WO2024048440A1 - Procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes, et dispositif de traitement de données, système de traitement de données, programme de traitement de données et kit associé - Google Patents

Procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes, et dispositif de traitement de données, système de traitement de données, programme de traitement de données et kit associé Download PDF

Info

Publication number
WO2024048440A1
WO2024048440A1 PCT/JP2023/030682 JP2023030682W WO2024048440A1 WO 2024048440 A1 WO2024048440 A1 WO 2024048440A1 JP 2023030682 W JP2023030682 W JP 2023030682W WO 2024048440 A1 WO2024048440 A1 WO 2024048440A1
Authority
WO
WIPO (PCT)
Prior art keywords
single nucleotide
data processing
risk
immunological
data
Prior art date
Application number
PCT/JP2023/030682
Other languages
English (en)
Japanese (ja)
Inventor
秀樹 大段
田中 友加 杉山
健太郎 井手
真裕 大平
裕之 田原
直樹 谷峰
Original Assignee
国立大学法人広島大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国立大学法人広島大学 filed Critical 国立大学法人広島大学
Publication of WO2024048440A1 publication Critical patent/WO2024048440A1/fr

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]

Definitions

  • the present invention relates to a data acquisition method for identifying immunological 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 immunological high-risk groups in organ transplantation.
  • Immunosuppressive therapy is commonly used as a method to prevent rejection after organ transplantation.
  • Typical immunosuppressive therapy involves suppressing T cell and B cell activity by administering oral drugs or oral and intravenous drugs.
  • Non-Patent Document 1 reports that immunosuppressive therapy can be optimized by a lymphocyte mixture test.
  • Non-Patent Document 2 reports on the relationship between rejection after liver transplantation and monogenic polymorphism of the FOXP3 gene.
  • Non-Patent Document 1 takes time and effort to put into practice, and is difficult to apply to all patients receiving organ transplants.
  • the technique described in Non-Patent Document 2 still has room to improve accuracy.
  • 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.
  • a data acquisition method for identifying immunological high-risk groups in organ transplantation includes: The method includes a step of detecting single nucleotide polymorphisms in 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.
  • a data processing device for identifying an immunological high-risk group in organ transplantation includes: an acquisition unit that acquires data on a single gene polymorphism in 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 gene polymorphism; a determination unit that determines whether the subject is in an immunological high-risk group for organ transplantation based on the calculated risk; It is equipped with
  • the genes for which monogenic polymorphism data are obtained include two or more genes selected from the first gene group consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff.
  • a kit for identifying immunological high-risk groups in organ transplantation includes: An article for detecting single nucleotide polymorphisms 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. ing.
  • FIG. 1 is a schematic diagram showing main parts of a data processing device according to an embodiment of the present invention.
  • 1 is an ROC curve related to acute rejection after liver transplantation according to an example of the present invention.
  • 1 is an ROC curve related to acute rejection after liver transplantation according to an example of the present invention.
  • 1 is an ROC curve related to acute rejection after liver transplantation according to an example of the present invention.
  • 1 is an ROC curve related to acute rejection after liver transplantation according to an example of the present invention.
  • Figure 3 is an ROC curve for the production of de novo donor-specific antibodies after liver transplantation, according to an example of the present invention.
  • Figure 3 is an ROC curve for the production of de novo donor-specific antibodies after liver transplantation, according to an example of the present invention.
  • Figure 3 is an ROC curve for the production of de novo donor-specific antibodies after liver transplantation, according to an example of the present invention.
  • 2 is an ROC curve regarding other infections after liver transplantation according to an example of the present invention.
  • FIG. 3 is an ROC curve for multiple diseases after kidney transplantation according to an embodiment of the present invention.
  • FIG. 3 is an ROC curve for multiple diseases after kidney transplantation according to an embodiment of the present invention.
  • a data acquisition method for identifying an immunological high-risk group in organ transplantation includes two types selected from a first gene group contained in a sample collected from a subject.
  • the method includes a step of detecting single nucleotide polymorphisms in the above genes.
  • the data acquisition method may further include the step of detecting single nucleotide polymorphisms in one or more genes selected from the second gene group, which are included in the sample collected from the subject.
  • the data acquisition method may further include the step of detecting single nucleotide polymorphisms in genes other than the first gene group and the second gene group.
  • the first gene group consists of FOXP3, HMGB1, PD-1, STAT4 and Baff.
  • the second gene group consists of IL12B, NLRP3, TNF ⁇ , CAV1 and CTLA4.
  • single nucleotide polymorphisms in two or more types of genes are detected. That is, single nucleotide polymorphisms in two or more genes included in the first gene group are detected, and, with an optional configuration, single nucleotide polymorphisms in one or more genes included in the second gene group are further detected. . Detection of these single nucleotide polymorphisms may be performed simultaneously or at different times.
  • the data acquisition method detects single nucleotide polymorphisms of only two types of genes from the first gene group. In one embodiment, the data acquisition method comprises determining a single nucleotide polymorphism of two or more (3, 4, 5, or more) genes from the first gene group (and optionally the second gene group). Detect types.
  • genes that detect single nucleotide polymorphisms the easier it is to identify immunologically high-risk groups in organ transplantation with high accuracy, targeting specific transplanted organs and specific diseases. From this point of view, it is preferable to detect single nucleotide polymorphisms in two or more genes included in the first gene group (and optionally the second gene group). However, in consideration of work effort, 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 is, for example, five or less.
  • the single gene polymorphism to be detected is one or more types selected from the group consisting of: ⁇ FOXP3:rs3761548 A carrier ⁇ HMGB1:rs2249825C carrier ⁇ HMGB1:rs1412125 CC ⁇ HMGB1:rs1412125 T carrier ⁇ PD-1: rs3608432 G carrier ⁇ PD-1: rs2227982 G carrier ⁇ STAT4:rs7574865GG ⁇ Baff: rs9514828 CC ⁇ Baff: rs12583006 TT ⁇ IL12B:rs6887965C carrier ⁇ IL12B:rs3212227 T carrier ⁇ NLRP3: rs4612666 TT ⁇ TNF ⁇ : rs1799964 CC ⁇ TNF ⁇ : rs1799724 TT ⁇ CAV1:rs3807994GG ⁇ CTLA4:rs5742909 T carrier
  • the numbers starting with rs are ref SNP IDs managed by NCBI.
  • One or two capital letters following the ref SNP ID represent the base.
  • the description of two capital letters indicates that the base is homozygous or heterozygous.
  • AA indicates that a gene with single nucleotide polymorphism genotype A is homozygous.
  • AG indicates that a gene having a single nucleotide polymorphism of genotype A and a gene of genotype G are heterozygous.
  • the description of one capital letter indicates that the compound contains at least the base.
  • a carrier indicates that the gene has a gene whose single nucleotide polymorphism genotype is A, and the gene may be either homozygous or heterozygous.
  • the combination of genes for which single nucleotide polymorphisms are detected may be one or more of the combinations shown in Table 1 below.
  • 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.
  • the immunological high-risk group identified by combinations 1 to 4-6 may be a high-risk group for rejection.
  • This rejection can be a rejection after liver or kidney transplantation.
  • the combination of genes for which single nucleotide polymorphisms are detected may be one or more of the combinations shown in Table 2 below.
  • 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 TNF ⁇ is preferably rs1799964 CC.
  • the immunological high-risk group identified by combinations 5 to 10-6 may be a high-risk group for rejection.
  • This rejection reaction may be a rejection reaction after liver transplantation.
  • the combination of genes for which single nucleotide polymorphisms are detected may be one or more of the combinations shown in Table 3 below.
  • 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 TNF ⁇ is preferably rs1799724 TT.
  • 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.
  • the immunological high-risk group identified in combinations 11-1 to 12-4 may be a high-risk group for infectious diseases. This infection may be a post-liver transplant infection.
  • the immunological high-risk group identified in combination 13 may be a high-risk group for rejection. This rejection reaction may be a rejection reaction after kidney transplantation.
  • single nucleotide polymorphisms of FOXP3, HMGB1 and PD-1 are detected.
  • a single nucleotide polymorphism in STAT4 may be detected.
  • single nucleotide polymorphisms in genes other than those mentioned above may be detected.
  • various immunological high-risk groups can be identified in a subject who has been transplanted with one or more types of organs selected from the group consisting of liver and kidney.
  • the embodiment of detecting single nucleotide polymorphisms of FOXP3, HMGB1, PD-1, and STAT4 can identify various immunological high-risk groups in liver transplanted subjects.
  • Embodiments that detect single nucleotide polymorphisms of FOXP3, HMGB1, and PD-1 can identify various immunological high-risk groups in kidney transplanted subjects.
  • the subject may have multiple organs transplanted. For example, a liver and kidney may have been transplanted. Alternatively, an organ other than the liver and/or kidney may have been transplanted. [1.2. Immunological high-risk group]
  • a subject included in an immunological high-risk group for organ transplantation refers to a subject whose immunological risk after organ transplant surgery is higher than that of a normal subject.
  • subjects included in an immunological high-risk group for organ transplantation have a higher risk than normal subjects of developing immunological complications, complications, and/or complications after organ transplant surgery.
  • subjects included in the immunological high-risk group for organ transplantation have a higher risk of developing rejection and/or infection after organ transplant surgery than normal subjects. Identifying immunological high-risk groups in organ transplantation can predict subjects who are at high risk of developing diseases (rejection, infection, etc.) associated with organ transplant surgery.
  • rejection reactions include hyperacute rejection, accelerated rejection, acute rejection, chronic rejection, etc.
  • Rejection reactions are classified according to the onset mechanism, and include cellular rejection reactions and antibody-related rejection reactions.
  • An example of cellular rejection includes T-cell rejection.
  • Examples of antibody-related rejection include rejection due to pre-existing donor-specific antibodies and/or de novo donor-specific antibodies.
  • the rejection reaction may be one or more of the rejection reactions described above.
  • infectious diseases associated with organ transplantation include pneumonia, enteritis, cystitis, and bloodstream infections caused by bacteria, viruses, fungi, and the like.
  • a specific example of such an infectious disease is a cytomegalovirus infection.
  • the infectious disease may be one or more of the infectious diseases mentioned above.
  • single nucleotide polymorphisms of the two or more types of genes described above are detected.
  • a medical professional such as a doctor may determine that the subject belongs to an immunological high-risk group based on data obtained by the data acquisition method.
  • those who are further determined to be in the high-risk group for rejection may receive treatment different from normal treatment. For example, lymphocyte mixing studies may be performed to select a more optimal immunosuppressive therapy.
  • the subject is not particularly limited.
  • the subject is a human.
  • the subject is a non-human mammal.
  • non-human mammals include artiodactyls (cows, boars, pigs, sheep, goats, etc.), perissodactyls (horses, etc.), rodents (mice, rats, hamsters, squirrels, etc.), and lagomorphs (rabbits, etc.). ), and meat (dogs, cats, ferrets, etc.).
  • the above-mentioned non-human mammals include not only domestic animals or companion animals, but also wild animals.
  • sample is not particularly limited as long as it is a sample in which single nucleotide polymorphisms can be detected.
  • the sample is a blood sample. Blood samples are preferred because they are easy to collect.
  • the organ to which the subject is transplanted may be any transplantable organ.
  • organs include the heart, lungs, liver, pancreas, kidneys, small intestine, and eyeball (cornea).
  • Multiple organs may be transplanted.
  • transplants include simultaneous heart-lung transplant, simultaneous liver-kidney transplant, simultaneous pancreatic-kidney transplant, and simultaneous liver-small-intestine transplant.
  • the organ transplant is a transplant involving liver and/or pancreas transplantation.
  • the organ transplant is a liver transplant and/or a kidney transplant.
  • the organ transplant may be a living donor transplant, a brain-dead transplant, or a cardiac arrest transplant.
  • the method for detecting single nucleotide polymorphisms is not particularly limited. Examples of detection methods include PCR (real-time PCR, qPCR, etc.), SSCP method, RFLP method, and microarray method.
  • detection methods include PCR (real-time PCR, qPCR, etc.), SSCP method, RFLP method, and microarray method.
  • the articles used in these methods such as PCR devices, primer pairs, probes, various reagents, etc.
  • the methods for their use are well known to those skilled in the art, and therefore detailed explanations will be omitted.
  • a data processing device for identifying an immunological high-risk group in organ transplantation will be described below with reference to FIG. In this specification, data that appears is explained using natural language. These data are usually written in computer-recognizable pseudo language, commands, parameters, machine language, etc.
  • FIG. 1 is a block diagram illustrating an exemplary main part of a data processing system 100 that includes a data processing device 50.
  • the data processing system 100 includes a detection section 70 and an output section 80 in addition to the data processing device 50.
  • the data processing system 100 based on data on single nucleotide polymorphisms of genes contained in samples collected from the subject, it is determined whether the subject is in an immunological high-risk group for organ transplantation. Can be judged.
  • the detection unit 70 is a block that detects single nucleotide polymorphisms in genes contained in samples collected from subjects.
  • components constituting the detection unit 70 include a PCR device, a detector (CCD camera, spectrometer, etc.), an excitation light source (laser oscillator, electromagnetic wave irradiator, etc.), a microarray scanner, a sequencer, a gene analysis device, and a device that controls these.
  • An example is a control device that The operation of the detection unit 70 is well known to those skilled in the art, so a description thereof will be omitted.
  • 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.
  • the detection unit 70 and/or the output unit 80 may be configured as an integrated device with the data processing device 50, or may be configured as separate devices.
  • the data processing device 50 includes a control section 10 and a storage section 20.
  • the control unit 10 controls each component according to information processing. Examples of the members constituting the control unit 10 include a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory).
  • the storage unit 20 stores data necessary for processing in the control unit 10.
  • a specific example of the storage unit 20 is an auxiliary storage device (hard disk drive, solid state drive, etc.).
  • 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.
  • the acquisition unit 1 acquires data on single nucleotide polymorphisms of genes contained in a sample collected from a subject. 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.
  • the single nucleotide polymorphism data acquired by the acquisition unit 1 includes single nucleotide polymorphism data of two or more genes selected from the first gene group consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff. It will be done.
  • the single nucleotide polymorphism data acquired by the acquisition unit 1 further includes single nucleotide polymorphism data of one or more genes selected from the second gene group consisting of IL12B, NLRP3, TNF ⁇ , CAV1, and CTLA4. You may be
  • the single nucleotide polymorphism data acquired by the acquisition unit 1 may further include single nucleotide polymorphism data 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 be the single nucleotide polymorphism data of the combination of genes described in section [1]. These single nucleotide polymorphisms may be the single nucleotide polymorphisms described in Section [1].
  • 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.
  • the genotype of single nucleotide polymorphism X is known to be A or G.
  • the genotype associated with single nucleotide polymorphism X can be any of "A/A homozygote", “G/G homozygote", or "A/G heterozygote”.
  • the data in which a specific single nucleotide polymorphism is associated with a detected genotype may be generated by the acquisition unit 1 or by another block included in the control unit 10. , it may be another block that is not included in the data processing device 50.
  • 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.
  • the calculation unit 2 associates it with the single nucleotide polymorphism X received from the acquisition unit 1, when A/A homo is positively correlated with a high risk group. If the genotype is A/A homozygous, the risk value increases. In this example, the calculation unit 2 does not change the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygous for G/G or heterozygous for A/G. or the risk value may be decreased.
  • the genetic type of A may be positively correlated with a 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 A/A homozygote or A/G heterozygote.
  • the calculation unit 2 does not need to change the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is G/G homozygous. The value may be decreased.
  • the calculation unit 2 does not change the risk value if the genotype associated with the single nucleotide polymorphism X received from the acquisition unit 1 is homozygous for A/A or heterozygous for A/G. or the risk value may be increased.
  • the calculation unit 2 calculates the risk.
  • the determination unit 3 determines whether the subject is in an immunological high-risk group based on the risk calculated by the calculation unit 2. The result determined by the determination unit 3 is sent to the output unit 80 and output.
  • the determination of whether the subject is in an immunological high-risk group is performed, for example, by comparing the risk value and a predetermined cutoff value.
  • the determination unit 3 may determine that the subject is in an immunological high-risk group when the risk value is equal to or greater than the cutoff value. If the risk value is less than the cutoff value, it may be determined that the subject is not in an immunological high-risk group.
  • the determination unit 3 may provide two or more cutoff values in the determination and classify into three or more types of determination results.
  • the determination unit 3 may, for example, provide a cutoff value X and a cutoff value Y in ascending order and make the determination as follows.
  • ⁇ Risk value is equal to or higher than cutoff value X: There is a very high possibility that the subject is in an immunological high-risk group.
  • ⁇ Risk value is greater than or equal to the cutoff value Y and less than X: The possibility that the subject is in an immunological high-risk group cannot be denied.
  • ⁇ Risk value is less than the cutoff value Y: The subject is not in an immunological high-risk group.
  • the determination unit 3 may change the cutoff value depending on the attributes of the subject.
  • attributes of the subject include age, gender, disease history, medical history, and transplanted organ.
  • the functions of the data processing device 50 may be implemented by a computer program.
  • This computer program is a control program for causing the computer to function as the data processing device 50.
  • the control program can cause the computer to function as each control block of the data processing device 50 (in particular, each unit included in the control unit 10).
  • the data processing device 50 includes a computer.
  • This computer includes one or more control devices (such as a processor) and one or more storage devices (such as a memory) as hardware for executing the above-mentioned control program.
  • control devices such as a processor
  • storage devices such as a memory
  • the control program may be recorded on one or more non-transitory and computer-readable recording media.
  • the recording medium may or may not be included in the data processing device 50. If the data processing device 50 is not equipped with a recording medium, the control program may be supplied to the data processing device 50 via any wired or wireless transmission medium.
  • each control block of the data processing device 50 may be implemented by a logic circuit.
  • a logic circuit for example, an integrated circuit in which logic circuits functioning as each control block of the data processing device 50 are formed is included in the scope of the present invention.
  • part or all of the functions of each control block of the data processing device 50 may be implemented by a quantum computer.
  • AI Artificial Intelligence
  • AI may operate in the data processing device 50, or may operate in other devices (edge computer, cloud server, etc.).
  • kits for identifying an immunological high-risk group in organ transplantation is a kit for identifying two or more genes selected from a first gene group contained in a sample collected from a subject. Equipped with an article for detecting single nucleotide polymorphism.
  • the kit may further include an article for detecting single nucleotide polymorphism in one or more genes selected from the second gene group.
  • the kit may further include an article for detecting single nucleotide polymorphisms in genes other than the first gene group and the second gene group.
  • the single nucleotide polymorphism detected by the kit may be a single nucleotide polymorphism of the gene combination described in Section [1].
  • the first gene group consists of FOXP3, HMGB1, PD-1, STAT4 and Baff.
  • the second gene group consists of IL12B, NLRP3, TNF ⁇ , CAV1 and CTLA4.
  • kit refers to a combination of reagents, etc. used for a specific purpose.
  • This application may be a medical application (such as a diagnostic application) or an experimental application.
  • the kit may include reagents and/or auxiliary materials.
  • a kit may include one or more containers (boxes, bottles, dishes, etc.) for storing reagents and/or ancillary materials.
  • Examples of articles for detecting single nucleotide polymorphisms include primer pairs and probes. Other examples of articles for detecting single nucleotide polymorphisms include DNA chips. The methods of using these articles are well known to those skilled in the art and will not be described in detail.
  • the present invention includes the following aspects.
  • An organ comprising the step of detecting single nucleotide polymorphisms 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. How to obtain data to identify immunological high-risk groups in transplants.
  • ⁇ 2> further comprising the step of detecting single nucleotide polymorphisms of one or more genes selected from a second gene group consisting of IL12B, NLRP3, TNF ⁇ , CAV1, and CTLA4, contained in the sample collected from the subject, ⁇ 1> Data acquisition method.
  • ⁇ 3> The data acquisition method according to ⁇ 1> or ⁇ 2>, wherein the disease that the immunologically high-risk group develops is one or more types selected from the group consisting of rejection and infection.
  • the organ transplant is a transplant of one or more types of organs selected from heart, lung, liver, pancreas, kidney, small intestine, and eyeball.
  • the organ to be transplanted in the organ transplant includes one or more types selected from the group consisting of liver and kidney,
  • ⁇ 6> A method for acquiring data according to any one of ⁇ 1> to ⁇ 5> that satisfies either (i) or (ii) below:
  • the organ to be transplanted in the organ transplant includes the liver, and the step of detecting single nucleotide polymorphisms of each gene of FOXP3, HMGB1, PD-1, and STAT4 contained in the sample collected from the subject.
  • the organ to be transplanted includes a kidney, and the method includes a step of detecting single nucleotide polymorphisms in the FOXP3, HMGB1, and PD-1 genes contained in the sample collected from the subject.
  • the genes for which monogenic polymorphism data are obtained include two or more genes selected from the first gene group consisting of FOXP3, HMGB1, PD-1, STAT4, and Baff.
  • a data processing device (50) for identifying high-risk groups for identifying high-risk groups
  • ⁇ 8> Data for identifying an immunological high-risk group in organ transplantation, comprising the data processing device (50), the detection unit (70), and the output unit (80) according to ⁇ 7>.
  • ⁇ 9> A data processing program for causing a computer to function as the data processing device (50) according to ⁇ 7>, wherein the computer is configured 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 described in ⁇ 9>.
  • one aspect of the present invention also includes a diagnostic method. Therefore, in each of the above aspects, “data acquisition method” may be read as “diagnosis method”.
  • diagnosis method One embodiment of the present invention relates to a diagnostic method for identifying immunological high-risk groups.
  • the ROC (Receiver Operating Characteristic) curves obtained are shown in FIGS. 2 to 11.
  • single nucleotide polymorphisms used as variables are shown below the ROC curves.
  • Single nucleotide polymorphisms written in italics are single nucleotide polymorphisms of genes included in the first gene group.
  • the single nucleotide polymorphism described in its true form is a single nucleotide polymorphism of a gene included in the second gene group.
  • ⁇ Test method Single nucleotide polymorphisms in the target gene were analyzed according to the following procedure.
  • Peripheral blood was collected from the patient using blood collection tubes containing anticoagulant.
  • the breakdown of treatments performed on patients was liver transplantation for 126 patients and kidney transplantation for 98 patients.
  • the breakdown of liver transplants was 106 cases of living donor liver transplants and 20 cases of brain-dead liver transplants.
  • the breakdown of kidney transplants was: 86 patients received living kidney transplants, 9 patients received brain-dead kidney transplants, and 3 patients received cardiac arrest kidney transplants.
  • DNA was extracted from leukocytes or mononuclear cells in peripheral blood. QIAamp DNA Blood Mini Kit (QIAGEN) was used for extraction. 3.
  • 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. Primers and the like used in the analysis were included in TaqMan (registered trademark) SNP genotyping Assays (Thermo Fisher Scientific) and i-densy (registered trademark) Pack Multitype UNIVERSAL (Arkray Inc.).
  • Univariate analysis was performed using a t-test regarding the association between single nucleotide polymorphisms obtained through the analysis and diseases that patients developed after transplantation.
  • single nucleotide polymorphisms with p ⁇ 0.1 as candidates related single nucleotide polymorphisms were extracted by a stepwise method.
  • a prediction model was created by logistic regression based on the extracted single nucleotide polymorphisms.
  • Example 1 Prediction of acute rejection after liver transplantation 1
  • FOXP3, HMGB1, PD-1, and STAT4 from the first gene group and IL12B from the second gene group, and determined the single nucleotide polymorphisms (the following) contained in these five genes.
  • ⁇ FOXP3:rs3761548 A carrier ⁇ HMGB1:rs2249825C carrier
  • the ROC curve created by logistic regression is shown in Figure 2.
  • the AUC (Area Under the Curve) of the obtained ROC curve was 0.734.
  • ROC curves were created by logistic regression based on four, three, or two types of single nucleotide polymorphisms among the five types of single nucleotide polymorphisms selected in Example 1. The obtained ROC curves and AUC are shown in FIGS. 3 to 5.
  • the AUC of the ROC curve based on the four types of single nucleotide polymorphisms was 0.697 to 0.724.
  • the AUC of the ROC curve based on the three types of single nucleotide polymorphisms was 0.663 to 0.706.
  • the AUC of the ROC curve based on the two types of single nucleotide polymorphisms was 0.671. In this way, even with a predictive model in which the types of single nucleotide polymorphisms were reduced, a sufficiently high AUC was obtained. According to the results of this example, patients with a high risk of acute rejection after liver transplantation could be predicted based on single nucleotide polymorphisms of two or more genes selected from the first gene group.
  • Example 3 Prediction 1 of production of de novo donor-specific antibodies after liver transplantation
  • the ROC curve created by logistic regression is shown in FIG.
  • the AUC of the obtained ROC curve was 0.809.
  • Example 4 Prediction of de novo donor-specific antibody production after liver transplantation 2
  • ROC curves were created by logistic regression based on five types, four types, or three types of single nucleotide polymorphisms. The obtained ROC curve and AUC are shown in FIGS. 7 and 8.
  • the AUC of the ROC curve based on the five types of single nucleotide polymorphisms was 0.728 to 0.762.
  • the AUC of the ROC curve based on the four types of single nucleotide polymorphisms was 0.708.
  • the AUC of the ROC curve based on the three types of single nucleotide polymorphisms was 0.682 to 0.697. In this way, even with a predictive model in which the types of single nucleotide polymorphisms were reduced, a sufficiently high AUC was obtained. According to the results of this example, patients at high risk of producing de novo donor-specific antibodies after liver transplantation were identified based on single nucleotide polymorphisms in two or more genes selected from the first gene group. I could predict it.
  • Example 5 Prediction of infection after liver transplantation
  • a predictive model for infection bloodstream infection or cytomegalovirus (CMV) infection
  • CMV cytomegalovirus
  • bloodstream infections we selected STAT4 and Baff from the first gene group and IL12B from the second gene group, and investigated the relationship between single nucleotide polymorphisms (see below) contained in these three genes and the disease.
  • HMGB1 and PD-1 from the first gene group
  • CTLA4 and TNF ⁇ from the second gene group.
  • the ROC curve created by logistic regression is shown in FIG.
  • the AUC of the obtained ROC curve was 0.671 for bloodstream infection and 0.740 for cytomegalovirus infection.
  • infections after liver transplantation can be detected based on single nucleotide polymorphisms in two or more genes selected from the first gene group. ) were able to predict which patients were at high risk.
  • Example 6 Prediction of rejection after kidney transplantation 1
  • FOXP3, HMGB1, and PD-1 all included in the first gene group
  • FOXP3, HMGB1, and PD-1 all included in the first gene group
  • ⁇ FOXP3:rs3761548 A carrier
  • ⁇ PD-1 rs3608432 G carrier
  • the ROC curve created by logistic regression is shown in FIG.
  • the AUC of the resulting ROC curves was 0.715 for T cell-mediated rejection, 0.706 for antibody-related rejection, and 0.562 for de novo donor-specific antibody production.
  • the results of this example suggest that the model for identifying immunologically high-risk groups created based on the results of liver transplantation can also be applied to identifying immunologically high-risk groups after kidney transplantation.
  • Example 7 Prediction of rejection after kidney transplantation 2
  • Baff and HMGB1 were selected from the first gene group, and the relationship between single nucleotide polymorphisms (see below) contained in these five genes and diseases was analyzed.
  • the ROC curve created by logistic regression is shown in FIG.
  • the AUC of the resulting ROC curves was 0.775 for antibody-related rejection and 0.649 for de novo donor-specific antibody production. According to the results of this example, patients with a high risk of rejection after kidney transplantation could be predicted based on single nucleotide polymorphisms of two or more genes selected from the first gene group.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Microbiology (AREA)
  • Biochemistry (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Biotechnology (AREA)
  • Biophysics (AREA)
  • Analytical Chemistry (AREA)
  • Immunology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

L'invention concerne un procédé simple et précis pour identifier des groupes immunologiques à haut risque dans une transplantation d'organes. Selon un mode de réalisation de la présente invention, un procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes comprend une étape de détection d'un polymorphisme mononucléotidique de deux gènes ou plus choisis dans un premier groupe de gènes constitué de FOXP3, HMGB1, PD-1, STAT4 et Baff, contenu dans un échantillon prélevé sur un sujet.
PCT/JP2023/030682 2022-08-31 2023-08-25 Procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes, et dispositif de traitement de données, système de traitement de données, programme de traitement de données et kit associé WO2024048440A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022-138497 2022-08-31
JP2022138497 2022-08-31

Publications (1)

Publication Number Publication Date
WO2024048440A1 true WO2024048440A1 (fr) 2024-03-07

Family

ID=90099878

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/030682 WO2024048440A1 (fr) 2022-08-31 2023-08-25 Procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes, et dispositif de traitement de données, système de traitement de données, programme de traitement de données et kit associé

Country Status (1)

Country Link
WO (1) WO2024048440A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006519440A (ja) * 2003-02-14 2006-08-24 インタージェネティックス インコーポレイテッド 疾患の増大リスクの統計学的同定法
JP2007503836A (ja) * 2003-09-04 2007-03-01 インタージェネティックス インコーポレイテッド 癌のリスク層別化のための遺伝的解析法
JP2015503923A (ja) * 2012-01-09 2015-02-05 オスロ ウニヴェルスィテーツスィーケフース ハーエフＯｓｌｏ Ｕｎｉｖｅｒｓｉｔｅｔｓｓｙｋｅｈｕｓ Ｈｆ 結腸直腸癌の解析のための方法およびバイオマーカー
WO2018079499A1 (fr) * 2016-10-25 2018-05-03 国立大学法人山口大学 Procédé d'aide à la prédiction du pronostic d'une transplantation de cellules souches hématopoïétiques allogéniques

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006519440A (ja) * 2003-02-14 2006-08-24 インタージェネティックス インコーポレイテッド 疾患の増大リスクの統計学的同定法
JP2007503836A (ja) * 2003-09-04 2007-03-01 インタージェネティックス インコーポレイテッド 癌のリスク層別化のための遺伝的解析法
JP2015503923A (ja) * 2012-01-09 2015-02-05 オスロ ウニヴェルスィテーツスィーケフース ハーエフＯｓｌｏ Ｕｎｉｖｅｒｓｉｔｅｔｓｓｙｋｅｈｕｓ Ｈｆ 結腸直腸癌の解析のための方法およびバイオマーカー
WO2018079499A1 (fr) * 2016-10-25 2018-05-03 国立大学法人山口大学 Procédé d'aide à la prédiction du pronostic d'une transplantation de cellules souches hématopoïétiques allogéniques

Non-Patent Citations (12)

* Cited by examiner, † Cited by third party
Title
CHANG YOUNGIL; SHAH TARIQ; MIN DAVID I.: "Association of genetic polymorphisms of macrophage inhibitory factor (MIF) and B-cell activating factor (BAFF) with the detection of donor specific antibodies in kidney allograft recipients", HUMAN IMMUNOLOGY, NEW YORK, NY, US, vol. 78, no. 10, 1 January 1900 (1900-01-01), US , pages 621 - 628, XP085242646, ISSN: 0198-8859, DOI: 10.1016/j.humimm.2017.06.001 *
ELISABETH A KASTELIJN;COLINE HM VAN MOORSEL;KARIN M KAZEMIER;SUZAN M ROOTHAAN;HENK JT RUVEN;JOHANNA M KWAKKEL-VAN ERP;ED A VAN DE : "A genetic polymorphism in the CAV1 gene associates with the development of bronchiolitis obliterans syndrome after lung transplantation", FIBROGENESIS & TISSUE REPAIR, BIOMED CENTRAL LTD, LONDON, UK, vol. 4, no. 1, 1 November 2011 (2011-11-01), London, UK , pages 24, XP021114615, ISSN: 1755-1536, DOI: 10.1186/1755-1536-4-24 *
FORCONI CATHERINE, GATAULT PHILIPPE, MIQUELESTORENA-STANDLEY ELODIE, NOBLE JOHAN, AL-HAJJ SALLY, GUILLEMAIN ROMAIN, STERN MARC, HO: "Polymorphism in programmed cell death 1 gene is strongly associated with lung and kidney allograft survival in recipients from CMV-positive donors", JOURNAL OF HEART AND LUNG TRANSPLANTATION, ELSEVIER, AMSTERDAM, NL, vol. 36, no. 3, 1 March 2017 (2017-03-01), AMSTERDAM, NL , pages 315 - 324, XP093143900, ISSN: 1053-2498, DOI: 10.1016/j.healun.2016.08.014 *
GORE E, RONDAAN C, VERSCHUUREN E A, GARD L, NIESTERS H G, VAN LEER-BUTER C, VERHOEVEN J G, BOER K, PEETERS A M, CLAHSEN-VAN GRONIN: "Abstract# D5. Long Term Longitudinal TTV Measurements in Lung Transplant Recipients", AMERICAN JOURNAL OF TRANSPLANTATION, vol. 22, no. Suppl 3, 1 June 2022 (2022-06-01), pages 1028, XP093143898 *
GOYAL, R.K. ; LIN, Y. ; SCHULTZ, K.R. ; FERRELL, R.E. ; KIM, Y. ; FAIRFULL, L. ; LIVOTE, E. ; YANIK, G. ; ATLAS, M.: "Tumor Necrosis Factor-@a Gene Polymorphisms Are Associated with Severity of Acute Graft-Versus-Host Disease Following Matched Unrelated Donor Bone Marrow Transplantation in Children: A Pediatric Blood and Marrow Transplant Consortium Study", BIOLOGY OF BLOOD AND MARROW TRANSPLANTATION, KLUGE CARDEN JENNINGS PUBLISHING, CHARLOTTESVILLE, VA, US, vol. 16, no. 7, 1 July 2010 (2010-07-01), US , pages 927 - 936.e1, XP027086114, ISSN: 1083-8791 *
H. YANG; Q. ZHOU; Z.M. CHEN; W.Q. CHEN; M.M. WANG; J.H. CHEN;: "Polymorphisms in STAT4 increase the risk of acute renal allograft rejection in the Chinese population", TRANSPLANT IMMUNOLOGY, ELSEVIER, NL, vol. 24, no. 4, 7 January 2011 (2011-01-07), NL , pages 216 - 219, XP028201338, ISSN: 0966-3274, DOI: 10.1016/j.trim.2011.01.001 *
KARIMI M H, MOTAZEDIAN M, GERAMIZADEH B, NIKEGHBALIAN S, YAGHOBI R, ABEDI F, HOSSIN AGHDAEE M, AZARPIRA N, ARABPOUR M, MALEKPOUR Z: "Association of the Co-stimulatory Molecules Polymorphisms with CMV Infection in Liver Transplant Recipients", INTERNATIONAL JOURNAL OF ORGAN TRANSPLANTATION MEDICINE, SHIRAZ UNIVERSITY OF MEDICAL SCIENCES, IRAN, vol. 2, no. 4, 1 January 2011 (2011-01-01), Iran, pages 171 - 177, XP093143907, ISSN: 2008-6482 *
KARIMI MOHAMMAD H, EBADI PADIDEH, POURFATHOLLAH ALI A: "Association of cytokine/costimulatory molecule polymorphism and allograft rejection: a comparative review", EXPERT REVIEW OF CLINICAL IMMUNOLOGY, FUTURE DRUGS LTD., LONDON, GB, vol. 9, no. 11, 1 November 2013 (2013-11-01), GB , pages 1099 - 1112, XP009553273, ISSN: 1744-666X, DOI: 10.1586/1744666X.2013.844462 *
PEROVIC VLADIMIR, MARKOVIC MILOS, KRAVLJACA MILICA, MILOSEVIC EMINA, DJORIC MILICA, PRAVICA VERA, NAUMOVIC RADOMIR: "Cytokine Gene Polymorphism Profiles in Kidney Transplant Patients - Association of +1188A/C RS3212227 SNP in the IL12B Gene Prevents Delayed Graft Function", ARCHIVES OF MEDICAL RESEARCH, INSTITUTO MEXICANO DEL SEGURO SOCIAL, MEXICO, MX, vol. 49, no. 2, 1 February 2018 (2018-02-01), MX , pages 101 - 108, XP093143903, ISSN: 0188-4409, DOI: 10.1016/j.arcmed.2018.04.015 *
SANTOS NAZLY; RODRÍGUEZ-ROMANOS ROCÍO; CÁMARA RAFAEL DE LA; BRUNET SALUT; NIETO JOSE B; BUÑO ISMAEL; MARTÍNEZ CARMEN; JIMÉNEZ-VELA: "PD-1 genotype of the donor is associated with acute graft-versus-host disease after HLA-identical sibling donor stem cell transplantation", ANNALS OF HEMATOLOGY, BERLIN, DE, vol. 97, no. 11, 17 July 2018 (2018-07-17), DE , pages 2217 - 2224, XP036605050, ISSN: 0939-5555, DOI: 10.1007/s00277-018-3438-y *
VERMA SAPANA, TANAKA YUKA, SHIMIZU SEIICHI, TANIMINE NAOKI, OHDAN HIDEKI: "Significant association between FOXP3 gene polymorphism and steroid‐resistant acute rejection in living donor liver transplantation", HEPATOLOGY COMMUNICATIONS, vol. 1, no. 5, 1 July 2017 (2017-07-01), pages 406 - 420, XP093143892, ISSN: 2471-254X, DOI: 10.1002/hep4.1052 *
YAMAMOTO MASATERU, KOBAYASHI TSUYOSHI, MASHIMA HIROAKI, MIKI DAIKI, KURODA SHINTARO, HAMAOKA MICHINORI, AIKATA HIROSHI, CHAYAMA KA: "PD1 gene polymorphism is associated with a poor prognosis in hepatocellular carcinoma following liver resection, cohort study", INTERNATIONAL JOURNAL OF SURGERY, SURGICAL ASSOCIATES, LONDON, GB, vol. 80, 1 August 2020 (2020-08-01), GB , pages 84 - 90, XP093143908, ISSN: 1743-9191, DOI: 10.1016/j.ijsu.2020.05.007 *

Similar Documents

Publication Publication Date Title
US20230399694A1 (en) Methods of monitoring immunosuppressive therapies in a transplant recipient
Freebern et al. GWAS and fine-mapping of livability and six disease traits in Holstein cattle
Schrider et al. Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia
US20220017891A1 (en) Improvements in variant detection
CA2771330C (fr) Procedes et materiaux permettant l'identification de races canines
CA3067634A1 (fr) Evaluation de conditions chez des sujets greffes a l'aide d'adn acellulaire specifique d'un donneur
US20200402613A1 (en) Improvements in variant detection
Johnson et al. Genotyping-by-sequencing (GBS) detects genetic structure and confirms behavioral QTL in tame and aggressive foxes (Vulpes vulpes)
JP2010022375A (ja) 移植拒絶を診断およびモニタリングするための方法および組成物
Friedenberg et al. Genotype imputation in the domestic dog
CN113874525A (zh) 使用无细胞信使rna的骨髓表征
Yan et al. Imputation-based whole-genome sequence association study reveals constant and novel loci for hematological traits in a large-scale swine F2 resource population
Osman et al. Importance of extended blood group genotyping in multiply transfused patients
Das et al. Polymorphism of fecundity genes (BMP15 and GDF9) and their association with litter size in Bangladeshi prolific Black Bengal goat
Sahana et al. A 0.5-Mbp deletion on bovine chromosome 23 is a strong candidate for stillbirth in Nordic Red cattle
Lo et al. Bovine major histocompatibility complex (BoLA) heterozygote advantage against the outcome of bovine leukemia virus infection
Myers et al. Integrated single-cell genotyping and chromatin accessibility charts JAK2V617F human hematopoietic differentiation
Rajar et al. Microbial DNA extraction of high-host content and low biomass samples: Optimized protocol for nasopharynx metagenomic studies
Duan et al. The application value of metagenomic and whole-genome capture next-generation sequencing in the diagnosis and epidemiological analysis of psittacosis
Borchert et al. RNA sequencing of whole blood in dogs with primary immune-mediated hemolytic anemia (IMHA) reveals novel insights into disease pathogenesis
O'Donnell et al. Absence of known feline MYH7 and MYBPC3 variants in a diverse cohort of cats with hypertrophic cardiomyopathy
US20220056531A1 (en) Prediction and characterization of dlbcl cell of origin subtypes
WO2024048440A1 (fr) Procédé d'acquisition de données pour l'identification de groupes immunologiques à haut risque dans la transplantation d'organes, et dispositif de traitement de données, système de traitement de données, programme de traitement de données et kit associé
Zou et al. Quantitative trait loci for porcine baseline erythroid traits at three growth ages in a White Duroc× Erhualian F 2 resource population
Rosario et al. Composite interval mapping and mixed models reveal QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23860202

Country of ref document: EP

Kind code of ref document: A1

DPE2 Request for preliminary examination filed before expiration of 19th month from priority date (pct application filed from 20040101)