Biliary tract cancer screening methods

Abstract

The present invention provides a means and a method for determining biliary tract cancer in a non-invasive and simple manner. Specifically, the present invention relates to a method, a device, and a kit for determining biliary tract cancer in a subject by measuring a tumor marker in urine from a urine sample derived from a subject.

Description

Technical Field

[0001] The present invention relates to a method, kit and device for determining biliary tract cancer of a subject based on the measured value of urinary tumor markers.

Background Art

[0002] Biliary tract cancer is a cancer with a relatively large number of cases in Japan, and since the 5-year survival rate, which is an indicator of prognosis, is also low, it is desirable to detect and treat it at an early stage. As tumor markers for biliary tract cancer for early detection, CEA, CA19-9, etc. in the blood are known, but the tumor specificity is low and the accuracy is not high. In addition, blood tests are invasive and are substantially limited to tests performed at medical institutions.

[0003] As tumor markers for cancers such as biliary tract cancer, for example, there are reports in Patent Documents 1 to 5. In Patent Document 1, genes and proteomics are mainly described as tumor markers. In Patent Documents 2 to 5, metabolites are described as tumor markers for cancers such as colorectal cancer, but the urinary tumor markers disclosed in this specification are not described.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Patent Document 5

Summary of the Invention

Problems to be Solved by the Invention

[0005] The present invention aims to provide means and methods for non-invasively and easily determining biliary tract cancer, which have been desired for some time. [Means for solving the problem]

[0006] In the process of searching for tumor markers in cancer urine, the inventors identified a group of markers associated with biliary tract cancer and found that by using these markers individually or in combination, biliary tract cancer can be diagnosed, risk predicted, and monitored in a simple and non-invasive manner.

[0007] In other words, the present invention relates to a method, apparatus, and kit for determining and / or monitoring biliary tract cancer in a subject by measuring urinary metabolites that are urinary tumor markers. Specific embodiments include the following:

[0008] [1] A method for determining biliary tract cancer in a subject, A step of measuring urinary tumor markers in a urine sample derived from a target, wherein the urinary tumor markers are cholates, chenodeoxycholic acid sulfate, compounds measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), glycochenodeoxycholic acid 3-sulfate, isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, glycocholates, compounds measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), compounds measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholic acid, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvic acid The above step comprises at least one urinary tumor marker selected from salts, lactates, cyclo(pro-hydroxypro), tryptophan, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured as having a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured as having a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsterone glucuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosterone glucuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolone glucuronide; Steps to determine biliary tract cancer in the subject based on the above measurement results. Methods that include...

[0009] [2] A device for detecting biliary tract cancer, A measuring unit for measuring urinary tumor markers in a urine sample, wherein the urinary tumor markers are cholates, chenodeoxycholic acid sulfate, compounds measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), glycochenodeoxycholic acid 3-sulfate, isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, glycocholates, compounds measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), compounds measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholic acid, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvate, A measurement unit comprising at least one urinary tumor marker selected from lactate, cyclo(pro-hydroxypro), tryptophan, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured with a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured with a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsterone glucuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosterone glucuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolone glucuronide, A comparison unit compares the measured value of the urinary tumor marker measured by the above measurement unit with a reference value or the previous measured value. The comparison unit used to determine biliary tract cancer is used to determine the results of the comparison obtained from the above comparison unit. A device characterized by being equipped with the following features.

[0010] [3] A kit for diagnosing biliary tract cancer, Cholates, chenodeoxycholic acid sulfate, compounds measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), glycochenodeoxycholic acid 3-sulfate, isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, glycocholates, compounds measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), compounds measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholate, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvate, lactate, cyclo(pro-hydroxypro), trypto A kit comprising means for measuring at least one urinary tumor marker selected from phan, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured with a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured with a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsteronylcuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosteronylcuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolonecuronide.

[0011] [4] A method for evaluating the effectiveness of treatment for biliary tract cancer, A step of measuring urinary tumor markers in a urine sample from a patient with biliary tract cancer who has been treated with an investigational drug or treatment, wherein the urinary tumor marker is a compound measured as a cholate, chenodeoxycholate sulfate, a compound measured as having a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), glycochenodeoxycholate 3-sulfate, isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, glycocholate, a compound measured as having a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), a compound measured as having a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholate, isoleucylglycine, phenylalanylhydroxyproline, 4 -Hydroxyphenylpyruvate, lactate, cyclo(pro-hydroxypro), tryptophan, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, compound measured as having a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode (C14H23N3O6), compound measured as having a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode (C6H11NO3), androsterone glucuronide, 3- The above step comprises at least one urinary tumor marker selected from hydroxyanthranilate, 11-beta-hydroxyandrosteronulcuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolonulcuronide. A step to evaluate the effectiveness of the investigational drug or treatment for biliary tract cancer based on the above measurement results. A method that includes this.

[0012] This specification incorporates the disclosure of Japanese Patent Application No. 2022-090965 filed on June 3, 2022, which is the basis of the priority of this application.

Advantages of the Invention

[0013] The present invention provides a method, apparatus, and kit for determining biliary tract cancer with low invasiveness, simplicity, and low cost. Since the test is performed using urine, the sampling method at the clinical site is also very simple, greatly improving the convenience for medical staff. Therefore, the present invention is useful in the fields of diagnosis, examination, treatment evaluation, and drug discovery of biliary tract cancer.

Brief Description of the Drawings

[0014] [Figure 1] It is a graph showing the top 30 metabolites when urinary metabolites related to biliary tract cancer are ranked by Random Forest (RF). [Figure 2] It is a graph (A) showing the calculation results of predicted values when 10 markers are applied to a cancer test model for biliary tract cancer, and a graph (B) showing the AUC of the cancer test model. [Figure 3] It is a graph (A) showing the calculation results of predicted values when 10 markers are applied to a cancer test model for biliary tract cancer, and a graph (B) showing the AUC of the cancer test model. [Figure 4] It is a graph (A) showing the calculation results of predicted values when 20 markers are applied to a cancer test model for biliary tract cancer, and a graph (B) showing the AUC of the cancer test model. [Figure 5] It is a graph (A) showing the calculation results of predicted values when 20 markers are applied to a cancer test model for biliary tract cancer, and a graph (B) showing the AUC of the cancer test model. [Figure 6] It is a graph (A) showing the calculation results of predicted values when 6 markers considered important for biliary tract cancer are applied to a cancer test model, and a graph (B) showing the AUC of the cancer test model. [Figure 7]Graph (A) showing the calculation results of prediction values when three markers considered important for biliary tract cancer are applied to a cancer test model, and graph (B) showing the AUC of the cancer test model. [Figure 8] Graph (A) showing the calculation results of prediction values when two markers considered important for biliary tract cancer are applied to a cancer test model, and graph (B) showing the AUC of the cancer test model. [Figure 9] Shows a configuration example of the apparatus to which the present invention is applied. [Figure 10] Graph showing the top 20 metabolites when urinary metabolites related to the difference between biliary tract cancer and healthy subjects are ranked by random forest (RF). [Figure 11] Graph (A) showing the calculation results of prediction values when twenty markers considered important for biliary tract cancer are applied to a cancer test model, and graph (B) showing the AUC of the cancer test model. [Figure 12] Graph (A) showing the calculation results of prediction values when ten markers considered important for biliary tract cancer are applied to a cancer test model, and graph (B) showing the AUC of the cancer test model. [Figure 13] Graph (A) showing the calculation results of prediction values when five markers considered important for biliary tract cancer are applied to a cancer test model, and graph (B) showing the AUC of the cancer test model.

Embodiments for Carrying Out the Invention

[0015] In the method, apparatus, and kit provided by the present invention, novel urinary tumor markers and marker groups related to biliary tract cancer are utilized. Since this urinary tumor marker is a metabolite with a difference in its urinary level related to the presence or absence of biliary tract cancer, it is useful for the detection of biliary tract cancer, risk prediction of biliary tract cancer, stage determination of biliary tract cancer, prognosis determination of biliary tract cancer, monitoring of biliary tract cancer, and / or monitoring of the treatment effect for biliary tract cancer.

[0016] The method for determining biliary tract cancer according to the present invention includes the steps of measuring urinary tumor markers in a urine sample derived from a subject, and determining biliary tract cancer in the subject based on the measurement results.

[0017] This invention relates to the diagnosis of biliary tract cancer. Biliary tract cancer refers to cancer (malignant tumor) that develops in the bile ducts and is classified into intrahepatic cholangiocarcinoma, extrahepatic cholangiocarcinoma (hilar cholangiocarcinoma, distal cholangiocarcinoma), gallbladder cancer, and ampullary cholangiocarcinoma. Biliary tract cancer can be primary, metastatic, or recurrent, and is further classified into stages based on its progression and extent of spread. The necessary treatment (surgical intervention, chemotherapy, radiotherapy, immunotherapy, etc.) differs depending on whether the cancer is primary, metastatic, or recurrent, and on its stage.

[0018] In one embodiment, the step of measuring urinary tumor markers involves measuring urinary tumor markers associated with biliary tract cancer. In this invention, the "urinary metabolites" or "urinary tumor markers" to be measured refer to the urinary metabolites listed in Table 1 below. Urinary metabolites are structurally stable and less susceptible to enzyme influence compared to substances in the blood, making them highly convenient as tumor markers. Furthermore, since urine is used as the sample, it can be easily collected from the subject, making it very easy to use for cancer screening. A "marker group" is a combination consisting of two or more urinary tumor markers.

[0019] "To measure" means to determine the relative abundance or absolute concentration of a metabolite in a urine sample. Relative abundance is the ratio of the measured intensity of the target metabolite to a standard substance that has been intentionally added. On the other hand, absolute concentration is calculated by creating a calibration curve (relationship between the concentration of a metabolite and the measured intensity of a metabolite) using the same metabolite in advance, and then calculating the absolute concentration from the measured intensity. In this invention, "measuring urinary tumor markers" may mean measuring a metabolite that is a urinary tumor marker, or measuring its derivatives or derivatives. "Derivatives" and "derivatives" mean substances derived from a metabolite that is a urinary tumor marker and substances derived from said metabolite, respectively. "Derivatives" and "derivatives" include, but are not limited to, fragments of metabolites and modified metabolites.

[0020] Table 1 below summarizes the main urinary tumor markers used in this invention. In the table, the "Metabolite" column shows the name of the metabolite whose structure is known as a result of a database search, or, if the structure is unknown, the symbol and estimated chemical formula. The estimated chemical formula is estimated from the "Measured Mass" and "Measurement Mode" and metabolite databases such as the Human Metabolome Database (HMDB). The CAS registry number in Table 1 is the de facto standard for chemical substance IDs and is a number used to identify chemical substances, while the HMDB ID is the ID of the HMDB, an online database of small molecule metabolites in the human body. In Table 1, the "Measured Mass" column shows the mass-to-charge ratio when detected by the detection means described in the "Measurement Mode" column. In the "Measurement Mode" column, "Neg," "Pos Early," and "Polar" refer to "the negative ion detection mode of the liquid chromatography-mass spectrometer (LC / MS)," "the positive ion detection mode of the liquid chromatography-mass spectrometer (LC / MS) optimized for hydrophilic compounds," and "the negative ion detection mode of the liquid chromatography-mass spectrometer (LC / MS) using a hydrophilic interaction liquid chromatography (HILIC) column, optimized for polar compounds," respectively. In this specification, "LC / MS pos early" is also simply referred to as "the positive ion detection mode of the liquid chromatography-mass spectrometer (LC / MS)." The measured masses in Table 1 are basically the masses of ionized metabolites, with one proton added or lost, resulting in a variation of ±1 from the original metabolite mass. However, depending on the measurement conditions, multiple protons or sodium may be added or lost, causing the measured mass to fluctuate accordingly. Alternatively, the mass spectrum of fragment ions obtained by cleaving the metabolite with energy may be measured.

[0021] [Table 1] TIFF0007838771000002.tif229159TIFF0007838771000003.tif93159

[0022] In one embodiment, the cholate shown in Table 1 is measured. Specifically, the compound measured with a mass of 407.280 in LC / MS negative ion detection mode is measured.

[0023] In one embodiment, the chenodeoxycholic acid sulfate (2) shown in Table 1 is measured. That is, the compound measured with a mass of 235.118 in LC / MS negative ion detection mode is measured.

[0024] In one embodiment, X-17686 (C10H12O6S) shown in Table 1 is measured. That is, the compound (C10H12O6S) measured with a mass of 259.028 in LC / MS negative ion detection mode is measured.

[0025] In one embodiment, glycochenodeoxycholic acid 3-sulfate, as shown in Table 1, is measured. Specifically, the compound measured with a mass of 263.628 in LC / MS negative ion detection mode is measured.

[0026] In one embodiment, isoleucylhydroxyproline shown in Table 1 is measured. Specifically, the compound measured with a mass of 245.150 in LC / MS positive ion detection mode is measured.

[0027] In one embodiment, the pro-hydroxy-pro compounds shown in Table 1 are measured. Specifically, compounds measured with a mass of 229.118 in LC / MS positive ion detection mode are measured.

[0028] In one embodiment, kynurenine shown in Table 1 is measured. Specifically, the compound measured with a mass of 209.092 in LC / MS positive ion detection mode is measured.

[0029] In one embodiment, the 4-methoxyphenol sulfate shown in Table 1 is measured. Specifically, the compound measured with a mass of 203.002 in LC / MS negative ion detection mode is measured.

[0030] In one embodiment, 5-hydroxylysine shown in Table 1 is measured. That is, the compound measured with a mass of 163.108 in LC / MS positive ion detection mode is measured.

[0031] In one embodiment, trans-4-hydroxyproline shown in Table 1 is measured. That is, the compound measured with a mass of 130.051 in LC / MS polarity detection mode is measured.

[0032] In one embodiment, glycylleucine shown in Table 1 is measured. That is, the compound measured with a mass of 189.123 in LC / MS positive ion detection mode is measured.

[0033] In one embodiment, the glycocholates shown in Table 1 are measured. Specifically, the compounds measured with a mass of 464.302 in LC / MS negative ion detection mode are measured.

[0034] In one embodiment, X-13728 (C7H10N4O3) shown in Table 1 is measured. That is, the compound (C7H10N4O3) measured with a mass of 197.068 in LC / MS negative ion detection mode is measured.

[0035] In one embodiment, X-21851 (C28H38N4O5) shown in Table 1 is measured. That is, the compound (C28H38N4O5) measured with a mass of 509.277 in LC / MS negative ion detection mode is measured.

[0036] In one embodiment, 3-hydroxykynurenine shown in Table 1 is measured. Specifically, the compound measured with a mass of 225.087 in LC / MS positive ion detection mode is measured.

[0037] In one embodiment, glycochenodeoxycholates shown in Table 1 are measured. Specifically, compounds measured with a mass of 448.307 in LC / MS negative ion detection mode are measured.

[0038] In one embodiment, isoleucylglycine shown in Table 1 is measured. Specifically, the compound measured with a mass of 187.116 in LC / MS negative ion detection mode is measured.

[0039] In one embodiment, phenylalanylhydroxyproline shown in Table 1 is measured. Specifically, the compound measured with a mass of 279.134 in LC / MS positive ion detection mode is measured.

[0040] In one embodiment, the 4-hydroxyphenylpyruvate shown in Table 1 is measured. Specifically, the compound measured with a mass of 179.035 in LC / MS negative ion detection mode is measured.

[0041] In one embodiment, the lactates shown in Table 1 are measured. Specifically, the compounds measured with a mass of 89.024 in LC / MS polarity detection mode are measured.

[0042] In one embodiment, the cyclo(pro-hydroxypro) compounds shown in Table 1 are measured. Specifically, the compounds measured with a mass of 211.108 in LC / MS positive ion detection mode are measured.

[0043] In one embodiment, tryptophan as shown in Table 1 is measured. Specifically, the compound measured with a mass of 205.097 in LC / MS positive ion detection mode is measured.

[0044] In one embodiment, N6-acetyllysine shown in Table 1 is measured. That is, the compound measured with a mass of 187.109 in LC / MS polarity detection mode is measured.

[0045] In one embodiment, gamma-glutamylphenylalanine, as shown in Table 1, is measured. Specifically, the compound measured with a mass of 295.129 in LC / MS positive ion detection mode is measured.

[0046] In one embodiment, leucylhydroxyproline shown in Table 1 is measured. Specifically, the compound measured with a mass of 245.150 in LC / MS positive ion detection mode is measured.

[0047] In one embodiment, X-18887 (C14H23N3O6) shown in Table 1 is measured. That is, the compound (C14H23N3O6) measured with a mass of 328.152 in LC / MS negative ion detection mode is measured.

[0048] In one embodiment, X-24475 (C6H11NO3) shown in Table 1 is measured. That is, the compound (C6H11NO3) measured with a mass of 146.081 in LC / MS positive ion detection mode is measured.

[0049] In one embodiment, the androsteronyl clonates shown in Table 1 are measured. Specifically, the compound measured with a mass of 465.249 in LC / MS negative ion detection mode is measured.

[0050] In one embodiment, the 3-hydroxyanthranilates shown in Table 1 are measured. Specifically, the compound measured with a mass of 154.050 in LC / MS positive ion detection mode is measured.

[0051] In one embodiment, the 11-beta-hydroxyandrosteronulcuronide shown in Table 1 is measured. Specifically, the compound measured with a mass of 481.244 in LC / MS negative ion detection mode is measured.

[0052] In one embodiment, the cystathionine shown in Table 1 is measured. Specifically, the compound measured with a mass of 221.060 in LC / MS positive ion detection mode is measured.

[0053] In one embodiment, carnosine shown in Table 1 is measured. Specifically, the compound measured with a mass of 227.114 in LC / MS positive ion detection mode is measured.

[0054] In one embodiment, proline as shown in Table 1 is measured. That is, the compound measured with a mass of 116.071 in LC / MS positive ion detection mode is measured.

[0055] In one embodiment, anserine shown in Table 1 is measured. That is, the compound measured with a mass of 239.115 in LC / MS negative ion detection mode is measured.

[0056] In one embodiment, the arabitol / xylitol compounds shown in Table 1 are measured. Specifically, the compounds measured with a mass of 151.061 in LC / MS polarity detection mode are measured.

[0057] In one embodiment, 3-hydroxy-2-ethylpropionate shown in Table 1 is measured. That is, the compound measured with a mass of 117.056 in LC / MS polarity detection mode is measured.

[0058] In one embodiment, the 2R,3R-dihydroxybutyrate shown in Table 1 is measured. Specifically, the compound measured with a mass of 119.035 in LC / MS polarity detection mode is measured.

[0059] In one embodiment, gamma-glutamyltyrosine shown in Table 1 is measured. Specifically, the compound measured with a mass of 311.124 in LC / MS negative ion detection mode is measured.

[0060] In one embodiment, 1-methyladenine shown in Table 1 is measured. Specifically, the compound measured with a mass of 150.077 in LC / MS positive ion detection mode is measured.

[0061] In one embodiment, the dihydroorotic acid shown in Table 1 is measured. Specifically, the compound measured with a mass of 157.026 in LC / MS polarity detection mode is measured.

[0062] In one embodiment, alanine as shown in Table 1 is measured. That is, the compound measured with a mass of 90.055 in LC / MS positive ion detection mode is measured.

[0063] In one embodiment, the 17-alpha-hydroxypregnenolonulcuronide shown in Table 1 is measured. Specifically, the compound measured with a mass of 509.276 in LC / MS negative ion detection mode is measured.

[0064] The mass spectrometer used to analyze the metabolites shown in Table 1 has very high resolution, allowing for mass measurement to approximately five decimal places. However, considering measurement errors, Table 1 is listed with three decimal places. When using a mass spectrometer with lower resolution, the mass will be measured as an integer or to one or two decimal places.

[0065] In one embodiment of the present invention, biliary tract cancer can be diagnosed and the effectiveness of treatment monitored using at least one marker from the urinary tumor markers shown in Table 1.

[0066] Furthermore, in this invention, by using at least two or more urinary tumor markers in combination, more accurate and precise diagnosis and monitoring of treatment effectiveness become possible. For example, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, or more markers can be combined. The combination of markers is not particularly limited. For example, in one embodiment, at least three types of urinary tumor markers are measured.

[0067] The urinary tumor markers shown in Table 1 can all be used individually to determine biliary tract cancer. In one embodiment, the marker includes at least glycochenodeoxycholic acid 3-sulfate, or glycocholate, or 4-hydroxyphenylpyruvate.

[0068] While individual urinary tumor markers can be compared and analyzed separately, the comparison and analysis become extremely complex when considering two or more urinary tumor markers due to the diverse combinations. Therefore, to determine which combinations of two or more markers are best, the following evaluation variables, the precision variable R2Y and the predictor variable Q2, can be used.

[0069]

number

[0070] Here, Yobs is the measured value, Ycalc is the calculated value using OPLS, and Ypred is the predicted value after cross-validation. TIFF0007838771000005.tif1318 represents the mean value. Cross-validation is a method in which data is divided, a portion is analyzed first, and the remaining portion is used to test the analysis and verify the validity of the analysis itself. According to this, the closer the value of the precision variable R2Y is to 1, the higher the accuracy of the model, and the closer the value of the predictor variable Q2 is to 1, the higher the predictiveness of the model. It is thought that by using combinations of high values ​​for these precision and predictor variables for the diagnosis of biliary tract cancer, more accurate diagnosis will be possible.

[0071] The combination of urinary tumor markers can be appropriately selected depending on the type of subject, sex, age, and purpose, including the determination of biliary tract cancer, monitoring of high-risk subjects (e.g., based on family history) or subjects with no abnormalities (monitoring for biliary tract cancer), or monitoring of treatment.

[0072] As an example of a method for identifying tumor markers in urine, partial least squares analysis, particularly OPLS-DA, a type of multivariate analysis, can be used. When performing multivariate analysis using multiple metabolites that vary between two groups, it may be difficult to understand the characteristics of the data if the multidimensional data is used as is. Therefore, it is preferable to reduce the data to two or three dimensions for visualization. As for multivariate analysis, it is also possible to use analytical methods known in this art, such as principal component analysis.

[0073] A urine sample refers to urine collected from a subject, and samples obtained by processing said urine (for example, urine to which preservatives such as toluene, xylene, or hydrochloric acid have been added).

[0074] Furthermore, the target is human urine. Since metabolic activity in the human body is expected to differ depending on race, etc., monogoloid individuals, including Japanese people, who are the subjects of analysis in this invention, are preferred, but are not limited to them. For example, it may be during mass screening such as health checkups or cancer screenings, during additional examinations after such mass screenings, or before and after surgery in hospitals, etc., or during treatment such as chemotherapy or radiation. In addition, even in the same individual, the concentration of urinary metabolites can easily fluctuate depending on the timing of collection and fluid intake. To standardize the amount of urinary metabolites in this random urine sample, the amount of creatinine or the osmotic pressure (osmorality) of the urine is generally measured and the amount of each metabolite is divided by these values ​​to normalize the values. Hereafter, the amount of urinary metabolites basically refers to the normalized amount.

[0075] Measurement of urinary tumor markers means measuring their amount or concentration in a urine sample, preferably semi-quantitatively or quantitatively, and the amount may be absolute or relative. The measurement can be performed directly or indirectly. Direct measurement involves measuring the amount or concentration based on a signal that directly correlates with the number of molecules of the urinary metabolite present in the sample. Such a signal is based, for example, on specific physical or chemical properties of the urinary metabolite. Indirect measurement is the measurement of a signal obtained from a secondary component (i.e., a component other than the urinary metabolite), such as a ligand, label, or enzymatic reaction product.

[0076] In one embodiment of the present invention, urinary tumor markers, i.e., urinary metabolites, are measured, but the measurement method is not particularly limited and can be any method or means known in the art. For example, urinary tumor markers can be measured by means for measuring physical or chemical properties specific to urinary metabolites, such as means for measuring accurate molecular weight or NMR spectrum. Examples of analytical instruments for measuring urinary metabolites include mass spectrometers, NMR analyzers, two-dimensional electrophoresis devices, chromatographs, and liquid chromatography-mass spectrometers (LC / MS). These analytical instruments may be used individually to measure urinary tumor markers, or multiple analytical instruments may be used to measure urinary tumor markers.

[0077] Alternatively, if reagents for detecting the target metabolites, such as immunoassay reagents or enzyme reagents, are available, such reagents can be used to measure the metabolites in the urine.

[0078] The urinary metabolites shown in Table 1 were identified by LC / MS; therefore, these urinary metabolites can be measured using LC / MS.

[0079] As described above, it is possible to measure urinary tumor markers contained in urine samples collected from subjects and determine biliary tract cancer in the subjects based on the results. Furthermore, urinary tumor markers may be measured in urine samples collected from subjects at multiple time points.

[0080] The present invention's method for diagnosing biliary tract cancer allows for early detection of the presence and progression of the cancer, aiding in detailed examinations and treatment decisions. The ability to diagnose biliary tract cancer with a simple test is expected to reduce the invasive risks associated with both treatment and examination. This method enables early treatment for biliary tract cancer and, in high-risk cases, allows for monitoring of the cancer's development. Furthermore, it enables monitoring of the effectiveness of biliary tract cancer treatment, allowing for consideration of stopping, continuing, or changing treatment based on its response. Moreover, because it utilizes urine samples, it is minimally invasive, has very low psychological barriers, and allows for simple and low-cost diagnosis of biliary tract cancer. Therefore, it is highly suitable for screening purposes for early detection, and since urine can be collected repeatedly, it is also suitable for routine monitoring.

[0081] The method for determining biliary tract cancer according to the present invention can be easily and simply performed by using a kit and / or apparatus equipped with means for measuring urinary tumor markers, which are urinary metabolites.

[0082] The biliary tract cancer diagnosis kit according to the present invention includes means for measuring at least one (preferably at least three) of the urinary tumor markers shown in Table 1 above.

[0083] An example of the kit of the present invention is a reagent set for mass spectrometry, which consists of, for example, isotope-labeled reagents, fractionation mini-columns, buffer solutions, etc. Another example of the kit is a reagent set for immunoassay, which consists of, for example, a substrate on which a primary antibody is immobilized, a secondary antibody, etc. Yet another example is a reagent set for enzyme reaction, which consists of, for example, enzymes, buffer solutions, etc. The kit of the present invention may also include instructions describing the procedures and protocols for carrying out the method of the present invention, a table showing reference values ​​or reference ranges used in the diagnosis of biliary tract cancer, etc.

[0084] The components included in the kit of the present invention may be provided individually or in a single container. Preferably, the kit of the present invention includes all the components necessary to carry out the method of the present invention, for example, as components in adjusted concentrations so that they can be used immediately.

[0085] The biliary tract cancer detection device according to the present invention comprises the following means: A measuring unit for measuring at least one (preferably at least three) of the urinary tumor markers shown in Table 1 above in a urine sample, A comparison unit compares the measured value of the urinary tumor marker measured by the above measurement unit with a reference value or the previous measured value. A determination unit that determines biliary tract cancer based on the comparison results obtained from the above comparison unit.

[0086] Furthermore, the biliary tract cancer detection device according to the present invention, when using multivariate analysis, comprises the following means: A measuring unit for measuring at least one (preferably at least three) of the urinary tumor markers shown in Table 1 above in a urine sample, A comparison unit compares calculated values ​​obtained by multivariate analysis from explanatory variables measured in the above measurement unit (amount or concentration of urinary tumor markers, or, for example, the ratio of observed ion intensity of urinary tumor markers that are increased or decreased in biliary tract cancer patients compared to benign or normal) with reference values ​​or previous calculated values ​​of the dependent variable, which is an indicator showing whether it is biliary tract cancer or normal, calculated based on a cancer screening model obtained by multivariate analysis based on accumulated data. A determination unit that determines biliary tract cancer based on the comparison results obtained from the above comparison unit.

[0087] The apparatus of the present invention is preferably a system in which the above-mentioned measuring unit, comparison unit, and determination unit are connected in such a way that they can operate relative to each other, so that the method of the present invention can be carried out. One embodiment of the apparatus of the present invention is shown in Figure 6.

[0088] Here, the measurement unit includes means for measuring urinary tumor markers in a urine sample, as described above, and is equipped with analytical instruments such as a mass spectrometer, NMR analyzer, two-dimensional electrophoresis apparatus, chromatograph, and liquid chromatography-mass spectrometry (LC / MS) apparatus.

[0089] The measurement unit includes a data analysis unit consisting of software and a computer that processes the measured values ​​obtained from the analytical devices described above. The data analysis unit calculates the amount or concentration of urinary tumor markers contained in the urine sample by referring to data such as calibration curves based on the measured values ​​obtained from the analytical devices described above. On the other hand, when using multivariate analysis, the data analysis unit calculates a calculated value of the dependent variable (an index indicating whether there is biliary tract cancer or no abnormality) calculated based on a cancer screening model obtained by multivariate analysis of the explanatory variables measured by the measurement unit (amount or concentration of urinary tumor markers, or, for example, the intensity ratio of observed ions of urinary tumor markers that increase or decrease in biliary tract cancer patients compared to benign or normal) measured by the measurement unit. The data analysis unit may include, for example, a signal display unit, a unit for analyzing measured values, a computer unit, etc.

[0090] Furthermore, the comparison unit reads reference values ​​for the amount or concentration of urinary tumor markers from a storage device (database), etc., and compares the measured value of urinary tumor markers measured by the measurement unit with the reference value. On the other hand, when using multivariate analysis, the comparison unit reads reference values ​​for the dependent variable from a storage device (database), etc., and compares the calculated value of the dependent variable obtained by the measurement unit with the reference value. In this case, the comparison unit selects and reads an appropriate reference value according to the type of urinary tumor marker. Alternatively, in the case of time-series monitoring of the same subject, the comparison unit reads the previous measurement value from a storage device (database), etc., and compares it with the measured value of urinary tumor markers measured by the measurement unit.

[0091] Furthermore, the determination unit determines biliary tract cancer based on the results of comparing the measured value of the urinary tumor marker with the reference value in the comparison unit, or based on the results of comparing the measured value of the urinary tumor marker at multiple time points in the comparison unit. On the other hand, when multivariate analysis is used, the determination unit determines biliary tract cancer based on the results of comparing the calculated value of the dependent variable with the reference value in the comparison unit, or based on the results of comparing the calculated value of the dependent variable at multiple time points in the comparison unit. Here, the determination unit acquires information indicating the presence of biliary tract cancer in the subject, the stage of biliary tract cancer, or whether there are any abnormalities. A preferred device is one that can be used without the knowledge of a specialist clinician, for example, an electronic device that simply requires the addition of a sample.

[0092] For example, the detection unit determines that a subject may have biliary tract cancer if the urinary tumor marker is above the reference value or the previous measurement value. Alternatively, the detection unit determines that a subject may be normal if the urinary tumor marker is below the reference value or the previous measurement value.

[0093] The apparatus of the present invention may further include a data storage unit, a data output / display unit, and the like.

[0094] In this specification, "determination (or assistance in determination) of biliary tract cancer" means not only detecting biliary tract cancer in a subject, but also predicting the risk of biliary tract cancer in a subject, determining the stage of biliary tract cancer in a subject, determining the prognosis of biliary tract cancer in a subject, monitoring biliary tract cancer in a subject, monitoring the effectiveness of treatment for biliary tract cancer present in a subject, and further assisting in the diagnosis of biliary tract cancer. In addition, in this invention, "determination" also includes continuous monitoring of biliary tract cancer that has already been detected or diagnosed, and confirmation of the detection or diagnosis of biliary tract cancer that has already been made.

[0095] The "diagnosis" made by the biliary tract cancer diagnosis method, diagnosis kit, and diagnosis device according to the present invention is intended to determine a statistically significant proportion of the subjects. Therefore, the "diagnosis" made by the biliary tract cancer diagnosis method, diagnosis kit, and diagnosis device according to the present invention includes cases where a correct result cannot necessarily be obtained for all subjects (i.e., 100%). The statistically significant proportion can be determined using various well-known statistical evaluation tools, such as determining confidence intervals, determining p-values, Student's t-test, Mann-Whitney test, etc. A preferred confidence interval is at least 90%. The p-value is preferably 0.1, 0.01, 0.05, 0.005, or 0.0001. More preferably, at least 60%, at least 80%, or at least 90% of the subjects can be appropriately determined by the biliary tract cancer diagnosis method, diagnosis kit, and diagnosis device according to the present invention.

[0096] A specific example of diagnosing biliary tract cancer is as follows: In one embodiment, urinary tumor markers in a target urine sample are measured, and the measured value is compared with a reference value or a previous measured value. When measuring multiple urinary tumor markers, each urinary tumor marker may be compared with its respective reference value or a previous measured value, or the calculated value of the dependent variable obtained by multivariate analysis may be obtained and compared with the reference value or a previous measured value.

[0097] The reference value is the amount or concentration of urinary tumor markers associated with biliary tract cancer, or the range of such amounts or concentrations, or the amount or concentration of urinary tumor markers that indicate no abnormality, or the range of such amounts or concentrations. On the other hand, when using multivariate analysis, the calculated value of the dependent variable that distinguishes biliary tract cancer / no abnormality becomes the reference value. For example, the reference value can be derived from healthy individuals (population) or individuals (population) at low risk of biliary tract cancer. Alternatively, the reference value can be derived from patients (patient population) who have biliary tract cancer (e.g., biliary tract cancer at a specific stage) or who have biliary tract cancer with a specific prognosis. The reference value applied to individual subjects may vary depending on various physiological parameters such as the type of animal, age, and sex.

[0098] Preferably, the correlation between the amount or concentration of tumor markers in urine and the presence of biliary tract cancer or a specific prognosis is recorded in a database. The measured values ​​of tumor markers in the urine sample can then be compared with reference values ​​in the database. Such a database is useful as a reference value or reference range that serves as an indicator of the presence or absence of biliary tract cancer (or biliary tract cancer at a specific stage), or as an indicator of prognosis.

[0099] The urinary tumor markers shown in Table 1 differ in quantity or concentration depending on the presence or absence of biliary tract cancer, and their quantity or concentration changes with the presence of biliary tract cancer and before or after the initiation of treatment. Specifically, the markers shown in Table 1 are elevated in quantity or concentration in patients with biliary tract cancer compared to subjects without biliary tract cancer. Therefore, if the markers shown in Table 1 are higher than the reference values ​​derived from the normal population (subjects without biliary tract cancer) or are equivalent to or higher than the reference values ​​derived from the patient population with biliary tract cancer, it can be said that the subject may have biliary tract cancer or is at high risk of developing it.

[0100] When comparing with a standard, it is also possible to determine the result by calculating a predicted value. In cancer screening using urinary tumor markers, multiple urinary tumor markers are often used to assess the risk of cancer, and in such cases, it is desirable to calculate a predicted value. For example, if three types of urinary tumor markers are selected, the risk of cancer is assessed using the predicted value given by the following prediction formula. Note that the intensity in the formula is the area value of the mass chromatogram corresponding to each mass. [Math 2] Predicted value = α × (intensity of urinary tumor marker 1) + β × (intensity of urinary tumor marker 2) +γ × (intensity of urinary tumor marker 3) + δ (In the formula, α, β, γ, and δ are constants.)

[0101] In the above cancer screening model, for example, a predicted value of 0 or greater indicates a high risk of cancer, while a value less than 0 indicates a low risk. However, the threshold for the predicted value is not limited to 0 and can be varied. Alternatively, the threshold may not be clearly defined, and the magnitude of the predicted value may be quantitatively correlated with the risk (probability) of cancer.

[0102] For example, the prediction formula can be expressed as follows: [Math 3] Prediction formula = 0.3336 × (intensity of urinary tumor marker 1) + 0.2408 × (intensity of urinary tumor marker 2) + 0.4132 × (intensity of urinary tumor marker 3) + 0.1234

[0103] In another embodiment, urine samples are collected from the subject at multiple time points, the amount of urinary tumor markers contained in the urine samples at each measurement time point is measured, and the measured values ​​of the urinary tumor markers are compared at each measurement time point. More specifically, the amount or concentration of the urinary tumor marker at a first time point (a) is compared with the amount or concentration of the urinary tumor marker at a second time point (b). If multivariate analysis is performed, for example, the calculated value of one component at the first time point is compared with the calculated value at the second time point. Measurements can be performed at least two, three, four, five, ten, fifteen, twenty, thirty, or more times over time, for example, with intervals of one day, two days, five days, one week, two weeks, three weeks, one month, two months, three months, six months, one year, two years, three years, five years, or longer. This comparison allows for time-series monitoring and can evaluate the progression of biliary tract cancer, metastasis or recurrence of biliary tract cancer, malignant transformation of benign tumors, and the development of biliary tract cancer from previously normal cases.

[0104] In another embodiment, the urinary tumor marker used in the present invention can be used to monitor the effect of treatment (therapeutic drug or treatment method) on biliary tract cancer in a subject. Specifically, (a) A step of measuring urinary tumor markers in a urine sample from a patient with biliary tract cancer before treatment with a drug or therapy, (b) A step of measuring urinary tumor markers in urine samples from patients with biliary tract cancer after treatment with a drug or therapy, (c) Repeat step (b) as needed. (d) A step to monitor the effectiveness of a drug or treatment for biliary tract cancer based on the measurement results of (a) to (c). Includes.

[0105] In the above method, a urine sample is collected from a patient with biliary tract cancer before treatment with a drug or therapy, and the urinary tumor markers in the urine sample are measured. After treatment with a drug or therapy is administered to a patient with biliary tract cancer, a urine sample is collected at appropriate intervals, and the urinary tumor markers in the urine sample are measured. For example, urine samples are collected immediately after treatment, 30 minutes, 1 hour, 3 hours, 5 hours, 10 hours, 15 hours, 20 hours, 24 hours (1 day), 2-10 days, 10-20 days, 20-30 days, and 1-6 months after treatment. The measurement of urinary tumor markers in the urine sample can be performed in the same manner as described above. By measuring urinary tumor markers before and after treatment, it is possible to monitor the effectiveness of the drug or therapy. Based on the monitoring results, it is helpful in considering stopping, continuing, or changing the treatment.

[0106] Furthermore, the method for diagnosing biliary tract cancer may be combined with other conventionally known diagnostic methods for biliary tract cancer. Such known diagnostic methods for biliary tract cancer include blood tests (measurement of cancer markers in the blood, liver function tests, etc.), imaging tests (e.g., abdominal ultrasound, computed tomography (CT), MRI, positron emission tomography (PET), etc.), endoscopy, and pathological examinations by biopsy or cytology.

[0107] Based on the above-mentioned assessment results, a physician can diagnose the biliary tract cancer in the subject and take appropriate measures. In other words, the present invention also relates to a method for determining and treating biliary tract cancer in a subject. For example, if biliary tract cancer is determined in a subject according to the method of the present invention and it is evaluated that there is a high probability that the subject has biliary tract cancer, the subject will be treated for biliary tract cancer or measures will be taken to prevent the progression of biliary tract cancer. Furthermore, if it is evaluated that the stage of biliary tract cancer in the subject is advanced or that there is a high probability that the prognosis for biliary tract cancer is poor, treatment will be continued or, if necessary, a change in treatment method will be considered. Alternatively, if the subject is evaluated as being at high risk but has not yet developed biliary tract cancer, biliary tract cancer may be monitored by measuring urinary tumor markers over time to avoid excessive examination and treatment. Furthermore, if it is evaluated that there is a high probability that biliary tract cancer is present in the subject, the presence of biliary tract cancer will be confirmed by performing other biliary tract cancer diagnostic methods as described above. In addition, based on the evaluation results before and after treatment, the effect of the treatment will be monitored and a decision will be made to stop, continue, or change the treatment. Furthermore, if no abnormalities are found, the patient's condition can be monitored over time by measuring tumor markers in the urine.

[0108] Biliary tract cancer can be treated with surgery (surgical resection), chemotherapy, radiotherapy, immunotherapy, proton therapy, heavy ion therapy, etc., either alone or in appropriate combinations. A person skilled in the art can appropriately select the treatment for biliary tract cancer, taking into consideration the type, stage, malignancy grade, sex, age and condition, response to treatment, and genetic polymorphisms (SNPs) present.

[0109] As an example of applying the present invention, cancer screening at a testing center will be described. At the testing center, information on cancer screening is provided in response to requests from those being screened. When applying for the initial screening, the person being screened may select the number of biomarkers to be tested. For example, the number of biomarkers could be 1 to 3 types of urinary tumor markers. This can also be used in combination with other biomarkers as a comprehensive cancer screening (analyzing various cancers at once).

[0110] Next, the testing center provides the person being tested with a test kit necessary for urine collection. If necessary, the kit will be sent by mail or other means. After receiving the test kit, the person being tested will provide or send the sample to the testing center. The testing center will store the sample frozen at approximately -80°C as needed for subsequent tests. However, if the target urinary metabolite is known to be stable with respect to temperature and elapsed time, storage may be limited to frozen at approximately -5°C, refrigerated at approximately 5°C, or at room temperature, rather than frozen at -80°C. The testing center will perform the initial test and send the test results to the person being tested.

[0111] After receiving the results of the initial examination, the person being examined may apply for a secondary examination or receive a more detailed diagnosis, depending on the findings. This makes it possible to confirm the suspicion of biliary tract cancer in the initial examination and even determine the stage of the biliary tract cancer.

[0112] Furthermore, the urinary tumor marker used in this invention can be used to evaluate the effectiveness of treatments (therapeutic drugs or treatment methods) for biliary tract cancer, or to screen for candidate therapeutic drugs for biliary tract cancer. Specifically, the method for evaluating the effectiveness of treatments for biliary tract cancer, or the method for screening for candidate therapeutic drugs for biliary tract cancer, is as follows: (a) A step of measuring urinary tumor markers in a urine sample from an animal with biliary tract cancer that has been treated with the investigational drug or treatment, (b) A step to evaluate the effectiveness of the investigational drug or treatment for biliary tract cancer based on the measurement results in (a). Includes.

[0113] In the method of the present invention, urine samples are collected from patients with biliary tract cancer or from humans without biliary tract cancer, and urinary tumor markers in the urine samples are measured. Preferably, urine samples are collected from humans with biliary tract cancer and urinary tumor markers in the urine samples are measured before administering the investigational drug or treatment. After animals with biliary tract cancer have been administered the investigational drug or treatment, urine samples are collected at appropriate times and urinary tumor markers in the urine samples are measured. For example, urine samples may be collected immediately after administration, 30 minutes, 1 hour, 3 hours, 5 hours, 10 hours, 15 hours, 20 hours, 24 hours (1 day), 2-10 days, 10-20 days, 20-30 days, and 1-6 months later. The measurement of urinary tumor markers in the urine samples and the determination of biliary tract cancer can be performed in the same manner as described above.

[0114] The types of investigational drugs or treatments subject to evaluation or screening are not particularly limited. For example, investigational drugs or treatments may include any material factors, specifically, naturally occurring molecules such as amino acids, peptides, oligopeptides, polypeptides, proteins, nucleic acids, lipids, carbohydrates (sugars, etc.), steroids, glycopeptides, glycoproteins, proteoglycans, etc.; synthetic analogs or derivatives of naturally occurring molecules such as peptide mimetic substances, nucleic acid molecules (aptamers, antisense nucleic acids, double-stranded RNA (RNAi), etc.); molecules that do not occur naturally, such as low-molecular-weight organic compounds (inorganic and organic compound libraries, or combinatorial libraries, etc.); and mixtures thereof. Furthermore, the drug or treatment may be a single substance, a complex composed of multiple substances, or food and diet. In addition to the material factors mentioned above, the investigational drug or treatment may also include radiation, ultraviolet light, etc.

[0115] Furthermore, the effectiveness of the investigational drug or treatment can be examined under several conditions. These conditions include the time or duration of treatment, the amount (large or small), and the number of times the investigational drug or treatment is administered. For example, multiple doses can be established by preparing a dilution series of the investigational drug. Additionally, when examining the additive or synergistic effects of multiple investigational drugs or treatments, a combination of drugs or treatments may be used.

[0116] By measuring urinary tumor markers in urine samples collected after treatment with the investigational drug or therapy and comparing them to the amount or concentration before treatment, it is possible to evaluate whether the investigational drug or therapy is effective in eliminating biliary tract cancer, reducing the size of biliary tract cancer, improving symptoms caused by biliary tract cancer, or stopping or slowing the progression of biliary tract cancer.

[0117] For example, in the case of the markers shown in Table 1, a lower post-treatment measurement than a pre-treatment measurement in patients with biliary tract cancer indicates that the investigational drug or treatment is effective, resulting in the disappearance of biliary tract cancer, reduction of biliary tract cancer, improvement of symptoms caused by biliary tract cancer, or cessation of biliary tract cancer progression. On the other hand, a higher post-treatment measurement than a pre-treatment measurement, or no significant difference from a pre-treatment measurement, indicates that the investigational drug or treatment is not effective in treating biliary tract cancer.

[0118] Based on the above, the method for evaluating the effectiveness of treatment for biliary tract cancer according to the present invention can be used to find therapeutic drugs or treatments for treating or preventing biliary tract cancer, and furthermore, to confirm the effectiveness of therapeutic drugs or treatments.

[0119] The present invention will be specifically described below with reference to examples, but these examples are provided solely for the purpose of explaining the present invention and are not intended to limit or restrict the scope of the invention disclosed in this application. [Examples]

[0120] [Example 1] Comprehensive analysis of urinary metabolites associated with biliary tract cancer At Nagoya University Hospital, urine samples were collected from 27 patients with biliary tract cancer with their permission before tumor resection and at 2 weeks and 4 weeks after tumor resection (a total of 62 samples). Patient information recorded included sample ID, date of sample collection, age, sex, pre- and post-operative status, osmorality, prognosis, diagnosis, surgical procedure, histopathological type, pathological margins, pathological lymph node metastasis, stage, intraoperative blood transfusion, medical history, and other marker measurements. The specific breakdown of the urine samples is as follows:

[0121] [Table 2]

[0122] The urinary metabolites (metabolomes) of patients were comprehensively analyzed using a liquid chromatography-mass spectrometer (LC / MS), which is suitable for highly sensitive analysis of mixed components in solutions. To detect as many metabolites as possible, positive and negative electrospray ionization was used for ionization, and multiple separation modes, such as reversed-phase chromatography and hydrophilic interaction chromatography, were used to separate the mixed components.

[0123] From the obtained mass spectra, metabolites are identified, i.e., peak annotation is performed, using a database. If metabolites not registered in the database are detected, structural estimation may be performed using tandem mass spectrometry (MS / MS), which actively generates fragment ions from the target ion. While MS / MS does not always provide a clear structural estimation, it is far simpler than isolating the target component and analyzing it using nuclear magnetic resonance (NMR). If candidate substances are narrowed down by MS / MS, the estimated structures are confirmed by actually synthesizing them and comparing the mass spectra and MS / MS spectra.

[0124] From the above analysis, 1524 types of urinary metabolites were detected, of which 1027 were identified as substances with known structures. For the unidentified, unknown structural compounds, information such as the mass-to-charge ratio (m / z), MS spectrum, MS / MS spectrum, retention time, and separation mode obtained from the measurements was available, so an ID was assigned and chemical formula identification was carried out.

[0125] For the following analyses, each measured value of a metabolite was normalized by osmolality. Further normalization was performed using the median (median = 1), and missing values ​​were substituted with the minimum value. Finally, a logarithmic transformation was performed. In some analyses, markers with large missing values ​​were excluded (for example, markers with a missing value of 50% or more in 27 preoperative cases were excluded). In marker discovery, exogenous metabolites such as drugs and foods were excluded. In OPLS-DA, standardization (auto scaling) was performed to unify the weights of each variable, setting the mean to 0 and the standard deviation to 1.

[0126] First, principal component analysis (PCA) was performed using all metabolites. The results showed a distinction between pre- and post-resection principal component values. This distinction was also observed at 2 weeks and 4 weeks post-surgery, with the 4-week post-surgery value being closer to the pre-surgery value. This suggests that the 2-week post-surgery value reflects transient effects from surgery and metabolic activity during healing. However, no clear distinctions were observed based on gender, disease site, prognosis, or survival rate.

[0127] Next, matched pair t-tests were performed between each group using all metabolites. The results showed significant differences in 494 metabolites between pre-resection and 2 weeks post-surgery (p<=0.05). Significant differences were found in 113 metabolites between pre-resection and 4 weeks post-surgery. Similar to PCA, it is presumed that the 2-week post-surgery analysis included metabolites related to biochemical changes associated with the effects of surgery or its recovery. Furthermore, 85 metabolites were evaluated as showing significant differences compared to pre-surgery in both the 2-week and 4-week post-surgery periods.

[0128] Next, random forest (RF) analysis was performed on 86 metabolites selected from the 113 metabolites that showed a significant difference between preoperative and 4 weeks postoperatively. These metabolites were excluded if they were exogenous or had a high deficiency rate. The analysis was performed for (i) preoperative vs. 2 weeks postoperatively, (ii) preoperative vs. 4 weeks postoperatively, and (iii) preoperative vs. 2 weeks and 4 weeks postoperatively. The results for (iii) preoperative vs. 2 weeks and 4 weeks postoperatively were used as the marker importance because they had the largest N and were relative to the tumor-free group (2 weeks and 4 weeks postoperatively). The top 30 markers, ranked by the average rank of three RF analyses, are shown in Table 3 and the graph in Figure 1 below.

[0129] [Table 3] TIFF0007838771000008.tif55159

[0130] As described above, 30 markers related to biliary tract cancer (presence or absence of tumor) were identified. Since the amount of these markers changes depending on the presence or absence of biliary tract cancer, measuring these markers makes it possible to determine the presence or absence of biliary tract cancer and to monitor the patient after surgery.

[0131] [Example 2] For the top 20 RF markers identified in Example 1, ROC curves were calculated before and after surgery (with or without tumor), and the AUC was determined. The results are shown in the table below. The AUC value represents the discriminative ability for biliary tract cancer when the indicated marker is used individually. An AUC value closer to 1 indicates higher discriminative ability, and generally, an AUC of 0.7 or higher can be considered a good model or good discriminative ability.

[0132] [Table 4]

[0133] The markers shown in Table 4 above were found to be able to evaluate the presence or absence of biliary tract cancer with high discriminatory ability, even when used alone.

[0134] [Example 3] Construction of a cancer screening model combining multiple markers A cancer screening model was constructed using multiple markers identified in Example 1. To evaluate the validity of the combined markers, an evaluation was performed using explanatory variables (representing the fit to the training data used to build the model) and predictor variables (indicating the predictive performance of the model (leave-one-out cross-validation)). The closer these variables are to 1, the better the model, and they are very effective indicators for verifying which model is highly valid. Specifically, the cancer screening model was obtained using the following indicators with OPLS discriminant analysis.

[0135]

number

[0136] Here, Yobs is the measured value, Ycalc is the calculated value using OPLS, and Ypred is the predicted value after cross-validation. TIFF0007838771000011.tif1315 represents the mean value. Cross-validation is a method in which data is divided, a portion is analyzed first, and the remaining portion is used to test the analysis and verify the validity of the analysis itself. According to this, the explanatory variable R2Y value represents how well the model fits the training data used to build the model, and the closer it is to 1, the higher the accuracy of the model. The predictive variable Q2 value indicates the predictive performance of the model (leave-one-out cross-validation), and the closer it is to 1, the higher the predictive ability of the model.

[0137] A cancer screening model is constructed for combinations of multiple markers. Here, the cancer screening model, for example, when using five types of markers, uses the concentration of each marker in the urine sample or the intensity of the ions corresponding to each marker (actually the area of ​​the mask chromatogram obtained by LC / MS measurement) to calculate a predicted value using the following formula: Prediction = α × (Marker 1 intensity) + β × (Marker 2 intensity) + γ × (Marker 3 intensity) + δ × (Marker 4 intensity) + ε × (Marker 5 intensity) + ζ (α, β, γ, δ, ε, ζ are constants) to determine the risk of cancer. Specifically, a higher predicted value indicates a higher risk of cancer, and a lower predicted value indicates a lower risk of cancer. If the determination is to distinguish between cancer and health, an appropriate threshold for the predicted value is set.

[0138] The top 10 or top 20 RF markers identified in Example 1 were evaluated using OPLS discriminant analysis to create cancer screening models. Specifically, cancer screening models were constructed for the following six cases. In Figures 2-8, the white bar graphs represent urine samples collected before biliary tract cancer resection, the black bar graphs represent urine samples collected two weeks post-surgery, and the shaded bar graphs represent urine samples collected four weeks post-surgery.

[0139] (1) Using the top 10 RF markers, a model was constructed for pre-operative and 2-week post-operative data, and applied to samples taken 4 weeks post-operatively. The results are shown in Figure 2A (predicted value) and B (AUC). The shaded bar graph represents the test samples. This cancer screening model had an explanatory variable R2Y of 0.706, a predictor variable Q2 of 0.632, and an AUC value (N=62) of 0.929. As can be seen from the figure, using a combination of multiple markers enables more accurate discrimination. Furthermore, similar results were obtained with both training and test data, indicating the high versatility of the model.

[0140] (2) Using the top 10 RF markers, a model was constructed for pre-operative and 4-week post-operative data, and applied to samples taken 2 weeks post-operatively. The results are shown in Figure 3A (predicted values) and B (AUC). The black bar graph represents the test samples. This cancer screening model had an explanatory variable R2Y of 0.694, a predictor variable Q2 of 0.537, and an AUC value (N=62) of 0.930. As can be seen from the figure, using a combination of multiple markers enables more accurate identification. Furthermore, similar results were obtained with both training and test data, indicating the high versatility of the model.

[0141] (3) Using the top 20 RF markers, a model was constructed for pre-operative and 2-week post-operative data, and applied to samples taken 4 weeks post-operatively. The results are shown in Figure 4A (predicted value) and B (AUC). The shaded bar graph represents the test samples. This cancer screening model had an explanatory variable R2Y of 0.773, a predictor variable Q2 of 0.689, and an AUC value (N=62) of 0.91. As can be seen from the figure, using a combination of multiple markers enables more accurate discrimination. Furthermore, similar results were obtained with both training and test data, indicating the high versatility of the model.

[0142] (4) Using the top 20 RF markers, a model was constructed for pre-operative and 4-week post-operative data, and applied to samples taken 2 weeks post-operatively. The results are shown in Figure 5A (predicted value) and B (AUC). The black bar graph represents the test samples. This cancer screening model had an explanatory variable R2Y of 0.755, a predictor variable Q2 of 0.561, and an AUC value (N=62) of 0.939. As can be seen from the figure, more accurate discrimination is possible by using a combination of multiple markers. Furthermore, similar results were obtained with both training and test data, indicating the high versatility of the model.

[0143] (5) Using six markers that were considered particularly important from the top 20 RF markers, models were constructed for pre-operative and post-operative periods (both 2 weeks and 4 weeks). These six markers are metabolites that also have a high ability to distinguish between biliary tract cancer and healthy individuals, as will be described later (Example 4). Specifically, these are glycochenodeoxycholate 3-sulfate (Table 3: position 4, Table 6: position 1), glycocholate (Table 3: position 12, Table 6: position 2), 4-hydroxyphenylpyruvate (Table 3: position 19, Table 6: position 6), glycochenodeoxycholate (Table 3: position 16, Table 6: position 9), trans-4-hydroxyproline (Table 3: position 10, Table 6: position 12), and kynurenine (Table 3: position 7, Table 6: position 18). The results are shown in Figure 6A (predicted value) and B (AUC). Here, the black bar graph represents postoperative samples (both 2 weeks and 4 weeks). The cancer screening model had an explanatory variable R2Y of 0.447, a predictor variable Q2 of 0.359, and an AUC value (N=62) of 0.903. As can be seen from the figure, using a combination of multiple markers enables more accurate identification.

[0144] (6) Using three markers that were considered particularly important from the top 20 RF markers, we constructed models for pre-operative and post-operative (both 2 weeks and 4 weeks) conditions. These three markers are metabolites that also have a high ability to distinguish between biliary tract cancer and healthy individuals, as will be described later (Example 4). Specifically, these are glycochenodeoxycholate 3-sulfate (Table 3: 4th, Table 6: 1st), glycocholate (Table 3: 12th, Table 6: 2nd), and 4-hydroxyphenylpyruvate (Table 3: 19th, Table 6: 6th). The results are shown in Figure 7A (predicted value) and B (AUC). Here, the black bar graph represents post-operative samples (both 2 weeks and 4 weeks). For this cancer screening model, the explanatory variable R2Y was 0.412, the predictor variable Q2 was 0.350, and the AUC value (N=62) was 0.883. As can be seen from the diagram, using a combination of multiple markers enables more accurate identification.

[0145] (7) Using two markers that were considered particularly important from the top 20 RF markers, we constructed models for pre-operative and post-operative (both 2 weeks and 4 weeks) conditions. These two markers are metabolites that also have a high ability to distinguish between biliary tract cancer and healthy individuals, as will be described later (Example 4). Specifically, these are glycochenodeoxycholate 3-sulfate (Table 3: 4th place, Table 6: 1st place) and glycocholate (Table 3: 12th place, Table 6: 2nd place). The results are shown in Figure 8A (predicted value) and B (AUC). Here, the black bar graph represents post-operative samples (both 2 weeks and 4 weeks). For this cancer screening model, the explanatory variable R2Y was 0.306, the predictor variable Q2 was 0.241, and the AUC value (N=62) was 0.809. As can be seen from the figure, more accurate discrimination is possible by using a combination of multiple markers.

[0146] [Example 4] Comprehensive analysis of urinary metabolites by comparing patients with biliary tract cancer and healthy individuals. With permission, urine samples were collected from 25 patients with biliary tract cancer before tumor resection. Additionally, urine samples were collected from 25 individuals who were deemed healthy through health checkups, as a control group. The specific breakdown of the urine samples is as follows:

[0147] [Table 5]

[0148] The urinary metabolites (metabolome) of the subjects were comprehensively analyzed using a liquid chromatography-mass spectrometer (LC / MS), which is suitable for highly sensitive analysis of mixed components in solution. To detect as many metabolites as possible, positive and negative electrospray ionization was used for ionization, and multiple separation modes, such as reversed-phase chromatography and hydrophilic interaction chromatography, were used to separate the mixed components.

[0149] From the obtained mass spectra, metabolites are identified, i.e., peak annotation is performed, using a database. If metabolites not registered in the database are detected, structural estimation may be performed using tandem mass spectrometry (MS / MS), which actively generates fragment ions from the target ion. While MS / MS does not always provide a clear structural estimation, it is far simpler than isolating the target component and analyzing it using nuclear magnetic resonance (NMR). If candidate substances are narrowed down by MS / MS, the estimated structures are confirmed by actually synthesizing them and comparing the mass spectra and MS / MS spectra.

[0150] From the above analysis, 1574 types of urinary metabolites were detected, of which 1070 were identified as substances with known structures.

[0151] For the following analyses, each measured value of a metabolite was normalized by osmolality. Further normalization was performed using the median (median = 1), and missing values ​​were substituted with the minimum value. Finally, a logarithmic transformation was performed. In some analyses, markers with a large rate of missing data were excluded. In marker discovery, exogenous metabolites such as drugs and foods were excluded. In OPLS-DA, standardization (auto scaling) was performed to unify the weights of each variable, setting the mean to 0 and the standard deviation to 1.

[0152] Significant difference tests were performed between groups using all metabolites. Here, the Wilcoxon rank-sum test, which can be applied to unpaired, nonparametric analyses, was used. As a result, there were significant differences between cancer patients and healthy controls for 293 metabolites excluding exogenous metabolites (p<=0.05). There were 32 metabolites common to both the 86 metabolites analyzed using random forest analysis in Example 1 and the 293 metabolites analyzed here.

[0153] Next, using 32 metabolites that showed a significant difference in tumor resection for biliary tract cancer and also showed a significant difference between cancer patients and healthy controls, random forest analysis (RF) was performed to distinguish between cancer patients and healthy controls. The top 20 markers, ranked by the average rank of three RF runs, are shown in Table 6 and the graph in Figure 10 below.

[0154] [Table 6]

[0155] As described above, 20 markers were identified that were associated with the presence or absence of biliary tract cancer tumors (Examples 1-3) and differed from those of healthy individuals. By measuring these markers, it becomes possible to perform early detection and screening of biliary tract cancer.

[0156] [Example 5] The top 20, top 10, or top 5 RF markers identified in Example 4 were evaluated using OPLS discriminant analysis to create cancer screening models. Specifically, cancer screening models were constructed for the following three cases. In Figures 11-13, the white bar graphs represent urine samples from biliary tract cancer patients (before tumor resection), and the black bar graphs represent urine samples from healthy individuals.

[0157] (1) A model was constructed comparing cancer patients with healthy controls using the top 20 RF markers. The results are shown in Figure 11A (predicted value) and B (AUC). This cancer screening model had an explanatory variable R2Y of 0.598, a predictor variable Q2 of 0.405, and an AUC value (N=50) of 0.934. As can be seen from the figure, high-precision discrimination is possible by using a combination of multiple markers.

[0158] (2) A model was constructed comparing cancer patients with healthy controls using the top 10 RF markers. The results are shown in Figure 12, A (predicted value) and B (AUC). This cancer screening model had an explanatory variable R2Y of 0.471, a predictor variable Q2 of 0.333, and an AUC value (N=50) of 0.941. As can be seen from the figure, high-precision discrimination is possible by using a combination of multiple markers.

[0159] (3) A model was constructed comparing cancer patients with healthy controls using the top 5 RF markers. The results are shown in Figure 13, A (predicted value) and B (AUC). This cancer screening model had an explanatory variable R2Y of 0.346, a predictor variable Q2 of 0.254, and an AUC value (N=50) of 0.899. As the number of markers decreases, the values ​​of the explanatory and predictor variables decrease, but the AUC value remains high. Furthermore, a smaller number of markers makes it possible to reduce the testing time, reagents, and data analysis costs per sample.

[0160] All publications, patents, and patent applications cited herein are incorporated herein by reference in their entirety.

Claims

1. A method for analyzing a urine sample from a subject to assist in the diagnosis of biliary tract cancer, A step of measuring urinary tumor markers in a urine sample derived from a subject, wherein the urinary tumor markers include at least one urinary tumor marker containing glycochenodeoxycholic acid 3-sulfate; The step of comparing the measured value of the above-mentioned urinary tumor marker with reference values ​​derived from healthy individuals or a healthy population, or individuals or a low-risk population for biliary tract cancer, or with the previous measured value. Includes, The above results serve as an indicator for diagnosing biliary tract cancer in the subjects.

2. The aforementioned urinary tumor markers include glycocholate, cholate, chenodeoxycholate sulfate, a compound measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, a compound measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), a compound measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholate, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvate, lactate, cyclo(pro-hydroxypro), tryptoph The method according to claim 1, further comprising at least one urinary tumor marker selected from α-, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured as having a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured as having a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsteronylcuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosteronylcuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolonecuronide.

3. The method according to claim 2, wherein at least three of the urinary tumor markers are measured.

4. The method according to claim 1, wherein if the urinary tumor marker is equal to or greater than the reference value or the previous measurement value, it serves as an indicator that the subject may have biliary tract cancer.

5. The method according to claim 1, wherein the determination of biliary tract cancer is the detection of biliary tract cancer in the subject, the prediction of the risk of biliary tract cancer in the subject, the determination of the stage of biliary tract cancer in the subject, the determination of the prognosis of biliary tract cancer in the subject, the monitoring of biliary tract cancer in the subject, or the monitoring of the effect of treatment for biliary tract cancer present in the subject.

6. The method according to claim 1, wherein the biliary tract cancer is selected from the group consisting of intrahepatic cholangiocarcinoma, hilar cholangiocarcinoma, distal cholangiocarcinoma, gallbladder cancer, and ampullary cholangiocarcinoma.

7. The method according to claim 1, wherein the measurement of the urinary tumor marker is performed by liquid chromatography-mass spectrometry (LC / MS).

8. A device for detecting biliary tract cancer, A measuring unit for measuring urinary tumor markers in a urine sample, wherein the urinary tumor marker includes at least one urinary tumor marker containing glycochenodeoxycholic acid 3-sulfate, A comparison unit compares the measured value of the urinary tumor marker measured by the above measurement unit with a reference value or the previous measured value. The comparison unit used to determine biliary tract cancer is used to determine the results of the comparison obtained from the above comparison unit. A device characterized by being equipped with the following features.

9. The aforementioned urinary tumor markers include glycocholate, cholate, chenodeoxycholate sulfate, a compound measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, a compound measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), a compound measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholate, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvate, lactate, cyclo(pro-hydroxypro), tryptoph The apparatus according to claim 8, further comprising at least one urinary tumor marker selected from α-, N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured as having a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured as having a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsteronylcuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosteronylcuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolonecuronide.

10. The apparatus according to claim 8, wherein if the urinary tumor marker is equal to or greater than the reference value or the previous measurement value, the determination unit determines that the subject may have biliary tract cancer.

11. The apparatus according to claim 9, wherein the measuring unit measures at least three types of urinary tumor markers.

12. The apparatus according to claim 8, wherein the measuring unit comprises a liquid chromatography-mass spectrometry (LC / MS) device.

13. A kit for diagnosing biliary tract cancer, A kit characterized by comprising means for measuring at least one urinary tumor marker containing glycochenodeoxycholic acid 3-sulfate.

14. The aforementioned urinary tumor markers include glycocholate, cholate, chenodeoxycholate sulfate, a compound measured with a mass-to-charge ratio of 259.028 in LC / MS negative ion detection mode (C10H12O6S), isoleucylhydroxyproline, pro-hydroxy-pro, kynurenine, 4-methoxyphenol sulfate, 5-hydroxylysine, trans-4-hydroxyproline, glycylleucine, a compound measured with a mass-to-charge ratio of 197.068 in LC / MS negative ion detection mode (C7H10N4O3), a compound measured with a mass-to-charge ratio of 509.277 in LC / MS negative ion detection mode (C28H38N4O5), 3-hydroxykynurenine, glycochenodeoxycholate, isoleucylglycine, phenylalanylhydroxyproline, 4-hydroxyphenylpyruvate, lactate, cyclo(pro-hydroxypro), and tryptopha. The kit according to claim 13, further comprising at least one urinary tumor marker selected from N6-acetyllysine, gamma-glutamylphenylalanine, leucylhydroxyproline, a compound (C14H23N3O6) measured as having a mass-to-charge ratio of 328.152 in LC / MS negative ion detection mode, a compound (C6H11NO3) measured as having a mass-to-charge ratio of 146.081 in LC / MS positive ion detection mode, androsteronylcuronide, 3-hydroxyanthranilate, 11-beta-hydroxyandrosteronylcuronide, cystathionine, carnosine, proline, anserine, arabitol / xylitol, 3-hydroxy-2-ethylpropionate, 2R,3R-dihydroxybutyrate, gamma-glutamyltyrosine, 1-methyladenine, dihydroorotic acid, alanine, and 17-alpha-hydroxypregnenolonecuronide.

15. The kit according to claim 13, which is a reagent set for mass spectrometry.

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