Cancer determination method

Abstract

To provide a screening of a sugar peptide marker having high sensitivity and specificity, and also to provide a cancer determination method using the screening.SOLUTION: A cancer determination method is the method where a value obtained by correcting a present amount of sugar peptide derived from an α1-antitrypsin and / or α2-macroglobulin in blood collected from a human by a present amount of sugar peptide derived from transferrin is defined as an indicator.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a method for determining cancer using a combination of the amount of a specific glycoprotein present in the blood and a tumor marker as an index. [Background technology]

[0002] Currently, cancer diagnosis is centered on imaging diagnostics such as endoscopy, PET, and MRI, but many patients undergo tests only after they have noticed symptoms, so detection is delayed and they are often diagnosed as advanced cancer with metastasis. The reality is that these imaging diagnostics are not necessarily widespread as tests that healthy people undergo regularly for early detection, due to reasons such as the great pain they cause to patients and the high cost burden. To detect asymptomatic early cancer, blood tests that are easier and less expensive than diagnostic imaging are desirable. However, the accuracy of biomarkers that can be used to diagnose cancer is insufficient, and they are currently only used for special purposes such as recurrence monitoring. It has often been very difficult to determine early cancer using only sample tests that use already known biomarkers.

[0003] It is known that the sugar chain structure of glycoproteins contained in serum changes with canceration, and in particular, fucosylation is known to progress. Non-Patent Document 1 discloses that fucosylated α1-antitrypsin is increased in patients with liver cancer compared to patients with liver cirrhosis.

[0004] Also disclosed is a method for determining the presence of solid cancer by quantifying urinary α1-antitrypsin using ELISA (Patent Document 1).

[0005] It has also been reported that α1-antitrypsin-derived peptides in blood samples increase in lung cancer (Patent Document 2).

[0006] It has also been reported that serum fucosylated α2-macroglobulin is increased in liver cancer patients (Patent Document 3).

[0007] Among the glycan markers that have already been reported, there is an example in which serum transferrin-derived glycans isolated by SDS-PAGE were analyzed by HPLC or the like to analyze their relationship with cancer progression (Non-Patent Document 2). In this study, it was reported that fucosylation was increased and biantennary or sialic acid-linked glycans were decreased in ovarian cancer patients.

[0008] There is also an example of analyzing the glycan structure of transferrin in the serum of pancreatic cancer patients (Non-Patent Document 3), which reported that transferrin with a glycan structure containing four sialic acids is increased in pancreatic cancer, and that transferrin with five sialic acids is decreased in pancreatic cancer.

[0009] It has also been reported that in liver cancer, transferrin, which has multiple fucoses bound to highly branched sugar chains, increases in the blood (Patent Document 4).

[0010] Non-Patent Document 4 reports a method for diagnosing lung cancer that uses a combination of conventional biomarkers such as CYFRA and AFP, as well as α1-antitrypsin, using random forest, support vector machine, linear discriminant analysis, or logistic regression.

[0011] In addition, Patent Document 5 discloses that serum α1-antitrypsin, carcinoembryonic antigen, and squamous cell carcinoma are A method for determining lung cancer susceptibility using a classification and regression tree analysis is disclosed that combines at least two proteins, including epithelial cell carcinoma antigen, retinol binding protein, transferrin, and haptoglobin.

[0012] In addition, Non-Patent Document 5 reveals that serum from liver cancer patients has increased levels of transferrin with a multibranched glycan structure (DSA-TRF) compared to healthy subjects. Furthermore, the paper reports a method for diagnosing liver cancer that combines DSA-TRF, TRF, total bilirubin, direct bilirubin, albumin, and total protein using a logistic regression method. [Prior art documents] [Patent documents]

[0013] [Patent Document 1] JP 2015-169608 A [Patent Document 2] JP 2015-105951 A [Patent Document 3] JP 2015-092167 A [Patent Document 4] JP 2014-134435 A [Patent Document 5] Special Publication No. 2010-528265 [Non-patent literature]

[0014] [Non-Patent Document 1] Cancer Epidemiology, Biomarkers & Prevention,vol.18, issue.6,p1914-1921(2009) [Non-Patent Document 2] Molecular & Cellular Proteomics,vol.18,p2191-2206(2019) [Non-Patent Document 3] Annals of Clinical & Laboratory Science,vol.46,No.3, p286-290(2016) [Non-Patent Document 4] The Journal of Thoracic and Cardiovascular Surgery, vol.143,issue.2,p421-427 (2012) [Non-Patent Document 5] Glycoconjugate Journal, vol.37, p231-240 (2020) Summary of the Invention [Problem to be solved by the invention]

[0015] As described in the above-mentioned literature, various methods have been proposed as methods for diagnosing cancer, but few of them have sufficient accuracy and are actually suitable for practical use in clinical settings. Therefore, an objective of the present invention is to provide a method for screening glycopeptide markers with high sensitivity and specificity suitable for early cancer detection, and a method for diagnosing cancer using the same. [Means for solving the problem]

[0016] In the present invention, it was found that the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin in a blood sample corrected by the amount of glycopeptides derived from transferrin is effective as an index for early cancer diagnosis. Furthermore, it was found that the sensitivity and specificity can be increased by combining one or more substances known as tumor markers in addition to the above, leading to the completion of this cancer diagnosis method. The cancer diagnosis method in this specification is a method for determining whether or not a patient is affected by a cancer, which can be used by doctors and others as a criterion for diagnosing cancer.

[0017] That is, the gist of the present invention lies in the following (1) to (26). (1) A method for determining cancer, which uses as an index the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin in blood collected from a human, corrected for the amount of glycopeptides derived from transferrin. (2) The glycopeptide used in the cancer diagnosis method is an α1-antitrypsin peptide having a sugar chain bound to the 70th or 271st asparagine residue of the amino acid sequence set forth in SEQ ID NO:1; an α2-macroglobulin peptide having a sugar chain bound to the asparagine residue at position 70 or 1424 of the amino acid sequence set forth in SEQ ID NO:2; A transferrin peptide having a sugar chain bound to the 432nd or 630th asparagine residue in the amino acid sequence set forth in SEQ ID NO:3. A method for determining whether cancer is present, comprising the steps of: (1) determining whether cancer is present; (3) The glycan structure of the glycopeptide used in the cancer diagnosis method is α1-antitrypsin peptide is A2G2S2 or A2G2S2F1, the α2-macroglobulin peptide is A2G2S2 or A2G2S2F1; the transferrin peptide is A2G2S2 or A3G3S2; A method for determining whether a cancer is present or not, comprising the steps of: (4) The amino acid sequence of the glycopeptide used in the cancer diagnosis method is α1-antitrypsin peptide YLGNATAIFFLPDEGK (SEQ ID NO: 6) or QLAHQSNSTNIFFSPVSIATAFAMLSLGTK (SEQ ID NO: 4); The α2-macroglobulin peptide is GNR (SEQ ID NO: 8) or VSNQTLSLFFTVLQDVPVR (SEQ ID NO: 9), The transferrin peptide comprises CGLVPVLAENYNK (SEQ ID NO: 10) or QQQHLFGSNVTDCSGNFCLFR (SEQ ID NO: 11); A method for determining whether or not cancer is present, comprising the steps of: (1) determining whether or not cancer is present; (5) In addition to the corrected value of the amount of glycopeptide present, the concentration of at least one tumor marker selected from the group consisting of CEA, CYFRA, CA19-9, SPan-1 antigen, CA125, SCC antigen, and AFP is used in combination as an index of the cancer determination method. A method for determining whether or not cancer is present, comprising the steps of: (1) determining whether or not cancer is present; (6) A method for combining the correction value of the abundance of the glycopeptide used in the cancer assessment method with the concentration of the tumor marker is a linear regression method or a logistic regression method. (5) A method for determining whether or not cancer is present. (7) The cancer is selected from lung cancer, colon cancer, and gastric cancer, a method of combining the correction value of the amount of glycopeptide present used in the cancer assessment method with the concentration of at least one tumor marker selected from CEA and CYFRA is a linear regression method; The index calculated based on the following formula 1 is used as an indicator. (6) A method for determining whether or not cancer is present. Equation 1: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CEA concentration) + D x (CYFRA concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value obtained by mass spectrometry of the subject's blood by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A~D are coefficients for each cancer, and A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0, A+B+C+D=1. (8) The values ​​of A to D shown in the formula 1 are within the ranges of A = 0.119 to 0.540, B = 0.089 to 0.345, C = 0.035 to 0.405, and D = 0.150 to 0.465, respectively. (7) A method for determining whether or not cancer is present. The values ​​A to D may be the coefficients for each cancer based on Table 10. (9) The cancer is selected from lung cancer, colon cancer, and gastric cancer; a method of combining the corrected value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CEA and CYFRA, is a logistic regression method; The estimated value calculated based on the following formula 2 is used as an index. (6) A method for determining whether or not cancer is present. Equation 2: Estimated value = 1 / [1 + e^-{A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CEA concentration) + D x (CYFRA concentration) + E}] Note that the concentration of each tumor marker refers to the logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, and satisfy A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0. (10) The values ​​of A to E shown in the formula 2 are within the ranges of A = 6.56 to 38.9, B = 5.54 to 28.8, C = 0.320 to 5.10, D = 1.69 to 6.71, and E = -6.44 to -32.9, respectively. (9) A method for determining whether or not cancer is present. The values ​​A to E may be the coefficients for each cancer based on Table 11. (11) The cancer is pancreatic cancer. a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CA19-9, SPan-1 antigen, and CA125, is a linear regression method; The index calculated based on the following formula 3 is used as an indicator. (6) A method for determining whether or not cancer is present. Equation 3: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value of the test value obtained by mass spectrometry of the subject's blood by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A~E are coefficients for each cancer, and satisfy A≧0, B≧0, C≧0, D≧0, E≧0, A+B>0, C+D+E>0, and A+B+C+D+E=1. (12) The values ​​of A to E shown in the formula 3 are within the ranges of A = 0.140 to 0.420, B = 0.135 to 0.406, C = 0.110 to 0.330, D = 0.049 to 0.148, and E = 0.065 to 0.196, respectively. The method for determining whether or not a cancer has occurred according to (11). The values ​​A to E may be the coefficients for each cancer based on Table 10. (13) The cancer is pancreatic cancer. a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CA19-9, SPan-1 antigen, and CA125, is a logistic regression method; The estimated value calculated based on the following formula 4 is used as an index. (6) A method for determining whether or not cancer is present. Equation 4: Estimated value = 1 / [1 + e^-{A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) + F}] The concentration of each tumor marker is calculated by logarithmically transforming (log10) the blood test value of the subject. A~E are coefficients for each cancer, and satisfy A≧0, B≧0, C≧0, D≧0, E≧0, A+B>0, C+D+E>0. (14) The values ​​of A to F shown in the formula 4 are within the ranges of A=6.99 to 21.0, B=3.49 to 10.5, C=0.255 to 0.765, D=5.66 to 17.0, E=1.02 to 3.06, and F=-36.8 to -12.3, respectively. (13) A method for determining whether or not cancer is present. The values ​​A to F may be the coefficients for each cancer based on Table 11. (15) The cancer is esophageal cancer. a method of combining the correction value of the amount of glycopeptide present, which is used in the cancer assessment method, with the concentration of at least one of tumor markers selected from CYFRA and SCC antigen is a linear regression method; The index calculated based on the following formula 5 is used as an indicator. (6) A method for determining whether or not cancer is present. Equation 5: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CYFRA concentration) + D x (SCC antigen concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value obtained by mass spectrometry by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A~D are coefficients for each cancer, and A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0, A+B+C+D=1. (16) The values ​​of A to D shown in the above formula 5 are within the ranges of A = 0.158 to 0.474, B = 0.097 to 0.290, C = 0.072 to 0.216, and D = 0.174 to 0.522, respectively. (15) A method for determining whether or not cancer is present. The values ​​A to D may be the coefficients for each cancer based on Table 10. (17) The cancer is esophageal cancer. a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CYFRA and SCC antigen is a logistic regression method; The estimated value calculated based on the following formula 6 is used as an index. (6) A method for determining whether or not cancer is present. Equation 6: Estimated value = 1 / [1 + e^-{A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CYFRA concentration) + D x (SCC antigen concentration) + E}] The concentration of each tumor marker is calculated by logarithmically transforming (log10) the blood test value of the subject. A to D are coefficients for each cancer, and satisfy A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0. (18) The values ​​of A to E shown in the formula 6 are within the ranges of A = 12.6 to 37.9, B = 5.85 to 17.6, C = 0.915 to 2.75, D = 2.80 to 8.39, and E = -33.2 to -11.1, respectively. (17) A method for determining whether or not cancer is present. The values ​​A to E may be the coefficients for each cancer based on Table 11. (19) The cancer is liver cancer. a method of combining the correction value of the amount of glycopeptide present used in the cancer assessment method with the concentration of at least one tumor marker selected from CEA and AFP is a linear regression method; The index calculated based on the following formula 7 is used as an index. (6) A method for determining whether or not cancer is present. Equation 7: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CEA concentration) + D x (AFP concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value obtained by mass spectrometry by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A~D are coefficients for each cancer, and A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0, A+B+C+D=1. (20) The values ​​of A to D shown in the above formula 7 are A=0.036 to 0.108, B=0.317 to 0.951, C=0.045 to 0.134, and D=0.147 to 0.440. (19) A method for determining whether or not cancer is present. The values ​​A to D may be the coefficients for each cancer based on Table 10. (21) The cancer is liver cancer. a method of combining the correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CEA and AFP, is a logistic regression method; The estimated value calculated based on the following formula 8 is used as an index. (6) A method for determining whether or not cancer is present. Equation 8: Estimated value = 1 / [1 + e^-{A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (AFP concentration) + E}] The concentration of each tumor marker is calculated by logarithmically transforming (log10) the blood test value of the subject. A to D are coefficients for each cancer, and satisfy A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0. (22) The values ​​of A to E shown in the above formula 8 are within the ranges of A = 7.61 to 22.8, B = 18.2 to 54.6, C = 1.03 to 3.09, D = 1.28 to 3.84, and E = -29.3 to -9.76, respectively. (21) A method for determining whether or not cancer is present. The values ​​A to E may be the coefficients for each cancer based on Table 11. (23) The cancer is lung cancer. A combination of the corrected amount of the glycopeptide used in the cancer assessment method and the concentration of at least one of tumor markers CEA and CYFRA is used as an index to assess squamous cell carcinoma. The method for determining whether or not a cancer has occurred according to any one of (7) to (10). (24) A cancer detection kit comprising a reagent for measuring the amount of glycopeptide derived from α1-antitrypsin and / or a reagent for measuring the amount of glycopeptide derived from α2-macroglobulin in blood, and a reagent for measuring the amount of glycopeptide derived from transferrin. (25) an acquisition unit for acquiring the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, and glycopeptides derived from transferrin present in blood collected from a subject; a calculation unit that calculates a value obtained by correcting the abundance of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, which is an index value for cancer diagnosis of the subject, based on the abundance of the glycopeptides acquired by the acquisition unit, by the abundance of glycopeptides derived from transferrin; and an output unit that outputs the value calculated by the calculation unit as an index for cancer determination. An apparatus for calculating index values ​​for cancer diagnosis. (26) The computer: Obtaining the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, and glycopeptides derived from transferrin present in blood collected from a test subject; Based on the amount of glycopeptide obtained, a value is calculated by correcting the amount of glycopeptide derived from α1-antitrypsin and / or α2-macroglobulin, which is an index value for determining whether the subject has cancer, by the amount of glycopeptide derived from transferrin; A program for calculating an index value for cancer diagnosis, for executing outputting the calculated value that serves as an index for cancer diagnosis. Effect of the Invention

[0018] The present invention makes it possible to provide a glycopeptide marker that has sufficient accuracy and is more sensitive and specific than conventional methods, as well as a cancer determination method and a cancer detection measurement kit or device using the same. [Brief description of the drawings]

[0019] [Figure 1] 1 shows the structure of the glycan A2G2S2 used in the cancer diagnosis of the present invention. [Diagram 2] 1 shows the structure of the glycan A2G2S2F1 used in the cancer diagnosis of the present invention. [Diagram 3]1 shows the structure of the glycan A3G3S2 used in the cancer diagnosis of the present invention. [Figure 4] This is a diagram comparing the indexes of healthy subjects and lung cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Diagram 5] This is a diagram comparing the estimated values ​​between healthy subjects and lung cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 6] This is a diagram comparing the indexes of healthy subjects and colorectal cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 7] This is a diagram comparing the estimated values ​​between healthy subjects and colorectal cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 8] This is a diagram comparing the indexes of healthy subjects and gastric cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 9] This is a diagram comparing the estimated values ​​between healthy subjects and stomach cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 10] This is a diagram comparing the indexes of healthy subjects and pancreatic cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 11] This is a diagram comparing the estimated values ​​between healthy subjects and pancreatic cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 12] This is a diagram comparing the indexes of healthy subjects and esophageal cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 13] This figure shows the comparison of estimated values ​​between healthy subjects and esophageal cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 14] This is a diagram comparing the indexes of healthy subjects and liver cancer patients using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 15] This is a graph comparing the estimated values ​​between healthy subjects and liver cancer patients using the logistic regression method. Cancer box plot (logistic regression method) is shown. [Figure 16] This is a diagram comparing the indexes of healthy subjects and lung cancer patients by lung cancer stage using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 17] This is a diagram comparing the indexes of healthy subjects and lung cancer patients by lung cancer tissue type using the linear regression method. Cancer box plot (linear regression method) is shown. [Figure 18] FIG. 1 illustrates an example of a configuration of a system according to an embodiment. [Figure 19] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Figure 20] FIG. 11 is a diagram showing an example of an operation flow of the index value calculation device for cancer determination. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] <Cancer diagnosis method> The present invention is a method for diagnosing cancer, which uses as an index the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin in blood collected from a human, corrected by the amount of glycopeptides derived from transferrin. More specifically, the present invention relates to a method for diagnosing cancer, which uses as an index the amount of glycopeptides derived from α1-antitrypsin in blood collected from a human, corrected by the amount of glycopeptides derived from transferrin. This is a method for diagnosing cancer, which uses one or two of the following indicators to determine cancer: a value corrected for the amount of glycopeptides derived from transferrin in blood collected from a human, and a value corrected for the amount of glycopeptides derived from α2-macroglobulin in blood collected from a human, by the amount of glycopeptides derived from transferrin in blood collected from a human. The method of correction is to divide the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin by the amount of glycopeptides derived from transferrin, but is not limited to this method. Any method can be used as long as it can correct factors such as analytical errors and individual differences contained in the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin. In the present invention, the amounts of glycopeptides derived from α1-antitrypsin, α2-macroglobulin, and transferrin in blood may be measured simultaneously in the same sample, or may be measured separately in the same sample.

[0021] α1-antitrypsin is a type of glycoprotein present in serum that inhibits various proteases and whose blood concentration increases in inflammatory diseases. α1-antitrypsin has a glycan portion that contains N-linked glycans and a protein portion that is α1-antitrypsin without the glycan portion, and the bond between the glycan portion and the protein portion is an N-glycosidic bond with asparagine.

[0022] α2-macroglobulin is a type of glycoprotein present in serum, which, like α1-antitrypsin, has the ability to inhibit various proteases and is also involved in the functional control of inflammatory cytokines and hormones. α2-macroglobulin has a glycan portion containing N-linked glycans and a protein portion that is the glycan portion of α2-macroglobulin minus the glycan portion, and the bond between the glycan portion and the protein portion is an N-glycosidic bond with asparagine.

[0023] Transferrin (also known as serotransferrin) is a type of protein contained in serum that binds and transports iron ions. Transferrin has a glycan portion that contains N-linked glycans and a protein portion that is the transferrin without the glycan portion, and the bond between the glycan portion and the protein portion is an N-glycosidic bond with asparagine.

[0024] In the present invention, examples of the amino acid sequences of the protein portion (hereinafter, sometimes referred to as the "protein portion") excluding the sugar chain portion (hereinafter, sometimes referred to as the "sugar chain portion") of α1-antitrypsin, α2-macroglobulin, and transferrin include the sequences described in SEQ ID NO: 1 (α1-antitrypsin), SEQ ID NO: 2 (α2-macroglobulin), and SEQ ID NO: 3 (transferrin) in the sequence listing. However, in carrying out the present invention, the sequences are not limited to these sequences, and the amino acid sequences described in SEQ ID NO: 1 to SEQ ID NO: 3 in the sequence listing may contain mutations (including cases of addition, deletion, and substitution of amino acids) within the range in which the effects of the present invention can be obtained. The protein portion of the glycopeptide used in the cancer assessment method of the present invention may be the full length of the protein portion of α1-antitrypsin, α2-macroglobulin, and transferrin, or a peptide constituting a part thereof. The peptide constituting a part of the protein portion may be any peptide that can be identified as a part of α1-antitrypsin, α2-macroglobulin, and transferrin by the measurement method described below, etc. For example, peptides obtained by fragmenting α1-antitrypsin, α2-macroglobulin, and transferrin with known proteases (e.g., trypsin, lysyl endopeptidase, etc.) can be used. Those skilled in the art can appropriately determine whether a sequence is usable based on the calculated AUC value or index described below.

[0025] The sugar chain portion of the glycopeptide used in the cancer assessment method of the present invention refers to a sugar chain that binds to the nitrogen atom of the amide group of the side chain of an asparagine residue of a protein. The sugar chain portion in the present invention is not particularly limited in its binding site, but it binds to the asparagine residues at positions 70, 107, and 271 (or residues corresponding to these residues) of the amino acid sequence of α1-antitrypsin described in SEQ ID NO: 1, the asparagine residues at positions 55, 70, 247, 396, 410, 869, 991, and 1424 (or residues corresponding to these residues) of the amino acid sequence of α2-macroglobulin described in SEQ ID NO: 2, and the asparagine residues at positions 432, 491, and 630 (or residues corresponding to these residues) of the amino acid sequence of transferrin described in SEQ ID NO: 3. Preferably, it binds to the asparagine residues at positions 70 and 271 of α1-antitrypsin, the asparagine residues at positions 70 and 1424 of α2-macroglobulin, and the asparagine residues at positions 432 and 630 of transferrin. More preferably, it binds to the asparagine residue at position 271 of α1-antitrypsin, the asparagine residue at position 70 of α2-macroglobulin, and the asparagine residue at position 432 of transferrin. In one embodiment, it binds to the asparagine residues at positions 70, 107, and 271 of α1-antitrypsin, the asparagine residues at positions 55, 70, and 1424 of α2-macroglobulin, and the asparagine residues at positions 432 and 630 of transferrin. In another embodiment, it binds to asparagine residues 70, 107, and 271 of α1-antitrypsin, asparagine residue 70 of α2-macroglobulin, and asparagine residue 432 of transferrin.

[0026] Sugars constituting the glycan portion include, but are not limited to, mannose, galactose, N-acetyl-D-glucosamine, sialic acid, and fucose. N-linked glycans include those that form branches with mannose as the base point, such as di-branched, tri-branched, and quadruple-branched chains. The N-linked glycan of the glycopeptide used in the present invention is preferably di-branched or tri-branched.

[0027] The structure of the glycan portion of the glycopeptide used in the cancer assessment method of the present invention is not particularly limited, but is preferably A2G2S2, A2G2S2F1 or A3G3S2, and more preferably A2G2S2 or A2G2S2F1 (wherein A represents the number of branches, G represents the number of galactose, S represents the number of sialic acids (N-acetylneuraminic acids), and F represents the number of fucose. Fucose may be either outer fucose or core fucose.) As preferred examples of specific structures, the structure of A2G2S2 is shown in FIG. 1, the structure of A2G2S2F1 in FIG. 2, and the structure of A3G3S2 in FIG. 3.

[0028] More preferred structures of the glycopeptide marker used in the present invention include a glycoprotein in which A2G2S2 is bound to Asn70 of α1-antitrypsin, a glycoprotein in which A2G2S2 is bound to Asn107 of α1-antitrypsin, a glycoprotein in which A2G2S2 is bound to Asn271 of α1-antitrypsin, a glycoprotein in which A2G2S2F1 is bound to Asn70 of α1-antitrypsin, a glycoprotein in which A2G2S2F1 is bound to Asn107 of α1-antitrypsin, a glycoprotein in which A2G2S2F1 is bound to Asn271 of α1-antitrypsin, etc. Two or more types of α1-antitrypsin peptides having sugar chains with different binding positions and structures can also be combined and used as glycopeptides derived from α1-antitrypsin to calculate an index based on the abundance.

[0029] More preferred structures of the glycopeptide markers used in the present invention are a glycoprotein in which A2G2S2 is bound to Asn55 of α2-macroglobulin, a glycoprotein in which A2G2S2 is bound to Asn70 of α2-macroglobulin, a glycoprotein in which A2G2S2 is bound to Asn1424 of α2-macroglobulin, a glycoprotein in which A2G2S2F1 is bound to Asn55 of α2-macroglobulin, a glycoprotein in which A2G2S2F1 is bound to Asn70 of α2-macroglobulin, a glycoprotein in which A2G2S2F1 is bound to Asn1424 of α2-macroglobulin, Examples of such glycoproteins include a glycoprotein in which A2G2S2F1 is bound to a 2-macroglobulin. Two or more types of α2-macroglobulin peptides having glycans with different binding positions and structures can be combined to produce α2-macroglobulin-derived glycopeptides, which can be used to calculate indicators based on abundance.

[0030] More preferred structures of the glycopeptide marker used in the present invention include a glycoprotein in which A2G2S2 is bound to Asn432 of transferrin, a glycoprotein in which A2G2S2 is bound to Asn630 of transferrin, a glycoprotein in which A3G3S2 is bound to Asn432 of transferrin, a glycoprotein in which A3G3S2 is bound to Asn630 of transferrin, etc. Two or more types of transferrin peptides having glycans with different binding positions and structures can also be combined and used as transferrin-derived glycopeptides to calculate an index based on the abundance.

[0031] Among these, particularly preferred are glycoproteins in which A2G2S2 is bound to Asn70 of α1-antitrypsin, glycoproteins in which A2G2S2 is bound to Asn107 of α1-antitrypsin, glycoproteins in which A2G2S2 is bound to Asn271 of α1-antitrypsin, glycoproteins in which A2G2S2 is bound to Asn70 of α2-macroglobulin, glycoproteins in which A2G2S2F1 is bound to Asn70 of α2-macroglobulin, glycoproteins in which A2G2S2 is bound to Asn1424 of α2-macroglobulin, glycoproteins in which A2G2S2 is bound to Asn432 of transferrin, and glycoproteins in which A2G2S2 is bound to Asn630 of transferrin.

[0032] The specimen used in the determination method of the present invention is blood collected from a human. In the present invention, blood means that any of whole blood, serum, and plasma can be used, and untreated blood may be used, or pretreated blood or the like may be used. Even when serum or plasma is described in this specification, the method can be carried out using any of blood, serum, and plasma, just as when blood is described.

[0033] In the present invention, a method is exemplified in which the total glycoprotein contained in human blood is extracted, the extracted total glycoprotein is fragmented into peptides using a protease or the like (hereinafter, the fragmented glycoprotein is referred to as "glycopeptide"), and the glycopeptide marker used in the present invention is detected from the resulting mixture of glycopeptides. In this case, the glycopeptide may be treated with a protease together with other proteins in the body fluid. The step of extracting the total glycoprotein and the step of fragmenting the total glycoprotein into peptides may be any method that is commonly used in the analysis of glycoproteins, and those skilled in the art can appropriately select and use the method, taking into consideration the measuring equipment required to realize the measurement, the complexity of the operation, and the like.

[0034] As an example, more specifically, in a process for extracting all proteins from human body fluids, first, a solvent is added in an amount 2 to 10 times that of the body fluid. The solvent may be any solvent that can precipitate proteins, and acetone, methanol, ethanol, trichloroacetic acid, hydrochloric acid aqueous solution, etc. are preferred, with acetone and methanol being particularly preferred. The precipitated proteins are denatured, reduced and alkylated, and fragmented into peptides using a protease. The protease may be any protease that can decompose proteins into peptides, and trypsin or lysyl endopeptidase may be used alone or in combination. The peptides after decomposition may be analyzed as they are, or may be analyzed after pretreatment such as concentrating glycopeptides using antibodies or lectins.

[0035] When trypsin and / or lysyl endopeptidase are used, the amino acid sequence of the peptide constituting a part of the protein portion of the glycopeptide marker used in the present invention is, for example, α1-antitrypsin peptide QLAHQSNSTNIFFSPVSIATAFAMLSLGTK (SEQ ID NO: 4), ADTHDEILEGLNFNLTEIPEAQIHEGFQELLR (SEQ ID NO: 5), and YLGNATAIFFLPDEGK (SEQ ID NO: 6), α2-macroglobulin peptides are GCVLLSYLNETVTVSASLESVR (SEQ ID NO: 7), GNR (SEQ ID NO: 8) (feature position: CARBOHYD 2; / qualifier: note= Asn side-chain linked to saccharide), and VSNQTLSLFFTVLQDVPVR (SEQ ID NO: 9), and transferrin peptides are CGLVPVLAENYNK (SEQ ID NO: 10), QQQHLFGSNVTDCSGNFCLFR (SEQ ID NO: 11), and INHCR (SEQ ID NO: 12), etc. The α1-antitrypsin peptides YLGNATAIFFLPDEGK (SEQ ID NO: 6) and QLAHQSNSTNIFFSPVSIATAFAMLSLGTK (SEQ ID NO: 4), the α2-macroglobulin peptides GNR (SEQ ID NO: 8) and VSNQTLSLFFTVLQDVPVR (SEQ ID NO: 9), the transferrin peptides CGLVPVLAENYNK (SEQ ID NO: 10) and QQQHLFGSNVTDCSGNFCLFR (SEQ ID NO: 11) are preferred, and the α1-antitrypsin peptide YLGNATAIFFLPDEGK (SEQ ID NO: 6), the α2-macroglobulin peptide GNR (SEQ ID NO: 8), and the transferrin peptide CGLVPVLAENYNK (SEQ ID NO: 10) are more preferred. The method of describing the amino acid sequence is in accordance with IUPAC-IUB.

[0036] These glycopeptides may be detected by any method, such as immunoassay, liquid chromatography (LC), mass spectrometry (MS), lectin array, electrophoresis, etc., but methods using a mass spectrometer (MS) are preferred, and in particular, liquid chromatography-mass spectrometry (LC-MS) or LC-MS / MS is preferred, with LC-MS being even more preferred.

[0037] The detection range of the mass spectrometer is not particularly limited as long as it can detect the glycopeptide used in the present invention as the detection target. The ionization method may be ESI or APCI, etc., but ESI is most preferred. The mass spectrometer may be any of quadrupole, TOF, ion trap, magnetic field, and Fourier transform types, but quadrupole types with high quantitative properties and TOF types with high sensitivity are particularly preferred. In addition, the ions to be detected are not limited to parent ions, and may be related ions such as fragment ions, adduct ions, and dimer ions.

[0038] The ion charge and isotope ion peaks of glycopeptides derived from α1-antitrypsin, α2-macroglobulin, and transferrin detected by the above method 13 The number of C's is not specified, but α1-antitrypsin contains trivalent ions and 13 Isotopic ion peaks containing two C, α2-macroglobulin is a doubly charged ion and 13 Isotopic ion peaks containing one C, doubly charged ions for transferrin and 13 It is preferable that the isotope ion peak contains two C atoms.

[0039] The amounts of glycopeptides derived from α1-antitrypsin, α2-macroglobulin, and transferrin in the present invention do not necessarily need to be determined in absolute amounts, but can be determined by quantifying the peak heights and peak areas of these glycopeptides. Either method may be used, but the method of quantifying the peak areas is more accurate and is preferred. As a method for quantifying these areas, any known method can be appropriately selected and used.

[0040] The cancer assessment method of the present invention uses as an index the amount of α1-antitrypsin-derived glycopeptide and / or α2-macroglobulin-derived glycopeptide present in blood collected from a human being corrected by the amount of transferrin-derived glycopeptide present. There is no particular limitation on the correction method, but it is preferable to divide the amount of α1-antitrypsin-derived glycopeptide and / or α2-macroglobulin-derived glycopeptide present by the amount of transferrin-derived glycopeptide present.

[0041] In the cancer diagnosis method of the present invention, α1-antitrypsin-derived glycopeptide and / or α2-macroglobulin-derived glycopeptide are used as glycopeptide markers, and one of these glycopeptide markers may be used alone, or two of these glycopeptide markers may be used in combination. For example, in addition to glycopeptides, a substance known as a known tumor marker may be used in combination as a marker for cancer diagnosis of the present invention. The number of tumor markers to be combined is not particularly limited, and one type may be used alone, or multiple types may be used in combination. They may be used in combination. Specifically, tumor markers such as AFP, HS-PSA, CA125, CA19-9, CEA, CYFRA, CA15-3, SCC antigen, NC-ST439, elastase 1, Dupan-2, anti-p53 antibody, and SPan-1 antigen are preferred. More preferred are AFP, CA125, CA19-9, CEA, CYFRA, SCC antigen, and SPan-1 antigen. Those skilled in the art can appropriately select from these tumor markers a combination that has sufficient accuracy such as sensitivity and specificity and use it.

[0042] For example, as a method for diagnosing cancer by combining the glycopeptide marker of the present invention with an existing tumor marker, a combination of a glycopeptide with at least one of the following tumor markers: AFP, HS-PSA, CA125, CA19-9, CEA, CYFRA, CA15-3, SCC antigen, NC-ST439, and SPan-1 antigen will be described, but the present invention is not limited thereto. The method of combining a glycopeptide marker that showed a particularly high AUC value in each cancer with a tumor marker can use a known regression analysis method, including, but not limited to, linear regression and nonlinear regression (logistic regression).

[0043] The linear regression method can be used by calculating and adding the explanatory variable X by a coefficient (hereinafter, this may be referred to as "index" in the present invention) as in the formula Y=aX1+bX2+cX3+.... The linear regression method may be performed by aligning the digits of the values ​​of the glycopeptide marker value (corrected value of the amount of glycopeptide present in blood) and the tumor marker value (tumor marker concentration in blood). For example, the tumor marker concentration may be logarithmically transformed (Log10) and normalized so that the average value is 0 and the standard deviation is 1 for each glycopeptide marker value and tumor marker concentration. For example, the amount of glycopeptide present may be the value obtained by dividing the deviation from the average value of the value obtained by mass spectrometry of the subject's blood by the standard deviation, and the value obtained by dividing the deviation from the average value of the test value of the subject's blood by the standard deviation after logarithmic transformation (log10) may be used as each tumor marker concentration. The average value is preferably determined based on the amount of glycopeptide present and the tumor marker concentration measured in case subjects. The average value may be a predetermined value based on previously obtained values ​​for case subjects.

[0044] Using these values, statistical tests such as the Student's T. TEST can be performed between two groups of healthy subjects and cancer patients, and each marker can be weighted to maximize the p-value, and an index can be calculated using the weighting coefficient.

[0045] As a method for calculating the index, for example, the index for each cancer can be calculated using the following formula. Lung cancer, colon cancer and gastric cancer index = A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (CYFRA concentration), Pancreatic cancer index = A x (abundance of α1-antitrypsin-derived glycopeptides / abundance of transferrin-derived glycopeptides) + B x (abundance of α2-macroglobulin-derived glycopeptides / abundance of transferrin-derived glycopeptides) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration), Esophageal cancer index = A × (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B × (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C × (CYFRA concentration) + D × (SCC antigen concentration), Liver cancer index = A × (abundance of α1-antitrypsin-derived glycopeptides / abundance of transferrin-derived glycopeptides) + B × (abundance of α2-macroglobulin-derived glycopeptides / abundance of transferrin-derived glycopeptides2) + C × (CEA concentration) + D × (AFP concentration).

[0046] On the other hand, in the logistic regression method, the explanatory variable X is expressed as Y=1 / {1+ e^-(aX1+bX2+cX3+…)} The probability of a certain event occurring (hereinafter, in the present invention, may be referred to as an "estimated value") can be predicted by applying this equation. In the logistic regression method, the tumor marker value (tumor marker concentration in blood) may be logarithmically transformed (Log10) to match the number of digits with the glycopeptide marker value (corrected value of the amount of glycopeptide present in blood). The values ​​thus obtained can be used to weight each marker using maximum likelihood methods to calculate estimates.

[0047] To calculate the estimated values ​​for each cancer, the following formula can be used. Estimated value for lung cancer, colon cancer, and stomach cancer = 1 / [1+ e^-{A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (CYFRA concentration) + E}], Pancreatic cancer estimate = 1 / [1+ e^-{A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) + F}], Esophageal cancer estimate = 1 / [1+ e^-{A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CYFRA concentration) + D x (SCC antigen concentration) + E}], Estimated liver cancer value = 1 / [1 + e^-{A × (abundance of α1-antitrypsin-derived glycopeptides / abundance of transferrin-derived glycopeptides) + B × (abundance of α2-macroglobulin-derived glycopeptides / abundance of transferrin-derived glycopeptides) + C × (CEA concentration) + D × (AFP concentration) + E}].

[0048] When the indices (linear regression method) and estimated values ​​(logistic regression method) for each cancer calculated by the above method are compared by dividing the samples into groups of non-cancer patients / healthy individuals and cancer patients, differences are observed between the non-cancer patient / healthy individual group and the cancer patient group. Therefore, it is possible to calculate the ROC curve or AUC from these graphs and set cutoff values ​​based on values ​​with sufficient accuracy.

[0049] For example, the cutoff value can be determined so that the sensitivity (= number of positive cancer patients / total number of cancer patients) is 50% or more and the specificity (= number of negative healthy subjects / total number of healthy subjects) is 98% or more for the index and estimated value of each cancer. In this case, a person skilled in the art can appropriately set appropriate values ​​for the sensitivity and specificity. These cutoff values ​​may be set for cancer determination, or can be appropriately set for each type of cancer, stage of cancer, etc. For example, in cases where the index increases with progression of the stage of lung cancer in both Japanese and foreigners, it is preferable to use the present invention because it allows for earlier detection. In addition, in cases where an increase in the index is observed for each type of cancer, for example, in lung cancer, the index increases more in squamous cell carcinoma and adenosquamous cell carcinoma than in adenocarcinoma, so it can be used to assist in distinguishing between types of cancer.

[0050] The cancer assessment method of the present invention can be carried out without specifying the site or organ of cancer. The type of cancer to be assessed is not limited, but the cancer assessment method of the present invention can be used for, for example, lung cancer, colon cancer, stomach cancer, pancreatic cancer, esophageal cancer, and liver cancer.

[0051] The method for determining cancer of the present invention can be carried out without limiting the tissue type, but can also be carried out by limiting the tissue type. For example, in the case of lung cancer determination, squamous cell carcinoma, particularly squamous cell carcinoma, is the most suitable. It can be used to determine whether the patient has epithelial cancer or adenosquamous carcinoma.

[0052] In the method for determining cancer of the present invention, the presence or absence of a subject is determined using a value (glycopeptide marker value), index, or estimated value obtained by correcting the amount of glycopeptide derived from α1-antitrypsin and / or α2-macroglobulin by the amount of glycopeptide derived from transferrin as an index. For example, the presence or absence of cancer can be determined by comparing the glycopeptide marker value, index, or estimated value with a preset threshold value (cutoff value). For example, when the glycopeptide marker value, index, or estimated value in the blood of the subject shows a significant difference from this cutoff value, it can be determined that the subject has cancer. In one example, when the glycopeptide marker value, index, or estimated value in the blood of the subject is equal to or greater than this preset cutoff value, it can be determined that the subject has cancer, and when the glycopeptide marker value, index, or estimated value in the blood of the subject is smaller than the cutoff value, it can be determined that the subject does not have cancer.

[0053] Such cut-off values ​​can be appropriately set and used by those skilled in the art. The cut-off value is preferably determined based on the glycopeptide marker values, indexes or estimated values ​​calculated by measuring the markers in case subjects and control subjects. The cut-off value may be a value determined in advance based on the values ​​already obtained for the case subjects and control subjects. It is preferable that the control subjects are not affected by a disease correlated with the measured marker.

[0054] For example, a cutoff value can be determined so that a predetermined percentage (which may be 100%) of case subjects are above the cutoff value, and a predetermined percentage (which may be 100%) of control subjects are below the cutoff value. The cutoff value may also be a previous value in the test subject, in which case changes from the previous state (worsening, recovery, etc.) can be determined.

[0055] <Cancer detection test kit> The cancer detection assay kit of the present invention is a cancer detection assay kit that includes a reagent for measuring the amount of glycopeptides derived from α1-antitrypsin and / or a reagent for measuring the amount of glycopeptides derived from α2-macroglobulin in blood, and a reagent for measuring the amount of glycopeptides derived from transferrin.

[0056] That is, the kit of the present invention contains one or more types of reagents for measuring the amount in blood of glycopeptide markers used in the cancer assessment method of the present invention. Examples of such reagents include reagents for pretreating proteins in blood, reagents such as enzymes for fragmenting proteins into peptides, and reagents for pretreating prepared glycopeptides for analysis by mass spectrometry or the like. Based on the amount of glycopeptide present in blood measured by the kit of the present invention, a glycopeptide marker value (a corrected value for the amount of glycopeptide present in blood), which is a value that serves as an index for cancer assessment in a subject, can be calculated and used as an index for cancer assessment. The kit of the present invention may further contain a reagent for measuring the concentration of a tumor marker in blood.

[0057] The kit of the present invention can be used not only for detecting cancer, but also for evaluating the therapeutic or preventive effects of cancer, and for evaluating candidate compounds for therapeutic or preventive drugs for cancer.

[0058] <Cancer diagnosis index value calculation device and system> FIG. 18 is a diagram showing an example of the configuration of the system of this embodiment. The system of this embodiment includes a glycopeptide abundance measurement device 10 and a cancer diagnosis index value calculation device 20. The glycopeptide abundance measurement device 10 measures the abundance of glycopeptide markers (glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, and glycopeptides derived from transferrin) present in blood collected from a subject. The cancer diagnosis index value calculation device 20 calculates a glycopeptide marker value (a corrected value of the glycopeptide abundance in blood) based on the measurement result of the glycopeptide abundance measurement device 10. Here, the glycopeptide abundance measurement device 10 and the cancer diagnosis index value calculation device 20 are separate entities, but may be integrated to operate as one glycopeptide marker value measurement device. The glycopeptide abundance measuring device 10 is not particularly limited, and the above-mentioned measuring devices can be used. The glycopeptide abundance measuring device 10 may be controlled by an index value calculation device 20 for cancer diagnosis. The system of the present invention may further include a blood tumor marker concentration measuring device (not shown). In this case, the cancer diagnosis index value calculation device 20 calculates a glycopeptide marker value (a corrected value of the amount of glycopeptide present in the blood), which is an index value for cancer diagnosis of the subject, based on the measurement results of the glycopeptide abundance measuring device 10, and calculates a tumor marker value (the concentration of the tumor marker in the blood) based on the measurement results of the tumor marker concentration measuring device, and can also calculate an index and an estimated value, which are values ​​that are indexes for cancer diagnosis of the subject, based on the glycopeptide marker value, the tumor marker value, and the coefficients of each cancer. Hereinafter, a case where an index and an estimated value are calculated by the index value calculation device 20 for cancer determination will be described as an example, but the present invention is not limited to the following aspect.

[0059] The index value calculation device 20 for cancer diagnosis includes an acquisition unit 21, a calculation unit 22, and an output unit 23. The index value calculation device 20 for cancer diagnosis calculates a glycopeptide marker value, an index, and / or an estimated value based on the amount of glycopeptide present measured by the glycopeptide amount measurement device 10, the concentration of the tumor marker calculated by the tumor marker concentration measurement device, and the coefficient of each cancer. The acquisition unit 21 acquires the amount of glycopeptide present in the blood of the subject and the concentration of the tumor marker calculated by the tumor marker concentration measurement device from the glycopeptide amount measurement device 10. The calculation unit 22 calculates the glycopeptide marker value, an index, and / or an estimated value based on the amount of glycopeptide present, the concentration of the tumor marker, and the coefficient of each cancer acquired by the acquisition unit 21. The output unit 23 outputs the index and / or the estimated value calculated by the calculation unit 22. Here, preferred embodiments of the formula for calculating the index and the estimated value include the formulas shown in Equations 1 to 8 above.

[0060] <Cancer diagnosis index value calculation program> The present invention includes a computer program for executing the calculation of the above-mentioned glycopeptide marker value, index and / or estimated value, and a computer-readable recording medium having the program recorded thereon. The recording medium having the program recorded thereon enables the calculation of the above-mentioned glycopeptide marker value, index and / or estimated value by causing a computer to execute the program.

[0061] FIG. 19 is a diagram showing an example of the hardware configuration of an information processing device. The information processing device 90 shown in FIG. 19 has a general computer configuration. The cancer diagnosis index value calculation device 20 is realized by using the information processing device 90 shown in FIG. 19. The information processing device 90 in FIG. 19 has a processor 91, a memory 92, a storage unit 93, an input unit 94, an output unit 95, and a communication control unit 96. These are connected to each other by a bus. The memory 92 and the storage unit 93 are computer-readable recording media. The hardware configuration of the information processing device is not limited to the example shown in FIG. 19, and components may be omitted, replaced, or added as appropriate.

[0062] The information processing device 90 has a processor 91 which loads a program stored in a recording medium into a working area of ​​a memory 92 and executes it, and each component, etc. is controlled through the execution of the program, thereby realizing functions consistent with a specified purpose.

[0063] The processor 91 is, for example, a CPU (Central Processing Unit) or a DSP (Digital Signal Processor).

[0064] The memory 92 includes, for example, a random access memory (RAM) and a read only memory (ROM). The memory 92 is also called a main storage device.

[0065] The storage unit 93 is, for example, an erasable programmable ROM (EPROM) or a hard disk drive (HDD, Hard Disk Drive). The storage unit 93 may also include a removable medium, i.e., a portable recording medium. The removable medium is, for example, a universal serial bus (USB) memory or a disk recording medium such as a compact disc (CD) or a digital versatile disc (DVD). The storage unit 93 is also called a secondary storage device.

[0066] The storage unit 93 stores various programs, various data, and various tables used in the information processing device 90 in a readable and writable recording medium. An operating system (OS), various programs, various tables, etc. are stored in the storage unit 93. The information stored in the storage unit 93 may be stored in the memory 92. Moreover, the information stored in the memory 92 may be stored in the storage unit 93.

[0067] The operating system is software that mediates between software and hardware, manages memory space, manages files, and manages processes and tasks. The operating system includes a communication interface. The communication interface is a program that exchanges data with other external devices connected via the communication control unit 96. The external devices include, for example, other information processing devices, external storage devices, etc.

[0068] The input unit 94 includes a keyboard, a pointing device, a wireless remote control, a touch panel, etc. The input unit 94 can also include an input device for video or images such as a camera, and an input device for audio such as a microphone.

[0069] The output unit 95 includes a display device such as an LCD (Liquid Crystal Display), an EL (Electroluminescence) panel, a CRT (Cathode Ray Tube) display, a PDP (Plasma Display Panel), etc., a printer, etc. The output unit 95 may also include an audio output device such as a speaker.

[0070] The communication control unit 96 connects to other devices and controls communication between the information processing device 90 and the other devices. The communication control unit 96 is, for example, a LAN (Local Area Network) interface board, a wireless communication circuit for wireless communication, or a communication circuit for wired communication. The LAN interface board and the wireless communication circuit are connected to a network such as the Internet.

[0071] In the information processing device 90, the processor deploys a program stored in the auxiliary storage unit in an executable manner in the working area of ​​the main storage unit, and controls peripheral devices and the like through the execution of the program. This allows the information processing device to realize functions that meet a predetermined purpose. The main storage unit and the auxiliary storage unit are recording media that can be read by the information processing device.

[0072] (Example of operation) Here, an example of the operation of the index value calculation device 20 for cancer determination will be described. In the following, an example will be described in which an index or an estimated value is calculated by the index value calculation device 20 for cancer determination, but the present invention is not limited to the following aspect. 20 is a diagram showing an example of an operation flow of the index value calculation device for cancer diagnosis. The index value calculation device 20 for cancer diagnosis calculates the abundance of glycopeptides calculated by the glycopeptide abundance measurement device 10. etc. The glycopeptide marker value is calculated based on the amount of glycopeptide present in the blood of the subject. In the glycopeptide abundance measuring device 10, the amount of glycopeptide present in the blood of the subject is calculated in advance. In addition, the index value calculation device 20 for cancer determination calculates a tumor marker value based on the concentration of the tumor marker calculated by the tumor marker concentration measuring device, etc. In the tumor marker concentration measuring device, the concentration of the tumor marker in the blood of the subject is calculated in advance. The index value calculation device 20 for cancer determination calculates an index or an estimated value, which is a value that serves as an index for cancer determination of the subject, based on the glycopeptide marker value, the tumor marker value, and the coefficient of each cancer.

[0073] In S101, the acquisition unit 21 of the cancer diagnosis index value calculation device 20 acquires data on the amount of glycopeptide present in the subject's blood from the glycopeptide amount measurement device 10, and acquires data on the concentration of the tumor marker in the subject's blood from the tumor marker concentration measurement device. The acquisition unit 21 may acquire data on the subject by having a user input the data via an input means or the like, by extracting the data from a memory means in which the data is stored in advance, by acquiring the data from another information processing device, or the like. The acquisition unit 21 may acquire values ​​of parameters equivalent to the amount of glycopeptide present and the concentration of the tumor marker.

[0074] In S102, the calculation unit 22 of the index value calculation device 20 for cancer determination calculates an index or an estimated value, which is a value serving as an index for cancer determination of a subject, based on the information acquired by the acquisition unit 21 and the coefficient for each cancer. The index or estimated value, which is a value serving as an index for cancer determination of a subject, is calculated, for example, by the calculation formulas shown in the above-mentioned formulas 1 to 8, as described above.

[0075] In S103, the output unit 23 of the cancer determination index value calculation device 20 outputs the index or estimated value calculated by the calculation unit 22 to an output device such as a display. The output unit 23 may also output the index or estimated value to another information processing device, etc. The user can recognize the presence or absence of cancer in the subject and the severity of the cancer from the output index or estimated value.

[0076] A program for causing a computer or other machine or device (hereinafter, referred to as a computer, etc.) to realize any of the above functions can be recorded on a recording medium readable by the computer, etc. Then, the computer, etc. can provide the function by reading and executing the program from the recording medium.

[0077] Here, a computer-readable recording medium refers to a recording medium that stores information such as data and programs by electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer, etc. Such a recording medium may be provided with elements that constitute a computer, such as a CPU and memory, and the CPU may be made to execute the program.

[0078] Among such recording media, those that are removable from a computer etc. include, for example, flexible disks, magneto-optical disks, CD-ROMs, CD-R / Ws, DVDs, DATs, 8mm tapes, memory cards and the like.

[0079] Further, examples of recording media fixed to a computer include a hard disk and a ROM. EXAMPLES

[0080] Next, the present invention will be described in more detail with reference to examples. However, the present invention is not limited to the following examples as long as it does not depart from the gist of the present invention.

[0081] [Measurement conditions] Glycopeptides present in serum were measured using liquid chromatography (Agilent 1260 Infinity II, Agilent Technologies) and mass spectrometry (Q-TOF6545, Agilent Technologies) under the following conditions. The liquid chromatography column used was Inertsil ODS4 (inner diameter 2.1 mm, length 100 mm, particle size 2 μm). The eluents used were A: 0.1% formic acid aqueous solution, B: 0.1% formic acid, 90% acetonitrile aqueous solution, and the B ratio was changed linearly from 10% to 56% over 20 minutes. The column oven temperature was 40°C, and the flow rate was 0.3 ml / min. Mass spectrometry was performed in negative mode, with capillary voltage: 4000 V, nebulizer gas volume: 30 psi, and dry gas 8 L / min (350°C).

[0082] [Example 1] [Detection and abundance measurement of α1-antitrypsin, α2-macroglobulin, and transferrin-derived glycopeptides in the serum of healthy subjects and cancer patients] Sera from foreigners (KAC, Sanfuco, Central Link) and Japanese people (Soken Medical Science Institute, LSI Medience, Kyushu Medical Center) were obtained after obtaining informed consent. First, the obtained serum samples were classified into the following groups (Table 1).

[0083] [Table 1]

[0084] Next, 400 μL of acetone was added to 20 μL of serum from each patient, and the mixture was centrifuged at 12,000 rpm for 5 minutes at room temperature to precipitate the proteins. After removing the supernatant, a denaturant containing urea was added to the precipitate to denature the proteins, followed by reductive alkylation. After removing the denaturant and reducing agent, trypsin and lysyl endopeptidase were added to fragment the proteins into peptides. The prepared glycopeptides were analyzed under the above-mentioned conditions using liquid chromatography (Agilent1260 Infinity II, Agilent Technologies) and a mass spectrometer (Q-TOF6545, Agilent Technologies) (hereinafter sometimes referred to as "LC-MS") to analyze the structures of α1-antitrypsin, α2-macroglobulin, and transferrin-derived glycopeptides contained in each serum, and the amount of each glycopeptide present was measured.

[0085] As standard samples, α1-antitrypsin, α2-macroglobulin, and transferrin (all manufactured by Sigma) derived from human plasma were fragmented into peptides using the method described above, and were then measured by LC-MS using the method described above. Peaks corresponding to α1-antitrypsin, α2-macroglobulin, and transferrin in the serum sample were extracted, and the glycan binding positions and glycan structures were determined from the mass numbers and MSMS results, confirming that the standard reagents and serum samples were consistent.

[0086] The abundances of α1-antitrypsin, α2-macroglobulin, and transferrin-derived glycopeptides were calculated from the areas of individual peaks obtained by a mass spectrometer, and the abundances of α1-antitrypsin and α2-macroglobulin-derived glycopeptides having the amino acid sequences and glycan structures in Table 2 were divided by the abundance of transferrin-derived glycopeptides to obtain the corrected values ​​(hereinafter referred to as "glycopeptides"). The glycopeptide marker values ​​in serum samples from Group 1 (non-cancer patients / healthy individuals) and Groups 2 to 7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients) were compared, and the AUC values ​​of the ROC curve were calculated. The results are shown in Tables 3 to 5. The AUC values ​​were calculated as follows.

[0087] The samples to be compared were divided into two groups (Group 1 (non-cancer patients / healthy subjects) and Groups 2-7 (lung cancer, colon cancer, stomach cancer, pancreatic cancer, esophageal cancer, and liver cancer patients)), and the cutoff (threshold) of the marker to be used for calculating the AUC value was changed from 0 to infinity to plot the sensitivity (positive rate of each cancer patient) and 1-specificity (negative rate of non-cancer patients / healthy subjects) to create an ROC curve. The ROC curve is drawn in a square that is 1x1 in height, and when sensitivity = 1 and specificity = 1 (i.e., when the cancer patient group can be completely distinguished from non-cancer patients / healthy subjects), it is a line that passes through the upper left vertex. The AUC (Area Under Curve) value is the area of ​​the lower right part of the square divided by the ROC curve (when sensitivity = 1 and specificity = 1, the AUC is 1).

[0088] In Table 3, the AUC values ​​of the ROC curves for Group 1 (non-cancer patients / healthy subjects) and Groups 2-7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients) were calculated for the values ​​corrected by dividing the abundance of each α1-antitrypsin-derived glycopeptide by the abundance of the transferrin-derived glycopeptide (TRFE_432_A2G2S2). The * in Table 3 indicates the combination of glycan binding site and glycan structure for each cancer with the highest AUC.

[0089] In Table 4, the AUC values ​​of the ROC curves for Group 1 (non-cancer patients / healthy subjects) and Groups 2 to 7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients) were calculated for the corrected values ​​obtained by dividing the abundance of each glycopeptide derived from α2-macroglobulin by the abundance of the transferrin-derived glycopeptide (TRFE_432_A2G2S2). The * in Table 4 indicates the combination of glycan binding site and glycan structure for each cancer that yielded the highest AUC.

[0090] In Table 5, the AUC values ​​of the ROC curves for the abundance of transferrin-derived glycopeptides were calculated for Group 1 (non-cancer patients / healthy subjects) and Groups 2 to 7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients). The * in Table 5 indicates the combination of glycan binding site and glycan structure for each cancer that yielded the highest AUC.

[0091] [Table 2]

[0092] [Table 3]

[0093] [Table 4]

[0094] [Table 5]

[0095] From Table 3, it can be seen that, in α1-antitrypsin-derived glycopeptides, the glycopeptide (A1AT_271_A2G2S2) having an A2G2S2 glycan bound to the amino acid sequence YLGNATAIFFLPDEGK (sequence number 6) (glycosylation site: Asn271) in lung cancer, colon cancer, gastric cancer, and liver cancer, the glycopeptide (A1AT_70_A2G2S2) having an A2G2S2 glycan bound to the amino acid sequence QLAHQSNSTNIFFSPVSIATAFAMLSLGTK (sequence number 4) (glycosylation site: Asn70) in pancreatic cancer, and the glycopeptide (A1AT_107_A2G2S2) having an A2G2S2 glycan bound to the amino acid sequence ADTHDEILEGLNFNLTEIPEAQIHEGFQELLR (sequence number 5) (glycosylation site: Asn107) in esophageal cancer tend to have a higher ability to identify cancer than other combinations. Furthermore, in all cancer types, A2G2S2 showed a higher AUC than the glycan structure of A2G2S2F1.

[0096] In addition, Table 4 shows that, in α2-macroglobulin-derived glycopeptides, the glycopeptide (A2M_70_A2G2S2) in which the glycan of A2G2S2 is bound to the amino acid sequence GNR (SEQ ID NO: 8) (glycosylation position: Asn70) in lung cancer, colon cancer, and liver cancer, and the glycopeptide (A2M_70_A2G2S2F1) in which the glycan of A2G2S2F1 is bound to the amino acid sequence GNR (SEQ ID NO: 8) (glycosylation position: Asn70) in gastric cancer, pancreatic cancer, and esophageal cancer tend to have higher cancer discrimination power than other combinations. In addition, in all cancer types, the amino acid sequence GNR (SEQ ID NO: 8) (glycosylation position: Asn70) showed a higher AUC than other amino acid sequences and glycosylation positions.

[0097] Furthermore, Table 5 shows that, in transferrin-derived glycopeptides, the glycopeptide (TRFE_432_A2G2S2) in which the A2G2S2 glycan is bound to the amino acid sequence CGLVPVLAENYNK (sequence number 10) (glycan binding position: Asn432) tends to have high cancer discrimination ability, regardless of the cancer type.

[0098] [Example 2] [Validation of glycopeptide markers] Using low concentration (serum from healthy subjects), medium concentration (1:1 mixture of serum from healthy subjects and serum from cancer patients), and high concentration (serum from cancer patients), the intra-day and inter-day reproducibility of the glycopeptide markers of α1-antitrypsin-derived glycopeptide (A1AT_271_A2G2S2) abundance / transferrin-derived glycopeptide (TRFE_432_A2G2S2) abundance and α2-macroglobulin-derived glycopeptide (A2M_70_A2G2S2) abundance / transferrin-derived glycopeptide (TRFE_432_A2G2S2) abundance was confirmed. The glycopeptide abundance was measured in the same manner as in Example 1.

[0099] For intraday reproducibility, pretreatment and measurement were repeated for three days (Days 1 to 3) for low-, medium-, and high-concentration samples (n = 5 for each sample), and the CV value (mean value of glycopeptide markers / standard deviation for each sample n = 5) for each day was calculated (Table 6). From Table 6, the intraday reproducibility CV value of A1AT_271_A2G2S2 / TRFE_432_A2G2S2 was 0.6 to 3.1%, and that of A2M_70_A2G2S2 / TRFE_432_A2G2S2 was 6.5 to 23.4%.

[0100] For inter-day reproducibility, pretreatment and measurement were repeated for three days (Days 1 to 3) for low-, medium-, and high-concentration samples (n=5 for each sample), and the CV values ​​(mean value of glycopeptide markers on Days 1 to 3 (n=3) / standard deviation) were calculated (Table 6). From Table 6, the inter-day reproducibility CV values ​​of A1AT_271_A2G2S2 / TRFE_432_A2G2S2 were 0.4 to 2.0%, and those of A2M_70_A2G2S2 / TRFE_432_A2G2S2 were 4.9 to 8.6%.

[0101] [Table 6]

[0102] [Example 3] [Measurement of existing tumor markers in the serum of healthy individuals and cancer patients] Using the serum samples from groups 1 to 7 used in Example 1, the concentrations of AFP, HS-PSA, CA125, CA19-9, CEA, CYFRA, CA15-3, SCC antigen, NC-ST439, and SPan-1 antigen (hereinafter sometimes referred to as "tumor markers") were also measured by CLIA (chemiluminescence immunoassay) and IRMA (immunoradiometric assay).

[0103] The tumor marker values ​​in serum samples from Group 1 (non-cancer patients / healthy subjects) and Groups 2 to 7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients) shown in Table 1 were compared, and the AUC value of the ROC curve was calculated in the same manner as in Example 1 (Table 7). Note that * in Table 7 indicates each Cancer tumor markers with particularly high AUCs are shown.

[0104] [Table 7]

[0105] Table 7 shows that CEA and CYFRA tend to have high cancer discrimination ability in patients with lung cancer, colon cancer, and gastric cancer, CA125, CA19-9, and SPan-1 antigen in pancreatic cancer, CYFRA and SCC antigen in esophageal cancer, and AFP and CEA in liver cancer.

[0106] [Example 4] [Cancer diagnosis by combining the glycopeptide marker of the present invention with existing tumor markers] A combination of a glycopeptide marker and at least one tumor marker selected from AFP, HS-PSA, CA125, CA19-9, CEA, CYFRA, CA15-3, SCC antigen, NC-ST439, and SPan-1 antigen is used as an index for the cancer assessment method. Glycopeptide markers and tumor markers that showed particularly high AUC values ​​for each cancer were selected from Tables 3 to 5 and Table 7 (Table 8). Note that the combination of glycopeptide markers and tumor markers described here is an example of the types of glycopeptides that can be used in the present invention, such as those in Table 2, and the scope of the present invention is not limited thereto.

[0107] [Table 8]

[0108] The glycopeptide markers and tumor markers shown in Table 8 were combined using linear regression and nonlinear regression (logistic regression). The linear regression calculates a value (hereinafter sometimes referred to as "index" in the present invention) obtained by multiplying and adding the explanatory variable X by a coefficient, such as Y=aX1+bX2+cX3+.... On the other hand, the logistic regression applies the explanatory variable X to the formula Y=1 / {1+ e^-(aX1+bX2+cX3+...)} to predict the probability of an event occurring (hereinafter sometimes referred to as "estimated value" in the present invention).

[0109] First, glycopeptide abundance and tumor marker concentration were obtained from the serum samples of groups 1 to 7 used in Example 1 by the same methods as in Examples 1 and 2. Next, these serum samples were divided into a training set and a test set at a 1:1 ratio (Table 9).

[0110] [Table 9]

[0111] In the linear regression method, in order to align the number of digits with the glycopeptide marker values, the tumor marker concentrations of the serum samples shown in Table 9 were logarithmically transformed (Log10). Next, each glycopeptide marker value (normalized after correction for α1-antitrypsin and / or α2-macroglobulin / transferrin) and tumor marker concentration were normalized so that the mean value was 0 and the standard deviation was 1.

[0112] In addition, a Student's T.TEST was performed between the two groups of healthy subjects and cancer patients in the training set, and each marker was weighted to maximize the p-value. The index was calculated for the test set samples using the weighting coefficients determined in the training set. The method for calculating the index is shown below.

[0113] In the linear regression method, the index for each cancer was calculated using the following formula: Lung, colon and stomach cancer index = A × (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B × (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C × (CEA concentration) + D × (CYFRA concentration), pancreatic cancer index = A × (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B × (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C × (CA19-9 concentration) + D × (SPan-1 antigen concentration). ) + E × (CA125 concentration), esophageal cancer index = A × (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B × (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C × (CYFRA concentration) + D × (SCC antigen concentration), liver cancer index = A × (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B × (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C × (CEA concentration) + D × (AFP concentration). The coefficients A to E for each cancer are shown in Table 10.

[0114] [Table 10]

[0115] In the logistic regression method, the blood values ​​shown in Table 9 were used to align the digits with those of the glycopeptide markers. Tumor marker concentrations in supernatant samples were logarithmically transformed (Log10).

[0116] Next, the training set was used to weight each marker using the maximum likelihood method, and estimates were calculated using the test set samples. The method for calculating estimates is shown below.

[0117] Using the logistic regression method, estimates for each cancer were calculated using the following formula: Estimated values ​​for lung, colon and stomach cancers = 1 / [1+ e^ -{A x (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B x (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C x (CEA concentration) + D x (CYFRA concentration) + E}], estimated value for pancreatic cancer = 1 / [1+ e^ -{A x (A1AT_271_A2G2S2 / TRFE_432_A2G2S2) + B x (A2M_70_A2G2S2 / TRFE_432_A2G2S2) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) + F}], estimated value for esophageal cancer = 1 / [1+ e^ -{A×(A1AT_271_A2G2S2 / TRFE_432_A2G2S2)+B×(A2M_70_A2G2S2 / TRFE_432_A2G2S2)+C×(CYFRA concentration)+D×(SCC antigen concentration)+E}], liver cancer estimated value = 1 / [1+e^ -{A×(A1AT_271_A2G2S2 / TRFE_432_A2G2S2)+B×(A2M_70_A2G2S2 / TRFE_432_A2G2S2)+C×(CEA concentration)+D×(AFP concentration)+E}]. The coefficients A to F for each cancer are shown in Table 11.

[0118] [Table 11]

[0119] In order to compare the index (linear regression method) and estimated value (logistic regression method) of each cancer calculated by the above method between group 1 (non-cancer patients / healthy subjects) and groups 2 to 7 (lung cancer, colon cancer, gastric cancer, pancreatic cancer, esophageal cancer, and liver cancer patients) in the test set, box plots (Figures 4 to 15) were created and the AUC value of the ROC curve was calculated. The AUC value of each cancer was calculated by the same method as in Example 1, and the results are shown in Table 12.

[0120] Box plots in Figures 4 to 15 are grouped by race and specimen collection facility (e.g., Japanese (1), Japanese (2), Foreigner (1), and Foreigner (2)). For healthy subjects, no facility differences were observed for any cancer type. For cancer patients, facility differences were observed for lung cancer, pancreatic cancer, and esophageal cancer. Additionally, differences were observed between healthy subjects and cancer patients in the index and estimated values ​​for each cancer type.

[0121] Next, the cut-off value was determined so that the sensitivity (= number of positive cancer patients / total number of cancer patients) was 50% or more and the specificity (= number of negative healthy subjects / total number of healthy subjects) was 98% or more for the index and estimate of each cancer. The cut-off values ​​for each cancer are as follows: Linear regression cut-off value (lung cancer: 0.587, colon cancer: 0.543, gastric cancer: 0.717, pancreatic cancer: 0.510, esophageal cancer: 1.05, liver cancer: 1.31), logistic regression cut-off value (lung cancer: 0.700, colon cancer: 0.833, gastric cancer: 0.713, pancreatic cancer: 0.900, esophageal cancer: 0.367, liver cancer: 0.480). The sensitivity and specificity of each cancer are shown in Table 12.

[0122] Results above the cut-off value were judged as positive, and results below the cut-off value were judged as negative, and the positive predictive value for each cancer (= number of positive cancer patients / total number of positive cases) was calculated (Table 12).

[0123] Furthermore, Table 12 shows that there was no significant difference in the AUC, sensitivity, specificity, or positive predictive value for each cancer between the linear regression method and the logistic regression method.

[0124] In addition, similar results to those in Table 12 were obtained when glycopeptide markers were combined with one of AFP, HS-PSA, CA125, CA19-9, CEA, CYFRA, CA15-3, SCC antigen, NC-ST439, or SPan-1 antigen.

[0125] [Table 12]

[0126] [Example 5] [Analysis of lung cancer by stage and histological type] In a manner similar to that used in Example 3, A1AT_271_A2G2S2 / TRFE_432_A2G2S2, A2M_70_A2G2S2 / TRFE_432_A2G2S2, CEA and CYFRA were combined, an index was calculated by linear regression, and analyses were performed by lung cancer stage and histological type.

[0127] First, the glycopeptide abundance, CEA concentration, and CYFRA concentration were obtained in the same manner as in Examples 1 and 2. For the serum samples used, the analysis by lung cancer stage is shown in Table 13, and the analysis by lung cancer tissue type is shown in Table 14.

[0128] [Table 13]

[0129] [Table 14]

[0130] As can be seen from Fig. 16, the index increases in both Japanese and foreigners as the stage of lung cancer progresses. Fig. 16 suggests that the use of the present invention makes it possible to detect cancer at an earlier stage. Also, Fig. 17 shows a tendency for the index to increase in squamous cell carcinoma and adenosquamous carcinoma more than in adenocarcinoma. [Industrial Applicability]

[0131] The present invention provides a method for determining cancer with high sensitivity and specificity.

[0132] <Description of sequence listing> SEQ ID NO: 1: Amino acid sequence of α1-antitrypsin (human) SEQ ID NO:2: Amino acid sequence of α2-macroglobulin (human) SEQ ID NO: 3: Amino acid sequence of transferrin (human) SEQ ID NO: 4: Amino acid sequence of glycopeptide 1 of α1-antitrypsin SEQ ID NO: 5: Amino acid sequence of glycopeptide 2 of α1-antitrypsin SEQ ID NO: 6: Amino acid sequence of glycopeptide 3 of α1-antitrypsin SEQ ID NO: 7: Amino acid sequence of glycopeptide 1 of α2-macroglobulin SEQ ID NO: 8: Amino acid sequence of glycopeptide 2 of α2-macroglobulin SEQ ID NO: 9: Amino acid sequence of glycopeptide 3 of α2-macroglobulin SEQ ID NO: 10: Amino acid sequence of glycopeptide 1 of transferrin SEQ ID NO: 11: Amino acid sequence of glycopeptide 2 of transferrin SEQ ID NO: 12: Amino acid sequence of glycopeptide 3 of transferrin

Claims

1. A method for determining cancer, which uses as an index the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin present in blood collected from a human, corrected by the amount of glycopeptides derived from transferrin.

2. The glycopeptide used in the cancer diagnosis method is an α1-antitrypsin peptide having a sugar chain bound to the 70th or 271st asparagine residue of the amino acid sequence set forth in SEQ ID NO:1; an α2-macroglobulin peptide having a sugar chain bound to the asparagine residue at position 70 or 1424 of the amino acid sequence set forth in SEQ ID NO: 2; A transferrin peptide having a sugar chain bound to the 432nd or 630th asparagine residue in the amino acid sequence set forth in SEQ ID NO:

3. The method for determining cancer according to claim 1.

3. The glycan structure of the glycopeptide used in the cancer diagnosis method is the α1-antitrypsin peptide is A2G2S2 or A2G2S2F1; the α2-macroglobulin peptide is A2G2S2 or A2G2S2F1; the transferrin peptide is A2G2S2 or A3G3S2; The method for determining cancer according to claim 1.

4. The amino acid sequence of the glycopeptide used in the cancer diagnosis method is α1-antitrypsin peptide YLGNATAIFFLPDEGK (SEQ ID NO: 6) or QLAHQSNSTNIFFSPVSIATAFAMLSLGTK (SEQ ID NO: 4); The α2-macroglobulin peptide is GNR (SEQ ID NO: 8) or VSNQTLSLFFTVLQDVPVR (SEQ ID NO: 9), The transferrin peptide comprises CGLVPVLAENYNK (SEQ ID NO: 10) or QQQHLFGSNVTDCSGNFCLFR (SEQ ID NO: 11); The method for determining cancer according to claim 1.

5. In addition to the corrected value of the amount of glycopeptide present, the concentration of at least one tumor marker selected from the group consisting of CEA, CYFRA, CA19-9, SPan-1 antigen, CA125, SCC antigen, and AFP is used in combination as an index for the cancer assessment method. The method for determining cancer according to claim 1.

6. A method for combining the correction value of the abundance of the glycopeptide used in the cancer assessment method with the concentration of the tumor marker is a linear regression method or a logistic regression method. The method for determining cancer according to claim 5 .

7. the cancer is selected from lung cancer, colon cancer, and gastric cancer; a method of combining the correction value of the amount of glycopeptide present used in the cancer assessment method with the concentration of at least one tumor marker selected from CEA and CYFRA is a linear regression method; The index calculated based on the following formula 1 is used as an index. The method for determining cancer according to claim 6. Formula 1: Index = A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (CYFRA concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value obtained by mass spectrometry of the subject's blood by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A>0, B>0, C>0, D>0, A+B>0, C+D>0, and A+B+C+D=1.

8. The values ​​of A to D shown in the formula 1 are within the ranges of A = 0.119 to 0.540, B = 0.089 to 0.345, C = 0.035 to 0.405, and D = 0.150 to 0.465, respectively. The method for determining cancer according to claim 7.

9. the cancer is selected from lung cancer, colon cancer, and gastric cancer; a method of combining the corrected value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CEA and CYFRA, is a logistic regression method; The estimated value calculated based on the following formula 2 is used as an index. The method for determining cancer according to claim 6. Equation 2: Estimated value = 1 / [1 + e^-{A x (amount of α1-antitrypsin-derived glycopeptide / amount of transferrin-derived glycopeptide) + B x (amount of α2-macroglobulin-derived glycopeptide / amount of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (CYFRA concentration) + E}] Note that the concentration of each tumor marker is the logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0.

10. The values ​​of A to E shown in the formula 2 are within the ranges of A = 6.56 to 38.9, B = 5.54 to 28.8, C = 0.320 to 5.10, D = 1.69 to 6.71, and E = -6.44 to -32.9, respectively. The method for determining cancer according to claim 9 .

11. The cancer is pancreatic cancer, a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CA19-9, SPan-1 antigen, and CA125, is a linear regression method; The index calculated based on the following formula 3 is used as an index: The method for determining cancer according to claim 6. Formula 3: Index = A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value of the test value obtained by mass spectrometry of the subject's blood by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A to E are coefficients for each cancer, where A>0, B>0, C>0, D>0, E>0, A+B>0, C+D+E>0, and A+B+C+D+E=1.

12. The values ​​of A to E shown in the formula 3 are within the ranges of A = 0.140 to 0.420, B = 0.135 to 0.406, C = 0.110 to 0.330, D = 0.049 to 0.148, and E = 0.065 to 0.196, respectively. The method for determining cancer according to claim 11.

13. The cancer is pancreatic cancer, a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CA19-9, SPan-1 antigen, and CA125, is a logistic regression method; The estimated value calculated based on the following formula 4 is used as an index. The method for determining cancer according to claim 6. Equation 4: Estimated value = 1 / [1 + e^-{A x (amount of α1-antitrypsin-derived glycopeptide / amount of transferrin-derived glycopeptide) + B x (amount of α2-macroglobulin-derived glycopeptide / amount of transferrin-derived glycopeptide) + C x (CA19-9 concentration) + D x (SPan-1 antigen concentration) + E x (CA125 concentration) + F}] However, the concentration of each tumor marker refers to the logarithmic transformation (log10) of the blood test value of the subject. A to E are coefficients for each cancer, where A≧0, B≧0, C≧0, D≧0, E≧0, A+B>0, C+D+E>0.

14. The values ​​of A to F shown in the formula 4 are within the ranges of A = 6.99 to 21.0, B = 3.49 to 10.5, C = 0.255 to 0.765, D = 5.66 to 17.0, E = 1.02 to 3.06, and F = -36.8 to -12.3, respectively. The method for determining cancer according to claim 13.

15. The cancer is esophageal cancer, a method of combining the correction value of the amount of glycopeptide present, which is used in the cancer assessment method, with the concentration of at least one of tumor markers selected from CYFRA and SCC antigen is a linear regression method; The index calculated based on the following formula 5 is used as an index. The method for determining cancer according to claim 6. Equation 5: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CYFRA concentration) + D x (SCC antigen concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value of the values ​​obtained by mass spectrometry by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A>0, B>0, C>0, D>0, A+B>0, C+D>0, and A+B+C+D=1.

16. The values ​​of A to D shown in the above formula 5 are within the ranges of A = 0.158 to 0.474, B = 0.097 to 0.290, C = 0.072 to 0.216, and D = 0.174 to 0.522, respectively. The method for determining cancer according to claim 15.

17. The cancer is esophageal cancer, a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CYFRA and SCC antigen is a logistic regression method; The estimated value calculated based on the following formula 6 is used as an index. The method for determining cancer according to claim 6. Equation 6: Estimated value = 1 / [1 + e^-{A x (abundance of α1-antitrypsin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + B x (abundance of α2-macroglobulin-derived glycopeptide / abundance of transferrin-derived glycopeptide) + C x (CYFRA concentration) + D x (SCC antigen concentration) + E}] However, the concentration of each tumor marker refers to the logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0.

18. The values ​​of A to E shown in the formula 6 are within the ranges of A = 12.6 to 37.9, B = 5.85 to 17.6, C = 0.915 to 2.75, D = 2.80 to 8.39, and E = -33.2 to -11.1, respectively. The method for determining cancer according to claim 17.

19. The cancer is liver cancer, a method of combining the correction value of the amount of glycopeptide present used in the cancer assessment method with the concentration of at least one tumor marker selected from CEA and AFP is a linear regression method; The index calculated based on the following formula 7 is used as an index. The method for determining cancer according to claim 6. Equation 7: Index = A x (α1-antitrypsin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + B x (α2-macroglobulin-derived glycopeptide abundance / transferrin-derived glycopeptide abundance) + C x (CEA concentration) + D x (AFP concentration) Here, the abundance of α1-antitrypsin-derived glycopeptide, the abundance of α2-macroglobulin-derived glycopeptide, and the abundance of transferrin-derived glycopeptide are values ​​obtained by dividing the deviation from the average value of the values ​​obtained by mass spectrometry by the standard deviation, The concentration of each tumor marker is calculated by dividing the deviation from the mean value by the standard deviation after logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A>0, B>0, C>0, D>0, A+B>0, C+D>0, and A+B+C+D=1.

20. The values ​​of A to D shown in the above formula 7 are A = 0.036 to 0.108, B = 0.317 to 0.951, C = 0.045 to 0.134, and D = 0.147 to 0.

440. The method for determining cancer according to claim 19.

21. The cancer is liver cancer, a method of combining a correction value of the amount of glycopeptide present, used in the cancer assessment method, with the concentration of at least one tumor marker selected from CEA and AFP, is a logistic regression method; The estimated value calculated based on the following formula 8 is used as an index. The method for determining cancer according to claim 6. Equation 8: Estimated value = 1 / [1 + e^-{A x (amount of α1-antitrypsin-derived glycopeptide / amount of transferrin-derived glycopeptide) + B x (amount of α2-macroglobulin-derived glycopeptide / amount of transferrin-derived glycopeptide) + C x (CEA concentration) + D x (AFP concentration) + E}] However, the concentration of each tumor marker refers to the logarithmic transformation (log10) of the blood test value of the subject. A to D are coefficients for each cancer, where A≧0, B≧0, C≧0, D≧0, A+B>0, C+D>0.

22. The values ​​of A to E shown in the above formula 8 are within the ranges of A = 7.61 to 22.8, B = 18.2 to 54.6, C = 1.03 to 3.09, D = 1.28 to 3.84, and E = -29.3 to -9.76, respectively. The method for determining cancer according to claim 21.

23. The cancer is lung cancer, The corrected value of the amount of glycopeptide used in the cancer assessment method and the concentration of at least one of the tumor markers CEA and CYFRA are used as an index to assess squamous cell carcinoma. Determine, The method for determining cancer according to any one of claims 7 to 10.

24. A cancer detection kit comprising a reagent for measuring the amount of glycopeptide derived from α1-antitrypsin and / or a reagent for measuring the amount of glycopeptide derived from α2-macroglobulin in blood, and a reagent for measuring the amount of glycopeptide derived from transferrin.

25. an acquisition unit for acquiring the amount of glycopeptide derived from α1-antitrypsin and / or α2-macroglobulin and glycopeptide derived from transferrin present in blood collected from a subject; a calculation unit that calculates a value obtained by correcting the abundance of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, which is an index value for cancer diagnosis of the subject, based on the abundance of the glycopeptides acquired by the acquisition unit, by the abundance of glycopeptides derived from transferrin; and an output unit that outputs the value calculated by the calculation unit as an index for cancer determination. An apparatus for calculating index values ​​for cancer diagnosis.

26. The computer Obtaining the amount of glycopeptides derived from α1-antitrypsin and / or α2-macroglobulin, and glycopeptides derived from transferrin present in blood collected from a test subject; Based on the amount of glycopeptide obtained, a value is calculated by correcting the amount of glycopeptide derived from α1-antitrypsin and / or α2-macroglobulin, which is an index value for cancer diagnosis in the subject, with the amount of glycopeptide derived from transferrin; A program for calculating an index value for cancer diagnosis, for executing outputting the calculated value that serves as an index for cancer diagnosis.

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