WO2016181912A1 - Procédé de création d'équation pour calculer le pronostic d'adénocarcinome du poumon en utilisant des facteurs immunitaires en tant qu'indices, et procédé de prédiction de pronostic associé - Google Patents

Procédé de création d'équation pour calculer le pronostic d'adénocarcinome du poumon en utilisant des facteurs immunitaires en tant qu'indices, et procédé de prédiction de pronostic associé Download PDF

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WO2016181912A1
WO2016181912A1 PCT/JP2016/063687 JP2016063687W WO2016181912A1 WO 2016181912 A1 WO2016181912 A1 WO 2016181912A1 JP 2016063687 W JP2016063687 W JP 2016063687W WO 2016181912 A1 WO2016181912 A1 WO 2016181912A1
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prognosis
data
expression
score
lung adenocarcinoma
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Japanese (ja)
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祥弘 大植
三喜男 岡
浩史 黒瀬
睿一 中山
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学校法人 川崎学園
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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
    • 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

Definitions

  • the present invention relates to a method for creating an arithmetic expression for predicting the prognosis of primary lung adenocarcinoma (hereinafter referred to as lung adenocarcinoma) and a method for estimating the prognosis of lung adenocarcinoma using the same. More specifically, a method for creating a prognostic calculation formula based on the relationship between the expression profile in tumor tissue and the prognosis of a plurality of factors associated with the prognosis of lung adenocarcinoma, and the calculation formula for lung adenocarcinoma using the calculation formula The present invention relates to a method for estimating prognosis.
  • XAGE1 as a cancer-related protein to be measured in Patent Document 2 and Non-Patent Document 2
  • Galectin-9 can be a prognostic factor for postoperative recurrence of lung cancer.
  • factors on the host side and prognosis only the relationship between various immune cell infiltration and prognosis was examined. However, a combination of a plurality of factors on the tumor side and the host side has not been studied.
  • 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 carrying out the method.
  • the present inventors tried to estimate the prognosis of lung adenocarcinoma by examining a plurality of immune factors in lung adenocarcinoma.
  • the present inventors have important PD-L1 (Programmed death-ligand 1) and Gal-9 (galectin-9) as tumor-side immunosuppressive factors in lung cancer, and in lung adenocarcinoma, It was found that the expression of XAGE1 having sex and the expression pattern of PD-L1 and Gal-9 molecules are associated with prognosis.
  • 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.
  • the present inventors have shown that prognosis is related to CD4 and CD8 T cell infiltration as a host factor, the degree of T cell infiltration, the presence or absence of XAGE1 expression, the intensity of PD-L1 expression, and the expression of Gal-9. It was found that the prognosis of lung adenocarcinoma patients can be more clearly distinguished by combining the intensities. Based on these findings, the present inventors have further investigated and have completed the present invention.
  • the present invention relates to the following.
  • Detecting the expression of PD-L1, Gal-9 and XAGE1 by immunostaining First data binarized from the first score specified based on the distribution range and expression intensity of tumor PD-L1 in the tumor tissue, and specified based on the distribution range and expression intensity of tumor Gal-9 in the tumor tissue
  • lung adenocarcinoma patients are divided into good prognosis group and bad prognosis group
  • a prognostic calculation formula creation method characterized in that an arithmetic expression for dividing 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.
  • a method for creating a prognostic formula, characterized in that an arithmetic formula for dividing into bad groups is obtained.
  • [3] Substituting the first and second scores detected from the subject's sample and the third data into the arithmetic expression obtained by the method described in [1] above, the prognosis of the subject is estimated by calculation.
  • Prognosis estimation method for lung adenocarcinoma [4] Substituting the first and second scores, the third data, and the fourth score detected from the specimen of the subject into the arithmetic expression obtained by the method described in [2] above, the prognosis of the subject is determined.
  • [5] A method for selecting an early lung cancer patient with a poor prognosis, characterized by using the prognosis estimation method for lung adenocarcinoma described in [3] or [4] above, and for performing 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] above.
  • a system capable of estimating prognosis by examining immune factors in lung adenocarcinoma.
  • treatment is selected according to the clinical stage and pathological stage, but by using a prognosis determination system using immune factors as an index separately from those stages, clinical early cancer Even so, patients who require aggressive multidisciplinary treatment can be selected.
  • patient selection is the basis for the development of new drug discovery and treatment methods.
  • the present invention is useful for elucidating the relationship between lung adenocarcinoma and immune factors, but weighting to immune factors is an important index for the development of new drugs such as cancer immunotherapy in lung adenocarcinoma.
  • the prognosis can be determined only by examining a minute tumor tissue such as a bronchoscopic specimen or a specimen obtained by CT-guided lung biopsy. Therefore, prognosis can be determined by collecting a minute clinical sample in a patient who cannot perform whole body search of the stage, and can contribute to the determination of the subsequent treatment policy.
  • the horizontal axis represents the time (month) after the operation, and the vertical axis represents the overall survival rate (%).
  • the horizontal axis represents the time after surgery (month)
  • the vertical axis represents the overall survival rate (%).
  • FIG. 1 Shows the derivation of prognostic function based on the expression of tumor side 3 factors (PD-L1, Gal-9, XAGE1) and host factor T cell invasion in early stage primary lung adenocarcinoma belonging to stage IA
  • the horizontal axis represents the time (month) after surgery, and the vertical axis represents the overall survival rate (%).
  • the horizontal axis represents the time (month) after surgery, and the vertical axis represents the overall survival rate (%).
  • “specimen” means a specimen derived from a plurality of lung adenocarcinoma patients used for creating an arithmetic expression and a specimen derived from a subject to be used for prognosis estimation.
  • the “specimen” is not particularly limited as long as it contains tumor tissue derived from lung adenocarcinoma, and includes excised specimens by surgery, bronchoscopic specimens, specimens by lung biopsy, and the like.
  • the “specimen” may be derived from any mammal (eg, human, mouse, rat, monkey, dog, cat, cow, horse, pig, sheep, goat, rabbit, hamster, etc.), more preferably human.
  • the form of the specimen may vary depending on the expression analysis means used.
  • a tissue section thinly sectioned with a microtome or the like, or a whole-mounted tissue or individual may be used as a specimen, but preferably a tissue section is used. It is done.
  • the tissue section may be a section in which frozen tissue is sectioned and embedded in paraffin or resin.
  • surgically excised specimens, bronchoscopic specimens and lung biopsy specimens may be directly subjected to flow cytometry analysis, mass cytometry analysis, genetic testing, etc. without being frozen or embedded in paraffin.
  • the “subject” is a lung adenocarcinoma patient whose prognosis is estimated.
  • the subject is usually an animal of the same species as the animal species from which the specimen used for the creation of the prognostic calculation formula according to the present invention is derived.
  • 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, etc., particularly preferably humans.
  • the prognosis of a cancer patient means prediction of the future state of cancer from a certain point of time, for example, at the time of cancer diagnosis or treatment (at the time of surgery, etc.).
  • the prognosis is good, and when the risk (probability) of cancer progression, recurrence, or metastasis is not high, the prognosis is good.
  • the prediction index is not specified, for example, overall survival ( ⁇ OS) is preferable.
  • the “total survival rate” generally means the proportion of the observed cases that can be confirmed to be alive after a specific period from the specific observation start time.
  • PD-L1 (Programmed death-ligand 1), whose expression is to be measured in the present invention, is encoded by the cluster of differentiation (274 (CD274) gene and is also known as CD274 or B7 homolog 1 (B7-H1). It is a transmembrane protein.
  • CD274 cluster of differentiation
  • B7-H1 B7 homolog 1
  • 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.
  • 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, and activated T cells. Various activities such as apoptosis induction, eosinophil migration, cancer cell apoptosis induction and cell adhesion have been reported.
  • Gal-9 is an immunosuppressive molecule on the tumor side 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.
  • XAGE1 (X Antigen Family, Member 1), whose expression is to be measured in the present invention, is a known cancer testis antigen encoded by the XAGE1 gene and also known as Cancer / Testis antigen 12.1 (CT12.1). . So far, five types of genes of XAGE1A-E have been identified, the related protein is GAGED2, and it is known that there are two types of isoforms, GAGED2a and GAGED2d. To date, four alternative splice variants of XAGE-1a, b, c, and d have been identified (Sato et al., Cancer Immunity, vol. 7, page 5 (2007)).
  • XAGE1 whose expression is to be measured 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.
  • the sequence of human GAGED2a is available as RefSeq No. NM_001097594.2
  • the sequence of human GAGED2d is available as RefSeq No. NM_001097596.2.
  • T cell means an immunocompetent cell as a host-side factor involved in tumor immunity, and means a CD4 positive T cell and a CD8 positive T cell.
  • CD4 positive T cells and CD8 positive T cells may be combined to form CD3 positive T cells.
  • 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 have been published in standard molecular biology textbooks such as Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). It is disclosed. Specifically, RNA isolation can be performed according to the manufacturer's instructions using a purification kit, buffer set, and protease obtained from a manufacturer such as Qiagen.
  • the method for measuring the expression level of the marker gene for 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 methods.
  • Microarray analysis methods 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.
  • the measurement means in the case of using protein as a detection target is not particularly limited, but preferably an immunostaining method can be used.
  • Specific means using the immunostaining method include immunohistochemical staining (IHC) method, 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 fragment generated by a Fab fragment or a Fab expression library. And a part of the antibody having antigen binding property.
  • Commercially available antibodies can be utilized for each of the factors of the present invention.
  • the antibody of each factor can be prepared by a method known per se using each factor or a part thereof as an antigen.
  • Immunohistochemical staining can be performed independently for each of the factors of the present invention.
  • the immunostaining of each of the factors of the present invention can be performed according to a method known per se (for example, revised 4th edition, Watanabe and Nakane Enzyme Antibody Method, published by Interdisciplinary Planning Co., Ltd., edited by 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.
  • Preferred embodiments include an immunostaining method using an enzyme antibody method from the viewpoint of immunostaining sensitivity, detection means, and the like.
  • the factor of the present invention is detected by an antibody labeled with an enzyme.
  • the labeling may be performed directly or indirectly using a secondary antibody.
  • improved methods such as the ABC method using biotin-streptavidin and the tyramide method (TSA method, Tyramide Signal Amplification) using tyramide (tyramide). realizable.
  • labeling enzyme peroxidase (HRP, horseradish peroxidase) and alkaline phosphatase (AP) are widely used, and can be suitably used in the present invention.
  • Antibody reagents and coloring 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 kit.
  • cell samples that have been deparaffinized or fixed as necessary are prepared, and pretreatment (for example, depigmentation or removal of biological dyes, blocking treatment) or antigen activation (warm bath, microwave treatment, autoclaving) as necessary.
  • pretreatment for example, depigmentation or removal of biological dyes, blocking treatment
  • antigen activation warm bath, microwave treatment, autoclaving
  • the primary antibody for example, 4 ° C. to room temperature for 0.5 to 24 hours.
  • the cell specimen is washed and reacted with the secondary antibody (for example, 4 ° C. to 4 ° C.).
  • the cell specimen is washed.
  • a coloring substrate such as diaminobenzidine (DAB) is added to cause color development.
  • DAB diaminobenzidine
  • counterstaining such as hematoxylin staining may be further performed.
  • the immunostained sample is imaged using an optical microscope equipped with a photographing device or a fluorescence microscope (in the case of fluorescence staining), 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, the two-dimensional data can be sufficiently analyzed. Image processing such as filtering may be performed before analysis.
  • Image analysis of immunostained cells can be performed using image analysis software mounted on the microscope used and 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 follow standard judgment criteria by a pathologist of immunohistochemical staining.
  • a cell suspension or cell suspension can be prepared from the collected lung adenocarcinoma tissue and used for analysis.
  • Cell suspensions or cell suspensions can be prepared using techniques well known to those skilled in the art. For example, cells are dispersed by mechanical disruption or treatment with proteases such as trypsin, collagenase, elastase, and pronase and hyaluronidase. Cell suspensions or cell suspensions can be obtained.
  • Flow cytometry analysis and mass cytometry analysis can be performed using a commercially available apparatus (for example, FACSCanto TM II flow cytometer, CyTOF2, etc.) according to the manufacturer's manual.
  • prognostic formula creation method for lung adenocarcinoma of the present invention.
  • One aspect of the prognostic formula 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 binarized from the first score specified based on the distribution range and expression intensity of tumor PD-L1 in the tumor tissue, and specified based on the distribution range and expression intensity of tumor Gal-9 in the tumor tissue Based on the second data binarized second score and the third data binarized based on the presence or absence of the expression of XAGE1, lung adenocarcinoma patients are divided into good prognosis group and bad prognosis group, A prognostic formula creation method (hereinafter referred to as the present invention) characterized in that an arithmetic formula for dividing into a good prognosis group and a bad prognosis
  • the number of the plurality of specimens is not particularly limited as long as the prognostic calculation formula according to the present invention can be created, but is, for example, 50 or more, preferably 100 or more.
  • the first score is specified based on the distribution range and expression intensity of tumor PD-L1 in the tumor tissue.
  • the method for specifying the first score is not particularly limited as long as it is performed in the same manner 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, the expression intensity (I: Intensity) of PD-L1 is evaluated based on the following criteria. I: Not detected 0: Weak 1: Moderate 2: Remarkable 3 Further, the proportion of cells having intensity I in all the nucleated cells in the specimen is determined, and the distribution range (D: Distribution) is defined as follows.
  • the product (D ⁇ I) of the expression intensity I and the distribution range D is calculated and used as the IHC score.
  • the IHC score is obtained for the cell membrane and the cytoplasm, respectively, and if at least one of the cell membrane or cytoplasm has an IHC score of 3 or more, the specimen is positive.
  • the second score is specified based on the distribution range and expression intensity of Gal-9 in the tumor tissue.
  • the method for specifying the second score is not particularly limited as long as it is performed in the same manner for each sample, but can be calculated by the following method as an example. (Calculation method) Based on the result 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 Further, the proportion of cells having intensity I in all the nucleated cells in the specimen is determined, and the distribution range (D: Distribution) is defined as follows.
  • the product (D ⁇ I) of the expression intensity I and the distribution range D is calculated and used as the IHC score.
  • the IHC score is obtained for the cell membrane and the cytoplasm, respectively, and if at least one of the cell membrane or cytoplasm has an IHC score of 3 or more, the specimen is positive.
  • the third data is obtained by binarizing as positive or negative based on the presence or absence of expression of XAGE1.
  • the method for acquiring the third data is not particularly limited as long as it is performed in the same manner for each sample, but can be calculated by the following method as an example. (Calculation method) Based on the result of detecting the expression of XAGE1 according to the method described above, the presence or absence of XAGE1-positive cells is counted. Among all the nucleated cells analyzed, the binarized as positive when XAGE1 positive cells are 1% or more and negative when less than 1% is defined as third data.
  • each lung adenocarcinoma patient is divided into a good prognosis group and a bad prognosis group, respectively.
  • Prognosis can be determined using overall survival. Specifically, as described in the examples below, for example, (A1) A specimen in which the first data is positive, the second data is negative, and the third data is negative, (A2) a specimen in which the first data is positive, the second data is positive, and the third data is negative; and (A3) Classifying samples with negative first data, negative second data and negative third data into groups with good prognosis; (B1) A specimen in which the first data is positive, the second data is negative, and the third data is positive, (B2) a specimen in which the first data is negative, the second data is positive, and the third data is negative; (B3) a specimen in which the first data is negative, the second data is positive, and the third data is positive; (B4) a specimen in which the first data is positive, the second data is positive, and the
  • the first score, the second score, and the third data are used as explanatory variables, and by performing multivariate analysis, an arithmetic expression for dividing into a good prognosis group and a bad prognosis group, that is, a prognosis arithmetic expression (hereinafter, simply “ It may be referred to as an “arithmetic expression”.)
  • the method for multivariate analysis is not particularly limited, but discriminant analysis is preferably used. Discriminant analysis is a method of obtaining sample data extracted from two or more populations and examining which population the sample data belongs to when there is unknown sample data belonging to which population.
  • multivariate data consisting of numerical values related to PD-L1, Gal-9 and XAGE1 expression are used as explanatory variables.
  • discriminant analysis techniques are known, and the most typical discriminant analysis includes discrimination by a linear discriminant function, but is not limited thereto. Moreover, it can carry out using commercially available statistical analysis software.
  • the arithmetic expression thus obtained can be expressed as follows.
  • a 0 , a 1 , a 2 , and a 3 also vary depending on the number of specimens of lung adenocarcinoma patients used to create the arithmetic expression.
  • the prognostic calculation formula created by the method of the present invention the following calculation formulas created in examples described later can be cited.
  • Another aspect of the prognostic formula 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 binarized from the first score specified based on the distribution range and expression intensity of tumor PD-L1 in the tumor tissue, and specified based on the distribution range and expression intensity of tumor Gal-9 in the tumor tissue The second data binarized from the second score, the third data binarized based on the presence or absence of expression of XAGE1, and the fourth score specified based on the rate of infiltration of T cells Based on the 4 data, the lung adenocarcinoma patients are divided into a good prognosis group and a bad prognosis group, and a good prognosis group is obtained by multivariate analysis using the first score, the second score, the third data, and the fourth score as explanatory variables.
  • a prognostic formula creation method characterized by obtaining formulas that are classified into bad groups (hereinafter also referred to as prognostic formula creation method 2 of the present invention).
  • the first score and the first data binarized from it, the second score and the second data binarized from it, and the third data are the same as in the above “prognostic formula creation method 1 of the present invention”. get. Further, the number of specimens to be used and preferred embodiments thereof are also the same as in the above “prognostic formula creation method 1 of the present invention”.
  • the fourth score is identified based on the rate of T cell infiltration into the tumor tissue.
  • the method for identifying the fourth score is not particularly limited as long as it is performed in the same manner 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 respectively determined, and the sum of these ratios is defined as the ratio of T cell infiltration into the tumor tissue.
  • the expression intensity of CD4 is classified as follows for each cell. Expression intensity; not detected 0; weak 1: moderate 2: remarkable 3 Cells having an expression intensity of 2 or more are determined as CD4 positive cells.
  • the proportion of the CD4 positive cells in all nucleated cells is determined.
  • the expression intensity of CD8 is classified as follows for each cell. 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.
  • the proportion of the CD8 positive cells in all nucleated cells is determined. The sum of the ratio of CD4 positive cells and the ratio of CD8 positive cells obtained according to the above is defined as the fourth score.
  • the results are plotted with the number of specimens on the horizontal axis and the fourth score on the vertical axis, and approximated by a cubic function.
  • Fourth data obtained by binarizing the inflection point of the approximate curve as a cut-off value, using a sample with a positive cell frequency more than the cut-off value as a positive sample, and a sample with a positive cell frequency less than that as a negative sample
  • An example of the cutoff value is shown in an example (Table 4) described later.
  • each lung adenocarcinoma patient is divided into a good prognosis group and a bad prognosis group, respectively.
  • Prognosis can be determined using overall survival. Specifically, as described in the 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 specimen 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) Specimens 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 a group having a good prognosis.
  • an arithmetic expression for dividing a good prognosis group and a bad prognosis group that is, a prognosis arithmetic expression, Ask.
  • the method for multivariate analysis is not particularly limited, but discriminant analysis is preferably used.
  • multivariate data consisting of numerical values relating to PD-L1, Gal-9, and XAGE1 expression and the rate of T cell infiltration are used as explanatory variables.
  • discriminant analysis techniques are known, and the most typical discriminant analysis includes discrimination by a linear discriminant function, but is not limited thereto. Moreover, it can carry out using commercially available statistical analysis software.
  • the arithmetic expression thus obtained can be expressed as follows.
  • a 0 , a 1 , a 2 , a 3, and a 4 also vary depending on the number of specimens of lung adenocarcinoma patients used to create the arithmetic expression.
  • the prognostic calculation formula created by the method of the present invention the following calculation formulas created in examples described later can be cited.
  • the present invention provides a method for estimating the prognosis of lung adenocarcinoma using the arithmetic expression created by the above-described prognostic arithmetic expression creating method of the present invention.
  • the prognosis estimation method for lung adenocarcinoma of the present invention will be described in detail.
  • One aspect of the prognosis estimation method for lung adenocarcinoma of the present invention is as follows. The first and second scores and third data detected from the subject's sample are substituted into the arithmetic expression for the arithmetic expression obtained by using the prognostic calculation expression creating method 1 of the present invention to determine the prognosis of the subject.
  • a prognosis estimation method for lung adenocarcinoma estimated by calculation (hereinafter also referred to as prognosis estimation method 1 of the present invention).
  • 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 specimen of the subject is not particularly limited as long as it is the same as the method performed 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 previously obtained arithmetic expressions.
  • prognostic score 1 When the value obtained by substituting into the arithmetic expression (hereinafter also referred to as prognostic score 1) is larger than 0, it is estimated that the prognosis is good, and when it is smaller than 0, it is estimated that the prognosis is poor.
  • Another aspect of the prognostic formula creation method of the present invention is as follows. For the arithmetic expression obtained using the prognosis arithmetic expression creating method 2 of the present invention, the first and second scores, the third data, and the fourth score detected from the subject's sample are substituted into the arithmetic expression.
  • a prognosis estimation method for lung adenocarcinoma in which the prognosis of a subject is estimated by calculation (hereinafter also referred to as prognosis estimation method 2 of the present invention).
  • 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 specimen of the subject is not particularly limited as long as it is the same as the method performed 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 previously obtained arithmetic expressions.
  • prognostic score 2 When the value (hereinafter also referred to as prognostic score 2) obtained by substituting into the arithmetic expression 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.
  • prognostic score 2 the infiltration rate of CD3 positive T cells combined with CD4 positive T cells and CD8 positive T cells may be used.
  • the prognostic estimation method of lung adenocarcinoma of the present invention is a local progression including an early stage cancer determined to be stage IA in the conventional stage classification or an early stage cancer determined to be stage IA, IB, IIA, IIB, or IIIA. It is possible to identify patients with a poor postoperative prognosis from lung cancer patients in the stage (hereinafter referred to as “locally advanced stage”). Of course, the prognostic estimation method of lung adenocarcinoma of the present invention can also be used to estimate the prognosis of patients with stage IIIB or stage IV lung cancer.
  • the prognostic estimation method of the present invention is capable of discriminating a poor prognosis patient from a lung adenocarcinoma patient in an early stage or a locally advanced stage and determining a recurrence rate of an early lung adenocarcinoma patient, which is impossible with conventional staging.
  • the prognosis estimation method of the present invention can provide a place for a new clinical trial for a group of patients with poor prognosis whose stage is a locally advanced stage, in particular, the treatment method can be applied. It is useful as a companion diagnostic method for selecting patients with poor prognosis of lung adenocarcinoma.
  • the prognostic estimation method of lung adenocarcinoma of the present invention can estimate the poor prognosis of lung cancer that cannot be predicted from the conventional staging classification. Therefore, the treatment method determined based on the conventional staging classification Can be used as a method for determining whether or not the application is effective for the lung cancer, that is, as a method for determining the effect of a treatment method for lung cancer patients.
  • the lesion site of interest may be the primary lesion or the metastatic site, regardless of the method, and if the disease recurs after surgery
  • the discriminating factor used for obtaining the discriminant function it can be applied to a therapeutic method that affects the immunosuppressive pathway, especially PD-L1 expression
  • Gal- 9 can be advantageously used to determine the effect of a treatment method that affects one or more of the expression of 9, the expression of XAGE1, and the T cell infiltration.
  • the prognostic estimation method of the present invention is useful as a method for predicting the recurrence rate of lung cancer after surgery, and is particularly useful as a method for predicting the recurrence rate of early lung adenocarcinoma. It is useful as a method of predicting the long-term recurrence rate exceeding year.
  • Test example 1 The expression of immunosuppressive molecules PD-L1 and Gal-9 in lung cancer cell lines was examined. The materials and methods used are shown below.
  • 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:
  • lung cancer tissues used Lung adenocarcinoma (A549, PC-9, HLC-1, II-18, OU-LC-ON, OU-LC-SK) Lung squamous cell carcinoma (Sq-1, EBC-1, RERF-LC-AI) (Method) Analysis of PD-L1 and Gal-9 expression in lung cancer cell lines by flow cytometry: Cell surface 1.
  • Lung cancer cell line is cultured in a flask with 10% FCS / RPMI for several days. 2. Remove cultured cells from flask with EDTA solution and wash with FACS buffer (1% FCS / 0.1% azide / PBS). 3. Use 1x10 5 cells, use PD-L1 (clone 29E.2A3), Gal-9 (clone 9M1-3) and their respective 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 is cultured in a flask with 10% FCS / RPMI for several days. 2.
  • Test example 2 Further analysis was performed on the expression of PD-L1 and Gal-9 molecules in lung cancer cell lines.
  • Various lung adenocarcinoma cell lines (A549, PC-9, HLC-1, II-18, OU-LC-ON, OU-LC-SK) were treated with IFN- ⁇ , and the cell surface and cells before and after treatment Changes in the expression of PD-L1 and Gal-9 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
  • FIG. 2B shows changes in expression by IFN- ⁇ treatment in the tested cell lines.
  • PD-L1 expression on the cell surface was increased by IFN- ⁇ treatment, but Gal-9 expression was not changed.
  • the concentration of Gal-9 in the culture supernatant after the treatment with the molecular target drug was measured using the lung cancer cell line PC-9 which was successful with the molecular target drug and OU-LC-SK which was not successful.
  • the molecular target drug treatment was performed by culturing the cells with 10 nM Afatinib for 96 hours. As a result, it was revealed that Gal-9 was released into the supernatant when the tumor was disintegrated with the molecular target drug (FIG. 2C).
  • a molecularly targeted drug releases Gal-9, a substance that strongly suppresses immunity, even if the tumor partially collapses. Therefore, this means that a combination therapy of a molecular target drug and an antibody drug targeting Gal-9 and Tim-3 is important.
  • Test example 3 Expression analysis of immunosuppressive molecules (PD-1, TIM-3, BTLA, LAG-3) on the surface of T cells (TIL) that infiltrate the cancer site was performed. Analysis was performed by flow cytometry in the same manner as described in Test Example 1 using the following antibodies.
  • 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
  • PD-L1 receptor PD-1 and Gal-9 receptor Tim-3 are highly expressed (FIG. 3A-B). It was shown that the PD-1 / PD-L1 and Tim-3 / Gal-9 pathways are important as immunosuppressive molecular pathways in lung adenocarcinoma.
  • Test example 4 The results of Test Example 1-3 showed that the PD-1 / PD-L1 and Tim-3 / Gal-9 pathways are important as immunosuppressive molecular pathways in lung adenocarcinoma. Therefore, we investigated the relationship between the presence of these molecules and the prognosis, including the XAGE1 antigen, which is known to have a prognosis related to immune response in lung adenocarcinoma.
  • 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 inventors) Immunostaining (XAGE1): 1. Deparaffinize 5 ⁇ m paraffin sections. 2. Antigen activation is performed in a microwave using 10 mmol / L citrate buffer (pH 6.0) using a pressure cooker for 10 minutes. 3. Inactivate endogenous peroxidase in 0.3% H 2 O 2 for 5 minutes. 4.
  • XAGE1 If the number of positive cells in the tissue is 1% or more, it is determined as positive 1; PD-L1, Gal-9: A positive score of 3 or more for each of the cell membrane score and cytoplasm score obtained by immunostaining. If at least one of the cell membrane or cytoplasm is positive, the specimen is determined to be positive. analysis: For the survival analysis, IBM SPSS Statistics 19 for Windows (IBM, New York, NY) was used. (result) FIG. 4 shows immunostained images with anti-PD-L1 antibody, anti-Gal-9 antibody or anti-XAGE1 antibody. PD-L1, Gal-9 and XAGE1 expression was observed in lung cancer tissues. The results of scoring and positive determination for PD-L1 and Gal-9 are shown in Tables 1 and 2 below.
  • FIG. 1 the result obtained about the relationship between the expression of PD-L1, Gal-9, and XAGE1 and prognosis is shown in FIG.
  • PD-L1 and Gal-9 are important as immunosuppressive factors on the tumor side.
  • lung adenocarcinoma the immunogenic XAGE1 expression and the expression pattern of PD-L1 and Gal-9 molecules are prognostic. I found it involved.
  • Test Example 5 Since the relationship between the tumor side factor and the prognosis was clarified by Test Example 4, the host side factor and the prognosis were examined next. That is, the relationship between the number of immunocompetent cells infiltrating and prognosis in lung cancer was examined. Furthermore, we investigated the relationship between prognosis by combining XAGE1 expression, which is a factor on the tumor side, which is strongly associated with prognosis, and the expression patterns of PD-L1 and Gal-9 molecules. (Materials and methods) Lung cancer tissue used: We used 120 cases of lung cancer tissue with prognostic and clinical information available. Immunostaining: Immunostaining of XAGE1, PD-L1, and Gal-9 was performed as described above in Test Example 4.
  • Immunostaining of host-side factors was performed in the same manner as PD-L1 and Gal-9 immunostaining using the following antibodies.
  • CD4 and CD8 expression was measured as% by the Pathology Laboratory using the Scan scope system, and staining intensity 2+ or higher was determined as positive cells.
  • Positive test The positive determination of XAGE1, PD-L1, and Gal-9 expression was performed in the same manner as in Test Example 2.
  • the positive determination of CD4 and CD8 was measured by the Pathology Laboratory using the Scan scope system as%, staining intensity 2+ or higher was regarded as positive cells, and data from 120 cases of lung adenocarcinoma were used. An approximate curve (cubic function) of 120 cases of data was created, and the positive cell frequency above the inflection point was defined as a positive sample, and the positive cell frequency less than that was defined as a negative sample.
  • analysis For the survival analysis, IBM SPSS Statistics 19 for Windows (IBM, New York, NY) was used.
  • Example 1 Based on the above findings, an attempt was made to create an arithmetic expression that enables estimation of prognosis using tumor tissue specimens obtained from a plurality of lung cancer patients.
  • 120 lung adenocarcinoma specimens that were positively tested for XAGE, PD-L1, Gal-9 expression and T cell infiltration in Test Example 5 were used.
  • Table 5 shows the standardized coefficient of each explanatory variable in the above cluster fractionation
  • Table 6 shows the results of univariate and multivariate Cox regression analysis with various factors.
  • the coefficients shown in Table 5 indicate the weighting of immune factors. Therefore, when PD-L1 is used as an index, in Cluster C and D, T cell infiltration is about 2 times better in prognosis, Gal-9 is about 1.5 times worse in prognosis, and XAGE1 expression is about 2 times worse in prognosis.
  • Example 2 ⁇ Test method> Using tumor tissue specimens obtained from 52 early stage lung adenocarcinoma patients classified in stage IA among 120 lung adenocarcinoma patients available for prognostic information and clinical information used in Example 1 Thus, in the same manner as in Example 1, even in the case of early cancer, as in Example 1, PD-L1 expression, Gal-9 expression, XAGE1 expression and T cell infiltration were used as discriminating factors. It was confirmed whether or not it can be estimated. FIG. 9 shows the relationship between the same class of clusters used in Example 1 and the prognosis. ⁇ Result> In the case of lung cancer at stage IA, cluster classification similar to that in Example 1 is possible, and it has been found that classification can be made when the prognosis is good or poor.
  • the prognostic estimation method of the present invention provides an opportunity for additional treatment or multidisciplinary treatment, which has been impossible in the past, for a group of patients with poor prognosis in stage IA lung cancer patients who are not subjected to additional treatment after surgery. It shows that it can be used as a companion diagnostic method for providing.
  • Example 3 The discriminant function obtained in Example 1 after surgery for lung cancer using the tumor tissue specimen obtained from lung adenocarcinoma patients for whom the prognostic information and clinical information of 120 people used in Example 1 are available [Equation 5]
  • the prognostic estimation was performed using the results of the actual follow-up, all of them (Pathological Stage I-IIIA), 62 patients with early stage lung adenocarcinoma (Pathological Stage I) classified as stage I, and FIG. 10 shows a group of patients with locally advanced lung adenocarcinoma (Pathological Stage II-IIIA) in the stage. Further, FIG.
  • FIG. 11 shows 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.
  • the follow-up period after surgery was 5 years, which is generally used as a standard period for determining whether there is no recurrence after lung cancer surgery.
  • ⁇ Result> As is clear from FIG. 10, it was confirmed that the prognosis of any stage of lung adenocarcinoma can be estimated by using [Equation 5]. Further, comparing the survival rates of the patient groups of FIG. 10 and FIG. 11 analyzing the same patient group, the survival rate of the good prognosis group (Cluster A) of FIG. 10 is 55.8%, whereas the good prognosis group of FIG.
  • Example 4 Monoclonal Rabbit anti-human PD-L1 antibody (clone SP-142 Spring Bioscience Corporation, Pleasanton, Calif.) was used as an antibody for immunostaining PD-L1, diluted 100-fold, and monoclonal as an antibody for immunostaining T cells.
  • Rabbit anti-human CD3 antibody (clone: SP-7, Nichirei Biosciences Inc., Tokyo, Japan) diluted 100-fold, without using cut-off, using T cell infiltration rate as the fourth score
  • the staging classification I for which prognostic information and clinical information are available is available.
  • the prognostic estimation method of the present invention is effective for prognosis over a long period of time.
  • 10 years after surgery was set as the follow-up period to confirm the sex.
  • ⁇ Result> As is clear from FIG. 12, it was confirmed that the prognosis of the lung adenocarcinoma patient group at other facilities can be estimated by using [Equation 5] as in the case of Example 3.
  • the prognostic estimation method of this invention has versatility.
  • the prognosis estimation method of the present invention is extremely useful as a method for predicting the recurrence rate over a long period of time in patients with early lung adenocarcinoma.
  • Examples 3 and 4 not only indicate that the prognostic estimation method of the present invention can be applied to lung cancer patients regardless of the stage or implementation facility, but usually additional treatment is performed after surgery. It is also very useful for discriminating patients with poor prognosis among lung cancer patients with no stage I stage, including postoperative treatment methods for those patients with poor prognosis and setting of inspection frequency necessary for early detection of recurrence. It is a useful tool for deciding treatment strategies.
  • the prognosis estimation method of the present invention can reliably discriminate poor prognosis patients in early lung adenocarcinoma patients, the prognosis improvement for patients who have been judged as poor prognosis in early lung cancer patients which has not been conventionally performed It is useful as a companion diagnostic method to improve the survival rate by providing additional treatment necessary for medical care and providing opportunities for multidisciplinary treatment or personalized medicine that were impossible in the past. .
  • a system that can determine the prognosis of lung adenocarcinoma.
  • treatment is selected according to the clinical stage and pathological stage, but by using a prognosis determination system using immune factors as an index separately from those stages, clinical early cancer Even so, patients who require aggressive multidisciplinary treatment can be selected.
  • the prognosis can be determined only by examining a minute tumor tissue such as a bronchoscopic specimen or a specimen obtained by CT-guided lung biopsy. Therefore, prognosis can be determined by collecting a minute clinical sample in a patient who cannot perform whole body search of the stage, and can contribute to the determination of the subsequent treatment policy.

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Abstract

L'invention concerne un procédé pour prédire plus facilement et rapidement le pronostic d'adénocarcinome du poumon, et un procédé pour créer une équation de calcul pour mettre en œuvre le procédé susmentionné. Le procédé selon la présente invention pour créer une équation pour calculer le pronostic est caractérisé par le fait qu'il consiste : à détecter l'expression de PD-L1, Gal-9 et XAGE1 dans des échantillons obtenus à partir d'une pluralité de patients atteints d'adénocarcinome du poumon à l'aide d'une technique d'immuno-coloration ; sur la base de premières données, lesdites premières données étant obtenues en rendant binaire un premier score qui est spécifié sur la base de la plage de distribution et de l'intensité d'expression de PD-L1 d'une tumeur dans un tissu tumoral, de deuxièmes données, lesdites deuxièmes données étant obtenues en rendant binaire un second score qui est spécifié sur la base de la plage de distribution et de l'intensité d'expression de Gal-9 d'une tumeur dans un tissu tumoral, et de troisièmes données, lesdites troisièmes données étant obtenues en rendant binaire sur la base de la présence ou de l'absence de l'expression de XAGE1, séparer les patients atteints d'adénocarcinome du poumon en un groupe à bon pronostic et un groupe à mauvais pronostic ; et à déterminer l'équation de calcul pour diviser le groupe à bon pronostic et le groupe à mauvais pronostic par analyse à variables multiples en utilisant le premier score, le deuxième score et les troisièmes données en tant que variables explicatives. Le procédé selon la présente invention pour prédire le pronostic d'adénocarcinome du poumon est caractérisé par le fait qu'il consiste à substituer un premier et un second scores et des troisièmes données détectés à partir d'un échantillon d'un sujet dans l'équation de calcul déterminée ci-dessus et à prédire le pronostic du sujet par calcul.
PCT/JP2016/063687 2015-05-08 2016-05-06 Procédé de création d'équation pour calculer le pronostic d'adénocarcinome du poumon en utilisant des facteurs immunitaires en tant qu'indices, et procédé de prédiction de pronostic associé WO2016181912A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018207866A1 (fr) 2017-05-11 2018-11-15 学校法人 川崎学園 Procédé d'examen d'un effet thérapeutique sur le cancer et composition permettant d'induire une réponse immunitaire
CN112582028A (zh) * 2020-12-30 2021-03-30 华南理工大学 一种肺癌预后预测模型、构建方法及装置
JP2022029824A (ja) * 2020-08-05 2022-02-18 憲一 佐藤 癌罹患判定方法、装置、およびプログラム

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012515334A (ja) * 2009-01-14 2012-07-05 ザ ユナイテッド ステイツ オブ アメリカ， アズ リプレゼンテッド バイ ザ セクレタリー， デパートメント オブ ヘルス アンド ヒューマン サービシーズ 比に基づく生体マーカーおよびそれを使用する方法
JP2014158500A (ja) * 2005-10-19 2014-09-04 Anselm (Institut National De La Sante Et De La Recherche Medicale) 患者の癌の進行およびその転帰の生体外予後診断方法、および該方法を実施するための手段

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014158500A (ja) * 2005-10-19 2014-09-04 Anselm (Institut National De La Sante Et De La Recherche Medicale) 患者の癌の進行およびその転帰の生体外予後診断方法、および該方法を実施するための手段
JP2012515334A (ja) * 2009-01-14 2012-07-05 ザ ユナイテッド ステイツ オブ アメリカ， アズ リプレゼンテッド バイ ザ セクレタリー， デパートメント オブ ヘルス アンド ヒューマン サービシーズ 比に基づく生体マーカーおよびそれを使用する方法

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
F. HOFFMANN-LA ROCHE: "U.S. FDA grants Breakthrough Therapy Designation for Roche's investigational cancer immunotherapy MPDL3280A (anti-PDL1) in non- small cell lung cancer", MEDIA RELEASE, pages 1 - 3, XP055329363, Retrieved from the Internet <URL:http://www.roche.com/med-cor-2015-02-02-e.pdf> [retrieved on 20160629] *
SCHULKENS I. A. ET AL.: "Galectin Expression Profiling Identifies Galectin-1 and Galectin- 9A5 as Prognostic Factors in Stage I/II Non- Small Cell Lung Cancer", PLOS ONE, vol. 9, no. 9, September 2014 (2014-09-01), pages 1 - 9, XP055329346 *
WAKI KAYOKO ET AL.: "PD-1 expression on peripheral blood T-cell subsets correlates with prognosis in non-small cell lung cancer", CANCER SCI, vol. 10 5, no. 10, October 2014 (2014-10-01), pages 1229 - 1235, XP055329355 *
YOSHIHIRO OUE ET AL.: "Haisengan de Fukusu no Men'eki Bunshi o Shihyo to suru Haisengan Kanja no Yogo Yosokuho", KANSAI . TOKAI CHIKU IKEI DAIGAKU SHIN GIJUTSU SETSUMEIKAI, 29 October 2015 (2015-10-29), Retrieved from the Internet <URL:https://shingi.jst.go.jp/past_abst/abst/p/15/kansaitokai_mednet/kansaitokai_mednet02.pdf> [retrieved on 20160629] *
YOSHIHIRO OUE: "Antibody, CD 4 and CD 8 T- cell response against XAGE-1b (GAGED2a) in non-small cell lung cancer patients", KAWASAKI MEDICAL JOURNAL, vol. 37, no. 4, 2011, pages 223 - 232 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018207866A1 (fr) 2017-05-11 2018-11-15 学校法人 川崎学園 Procédé d'examen d'un effet thérapeutique sur le cancer et composition permettant d'induire une réponse immunitaire
CN110582701A (zh) * 2017-05-11 2019-12-17 学校法人川崎学园 检查癌症治疗效果的方法和用于诱导免疫应答的组合物
JPWO2018207866A1 (ja) * 2017-05-11 2020-05-28 学校法人 川崎学園 がん治療効果の検査方法及び免疫応答誘導用組成物
JP7216420B2 (ja) 2017-05-11 2023-02-01 学校法人 川崎学園 がん治療効果の検査方法及び免疫応答誘導用組成物
US11709165B2 (en) 2017-05-11 2023-07-25 Kawasaki Gakuen Educational Foundation Examination method for prediction of effect of treatment of cancer based on detection of cancer/testis antibodies
JP2022029824A (ja) * 2020-08-05 2022-02-18 憲一 佐藤 癌罹患判定方法、装置、およびプログラム
JP7157941B2 (ja) 2020-08-05 2022-10-21 憲一 佐藤 癌罹患判定方法、装置、およびプログラム
CN112582028A (zh) * 2020-12-30 2021-03-30 华南理工大学 一种肺癌预后预测模型、构建方法及装置
CN112582028B (zh) * 2020-12-30 2023-10-13 华南理工大学 一种肺癌预后预测模型、构建方法及装置

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