expressed (FIG. 3D).
Gal-9 was added to the T cells (1 × 10 4 cells) at the concentrations shown in the figure and the cells were cultured for 8 hours. As a result, the cells apoptotic (FIG. 3E). In addition, anti-Gal-9 antibody (1 μg / ml) was added to Gal-9 (300 pg / ml) using the T cells (1 × 10 4 cells) and cultured for 12 hours. Inhibited (FIG. 3F).

試験例４
試験例１−３の結果により肺腺癌においては免疫抑制分子経路としてPD-1/PD-L1及びTim-3/Gal-9経路が重要であることが示された。そこで、肺腺癌で免疫応答が予後に関連することが知られているXAGE1抗原も含め、これらの分子の発現の有無と予後との関連を検討した。
（材料と方法）
使用した肺癌組織：361例（Tissue Micro Array LK400-1,LK400-2:肺腺癌194例、肺扁平上皮癌112例、肺小細胞癌12例、その他の組織型11例、転移性肺腫瘍32例）
株式会社パソロジー研究所（富山，日本）より提供

使用した免疫染色抗体：
・Rabbit anti-PD-L1 Ab (Catalog number: 4059 ProSci incorporated Poway,CA,USA)
・Mouse anti-human Galectin-9 mAb (clone 9M1-3 GalPharma Co., Ltd, Kagawa, Japan)
・XAGE1モノクローナル抗体（USO 9-13：本発明者らが作製）

免疫染色（XAGE1）：
1. 5μmパラフィン切片を脱パラフィン化。
2. 抗原賦活化はマイクロウェーブで10 mmol/L citrate buffer (pH 6.0)内で 圧力釜を使用し10分。
3. 内在性ペルオキシダーゼを0.3% H2O2for 5分で不活性化。
4. XAGE1モノクローナル抗体を2 μg/mL使用し4℃でオーバーナイト。
5. 洗浄後streptavidin-biotin complex (SimpleStain MAX-PO kit; Nichirei, Tokyo, Japan)に引き続きH2O2入り3, 3'-diaminobenzidine で発色。

その他の免疫染色（パソロジー研究所による）：
1. 脱パラフィン操作
2. 抗原賦活
抗原賦活処理液 KN9（Code:KN-09001, パソロジー研究所)で95℃40分間処理、室温で冷却20分間
3. 内因性ペルオキシダーゼ処理
3%過酸化水素水で、室温 10分間
4. 蒸留水で数回洗浄し、KN Buffer (Code : KN-09002, パソロジー研究所)に室温で5分間浸漬し、平衡化
5. 一次抗体反応 （室温）
6. KN Bufferで数回洗浄
7. 二次抗体反応（室温 30分間）
Envision＋Dual Link HRP標識・ポリマー試薬（Dako社：K4061）
8. KN Bufferで数回洗浄
9. DAB発色 ［Dako DAB+］ （室温 10分間）
10. 蒸留水で洗浄
11. ヘマトキシリン（細胞核を染色） 室温 3分
12. 水洗、脱水、透徹、封入

スコアリング：
・免疫染色
XAGE1：
組織内に1％以上の陽性細胞数があれば陽性と判定する。
PD-L1，Gal-9：
免疫染色を施行した後、2切片を用いて、細胞膜および細胞質の染色強度I（Intensity）と範囲D（Distribution; 強度Iの細胞が何%存在するか）を計測。
強度I : no expression 0, weak 1, moderate 2, marked 3
範囲D : 0% 0, 1〜50% 1, 51〜100% 2
免疫染色スコアIHC Score = D x I
細胞膜では、2切片の内高い方を、その組織の細胞膜スコアとする。同様に、細胞質でも2切片の内高い方を、その組織の細胞膜スコアとする。

陽性判定：
XAGE1：
組織内に1％以上の陽性細胞数があれば陽性1、1％未満であれば0と判定する。
PD-L1，Gal-9：
免疫染色で求められた細胞膜スコア、細胞質スコアのそれぞれについて、3以上で陽性。細胞膜もしくは細胞質の少なくとも一方が陽性の場合、その検体は陽性であると判定する。

解析：
生存解析にはIBM SPSS Statistics 19 for Windows (IBM, New York, NY)を用いた。

（結果）
抗PD-L1抗体、抗Gal-9抗体または抗XAGE1抗体での免疫染色画像を図４に示す。肺癌組織においてPD-L1、Gal-9およびXAGE1の発現が認められた。
また、PD-L1及びGal-9についての上記スコアリング及び陽性判定の結果を下記表１及び２に示す。 Test example 4
The results of Test Examples 1-3 showed that PD-1 / PD-L1 and Tim-3 / Gal-9 pathways are important as immunosuppressive molecular pathways in lung adenocarcinoma. Therefore, the relationship between the presence or absence of the expression of these molecules, including the XAGE1 antigen, which is known to be associated with prognosis in lung adenocarcinoma, and prognosis was examined.
(Materials and methods)
Lung cancer tissue used: 361 (Tissue Micro Array LK400-1, LK400-2: 194 lung adenocarcinoma, 112 lung squamous cell carcinoma, 12 small cell lung cancer, 11 other histological types, 11 metastatic lung tumors 32 cases)
Provided by Pathology Laboratory Co., Ltd. (Toyama, Japan)

Immunostaining antibodies used:
・ Rabbit anti-PD-L1 Ab (Catalog number: 4059 ProSci incorporated Poway, CA, USA)
・ Mouse anti-human Galectin-9 mAb (clone 9M1-3 GalPharma Co., Ltd, Kagawa, Japan)
・ XAGE1 monoclonal antibody (USO 9-13: prepared by the present inventors)

Immunostaining (XAGE1):
1. Deparaffinize 5 μm paraffin sections.
2. Reactivate the antigen by microwave in a 10 mmol / L citrate buffer (pH 6.0) using a pressure cooker for 10 minutes.
3. Inactivated endogenous peroxidase with 0.3% H 2 O 2 for 5 minutes.
4. Overnight at 4 ° C using 2 μg / mL of XAGE1 monoclonal antibody.
5. After washing, color is developed with 3,3'-diaminobenzidine containing H 2 O 2 following the streptavidin-biotin complex (SimpleStain MAX-PO kit; Nichirei, Tokyo, Japan).

Other immunostainings (by Pathology Institute):
1. Deparaffinization operation
2. Antigen activation Antigen activation treatment solution KN9 (Code: KN-09001, Pathology Laboratory) treated at 95 ° C for 40 minutes, cooled at room temperature for 20 minutes
3. Endogenous peroxidase treatment
3% hydrogen peroxide solution at room temperature for 10 minutes
4. Wash several times with distilled water, immerse in KN Buffer (Code: KN-09002, Pathology Laboratory) for 5 minutes at room temperature, and equilibrate
5. Primary antibody reaction (room temperature)
6. Wash several times with KN Buffer
7. Secondary antibody reaction (room temperature 30 minutes)
Envision + Dual Link HRP label / polymer reagent (Dako: K4061)
8. Wash several times with KN Buffer
9. DAB coloring [Dako DAB +] (room temperature for 10 minutes)
10. Wash with distilled water
11. Hematoxylin (stains cell nuclei) 3 minutes at room temperature
12. Washing, dehydrating, clearing, enclosing

Scoring:
・ Immunostaining
XAGE1:
If the number of positive cells is 1% or more in the tissue, it is determined to be positive.
PD-L1, Gal-9:
After immunostaining, the staining intensity I (Intensity) and range D (Distribution; what percentage of cells with intensity I are present) of the cell membrane and cytoplasm were measured using the two sections.
Strength I: no expression 0, weak 1, moderate 2, marked 3
Range D: 0% 0, 1 ~ 50% 1, 51 ~ 100% 2
Immunostaining score IHC Score = D x I
For the cell membrane, the higher of the two sections is taken as the cell membrane score of the tissue. Similarly, in the cytoplasm, the higher of the two sections is used as the cell membrane score of the tissue.

Positive judgment:
XAGE1:
If the number of positive cells in the tissue is 1% or more, it is judged as positive 1;
PD-L1, Gal-9:
Positive at 3 or more for each of the cell membrane score and cytoplasmic score determined by immunostaining. If at least one of the cell membrane and the cytoplasm is positive, the specimen is determined to be positive.

analysis:
For survival analysis, IBM SPSS Statistics 19 for Windows (IBM, New York, NY) was used.

(result)
FIG. 4 shows an image of immunostaining with an anti-PD-L1 antibody, an anti-Gal-9 antibody, or an anti-XAGE1 antibody. Expression of PD-L1, Gal-9 and XAGE1 was observed in lung cancer tissues.
Tables 1 and 2 below show the results of the above scoring and positive judgment for PD-L1 and Gal-9.

XAGE1についての陽性判定の結果を下記表３に示す。   Table 3 shows the results of the positive determination for XAGE1.

更に、PD-L1、Gal-9、XAGE1の発現と予後との関係について得られた結果を図５に示す。
肺癌では腫瘍側の免疫抑制因子としてPD-L1とGal-9が重要であり、さらに、肺腺癌において、免疫原性を有するXAGE1発現とPD-L1、Gal-9分子の発現パターンが予後に関与することがわかった。Further, the results obtained regarding the relationship between the expression of PD-L1, Gal-9, and XAGE1 and the prognosis are shown in FIG.
In lung cancer, PD-L1 and Gal-9 are important as immunosuppressive factors on the tumor side.In lung adenocarcinoma, the expression pattern of immunogenic XAGE1 and the expression pattern of PD-L1 and Gal-9 molecules are prognostic. Turned out to be involved.

試験例５
試験例４により、腫瘍側の因子の予後との関連が明らかになったので、次に宿主側の因子と予後についての検討を行った。即ち、肺癌における免疫担当細胞の浸潤の多寡と予後との関連性について調べた。さらに、予後との関連が強い腫瘍側の因子であるXAGE1発現とPD-L1、Gal-9分子の発現パターンとそれぞれ組み合わせて予後との関連性について調べた。
（材料と方法）
使用した肺癌組織：
予後情報と臨床情報が利用可能な120例の肺癌組織を用いた。

免疫染色：
XAGE1、PD-L1、Gal-9の免疫染色は試験例４において上述した通りに行った。
宿主側の因子の免疫染色は、以下の抗体を用いてPD-L1、Gal-9の免疫染色と同様にして行った。
・CD4 (abcam社ab133616) 希釈倍率 1:100 30分反応
・CD8 (BIOSB社 BIOSB5173) 希釈倍率 1:50 30分反応
・Foxp3 (abcam社ab20034) 希釈倍率 1:100 30分反応

スコアリング：
XAGE1、PD-L1、Gal-9発現のスコアリングは試験例４と同様にして行った。
CD4、CD8発現は、パソロジー研究所により、Scan scopeシステムを使用して%として計測し、染色強度2+以上を陽性細胞と判定した。

陽性判定：
XAGE1、PD-L1、Gal-9発現の陽性判定は試験例２と同様にして行った。
CD4、CD8の陽性判定は、パソロジー研究所により、Scan scopeシステムを使用して％として計測し、染色強度2+以上を陽性細胞とし、肺腺癌120例のデータを使用した。120例のデータの近似曲線（三次関数）を作成し、その変曲点以上の陽性細胞数頻度を陽性検体、それ未満の陽性細胞数頻度を陰性検体とした。

解析：
生存解析にはIBM SPSS Statistics 19 for Windows (IBM, New York, NY)を用いた。

（結果）
宿主側の因子の免疫染色画像（Ａ）、その発現強度のスコア付けと頻度（Ｂ）、およびCD4陽性T細胞の割合と検体数との関係のプロットの結果より二値化する基準（Ｃ）を図６に示す。上記近似曲線における変曲点に基づいて、T細胞の各パラメータについて以下のカットオフ値が得られた。 Test example 5
Test Example 4 revealed the relationship between tumor side factors and prognosis. Next, host side factors and prognosis were examined. That is, the relationship between the degree of infiltration of immunocompetent cells in lung cancer and the prognosis was examined. Furthermore, we examined the association with prognosis by combining XAGE1 expression, which is a tumor-side factor strongly associated with prognosis, with PD-L1 and Gal-9 molecule expression patterns.
(Materials and methods)
Lung cancer tissue used:
We used 120 lung cancer tissues for which prognostic information and clinical information were available.

Immunostaining:
Immunostaining of XAGE1, PD-L1, and Gal-9 was performed as described above in Test Example 4.
Immunostaining for host factors was performed in the same manner as for PD-L1 and Gal-9 immunostaining using the following antibodies.
・ CD4 (abcam ab133616) dilution 1: 100 30 minutes reaction ・ CD8 (BIOSB BIOSB5173) dilution 1:50 30 minutes reaction ・ Foxp3 (abcam ab20034) dilution 1: 100 30 minutes reaction

Scoring:
Scoring of XAGE1, PD-L1, and Gal-9 expression was performed in the same manner as in Test Example 4.
CD4 and CD8 expression were measured as% using a Scan scope system by the Pathology Laboratory, and staining intensity 2+ or more was determined as positive cells.

Positive judgment:
Positive determination of XAGE1, PD-L1, and Gal-9 expression was performed in the same manner as in Test Example 2.
CD4 and CD8 positive judgment was measured by the Pathology Laboratory using a Scan scope system as%, and staining intensity of 2+ or more was defined as positive cells, and data of 120 cases of lung adenocarcinoma was used. An approximate curve (cubic function) of the data of 120 cases was created, and the frequency of positive cells at or above the inflection point was defined as a positive sample, and the frequency of positive cells below that inflection point was defined as a negative sample.

analysis:
For survival analysis, IBM SPSS Statistics 19 for Windows (IBM, New York, NY) was used.

(result)
Criteria for binarization based on the results of immunostaining images of host-side factors (A), scoring and frequency of expression intensity (B), and plots of the relationship between the ratio of CD4-positive T cells and the number of specimens (C) Is shown in FIG. Based on the inflection point in the above approximate curve, the following cutoff values were obtained for each parameter of the T cell.

更に、PD-L1、Gal-9又はXAGE1の発現及びＴ細胞浸潤と予後との関係について図７に示す結果が得られた。PD-L1、Gal-9又はXAGE1発現の多寡及び腫瘍局所浸潤Ｔ細胞の多寡と予後との関連性が強く示された。   Furthermore, the results shown in FIG. 7 were obtained regarding the relationship between PD-L1, Gal-9 or XAGE1 expression and T cell infiltration and prognosis. The relationship between PD-L1, Gal-9 or XAGE1 expression levels and tumor local infiltration T cell levels and prognosis was strongly shown.

実施例１
以上の知見に基づき、複数の肺癌患者より得られた腫瘍組織検体を用いて、予後の推定を可能とする演算式の作成を試みた。
実験では、試験例５においてXAGE、PD-L1、Gal-9の発現、及びT細胞浸潤について陽性判定を行った120例の肺腺癌検体を用いた。120例の検体は以下の8グループに分類された。
a. PD-L1陽性、Gal-9陰性、XAGE陰性 (n=21)
b. PD-L1陽性、Gal-9陰性、XAGE陽性 (n=8)
c. PD-L1陰性、Gal-9陽性、XAGE陰性 (n=11)
d. PD-L1陰性、Gal-9陽性、XAGE陽性 (n=2)
e. PD-L1陽性、Gal-9陽性、XAGE陰性 (n=19)
f. PD-L1陽性、Gal-9陽性、XAGE陽性 (n=5)
g. PD-L1陰性、Gal-9陰性、XAGE陰性 (n=36)
h. PD-L1陰性、Gal-9陰性、XAGE陽性 (n=18)
各グループと予後との関係を図８Ａに示す。これらの腫瘍側の因子である３つの分子発現を組み合わせることによって予後が判定できることが分かった。すなわち、PD-L1の発現は予後良好、Gal-9及びXAGE1発現は予後不良因子であることが判明した。そして、上記のグループa、e、gを予後の良いグループ（クラスターA; n=76）に、グループb、c、d、f、hを予後の悪いグループ（クラスターB; n=44）に区分できることも分かった（図８Ｂ）。これらに基づいて、PD-L1のtotal score（第1スコア）、Gal-9のtotal score（第2スコア）、XAGE1の発現の有無（第3データ）を説明変数とする判別解析を行い、予後の良いグループと悪いグループに区分する演算式を求めた。判別解析は、IBM SPSS Statistics 19 for Windows (IBM, New York, NY)を用いて行った。結果、以下の判別関数が得られた。 Example 1
Based on the above findings, an attempt was made to create an arithmetic expression that enables the estimation of prognosis using tumor tissue samples obtained from a plurality of lung cancer patients.
In the experiment, 120 cases of lung adenocarcinoma specimens which were positively determined for the expression of XAGE, PD-L1, Gal-9 and T cell infiltration in Test Example 5 were used. The 120 samples were divided into the following eight groups:
a. PD-L1 positive, Gal-9 negative, XAGE negative (n = 21)
b. PD-L1 positive, Gal-9 negative, XAGE positive (n = 8)
c. PD-L1 negative, Gal-9 positive, XAGE negative (n = 11)
d. PD-L1 negative, Gal-9 positive, XAGE positive (n = 2)
e. PD-L1 positive, Gal-9 positive, XAGE negative (n = 19)
f. PD-L1 positive, Gal-9 positive, XAGE positive (n = 5)
g. PD-L1 negative, Gal-9 negative, XAGE negative (n = 36)
h. PD-L1 negative, Gal-9 negative, XAGE positive (n = 18)
FIG. 8A shows the relationship between each group and prognosis. It was found that the prognosis can be determined by combining the expression of the three molecules, which are factors on the tumor side. That is, it was revealed that the expression of PD-L1 was a favorable prognosis, and the expression of Gal-9 and XAGE1 was a poor prognosis factor. The above groups a, e, and g are classified into groups with good prognosis (cluster A; n = 76), and groups b, c, d, f, and h into groups with poor prognosis (cluster B; n = 44). It was also found that it could be done (FIG. 8B). Based on these, discriminant analysis was performed using PD-L1 total score (first score), Gal-9 total score (second score), and the presence or absence of XAGE1 expression (third data) as the explanatory variables, and the prognosis was determined. An arithmetic expression to divide into good and bad groups was obtained. Discriminant analysis was performed using IBM SPSS Statistics 19 for Windows (IBM, New York, NY). As a result, the following discriminant function was obtained.

更に、腫瘍側の因子である上記３つの分子発現に加えて、宿主因子であるT細胞浸潤の多寡も組み合わせて、予後の推定を可能とする演算式の作成を試みた。T細胞浸潤の割合は、CD4陽性細胞の%とCD8陽性細胞の%の和により求めた。
120例の検体は、i)クラスターAに含まれかつT細胞浸潤陽性(n=52)、ii)クラスターAに含まれかつT細胞浸潤陰性(n=24)、iii)クラスターBに含まれかつT細胞浸潤陽性(n=32)、iv)クラスターBに含まれかつT細胞浸潤陰性(n=12)の4つのグループに分類された。各グループと予後との関係を図８Ｄに示す。そして、上記のグループi)を予後の良いグループ（クラスターC; n=52）に、グループii)、iii)、iv)を予後の悪いグループ（クラスターD; n=68）に更に区分できることも分かった（図８Ｅ）。これらに基づいて、PD-L1のtotal score（第1スコア）、Gal-9のtotal score（第2スコア）、XAGE1の発現の有無（第3データ）、T細胞浸潤の割合（第4スコア）を説明変数とする判別解析を行い、予後の良いグループと悪いグループに区分する演算式を求めた。判別解析は、IBM SPSS Statistics 19 for Windows (IBM, New York, NY)を用いて行った。結果、更に明確に予後を判定できる以下の判別関数が得られた。Furthermore, in addition to the expression of the above three molecules, which are factors on the tumor side, the amount of T cell infiltration, which is a host factor, was also combined, and an attempt was made to create an arithmetic expression capable of estimating prognosis. The percentage of T cell infiltration was determined by the sum of% of CD4 positive cells and% of CD8 positive cells.
120 samples were i) included in cluster A and positive for T cell infiltration (n = 52), ii) included in cluster A and negative for T cell infiltration (n = 24), iii) included in cluster B and T cells were positively infiltrated (n = 32), and iv) were classified into four groups included in cluster B and negative for T cell infiltration (n = 12). FIG. 8D shows the relationship between each group and prognosis. It can also be seen that the above group i) can be further divided into groups with good prognosis (cluster C; n = 52) and groups ii), iii) and iv) into groups with poor prognosis (cluster D; n = 68). (FIG. 8E). Based on these, PD-L1 total score (first score), Gal-9 total score (second score), presence / absence of XAGE1 expression (third data), percentage of T cell infiltration (fourth score) Was used as an explanatory variable, and an arithmetic expression for classifying into a good prognosis group and a poor prognosis group was obtained. Discriminant analysis was performed using IBM SPSS Statistics 19 for Windows (IBM, New York, NY). As a result, the following discriminant function that can more clearly determine the prognosis was obtained.

上記のクラスター分別における各説明変数の標準化係数（standardized coefficient value）を表５に、また種々の因子による単変量及び多変量Cox回帰分析の結果を表６に示す。   Table 5 shows standardized coefficient values of each explanatory variable in the above-described cluster classification, and Table 6 shows the results of univariate and multivariate Cox regression analysis by various factors.

表５に示す係数は免疫因子の重み付けを示している。従ってPD-L1を指標とした時にCluster C, Dにおいて、T細胞浸潤は約2倍予後良好、Gal-9は約1.5倍予後不良、XAGE1発現は約2倍予後不良であることが分かる。   The coefficients shown in Table 5 indicate the weighting of the immune factors. Therefore, it can be seen that when PD-L1 is used as an index, T cell infiltration is about 2 times better in Cluster C and D, Gal-9 is about 1.5 times worse in prognosis, and XAGE1 expression is about 2 times worse in prognosis.

実施例２
<試験方法>
実施例１で用いた予後情報と臨床情報が利用可能な１２０名の肺腺癌患者中の病期分類ＩＡ期に分類される５２名の早期肺腺癌患者より得られた腫瘍組織検体を用いて、実施例１と同様の方法により、早期癌の場合においても実施例１と同様に、ＰＤ−Ｌ１の発現、Ｇａｌ−９の発現、ＸＡＧＥ１の発現及びＴ細胞浸潤を識別因子として用い、予後の推定ができるかどうかの確認を行った。
実施例１で用いたのと同じ分類のクラスターと予後との関係を図９に示す。
<結果>
病期分類ＩＡ期の肺癌の場合にも、実施例１と同様のクラスター分類が可能であり、その予後が良い場合と不良の場合に区分できることが判明した。
この結果は、本発明の予後推定方法を用いることにより、従来識別が困難とされていた病期分類ＩＡ期の早期癌患者中の予後不良の患者群の識別ができることを示している。
さらに、本発明の予後推定方法は、術後追加治療が行われない病期分類ＩＡ期の肺癌患者中の予後不良患者群に対し、追加治療や従来不可能だった集学的治療の機会を提供するためのコンパニオン診断方法として用いることができることを示している。 Example 2
<Test method>
A tumor tissue sample obtained from 52 early lung adenocarcinoma patients classified as stage IA in 120 lung adenocarcinoma patients for which prognosis information and clinical information used in Example 1 were available was used. In the same manner as in Example 1, PD-L1 expression, Gal-9 expression, XAGE1 expression and T cell infiltration were used as discriminants in the same manner as in Example 1 in the same manner as in Example 1, and the prognosis was evaluated. It was confirmed whether or not estimation was possible.
The relationship between clusters of the same classification as used in Example 1 and prognosis is shown in FIG.
<Result>
In the case of stage IA lung cancer, the same cluster classification as in Example 1 was possible, and it was found that the prognosis could be classified into cases with good prognosis and cases with poor prognosis.
These results indicate that the use of the prognostic estimation method of the present invention enables the identification of a group of patients with poor prognosis among early stage cancer patients in stage IA stage, which had previously been difficult to identify.
Furthermore, the prognosis estimating method of the present invention provides an opportunity for additional treatment or multidisciplinary treatment that was previously impossible for a group of patients with poor prognosis in stage IA lung cancer patients without additional postoperative treatment. It shows that it can be used as a companion diagnostic method for providing.

実施例３
<試験方法>
実施例１で用いた１２０名の予後情報と臨床情報が利用可能な肺腺癌患者より得られた腫瘍組織検体を用いて、肺癌の術後の実施例１で求めた判別関数［数５］を用いて予後推定を行い、実際にその予後を追跡した結果を、全員(Pathological Stage Ｉ-ＩＩＩＡ)、病期分類Ｉ期に分類される６２名の早期肺腺癌患者(Pathological Stage Ｉ)及び局所進行期の肺腺癌患者(Pathological Stage ＩＩ-ＩＩＩＡ)の病期の患者群に分けて、図１０に示す。さらに、当該１２０名の肺癌患者を対象に、実施例１で求めた判別関数［数６］を用いて予後推定を行い、実際にその予後を追跡した結果を図１１に示す。なお、術後の追跡期間は、肺癌術後の再発無しの判断を行う目安の期間として一般に使用されている５年間とした。
<結果>
図１０から明らかなように、［数５］を用いることにより、いずれの病期の肺腺癌患者においても、その予後推定が可能であることが確認された。
また、同じ患者群を解析した図１０と図１１の患者群の生存率を比較すると、図１０の予後良好群(Cluster A)では生存率が55.8%であるのに対し図１１の予後良好群(Cluster C)では生存率が62.0%と高くなっていることから、免疫因子として宿主側因子を加えた［数６］を用いることにより、より精度の高い予後推定が可能であることが確認された。
これらの結果は、本発明の［数1］に基づき求めた演算式を用いる予後推定方法により、病期分類ＩＩ-ＩＩＩＡの肺腺癌患者はもとより、従来識別が困難とされていた病期分類Ｉ期の早期癌患者中の予後不良の患者を識別ができることを示している。さらに、これらの結果は、［数３］に基づき求めた演算式を用いる予後推定方法により、［数1］に基づき求めた演算式を用いるよりもさらに精度よく肺腺癌の予後推定ができることを示している。 Example 3
<Test method>
Discriminant function obtained in Example 1 after surgery for lung cancer using a tumor tissue sample obtained from a lung adenocarcinoma patient for which 120 prognostic information and clinical information are available in Example 1 [Equation 5] The results of estimating the prognosis by using the results of the actual prognosis, all (Pathological Stage I-IIIA), 62 early lung adenocarcinoma patients (Pathological Stage I) classified into stage I stage and FIG. 10 shows a group of patients at the stage of locally advanced lung adenocarcinoma (Pathological Stage II-IIIA). Further, the results of estimating the prognosis of the 120 lung cancer patients using the discriminant function [Equation 6] obtained in Example 1 and actually tracking the prognosis are shown in FIG. The postoperative follow-up period was set to 5 years, which is generally used as a reference period for judging the absence of recurrence after lung cancer operation.
<Result>
As is clear from FIG. 10, it was confirmed that the prognosis can be estimated for lung adenocarcinoma patients of any stage by using [Equation 5].
In addition, comparing the survival rates of the patient groups of FIG. 10 and FIG. 11 in which the same patient group was analyzed, the survival rate was 55.8% in the favorable prognosis group (Cluster A) of FIG. (Cluster C) has a high survival rate of 62.0%, confirming that it is possible to estimate the prognosis with higher accuracy by using [Equation 6] to which host-side factors are added as immune factors. Was.
These results are based on the prognosis estimation method using the arithmetic expression obtained based on [Equation 1] of the present invention, which is used for lung adenocarcinoma patients of stage classification II-IIIA, as well as for stage classification previously difficult to identify. This shows that patients with poor prognosis among early stage I cancer patients can be identified. Furthermore, these results show that the prognosis estimation method using the arithmetic expression calculated based on [Equation 3] can more accurately estimate the prognosis of lung adenocarcinoma than using the arithmetic expression calculated based on [Equation 1]. Is shown.

実施例４
<試験方法>
PD-L1の免疫染色用抗体として、モノクローナルRabbit anti-human PD-L1抗体(clone SP-142 Spring Bioscience Corporation, Pleasanton, CA）を１００倍希釈して用い、T細胞の免疫染色用抗体として、モノクローナル Rabbit anti-human CD3抗体(clone:SP-7, Nichirei Biosciences Inc., Tokyo, Japan)を１００倍希釈して用い、Cut-offを使用せず、T細胞の浸潤率をそのまま第４スコアとして使用した以外は、実施例1と同じ方法で、実施例１で用いた肺腺癌患者群のデータを収集した大学病院とは異なる大学病院において予後情報と臨床情報が利用可能な病期分類Ｉの肺腺癌患者より得られた腫瘍組織検体について、実施例１で求めた［数５］を用いて予後推定を行う検証コホート研究を実施し、術後の生存率(Overall survival)及び無病生存率(Disease-free survival)を追跡した。結果を図１２に示す。なお、近年、全国がん（成人病）センター協議会が公表している全がん協生存率として、通常の肺癌治癒の目安として用いられる５年生存率に加えて１０年生存率が公表されるようになり、それによると早期肺腺癌であっても術後５年経過後においても原病死があることが明らかになってきたので、本発明の予後推定方法の長期にわたる予後予測の有効性を確認するために本研究では追跡期間として術後１０年間を設定した。
<結果>
図１２から明らかなように、実施例３の場合と同様に、他施設の肺腺癌患者群においても、［数５］を用いることにより、その予後推定が可能であることが確認された。また、予後良好と判定された早期肺癌患者では、術後５年以降１０年を経過しても全員が無病状態にあることが確認された。
この結果は、本発明の予後推定方法が汎用性を有していることを示している。さらに、本発明の予後推定方法は、早期肺腺癌患者の長期にわたる再発率の予測方法として極めて有用であることが明らかになった。 Example 4
<Test method>
As an antibody for PD-L1 immunostaining, monoclonal Rabbit anti-human PD-L1 antibody (clone SP-142 Spring Bioscience Corporation, Pleasanton, Calif.) Was used by diluting it 100-fold and used as an antibody for T cell immunostaining. Use Rabbit anti-human CD3 antibody (clone: SP-7, Nichirei Biosciences Inc., Tokyo, Japan) diluted 100-fold, and use the infiltration rate of T cells as the fourth score without using Cut-off Prognostic information and clinical information are available in a university hospital different from the university hospital that collected the lung adenocarcinoma patient group data used in Example 1 in the same manner as in Example 1 except that For a tumor tissue sample obtained from a lung adenocarcinoma patient, a validation cohort study was performed to estimate prognosis using [Equation 5] obtained in Example 1, and the postoperative survival rate (Overall survival) and disease-free survival rate (Disease-free survival) was followed. The result is shown in FIG. In recent years, the 10-year survival rate has been published in addition to the 5-year survival rate, which is used as a standard for the cure of lung cancer, as the overall cancer cooperative survival rate published by the National Cancer Center for Adults (Adults Diseases) Council. It has become clear that even in the case of early lung adenocarcinoma, even after 5 years from the operation, there is a primary mortality, so that the prognosis estimation method of the present invention is effective for long-term prognosis prediction. In this study, a follow-up period of 10 years after surgery was set to confirm sex.
<Result>
As is clear from FIG. 12, as in the case of Example 3, it was confirmed that the prognosis of the lung adenocarcinoma patient group at another facility can be estimated by using [Equation 5]. In addition, it was confirmed that all of the early lung cancer patients determined to have a favorable prognosis were in a disease-free state even after 10 years from 5 years after the operation.
This result indicates that the prognosis estimation method of the present invention has versatility. Furthermore, the prognostic estimation method of the present invention was found to be extremely useful as a method for predicting the long-term recurrence rate of early lung adenocarcinoma patients.

実施例３及び４の結果は、本発明の予後推定方法が、病期や実施施設の如何にかかわらず肺癌患者に適用できることを示しているだけでなく、通常は術後に追加治療が行われない病期分類Ｉ期の肺癌患者中の予後不良患者の判別にもきわめて有用であり、当該予後不良患者の術後の治療方法や再発の早期発見に必要な検査頻度の設定等、術後の治療方針の決定にも有用なツールとなることを示している。さらに、本発明の予後推定方法は、早期肺腺癌患者中の予後不良患者を確実に判別できるので、従来は行われていなかった早期肺癌患者中の予後不良と判別された患者に対し予後改善に必要な追加治療の実施や、従来不可能だった集学的治療或いは個別化医療の適用機会を提供し、その生存率を向上させるためのコンパニオン診断方法としても有用であることを示している。   The results of Examples 3 and 4 not only indicate that the prognosis estimation method of the present invention can be applied to lung cancer patients regardless of the stage or facility, but also that additional treatment is usually performed after surgery. It is also very useful for discriminating patients with poor prognosis in patients with stage I lung cancer that do not have a staging classification, such as setting post-operative treatment methods for such patients with poor prognosis and setting the examination frequency necessary for early detection of recurrence. It is a useful tool for deciding treatment strategies. Furthermore, since the prognosis estimation method of the present invention can reliably discriminate patients with poor prognosis in patients with early lung adenocarcinoma, the prognosis of patients with early prognosis that had not been conventionally determined as poor prognosis in patients with early lung cancer has been improved. To provide additional treatments necessary for the treatment and to apply multidisciplinary treatments or personalized medicines that were not possible before, indicating that it is also useful as a companion diagnostic method to improve the survival rate .

本発明によれば、肺腺癌の予後の判定が可能となるシステムが提供される。実臨床においては、臨床病期及び病理学的病期によって治療法が選択されるが、それらの病期とは別に免疫因子を指標とした予後判定システムを用いることで、臨床的な早期がんであっても、積極的な集学的治療が必要な患者を選択できる。また、本発明は、気管支鏡検体やＣＴガイド下肺生検による検体といった微小な腫瘍組織を検討するだけで予後を判定できる。従って、病期の全身検索ができないような患者において、微小な臨床検体を採取することで予後判定が可能であり、その後の治療方針の決定に貢献することができる。   According to the present invention, there is provided a system capable of determining the prognosis of lung adenocarcinoma. In the actual clinical setting, treatment is selected depending on the clinical stage and pathological stage, but by using a prognostic system using immune factors as an index separately from those stages, clinical early cancer Even so, patients who need active multidisciplinary treatment can be selected. In addition, the present invention can determine the prognosis only by examining a small tumor tissue such as a bronchoscope specimen or a specimen obtained by CT-guided lung biopsy. Therefore, prognosis can be determined by collecting a small clinical specimen in a patient whose whole body cannot be searched for the disease stage, which can contribute to the determination of a subsequent treatment policy.

本出願は、日本で出願された特願２０１５−０９６０１３（出願日：２０１５年５月８日）、特願２０１５−２１２３９３（出願日：２０１５年１０月２８日）、及び特願２０１６−０５２６４４（出願日：２０１６年３月１６日）を基礎としており、その内容は本明細書に全て包含されるものである。   The present application has been filed in Japanese Patent Application No. 2015-096013 (filing date: May 8, 2015), Japanese Patent Application No. 2015-212393 (filing date: October 28, 2015), and Japanese Patent Application No. 2006-052644 (Japanese Patent Application). (Filing date: March 16, 2016), the contents of which are incorporated in full herein.

Claims (7)

複数の肺腺癌患者の各検体について、
免疫染色法によりＰＤ−Ｌ１、Ｇａｌ−９およびＸＡＧＥ１の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、
第１スコアと第２スコアと第３データを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法。For each specimen of multiple lung adenocarcinoma patients,
The expression of PD-L1, Gal-9 and XAGE1 was detected by immunostaining,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Lung adenocarcinoma patients are divided into a good prognosis group and a poor prognosis group based on the second data obtained by binarizing the second score, and the third data binarized based on the presence or absence of XAGE1,
A method for creating a prognosis calculation formula, wherein a calculation formula for classifying into a good prognosis group and a bad prognosis group is obtained by multivariate analysis using the first score, the second score, and the third data as explanatory variables. 複数の肺腺癌患者の各検体について、
免疫染色法によりＰＤ−Ｌ１、Ｇａｌ−９、ＸＡＧＥ１、ＣＤ４およびＣＤ８の発現を検出し、
腫瘍組織における腫瘍のＰＤ−Ｌ１の分布範囲と発現強度に基づき特定される第１スコアを二値化した第１データと、腫瘍組織における腫瘍のＧａｌ−９の分布範囲と発現強度に基づき特定される第２スコアを二値化した第２データと、ＸＡＧＥ１の発現の有無に基づき二値化した第３データと、Ｔ細胞の浸潤の割合に基づき特定される第４スコアを二値化した第４データに基づき肺腺癌患者を予後の良いグループと悪いグループに区分し、第１スコアと第２スコアと第３データと第４スコアとを説明変数とする多変量解析により予後の良いグループと悪いグループに区分する演算式を求めることを特徴とする予後演算式作成方法。For each specimen of multiple lung adenocarcinoma patients,
Detecting the expression of PD-L1, Gal-9, XAGE1, CD4 and CD8 by immunostaining,
First data obtained by binarizing a first score specified based on the distribution range and expression intensity of PD-L1 of the tumor in the tumor tissue, and specified based on the distribution range and expression intensity of Gal-9 of the tumor in the tumor tissue Second data obtained by binarizing the second score, third data obtained by binarizing based on the presence or absence of XAGE1, and fourth data obtained by binarizing the fourth score specified based on the percentage of infiltration of T cells. Based on the 4 data, the lung adenocarcinoma patients were classified into a good prognosis group and a bad prognosis group, and a group with a good prognosis was determined by multivariate analysis using the first score, the second score, the third data, and the fourth score as explanatory variables. A method for creating a prognostic formula, wherein a formula for classifying a bad group is obtained. 請求項１に記載の方法により求められた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データを代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法。   A lung adenocarcinoma for which the prognosis of the subject is estimated by calculation by substituting the first and second scores and the third data detected from the subject's sample into the calculation formula obtained by the method according to claim 1. Prognosis estimation method. 請求項２に記載の方法により求められた演算式に対し、被験者の検体から検出される第１・第２スコアと第３データと第４スコアを代入して前記被験者の予後を演算により推定する肺腺癌の予後推定方法。   3. The prognosis of the subject is estimated by calculation by substituting the first and second scores, the third data, and the fourth score detected from the subject's sample into the calculation formula obtained by the method according to claim 2. Method for estimating prognosis of lung adenocarcinoma. 請求項３又は４に記載の肺腺癌の予後推定方法を用いることを特徴とする、予後不良な早期乃至局所進行期の肺癌患者を選別し、予後改善に必要な治療を実施するためのコンパニオン診断方法。   A companion for selecting a lung cancer patient with a poor prognosis in an early stage or a locally advanced stage, and performing a treatment necessary for improving the prognosis, characterized by using the method for estimating prognosis of lung adenocarcinoma according to claim 3 or 4. Diagnostic method. 請求項３又は４に記載の肺腺癌の予後推定方法を用いることを特徴とする、肺腺癌治療方法の効果判定方法。   A method for determining the effect of a method for treating lung adenocarcinoma, comprising using the method for estimating prognosis of lung adenocarcinoma according to claim 3 or 4. 被験者が早期肺癌患者であり、肺腺癌の予後推定方法が当該患者における術後の再発率を判定するものである、請求項３又は４に記載の肺腺癌の予後推定方法。   The method for estimating prognosis of lung adenocarcinoma according to claim 3 or 4, wherein the subject is an early lung cancer patient, and the method for estimating prognosis of lung adenocarcinoma is to determine a postoperative recurrence rate in the patient.
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