Immune checkpoint inhibitors and screening methods

Abstract

To provide means of suppressing immune checkpoints based on signaling pathways independent of the PD1 pathway, and to provide methods for in silico search for genes belonging to signaling pathways independent of the PD1 pathway.SOLUTION: Provided are an immune checkpoint inhibitor and anticancer drug resistance suppressant, including a kynureninase inhibitor. Also provided are a method for predicting the prognosis of a cancer patient or the sensitivity of a cancer patient to an anticancer drug based on kynureninase activity, and a method for screening genes related to immune checkpoints based on mutual information.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to an immune checkpoint inhibitor, an anticancer drug resistance suppressor, a method for predicting the prognosis of a cancer patient, a method for predicting sensitivity to an anticancer drug, a method for screening genes related to immune checkpoints, and a computer program for executing the methods. [Background technology]

[0002] The most common cause of death in Japan by disease is malignant tumors (cancer), and it is said that currently one in two people will develop cancer and one in three will die from it. According to the World Health Organization (WHO), there were approximately 10 million deaths from malignant tumors worldwide in 2020, with one in six people dying from cancer. Among malignant tumors that cause death, the most common are breast cancer and lung cancer (1.8 million people), followed by colorectal cancer (910,000 people), liver cancer, stomach cancer, breast cancer, etc.

[0003] In recent years, cancer immunotherapy has been attracting attention as a therapeutic strategy for cancer cells that are resistant to anticancer drugs. In addition to the signal transduction pathway that occurs when T cell receptors recognize major histocompatibility complexes that present cancer antigens as a signal transduction pathway between cancer cells and T cells that recognize and attack them, it has been reported that there is also a mechanism of auxiliary signals that act to promote or suppress immunity, and interest is growing in the possibility of treatments that focus on this mechanism.

[0004] Molecules that can induce immunosuppressive signals in the above mechanism are called "immune checkpoint molecules." Among them, the PD-1 receptor on T cells involved in the PD1 pathway and the ligand PD-L1 expressed on cancer cells are well known.

[0005] However, only about 10% of cases are effective in PD-1-targeted therapy (Non-Patent Document 1), suggesting that the contribution of the PD1 pathway to immune checkpoint function is not as high as previously assumed. For this reason, there is a demand for the development of a new method for suppressing immune checkpoints that can provide therapeutic effects even in cases in which PD-1-targeted therapy is ineffective. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] JAMA Network Open.2020;3(10):e2017675 Summary of the Invention [Problem to be solved by the invention]

[0007] An objective of the present invention is to provide a means for suppressing immune checkpoints based on signaling pathways independent of the PD1 pathway (or with a different mechanism of action), and to provide a method for in silico searching for genes belonging to signaling pathways independent of the PD1 pathway.

[0008] The present inventor aims to accurately calculate the mutual information between random variables easily and with a small amount of calculation, even when using data that handles random variables that can take on a large number of values, and to efficiently and accurately identify the interdependence between subjects regarding the random variables. [Means for solving the problem]

[0009] The present inventors have been conducting intensive research, and by applying a method for analyzing the interdependence between two events that they developed independently, they have found that kynureninase, which belongs to the tryptophan metabolic pathway, contributes to immune checkpoints independently of the PD1 pathway. Furthermore, they have found that, unlike many cancer genes and tumor suppressor genes, functional suppression mutations of the kynureninase gene tend to improve the prognosis of cancer patients. The present invention is based on these novel findings and provides the following:

[0010] [1] Immune checkpoint inhibitors, including kynureninase inhibitors. [2] The immune checkpoint inhibitor according to [1], wherein the kynureninase inhibitor comprises one or more compounds selected from the group consisting of PF-04859989, BFF-122, BFF-816, S-ESBA, NS-1502, 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)benzenesulfonamide, 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)ethyl benzenesulfonamide, glycyrrhizinic acid, glycyrrhetinic acid, carbenoxolone, estradiol disulfate, 4-(3-hydroxyphenyl)homoserine, and analogs and derivatives thereof, and salts thereof. [3] A pharmaceutical composition comprising the immune checkpoint inhibitor described in [1] or [2]. [4] Anticancer drug resistance suppressants, including kynureninase inhibitors. [5] A pharmaceutical composition comprising the anticancer drug resistance inhibitor described in [4]. [6] A method for predicting the prognosis of a cancer patient, comprising: determining the activity of kynureninase in cancer tissue derived from the patient; The method, wherein the activity is lower than the activity of wild-type kynureninase, indicating a good prognosis for the patient. [7] The method according to [6], wherein the activity of the kynureninase is determined by analysis of SNPs in the kynureninase gene or its transcription product. [8] A method for predicting sensitivity of a cancer patient to an anticancer drug, comprising: determining the activity of kynureninase in cancer tissue derived from the patient; The method, wherein the activity is lower than the activity of wild-type kynureninase, indicating that the patient has high sensitivity to an anti-cancer drug. [9] A biomarker for predicting the prognosis of a cancer patient, comprising a mutant kynureninase having an amino acid sequence selected from the group consisting of SEQ ID NOs: 3 to 5 or a fragment thereof, or a transcription product of the kynureninase gene encoding same or a nucleic acid fragment thereof.

[10] A biomarker for predicting sensitivity to an anticancer drug, comprising a mutant kynureninase having an amino acid sequence selected from the group consisting of SEQ ID NOs: 3 to 5 or a fragment thereof, or a transcription product of the kynureninase gene encoding same or a nucleic acid fragment thereof.

[11] A kit comprising a reagent for measuring the biomarker described in [9] or

[10] .

[0011]

[12] A method for screening genes related to immune checkpoints based on mutual information, the mutual information being the mutual information of two or more first events including a criterion for gene expression and a second event including a criterion for expression of a candidate gene, the two or more first events including an event including a criterion for expression of a PD1 pathway-related gene and an event including a criterion for expression of an immune checkpoint-related gene, the second event including a criterion for expression of a candidate gene, the number of criteria included in the first events being m and the number of criteria included in the second events being n, the method comprising: a sample number counting step of counting the number of samples in all cells consisting of one criterion selected from the first events and one criterion selected from the second events into an m×n frequency distribution table; a relative frequency counting step of calculating a simultaneous relative frequency from the sample numbers in all cells of the frequency distribution table and counting the results in an m×n contingency table; an individual mutual information determining step of determining an individual mutual information of each cell based on the simultaneous relative frequency in all cells; and a relative frequency counting step of calculating the relative frequency of the first event and the second event from the individual mutual information. The method includes a mutual information calculation step of calculating mutual information, and an immune checkpoint determination step of determining that the candidate gene is an immune checkpoint-related gene based on the calculated mutual information, wherein each of m and n is an integer of 2 or more, and at least one of them is 3 or more, the simultaneous relative frequency is a ratio of the number of samples corresponding to a certain cell to the number of all samples, and the individual mutual information determination step includes, for each cell, determining (a) the simultaneous relative frequency of a cell, the marginal relative frequency of the cell with respect to a criterion in a first event, and (b) a conversion step of converting the m×n contingency table into a 2×2 contingency table including binary data for the criterion in each of the first and second events based on the marginal relative frequencies for the criterion in two events; and (b) a calculation step of calculating the strength of the correlation between the criterion in the first and second events as the individual mutual information from the 2×2 contingency table using multidimensional mutual information, Fisher's exact probability, or hypergeometric distribution, wherein the mutual information calculation step includes (i) calculating the mutual information from the individual mutual information by the formula (I) [Number 1] TIFF2025031671000001.tif1266 (wherein MI represents the mutual information, and MI kl represents the individual mutual information), or (ii) a frontier mutual information selection step of comparing the individual mutual informations with each other and selecting the individual mutual information with the largest absolute value as the frontier mutual information, and a frontier mutual information utilization step of calculating the mutual information based on the frontier mutual information, wherein the calculation of the mutual information in the frontier mutual information utilization step is based on the formula (II): [Number 2] TIFF2025031671000002.tif1083 (wherein MI represents the mutual information, and MI front indicates the frontier mutual information), and the immune checkpoint determination step includes an event selection step of selecting a predetermined number of events from the two or more first events based on the magnitude of the mutual information value, a function aggregation step of aggregating functions of genes related to the selected events, and a determination step of determining that the candidate gene is an immune checkpoint-related gene if the aggregated functions include an activation function of one or more signal transduction pathways related to an immune checkpoint and an inhibitory function of one or more immune checkpoints via the PD1 pathway.

[13] The method according to

[12] , wherein the calculation using the multidimensional mutual information is carried out based on the following formula (III): [Number 3] TIFF2025031671000003.tif13102 (in the formula, MI kl denotes the individual mutual information of a cell consisting of the k-th criterion of the first event and the l-th criterion of the second event, and X k’ denotes the k'th criterion of the first event in the 2 × 2 contingency table, and Y l’ denotes the l'th criterion of the second event in the 2 × 2 contingency table, and p(X k’ ,Y l’ ) is the standard X k’ and criterion Y l’ The simultaneous relative frequency of cells consisting of p(Xk’ ) is the standard X k’ The marginal relative frequencies for p(Y l’ ) is the standard Y l’ (shows marginal relative frequencies for

[14] The method according to

[12] or

[13] , wherein the total number of samples is 1,000 or more. [14-1] The method described in any of

[12] to

[14] , wherein the immune checkpoint judgment step is carried out when the mutual information is equal to or greater than a predetermined level with respect to the expression level of one or more genes related to an immune checkpoint. [14-2] The method described in [14-1], wherein one or more genes related to the immune checkpoint include a kynureninase gene.

[0012]

[15] A processing program for screening genes related to immune checkpoints based on mutual information, the mutual information being the mutual information of two or more first events including a criterion for gene expression and a second event including a criterion for expression of a candidate gene, the two or more first events including an event including a criterion for expression of a PD1 pathway-related gene and an event including a criterion for expression of an immune checkpoint-related gene, the second event including a criterion for expression of a candidate gene, the computer being programmed to perform a sample number counting step of counting the sample numbers in all cells consisting of one criterion selected from the first events and one criterion selected from the second events into an m×n frequency distribution table, where m is the number of criteria included in the first events and n is the number of criteria included in the second events; a relative frequency counting step of calculating simultaneous relative frequencies from the sample numbers in all cells of the frequency distribution table and counting the frequencies into an m×n contingency table; an individual mutual information determining step of determining individual mutual information for each cell based on the simultaneous relative frequencies in all cells; and a step of calculating the individual mutual information for the first events and the second events from the individual mutual information. and an immune checkpoint determination step of determining that the candidate gene is an immune checkpoint-associated gene based on the calculated mutual information, wherein each of m and n is an integer equal to or greater than 2, and at least one of them is equal to or greater than 3, the simultaneous relative frequency is a ratio of the number of samples corresponding to a certain cell to the number of all samples, and the individual mutual information determination step is configured to execute, for each cell, (a) a simultaneous relative frequency of a cell, a marginal relative frequency of the cell with respect to a criterion in a first event, a conversion step of converting the m×n contingency table into a 2×2 contingency table containing binary data for the criterion in each of the first and second events based on the marginal relative frequencies for the criterion in the first and second events, and (b) a calculation step of calculating the strength of the correlation between the criterion in the first and second events as the individual mutual information from the 2×2 contingency table using multidimensional mutual information, Fisher's exact probability or hypergeometric distribution, wherein the mutual information calculation step (i) calculates the mutual information from the individual mutual information by the following formula (I): [Number 4] TIFF2025031671000004.tif1264 (wherein MI represents the mutual information, and MI kl represents the individual mutual information), or (ii) a frontier mutual information selection step of comparing the individual mutual informations with each other and selecting the individual mutual information with the largest absolute value as the frontier mutual information, and a frontier mutual information utilization step of calculating the mutual information based on the frontier mutual information, wherein the calculation of the mutual information in the frontier mutual information utilization step is based on the formula (II): [Number 5] TIFF2025031671000005.tif1185 (wherein MI represents the mutual information, and MI front indicates the frontier mutual information), and the immune checkpoint determination step includes an event selection step of selecting a predetermined number of events from the two or more first events based on the magnitude of the mutual information value, a function aggregation step of aggregating functions of genes related to the selected events, and a determination step of determining that the candidate gene is an immune checkpoint-related gene if the aggregated functions include an activation function of one or more signal transduction pathways related to an immune checkpoint and an inhibitory function of one or more immune checkpoints via the PD1 pathway. [15-1] The program described in

[15] , in which the immune checkpoint judgment process is carried out when the mutual information is above a predetermined level with respect to the expression level of one or more genes related to the immune checkpoint. [15-2] The program described in [15-1], wherein one or more genes related to the immune checkpoint include a kynureninase gene. Effect of the Invention

[0013] The immune checkpoint inhibitor of the present invention can inhibit immune checkpoint function.

[0014] The anticancer drug resistance suppressor of the present invention can increase the sensitivity of cancer patients to anticancer drugs.

[0015] According to the prediction method of the present invention, it is possible to predict the prognosis of a cancer patient and its sensitivity to an anticancer drug.

[0016] According to the screening method and program of the present invention, genes related to immune checkpoints can be screened. [Brief description of the drawings]

[0017] [Figure 1] 1 is a flow chart showing steps of a method for screening genes. [Diagram 2] 13 is a flowchart showing details (S0110 and S0111) of the individual mutual information determination step (S0103). [Diagram 3] 13 is a flowchart showing details (S0112 to S0114) of the mutual information calculation step (S0104). [Figure 4] 13 is a flowchart showing details (S0115 to S0117) of the immune checkpoint assessment step (S0105). [Diagram 5] 1 is a graph showing the relationship between the expression level of kynureninase and the survival rate of lung adenocarcinoma patients, in which "a" shows the survival curve of individuals with normal expression levels of kynureninase, and "b" shows the survival curve of individuals with significantly high expression levels of kynureninase. [Figure 6] 6A and 6B are graphs showing the relationship between the expression level of kynureninase and the survival rate of patients with other cancers. FIG. 6A shows the survival curve of a patient with pancreatic cancer. FIG. 6B shows the survival curve of a patient with breast cancer. FIG. 6C shows the survival curve of a patient with clear cell renal carcinoma. FIG. 6D shows the survival curve of a patient with hepatocellular carcinoma. FIG. 6E shows the survival curve of a patient with gastric cancer. FIG. 6F shows the survival curve of a patient with uterine carcinosarcoma. In the figures, "a" shows the survival curve of an individual with normal expression levels of kynureninase, "b" shows the survival curve of an individual with high expression levels of kynureninase, and "c" shows the survival curve of an individual with low expression levels of kynureninase. [Figure 7] FIG. 1 shows signaling pathways with large values ​​of mutual information for kynureninase gene expression, where the dotted bars indicate unknown activation z-scores and the arrows indicate immune checkpoint-related signaling pathways. [Figure 8] 1 is a graph showing the relationship between kynureninase activity and the survival rate of cancer patients. In the figure, "a" shows the survival curve of individuals with normal expression levels of kynureninase, and "b" shows the survival curve of individuals with high expression levels of kynureninase. In the figure, "c" shows the survival curve of individuals with a mutation in the kynureninase gene that can reduce activity, and "d" shows the survival curve of individuals without such a mutation in the kynureninase gene. [Figure 9] 9A and 9B are graphs showing the relationship between the activity of other genes in the tryptophan metabolic pathway and the survival rate of cancer patients. FIG. 9A shows the results for the indoleamine-2,3-dioxygenase 1 (IDO1) gene. FIG. 9B shows the results for the indoleamine-2,3-dioxygenase 2 (IDO2) gene. FIG. 9C shows the results for the tryptophan-2,3-dioxygenase 2 (TDO2) gene. In the figures, "a" shows the survival curves of individuals with mutations in each gene that may reduce activity, and "b" shows the survival curves of individuals without such mutations in each gene. [Figure 10] 10A and 10B are graphs showing the relationship between the activity of oncogenes and tumor suppressor genes and the survival rate of cancer patients. FIG. 10A shows the results for the epidermal growth factor receptor (EGFR) gene, which is an oncogene. FIG. 10B shows the results for the Kirsten rat sarcoma viral oncogene homolog (KRAS) gene, which is an oncogene. FIG. 10C shows the results for the tumor protein p53 (TP53) gene, which is a tumor suppressor gene. FIG. 10D shows the results for the cyclin-dependent kinase inhibitor 2A (CDKN2A) gene, which is a tumor suppressor gene. In the figure, "a" shows the survival curve of individuals who have a mutation in each gene that can change activity, and "b" shows the survival curve of individuals who do not have such a mutation in each gene. [Figure 11]11A and 11B show the results for the programmed cell death-ligand 1 (PD-L1) gene and the programmed cell death-ligand 2 (PD-L2) gene, respectively. In the figures, "a" shows the survival curves of individuals with SNP-based mutations in each gene, and "b" shows the survival curves of individuals without such mutations. [Figure 12] 12A and 12B are graphs showing the relationship between mutations in immunosuppressive genes and survival rates of cancer patients. FIG. 12A shows the results for the Hepatitis A Virus Cellular Receptor 2 (HAVCR2) gene. FIG. 12B shows the results for the Lymphocyte Activation Gene 3 (LAG3) gene. In the figures, "a" shows the survival curves of individuals with SNP-based mutations in each gene, and "b" shows the survival curves of individuals without such mutations. [Figure 13] 1 is a graph showing the relationship between kynureninase mutations and survival rates of cancer patients, where "a" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene and "b" shows the survival curves of individuals without such mutations. [Figure 13-1] 1 is a graph showing the relationship between kynureninase mutations and survival rates of cancer patients, where "a" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene and "b" shows the survival curves of individuals without such mutations. [Figure 14] 1 is a graph showing the relationship between kynureninase mutations and survival rates of cancer patients, in which "a" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene that are structure-disruptive, "b" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene that are not structure-disruptive, and "c" shows the survival curves of individuals without such mutations. [Figure 14-1]1 is a graph showing the relationship between kynureninase mutations and survival rates of cancer patients, in which "a" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene that are structure-disruptive, "b" shows the survival curves of individuals with SNP-based mutations in the kynureninase gene that are structure-disruptive, and "c" shows the survival curves of individuals without such mutations. [Figure 15] This is a diagram showing signaling pathways with large mutual information values ​​for HAVCR2 gene expression. In the figure, the darker the color in the bar, the larger the activation z score, the mid-dotted bar indicates an unknown activation z score, and the middle-slanted bar indicates a negative activation z score. In the figure, the arrows indicate signaling pathways related to immune checkpoints. [Figure 16] 1 is a graph showing the relationship between HAVCR2 activity and the survival rate of cancer patients, in which "a" shows the survival curve of individuals with a mutation in the HAVCR2 gene that can reduce activity, and "b" shows the survival curve of individuals without such a mutation in the HAVCR2 gene. [Figure 17] 1 is a graph showing the relationship between HAVCR2 mutations and survival rates of cancer patients, in which "a" shows the survival curves of individuals with SNP-based mutations in the HAVCR2 gene, and "b" shows the survival curves of individuals without such mutations. [Figure 18] 1 is a graph showing the relationship between HAVCR2 mutations and the survival rate of cancer patients, in which "a" shows the survival curve of individuals with SNP-based mutations in the HAVCR2 gene that are structurally disruptive, "b" shows the survival curve of individuals with SNP-based mutations in the HAVCR2 gene that are not structurally disruptive, and "c" shows the survival curve of individuals without such mutations. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] 1. Immune checkpoint inhibitors The first aspect of the present invention is an immune checkpoint inhibitor. The immune checkpoint inhibitor of the present invention contains a kynureninase inhibitor as an active ingredient and has an immune checkpoint inhibitory function. The immune checkpoint inhibitor of the present invention can be an active ingredient of a pharmaceutical composition.

[0019] Terms used in this specification are defined below. As used herein, "kynureninase" refers to a hydrolase that acts in the kynurenine pathway, which is one of the pathways that metabolize tryptophan. Kynureninase is known to be an enzyme that uses pyridoxal phosphate (PLP; vitamin B6) as a coenzyme and decomposes L-kynurenine into anthranilic acid and L-alanine. Kynureninase is sometimes abbreviated as kynureninase or KYNU. It is known that some intermediate metabolites in the kynurenine pathway act on the immune system and nervous system.

[0020] Wild-type kynureninase in the present specification also includes kynureninase variants having activity functionally equivalent to that of the kynureninase shown in SEQ ID NO: 1 and kynureninase orthologues of other organisms. Specifically, wild-type kynureninase includes an amino acid sequence in which one or more amino acids have been deleted, substituted or added in the amino acid sequence shown in SEQ ID NO: 1, or a kynureninase having 90%, 95% or more, 97% or more, 98% or more, or 99% or more amino acid identity to the amino acid sequence shown in SEQ ID NO: 1.

[0021] In the present specification, "multiple" refers to, for example, 2 to 20, 2 to 15, 2 to 10, 2 to 7, 2 to 5, 2 to 4, or 2 to 3. In addition, the amino acid substitution is preferably a conservative amino acid substitution. "Conservative amino acid substitution" refers to a substitution between amino acids with similar properties such as charge, side chain, polarity, and aromaticity. Amino acids with similar properties can be classified into, for example, basic amino acids (arginine, lysine, histidine), acidic amino acids (aspartic acid, glutamic acid), uncharged polar amino acids (glycine, asparagine, glutamine, serine, threonine, cysteine, tyrosine), nonpolar amino acids (leucine, isoleucine, alanine, valine, proline, phenylalanine, tryptophan, methionine), branched chain amino acids (leucine, valine, isoleucine), aromatic amino acids (phenylalanine, tyrosine, tryptophan, histidine), and the like.

[0022] As used herein, "amino acid identity" can be obtained by aligning two amino acid sequences, introducing gaps as necessary to maximize the degree of amino acid identity between the two, and determining the ratio (%) of identical amino acid residues between two amino acid sequences to the total amino acid residues of kynureninase consisting of the amino acid sequence shown in SEQ ID NO: 1. Amino acid identity can be calculated using a protein search system such as BLAST or FASTA.

[0023] A "kynureninase gene" is a gene that encodes the kynureninase. A specific example of the kynureninase gene is a human kynureninase gene that encodes a human kynureninase having the amino acid sequence shown in SEQ ID NO: 1. More specifically, an example of the kynureninase gene is a gene having the base sequence shown in SEQ ID NO: 2.

[0024] The kynureninase gene also includes kynureninase variants having activity functionally equivalent to that of the kynureninase encoded by the kynureninase gene shown in SEQ ID NO: 2, and kynureninase genes encoding kynureninase orthologues of other organisms. Specifically, the kynureninase gene includes a nucleotide sequence in which one or more nucleotides are deleted, substituted, or added in the nucleotide sequence shown in SEQ ID NO: 2, or a kynureninase gene having 90%, 95% or more, 97% or more, 98% or more, or 99% or more nucleotide identity to the nucleotide sequence shown in SEQ ID NO: 2. Furthermore, the kynureninase gene includes a gene consisting of a nucleotide sequence that hybridizes under highly stringent conditions with a nucleic acid fragment consisting of a part of the nucleotide sequence complementary to the nucleotide sequence shown in SEQ ID NO: 2, and which encodes a protein having activity functionally equivalent to that of kynureninase.

[0025] As used herein, "base identity" can be obtained by aligning two base sequences, introducing gaps as necessary to maximize the degree of base identity between the two sequences, and then determining the percentage (%) of identical bases between the two base sequences relative to the total bases of the kynureninase gene consisting of the base sequence shown in SEQ ID NO: 2.

[0026] In the present specification, "hybridizing under high stringency conditions" can be performed by hybridization and washing under low salt concentration and / or high temperature conditions. For example, incubation with a probe in 6xSSC, 5xDenhardt's reagent, 0.5%SDS, 100μg / mL denatured fragmented salmon sperm DNA at 65℃-68℃, followed by washing in a washing solution of 2xSSC, 0.1%SDS starting from room temperature, lowering the salt concentration in the washing solution to 0.1xSSC, and raising the temperature to 68℃ until background signals are no longer detected. High stringency hybridization conditions are described in Green, MR and Sambrook, J., 2012, Molecular Cloning: A Laboratory Manual Fourth Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, and can be used as a reference.

[0027] Such nucleotide sequence information of the kynureninase gene can be searched for in public databases (GenBank, EMBL, DDBJ). For example, based on the known nucleotide sequence information of the kynureninase gene shown in SEQ ID NO: 2, genes with high nucleotide identity can be searched for and obtained from the database.

[0028] "Immune checkpoint" refers to a mechanism that regulates immune responses to self-antigens and the level of immune responses in order to maintain immune homeostasis in the body. Cancer cells contain cells that utilize this immune checkpoint mechanism to suppress immune responses to the cancer cells themselves. This allows the cancer cells to survive and grow without being controlled by the immune system.

[0029] As used herein, the term "immune checkpoint-associated gene" refers to any gene associated with an immune checkpoint mechanism. The immune checkpoint-associated gene as used herein includes both genes that promote and suppress the function of the immune checkpoint mechanism.

[0030] "Cancer" refers to a disease characterized by unregulated cell proliferation, and is also called a malignant neoplasm, a malignant tumor, etc. In this specification, cancer does not include a benign tumor.

[0031] The immune checkpoint inhibitor of the present invention contains a kynureninase inhibitor as an active ingredient.

[0032] In the present invention, a "kynureninase inhibitor" refers to a drug that inhibits the activity of the kynurenine pathway and suppresses the production of anthranilic acid. The mode of inhibition is not particularly limited, and includes inhibition of the expression or function of the kynureninase gene, as well as any substance that inhibits the action of kynureninase. Preferably, it refers to a substance that specifically inhibits kynureninase.

[0033] Kynureninase inhibitors in the present invention include, but are not limited to, known low molecular weight compounds and their salts, antibodies and their active fragments, and nucleic acid medicines, which may be in the form of prodrugs. The "prodrug" here refers to a low molecular weight compound that undergoes a chemical change under physiological conditions and is consequently converted into an active form of an inhibitor.

[0034] The molecule that inhibits the function of kynureninase may be any of biomolecules such as proteins and other compounds. Examples of such compounds include PF-04859989 ((3S)-3-amino-1-hydroxy-3,4-dihydroquinolin-2(1H)-one), BFF-122 ((3S)-10-(4-aminopiperazin-1-yl)-9-fluoro-3-methyl-7-oxo-2,3,6,7-tetrahydro-5H-pyrido[1,2,3-de]-1,4-benzoxazine-6-carboxylic acid), BFF-816 ((Z)-5-(3-(4-oxo-1,2,5,6-tetrahydro-4H-pyrrolo[3,2,1-ij]quinolin-7-yl)benzoyl). 5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)-3-phenylpropanoic acid), 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)benzenesulfonamide, 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)ethyl benzenesulfonamide, glycyrrhizic acid, glycyrrhetinic acid, carbenoxolone, estradiol disulfate, 4-(3-hydroxyphenyl)homoserine, and analogs and derivatives thereof, and salts thereof.

[0035] The type of derivative is not particularly limited as long as it has an immune checkpoint inhibitory effect, and examples thereof include compounds in which one or more functional groups have been introduced into the original compound, etc. Preferably, the derivative is a compound in which one or more functional groups have been introduced into the original compound.

[0036] The type of functional group introduced at this time is not particularly limited. For example, it may be any of a hydrophobic group (including a hydrocarbon group, a carbonyl group, etc.), a hydrophilic group (including, for example, a hydroxy group, a carboxy group, a sulfo group, an amino group, an aldehyde group, etc.), an acidic group (including, for example, a carboxy group, a sulfo group, an amino group, etc.), a neutral group (including, for example, a hydroxy group, an aldehyde group, a hydrocarbon group, a carbonyl group, etc.), and a basic group (including, for example, an amino group, etc.).

[0037] The degree of difference between the original compound and the derivative is not particularly limited, and is, for example, 20 atoms or less, 19 atoms or less, 18 atoms or less, 17 atoms or less, 16 atoms or less, 15 atoms or less, 14 atoms or less, 13 atoms or less, 12 atoms or less, 11 atoms or less, 10 atoms or less, 9 atoms or less, 8 atoms or less, 7 atoms or less, 6 atoms or less, 5 atoms or less, 4 atoms or less, 3 atoms or less, or 2 atoms or less.

[0038] For example, derivatives of 4-(3-hydroxyphenyl)homoserine include those in which a functional group has been introduced to the carbon of the 3-hydroxyphenyl group. The position at which the functional group is introduced is not particularly limited, but for example, a functional group can be introduced to the 2-position carbon of the 3-hydroxyphenyl group. The functional group to be introduced is not particularly limited, but preferably a hydrophilic group or a basic group can be used. Specific examples of derivatives of 4-(3-hydroxyphenyl)homoserine include the compounds shown below in which an amino group has been introduced to the 2-position carbon of the 3-hydroxyphenyl group.

[0039] [ka]

[0040] In the present specification, the term "salt thereof" refers to a salt of the low molecular weight compound. Any pharma- ceutically acceptable salt may be used, and examples thereof include, but are not limited to, alkali metal salts (e.g., sodium salt, potassium salt, etc.), alkaline earth metal salts (e.g., calcium salt, magnesium salt, etc.), and amine salts (e.g., tri(n-butyl)amine salt, triethylamine salt, pyridine salt, amino acid salt, etc.). In the present invention, one or more selected from these salts may be used as the "kynureninase inhibitor or a salt thereof."

[0041] Furthermore, examples of antibodies include anti-kynureninase antibodies that specifically recognize and bind to kynureninase.

[0042] As used herein, "antibody" refers to an immunoglobulin, a chimeric antibody, a humanized antibody or a synthetic antibody. As used herein, the term "active fragment thereof" refers to a partial region of the anti-kynureninase antibody described above, and refers to a polypeptide chain or a complex thereof having substantially the same activity as the antigen-specific binding activity of the antibody. For example, a polypeptide chain or a complex thereof having at least one light chain variable region (VL) and at least one heavy chain variable region (VH) is an example of such an active fragment. Specific examples include antibody fragments generated by cleaving immunoglobulins with various peptidases. More specific examples include Fab, F(ab')2, Fab', etc.

[0043] The antibody etc. may be modified by glycosylation, acetylation, formylation, amidation, phosphorylation, pegylation (PEG), etc. Furthermore, as described later in the complex quantification step, the antibody etc. may be labeled.

[0044] The antibodies of the present invention may be derived from any animal, including mammals and birds, such as mice, rats, guinea pigs, rabbits, goats, donkeys, sheep, camels, horses, chickens, or humans.

[0045] When the antibody is an immunoglobulin, it may be either a polyclonal or a monoclonal antibody, and the immunoglobulin may be of any class, such as IgG, IgE, IgM, IgA, IgD, and IgY, or any subclass, such as IgG1, IgG2, IgG3, IgG4, IgA1, IgA2, etc.

[0046] Further, examples of nucleic acid drugs include kynureninase, or a nucleic acid aptamer or an RNA interference molecule that specifically recognizes and binds to kynureninase gene or mRNA.

[0047] As used herein, the term "nucleic acid aptamer" refers to a DNA aptamer or RNA aptamer that specifically binds to kynureninase. A nucleic acid aptamer refers to a ligand molecule that strongly and specifically binds to kynureninase or the like due to a three-dimensional structure formed based on the secondary structure and further tertiary structure of a single-stranded nucleic acid molecule via hydrogen bonds or the like. When a nucleic acid aptamer has the ability to specifically inhibit or suppress a function such as the physiological activity of kynureninase, the nucleic acid aptamer can be a functional inhibitor of kynureninase.

[0048] The term "RNA interference molecule" refers to a substance that can induce RNA interference (RNAi) in vivo and suppress (silence) the expression of a target kynureninase gene through degradation of the transcription product of the gene. Examples include microRNA (miRNA) (including pri-miRNA and pre-miRNA), short hairpin RNA (shRNA), and small interference RNA (siRNA).

[0049] In this specification, "miRNA" refers to a single-stranded non-coding RNA of 18 to 25 bases in length that exists in the living body and regulates the expression of the kynureninase gene. After being transcribed from the genome in a single-stranded precursor state called pri-miRNA, miRNA is processed into a further single-stranded precursor state called pre-miRNA by an endonuclease called Drosha in the nucleus, and becomes a mature double-stranded miRNA consisting of an miRNA strand and an miRNA star strand by the action of an endonuclease called Dicer outside the nucleus, of which the miRNA strand is incorporated into the RISC (RNA-induced silencing complex) complex to become a mature single-stranded miRNA that suppresses gene expression (David P. Bartel, Cell, Vol. 116, 281-297, January 23, 2004).

[0050] As used herein, "shRNA" refers to a single-stranded RNA in which the sense strand and antisense strand of the below-mentioned siRNA or mature double-stranded miRNA are linked by a short spacer sequence having an appropriate sequence. In other words, in an shRNA, the sense region and the antisense region are base-paired with each other within a single molecule to form a stem structure, and at the same time, the spacer sequence forms a loop structure, thereby forming a hairpin-type stem-loop structure as a whole molecule.

[0051] As used herein, "siRNA" refers to a small double-stranded RNA consisting of a sense strand (passenger strand) having a base sequence corresponding to a part of a gene, and its antisense strand (guide strand).

[0052] The degree of inhibition by the kynureninase inhibitor used in the immune checkpoint inhibitor of the present invention is not particularly limited as long as the activity of kynureninase is reduced compared to when the inhibitor is not applied.

[0053] 2. Anticancer drug resistance inhibitors The second aspect of the present invention is an anticancer drug resistance suppressor. The anticancer drug resistance suppressor of the present invention contains a kynureninase inhibitor as an active ingredient and has a function of suppressing anticancer drug resistance. The anticancer drug resistance suppressor of the present invention can be an active ingredient of a pharmaceutical composition.

[0054] In the present invention, the term "anticancer drug resistance suppressor" refers to a drug that has the effect of suppressing a mechanism that may act to exert resistance against anticancer drug treatment. Examples of the mechanism to be suppressed include immune checkpoints. The specific configuration of the anticancer drug resistance suppressor of the present invention is similar to that of an immune checkpoint inhibitor.

[0055] 3. Pharmaceutical Compositions The third aspect of the present invention is a pharmaceutical composition. The pharmaceutical composition of the present invention contains an immune checkpoint inhibitor and / or an anticancer drug resistance suppressor as an active ingredient as an essential component, and contains a solvent and a pharma- ceutical acceptable carrier as optional components. The composition of the present invention can inhibit immune checkpoint function in a subject or improve the sensitivity of the subject to an anticancer drug.

[0056] (1) Active ingredient The pharmaceutical composition of the present invention includes, as essential active ingredients, the immune checkpoint inhibitor described in the first aspect and / or the anticancer agent resistance suppressor described in the second aspect.

[0057] The configurations of the immune checkpoint inhibitor and the anticancer drug resistance suppressor are described in detail in the first and second aspects, and therefore detailed explanations will be omitted here. The pharmaceutical composition of the present invention can contain one or more active ingredients.

[0058] The content of the active ingredient contained in the pharmaceutical composition of the present invention is not particularly limited. In general, the content varies depending on the type of active ingredient, the dosage form, and the type of the solvent and carrier, which are other components described later. Therefore, it may be appropriately determined taking into account each condition. It is sufficient that an effective amount of the active ingredient is contained in a single application amount of the pharmaceutical composition. However, when it is necessary to administer a large amount of the pharmaceutical composition to a subject to obtain the pharmacological effect of the active ingredient, it may be administered in several divided doses to reduce the burden on the subject. In this case, the amount of the active ingredient may be sufficient as long as the total amount contains an effective amount.

[0059] The term "effective amount" refers to an amount necessary for the active ingredient to function and to which it gives little or no harmful side effects to the subject to which it is applied. This effective amount may vary depending on various conditions such as the subject's information, the application route, and the number of applications. Therefore, when the pharmaceutical composition is used as a medicine, the content of the active ingredient is ultimately determined by the judgment of a doctor or pharmacist.

[0060] As used herein, the term "subject" refers to a subject to which the immune checkpoint inhibitor according to the first aspect, the anticancer drug resistance suppressant according to the second aspect, or the pharmaceutical composition of this aspect is applied. For example, it is a cell (including cultured cell), a tissue, an organ, or an individual. The subject animal species is preferably a mammal, and includes, for example, primates such as humans, chimpanzees, and gorillas, rodents such as mice, rats, and guinea pigs, cows, horses, pigs, sheep, goats, llamas, camels, dogs, cats, rabbits, and the like. The preferred animal species is human.

[0061] In the present invention, the subject may be healthy or may be suffering from some disease. Examples of the subject include a subject experiencing or suspected of experiencing immunosuppression due to immune checkpoint molecules, a subject having or suspected of having cancer, a subject who is refractory to cancer immunotherapy or has reduced responsiveness to cancer immunotherapy, etc. Such refractoryness or reduced responsiveness to cancer immunotherapy may be due to immune checkpoints, particularly dendritic cells, B cells (CD19 + B cells), monocytes (CD14+ Preferably, the immune checkpoint is triggered in antigen-presenting cells, such as monocytes.

[0062] In this specification, "subject information" refers to various information related to the characteristics and condition of a subject. For example, when the subject is a human individual, information includes age, weight, sex, general health condition, presence or absence of disease, progression or severity of disease, drug sensitivity, presence or absence of concomitant drugs, and resistance to treatment.

[0063] The pharmaceutical composition of the present invention can also be used in combination with one or more other known drugs. There is no particular limitation on the drugs that can be used in combination, and examples of such drugs include drugs that have an inhibitory effect on immune checkpoints, anticancer drug resistance suppressors, anticancer drugs, immune enhancers, antibiotics, anti-inflammatory drugs, antipyretics, and analgesics.

[0064] (2) Solvent The pharmaceutical composition of the present invention may contain a pharma- ceutically acceptable solvent as necessary. The term "pharma-ceutically acceptable solvent" refers to a solvent commonly used in the field of formulation technology. Examples of the solvent include water or an aqueous solution, or an organic solvent. Examples of the aqueous solution include physiological saline, an isotonic solution containing glucose or other auxiliary agents, a phosphate buffer, and a sodium acetate buffer. Examples of the auxiliary agents include D-sorbitol, D-mannose, D-mannitol, sodium chloride, and other low-concentration nonionic surfactants, polyoxyethylene sorbitan fatty acid esters, etc. Examples of the organic solvent include ethanol.

[0065] (3) Carrier The pharmaceutical composition of the present invention may contain a pharma- ceutically acceptable carrier as necessary. The term "pharma-ceutically acceptable carrier" refers to additives commonly used in the field of formulation technology. Examples include excipients, binders, disintegrants, fillers, emulsifiers, flow additive regulators, lubricants, human serum albumin, etc.

[0066] Excipients include, for example, sugars such as monosaccharides, disaccharides, cyclodextrins and polysaccharides, metallic salts, citric acid, tartaric acid, glycine, polyethylene glycol, Pluronic®, kaolin, silicic acid, or combinations thereof.

[0067] Binders include, for example, starch paste using vegetable starch, pectin, xanthan gum, simple syrup, glucose solution, gelatin, tragacanth, methylcellulose, hydroxypropylmethylcellulose, sodium carboxymethylcellulose, shellac, paraffin, polyvinylpyrrolidone, or combinations thereof.

[0068] Disintegrants include, for example, the above-mentioned starches, lactose, carboxymethyl starch, cross-linked polyvinylpyrrolidone, agar, laminaran powder, sodium bicarbonate, calcium carbonate, alginic acid or sodium alginate, polyoxyethylene sorbitan fatty acid esters, sodium lauryl sulfate, stearate monoglyceride, or salts thereof.

[0069] Examples of fillers include petrolatum, the aforementioned sugars and / or calcium phosphate.

[0070] Examples of emulsifiers include sorbitan fatty acid esters, glycerin fatty acid esters, sucrose fatty acid esters, and propylene glycol fatty acid esters.

[0071] Examples of flow regulators and lubricants include silicates, talc, stearates or polyethylene glycol.

[0072] In addition to the above, if necessary, the composition may appropriately contain solubilizers, suspending agents, diluents, dispersing agents, surfactants, soothing agents, stabilizers, absorption promoters, bulking agents, moisturizing agents, humectants, adsorbents, flavorings, deodorants, disintegration inhibitors, coating agents, colorants, preservatives, antiseptics, antioxidants, fragrances, flavorings, sweeteners, buffers, isotonicity agents, and the like that are commonly used in pharmaceutical compositions.

[0073] Carriers are used to avoid or inhibit decomposition of the active ingredient by enzymes and the like in the subject's body, as well as to facilitate formulation and administration methods and maintain the dosage form and medicinal efficacy, and may be used appropriately as needed.

[0074] (4) Drug delivery system particles (DDS particles) The pharmaceutical composition of the present invention may contain DDS particles as necessary. DDS particles refer to particles that contain active ingredients or other carriers inside, deliver the contents, especially the active ingredients, to the target site without decomposing them, and control the drug distribution in the body in terms of time and quantity. Since the active ingredient of the pharmaceutical composition of the present invention is a peptide or nucleic acid, the use of DDS particles is also suitable for protecting the active ingredient from degradation by proteases and nucleases in the body after administration. Any type of DDS particle may be used. Examples include liposomes, polymeric micelles, and virus particles.

[0075] (5) Dosage form The dosage form of the pharmaceutical composition of the present invention is not particularly limited as long as it is in a form that allows the active ingredient to be delivered to the target site in the body of the subject without being inactivated.

[0076] The specific dosage form varies depending on the application method described below. The application method can be roughly divided into parenteral administration and oral administration, so a dosage form suitable for each administration method may be used.

[0077] For example, if the administration method is parenteral administration, the preferred dosage form is a liquid that can be administered directly to the target site or systemically via the circulatory system. A preferred example of a liquid is an injection. The injection can be formulated by appropriately combining the solvent, the excipient, emulsifier, suspending agent, surfactant, stabilizer, pH regulator, etc., and mixing them in a unit dose form required for generally accepted pharmaceutical practice.

[0078] If the method of administration is oral administration, preferred dosage forms include solid agents (including tablets, capsules, drops, and lozenges), granules, powders, powders, and liquid agents (including oral waters, emulsions, and syrups). If the agent is a solid agent, it can be made into a dosage form with a coating known in the art, such as sugar-coated tablets, gelatin-coated tablets, enteric-coated tablets, film-coated tablets, double tablets, and multi-layer tablets, as necessary.

[0079] The specific shape and size of each of the above dosage forms are not particularly limited as long as they are within the range of dosage forms known in the art. The pharmaceutical composition of the present invention may be prepared according to a conventional method in the art.

[0080] (6) Application method The pharmaceutical composition of the present invention may be administered orally or parenterally. Generally, oral administration is systemic administration, while parenteral administration can be further divided into systemic administration and local administration. Local administration includes, for example, intramuscular administration, subcutaneous administration, tissue administration, and organ administration, and systemic administration of parenteral administration includes administration into the circulatory system, for example, intravenous administration (intravenous injection), intraarterial administration, and intralymphatic administration. When the pharmaceutical composition of the present invention is administered locally, it may be administered directly to the liver by injection or the like. When the pharmaceutical composition is administered systemically, it may be administered into the circulatory system, for example, by intravenous injection. The dosage may be an amount effective for the active ingredient to take effect. The effective amount is appropriately selected according to the information of the subject, as described above.

[0081] (7) Applicable diseases The disease to which the present invention is applicable is not particularly limited, but may be, for example, cancer. The type of cancer is not particularly limited. For example, leukemia, lymphoma, Hodgkin's disease, non-Hodgkin's lymphoma, myeloma, multiple myeloma, brain tumor, breast cancer, uterine cancer, cervical cancer, ovarian cancer, esophageal cancer, stomach cancer, appendix cancer, colon cancer, liver cancer, hepatocellular carcinoma, gallbladder cancer, bile duct cancer, pancreatic cancer, adrenal cancer, gastrointestinal stromal tumor, mesothelioma, head and neck cancer, laryngeal cancer, oral cancer, floor of the mouth cancer, gingival cancer, tongue cancer, buccal mucosa cancer, salivary gland cancer, paranasal sinus cancer, maxillary sinus cancer, frontal sinus cancer, ethmoid sinus cancer, sphenoid sinus cancer, thyroid cancer, kidney cancer, lung cancer, malignant melanoma, osteosarcoma, prostate cancer, testicular tumor (testicular cancer), renal cell cancer, renal clear cell carcinoma, bladder cancer, rhabdomyosarcoma, skin cancer, anal cancer, etc. may be mentioned.

[0082] 4. Biomarkers A fourth aspect of the present invention is a biomarker for predicting the prognosis of a cancer patient and / or for predicting sensitivity to an anticancer drug. The biomarker of the present invention can be used to determine the activity of kynureninase in the method for prediction of the sixth aspect.

[0083] In the present invention, a mutant kynureninase having any one of the amino acid sequences shown in the group consisting of SEQ ID NOs: 3 to 5 or a fragment thereof, or a transcription product of the kynureninase gene encoding the same or a nucleic acid fragment thereof can be a biomarker.

[0084] An example of a "transcription product of the kynureninase gene" is kynureninase mRNA. The mRNA may be either a pre-mRNA or a mature mRNA. Normally, a pre-mRNA is immediately spliced ​​in the nucleus to become a mature mRNA, and therefore the transcription product of the kynureninase gene that is essentially a biomarker of the present invention is a mature kynureninase mRNA.

[0085] Kynureninase used as a biomarker in the present invention has amino acid substitutions at positions 89, 208, and / or 217 compared to wild-type kynureninase, and has low activity. Therefore, when a peptide fragment or a nucleic acid fragment is used as a biomarker in the present invention, it is a fragment that can distinguish mutant kynureninase from wild-type kynureninase. Examples of such fragments include an amino acid sequence containing one or more of the above three amino acid positions or a base sequence corresponding thereto.

[0086] The "peptide fragment" in the present specification refers to a peptide fragment consisting of a part of the amino acid sequence constituting a protein, and can be identified as a fragment of the protein from the amino acid sequence constituting the fragment. In the case of kynureninase, the peptide may generally be a peptide consisting of 20 to 200, 30 to 150, 40 to 100, or 50 to 80 consecutive amino acid residues of the full-length amino acid sequence.

[0087] As used herein, the term "nucleic acid fragment" refers to a nucleic acid fragment consisting of a part of the base sequence constituting an mRNA, and can be identified as a fragment of the mRNA from the base sequence constituting the fragment. In the case of kynureninase mRNA, it is usually sufficient that the nucleic acid consists of 40 to 600, 50 to 450, 60 to 300, or 70 to 200 consecutive bases of the full-length base sequence.

[0088] 5. Kit A fifth aspect of the present invention is a kit comprising a reagent for measuring the biomarker according to the fourth aspect. The kit of the present invention can be used in the method for prediction described below.

[0089] Reagents for measuring biomarkers include, for example, the above-mentioned antibodies or antibody fragments, or probes or primers. The kit may further include known immunohistochemical staining, ELISA, Western blot, or RT-PCR reagents, such as labeling reagents, buffers, color-developing substrates, secondary antibodies, blocking agents, and instruments and controls required for testing.

[0090] 6. Methods for Prediction A sixth aspect of the present invention is a method for predicting the prognosis of a cancer patient and / or predicting the sensitivity of a cancer patient to an anticancer drug. The method of this aspect includes an activity determining step. In this method, a judgment can be made based on the activity determined by the activity determining step. The activity determining step can be performed, for example, in vitro and / or in silico.

[0091] (1)Activity determination step In the activity determining step, the activity of kynureninase in cancer tissue derived from a patient is determined.

[0092] The method for determining the activity in this step is not particularly limited as long as the activity of the kynureninase of the patient to be determined can be compared with the activity of the wild-type kynureninase, and can be, for example, by measuring the biomarker described in the fourth aspect, predicting based on a mutation in the kynureninase gene, measuring the activity of kynureninase itself, or a combination thereof.

[0093] The cancer tissue used is not particularly limited, and may be obtained from a patient by, for example, biopsy or resection surgery. The cancer tissue may be composed of cancer cells. The cancer tissue may be used as is for measurement or the like, or may be appropriately pretreated for measurement. For example, when performing detection by immunohistochemical staining, a paraffin-embedded section may be prepared from a patient-derived sample. Also, for example, when performing detection by Western blot or RT-PCR, a protein extract or an mRNA extract may be prepared from a patient-derived sample.

[0094] When measuring a biomarker, the measurement of the biomarker includes measuring the presence or absence of expression, or the magnitude of the expression amount or expression concentration, etc. In this specification, the term "measurement" includes detection, qualitative, quantitative, and semi-quantitative.

[0095] When the biomarker to be measured is kynureninase or a peptide fragment thereof, the measurement method may be any known protein quantification method, and is not particularly limited, but examples thereof include immunological detection methods.

[0096] An "immunological detection method" is a method for measuring the amount of a target molecule, which is an antigen, using an antibody or an antibody fragment that specifically binds to the target molecule.

[0097] An example of an antibody that specifically binds to kynureninase that can be used in this step is, in the case of human kynureninase, an antibody whose epitope is one or more amino acids selected from the region consisting of the amino acid sequence from position 1 to position 216 of human kynureninase.

[0098] Immunological detection methods include, for example, immunohistochemical staining, enzyme immunoassay (including ELISA and EIA), Western blotting, radioimmunoassay (RIA), immunoprecipitation, and flow cytometry.

[0099] A known method can be used for the "immunohistochemical staining method". For example, a patient-derived sample may be fixed in formalin, embedded in paraffin, sliced ​​into tissue pieces, and attached to a slide glass to be used as a section sample. The section sample may be optionally heat-treated to activate antigens, and then immunohistochemical staining may be performed on the section sample using a commercially available detection system such as Dako EnVision+System (Agilent).

[0100] Furthermore, when the biomarker to be measured is a kynureninase gene transcription product or a nucleic acid fragment thereof, the measurement method may be any known nucleic acid quantification method, and is not particularly limited, but examples thereof include a nucleic acid amplification method using a primer, a hybridization method using a probe, and RNA sequencing.

[0101] The term "nucleic acid amplification method" refers to a method in which a specific region of a target nucleic acid is amplified by a nucleic acid polymerase using a forward / reverse primer set. Examples of nucleic acid amplification methods include PCR (polymerase chain reaction) methods such as RT-PCR (reverse transcription polymerase chain reaction).

[0102] The "hybridization method" is a method in which a nucleic acid fragment having a base sequence complementary to all or part of the base sequence of a target nucleic acid to be detected is used as a probe, and a target nucleic acid or a fragment thereof is detected and quantified by utilizing base pairing between the nucleic acid and the probe. There are several known hybridization methods using different detection means, including, for example, Northern hybridization (Northern blot hybridization), in situ hybridization, and microarray.

[0103] Nucleic acid strands such as primers and probes can be appropriately designed by methods known to those skilled in the art based on known biomarker sequence information, and obtained by known production methods such as chemical synthesis.

[0104] The above-mentioned measurement methods are all known in the art. Therefore, the specific measurement method may be performed according to a known method. For example, the method described in Green, MR and Sambrook, J., 2012 (mentioned above) may be used as a reference.

[0105] When determining the activity of kynureninase by prediction based on mutations in the kynureninase gene, the sequence of the kynureninase gene of the patient is at least partially identified, and the sample used for extracting the kynureninase gene in this case may be derived from cancer tissue or from normal tissue.

[0106] The method for extracting nucleic acid from a sample is not particularly limited. Nucleic acid can be extracted using a conventionally known buffer solution, device, kit, etc. under conditions that do not degrade the target polynucleotide. For example, nucleic acid can be extracted by the acidic phenol method, phenol / chloroform extraction method, etc. Also, a commercially available nucleic acid extraction kit can be used. Examples of the nucleic acid extraction kit that can be used include DNeasy Blood & Tissue Kit (QIAGEN), Quick-DNA kit (Zymoresearch), etc.

[0107] The identification method is not particularly limited. For example, the above-mentioned nucleic acid amplification method, RNA sequencing, and microarray-based method, as well as a method of identifying the sequence of genomic DNA by DNA sequencing, can be used.

[0108] The type of mutation used for prediction is not particularly limited, but is preferably a mutation that increases or decreases the expression level or a mutation that can be predicted to have a relationship with the structure or function of the protein. Examples of mutations that increase or decrease the expression level include an increase or decrease in the copy number of the kynureninase gene, and a mutation in the transcriptional regulatory region of the kynureninase gene. Examples of mutations that can be predicted to have a relationship with the structure or function of the protein include a mutation based on SNP in the kynureninase gene or its transcription product. In this case, in this step, the activity of kynureninase is determined by analyzing SNP in the kynureninase gene or its transcription product.

[0109] A "genetic polymorphism" refers to a mutation in genomic DNA that is held by 1% or more of individuals of the same species. A "single nucleotide polymorphism (SNP)" refers to a genetic polymorphism in which the base sequence on genomic DNA differs by only one base. It is believed that there are approximately 10 million SNPs in total throughout the genome. Genome SNP information can be obtained from databases such as dbSNP (https: / / www.ncbi.nlm.nih.gov / snp / ) of the National Center for Biotechnology Information (NCBI). In particular, hundreds of thousands to millions of SNPs are said to be highly related to human diseases, biological reactions, drug sensitivity, etc. A single nucleotide polymorphism held by an individual on its genomic DNA typically consists of two types, a wild-type allele and one type of mutant allele, but there are also cases where an individual has two or more types of mutant alleles.

[0110] The method of analyzing SNP is not particularly limited. For example, a direct sequencing method in which a DNA fragment containing SNP is amplified to read the base sequence, a method using a probe containing an SNP site, a method using a protein that exhibits different behavior depending on the presence of a mismatch, etc. are known. Examples of the method using a probe containing an SNP site include a cycling probe method that applies real-time PCR, a bead array method, and a microarray (DNA chip) method.

[0111] Information on whether the detected SNP changes the structure of kynureninase or not, or whether the detected SNP changes the activity of kynureninase or not, can be obtained from many known databases, and can be determined by many algorithms. Examples of such databases include cBioPortal, etc. Examples of algorithms for determination include SIFT, PolyPhen-2, or a combination thereof, etc.

[0112] In addition, for example, a mutation at a specific position, not limited to SNP, can be determined to be a mutation that changes the structure of kynureninase. For example, in the case of human kynureninase, a mutation in the aminotransferase class V domain consisting of amino acids at positions 119 to 387 can be determined to be a mutation that changes the structure and / or activity of the protein. In addition, for example, a mutation in a range including one or more amino acids at positions 137, 138, 165 to 168, 221, 250, 253, 275, 305, 333, and several amino acids before and after them, which are pyridoxal phosphate binding sites, as well as a mutation in a range including one or more amino acids at positions 89, 208, and 217 can be determined to be a mutation that changes the structure and / or activity of the protein. The type of mutation is not particularly limited, and examples thereof include amino acid deletion mutation, addition mutation, missense mutation, nonsense mutation, frameshift mutation, and combinations thereof. Specific examples of the mutations include, but are not limited to, G73E mutation, E89K mutation, I208N mutation, V217M mutation, T198A mutation, W272S mutation, G287D mutation, and the like.

[0113] When measuring the activity of kynureninase itself, the method is not particularly limited. For example, the activity can be measured by measuring the catalytic activity of kynureninase, the amount of anthranilic acid, which is the product of the reaction catalyzed by kynureninase, or the amount of L-kynurenine, which is the substrate of the reaction. Any known method, including measurement of Km value, can be used to measure the catalytic activity. Examples of methods for measuring the amount of anthranilic acid include a method using cobalt ions and a method using high performance liquid chromatography (HPLC). Examples of methods for measuring the amount of L-kynurenine include a method using ELISA or HPLC.

[0114] (2) Activity-based judgment In the method of the present invention, the prognosis of a cancer patient and / or the sensitivity of a cancer patient to an anticancer drug is predicted based on the activity determined in the activity determining step.

[0115] In the method of the present invention, when predicting prognosis, a lower activity than that of wild-type kynureninase indicates a better prognosis for the patient, and when predicting sensitivity to anticancer drugs, a lower activity than that of wild-type kynureninase indicates a higher sensitivity of the patient to the anticancer drug.

[0116] For example, when kynureninase activity is measured by measuring a biomarker, activity can be determined to be lower than wild-type kynureninase activity if the biomarker is detected or is detected in greater amounts than in a control sample.

[0117] The control sample may be derived from a healthy individual (e.g., a healthy person) or from normal tissue of the patient, where "healthy individual" refers to a healthy individual of the same species as the patient who is not suffering from cancer.

[0118] The method for comparing values ​​is not particularly limited. For example, a simple comparison of absolute values, a comparison based on statistical significance, a comparison based on a cutoff value, or a combination of these may be used.

[0119] The method for setting the cutoff value is not particularly limited, and for example, it can be determined by ROC (receiver operating characteristic curve) analysis. The criteria for determining the cutoff value are not particularly limited. For example, the cutoff value can be set so that the specificity or sensitivity is at a certain level or higher, and / or the AUC (area under the curve) is at a certain level or higher.

[0120] "Prognosis" refers to the predicted course of a disease. Examples of prognosis include the survival time of a cancer patient and the predicted course (e.g., life or death) after cancer treatment (including treatment with anticancer drugs or surgery). "Prediction of prognosis" may be a prediction of survival time, or a prediction of survival rate after a certain period of time (e.g., 1, 3, 5, 10, 15, or 20 years or more) from a given time (e.g., surgery). Prediction of prognosis includes the risk of cancer recurrence. Prediction of prognosis can also be called prognosis judgment, evaluation, or diagnosis.

[0121] As used herein, "sensitivity to anticancer drugs" refers to the degree of therapeutic effect that a patient can obtain by administering a normal amount of an anticancer drug. High sensitivity refers to a therapeutic effect greater than that normally obtained by a normal amount of an anticancer drug, and low sensitivity refers to a therapeutic effect less than that normally obtained by a normal amount of an anticancer drug.

[0122] According to the present invention, when a cancer patient is predicted to have high sensitivity to an anticancer drug, the anticancer drug can be administered to the patient.Also, when a cancer patient is predicted to have low sensitivity to an anticancer drug, for example, a treatment method other than an anticancer drug or a treatment method using an anticancer drug resistance suppressor can be selected.

[0123] Specifically, "poor prognosis" refers to poor clinical outcomes (e.g., high recurrence rate after cancer resection surgery, low disease (cancer)-specific survival rate, or low overall survival rate). In the case of poor prognosis, the 5-year survival rate after cancer resection surgery may be less than 90%, less than 85%, less than 80%, less than 75%, less than 70%, less than 65%, or less than 60%. In the present invention, survival rate means cumulative survival rate. In the present invention, survival rate may be disease (cancer)-specific survival rate or overall survival rate.

[0124] A "good prognosis" refers to a good clinical outcome. A good prognosis may mean a 5-year survival rate of 90% or more or 95% or more after surgery to remove the cancer.

[0125] According to the present invention, it is possible to predict the prognosis of a cancer patient, and based on the result, it is possible to determine a treatment plan (e.g., type of anticancer drug, dosage, administration interval, etc.) or determine the interval for testing for lung cancer recurrence and metastasis.

[0126] According to the present invention, when a cancer patient is predicted to have a poor prognosis, an anticancer agent may be administered to the patient to prevent recurrence of cancer, improve the prognosis, or improve the survival rate.

[0127] When selecting an anticancer drug or other treatment method, multiple types of drugs and treatment methods can be used alone or in combination. In this case, the route of administration of the drug is not particularly limited.

[0128] 7. How to Screen for Genes The seventh aspect of the present invention is a method for screening genes. As shown in Fig. 1, the method of the present invention includes a sample number counting step (S0101), a relative frequency counting step (S0102), an individual mutual information determination step (S0103), a mutual information calculation step (S0104), and an immune checkpoint judgment step (S0105) as essential steps, and includes a confidence interval calculation step (S0106) as an optional step. According to the method of this aspect, genes related to immune checkpoints can be screened based on the mutual information.

[0129] The methods of the present invention screen for genes associated with immune checkpoints based on identifying an interdependence of two or more first events and a second event that includes criteria related to expression of candidate genes.

[0130] (1) Events and Criteria The two or more first events including a criterion for gene expression include an event including a criterion for expression of a PD1 pathway-related gene and an event including a criterion for expression of an immune checkpoint-related gene, and the second event includes a criterion for expression of a candidate gene.

[0131] The criteria for the expression of immune checkpoint-associated genes include any changes associated with immune checkpoint-associated genes, such as changes in the expression level of immune checkpoint-associated genes, changes in immune checkpoint-associated genes, changes in the activity of immune checkpoint-associated proteins, changes in the signal transduction pathway of immune cells, and the like.

[0132] The specific immune checkpoint-related gene is not particularly limited. Numerous genes related to immune checkpoints are known in the art. For example, epidermal growth factor receptor (EGFR) gene, Kirsten rat sarcoma viral oncogene homolog (KRAS) gene, tumor protein p53 (TP53) gene, cyclin-dependent kinase inhibitor 2A (CDKN2A) gene, inducible T cell costimulator (ICOS) gene, CD40 ligand (CD40L) gene, CD28 gene, CD80 gene, CD86 gene, cytotoxic T lymphocyte-associated protein 4 (CTLA-4) gene, herpes virus entry mediator (HVEM) gene, Programmed cell death ligand 1 (PD-L1) gene, Programmed cell death 1 (PD-1) gene, BCL2-like protein 1 (BCL2L1) gene, Cbl proto-oncogene B (CBLB) gene, CD247 gene, CD274 gene, CD28 gene, CD80 gene, cyclin-dependent kinase 2 (CDK2) gene, cyclin-dependent kinase inhibitor 1B (CDKN1B) gene, cellular inhibitor of PP2A (CIP2A) gene, casein kinase 2 (Ck2) gene, C-terminal Src kinase (CSK) gene gene, ETS transcription factor 1 (ELK1) gene, F-box protein 38 (FBXO38) gene, forkhead box P3 (FOXP3) gene, glycogen synthase kinase 3 beta (GSK3B) gene, major histocompatibility complex class IB (HLA-B) gene, interferon gamma (IFNG) gene, interferon gamma receptor 1 (IFNGR1) gene, interferon gamma receptor 2 (IFNGR2) gene, interleukin 2 receptor (IL-2R) gene, interleukin 1 2 (IL12) gene, interleukin 2 (IL2) gene, interleukin 4 (IL4) gene, Janus kinase (JAK) gene, linker for activation of T-cells (LAT) gene, large tumor suppressor kinase (LATS) gene, lymphocyte cell-specific protein tyrosine kinase (LCK) gene, lymphocyte cytoplasmic protein 2 (LCP2) gene, MHC class I gene, MHC class II gene, MHC class I subunit α (MHCI-α) gene, MHC class II subunit β (MHCII-β) gene,MHC class II subunit beta (MHCII-α) gene, mir-21 gene, programmed cell death 1 (PDCD1) gene, programmed cell death 1 ligand 2 (PDCD1LG2) gene, programmed cell death 4 (PDCD4) gene, phosphoinositide 3-kinase (PI3K), phospholipase C gamma 1 (PLCG1) gene, protein kinase C theta (PRKCQ) gene, phosphatase tensin homolog (PTEN) gene, protein tyrosine phosphatase nonreceptor type 11 (PTPN11) gene, RAS guanyl releasing protein 1 (RASGRP1) gene, S-phase kinase Examples of such genes include the kynureninase-associated protein 2 (SKP2) gene, the SMAD family member 3 (SMAD3) gene, the signal transducer and activator of transcription 5A (STAT5a) gene, the signal transducer and activator of transcription 5b (STAT5b) gene, the transforming growth factor beta (TGFβ) gene, the tumor necrosis factor (TNF) gene, the TNF receptor gene, the T cell receptor alpha locus (TRA) gene, the T cell receptor beta locus (TRB) gene, the Yes1-associated transcription factor (YAP1) gene, and the T cell receptor-associated protein kinase zeta chain 70 (ZAP70). In addition, the kynureninase gene and the hepatitis A virus cell receptor 2 (HAVCR2) gene can also be used. HAVCR2 is a protein belonging to the immunoglobulin superfamily and the TIM family, and is known as a Th1-specific cell surface protein. It is also known as TIM3 and has functions such as regulating macrophage activation, suppressing Th1-mediated autoimmune reactions, and promoting immune tolerance.

[0133] The first event includes an event that involves a measure of expression of a PD1 pathway-related gene.

[0134] The term "PD1 pathway" refers to a signal transduction pathway based on the binding of programmed cell death ligand (PD-L) to programmed cell death 1 (PD-1). Known examples of programmed cell death ligands include programmed cell death ligand 1 (PD-L1) and programmed cell death ligand 2 (PD-L2). The PD1 pathway in this specification may relate to any ligand, for example, the PDL1-PD1 pathway relating to PD-L1. The PD1 pathway in this specification includes any molecule that is directly or indirectly regulated by the cytoplasmic domain of PD-1 phosphorylated by the binding of PD-L to PD-1. Examples of such molecules include SH2 domain-containing protein tyrosine phosphatase 2 (SHP2), ZAP70, PI3K, and any molecule that is regulated by these molecules. Examples of changes in the signal transduction pathway include changes in the expression level or activity of proteins involved in the pathway.

[0135] The first event includes two or more events including criteria related to gene expression. Each event belonging to the first event can include criteria related to the expression of multiple genes, but preferably, each event is related to one type of gene. If necessary, the first event can include an event related to genes other than PD1 pathway-related genes and immune checkpoint-related genes.

[0136] The number of events used as the first event is not particularly limited as long as it is two or more. For example, it can be 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 15,000 or more, 16,000 or more, 17,000 or more, 18,000 or more, 19,000 or more, 20,000 or more, 50,000 or more, or 100,000 or more.

[0137] Of the criteria included in each event, the number of criteria related to the expression level of the gene related to each event may be 1 or more, and is not particularly limited. For example, each of them can be independently 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more. In addition, for example, of the criteria included in each event, 1 / 4 or more, 1 / 3 or more, 1 / 2 or more, 2 / 3 or more, 3 / 4 or more, or all of them can be the criteria related to these.

[0138] A candidate gene is a gene whose association with an immune checkpoint is determined by the method of the present invention. The type of candidate gene is not particularly limited. For example, the candidate gene may be a gene whose association with an immune checkpoint has been suggested, or a gene whose association with an immune checkpoint is not known. The second event may include multiple criteria regarding the expression of one type of candidate gene, or may include criteria regarding the expression of multiple candidate genes.

[0139] The number of criteria for the expression of the candidate genes included in the second event may be 1 or more, and is not particularly limited. For example, the number of criteria can be independently 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more. In addition, for example, one-quarter or more, one-third or more, one-half or more, two-thirds or more, three-quarters or more, or all of the criteria included in each event can be the criteria for these.

[0140] In the method of the present invention, for each of two or more first events, the steps from the sample number counting step (S0101) to the mutual information calculation step (S0104), and in the case of calculating a confidence interval, the step up to the confidence interval calculation step (S0106) are repeated the same number of times as the number of events. The repetition may involve repeating all of the above steps as a series of steps, repeating each step, or repeating some of the steps together. Even in this case, some of the steps, such as the sample number counting step (S0101), may be performed with data for multiple events compiled together.

[0141] The number of criteria included in the first event is represented as m, and the number of criteria included in the second event is represented as n. However, when the first event and / or the second event includes multiple events, the number of criteria constituting each of the events may be the same or different.

[0142] There are no particular limitations on the types of specific criteria included in each event. Examples include changes in signal transduction pathways, changes in protein activity, changes in gene expression levels (changes in the amount of transcripts such as RNA and proteins), and changes in genes (including epigenetic changes). Epigenetic changes broadly include changes that are inherited through cell division and are independent of changes in DNA base sequences.

[0143] Examples of genetic changes include mutations in the gene sequence, changes in the expression product of the gene, changes in the modification of the gene, and the like.

[0144] Examples of gene sequence mutations include mutations in the base sequence of a gene, changes in the copy number of a gene on a chromosome, and changes in gene modification. Examples of gene sequence mutations include point mutations (including mutations based on SNPs), addition of base sequences to a gene, and deletion of base sequences in a gene. Examples of gene expression products include proteins, mRNA, and miRNA (micro-RNA). Examples of changes in gene expression products include changes in the expression level of a gene expression product, changes in the expression site of a gene expression product, formation of a complex of a gene expression product, and degradation of a complex of a gene expression product.

[0145] Examples of gene modifications include DNA methylation and histone modifications. Examples of histone modifications include acetylation, methylation, ubiquitination, phosphorylation, and sumoylation. Examples of gene modifications include post-translational modifications. Examples of post-translational modifications include functional group addition, protein or peptide addition, conversion of the chemical properties of amino acids, and structural conversion. Examples of functional group addition include acylation, acetylation, alkylation, amidation, biotinylation, formylation, gamma carboxylation, glutamylation, glycosylation, glycylation, heme, hydroxylation, iodination, isoprenylation, lipoylation (prenylation, GPI anchor formation, myristoylation, farnesylation, geranylgeranylation, etc.), addition of covalent bonds to nucleotides or derivatives (ADP-ribosylation, FAD binding, etc.), oxidation-reduction reactions, polyethylene glycolation, phosphatidylinositol, phosphopantetheinylation, phosphorylation, pyroglutamic acid formation, racemization, tyrosine sulfation, and selenoylation. Examples of protein or peptide addition include ISGylation, SUMOylation, and ubiquitination. Examples of conversion of the chemical properties of amino acids include citrullination, deamination, and deamidation. Examples of structural conversion include disulfides and those caused by proteases.

[0146] In the first event and the second event, the first and the second are symbols for distinguishing the first event from the second event, and do not limit the order of these events.

[0147] Examples of events include those represented by presence / absence and those represented by numerical values. Examples of those represented by numerical values ​​include those represented by discrete quantities greater than 2 and those represented by continuous quantities. The first event and the second event may be represented differently, for example, the first event may be represented by presence / absence and the second event may be represented by discrete quantities greater than 2.

[0148] In the method of the present invention, information on each event can be used regardless of its type. Even when different events are repeatedly analyzed, the method of the present invention can be processed using a common method, so that a unified analysis can be easily performed. Therefore, the method of the present invention is suitable for use in unifying data containing information on multiple events, for example, multiple genes, to identify the interdependence of these events.

[0149] The method of the present invention uses a data set that comprises relative frequencies for a first event involving a plurality of criteria and a second event involving a plurality of criteria. The relative frequencies are positive values ​​that sum to one.

[0150] In a data set, for example, each of N samples includes information on a first event and information on a second event. Examples of event information include (1) information on whether the event occurred or did not occur in the sample if the event is expressed as presence or absence, and (2) information on the sample if the event is expressed as a numerical value.

[0151] The criteria included in each event include, for example, the presence or absence of a certain phenomenon or its type, or the properties of each event, when the event is expressed by the presence or absence of a specific phenomenon or a difference in properties, and includes, but is not limited to, a numerical range defined by a threshold value when the event is expressed by a numerical value. When the threshold value is used, the event can be classified by the criteria, for example, according to whether the numerical value is equal to or greater than the threshold value, or whether the numerical value is less than the threshold value.

[0152] The criteria may be set in such a way that there are samples that are not classified into any of the criteria. For example, when an event is expressed numerically, it is not necessary to set a threshold for all ranges of values ​​that the sample can take to classify them, and a certain range can be delimited and then a criterion can be set within the delimited range. Here, the certain range, in the case of a numerical range, can be, for example, a range excluding outliers, a range delimited by percentiles, a range delimited by the size of the standard deviation from the average value, a range delimited by any other numerical value, or a range delimited by an index that can be used by a general statistical method. In other cases, the range can be arbitrarily set according to various purposes, for example, so that a major phenomenon is included or so that the phenomenon of interest is more clearly defined.

[0153] The criteria included in one event may be related to only one phenomenon or property, or may be a combination of criteria related to multiple different phenomena or properties. In addition, when a combination is used, all possible combinations may be used as criteria, or only a part of them may be used as criteria.

[0154] The criterion for the first event and the criterion for the second event may be the same or different. For example, when two events are expressed by numerical values, the threshold value for the first event and the threshold value for the second event may be the same or different numerical values.

[0155] Conventionally, it has been extremely difficult to calculate the mutual information between events that include a huge number of criteria because the amount of calculation increases dramatically. According to the present invention, the more criteria included in an event, as described below, the more accurately the mutual information can be approximated, and even when such an event is used, the mutual information can be accurately calculated with a small amount of calculation.

[0156] (2) Sample size calculation process (S0101) and relative frequency calculation process (S0102) In the method of the present invention, first, data is tabulated into an m×n contingency table. Specifically, this is performed in two steps: a sample number tabulation step (S0101) and a relative frequency tabulation step (S0102).

[0157] The sample number counting step (S0101) is a step of counting the sample numbers in all cells, for a cell consisting of one criterion selected from a first event and one criterion selected from a second event, into an m×n frequency distribution table.

[0158] The relative frequency counting step (S0102) is a step of calculating the simultaneous relative frequency from the number of samples in all cells of the frequency distribution table, and counting the results in an m×n contingency table.

[0159] Here, as described above, the number of criteria included in the first event is m, and the number of criteria included in the second event is n, and the joint relative frequency refers to the ratio of the number of samples that fall into a certain cell to the total number of samples.

[0160] m is an integer of 2 or more, and at least some of the two or more events belonging to the first event may include different numbers of criteria, or all of them may include the same number of criteria. n is an integer of 2 or more, and may be the same as or different from m. Furthermore, at least one of m and n is 3 or more. The size of m and n is not particularly limited, but for example, at least one of them is 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more. In addition, the number of data in the dataset, that is, the total number of samples, is hereinafter referred to as N. N is an integer of 2 or more. Although not particularly limited, examples of N include values ​​such as 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, and 100,000 or more. The larger m and n are, the more precisely the interdependence of the first and second events can be identified.

[0161] For example, in a 2×2 contingency table in which data on a first event and data on a second event are both expressed as presence / absence, the number of samples from the data set to the contingency table may be tallied by tallying a, b, c, and d, which are the numbers of samples that meet the conditions in each column in Table 1 below. The sum of a through d is the total number N of samples included in the data set.

[0162] [Table 1]

[0163] In tabulating the contingency table, it is not necessary to use a table, as long as the number of samples corresponding to a, b, c, and d, which are the number of samples that meet the above conditions, is tabulated. In this example, for example, it can be performed as follows. First, the following conditions are set: (1) a condition that the first event exists and the second event exists; (2) a condition that the first event exists and the second event does not exist; (3) a condition that the first event does not exist and the second event exists; and (4) a condition that the first event does not exist and the second event does not exist. Next, it is determined which of the conditions (1) to (4) each of the N samples falls under, and each of the N samples is classified into each of the above conditions. This is repeated for all N samples, and the number of samples classified into each condition is tabulated. In this case, (1) a is the number of samples, out of all N samples, in which the first event is “yes” and the second event is “yes,” (2) b is the number of samples, out of all N samples, in which the first event is “yes” and the second event is “no,” (3) c is the number of samples, out of all N samples, in which the first event is “no” and the second event is “yes,” and (4) d is the number of samples, out of all N samples, in which the first event is “no” and the second event is “no.”

[0164] Alternatively, instead of the above sample numbers, the relative frequencies may be directly counted and used in the method of the present invention, or the data expressed as relative frequencies may be used as is in the method of the present invention. In this case, the counting can be performed by simultaneously performing the relative frequency counting step (S0102) and the sample number counting step (S0101), or by performing only the relative frequency counting step (S0102).

[0165] (3) Individual mutual information determination step (S0103) and mutual information calculation step (S0104) According to the findings of the present inventors, the mutual information MI between a first event including m criteria and a second event including n criteria can be calculated based on the individual mutual information between a specific value of the first event and a specific criterion of the second event. Specifically, the sum of the mutual information MI or the frontier mutual information MI having the largest absolute value can be calculated based on the individual mutual information between a specific value of the first event and a specific criterion of the second event. front Using the formula m×n×MI front The mutual information MI can be approximated by / log(m×n-1).

[0166] The calculation of the mutual information is performed by two steps, an individual mutual information determination step (S0103) and a mutual information calculation step (S0104). The calculation of the mutual information is usually performed after the counting. However, for example, a part or all of the calculation of the mutual information may be started before the counting is completed.

[0167] The individual mutual information determining step (S0103) is a step of determining the individual mutual information of each cell based on the simultaneous relative frequency in all cells.

[0168] The mutual information calculation step (S0104) is a step of calculating the mutual information of the first event and the second event from the individual mutual information.

[0169] Usually, the mutual information calculation step (S0104) is performed after the individual mutual information determination step (S0103). However, for example, in particular in the mutual information calculation step (S0104), when the frontier mutual information described later is not used, it is not necessary to perform the mutual information calculation step (S0104) after determining the individual mutual information of all cells in the individual mutual information determination step (S0103). For example, the calculation of the mutual information may be started sequentially from the determined individual mutual information. Also, even when the frontier mutual information is used, the selection of the frontier mutual information may be started during the individual mutual information determination step (S0103).

[0170] Here, mutual information is a quantity used in information theory that represents a measure of the interdependence of two random variables. Mutual information is a measure of the amount of information shared by a first random variable X and a second random variable Y. In general, the mutual information MI between two discrete random variables X and Y is calculated, for example, by the following formula:

[0171]

number

[0172] Furthermore, the mutual information I(X;Y) between two continuous random variables X and Y is calculated, for example, by the following formula:

[0173]

number

[0174] In the above example, the mutual information of an m×n contingency table for two events X and Y is called MI. Let k be the criterion for one event X and l be the criterion for one event Y. The mutual information of a 2×2 contingency table centered on cell (k, l), which is the combination of k and l, is called the individual mutual information MI. kl The individual mutual information of each cell is determined based on the joint relative frequency.

[0175] (3-1) Individual mutual information determination step (S0103) As shown in FIG. 2, the individual mutual information determination step (S0103) includes two steps: a conversion step (S0110) and a calculation step (S0111).

[0176] For each cell, the transformation step (S0110) is a step of transforming, for each cell, the m×n contingency table into a 2×2 contingency table including binary data for the criterion in each of the first and second events.

[0177] The calculation step (S0111) is a step of calculating, for each cell, the strength of the criterion correlation between the first and second events as individual mutual information from the 2×2 contingency table.

[0178] The calculation step (S0111) can usually be performed after the conversion step (S0110). Here, for example, the conversion step (S0110) may be performed for all cells before the calculation step (S0111), or the conversion step (S0110) and the calculation step (S0111) may be performed for each cell.

[0179] In the above-mentioned transformation step (S0110), the 2×2 contingency table can be transformed based on the joint relative frequency of a cell, the marginal relative frequency of the cell for the criterion in the first event, and the marginal relative frequency of the cell for the criterion in the second event, where the marginal relative frequency refers to the sum of the joint relative frequencies in one column or one row.

[0180] Specifically, in the m × n contingency table for the two events X and Y mentioned above, the joint relative frequency of cell (k, l) is p(X k ,Y l ), and the marginal relative frequencies of the kth column and lth row are p(X k ) and p(Y l ) Then, the 2 × 2 contingency table with cell (k, l) at center is expressed as the following table.

[0181] [Table 2]

[0182] The individual mutual information of each cell is calculated in a calculation step (S0111). The method used here is not particularly limited, but it can be calculated using, for example, multidimensional mutual information, Fisher's exact probability, or hypergeometric distribution.

[0183] An example of a method using multidimensional mutual information is a method carried out based on the following formula (III).

[0184]

number

[0185] Examples of the method using Fisher's exact probability include a method using Fisher's exact probability as is or after modification. Examples of the method using Fisher's exact probability after modification include calculation by -log(P) / N (P represents Fisher's exact probability).

[0186] As an example of a method using the hypergeometric distribution, the main term of Fisher's exact probability, P, is described later in the section on the application of meta-analysis. main A method using the above method is exemplified.

[0187] (3-2) Mutual information calculation step (S0104) As shown in FIG. 3, the mutual information calculation step (S0104) includes the following two procedures depending on whether or not frontier mutual information is used (E0102): (i) When frontier mutual information is not used, the mutual information calculation step includes a direct calculation step (S0112); (ii) In the case where frontier mutual information is used, the mutual information calculation step includes a frontier mutual information selection step (S0113) and a frontier mutual information use step (S0114).

[0188] (3-2-1) When frontier mutual information is not used In the direct calculation step (S0112), the mutual information is calculated from the individual mutual information by the formula (I).

[0189]

number

[0190] As shown below, when m and n are each sufficiently large, the right side of formula (I) approaches the left side infinitesimally, and formula (I) can be regarded as an equality.

[0191] "Sufficiently large" refers to, for example, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, or 10,000 or more.

[0192] (3-2-2) When using frontier mutual information The case where the frontier mutual information is used will be described below. As will be described below, the inventors have found that by using the individual mutual information (frontier mutual information) with the largest absolute value as a representative value indicating the interdependence between the first and second events, the mutual information can be accurately calculated easily with a small amount of calculation.

[0193] In the frontier mutual information selection step (S0113), the individual mutual information is compared with each other, and the individual mutual information having the largest absolute value is selected as the frontier mutual information.

[0194] In the frontier mutual information utilizing step (S0114), mutual information is calculated based on the selected frontier mutual information.

[0195] Here, this calculation is performed according to the following formula (II).

[0196]

number

[0197] As shown below, when m and n are each sufficiently large, the right side of formula (II) approaches the left side infinitesimally, and formula (II) can be regarded as an equality.

[0198] The sufficiently large values ​​of m and n are as described above for formula (I).

[0199] The use of frontier mutual information can be appropriately determined depending on the magnitudes of m and n, the performance of the computer used, other conditions, purposes, etc. For example, frontier mutual information can be used when m and n are each sufficiently large.

[0200] Here, "sufficiently large" refers to, for example, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more.

[0201] Below, we compare the mutual information MI and the individual mutual information MI mentioned above. kl and frontier mutual information MI front The present inventors have found the following information regarding the relationship between the random variables and the event. This information is the basis for the calculation of the mutual information MI by these steps. In the following, the mutual information regarding two random variables is described in terms of the relationship to be handled in mathematical formulas, but by replacing the random variables with events, the information can be directly applied to the method of the present invention.

[0202] Let the mutual information of an m×n contingency table for two random variables X and Y be MI. Let k be one value that the random variable X can take, and l be one value that the random variable Y can take. The mutual information of a 2×2 contingency table centered on cell (k, l), which is their combination, is the individual mutual information MI kl Furthermore, let the simultaneous relative frequency of cell (k, l) be p(X k ,Y l ), and the marginal relative frequencies of the kth column and lth row are p(X k ) and p(Y l ) Then, the MI between random variables X and Y and the MI in all cells are kl The difference between the total and the total is expressed as follows:

[0203]

number

[0204] Divide each cell of the m × n contingency table into h uniform parts along both rows and columns, and perform MI for all the divided cells. kl If we take the sum of these, we get the following formula:

[0205]

number

[0206] Define the left side of the above equation as Δ(h). Using L'Hopital's rule, calculate the limit of each term on the right side when h is set to infinity. Then, the limits of the first and second terms are both p(X k ,Y l ) The third term is approximated as follows:

[0207]

number

[0208] The fourth and fifth terms are approximately:

[0209]

number

[0210] These limits and p(X k ,Y l ) and p(X k ) and p(Y l ) is equal, we can prove that the limit on the right hand side is zero. Therefore,

[0211]

number

[0212] Therefore, when m and n are sufficiently large, the following formula (I) holds:

[0213]

number

[0214] Next, according to the findings of the present inventors, the individual mutual information MI kl is exponentially distributed, as shown below.

[0215] First, the mutual information of an m×n contingency table is defined as follows, where k and l are 1 to m and 1 to n, respectively:

[0216]

number

[0217] Note that the sum of the expected values ​​of each random variable is 1, as shown below.

[0218]

number

[0219] The individual mutual information for cell (k,l) is defined as follows:

[0220]

number

[0221] Now, as shown in the above formula (I), when m and n are sufficiently large, the following holds:

[0222]

number

[0223] The following holds for the probability p(MI) that the mutual information between X and Y is MI.

[0224]

number

[0225] MI for k and l kl The sum of the second, third, and fourth terms on the right hand side of converges to 0. Therefore, MI kl becomes more independent of the simultaneous relative frequencies of other cells. When X and Y are uniformly distributed, the marginal relative frequencies of the kth column and lth row asymptotically approach 1 / m and 1 / n, respectively, and as m and n become larger, the dependence on the marginal relative frequencies of other rows and columns decreases. Therefore, when m and n are sufficiently large, from a microscopic point of view, X and Y can be regarded as X and Y, respectively. k and Y l By doing so, MI kl The above formula for p(MI) can also be applied to

[0226] That is, M.I. kl follows an exponential distribution. On the other hand, from a macroscopic point of view, MI kl The average value of is MI / (m×n) from the above formula. That is, x=MI kl The probability density function p(x) of is expressed as follows:

[0227]

number

[0228] mutual information MI kl follows the exponential distribution as above. Therefore, let j be the MI kl If we take the rank of the absolute value of , then the following holds:

[0229]

number

[0230]

number

[0231] Therefore, M.I. front is the overall MI and the average MI kl Therefore, the overall MI is approximately proportional to MI front It is calculated as follows:

[0232]

number

[0233] As described above, when n and m are sufficiently large, the mutual information MI between a first event containing m criteria and a second event containing n criteria is m×n×MI front / log(m×n-1). The frontier mutual information MI front is the frontier mutual information MI having the maximum absolute value among the mutual information between a specific value of the first event and a specific value of the second event. front It is.

[0234] (4) Immune checkpoint assessment process (S0105) In this step, the candidate gene is determined to be an immune checkpoint-related gene based on the calculated mutual information.

[0235] As shown in FIG. 4, this process includes an event selection step (S0115), a function compilation step (S0116), and a judgment step (S0117).

[0236] In the event selection step (S0115), a predetermined number of events are selected from the first event based on the magnitude of the mutual information value.

[0237] In this step, it is preferable to select events with large mutual information values.

[0238] The selection criteria are not particularly limited. For example, events with a mutual information value of a certain amount or more may be selected, and a certain number of events may be selected in descending order of the mutual information value. The criteria for selection based on the mutual information value are not particularly limited, and for example, events with mutual information of 1 / N or more, 2 / N or more, 2.5 / N or more, 3 / N or more, or 3.5 / N or more may be selected.

[0239] The number of events selected in the event selection step is not particularly limited as long as it is 1 or more. For example, it can be 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, or 5,000 or more. Also, for example, of the events used as the first event, half or less, third or less, quarter or less, fifth or less, sixth or less, seventh or less, eighth or less, ninth or less, or tenth or less can be selected.

[0240] In the function tabulation step (S0116), the functions of genes related to the selected events are tabulated.

[0241] In this step, the functions of the genes related to the selected events as well as the functions of the candidate genes can be tabulated.

[0242] The method of counting is not particularly limited as long as it can identify the signal transduction pathway that the gene activates or inhibits. For example, any known enrichment analysis method can be used. As such a method, for example, gene ontology analysis, pathway analysis, network analysis, etc. can be used. These methods can be performed using any known software. Specific software includes, but is not limited to, Panther, GOrilla, DAVID, Metascape for gene ontology analysis, and Partek Pathway for pathway analysis. In addition, general-purpose software that can perform these analyses includes, for example, Gene Set Enrichment Analysis (GSEA), Ingenuity Pathway Analysis (IPA) (registered trademark), iPathwayGuide, R, etc.

[0243] In this step, only signaling pathways having a certain p-value or less can be counted based on the enrichment p-value. In this case, the p-value is usually Log(p-value), which indicates goodness of fit. For example, but not limited to, signaling pathways having a -Log(p-value) value of 10 or more, 15 or more, or 20 or more can be counted.

[0244] In the determination step (S0117), if the collected functions include an activation function of one or more signal transduction pathways related to immune checkpoints and an inhibitory function of one or more immune checkpoints via the PD1 pathway, the candidate gene is determined to be an immune checkpoint-related gene.

[0245] The number of signal transduction pathways used to determine the activation function of an immune checkpoint is not particularly limited, and may be one or more. For example, when activation function is observed in two or more, three or more, four or more, five or more, six or more, or seven or more signal transduction pathways, it can be considered that the activation function of a signal transduction pathway related to an immune checkpoint is included. The signal transduction pathway activated here is not particularly limited, and examples thereof include a Th1 / Th2 activation pathway, a cytokine signal transduction pathway, a T cell differentiation pathway, a NK cell signal transduction pathway, an autoimmune pathway, a dendritic cell pathway, a helper T cell pathway, and a pattern recognition receptor pathway.

[0246] The one or more immune checkpoint functions mediated by the PD1 pathway to be inhibited are not particularly limited as long as they are pathways related to the PD1 pathway among pathways related to immune checkpoints. The PD1 pathway to be inhibited may be related to any ligand, for example, the PDL1-PD1 pathway related to PD-L1. For example, the functions include those related to cancer therapy mediated by the PD1 pathway, and those related to immunotherapy mediated by the PD1 pathway.

[0247] The method of determining the suppression and activation functions used in the determination is not particularly limited, but for example, the activation z score can be used as an index. For example, when the activation z score is, for example, 0 or more, 1 or more, 2 or more, 3 or more, 3.1 or more, 3.2 or more, 3.3 or more, 3.5 or more, 4 or more, 5 or more, 6 or more, or 7 or more, it can be determined that the signal transduction pathway has an activation function. In addition, when the activation z score is, for example, less than 0, less than -1, less than -2, less than -3, less than -3.1, less than -3.2, or less than -3.3, it can be determined that the signal transduction pathway has an inhibition function.

[0248] This method makes it possible to identify immune checkpoint genes in signaling pathways independent of the PD1 pathway.

[0249] Further confirmation can be performed on the immune checkpoint genes identified by this method to determine whether they change the prognosis of cancer patients based on their expression levels, mutations, etc. For example, in silico confirmation can be performed based on information on gene expression and mutations in patients obtained from public databases such as The Cancer Genome Atlas (TCGA) (http: / / cancergenome.nih.gov / ) in the United States.

[0250] One or more of the event selection step (S0115), function aggregation step (S0116), and judgment step (S0117) can be performed only when the candidate gene satisfies certain conditions. The conditions in this case are not particularly limited, but for example, these steps can be performed when the mutual information regarding the expression levels of the candidate gene and one or more genes related to immune checkpoints is equal to or higher than a predetermined level.

[0251] In this case, the predetermined level is not particularly limited, but for example, these steps can be performed when the above-mentioned -Log(p value) and / or activation z score is equal to or greater than a certain value. The specific values ​​used in this judgment are not particularly limited, but for example, the above-mentioned numerical values ​​can be used for each value.

[0252] The gene used for this judgment is not particularly limited as long as it is an immune checkpoint-related gene. For example, the various genes described above in the section "(1) Events and Criteria" of this embodiment can be used. Preferably, for example, the kynureninase gene can be used.

[0253] As described above, the judgment as to whether or not to perform each step may be made based on a value calculated separately as the mutual information between the target immune checkpoint-associated gene and the candidate gene. In this case, for example, the mutual information between the target immune checkpoint-associated gene and the candidate gene is calculated, and if the value is equal to or higher than the above-mentioned predetermined level, the mutual information between two or more first events including a criterion related to the expression of the gene according to this embodiment and a second event including a criterion related to the expression of the candidate gene may be calculated to judge whether or not the gene is an immune checkpoint.

[0254] (5) Confidence interval calculation process (S0106) When the method of the present invention evaluates the mutual information (E0101), it may further include a confidence interval calculation step (S0106) of calculating a confidence interval of the mutual information after the mutual information calculation step (S0104). This step allows the accuracy of the mutual information calculated in the mutual information calculation step (S0104) to be evaluated.

[0255] For the calculation, any method known in the art can be used. The method is not particularly limited, but for example, the distribution of the maximum value of samples extracted from an exponential distribution can be used.

[0256] The width of the confidence interval is not particularly limited and may be, for example, 90%, 95%, 99%, or 99.5%.

[0257] Specifically, for example, the 95% confidence interval of the mutual information can be calculated by the following formula. It is generally known that as the values ​​of m and n increase, the confidence interval narrows and the accuracy of the mutual information increases.

[0258]

number

[0259] The immune checkpoint assessment step (S0105) may include an integration step, in which mutual information between one or more different contingency tables is integrated by meta-analysis.

[0260] According to meta-analysis, multiple mutual information calculated based on data obtained under different conditions, such as data on different types of specimens, can be integrated and analyzed.

[0261] There are various methods known for synthesis using meta-analysis. For example, Rosenthal, R. (1984). Meta-analytic procedures for social research. Beverly Hills, CA: Sage uses a method that synthesizes p-values ​​obtained under multiple different conditions to arrive at a p overall A method for calculating the mutual information is explained. In addition, the mutual information of one or more different contingency tables can be compared and the contingency tables can be ranked. For example, by unifying one event in all contingency tables, events that are highly interdependent with that event can be found.

[0262] Usually, the indices to which meta-analysis can be applied are limited to those in the field of probability theory, so meta-analysis cannot be applied to mutual information included in information theory. However, the present inventors have found that, even though mutual information is an index of information theory, meta-analysis, which is an analysis method of probability theory, can be applied as described below. The details are described below.

[0263] As described above, the calculation of the individual mutual information between a particular value of the first random variable and a particular value of the second random variable can be performed according to the following formula:

[0264]

number

[0265] More specifically, this can be done by the following method: Let k be the value selected for the first random variable, and l be the value selected for the second random variable, and determine the relative frequency (frequency divided by the total sample size N) in the data set according to the following 2 x 2 contingency table.

[0266] [Table 3]

[0267] Using the relative frequencies X0 to X3 in Table 3 above, the individual mutual information MI kl Calculate.

[0268]

number

[0269] The inventors have found that the individual mutual information MI kl is Fisher's exact probability P using the total sample size N. Fkl is related by the following formula. This approximation formula can be treated as an equality when the total sample size N is sufficiently large.

[0270] Here, a sufficiently large total number of samples N refers to, for example, 20 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, 5,000 or more, or 10,000 or more. In addition, for example, the average number of samples per cell may be 1 or more, 5 or more, 10 or more, 50 or more, 100 or more, 500 or more, 1,000 or more, or 5,000 or more.

[0271]

number

[0272] This is shown below. Individual mutual information MI kl is expressed by the above formula using X0 to X3. On the other hand, Fisher's exact probability P Fkl The main term P mainwould be as follows:

[0273]

number

[0274]

number

[0275] Using Stirling’s formula, log(N!) is approximated by N×log(N)-N, and then the relation X0+X1+X2+X3=1 is applied to obtain the above approximation formula as follows:

[0276]

number

[0277] In this way, the mutual information MI kl is the total sample size N and Fisher's exact probability P Fkl It has been shown that there is a certain relationship between the Fisher exact probability P Fkl can be integrated using meta-analysis. Therefore, as described above, by using the above formula and meta-analysis, p-values ​​obtained under multiple different study conditions can be integrated, and mutual information can be calculated based on data sets obtained under multiple different study conditions (i.e., multiple contingency tables).

[0278] When the total number of samples N is sufficiently large, a specific cell is selected using the frequency of the cell as an index, and in the individual mutual information determination step (S0103), the individual mutual information is determined for only that cell. Based on the individual mutual information calculated there, the mutual information is calculated using the frontier mutual information in the mutual information calculation step (S0104), thereby making it possible to carry out the method of the present invention more simply.

[0279] Here, a sufficiently large total number of samples N refers to, for example, 100 or more, 500 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more. Also, for example, the average number of samples per cell may be 10 or more, 50 or more, 100 or more, 1,000 or more, 5,000 or more, 10,000 or more, 50,000 or more, or 100,000 or more.

[0280] The selection of a particular cell is performed using the frequency or simultaneous relative frequency of the cell in the data set as an index. front In this case, multiple cells may be selected. In that case, the individual mutual information MI kl Calculate the MI with the largest value. kl The cells showing MI front The selection may be made based on the deviation from the average. For example, the simultaneous relative frequency of one cell may be compared with the simultaneous relative frequency of another cell, and a cell whose simultaneous relative frequency is more deviated from the average may be selected. In this case, for example, the selection may be made based on the deviation p(x,y)-1 / (m×n) of the simultaneous relative frequency p(x,y) from the average value. In this case, for example, the cell with the largest absolute value of the deviation may be selected.

[0281] In addition, the cell with the maximum or minimum frequency or simultaneous relative frequency is MI. front Therefore, the selection may be performed based on the maximum or minimum frequency or joint relative frequency. For example, both the cell with the maximum frequency and the cell with the minimum frequency are selected, and the MI is calculated. kl By comparing the MI front A further selection may be made of cells that can provide

[0282] In addition, when N<(m×n), cells with a frequency of 0 will appear. In any method of the present invention, cells with a frequency of 0 may be included or excluded from the calculation. When these cells are included, for example, in the case of MI klThe maximum value of may identify either the cell with the largest p(x,y) or the cell with the smallest p(x,y) (i.e., the cell with p(x,y)=0), or both. In this case, the cell with the largest p(x,y) is the MI. front The content of the sufficiently large total number of samples N is as described above in the section on the integration step.

[0283] The findings that the present inventors have found and that serve as the basis for this are given below.

[0284] When the joint distribution density function p(x,y) of random variables x and y is a uniform distribution, its mean (Mean) is 1 / (m×n) and its standard deviation (SD) is the square root of the variance (1 / (N×m×n). The MI where p(x,y) is Mean-X×SD kl MI is the mean + X × SD of the values kl The ratio (Ratio(XSD)) to the value of is expressed as follows:

[0285]

number

[0286] In the formula, each σ represents the following:

[0287]

number

[0288] Then, when N is sufficiently large, the following holds:

[0289]

number

[0290] Thus, when N is sufficiently large, the mutual information MI kl is nearly symmetrical on either side of the average value 1 / mn.

[0291] Therefore, if N is large enough, the mutual information MI kl is assumed to be symmetric around the mean value 1 / mn, and p(X k ,Y l ) is higher or lower, and the MI kl This allows for a more efficient calculation of MI. front This can lead to:

[0292] In this specification, various formulas shown as formulas that can be used in the method may be subjected to any processing as long as it does not impair the purpose achieved by the method. For example, a constant term may be added to the formula, or any coefficient or exponent may be added and used, or a root of the formula may be used.

[0293] Even when calculating mutual information for events including a huge number of criteria, the mutual information between events can be easily and accurately calculated with a small amount of calculation, and the interdependence between objects can be accurately identified. Therefore, according to the present invention, a large-scale analysis can be performed using the entire data set consisting of a wide variety of data, so that the method of the present invention is suitable for implementation by a computer. In the present invention, any of the above methods may be performed by a calculation device including a computer, or by a computer program for executing the method.

[0294] 8. Program A seventh aspect of the present invention is a processing program for screening genes associated with immune checkpoints based on mutual information.

[0295] As shown in Figure 1, this program is configured to cause a computer to execute a sample number counting step (S0101), a relative frequency counting step (S0102), an individual mutual information determining step (S0103), a mutual information calculating step (S0104), and an immune checkpoint assessment step (S0105).

[0296] The details of each step executed by the program of the present invention are as described in the sixth aspect.

[0297] This program is configured to execute a conversion step (S0110) and a calculation step (S0111) as shown in FIG. 2, as an individual mutual information determination step (S0103).

[0298] Moreover, this program is configured to execute a direct calculation step (S0112) or execute a frontier mutual information selection step (S0113) and a frontier mutual information utilization step (S0114) as a mutual information calculation step (S0104) as shown in FIG. 3. Here, whether to execute the frontier mutual information selection step (S0113) and the frontier mutual information utilization step (S0114) is selected depending on whether or not the frontier mutual information is utilized (E0102). The necessity of utilizing the frontier mutual information depends on, for example, whether or not the user requests the utilization of the frontier mutual information. Alternatively, it may be configured to be selected according to the type of data to be processed. For example, it may be configured to select the utilization of the frontier mutual information when n and m of the data to be processed are sufficiently large.

[0299] Furthermore, this program is configured to execute an event selection step (S0115), a function compilation step (S0116), and a judgment step (S0117) as shown in FIG.

[0300] The program of the present invention may be configured to ask the user for further instructions. For example, the program may be configured to ask the user for instructions as to whether or not to evaluate the mutual information (E0101), and if the evaluation is to be performed, a confidence interval calculation step (S0106) for calculating a confidence interval for the mutual information may be executed after the mutual information calculation step (S0104).

[0301] There are no particular limitations on the timing and number of times instructions are requested from the user. For example, all instructions may be requested at once at the beginning, or instructions may be requested multiple times each time an instruction is required.

[0302] Also, for example, the program of the present invention may be configured to be able to omit the sample number counting step (S0101) according to the type of data to be processed or according to a user request. For example, when the sum of the frequencies of the data to be processed is 1, the sample number counting step (S0101) can be omitted.

[0303] The program of the present invention can also be configured to perform all steps, request instructions from the user, and output only the requested information when the device has sufficient performance, etc. In this case, for example, it is also possible to perform all steps and output all information without requesting instructions from the user.

[0304] The computer program may be stored in a recording medium, for example, a CD-ROM, a DVD, or other optical reading means, a semiconductor memory, a flexible disk, a hard disk, or other information storage means. EXAMPLES

[0305] <Example 1: Search for genes whose increased expression contributes to cancer prognosis> (the purpose) Using in silico analysis based on gene mutation and gene expression data of cancer patients, we searched for genes whose increased expression levels lead to a worsening of cancer prognosis.

[0306] (method) We downloaded data from The Cancer Genome Atlas (TCGA) (http: / / cancergenome.nih.gov / ) of the United States for a total of 6,664 cancer patient samples, which contained information on approximately 18,000 genes.

[0307] Using data from lung adenocarcinoma patients (sample size: 477), we extracted genes that met all of the following conditions: (1) mRNA expression levels are within the top 10% of all genes; (2) The frequency of mutations in that gene is 3% or more among all patients in the data; (3) For survival curves, the p-value of the log-rank test comparing a group of mutations including multiple changes in that gene with individuals without mutations is sufficiently small; (4) For the genetic alterations included in the mutation group giving the lowest p-value in the log-rank test, the proportion of patients with those alterations relative to the total number of patients in the data set must be 15% or more in total.

[0308] In addition, the relative mortality rate was calculated by taking the mortality rate of control patients with normal expression of the kynureninase gene as 100%.

[0309] (result) As a result of the analysis, the kynureninase gene was extracted as the gene with the lowest p-value in the log-rank test. Figure 5 shows a survival curve indicating the relationship between the expression level of kynureninase and the survival rate of lung adenocarcinoma patients. Compared to control individuals ("a" in Figure 5), individuals with significantly high expression of the kynureninase gene (more than two standard deviations above the average or where gene amplification has occurred) ("b" in Figure 5) had a significantly worse prognosis, with a p-value of 9.335E-5. Furthermore, the relative mortality rate of individuals with significantly high expression of the kynureninase gene was significantly lower than that of control individuals, at 21.38%.

[0310] <Example 2: Relationship between expression level of kynureninase gene and prognosis of various cancers> (the purpose) We investigated the effect of kynureninase gene expression levels on cancer prognosis in various cancers.

[0311] (method) The survival curves of the following two groups of patients with respect to the kynureninase gene for various cancers were compared using the log-rank test: Pancreatic cancer patients (sample size: 127); individuals with expression levels 2x higher than the mean or with gene amplification, and other control individuals Breast cancer patients (sample size: 1,043); individuals with expression levels greater than 1 standard deviation above the mean or increased DNA copy number, and other control individuals Renal clear cell carcinoma patients (sample size: 521); individuals with expression levels 2x or higher than the mean or with gene amplification, and other control individuals Hepatocellular carcinoma patients (sample size: 257); individuals with expression levels higher than the mean by 0.5 times the standard deviation, gene amplification, or increased DNA copy number, and other control individuals Gastric cancer patients (sample size: 442); individuals with expression levels higher than the mean by 1 times the standard deviation, and other control individuals - Patients with uterine carcinosarcoma (sample size: 57); individuals with expression levels lower than the mean by more than one standard deviation or with deletion of both alleles, and other control individuals.

[0312] As in Example 1, the relative mortality rate was calculated by taking the mortality rate of control individuals with normal expression of the kynureninase gene as 100%.

[0313] (result) Figure 6 shows survival curves indicating the relationship between the expression level of kynureninase and the survival rate of patients with various cancers. Compared with individuals with normal expression of the kynureninase gene ("a" in Figure 6), individuals with high expression of the kynureninase gene ("b" in Figure 5) had a significantly worse prognosis. Individuals with low expression of the kynureninase gene ("c" in Figure 5) had a significantly better prognosis. The p-values ​​and relative mortality rates for various cancers are shown in Table 4.

[0314] [Table 4]

[0315] <Example 3: Identification of signal transduction pathways associated with kynureninase genes> (the purpose) The signal transduction pathways in which the kynureninase gene is involved were investigated.

[0316] (method) We downloaded data from the US TCGA for a total of 6,664 cancer patient samples, which contained information on approximately 18,000 genes.

[0317] First, the mRNA expression data for the kynureninase gene in each cancer patient was combined with DNA copy number and mutations to obtain a one-dimensional random variable, and similar one-dimensional random variables were created for approximately 18,000 other genes.

[0318] The individual relative frequencies of the one-dimensional random variable values ​​for the kynureninase gene and the random variable values ​​for each of the other genes thus created were tabulated in a 47 x 47 contingency table (including a total of 47 criteria for the kynureninase gene and each of the other genes, combining 7 criteria for the range of mRNA expression levels from Mean-3SD to Mean+3SD divided into 7 equal parts, 5 criteria for the range of DNA copy number values ​​from 0 to 4, and 2 criteria for the presence or absence of mutations). In each contingency table, the cell with the maximum relative frequency was selected, and the individual mutual information of that cell was calculated using MI. front This MI front Using mnMI front The overall mutual information was calculated according to the formula / log(mn-1). The mutual information obtained from each contingency table was sorted in order of magnitude, and a list of genes with the largest mutual information with the kynureninase gene was created. Since the values ​​of m and n were common to each contingency table, this list was used as the MI front It was the same as the list sorted by size.

[0319] The top 2,000 genes with the highest calculated mutual information values ​​with the kynureninase gene and the kynureninase gene, a total of 2,001 genes, were subjected to Canonical Pathways analysis using Qiagen's Ingenuity Pathway Analysis (IPA) (registered trademark) analysis software.

[0320] For the gene that had the largest mutual information value with the kynureninase gene, a list of genes with large mutual information with that gene was created in the same manner as for the kynureninase gene.

[0321] (result) The top results for the canonical pathways in IPA are shown in Figure 7 along with the p-values ​​and activation z-scores obtained from IPA.

[0322] It was found that signal transduction pathways involved in immune checkpoints, such as the Th1 / Th2 activation pathway, cytokine signal transduction pathway, T cell differentiation pathway, NK cell signal transduction pathway, autoimmune pathway, inflammatory pathway, and PD1 pathway, were significantly affected by the expression of the kynureninase gene. The goodness of fit-Log(p-value) values ​​of these pathways all exceeded 20, and the significance was extremely high. In addition, the activation z-scores of each pathway were generally extremely high, ranging from 3.28 to 7.04. Surprisingly, the activation z-score of the PDL1-PD1 pathway was negative, at -3.395 (hatched in Figure 7). This indicates that the kynureninase gene regulates the immune checkpoint mechanism based on a pathway independent of the PD1 pathway.

[0323] In addition, the gene with the highest mutual information value with the kynureninase gene was the hepatitis A virus cellular receptor 2 (HAVCR2) gene. This gene is an immune checkpoint gene that is beginning to attract attention as a next-generation therapeutic target. When comparing the lists of the top 100 genes with the highest mutual information created for the kynureninase gene and the HAVCR2 gene, 72 genes were found to be in common. This strongly suggests that the two genes function in concert.

[0324] These results indicate that the kynureninase gene may be a promising therapeutic target for regulating immune checkpoints.

[0325] Example 4: Relationship between kynureninase activity and prognosis of cancer patients (the purpose) We investigated the effect of kynureninase activity on the prognosis of cancer patients.

[0326] (method) We analyzed the change in survival curve according to kynureninase activity in the data of 10,803 cancer patients obtained from the US TCGA. The data were divided into two groups based on the following two criteria regarding kynureninase activity, and the survival curves were compared using the log-rank test: - Individuals with expression levels 2x or more higher than the mean or increased DNA copy number (1,447 cases) and other control individuals (9,356 cases) - Individuals with mutations that could reduce activity (134 cases) and other control individuals (10,669 cases).

[0327] The fact that the mutation may reduce activity was determined based on calculation results using Sift and Polyphen-2.

[0328] Similarly, we investigated the prognostic impact of mutations that could change the activity of other genes. The abbreviations of the genes and the number of individuals in each group are shown in Table 5.

[0329] [Table 5]

[0330] (result) The results are shown in Figures 8 to 12.

[0331] Consistent with the results previously seen for various cancers, patients with high expression of the kynureninase gene, i.e., patients with overall elevated kynureninase activity ("b" in Figure 8A), had a significantly worse prognosis than control individuals ("a" in Figure 8A), with a p-value of 5.797E-4. The median survival time was approximately 82.9 months for control individuals, whereas it was approximately 57.3 months for individuals with elevated kynureninase activity. On the other hand, patients with mutations in the kynureninase gene and reduced kynureninase activity ("c" in Figure 8B) had a significantly better prognosis than control individuals ("d" in Figure 8B), with a p-value of 6.422E-3. The median survival time was approximately 78.4 months for control individuals, whereas it was approximately 151.3 months for individuals with reduced kynureninase activity, a significant increase in survival time.

[0332] This suggests that reducing kynureninase activity due to mutations in the gene may improve patient prognosis in a wide range of cancers.

[0333] Similar trends were observed for mutations that could reduce activity in other genes in the tryptophan metabolic pathway (Figure 9), but none of them were significantly associated with a poor prognosis (IDO1: p = 0.537; IDO2: p = 0.888; TDO2: p = 0.624). This suggests that kynureninase is a promising protein among other proteins in the tryptophan metabolic pathway as a therapeutic target for cancer.

[0334] Thus, cases in which gene mutations improve prognosis are not common in cancer-related genes. In fact, when analysis was performed using a similar method, as shown in Figure 10, mutations that could change activity in both cancer genes (Figures 10A and B) and tumor suppressor genes (Figures 10C and D) significantly worsened prognosis (EGFR: p = 1.44E-15; KRAS: p = 1.765E-3; TP53: p = 0.0; CDKN2A: p = 8.760E-5). This shows that the kynureninase gene is heterogeneous among cancer-related genes.

[0335] Furthermore, when a similar analysis was performed on known immune checkpoint genes, it was found that, like the kynureninase gene, mutations that can reduce the activity of immune checkpoint genes tend to improve the prognosis of cancer patients (Figures 11 and 12). This is a novel finding, as no reports from this perspective have been published to date. Furthermore, it was suggested that the kynureninase gene has a greater impact on the prognosis of cancer patients than other immune checkpoint genes (PD-1: p=0.210; PD-L1: p=0.0625; PD-L2: p=0.0138; HAVCR2: p=5.931E-3; LAG3: p=0.0627).

[0336] <Example 5: Relationship between SNPs in the kynureninase gene and prognosis of cancer patients> (the purpose) We investigated the type of SNPs in the kynureninase gene that affect the prognosis of cancer patients.

[0337] (method) We analyzed the change in survival curve according to the type of kynureninase mutation in data from TCGA in the United States, which included a total of 9,003 cancer patient samples without gene amplification of the kynureninase gene. The data were divided into groups based on the following two criteria regarding SNPs in the kynureninase gene, and the survival curves were compared using the log-rank test: -Individuals with amino acid substitutions based on SNPs (47 cases) and other control individuals (8,956 cases) -Individuals with SNP-based structure-disruptive mutations (28 cases), individuals with SNP-based non-structure-disruptive mutations (19 cases), and the rest of the control individuals (8,956 cases).

[0338] Whether a mutation was structure-disruptive was determined based on the known algorithms SIFT and PolyPhen-2.

[0339] (result) The results are shown in Figures 13-1 and 14-1.

[0340] Patients with SNP-based mutations in the kynureninase gene ("a" in Figure 13-1) had a better prognosis than control individuals ("b" in Figure 13-1). Although this trend was not significant (p=0.061), it was still noticeable.

[0341] Furthermore, when SNP-based mutations were classified as structurally disruptive or not (Figure 14-1), individuals with structurally disruptive mutations ("a" in Figure 14-1) had a significantly better prognosis than control individuals ("c" in Figure 14-1) (p = 0.008). On the other hand, there was no significant difference in survival curves between individuals with non-structurally disruptive mutations ("b" in Figure 14-1) and control individuals ("c" in Figure 14-1) (p = 0.561). Thus, even for SNP-based mutations, there was a significant difference in prognosis depending on whether the mutation was structurally disruptive or not (p = 0.036). Mutations classified as structurally disruptive in this analysis included E89K mutation (sequence number 3), I208N mutation (sequence number 4), V217M mutation (sequence number 5), G73E mutation (sequence number 6), W272S mutation (sequence number 7), G287D mutation (sequence number 8), and other mutations.

[0342] These results suggested that the improved prognosis due to kynureninase gene mutations is based on the loss of kynureninase function.

[0343] <Example 6: Prediction of immune checkpoint regulators based on kynureninase gene and PD1 pathway> (the purpose) In silico analysis verified the predictive potential of immune checkpoint regulators based on high mutual information with kynureninase gene expression levels and negative correlation with PD1 pathway activity.

[0344] (method) In accordance with Example 3, the mutual information value with the expression level of the kynureninase gene was calculated. As a result, the HAVCR2 gene was found to have the highest mutual information value, with a -Log(p-value) value of 30.38.

[0345] Next, this information on the expression level of the HAVCR2 gene was used instead of information on the expression level of the kynureninase gene, and the mutual information value with the expression level of the HAVCR2 gene was calculated and standard pathway analysis was performed in accordance with Example 3.

[0346] (result) The top results for the canonical pathways in IPA are shown in FIG. 15 along with the p-values ​​and activation z-scores obtained from IPA.

[0347] It was found that signaling pathways involved in immune checkpoints, such as the Th1 / Th2 activation pathway, cytokine signaling pathway, T cell differentiation pathway, NK cell signaling pathway, autoimmune pathway, inflammatory pathway, and PD1 pathway, were significantly affected by the expression of the HAVCR2 gene. The goodness-Log(p-value) values ​​of these pathways all exceeded 20, and the significance was extremely high. In addition, the activation z-scores of each pathway were generally very high, ranging from 5.33 to 12.38. As expected, the activation z-score of the PDL1-PD1 pathway was negative, at -3.920 (Figure 15).

[0348] The HAVCR2 gene has been reported to regulate immune checkpoints.

[0349] This confirmed the usefulness of searching for immune checkpoint regulators using the correlation between the expression level of the kynureninase gene and the activity of the PD1 pathway as indicators.

[0350] <Example 7: Relationship between HAVCR2 activity and prognosis of cancer patients> (the purpose) We investigated the effect of HAVCR2 activity on the prognosis of cancer patients.

[0351] (method) As in Example 4, the change in survival curve according to HAVCR2 activity was analyzed in data of 10,803 cancer patients in total samples obtained from the US TCGA. The data were divided into two groups based on the following criteria regarding HAVCR2 activity, and the survival curves were compared by the log-rank test: - 68 individuals with mutations that could reduce activity, and 10,735 control individuals without such mutations.

[0352] The fact that the mutation may reduce activity was determined based on calculation results using Sift and Polyphen-2.

[0353] (result) The results are shown in Figure 16.

[0354] Consistent with the results seen for the kynureninase gene, patients with mutations in the HAVCR2 gene and reduced kynureninase activity ("a" in FIG. 16) had a significantly better prognosis than control individuals ("b" in FIG. 16), with a p-value of 5.931E-3. The median survival time was approximately 78.9 months for control individuals, whereas for individuals with reduced HAVCR2 activity, 50% of deaths were not observed during the 160-month observation period, resulting in a significantly longer survival time.

[0355] This suggests that a decrease in the activity of the HAVCR2 gene due to mutations in the gene, similar to that of the kynureninase gene, may improve the prognosis of cancer patients.

[0356] <Example 8: Relationship between SNPs in the HAVCR2 gene and prognosis of cancer patients> (the purpose) We investigated the type of SNPs in the HAVCR2 gene that affect the prognosis of cancer patients.

[0357] (method) The analysis was performed in the same manner as in Example 5. The data of 10,829 cancer patient samples without gene amplification of the HAVCR2 gene obtained from the US TCGA were used to analyze the change in survival curve according to the type of HAVCR2 mutation. The data were divided into groups according to the following two criteria regarding SNPs of the HAVCR2 gene, and the survival curves were compared by the log-rank test: -Individuals with amino acid substitutions based on SNPs (40 cases) and control individuals without such substitutions (10,789 cases) Ten individuals with SNP-based disruptive mutations, 30 individuals with SNP-based non-disruptive mutations, and 10,789 other control individuals.

[0358] Whether a mutation was structure-disruptive was determined based on the known algorithms SIFT and PolyPhen-2.

[0359] (result) The results are shown in Figures 17 and 18.

[0360] Patients with SNP-based mutations in the HAVCR2 gene ("a" in Figure 17) had a significantly better prognosis than control individuals ("b" in Figure 17) (p=0.028).

[0361] Furthermore, when SNP-based mutations were classified as being structurally disruptive or not (Figure 18), individuals with structurally disruptive mutations ("a" in Figure 18) had a significantly better prognosis than control individuals ("c" in Figure 18) (p=0.035), with a 7-year survival rate of 100%. On the other hand, there was no significant difference in survival curves between individuals with non-structurally disruptive mutations ("b" in Figure 18) and control individuals ("c" in Figure 18) (p=0.13). Thus, even for SNP-based mutations, there was a significant tendency for prognosis to differ depending on whether the mutation was structurally disruptive or not (p=0.084).

[0362] These results suggest that the HAVCR2 gene isolated in Example 6 has an effect on the prognosis of cancer patients similar to that of the kynureninase gene. This suggests that a gene that affects cancer prognosis similarly to the kynureninase gene can be isolated by analysis based on a positive correlation with the activity of immune checkpoint regulators such as kynureninase and a negative correlation with the activity of the PD1 pathway.

Claims

1. Immune checkpoint inhibitors, including kynureninase inhibitors.

2. The immune checkpoint inhibitor of claim 1, wherein the kynureninase inhibitor comprises any one or more compounds selected from the group consisting of PF-04859989, BFF-122, BFF-816, S-ESBA, NS-1502, 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)benzenesulfonamide, 4-(5,6-dichloro-1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)ethyl benzenesulfonamide, glycyrrhizic acid, glycyrrhetinic acid, carbenoxolone, estradiol disulfate, 4-(3-hydroxyphenyl)homoserine, and analogs and derivatives thereof, and salts thereof.

3. A pharmaceutical composition comprising the immune checkpoint inhibitor of claim 1 or 2.

4. An anti-cancer drug resistance suppressant, including a kynureninase inhibitor.

5. A pharmaceutical composition comprising the anticancer drug resistance suppressant according to claim 4.

6. A method for predicting the prognosis of a cancer patient, comprising: determining the activity of kynureninase in cancer tissue derived from the patient; The method, wherein the activity is lower than the activity of wild-type kynureninase, indicating a good prognosis for the patient.

7. The method of claim 6, wherein the activity of the kynureninase is determined by analysis of SNPs in the kynureninase gene or its transcription product.

8. A method for predicting sensitivity of a cancer patient to an anticancer drug, comprising: determining the activity of kynureninase in cancer tissue derived from the patient; The method, wherein the activity is lower than the activity of wild-type kynureninase, indicating that the patient has high sensitivity to an anti-cancer drug.

9. A biomarker for predicting the prognosis of a cancer patient, comprising a mutant kynureninase having an amino acid sequence selected from the group consisting of SEQ ID NOs: 3 to 5, or a fragment thereof, or a transcription product of the kynureninase gene encoding same, or a nucleic acid fragment thereof.

10. A biomarker for predicting sensitivity to an anticancer drug, comprising a mutant kynureninase having an amino acid sequence selected from the group consisting of SEQ ID NOs: 3 to 5 or a fragment thereof, or a transcription product of the kynureninase gene encoding the same or a nucleic acid fragment thereof.

11. A kit comprising a reagent for measuring the biomarker according to claim 9 or 10.

12. A method for screening genes related to immune checkpoints based on mutual information, comprising: the mutual information being the mutual information of two or more first events including a measure related to the expression of a gene and a second event including a measure related to the expression of a candidate gene; The two or more first events include an event including a criterion for expression of a PD1 pathway-related gene, and an event including a criterion for expression of an immune checkpoint-related gene; the second event comprises a measure of expression of a candidate gene; a sample number counting step of counting the number of samples in all cells, where the number of criteria included in the first event is m and the number of criteria included in the second event is n, for a cell consisting of one criterion selected from the first event and one criterion selected from the second event, into an m×n frequency distribution table; a relative frequency counting step of calculating simultaneous relative frequencies from the number of samples in all cells of the frequency distribution table and counting the frequencies into an m×n contingency table; an individual mutual information determination step of determining individual mutual information for each cell based on the simultaneous relative frequency for all cells; a mutual information calculation step of calculating mutual information of the first event and the second event from the individual mutual information; and An immune checkpoint determination step of determining that the candidate gene is an immune checkpoint-related gene based on the calculated mutual information. Including, m and n are each an integer of 2 or more, and at least one of them is 3 or more; The simultaneous relative frequency is a ratio of the number of samples corresponding to a certain cell to the total number of samples, The individual mutual information determining step includes: For each cell, (a) transforming the m×n contingency table into a 2×2 contingency table containing binary data for a criterion in each of the first and second events based on the joint relative frequency of a cell, the marginal relative frequency of the cell for the criterion in a first event, and the marginal relative frequency of the criterion in a second event; and (b) calculating the strength of the criterion correlation between the first and second events as the individual mutual information from the 2×2 contingency table using multidimensional mutual information, Fisher's exact probability, or hypergeometric distribution; Including, The mutual information calculation step includes: (i) calculating the mutual information from the individual mutual information according to formula (I) [0010] (In the formula, MI represents the mutual information, and MI ｋｌ indicates the individual mutual information) or (ii) a frontier mutual information selection step of comparing the individual mutual information with each other and selecting the individual mutual information with the largest absolute value as the frontier mutual information; and a frontier mutual information utilization step of calculating the mutual information based on the frontier mutual information; The calculation of the mutual information in the frontier mutual information utilization step is carried out according to the formula (II). [0025] (In the formula, MI represents the mutual information, and MI ｆｒｏｎｔ indicates the frontier mutual information) It is carried out by The immune checkpoint determination step includes: an event selection step of selecting a predetermined number of events from the two or more first events based on the magnitude of the mutual information value; a function tabulation step of tabulating the functions of genes related to the selected events; a determination step of determining that the candidate gene is an immune checkpoint-related gene when the collected functions include an activation function of one or more signal transduction pathways related to an immune checkpoint and an inhibition function of one or more immune checkpoints via the PD1 pathway; Including, The method.

13. The method according to claim 12, wherein the immune checkpoint determination step is performed when the mutual information is equal to or greater than a predetermined level for the expression levels of one or more genes related to the immune checkpoint.

14. The method of claim 13, wherein the one or more genes related to immune checkpoints include a kynureninase gene.

15. The method according to any one of claims 12 to 14, wherein the calculation using the multidimensional mutual information is performed based on the following formula (III): [0030] (In the formula, M.I. ｋｌ denotes the individual mutual information of a cell consisting of the kth criterion of the first event and the lth criterion of the second event, X ｋ’ denotes the k'th criterion for the first event in the 2 x 2 contingency table, Y ｌ’ denotes the l′-th criterion for the second event in the 2×2 contingency table, p(X ｋ’ , Y ｌ’ ) is the standard X ｋ’ and criterion Y ｌ’ indicates the simultaneous relative frequency of cells consisting of ｋ’ ) is the standard X ｋ’ Show the marginal relative frequencies for p(Y ｌ’ ) is the standard Y ｌ’ (shows marginal relative frequencies for

16. The method according to any one of claims 12 to 14, wherein the total number of samples is 1,000 or more.

17. A processing program for screening genes related to immune checkpoints based on mutual information, the mutual information being the mutual information of two or more first events including a measure related to the expression of a gene and a second event including a measure related to the expression of a candidate gene; The two or more first events include an event including a criterion for expression of a PD1 pathway-related gene, and an event including a criterion for expression of an immune checkpoint-related gene; the second event comprises a measure of expression of a candidate gene; On the computer, a sample number counting step of counting the number of samples in all cells, where the number of criteria included in the first event is m and the number of criteria included in the second event is n, for a cell consisting of one criterion selected from the first event and one criterion selected from the second event, into an m×n frequency distribution table; a relative frequency counting step of calculating simultaneous relative frequencies from the number of samples in all cells of the frequency distribution table and counting the frequencies into an m×n contingency table; an individual mutual information determination step of determining individual mutual information for each cell based on the simultaneous relative frequency for all cells; a mutual information calculation step of calculating mutual information of the first event and the second event from the individual mutual information; and An immune checkpoint determination step of determining that the candidate gene is an immune checkpoint-related gene based on the calculated mutual information. configured to execute m and n are each an integer of 2 or more, and at least one of them is 3 or more; The simultaneous relative frequency is a ratio of the number of samples corresponding to a certain cell to the total number of samples, The individual mutual information determining step includes: For each cell, (a) transforming the m×n contingency table into a 2×2 contingency table containing binary data for a criterion in each of the first and second events based on the joint relative frequency of a cell, the marginal relative frequency of the cell for the criterion in a first event, and the marginal relative frequency of the criterion in a second event; and (b) calculating the strength of the criterion correlation between the first and second events as the individual mutual information from the 2×2 contingency table using multidimensional mutual information, Fisher's exact probability, or hypergeometric distribution; Including, The mutual information calculation step includes: (i) calculating the mutual information from the individual mutual information according to formula (I) [0045] (In the formula, MI represents the mutual information, and MI ｋｌ indicates the individual mutual information) or (ii) a frontier mutual information selection step of comparing the individual mutual information with each other and selecting the individual mutual information with the largest absolute value as the frontier mutual information; and a frontier mutual information utilization step of calculating the mutual information based on the frontier mutual information; The calculation of the mutual information in the frontier mutual information utilization step is carried out according to the formula (II). [0050] (In the formula, MI represents the mutual information, and MI ｆｒｏｎｔ indicates the frontier mutual information) It is carried out by The immune checkpoint determination step includes: an event selection step of selecting a predetermined number of events from the two or more first events based on the magnitude of the mutual information value; a function tabulation step of tabulating the functions of genes related to the selected events; a determination step of determining that the candidate gene is an immune checkpoint-related gene when the collected functions include an activation function of one or more signal transduction pathways related to an immune checkpoint and an inhibition function of one or more immune checkpoints via the PD1 pathway; Including, The program.

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