Method for detecting risk value, method for assisting prediction of possibility of onset of icans, medicine, and kit for predicting onset of icans

Abstract

Provided are a method for detecting a risk value, a method for assisting the prediction of the possibility of the onset of immune effector cell-associated neurotoxicity syndrome (ICANS), a medicine, and a kit for predicting the onset of ICANS, which can be used for clinical examination methods for evaluating the risk of the onset of ICANS. Provided are: a method for detecting a risk value, which is for use in the assistance of the prediction of the onset of ICANS, the method including a step for employing, as the risk value, a protein amount ratio which is calculated from the protein amounts of two types of biomarker proteins selected from among specific proteins contained in a test sample obtained from a living body; a method for assisting the prediction of the possibility of the onset of ICANS; a medicine; and a kit for predicting the onset of ICANS.

Description

Method for detecting risk value, method for assisting in prediction of likelihood of ICANS onset, medicine, and kit for predicting ICANS onset

[0001] The present invention relates to a technology for predicting the risk and likelihood of developing ICANS, as well as a medicine and a kit using the same. This application claims priority to U.S. Patent Application No. 63 / 689,774, provisionally filed in the U.S. on September 2, 2024, the contents of which are incorporated herein by reference.

[0002] Leukemia is considered one of the most difficult-to-treat diseases, with approximately 14,000 people diagnosed with the disease annually in Japan (National Cancer Center Cancer Statistics 2018 Cancer Statistics Forecast). Genetically modified T-cell therapy (CAR-T cell therapy, CAR-T therapy) has been approved and is being used as a new treatment for some of these leukemias. Chimeric antigen receptor (CAR) T-cell therapy uses genetically modified T cells to target CD19 (B lymphocyte antigen, T cell surface antigen) and is known as a powerful immunotherapy for B-cell malignancies. This treatment artificially strengthens the patient's immune cells (T cells) and enhances their ability to attack cancer. Numerous facilities have been established within Japan, and the treatment is becoming increasingly widespread.

[0003] As a technology for producing genetically modified T cells (CAR-T cells) used in CAR-T therapy, for example, Patent Document 1 discloses a method for producing CAR-T cells, a nucleic acid transfer carrier, and a kit, which include a stimulation step of stimulating a cell population containing T cells with an antibody that activates T cells, a gene transfer step of contacting the cell population with a nucleic acid transfer carrier containing lipid particles and a first nucleic acid containing a CAR gene and a second nucleic acid containing a transposase gene encapsulated in the lipid particles, and a culture step of culturing the cell population after the gene transfer step. This technology enables efficient transfer of a CAR gene into T cells using a simple procedure, and aims to obtain CAR-T cells that express a high amount of the CAR gene.

[0004] Patent No. 7653113

[0005] However, it has been reported that this CAR-T therapy may also be accompanied by side effects such as immune effector cell-associated neurotoxicity syndrome (ICANS) and cytokine release syndrome (CRS). ICANS, observed in up to 64% of clinical trial patients, is known to present with symptoms such as encephalopathy, tremors, confusion, aphasia, somnolence, agitation, hypoesthesia, memory impairment, dysarthria, hallucinations, and changes in mental status, and in the worst cases can lead to death, making it a problem associated with CAR-T therapy. However, the cause of ICANS has yet to be elucidated, and it has previously been impossible to predict the onset of ICANS before CAR-T infusion.

[0006] The present invention has been made in view of the above background, and its object is to provide a method for detecting a risk value that can be used in a clinical test method for evaluating the risk of developing ICANS, a method for assisting in predicting the possibility of developing ICANS, a pharmaceutical, and a kit for predicting the development of ICANS.

[0007] The present invention includes the following aspects: [1] A method for detecting a risk value used to assist in predicting the likelihood of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a step of calculating a protein quantity ratio between the respective protein quantities of two biomarker proteins selected from proteins contained in a specimen sample obtained from a living body, and defining the risk value as the ratio, wherein the biomarker proteins are selected from complement components, blood coagulation factors, serpin family, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases. [2] The biomarker proteins include complement components C1RL, C1R, C1QB, C1QC, C9, C4A, C4B, blood coagulation factors F5, F12, VWF, serpin family members SERPINA10, SERPINA1, SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA or NAGLU. , SIL1, a nucleotide exchange factor for ER-localized chaperone BiP; COL1A2, MFAP4, or TNXB, an extracellular matrix protein; GASK1B, a Golgi-associated kinase; HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1, a lysosomal enzyme; IGFBP7, an IGF regulatory factor; or CPQ, CPVL, TPP1, or ERAP1, a peptidase. [3] The method for detecting a risk score according to [1], wherein the two biomarker proteins include C1RL, FUCA2, or F12. [4] The method for detecting a risk score according to [1] to [3], wherein the specimen sample is blood, plasma, or cerebrospinal fluid. [5] The method for detecting a risk score according to [1] to [4], wherein the living body is a living body prior to undergoing gene-modified T-cell therapy.[6] A method for assisting in prediction of the likelihood of developing ICANS using the risk value detection method of any one of [1] to [5], comprising a step of selecting, for a plurality of living organisms containing one or more specimens that have developed ICANS, two types of biomarker proteins such that an AUC value showing a correlation between an increase in the risk value before and after the onset of ICANS in the specimen and the incidence of ICANS in the plurality of living organisms is 0.9 or greater. [7] A method for assisting in prediction of the likelihood of developing ICANS using the risk value detection method of [1], comprising a step of measuring the protein quantity ratios of three or more different biomarker proteins contained in specimen samples obtained from the same living organism. [8] A pharmaceutical for reducing the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a component that inhibits a biomarker protein, wherein the biomarker protein is selected from a complement component, a blood coagulation factor, a serpin family member, a very low density lipoprotein, an acute phase plasma protein, α-L-fucosidase, a glycosylation enzyme, a nucleotide exchange factor for the ER-localized chaperone BiP, an extracellular matrix, a Golgi-associated kinase, a lysosomal enzyme, an IGF regulatory factor, or a peptidase. [9] The biomarker proteins include complement components C1RL, C1R, C1QB, C1QC, C9, C4A, C4B, blood coagulation factors F5, F12, VWF, serpin family members SERPINA10, SERPINA1, SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA or NA. the nucleotide exchange factor for ER-localized chaperone BiP is selected from the group consisting of GLUT3, SIL1, a nucleotide exchange factor for ER-localized chaperone BiP, COL1A2, MFAP4, and TNXB, an extracellular matrix protein; GASK1B, a Golgi-associated kinase; HEXB, FUCA1, LAMP2, CTSH, CTSC, and LAMP1, a lysosomal enzyme; IGFBP7, an IGF regulator; or CPQ, CPVL, TPP1, or ERAP1, a peptidase.

[10] A kit for predicting the onset of ICANS, used in the method for detecting a risk value according to any one of [1] to [6], comprising a means for detecting the amount of one or more of the biomarker proteins.

[0008] Embodiments of the present invention also include the following aspects: [1A] A method for detecting a risk value used to assist in predicting the likelihood of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a step of calculating a protein quantity ratio between the respective protein quantities of two biomarker proteins selected from proteins contained in a specimen sample obtained from a living body, and defining the risk value as the ratio, wherein the biomarker proteins are selected from complement components, blood coagulation factors, serpin family proteins, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix proteins, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases. [2A] The biomarker proteins include complement components C1RL, C1R, C1QB, C1QC, C9, C4A, C4B, blood coagulation factors F5, F12, VWF, serpin family members SERPINA10, SERPINA1, SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA or NAGLU, The method for detecting a risk value according to [1A], wherein the biomarker protein is selected from SIL1, a nucleotide exchange factor for ER-localized chaperone BiP, COL1A2, MFAP4, or TNXB, an extracellular matrix protein, GASK1B, a Golgi-associated kinase, HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1, a lysosomal enzyme, IGFBP7, an IGF regulatory factor, or CPQ, CPVL, TPP1, or ERAP1, a peptidase. [3A] The method for detecting a risk value for use in assisting in predicting the likelihood of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising the steps of: determining the protein amount / 1 of a biomarker protein contained in a specimen sample obtained from a living body as a protein amount ratio; and determining the protein amount ratio as the risk value, wherein the biomarker protein is FUCA2. [4A] The method for detecting a risk value according to [1A], wherein the two biomarker proteins include C1RL, FUCA2, or F12.[5A] The method for detecting a risk value according to [1A] to [4A], wherein the specimen sample is blood, plasma, or cerebrospinal fluid. [6A] The method for detecting a risk value according to [1A] to [5A], wherein the living body has not yet undergone gene-modified T-cell therapy. [7A] A method for assisting in prediction of the likelihood of developing ICANS, comprising the step of determining, for the risk value detection methods of [1A] to [6A], that the likelihood of developing ICANS is high if the risk value is 0.9 or higher. [8A] (i) A method for detecting a risk value according to [1A], comprising the step of measuring the protein abundance ratios of three or more different biomarker proteins contained in specimen samples obtained from the same living body, or (ii) a method for assisting in prediction of the likelihood of developing ICANS, comprising the method for detecting a risk value according to [1A] and the method for detecting a risk value according to [3A], for specimen samples obtained from the same living body, and the step of determining, for each of the risk values, that the likelihood of developing ICANS is high if both of the risk values ​​are 0.9 or higher. [9A] A pharmaceutical for reducing the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a component that inhibits a biomarker protein, wherein the biomarker protein is selected from a complement component, a blood coagulation factor, a serpin family member, a very low density lipoprotein, an acute phase plasma protein, α-L-fucosidase, a glycosylation enzyme, a nucleotide exchange factor for the ER-localized chaperone BiP, an extracellular matrix, a Golgi-associated kinase, a lysosomal enzyme, an IGF regulatory factor, or a peptidase.[10A] The biomarker protein is selected from the group consisting of complement components C1RL, C1R, C1QB, C1QC, C9, C4A, and C4B, blood coagulation factors F5, F12, and VWF, serpin family members SERPINA10, SERPINA1, and SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, and CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA, and NA. the medicament according to [9A], wherein the biomarker protein is selected from the group consisting of GLUT3, a nucleotide exchange factor for ER-localized chaperone BiP, SIL1, an extracellular matrix protein, COL1A2, MFAP4, or TNXB; a Golgi-associated kinase, GASK1B; a lysosomal enzyme, HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1; an IGF regulator, IGFBP7; or a peptidase, CPQ, CPVL, TPP1, or ERAP1. [11A] A kit for predicting the onset of ICANS, used in the method for detecting a risk value according to [1A] to [6A], comprising a means for detecting the amount of any one or more of the biomarker proteins.

[0009] The present invention provides a method for detecting a risk value that can be used in a clinical test method for evaluating the risk of developing ICANS, a method for assisting in predicting the possibility of developing ICANS, a medicine, and a kit for predicting the development of ICANS.

[0010] 1 is a diagram showing a PCA score plot of Test Example 1.

[0034] FIG. 1 is a graph showing a PLS-DA score plot of Test Example 1.

[0035] FIG. 1 is a schematic diagram showing cluster classification by k-means of factors of Test Example 1.

[0036] FIG. 2 is an ROC curve and dot plot of C1RL / FUCA2 of Test Example 2.

[0037] FIG. 3 is an ROC curve and dot plot of C1RL / SIL1 of Test Example 2.

[0038] FIG. 4 is an ROC curve and dot plot of C9 / GASK1B of Test Example 2.

[0039] FIG. 5 is an ROC curve and dot plot of F12 / COL1A2 of Test Example 2.

[0039] FIG. 6 is a graph showing score plots of ICANS-negative and ICANS-positive groups of Test Example 3.

[0039] FIG. 7 is a graph showing a plot of VIP scores of Test Example 3.

[0039] FIG. 8 is a graph showing the relationship between the onset of ICANS and an elevation of the complement system of Test Example 3.

[0039] FIG. 9 is a graph showing basic information of patients from whom the 1st and 2nd cohorts of Test Example 4 were acquired. 1 is an ROC curve and dot plot of C1RL / FUCA2 in Test Example 4. 2 is an ROC curve and dot plot of F12 / FUCA2 in Test Example 4. 3 is a graph showing a PLS-DA score plot in Test Example 4. 4 is a schematic diagram showing the relationship between contributing factors of the 1st cohort and the 2nd cohort in Test Example 4. 5 is a schematic diagram showing cluster classification by k-means of factors in Test Example 4. 6 is a graph of GO analysis in this Example 4.

[0011] Hereinafter, the risk value detection method, the method for assisting in the prediction of the likelihood of ICANS onset, the pharmaceutical, and the kit for predicting ICANS onset according to the present invention will be described with reference to embodiments, although the present invention is not limited to the following embodiments.

[0012] (Risk Value Detection Method) (First Embodiment) The risk value detection method of this embodiment is a method for detecting a risk value used to assist in predicting the likelihood of developing ICANS (immune cell-associated neurotoxicity syndrome). The risk value detection method of this embodiment includes a step of calculating a protein quantity ratio from the respective protein quantities of two biomarker proteins selected from proteins contained in a specimen sample obtained from a living organism, and determining the risk value as the ratio. The biomarker proteins can be selected from a complement component, a blood coagulation factor, a serpin family, a very low density lipoprotein, an acute phase plasma protein, α-L-fucosidase, a glycosylation enzyme, a nucleotide exchange factor for the ER-localized chaperone BiP, an extracellular matrix, a Golgi-associated kinase, a lysosomal enzyme, an IGF regulatory factor, or a peptidase.

[0013] Use to assist in predicting the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome) broadly refers to, for example, cases where the risk value is used as one of the data used in diagnosing ICANS, or is included as part of the steps of a diagnostic method for ICANS.

[0014] Here, the risk value in this embodiment refers to a value that serves as a basis for determining a high risk when the value is large. In this embodiment, the higher the risk value, the higher the predicted likelihood of developing ICANS. A high risk value may be determined, for example, when a standard risk value is established for the likelihood of developing ICANS and the measured risk value is higher than that standard risk value. The standard risk value may be determined by measuring the risk values ​​of one or more subjects other than the subject (living body) to be measured, preferably a sufficiently large number of control subjects, and then setting the standard risk value from the measured risk value. Furthermore, the risk value measured in a living body that has not yet developed ICANS may be used as an example of a sufficiently low risk value, or the standard risk value may be set using multiple examples of sufficiently low risk values. For example, the risk values ​​may be measured for multiple subjects other than the patient to be measured, and a representative value (such as the mean, median, or mode) of the risk values ​​measured from the multiple subjects may be used as the standard risk value. The representative value is preferably the mean value. Furthermore, an acceptable range of standard risk values ​​may be established from the risk values ​​measured from these multiple subjects, and the risk value of the patient being measured may be determined to be high or low compared to that range. The multiple subjects may be, for example, subjects who have undergone genetically modified T cell therapy but have not developed the side effect ICANS (those who have not yet developed ICANS). Furthermore, a method may be used in which, for example, a value measured sufficiently in the past for the same subject being measured is used to determine that the risk value is high. For example, a value measured sufficiently in the past for the same subject may be used as the standard risk value. Furthermore, a risk value may be determined to be high when the absolute value of the risk value is greater than a certain level. For example, as described below, when a risk value is calculated using the protein amount of a factor whose protein amount increases after the onset of ICANS as the numerator and the protein amount of a factor whose protein amount decreases after the onset of ICANS as the denominator, if this risk value exceeds 1.0, the risk value for the possibility of developing ICANS may be determined to be high.

[0015] As a specific measurement process, as described below, a protein amount ratio is calculated using the protein amount of a factor whose protein amount increases after the onset of ICANS as the numerator and the protein amount of a factor whose protein amount decreases as the denominator, and if this protein amount ratio is increased, it may be determined that the risk value has increased.

[0016] In the risk value detection method of this embodiment, the step of converting the protein quantity ratio into the risk value will be described. First, a specimen sample is obtained from a living organism. The living organism broadly refers to organisms that can be used in ICANS research, including ICANS and similar syndromes, and ICANS research. When the risk value detection method is used as a numerical value for the likelihood of human ICANS development, the living organism here refers to humans. Organisms that can be used in ICANS research include animals with cranial nerve tissue and organs, such as laboratory animals such as mice, rats, rabbits, and other livestock.

[0017] The specimen sample can be obtained from a living body by various means. A liquid specimen is preferred because of its ease of handling and detection. Liquid specimens are also preferred. Furthermore, for the purpose of detecting biomarkers related to ICANS, the liquid specimen is more preferably the spinal fluid of the cranial nerve tissue, and cerebrospinal fluid (CSF) is particularly preferred.

[0018] In this embodiment, it is also preferable that the living body is a living body before genetically modified T-cell therapy (CAR-T therapy). By detecting the risk value for the living body before genetically modified T-cell therapy, it is possible to predict the possibility of ICANS onset and the possibility of side effects occurring before CAR-T therapy is performed.

[0019] In this embodiment, the risk value is the protein amount ratio calculated from the respective protein amounts of two biomarker proteins selected from proteins contained in a specimen.

[0020] The biomarker protein is preferably selected from complement components, blood coagulation factors, serpin family members, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix proteins, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases.

[0021] More specifically, the biomarker proteins include complement components C1RL, C1R, C1QB, C1QC, C9, C4A, and C4B, blood coagulation factors F5, F12, and VWF, serpin family members SERPINA10, SERPINA1, and SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, and CD163, α-L-fucosidase FUCA1 or FUCA2, and MANBA. or NAGLU, SIL1, a nucleotide exchange factor for ER-localized chaperone BiP, COL1A2, MFAP4, or TNXB, an extracellular matrix protein; GASK1B, a Golgi-associated kinase; HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1, a lysosomal enzyme; IGFBP7, an IGF regulator; or CPQ, CPVL, TPP1, or ERAP1, a peptidase.

[0022] Furthermore, it is preferable that one of the two biomarker proteins includes C1RL, FUCA2, or F12, and it is more preferable that two of the two biomarker proteins are selected from these.

[0023] When determining the protein quantity ratio, one of the biomarker proteins is used as the numerator and the other as the denominator. Here, as described below, the components (factors) of the biomarker proteins include factors that may increase with the onset of ICANS and factors that may decrease. When determining the protein quantity ratio, the factor that may increase is used as the numerator and the factor that may decrease is used as the denominator.

[0024] Examples of the biomarker proteins that may be increased include the complement components, blood coagulation factors, serpin family, very low density lipoproteins, acute phase plasma proteins, etc. Examples of the biomarker proteins that may be decreased include the α-L-fucosidase, sugar chain metabolic enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix, Golgi-associated kinase, lysosomal enzymes, IGF regulatory factors, peptidases, etc.

[0025] Further specific examples of the biomarker proteins that may be increased include C1RL, C9, F12, APOC2, SERPINA10, and ORM2. Further specific examples of the biomarker proteins that may be decreased include FUCA2, SIL1, GASK1B, COL1A2, IBP7, MFAP4, HEXB, IGFBP7, CPVL, TPP1, and TNXB.

[0026] Furthermore, when selecting two factors to be used as the numerator and denominator from the biomarker proteins when calculating the protein quantity ratio, it is preferable to first examine the protein quantity ratio of the two factors before and after the onset of ICANS, and select a factor with a high AUC of the correlation between the protein quantity ratio and the incidence rate of ICANS. Here, the AUC value is determined from an ROC curve graph created from the true positive rate and false positive rate of the protein quantity ratio against the incidence rate of ICANS. The two factors may be selected such that the AUC is 0.90 or higher, preferably 0.92 or higher, and more preferably 0.94 or higher.

[0027] It is particularly preferable to select C1RL as the numerator and FUCA2 as the denominator for the two factors of the biomarker protein. It is also particularly preferable to select C1RL as the numerator and SIL1 as the denominator. These have an AUC of 0.94 or more. It is also particularly preferable to select C9 as the numerator and GASK1B as the denominator. It is also particularly preferable to select F12 as the numerator and COL1A2, GASK1B, or FUCA2 as the denominator. These have an AUC of 0.92 or more.

[0028] (Second embodiment) In another embodiment, there is provided a method for detecting a risk value to be used to assist in predicting the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising the steps of: determining the protein amount / 1 of a biomarker protein contained in a specimen sample obtained from a living body as a protein amount ratio; and determining the reciprocal of the protein amount ratio as the risk value; and determining the protein amount ratio as the risk value, wherein the biomarker protein is FUCA2.

[0029] The protein amount of FUCA2 decreases in living organisms in which ICANS has developed, and the protein amount of FUCA2 has a negative correlation with the onset of ICANS. That is, the protein amount ratio is the ratio of the protein amount of FUCA2 divided by 1, and the reciprocal of this protein amount ratio is the risk value. As shown in the examples described below, the correlation between this risk value using FUCA2 and the onset of ICANS is 0.950 or more when the AUC value is measured. That is, when the biomarker protein is FUCA2, the risk value can be measured simply by measuring the protein amount of FUCA2. Other configurations are the same as those of the first embodiment.

[0030] (Method for assisting in prediction of likelihood of developing ICANS) The method for assisting in prediction of likelihood of developing ICANS of this embodiment is a method for obtaining information for determining the likelihood of developing ICANS. The method for assisting in prediction of likelihood of developing ICANS of this embodiment can be used for diagnosis, prevention, etc. of ICANS by using it in combination with other methods.

[0031] In one aspect, the method of this embodiment for assisting in prediction of the likelihood of developing ICANS is a method for assisting in prediction of the likelihood of developing ICANS using the risk value detection method described above, and includes a step of selecting the two biomarker proteins for a plurality of living organisms including one or more specimens that have developed ICANS, such that the AUC value showing the correlation between the increase in the risk value before and after the onset of ICANS in the specimens and the incidence of ICANS in the plurality of living organisms is 0.9 or more.

[0032] This method selects biomarker proteins or combinations thereof that are particularly excellent in predicting the likelihood of developing ICANS when measuring risk values. As described above, the two factors may be selected such that the AUC is 0.90 or more, preferably 0.92 or more, and more preferably 0.94 or more.

[0033] In one aspect, the method for assisting in the prediction of the likelihood of developing ICANS of this embodiment preferably includes the step of (i) measuring the protein abundance ratios of three or more different biomarker proteins contained in a specimen sample obtained from the same living organism in the risk value detection method. This risk value detection method involves selecting at least three or more of the biomarkers described above, measuring their protein abundance ratios, and measuring the three or more risk values. These three or more biomarkers may or may not include FUCA2.

[0034] Another detection method may include (ii) a method for detecting a risk value using the different biomarker proteins and a method for detecting a risk value using FUCA2 for a specimen sample obtained from the same living organism. The risk value detection method (ii) measures the reciprocal of the amount of at least one protein selected from the above-mentioned biomarkers and the amount of a protein using FUCA2 as the biomarker, thereby measuring two or more risk values. The one or more biomarkers may or may not include FUCA2.

[0035] The method may also include a step of determining that there is a high possibility of ICANS development if each of the risk values ​​is elevated compared to a standard risk value obtained from one or more other subjects or a standard risk value obtained from the same subject at a different time. The risk value detection methods (i) and (ii) are included, and the high possibility of ICANS development is determined if both are elevated compared to the standard risk values. As described above, the standard risk value is more preferably calculated based on risk values ​​obtained from one or more subjects other than the subject for which the ICANS development possibility is being determined, preferably from one or more subjects who have not yet developed ICANS, and calculated by averaging these risk values. In another embodiment, the standard risk value may be calculated based on a risk value obtained from the living body for which the ICANS development possibility is being determined at a different time, for example, sufficiently in the past.

[0036] (Drug) The pharmaceutical for reducing the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome) of this embodiment contains a component that inhibits the biomarker protein. Since the biomarker protein is likely to be correlated with the development of ICANS, inhibiting the activity of the biomarker protein can reduce the possibility of developing ICANS.

[0037] The biomarker protein is preferably selected from complement components, blood coagulation factors, serpin family, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases.

[0038] Biomarker proteins include complement components C1RL, C1R, C1QB, C1QC, C9, C4A, and C4B, blood coagulation factors F5, F12, and VWF, serpin family members SERPINA10, SERPINA1, and SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, and CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA, or More preferably, the nucleotide exchange factor is selected from NAGLU, SIL1 which is a nucleotide exchange factor for the ER-localized chaperone BiP, COL1A2, MFAP4, or TNXB which is an extracellular matrix protein, GASK1B which is a Golgi-associated kinase, HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1 which is a lysosomal enzyme, IGFBP7 which is an IGF regulator, or CPQ, CPVL, TPP1, or ERAP1 which is a peptidase.

[0039] Furthermore, among the biomarker proteins whose expression levels change with the onset of ICANS, it is preferable to select and inhibit a biomarker protein whose expression level may increase with the onset of ICANS. Examples of the biomarker proteins whose expression level may increase include the complement components, blood coagulation factors, serpin family, very low density lipoproteins, acute phase plasma proteins, etc. Further specific examples of the biomarker proteins whose expression level may increase include C1RL, C9, F12, APOC2, SERPINA10, and ORM2.

[0040] The component that inhibits the biomarker protein can be selected from a wide range of components that interfere with the biomarker protein and affect its activity. For example, an antibody to the biomarker protein can be used. Other chemical substances that inhibit the biomarker protein can also be used. Furthermore, the component may inhibit the biomarker protein from the expression stage. For example, a means for knocking out the gene for the biomarker protein can be used.

[0041] In a particularly preferred embodiment, components that inhibit the biomarker proteins are approved and commercially available and can be used, such as eculizumab (anti-C5 antibody), ravulizumab (anti-C5 antibody), avacopan (C5A receptor antagonist), and pegcetacoplan (C3 inhibitor).

[0042] (ICANS onset prediction kit) The ICANS onset prediction kit of this embodiment is a kit for predicting ICANS onset used in the risk value detection method, and includes a means for detecting the protein amount of one or more of the biomarker proteins. The means for detecting the protein amount may include conventionally known equipment, reagents, etc. The ICANS onset prediction kit may also include other reagents, instruments, devices, etc. used in other detection kits.

[0043] [Effects of this embodiment] This embodiment provides a method for detecting a risk value, a method for assisting in predicting the possibility of developing ICANS, a medicine, and a kit for predicting the development of ICANS, which can be used in clinical testing methods for evaluating the risk of developing ICANS.

[0044] As will be described in the Examples below, the present invention identified biomarkers that can predict the risk of developing ICANS, a side effect of CAR-T therapy in patients with B-cell tumors, by measuring several proteins that were discovered to be risk factors for the disease. This enabled the identification of protein markers and their combinations that can be used in standard clinical testing methods.

[0045] The present inventors have previously noted that analysis of CSF before CAR-T infusion has revealed that complement system and serpin family levels are elevated in patients who have developed ICANS compared to patients who have not. Therefore, by calculating the quantitative ratio of several proteins, including complement system proteins, they have discovered a biomarker that can predict the risk of developing ICANS with high accuracy.

[0046] Evaluating the risk of developing ICANS before CAR-T treatment can improve the accuracy of treatment and also enable emergency treatment at the time when symptoms first appear. Regarding the biomarkers used in this embodiment, several complement-targeted drugs have been approved and are on the market in Japan. For example, Soliris (eculizumab) (anti-C5 antibody), Ultomiris (ravulizumab) (anti-C5 antibody), Tabneos (avacopan) (C5A receptor antagonist), and Empavelli (pegcetacoplan) (C3 inhibitor) have been approved. By administering these complement-targeted drugs during CAR-T treatment, etc., it may be possible to reduce the risk of developing ICANS.

[0047] [Other Embodiments] This embodiment also includes the following embodiments as other aspects. Another embodiment of this embodiment is a method for predicting the possibility of developing ICANS, using the method for detecting a risk value. Another embodiment of this embodiment is a method for diagnosing ICANS, using the method for detecting a risk value. Another embodiment of this embodiment is a method for producing a therapeutic or preventive agent for ICANS, using a component that inhibits the biomarker protein. Another embodiment of this embodiment is use of a component that inhibits the biomarker protein for producing a therapeutic or preventive agent for ICANS. Another embodiment of this embodiment is use of a component that inhibits the biomarker protein for treating or preventing ICANS. Another embodiment of this embodiment is a method for treating or preventing ICANS, using the component that inhibits the biomarker protein.

[0048] The present invention will be described in detail below with reference to examples, but the present invention is not limited to these examples.

[0049] [Study Method] This study was approved by the Kyushu University Ethics Committee (IRB number: 22213) and conducted in accordance with the Declaration of Helsinki. This study involved 29 patients who underwent CD19-targeted CAR-T therapy for large B-cell lymphoma (LBCL) at Kyushu University Hospital. CRS and ICANS were classified according to the American Society for Transplantation and Cellular Therapy (ASTCT) classification.

[0050] (Collection of Cerebrospinal Fluid Samples) Residual CSF samples from LBCL patients who had completed clinical testing for CAR-T therapy were collected before CAR-T cell infusion and centrifuged at 1,800 xg for 5 minutes at room temperature, and the supernatants were stored at -80°C.

[0051] (Trypsin / Lys-C Digestion for MS Analysis) CSF proteins were trypsin-digested using the Rapid-Digestion Trypsin / Lys-C Kit (Promega, #VA1061) according to the manufacturer's instructions. Briefly, 10 μg of CSF protein was diluted with 80 μL of Rapid Digest Buffer, and 1 μL of 200 mM tris(2-carboxyethyl)phosphine (TCEP) was added and incubated at 37°C for 45 minutes. 2.5 μL of 200 mM iodoacetamide was then added and incubated at room temperature for 60 minutes. Next, 10 μL of 0.1 μg / μL trypsin / Lys-C was added, and the mixture was incubated at 70°C for 60 minutes. The reaction was stopped by adding 10 μL of 1% formic acid.

[0052] (LC-MS / MS Analysis) Proteomics analysis was performed using the EVOSEP ONE / Q-Exactive platform. 20 μL of the peptide digest was trapped on an Evotip pure C18 (EV2013, Evosep) according to the manufacturer's instructions and separated on a Performance column (15 cm x 150 μm, 1.5 μm: EV1137, Evosep) connected to an Evosep One system (Evosep Biosystems). Peptides were separated using the extended method (gradient length 88.0 min, flow rate 220 nL / min). Eluted peptides were analyzed on a Q-Exative Orbitrap mass spectrometer (Thermo Fisher Scientific).

[0053] Mass spectra were acquired using data-independent acquisition (DIA). MS1 spectra were collected in the m / z range of 430-860 m / z with a resolution of 35,000, an AGC target of 2 x 10 5 , and a maximum injection time of 50 ms. MS2 spectra were collected in the m / z range of 200-1800 m / z with a resolution of 35,000, and the resolution width of MS2 was set to 20 m / z with a window pattern of 430-860 m / z.

[0054] (Protein Identification and Data Analysis) DIA data were analyzed using DIA-NN software, referencing a spectral library created from FASTA files of human proteins. Multivariate data analysis (principal component analysis, clustering heat map analysis, and PLS discriminant model creation) was performed using MetaboAnalyst 6.0 (https: / / www.metaboanalyst.ca). Protein network analysis was performed using STRING version 12.0 (string-db.org). The pROC package in R was used to calculate the AUC values ​​of ROC curves and 95% confidence intervals using the bootstrap method. GraphPad Prism 8 software was used for other statistical analyses and visualizations.

[0055] [Test Example 1: Search for biomarkers to predict the risk of developing ICANS] The overall flow of the test was as follows: 1. The amounts of 1,356 types of proteins were measured using MS. 2. 864 types of proteins that met the following conditions were detected: (i) The p-value of the Welch two-sample t-test was less than 0.05. (ii) The lower limit AUC value of the 95% bootstrap confidence interval (CI) of the ROC curve was 0.6 or more. (iii) The average signal intensity in MS was 1 x 10 ５3. 46 proteins belonging to the ICANS-positive group were classified as ICANS predictors. Compared to the ICANS-negative group, 6 proteins were increased and 40 proteins were decreased. 4. A protein ratio index of 240 proteins was created, with the numerator containing the 6 increased proteins and the denominator containing the 40 decreased proteins. 5. For each index, the AUC value of the ROC curve was used to evaluate its performance as an ICANS predictor.

[0056] This study included 29 LBCL patients who received CAR-T therapy, using Tisa-cel (n=10), Axi-cel (n=12), and Liso-cel (n=7). This analysis did not take into account differences in CAR-T products (although differences in the incidence of ICANS have been reported between products, the sample size of this study did not provide the statistical power to detect such differences).

[0057] Cerebrospinal fluid (CSF) proteins were analyzed by data-independent mass spectrometry (DIA-MS). A total of 1,350 proteins were identified from all samples, and a PCA score plot was performed using the protein profiles by principal component analysis (PCA). In the PCA score plot, one sample showing an abnormal shift from the other samples was identified as an "outlier." Figure 1 shows the PCA score plot for this study example 1. In the figure, the sample marked with a star indicates an outlier. This sample had an abnormally high total protein level of 498 mg / dL, which may have caused artifacts in the mass spectrometry measurement. Figure 2 shows basic information for the 11 patients who developed ICANS in this study example 1. Eleven patients developed ICANS (grade 1 or higher was classified as ICANS positive), and there were no significant differences between the two groups in terms of age, sex, CAR-T product, laboratory values ​​including total protein (TP), glucose (Glu), Na, K, LDH, and WBC, or incidence and grade of CRS.

[0058] Using the 28 samples excluding the outlier samples, partial least squares discriminant analysis (PLS-DA) was performed to distinguish between two patient groups with and without ICANS. Figure 3 is a graph showing the PLS-DA score plot of this Test Example 1. The dots in the figure correspond to each sample group, and the ellipses correspond to the 95% confidence interval for each group. Partial least squares regression discriminant analysis (PLS-DA) was performed using cerebrospinal fluid (CSF) proteomic data and clinical test values ​​to classify the presence or absence of ICANS. A model constructed using the score plot was able to separate the two groups, and the contribution of each factor to this model was reflected in the VIP score. Specifically, the PLS-DA score plot separated the ICANS-positive group (n = 11) and the ICANS-negative group (n = 18), with the first two latent variables accounting for 30.4% (component 1) and 8.7% (component 2) of the total variance. When the top 30 factors contributing to component 1 were visualized and classified in a heat map (not shown), PZP, APOC3, C1RL, C9, etc. were extracted as showing strongly positive values.

[0059] The top 100 factors contributing to both component 1 and component 2 were subjected to STRING analysis, which visualizes the protein-protein network and clarifies their biological roles, and then classified into five clusters using the k-means method. Figure 4 is a schematic diagram showing the cluster classification of the factors in this Test Example 1 using the k-means method. Each cluster was annotated with a biological process using gene ontology analysis.

[0060] Cluster 1 involved the degradation process of carbohydrate derivatives, cluster 2 complement activation, cluster 3 chylomicron remnant removal, and cluster 4 extracellular matrix-related factors.

[0061] Furthermore, gene ontology enrichment analysis was performed on factors within each cluster. These clusters were annotated to biological pathways, such as hydrolase functions acting on lysosomes and glycosyl bonds (cluster 1), complement activation and humoral immunity (cluster 2), LDL particle clearance (cluster 3), and extracellular matrix (cluster 4), suggesting their involvement in the pathogenesis of ICANS. Notably, proteins belonging to cluster 2, such as C1RL, C3, C5, and C9, which are essential for complement activation, were elevated in the ICANS-positive group.

[0062] These results highlight the potential involvement of complement pathway activation in the pathogenesis of ICANS, and its inhibition may be a potential therapeutic target. However, detailed analysis of how these pathways relate to the pathophysiology of ICANS remains a challenge. In this regard, the emergence of new drugs targeting complement proteins may be a promising avenue for this approach.

[0063] [Test Example 2: Search for Highly Sensitive ICANS Predictors] Next, a strategy was adopted to find highly sensitive biomarkers for predicting the occurrence of ICANS. The overall flow of the test was as follows: 1. The protein amount of CSF proteins was measured using MS. 2. 864 proteins meeting the following conditions were detected: (i) The p-value of the Welch two-sample t-test was less than 0.05. (ii) The lower limit AUC value of the 95% bootstrap confidence interval (CI) of the ROC curve was 0.6 or more. (iii) The average signal intensity in MS was 1 x 10 ５ 3. 46 proteins belonging to the ICANS-positive group were classified as ICANS predictors. Compared to the ICANS-negative group, 6 proteins were increased and 40 proteins were decreased. 4. A protein ratio index of 240 proteins was created, with the numerator containing the 6 increased proteins and the denominator containing the 40 decreased proteins. 5. For each index, the AUC value of the ROC curve was used to evaluate its performance as an ICANS predictor.

[0064] Among the 864 proteins commonly identified in all samples, the signal intensity threshold (>1 × 10 ５ Forty-six proteins meeting the criteria of variability (AUC > 0.6), single discriminant performance criteria (AUC > 0.6), and t-test p-value (p < 0.05) were screened as candidate factors. Of these, six factors increased in the ICANS-positive group, while the remaining 40 factors decreased. Therefore, a composite ratio index (increased vs. decreased factors) was calculated to evaluate the possibility of discriminating between ICANS-positive and ICANS-negative groups, along with their individual performance. A composite ratio was created by placing the increased protein in the numerator and the decreased protein in the denominator for the amounts of two proteins in the ICANS-positive group. A receiver operating characteristic curve (ROC) curve was created to classify the presence or absence of ICANS. The 95% confidence interval for the AUC was calculated using the bootstrap method with 1,000 stratified replicates.

[0065] Table 1 shows the combinations of factors and their performance evaluations. The composite ratio index is shown when the increased factor is the numerator and the decreased factor is the denominator. The AUC value for each combination, i.e., the performance against ICANS onset, is shown in rank order. Note that FUCA2 in the table is a decreased factor, and Rank 2 indicates the AUC value of the correlation between the fluctuation of the value of FUCA2 alone and the onset of ICANS. That is, for FUCA2, a decrease in the protein ratio correlates with the onset of ICANS.

[0066]

[0067] Figures 5 to 8 show (a) the ROC curves and (b) dot plots of the ratios between the two groups for the top four high-performing combinations in Table 1. An asterisk indicates a p-value of less than 0.0001 (t-test). Figure 5 shows the ROC curve and dot plot for C1RL / FUCA2 in Test Example 2. Figure 6 shows the ROC curve and dot plot for C1RL / SIL1 in Test Example 2. Figure 7 shows the ROC curve and dot plot for C9 / GASK1B in Test Example 2. Figure 8 shows the ROC curve and dot plot for F12 / COL1A2 in Test Example 2.

[0068] Among the combinations of two factors, 19 ratio factors had an AUC value of over 0.9. Among them, the combined ratio of C1RL and FUCA2 most effectively distinguished between ICANS-positive and -negative groups, with an AUC value of 0.947 (95% CI 0.829-1.0) of the ROC curve. FUCA2 alone (decreased) showed an AUC value of 0.957.

[0069] Although no significant correlations were found between factors constituting a single composite ratio in most cases, significant negative correlations were found between C1RL and SIL1, C9 and GASK1B, F12 and COL1A2, and C1RL and FUCA2 in some cases, suggesting biological relevance. Previously, serum neurofilament light chain (NfL) 25 and plasma fibrinogen 14 have been reported as biomarkers for assessing the risk and severity of ICANS. However, their discriminatory ability was not very high, with an AUC of 0.71 for NfL and 0.724 for fibrinogen, respectively. In contrast, the cerebrospinal fluid (CSF) biomarker C1RL / FUCA2 investigated in this study showed remarkable performance with an AUC of 0.95, significantly exceeding previously reported biomarkers.

[0070] Test Example 3: Discovery of risk factors for the development of ICANS following CAR-T treatment using CSF The protein amounts of 21 CSF samples obtained from patients before CAR-T treatment were examined to investigate the association with the development of ICANS in these patients. Of the 21 patient samples, 13 patients did not develop ICNAS after CAR-T treatment, and 8 patients developed ICNAS after CAR-T treatment. As in Test Example 2, the protein amounts of CSF proteins were measured using MS.

[0071] Figure 9 is a graph showing the score plot of the ICANS-negative and ICANS-positive groups in Test Example 3. The score plot separated the ICANS-negative group (-) and the ICANS-positive group (+). Figure 10 is a graph showing the plot of the VIP score in Test Example 3.

[0072] 11 is a graph showing the relationship between the onset of ICANS and an increase in the complement system in Test Example 3. For each of the factors (a) C4b, (b) C3, (c) C2, (d) C9, (e) C5, and (f) C1RL, patients who subsequently developed ICANS after CAR-T treatment (+, positive group) experienced an increase in these factors compared to patients who did not develop ICANS (-, negative group). It was shown that an increase in the complement system (C1RL, C2, C3, C4b, C5, C9) in CSF before CAR-T is a risk factor for the onset of ICANS. Analysis of CSF before CAR-T infusion shows that the complement system and serpin family (SERPING1, SERPINC1, SERPINA3) are elevated in patients who develop ICNAS compared to patients who do not develop the disease. Therefore, by analyzing the CSF of patients before CAR-T infusion and analyzing the protein levels of the complement system and serpin family, it is possible to predict the possibility of developing ICANS after CAR-T infusion.

[0073] [Test Example 4: Search including verification of ICANS predictive factors] The factors searched for from the samples of patients in Test Example 2 were used as the 1st cohort, and the same operation was performed again to obtain factors for a 2nd cohort independent of these. The 2nd cohort (n = 10, including 7 ICANS-positive and 3 ICANS-negative patients), which is an expanded cohort composed of cerebrospinal fluid samples collected from different departments of the same hospital and in which participants do not overlap between cohorts, was analyzed. Figure 12 is a diagram showing basic information of patients who obtained the 1st cohort and 2nd cohort in this Test Example 4. For the 1st cohort and the 2nd cohort, as in Test Example 2, a composite ratio index was calculated using the increased factor as the numerator and the decreased factor as the denominator, and the AUC value for each combination, i.e., the performance for ICANS onset, was evaluated in rank order. The top five high-performing ratio factors validated in the second cohort, and the 95% confidence intervals (95% CI) of the AUC were calculated using the bootstrap method with 1000 stratified replications. The results of this comprehensive evaluation are shown in Table 2.

[0074]

[0075] Figure 13 shows the ROC curve and dot plot of C1RL / FUCA2 in this Test Example 4. For the 1st cohort, (a) shows the ROC curve and (b) shows the dot plot, and for the 2st cohort, (c) shows the ROC curve and (d) shows the dot plot. Figure 14 shows the ROC curve and dot plot of F12 / FUCA2 in this Test Example 4. For the 1st cohort, (a) shows the ROC curve and (b) shows the dot plot, and for the 2st cohort, (c) shows the ROC curve and (d) shows the dot plot. An asterisk * indicates a p-value of less than 0.05, *** indicates a p-value of less than 0.001, and *** indicates a p-value of less than 0.0001.

[0076] The C1RL / FUCA2 ratio factor, which showed the best performance in the first cohort, achieved an AUC of 1.0 (95% confidence interval: 0.81-1.0), and the F12 / FUCA2 ratio factor also showed high performance with an AUC of 1.0 (95% confidence interval: 0.71-1.0), confirming the reliability of these biomarkers for predicting the onset of ICANS. Both ratio values ​​were significantly elevated in the positive group compared to the negative group.

[0077] In order to elucidate the molecular mechanism of ICANS pathogenesis, PLS-DA was performed using 10 samples collected from the 2nd cohort in the same manner as in Test Example 1. Figure 15 is a graph showing the PLS-DA score plot of Test Example 4. As shown in the figure, a clear separation between the ICANS-positive and -negative groups was revealed.

[0078] 16 is a schematic diagram showing the relationship between the contributing factors of the first cohort and the second cohort in Test Example 4. It was revealed that some of the contributing factors were common between the two cohorts.

[0079] For these factors, STRING analysis was performed to visualize the network between proteins and clarify their biological roles, as in Test Example 1, and the factors were classified into clusters. Figure 17 is a schematic diagram showing the cluster classification of the factors in this Test Example 4 using the k-means method. Figure 18 is a graph of the GO analysis in this Example 4. (a) shows cluster 1, (b) shows cluster 2, and (c) shows cluster 3.

[0080] STRING network GO analysis identified three significant clusters. Cluster 2, in particular, suggested complement activation involving C1RL and other complement factors. These findings suggest that activation of the complement pathway plays a role in the pathogenesis of ICANS and represents a potential therapeutic target. Further analysis is needed to clarify the contribution of these factors and pathways, and novel complement-targeting drugs offer promising therapeutic avenues.

[0081] In the future, identifying blood biomarkers that correlate with and are comparable to CSF ​​biomarkers and whether these CSF biomarkers correlate with blood concentrations may facilitate the development of more non-invasive and convenient clinical testing methods. Furthermore, studies with larger cohorts than those used in this study are anticipated to be effective. This will allow for more detailed verification of differences between CAR-T formulations, the accuracy of the biomarkers, and the extent to which these biomarkers correlate with the severity (grade) of ICANS. In conclusion, the results of this study identify highly discriminatory CSF biomarkers for predicting ICANS and provide insights into its pathogenesis. Clinical application of these biomarkers may potentially optimize the delivery of CAR-T therapy, increasing efficacy while reducing side effects.

[0082] While preferred embodiments of the present invention have been described and illustrated, it should be understood that these are exemplary of the present invention and should not be considered limiting. Additions, omissions, substitutions, and other modifications can be made without departing from the spirit or scope of the present invention. Accordingly, the present invention is not to be deemed limited by the foregoing description, but is limited only by the scope of the appended claims.

[0083] According to the present invention, there are provided a method for detecting a risk value, a method for assisting in predicting the possibility of developing ICANS, a medicine, and a kit for predicting the development of ICANS, which can be used in clinical testing methods for evaluating the risk of developing ICANS.

Claims

1. A method for detecting a risk value used to assist in predicting the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a step of calculating a protein quantity ratio from the respective protein quantities of two biomarker proteins selected from proteins contained in a specimen sample obtained from a living body, and setting the risk value as the protein quantity ratio, wherein the biomarker proteins are selected from complement components, blood coagulation factors, serpin family, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for ER-localized chaperone BiP, extracellular matrix, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases.

2. The biomarker proteins are complement components C1RL, C1R, C1QB, C1QC, C9, C4A, C4B, blood coagulation factors F5, F12, VWF, serpin family members SERPINA10, SERPINA1, SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA or NAGLU, The method for detecting a risk value according to claim 1, wherein the risk factor is selected from SIL1, a nucleotide exchange factor for ER-localized chaperone BiP, COL1A2, MFAP4, or TNXB, an extracellular matrix protein; GASK1B, a Golgi-associated kinase; HEXB, FUCA1, LAMP2, CTSH, CTSC, or LAMP1, a lysosomal enzyme; IGFBP7, an IGF regulator; or CPQ, CPVL, TPP1, or ERAP1, a peptidase.

3. The method for detecting a risk value according to claim 1, wherein the two biomarker proteins include C1RL, FUCA2 or F12.

4. The method for detecting a risk value according to claim 1 or 2, wherein the specimen sample is blood, plasma, or cerebrospinal fluid.

5. The method for detecting a risk value according to claim 1 or 2, wherein the living body is a living body before undergoing genetically modified T cell therapy.

6. A method for assisting in the prediction of the likelihood of developing ICANS using the risk value detection method of claim 1 or 2, comprising the step of selecting the two biomarker proteins for a plurality of living organisms containing one or more specimens that have developed ICANS, such that the AUC value showing the correlation between the increase in the risk value before and after the onset of ICANS in said specimens and the incidence of ICANS in said plurality of living organisms is 0.9 or greater.

7. Regarding the risk value detection method of claim 1, a method for assisting in the prediction of the possibility of developing ICANS, which includes a step of measuring the protein quantity ratios of three or more different biomarker proteins contained in a specimen sample obtained from the same living organism.

8. A pharmaceutical for reducing the possibility of developing ICANS (immune cell-associated neurotoxicity syndrome), comprising a component that inhibits a biomarker protein, wherein the biomarker protein is selected from complement components, blood coagulation factors, serpin family members, very low density lipoproteins, acute phase plasma proteins, α-L-fucosidase, glycosylation enzymes, nucleotide exchange factors for the ER-localized chaperone BiP, extracellular matrix, Golgi-associated kinases, lysosomal enzymes, IGF regulatory factors, and peptidases.

9. The biomarker proteins are complement components C1RL, C1R, C1QB, C1QC, C9, C4A, C4B, blood coagulation factors F5, F12, VWF, serpin family members SERPINA10, SERPINA1, SERPING1, very low density lipoprotein APOC2, acute phase plasma proteins ORM2, HSPA13, CD163, α-L-fucosidase FUCA1 or FUCA2, MANBA or NA. The pharmaceutical of claim 8, wherein the nucleotide exchange factor is selected from the group consisting of GLU, SIL1, a nucleotide exchange factor for ER-localized chaperone BiP, COL1A2, MFAP4, and TNXB, an extracellular matrix protein; GASK1B, a Golgi-associated kinase; HEXB, FUCA1, LAMP2, CTSH, CTSC, and LAMP1, a lysosomal enzyme; IGFBP7, an IGF regulator; or CPQ, CPVL, TPP1, or ERAP1, a peptidase.

10. A kit for predicting the onset of ICANS, used in the method for detecting a risk value according to claim 1 or 2, comprising a means for detecting the amount of one or more of the biomarker proteins.

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