Data acquisition method for identifying immunological high-risk groups in organ transplantation, and data processing device , data processing system, data processing program, and kit associated therewith

Abstract

Provided is a simple and accurate method for identifying immunological high-risk groups in organ transplantation. A method for acquiring data for identifying immunological high-risk groups in organ transplantation according to an embodiment of the present invention includes a step for detecting a single nucleotide polymorphism of two or more genes selected from a first gene group consisting of FOXP3, HMGB1, PD-1, STAT4 and Baff, contained in a sample collected from a subject.

Description

Method for acquiring data for identifying immunologically high-risk groups in organ transplantation, and related data processing device, data processing system, data processing program, and kit

[0001] The present invention relates to a method for acquiring data for identifying immunologically high-risk groups in organ transplantation. The present invention also relates to a data processing device, a data processing system, and a data processing program for identifying immunologically high-risk groups in organ transplantation. The present invention further relates to a kit for identifying immunologically high-risk groups in organ transplantation.

[0002] Immunosuppressive therapy is commonly used to prevent rejection after organ transplantation. In typical immunosuppressive therapy, the activity of T cells and B cells is suppressed by oral administration or oral and intravenous administration.

[0003] However, despite immunosuppressive therapy, there exists a certain group of patients who develop immunological complications, concurrent diseases, or accidents, such as rejection and infection. Methods for identifying such immunologically high-risk patients are being researched and developed. Non-Patent Document 1 reports that a mixed lymphocyte test can optimize immunosuppressive therapy. Non-Patent Document 2 reports the relationship between rejection after liver transplantation and a genetic polymorphism in the FOXP3 gene.

[0004] Tanaka, Tashiro, Onoe et al, “Optimization of immunosuppressive therapy based on a multiparametric mixed lymphocyte reaction assay reduces infectious complications and mortality in living donor liver transplant recipients”, Transplantation Proceedings, 44, 555-559 (2012)Verma, Tanaka et al, “Significant Association Between FOXP3 Gene Polymorphism and Steroid-Resistant Acute Rejection in Living Donor Liver Transplantation”, HEPATOLOGY COMMUNICATIONS, VOL.1, NO.5, 2017

[0005] However, there is still room for improvement in the above-mentioned conventional technologies. The technology described in Non-Patent Document 1 is time-consuming to put into practice and difficult to apply to all patients receiving organ transplants. The technology described in Non-Patent Document 2 still has room for improvement in accuracy.

[0006] The present invention has been made in view of the above-mentioned problems, and aims to provide a simple and highly accurate method for identifying immunologically high-risk groups in organ transplantation.

[0007] A method for obtaining data for identifying an immunologically high-risk group in organ transplantation according to one embodiment of the present invention includes the step of detecting single nucleotide polymorphisms in two or more genes selected from a first group of genes consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff, contained in a sample collected from a subject.

[0008] A data processing device for identifying an immunological high-risk group in organ transplantation according to one aspect of the present invention comprises: an acquisition unit that acquires data on a single polymorphism of a gene contained in a sample collected from a subject; a calculation unit that calculates an immunological risk in organ transplantation from the acquired data on the single polymorphism; and a determination unit that determines whether the subject is in an immunological high-risk group in organ transplantation based on the calculated risk, wherein the genes for which data on a single polymorphism is acquired include two or more types of genes selected from a first gene group consisting of FOXP3, HMGB1, PD-1, STAT4 and Baff.

[0009] A kit for identifying immunologically high-risk groups in organ transplantation according to one embodiment of the present invention includes an item for detecting single nucleotide polymorphisms in two or more genes selected from a first group of genes consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff, contained in a sample collected from a subject.

[0010] According to one aspect of the present invention, a simple and highly accurate method for identifying immunologically high-risk groups in organ transplantation is provided.

[0011] FIG. 1 is a schematic diagram showing the main parts of a data processing device according to an embodiment of the present invention. FIG. 2 is an ROC curve relating to acute rejection after liver transplantation according to an example of the present invention. FIG. 3 is an ROC curve relating to acute rejection after liver transplantation according to an example of the present invention. FIG. 4 is an ROC curve relating to acute rejection after liver transplantation according to an example of the present invention. FIG. 5 is an ROC curve relating to production of de novo donor-specific antibodies after liver transplantation according to an example of the present invention. FIG. 6 is an ROC curve relating to production of de novo donor-specific antibodies after liver transplantation according to an example of the present invention. FIG. 7 is an ROC curve relating to other infectious diseases after liver transplantation according to an example of the present invention. FIG. 8 is an ROC curve relating to multiple diseases after kidney transplantation according to an example of the present invention.

[0012] An embodiment of the present invention will be described below, but the present invention is not limited to these specific examples. The present invention can be modified in various ways within the scope of the claims. New embodiments or examples can be obtained by appropriately combining the technical means disclosed in different embodiments or examples. These new embodiments or examples are also included in the technical scope of the present invention.

[0013] All of the scientific and patent literature mentioned herein is hereby incorporated by reference.

[0014] Unless otherwise specified in this specification, the expression "A to B" representing a range of numerical values ​​means "greater than or equal to A and less than or equal to B."

[0015] [1. Data Acquisition Method] According to one embodiment of the present invention, a data acquisition method for identifying immunologically high-risk groups in organ transplantation includes a step of detecting single nucleotide polymorphisms in two or more genes selected from a first gene group contained in a sample collected from a subject. The data acquisition method may further include a step of detecting single nucleotide polymorphisms in one or more genes selected from a second gene group contained in the sample collected from the subject. The data acquisition method may further include a step of detecting single nucleotide polymorphisms in genes other than the first gene group and the second gene group.

[0016] [1.1. First Gene Group and Second Gene Group] The first gene group consists of FOXP3, HMGB1, PD-1, STAT4, and Baff. The second gene group consists of IL12B, NLRP3, TNFα, CAV1, and CTLA4.

[0017] In a data acquisition method according to one embodiment of the present invention, single nucleotide polymorphisms in two or more genes are detected. That is, single nucleotide polymorphisms in two or more genes included in a first gene cluster are detected, and optionally, single nucleotide polymorphisms in one or more genes included in a second gene cluster are further detected. These single nucleotide polymorphisms may be detected simultaneously or at different times.

[0018] In one embodiment, the data acquisition method detects single nucleotide polymorphisms in only two types of genes from the first group of genes. In one embodiment, the data acquisition method detects single nucleotide polymorphisms in two or more types of genes (three, four, five or more types) from the first group of genes (and optionally the second group of genes).

[0019] The fewer the types of genes for which single nucleotide polymorphisms are detected, the lower the testing costs. Furthermore, the fewer the types of genes for which single nucleotide polymorphisms are detected, the easier it is to identify immunologically high-risk groups in organ transplantation for a wide range of transplant organs and diseases. From this perspective, a preferred embodiment detects single nucleotide polymorphisms in only two types of genes included in the first gene group.

[0020] The more types of genes for which single nucleotide polymorphisms are detected, the easier it is to identify immunological high-risk groups in organ transplantation with high accuracy for specific transplant organs and specific diseases. From this perspective, an embodiment in which single nucleotide polymorphisms of two or more types of genes contained in the first gene group (and optionally the second gene group) are detected is preferred. However, considering the effort required for the work, it is preferable that the number of genes for which single nucleotide polymorphisms are detected in the first gene group and the second gene group be, for example, five or less.

[0021] In one embodiment, the single genetic polymorphism to be detected is one or more selected from the group consisting of:・FOXP3: rs3761548 A carrier ・HMGB1: rs2249825 C carrier ・HMGB1: rs1412125 CC ・HMGB1: rs1412125 T carrier ・PD-1: rs3608432 G carrier ・PD-1: rs2227982 G carrier・STAT4: rs7574865 GG ・Baff: rs9514828 CC ・Baff: rs12583006 TT ・IL12B: rs6887965 C carrier ・IL12B: rs3212227 T carrier ・NLRP3: rs4612666 TT ・TNFα :rs1799964 CC ・TNFα :rs1799724 TT ・CAV1: rs3807994 GG ・CTLA4: rs5742909 T carrier

[0022] In the above list, numbers beginning with rs are ref SNP IDs managed by NCBI. One or two capital letters following the ref SNP ID represent a base. A description of two capital letters indicates that the base is present in a homozygous or heterozygous state. For example, "AA" indicates that a gene whose genotype at a single nucleotide polymorphism is A is present in a homozygous state. For example, "AG" indicates that a gene whose genotype at a single nucleotide polymorphism is A and a gene whose genotype at a single nucleotide polymorphism is G are present in a heterozygous state. A description of one capital letter indicates that at least the base is present. For example, "A carrier" indicates that a gene whose genotype at a single nucleotide polymorphism is A is present, and the gene may be either homozygous or heterozygous.

[0023] In one embodiment, the combination of genes for detecting single nucleotide polymorphisms may be one or more of the combinations shown in Table 1 below.

[0024]

[0025] In Table 1, the single nucleotide polymorphism detected in FOXP3 is preferably rs3761548 A carrier. The single nucleotide polymorphism detected in HMGB1 is preferably rs2249825 C carrier. The single nucleotide polymorphism detected in PD-1 is preferably rs3608432 G carrier. The single nucleotide polymorphism detected in STAT4 is preferably rs7574865 GG. The single nucleotide polymorphism detected in IL12B is preferably rs6887965 C carrier.

[0026] The immunological high-risk group identified by the combinations 1 to 4-6 may be a high-risk group for rejection, which may be after liver or kidney transplantation.

[0027] In one embodiment, the combination of genes for detecting single nucleotide polymorphisms may be one or more of the combinations shown in Table 2 below.

[0028]

[0029] In Table 2, the single nucleotide polymorphism detected in FOXP3 is preferably rs3761548 A carrier. The single nucleotide polymorphism detected in HMGB1 is preferably rs2249825 C carrier. The single nucleotide polymorphism detected in PD-1 is preferably rs3608432 G carrier. The single nucleotide polymorphism detected in STAT4 is preferably rs7574865 GG. The single nucleotide polymorphism detected in CAV1 is preferably rs3807994 GG. The single nucleotide polymorphism detected in NLRP3 is preferably rs4612666 TT. The single nucleotide polymorphism detected in TNFα is preferably rs1799964 CC.

[0030] The immunological high-risk group identified by the combinations 5 to 10-6 may be a high-risk group for rejection, which may be a rejection after liver transplantation.

[0031] In one embodiment, the combination of genes for detecting single nucleotide polymorphisms may be one or more of the combinations shown in Table 3 below.

[0032]

[0033] In Table 3, the single nucleotide polymorphism detected in Baff is preferably rs9514828 CC or rs12583006 TT. The single nucleotide polymorphism detected in STAT4 is preferably rs7574865 GG. The single nucleotide polymorphism detected in HMBG1 is preferably rs1412125 CC or rs1412125 T carrier. The single nucleotide polymorphism detected in PD-1 is preferably rs2227982 G carrier. The single nucleotide polymorphism detected in IL12B is preferably rs3212227 T carrier. The single nucleotide polymorphism detected in CTLA4 is preferably rs5742909 T carrier. The single nucleotide polymorphism detected in TNFα is preferably rs1799724 TT.

[0034] More preferably, in combination 11-1 or 11-2, the single nucleotide polymorphism detected in Baff is rs9514828 CC. More preferably, in combination 13, the single nucleotide polymorphism detected in Baff is rs12583006 TT. More preferably, in combinations 12-1 to 12-4, the single nucleotide polymorphism detected in HMBG1 is rs1412125 CC. More preferably, in combination 13, the single nucleotide polymorphism detected in HMBG1 is rs1412125 T carrier.

[0035] The immunological high-risk group identified in combinations 11-1 to 12-4 may be a high-risk group for infectious diseases. This infectious disease may be an infectious disease after liver transplantation. The immunological high-risk group identified in combination 13 may be a high-risk group for rejection. This rejection may be a rejection after kidney transplantation.

[0036] (Preferable Gene Combinations) In a preferred embodiment, single nucleotide polymorphisms in FOXP3, HMGB1, and PD-1 are detected. In this embodiment, single nucleotide polymorphisms in STAT4 may also be detected. In this embodiment, single nucleotide polymorphisms in genes other than those mentioned above may also be detected.

[0037] According to this data acquisition method, various immunological high-risk groups can be identified in subjects who have received one or more organ transplants selected from the group consisting of liver and kidney. The embodiment in which single nucleotide polymorphisms in FOXP3, HMGB1, PD-1, and STAT4 are detected can identify various immunological high-risk groups in subjects who have received liver transplants. The embodiment in which single nucleotide polymorphisms in FOXP3, HMGB1, and PD-1 are detected can identify various immunological high-risk groups in subjects who have received kidney transplants. In this embodiment, the subject may have received multiple organ transplants. For example, the subject may have received both a liver and a kidney transplant. Alternatively, the subject may have received an organ other than a liver and / or a kidney transplant. [1.2. High Immunological Risk Groups]

[0038] As used herein, the term "subjects in an immunological high-risk group for organ transplantation" refers to subjects who have a higher immunological risk after organ transplantation than normal subjects. For example, subjects in an immunological high-risk group for organ transplantation have a higher risk of developing immunological complications, concurrent diseases, and / or accidents after organ transplantation than normal subjects. For example, subjects in an immunological high-risk group for organ transplantation have a higher risk of developing rejection and / or infection after organ transplantation than normal subjects. Identifying an immunological high-risk group for organ transplantation can be used to predict subjects who are at high risk of developing diseases associated with organ transplantation (such as rejection and infection).

[0039] Rejection reactions can be classified by the time of onset, including hyperacute rejection, accelerated rejection, acute rejection, and chronic rejection. Rejection reactions can be classified by the mechanism of onset, including cellular rejection and antibody-mediated rejection. An example of cellular rejection is T-cell-mediated rejection. An example of antibody-mediated rejection is rejection due to pre-existing donor-specific antibodies and / or de novo donor-specific antibodies. In one embodiment of the present invention, the rejection reaction can be one or more of the above-mentioned rejection reactions. Examples of infections associated with organ transplantation include pneumonia, enteritis, cystitis, and bloodstream infections caused by bacteria, viruses, fungi, etc. A specific example of such an infection is cytomegalovirus infection. In one embodiment of the present invention, the infection can be one or more of the above-mentioned infections.

[0040] In a data acquisition method according to one embodiment of the present invention, single nucleotide polymorphisms in two or more of the aforementioned genes are detected. As a result, detection of a specific single nucleotide polymorphism supports the identification of a subject as belonging to the immunological high-risk group. A medical professional, such as a physician, may determine that a subject belongs to the immunological high-risk group based on data obtained by the data acquisition method. Among subjects identified as belonging to the immunological high-risk group, subjects further identified as belonging to the high-risk group for rejection may receive treatment different from the usual treatment. For example, a mixed lymphocyte test may be performed to select a more optimal immunosuppressive therapy. In clinical practice, the mixed lymphocyte test can be performed more efficiently if a patient group identified as belonging to the immunological high-risk group in advance as being at high risk for rejection is targeted. Among subjects identified as belonging to the immunological high-risk group, subjects identified as belonging to the high-risk group for infection may receive prophylactic administration of antibacterial agents, antiviral agents, antifungal agents, etc., or the administration period of these agents may be extended. [1.3. Subjects, Samples, and Transplant Organs]

[0041] As used herein, the term "subject" is not particularly limited. In one embodiment, the subject is a human. In one embodiment, the subject is a non-human mammal. Examples of non-human mammals include ungulates (cattle, wild boars, pigs, sheep, goats, etc.), perissodactyls (horses, etc.), rodents (mice, rats, hamsters, squirrels, etc.), lagomorphs (rabbits, etc.), and carnivores (dogs, cats, ferrets, etc.). The aforementioned non-human mammals include wild animals as well as livestock and companion animals (pets).

[0042] As used herein, the term "sample" is not particularly limited as long as it is a sample from which a single nucleotide polymorphism can be detected. In one embodiment, the sample is a blood sample. Blood samples are preferred in that they are easy to collect, etc.

[0043] As used herein, the organ to be transplanted into a subject may be any transplantable organ. Examples of such organs include the heart, lung, liver, pancreas, kidney, small intestine, and eyeball (cornea). Multiple organs may be transplanted. Examples of such transplants include simultaneous heart-lung transplantation, simultaneous liver-kidney transplantation, simultaneous pancreas-kidney transplantation, and simultaneous liver-small intestine transplantation. In one embodiment, the organ transplantation involves transplantation of the liver and / or pancreas. In one embodiment, the organ transplantation is liver transplantation and / or kidney transplantation. The organ transplantation may be a living donor transplantation, a transplantation under brain-dead conditions, or a transplantation under cardiac arrest.

[0044] [1.4. Single Nucleotide Polymorphism Detection Method] The method for detecting a single nucleotide polymorphism is not particularly limited. Examples of detection methods include PCR (real-time PCR, qPCR, etc.), SSCP, RFLP, and microarray. Items used in these methods (PCR devices, primer pairs, probes, various reagents, etc.) and methods for using them are well known to those skilled in the art, so detailed explanations will be omitted.

[0045] 2. Data Processing Apparatus A data processing apparatus for identifying immunologically high-risk groups in organ transplantation according to one embodiment of the present invention will be described below with reference to Figure 1. In this specification, the data that appears are explained in natural language. These data are usually written in computer-recognizable pseudo-language, commands, parameters, machine language, etc.

[0046] 1 is a block diagram showing an exemplary main part of a data processing system 100 including a data processing device 50. In addition to the data processing device 50, the data processing system 100 includes a detection unit 70 and an output unit 80. The data processing system 100 makes it possible to determine whether a subject is in an immunological high-risk group for organ transplantation based on data on single nucleotide polymorphisms of genes contained in a sample collected from the subject.

[0047] The detection unit 70 is a block that detects single nucleotide polymorphisms in genes contained in a sample collected from a subject. Examples of components that make up the detection unit 70 include a PCR device, a detector (such as a CCD camera or a spectrometer), an excitation light source (such as a laser oscillator or an electromagnetic wave irradiator), a microarray scanner, a sequencer, a genetic analysis device, and a control device that controls these. The operation of the detection unit 70 is well known to those skilled in the art, so a description thereof will be omitted.

[0048] The output unit 80 is a block that outputs the determination result determined by the determination unit 3. A specific example of the output unit 80 is a display device such as a display. The output unit 80 may output only one determination result, or may output two or more determination results.

[0049] The detection unit 70 and / or the output unit 80 may be configured as a device integrated with the data processing device 50, or may be configured as separate devices.

[0050] The data processing device 50 includes a control unit 10 and a storage unit 20. The control unit 10 controls each component in accordance with information processing. Examples of components that make up the control unit 10 include a CPU (Central Processing Unit), RAM (Random Access Memory), and ROM (Read Only Memory). The storage unit 20 stores data necessary for processing by the control unit 10. Specific examples of the storage unit 20 include auxiliary storage devices (hard disk drives, solid state drives, etc.).

[0051] The control unit 10 includes an acquisition unit 1, a calculation unit 2, and a determination unit 3. Each functional block and the processing executed by the functional block will be described below.

[0052] [2.1. Acquisition Unit] The acquisition unit 1 acquires data on single nucleotide polymorphisms of genes contained in a sample collected from a subject. The single nucleotide polymorphism data is generated by the detection unit 70. The single nucleotide polymorphism data acquired by the acquisition unit 1 is sent to the calculation unit 2.

[0053] The single nucleotide polymorphism data acquired by the acquisition unit 1 includes data on single nucleotide polymorphisms of two or more genes selected from a first gene group consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff. The single nucleotide polymorphism data acquired by the acquisition unit 1 may further include data on single nucleotide polymorphisms of one or more genes selected from a second gene group consisting of IL12B, NLRP3, TNFα, CAV1, and CTLA4. The single nucleotide polymorphism data acquired by the acquisition unit 1 may further include data on single nucleotide polymorphisms of genes other than the first gene group and the second gene group. The single nucleotide polymorphism data acquired by the acquisition unit 1 may also be data on single nucleotide polymorphisms of the gene combinations described in Section [1]. These single nucleotide polymorphisms may be the single nucleotide polymorphisms described in Section [1].

[0054] The single nucleotide polymorphism data sent from the acquisition unit 1 to the calculation unit 2 may be data in which a specific single nucleotide polymorphism is associated with a detected genotype. For example, it is assumed that the genotype of single nucleotide polymorphism X is known to be A or G. In this case, the genotype associated with single nucleotide polymorphism X may be any one of "A / A homozygote," "G / G homozygote," or "A / G heterozygote."

[0055] The data in which a specific single nucleotide polymorphism is associated with a detected genotype may be generated by the acquisition unit 1, another block included in the control unit 10, or another block not included in the data processing device 50.

[0056] [2.2. Calculation Unit] The calculation unit 2 calculates the immunological risk in organ transplantation from the single nucleotide polymorphism data received from the acquisition unit 1. The risk may be in the form of a quantified risk value. The risk calculated by the calculation unit 2 is sent to the determination unit 3.

[0057] For example, in the case where, with respect to the aforementioned single nucleotide polymorphism X, homozygosity of A / A is positively correlated with the high-risk group, the calculation unit 2 increases the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygosity of A / A. In this example, the calculation unit 2 may not change the risk value or may decrease the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygosity of G / G or heterozygosity of A / G.

[0058] Furthermore, for example, with respect to the aforementioned single nucleotide polymorphism X, there is a case where the genotype of A is positively correlated with the high-risk group. In this example, the calculation unit 2 increases the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygous A / A or heterozygous A / G. In this example, the calculation unit 2 may not change the risk value or may decrease the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygous G / G.

[0059] Furthermore, for example, when homozygote G / G is negatively correlated with the high-risk group for the aforementioned single nucleotide polymorphism X, the calculation unit 2 decreases the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygote G / G. In this example, the calculation unit 2 may not change the risk value or may increase the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygote A / A or heterozygote A / G.

[0060] By performing this process for other single nucleotide polymorphisms, the calculation unit 2 calculates the risk.

[0061] [2.3. Determination Unit] The determination unit 3 determines whether or not the subject is in an immunological high-risk group based on the risk calculated by the calculation unit 2. The result of the determination by the determination unit 3 is sent to the output unit 80 and output.

[0062] The determination of whether the subject is in the immunological high-risk group is performed, for example, by comparing the risk value with a predetermined cutoff value. The determination unit 3 may determine that the subject is in the immunological high-risk group when the risk value is equal to or greater than the cutoff value. The determination unit 3 may determine that the subject is not in the immunological high-risk group when the risk value is less than the cutoff value.

[0063] The determination unit 3 may set two or more cutoff values ​​for the determination and classify the determination results into three or more types. For example, the determination unit 3 may set cutoff values ​​X and Y in descending order and make the determination as follows: Risk value is cutoff value X or more: There is a very high possibility that the subject is in the immunological high-risk group. Risk value is cutoff value Y or more but less than X: It cannot be denied that the subject is in the immunological high-risk group. Risk value is less than cutoff value Y: The subject is not in the immunological high-risk group.

[0064] The determination unit 3 may change the cutoff value depending on the attributes of the subject, such as age, sex, disease history, medical history, and transplanted organs.

[0065] 2.4 Software Implementation The functions of the data processing device 50 may be implemented by a computer program. This computer program is a control program for causing a computer to function as the data processing device 50. The control program causes a computer to function as each control block of the data processing device 50 (particularly each unit included in the control unit 10).

[0066] In this embodiment, the data processing device 50 includes a computer. This computer includes, as hardware for executing the control program, one or more control devices (e.g., processors) and one or more storage devices (e.g., memories). The functions described in the above embodiment can be realized by executing the control program using the control devices and storage devices.

[0067] The control program may be recorded on one or more non-transitory computer-readable recording media. The recording media may or may not be included in the data processing device 50. If the data processing device 50 does not include a recording medium, the control program may be supplied to the data processing device 50 via any wired or wireless transmission medium.

[0068] Some or all of the functions of each control block of the data processing device 50 may be implemented by a logic circuit. For example, an integrated circuit formed with a logic circuit that functions as each control block of the data processing device 50 is included in the scope of the present invention. As another example, some or all of the functions of each control block of the data processing device 50 may be implemented by a quantum computer.

[0069] The processing described in the above embodiment may be performed by AI (Artificial Intelligence). In this embodiment, the AI ​​may operate in the data processing device 50 or may operate in another device (such as an edge computer or a cloud server).

[0070] [3. Kit] A kit for identifying an immunological high-risk group in organ transplantation according to one embodiment of the present invention includes an item for detecting single nucleotide polymorphisms in two or more genes selected from a first gene group contained in a sample collected from a subject. The kit may further include an item for detecting single nucleotide polymorphisms in one or more genes selected from a second gene group. The kit may further include an item for detecting single nucleotide polymorphisms in genes other than the first gene group and the second gene group. Furthermore, the single nucleotide polymorphism detected by the kit may be a single nucleotide polymorphism in a combination of genes described in Section [1].

[0071] The first group of genes consists of FOXP3, HMGB1, PD-1, STAT4, and Baff, and the second group of genes consists of IL12B, NLRP3, TNFα, CAV1, and CTLA4.

[0072] As used herein, the term "kit" refers to a combination of reagents and the like for a specific purpose. This purpose may be medical (such as diagnostic) or experimental.

[0073] The kit may include reagents and / or ancillary materials. The kit may include one or more containers (boxes, bottles, dishes, etc.) for storing the reagents and / or ancillary materials.

[0074] Examples of the product for detecting single nucleotide polymorphisms include primer pairs and probes. Other examples of the product for detecting single nucleotide polymorphisms include DNA chips. The methods of using these products are well known to those skilled in the art, so detailed descriptions will be omitted.

[0075] [4. Others] The present invention includes the following aspects. <1> A method for obtaining data for identifying an immunologically high-risk group in organ transplantation, comprising the step of detecting single nucleotide polymorphisms in two or more genes selected from a first group of genes consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff, contained in a sample collected from a subject. <2> The method for obtaining data according to <1>, further comprising the step of detecting single nucleotide polymorphisms in one or more genes selected from a second group of genes consisting of IL12B, NLRP3, TNFα, CAV1, and CTLA4, contained in a sample collected from the subject. <3> The method for obtaining data according to <1> or <2>, wherein the disease(s) that the immunologically high-risk group develops are one or more selected from the group consisting of rejection and infectious diseases. <4> The method for obtaining data according to any of <1> to <3>, wherein the organ transplant is a transplant of one or more organs selected from the group consisting of heart, lung, liver, pancreas, kidney, small intestine, and eyeball. <5> The method for obtaining data according to any one of <1> to <4>, wherein the organ to be transplanted comprises one or more types selected from the group consisting of a liver and a kidney, and the method comprises detecting single nucleotide polymorphisms in genes comprising FOXP3, HMGB1, PD-1, and optionally STAT4, contained in a sample collected from the subject. <6> The method for obtaining data according to any one of <1> to <5>, wherein the method satisfies either (i) or (ii) below: (i) the organ to be transplanted comprises a liver, and the method comprises detecting single nucleotide polymorphisms in the genes FOXP3, HMGB1, PD-1, and STAT4 contained in a sample collected from the subject; or (ii) the organ to be transplanted comprises a kidney, and the method comprises detecting single nucleotide polymorphisms in the genes FOXP3, HMGB1, PD-1, and STAT4 contained in a sample collected from the subject.<7> A data processing device (50) for identifying an immunologically high-risk group in organ transplantation, comprising: an acquisition unit (1) that acquires data on a single polymorphism of a gene contained in a sample collected from a subject; a calculation unit (2) that calculates an immunological risk in organ transplantation from the acquired data on the single polymorphism; and a determination unit (3) that determines whether the subject is in an immunologically high-risk group in organ transplantation based on the calculated risk, wherein the genes for which data on the single polymorphism is acquired include two or more genes selected from a first gene group consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff. <8> A data processing system (100) for identifying an immunologically high-risk group in organ transplantation, comprising the data processing device (50) according to <7>, a detection unit (70), and an output unit (80). <9> A data processing program for causing a computer to function as the data processing device (50) according to <7>, the data processing program causing a computer to function as the acquisition unit (1), the calculation unit (2), and the determination unit (3). <10> A computer-readable recording medium storing the data processing program according to <9>. <11> A kit for identifying immunologically high-risk groups in organ transplantation, comprising an article for detecting single nucleotide polymorphisms in two or more genes selected from a first group of genes consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff, which are contained in a sample collected from a subject.

[0076] Another aspect of the present invention includes a diagnostic method. Therefore, in each of the above aspects, the term "data acquisition method" may be read as "diagnostic method." One embodiment of the present invention relates to a diagnostic method for identifying immunologically high-risk groups.

[0077] The present invention will be described below based on examples. The examples shown below are merely examples, and are not intended to limit the present invention.

[0078] In the examples, the obtained ROC (Receiver Operating Characteristic) curves are shown in Figures 2 to 11. In these figures, the single nucleotide polymorphisms used as variables are written below the ROC curves. Single nucleotide polymorphisms written in italics are single nucleotide polymorphisms of genes included in the first gene cluster. Single nucleotide polymorphisms written in plain text are single nucleotide polymorphisms of genes included in the second gene cluster.

[0079] [Test Method] Single nucleotide polymorphisms in target genes were analyzed according to the following procedure. 1. Peripheral blood was collected from patients using blood collection tubes containing anticoagulant. The treatments the patients underwent included liver transplants: 126 patients and kidney transplants: 98 patients. The liver transplants included living donor liver transplants: 106 patients and brain-dead donor liver transplants: 20 patients. The kidney transplants included living donor kidney transplants: 86 patients, brain-dead donor kidney transplants: 9 patients, and heart-stopping donor kidney transplants: 3 patients. 2. DNA was extracted from leukocytes or mononuclear cells in peripheral blood. A QIAamp DNA Blood Mini Kit (QIAGEN) was used for extraction. 3. The extracted DNA was stored at 4°C until use. 4. Single nucleotide polymorphisms in the target genes contained in the extracted DNA samples were analyzed by real-time PCR. The primers used in the analysis were those included in TaqMan (registered trademark) SNP genotyping Assays (Thermo Fisher Scientific) and i-densy (registered trademark) Pack Multitype UNIVERSAL (Arkray Inc.).

[0080] The association between the single nucleotide polymorphisms obtained by the analysis and the diseases that patients developed after transplantation was analyzed by univariate analysis using a t-test. Single nucleotide polymorphisms with p<0.1 were selected as candidates, and associated single nucleotide polymorphisms were extracted using a stepwise method. A prediction model was created using logistic regression based on the extracted single nucleotide polymorphisms.

[0081] Example 1: Prediction of acute rejection after liver transplantation 1 A prediction model for acute rejection after liver transplantation was created. Specifically, FOXP3, HMGB1, PD-1, and STAT4 were selected from the first gene group, and IL12B was selected from the second gene group, and the association between single nucleotide polymorphisms (see below) contained in these five genes and disease was analyzed. FOXP3: rs3761548 A carrier HMGB1: rs2249825 C carrier PD-1: rs3608432 G carrier STAT4: rs7574865 GG IL12B: rs6887965 C carrier

[0082] The ROC curve generated by logistic regression is shown in Figure 2. The AUC (Area Under the Curve) of the obtained ROC curve was 0.734.

[0083] Example 2: Prediction of acute rejection after liver transplantation 2 ROC curves were created by logistic regression based on four, three, or two of the five single nucleotide polymorphisms selected in Example 1. The obtained ROC curves and AUCs are shown in Figures 3 to 5.

[0084] As shown in Figure 3, the AUC of the ROC curve based on four types of single nucleotide polymorphisms was 0.697 to 0.724. As shown in Figures 4 and 5, the AUC of the ROC curve based on three types of single nucleotide polymorphisms was 0.663 to 0.706. As shown in Figure 5, the AUC of the ROC curve based on two types of single nucleotide polymorphisms was 0.671. Thus, even with a prediction model in which the number of single nucleotide polymorphisms was reduced, a sufficiently high AUC was obtained. According to the results of this example, patients at high risk of acute rejection after liver transplantation could be predicted based on single nucleotide polymorphisms of two or more types of genes selected from the first gene group.

[0085] Example 3: Prediction of de novo donor-specific antibody production after liver transplantation (1) A prediction model for the production of de novo donor-specific antibodies (de novo DSA) after liver transplantation was created. Specifically, FOXP3, HMGB1, PD-1, and STAT4 were selected from the first gene group, and CAV1, NLRP3, and TNFα were selected from the second gene group, and the association between single nucleotide polymorphisms (see below) contained in these seven genes and disease was analyzed. ・FOXP3: rs3761548 A carrier ・HMGB1: rs2249825 C carrier ・PD-1: rs3608432 G carrier ・STAT4: rs7574865 GG ・CAV1: rs3807994 GG ・NLRP3: rs4612666 TT ・TNFα: rs1799964 CC

[0086] The ROC curve generated by logistic regression is shown in Figure 6. The AUC of the obtained ROC curve was 0.809.

[0087] Example 4: Prediction of de novo donor-specific antibody production after liver transplantation 2 ROC curves were created by logistic regression based on five, four, or three of the seven single nucleotide polymorphisms selected in Example 3. The obtained ROC curves and AUCs are shown in Figures 7 and 8.

[0088] As shown in Figure 7, the AUC of the ROC curve based on five types of single nucleotide polymorphisms was 0.728 to 0.762. As shown in Figure 7, the AUC of the ROC curve based on four types of single nucleotide polymorphisms was 0.708. As shown in Figure 8, the AUC of the ROC curve based on three types of single nucleotide polymorphisms was 0.682 to 0.697. Thus, even with a prediction model that reduced the number of single nucleotide polymorphisms, a sufficiently high AUC was obtained. According to the results of this example, patients at high risk of developing de novo donor-specific antibodies after liver transplantation could be predicted based on single nucleotide polymorphisms in two or more genes selected from the first gene group.

[0089] Example 5: Prediction of infections after liver transplantation A prediction model for infections (bloodstream infections or cytomegalovirus (CMV) infections) after liver transplantation was created. With regard to bloodstream infections, STAT4 and Baff were selected from the first gene group, and IL12B was selected from the second gene group, and the association between single nucleotide polymorphisms (see below) contained in these three genes and the disease was analyzed. With regard to cytomegalovirus infections, HMGB1 and PD-1 were selected from the first gene group, and CTLA4 and TNFα were selected from the second gene group, and the association between single nucleotide polymorphisms (see below) contained in these four genes and the disease was analyzed.

[0090] (Bloodstream infection) ・STAT4: rs7574865 GG ・Baff: rs9514828 CC ・IL12B: rs3212227 T carrier (Cytomegalovirus infection) ・HMGB1: rs1412125 CC ・PD-1: rs2227982 G carrier ・CTLA4: rs5742909 T carrier ・TNFα: rs1799724 TT

[0091] ROC curves generated by logistic regression are shown in Figure 9. The AUC of the obtained ROC curves was 0.671 for bloodstream infections and 0.740 for cytomegalovirus infections. The results of this example demonstrate that patients at high risk of infections (bloodstream infections, cytomegalovirus infections, etc.) after liver transplantation can be predicted based on single nucleotide polymorphisms in two or more genes selected from the first gene group.

[0092] Example 6: Prediction of rejection after kidney transplantation 1 A prediction model for T cell-mediated rejection, antibody-mediated rejection, and de novo donor-specific antibody production after kidney transplantation was created. Specifically, FOXP3, HMGB1, and PD-1 (all included in the first gene group), which were useful for identifying immunological high-risk groups after liver transplantation in Examples 1 to 5, were selected, and the association between single nucleotide polymorphisms (see below) contained in these three genes and disease was analyzed. FOXP3: rs3761548 A carrier HMGB1: rs2249825 C carrier PD-1: rs3608432 G carrier

[0093] The ROC curves generated by logistic regression are shown in Figure 10. The AUCs of the obtained ROC curves were 0.715 for T cell-mediated rejection, 0.706 for antibody-mediated rejection, and 0.562 for de novo donor-specific antibody production. The results of this example suggest that the immunological high-risk group identification model generated based on the results of liver transplantation can also be applied to identifying immunological high-risk groups after kidney transplantation.

[0094] Example 7: Prediction of rejection after kidney transplantation 2 A prediction model for rejection after kidney transplantation (antibody-mediated rejection and de novo donor-specific antibody production) was created. Specifically, Baff and HMGB1 were selected from the first gene group, and the association between single nucleotide polymorphisms (see below) contained in these five genes and disease was analyzed. Baff: rs12583006 TT HMGB1: rs1412125 CC

[0095] The ROC curves generated by logistic regression are shown in Figure 11. The AUC of the obtained ROC curves was 0.775 for antibody-mediated rejection and 0.649 for de novo donor-specific antibody production. The results of this example indicate that patients at high risk of rejection after kidney transplantation could be predicted based on single nucleotide polymorphisms in two or more genes selected from the first gene group.

[0096] The present invention can be used in organ transplantation therapy, etc.

Claims

1. A method for obtaining data for identifying a high-risk group of rejection in organ transplantation of organs including the liver, comprising: a step of detecting a single nucleotide polymorphism of three or more genes selected from the group consisting of FOXP3, HMGB1, PD-1, and STAT4, which are included in a sample collected from a subject.

2. The method for obtaining data according to claim 1, further comprising a step of detecting a single nucleotide polymorphism of one or more genes selected from a second gene group consisting of IL12B, NLRP3, TNFα, CAV1, and CTLA4, which are included in the sample collected from the subject.

3. A method for obtaining data for identifying a high-risk group of rejection in organ transplantation of one or more organs selected from the group consisting of the liver and the kidney, comprising: a step of detecting a single nucleotide polymorphism of a gene including FOXP3, HMGB1, PD-1, and optionally STAT4, which are included in a sample collected from a subject.

4. The method for obtaining data according to claim 3, satisfying any one of the following (i) or (ii): (i) The organ transplanted in the organ transplantation includes the liver, and includes a step of detecting a single nucleotide polymorphism of each gene of FOXP3, HMGB1, PD-1, and STAT4 included in the sample collected from the subject; (ii) The organ transplanted in the organ transplantation includes the kidney, and includes a step of detecting a single nucleotide polymorphism of each gene of FOXP3, HMGB1, and PD-1 included in the sample collected from the subject.

5. A data processing device for identifying a high-risk group of rejection in organ transplantation of organs including the liver, comprising: an acquisition unit that acquires data of a single gene polymorphism of a gene included in a sample collected from a subject; a calculation unit that calculates a risk of rejection from the acquired data of the single gene polymorphism; a determination unit that determines whether or not the subject is in a high-risk group of rejection based on the calculated risk; and is provided with: The data processing device, wherein the gene from which data of a single gene polymorphism is acquired includes three or more genes selected from the group consisting of FOXP3, HMGB1, PD-1, and STAT4.

6. A data processing device for identifying a high-risk group of rejection in organ transplantation of one or more organs selected from the group consisting of the liver and the kidney, An acquisition unit that acquires data on a single nucleotide polymorphism of a gene included in a sample collected from a subject; A calculation unit that calculates the risk of rejection from the acquired data on the single nucleotide polymorphism; A determination unit that determines whether or not the subject is in a high-risk group for rejection based on the calculated risk; and includes; A data processing device, wherein the gene from which data on a single nucleotide polymorphism is acquired includes FOXP3, HMGB1, PD-1, and optionally STAT4.

7. A data processing system for identifying a high-risk group for rejection in organ transplantation of one or more types selected from the group consisting of the liver and the kidney, comprising the data processing device according to claim 5 or 6, a detection unit, and an output unit.

8. A data processing program for causing a computer to function as the data processing device according to claim 5 or 6, the data processing program for causing the computer to function as the acquisition unit, the calculation unit, and the determination unit.

9. A computer-readable recording medium storing the data processing program according to claim 8.

10. An article for detecting single nucleotide polymorphisms of three or more genes selected from the group consisting of FOXP3, HMGB1, PD-1, and STAT4 included in a sample collected from a subject, a kit for identifying a high-risk group for rejection in organ transplantation of an organ including the liver.

11. An article for detecting single nucleotide polymorphisms of genes including FOXP3, HMGB1, PD-1, and optionally STAT4 included in a sample collected from a subject, a kit for identifying a high-risk group for rejection in organ transplantation of one or more types selected from the group consisting of the liver and the kidney.

12. A data acquisition method for identifying a high-risk group for bloodstream infection in liver transplantation, comprising: a step of detecting single nucleotide polymorphisms of genes of STAT4, Baff, and IL12B included in a sample collected from a subject.

13. A data processing device for identifying a high-risk group for bloodstream infection in liver transplantation, comprising: an acquisition unit that acquires data on a single nucleotide polymorphism of a gene included in a sample collected from a subject; a calculation unit that calculates the risk of bloodstream infection from the acquired data on the single nucleotide polymorphism; A determination unit that determines whether or not the subject belongs to a high-risk group for bloodstream infection based on the calculated risk; comprising; A data processing device in which the gene for which polymorphism data is acquired includes STAT4, Baff, and IL12B.

14. A data processing system for identifying a high-risk group for bloodstream infection in liver transplantation, comprising the data processing device according to claim 13, a detection unit, and an output unit.

15. A data processing program for causing a computer to function as the data processing device according to claim 13, the program causing the computer to function as the acquisition unit, the calculation unit, and the determination unit.

16. A computer-readable recording medium storing the data processing program according to claim 15.

17. A kit for identifying a high-risk group for bloodstream infection in liver transplantation, comprising an article for detecting single nucleotide polymorphisms of the genes of STAT4, Baff, and IL12B contained in a sample collected from a subject.