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WO2022059785A1 - Procédé pour prédire la réponse à un inhibiteur de point de contrôle immunitaire - Google Patents

Procédé pour prédire la réponse à un inhibiteur de point de contrôle immunitaire Download PDF

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WO2022059785A1
WO2022059785A1 PCT/JP2021/034399 JP2021034399W WO2022059785A1 WO 2022059785 A1 WO2022059785 A1 WO 2022059785A1 JP 2021034399 W JP2021034399 W JP 2021034399W WO 2022059785 A1 WO2022059785 A1 WO 2022059785A1
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subject
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immune checkpoint
predetermined threshold
abundance
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Japanese (ja)
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優 砂川
亮 的場
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Non Profit Organization Japan Clinical Cancer Research Organization
Ono Pharmaceutical Co Ltd
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Non Profit Organization Japan Clinical Cancer Research Organization
Ono Pharmaceutical Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P37/00—Drugs for immunological or allergic disorders
    • A61P37/02—Immunomodulators
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C12Q1/06—Quantitative determination
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing

Definitions

  • the present disclosure relates to a method of predicting a response to an immune checkpoint inhibitor.
  • Immune checkpoint inhibitors are a new therapeutic method that cancels the immunosuppressive mechanism and activates the immune response to cancer.
  • anti-CTLA-4 cytotoxic T lymphocyte-associated antibody-
  • the antibody ipilimumab and the anti-PD-1 (programmed cell death-1) antibody nivolumab and pembrolizumab have been approved both inside and outside Japan and are used in cancer treatment.
  • Immune checkpoint inhibitors revolutionized cancer treatment and gave the gospel to cancer patients who were previously difficult to cure.
  • immune checkpoint inhibitors do not exert a uniform effect on all cancer patients, and from the data on progression-free survival, anti-PD-1 antibody, which is one of the immune checkpoint inhibitors, is used.
  • anti-PD-1 antibody which is one of the immune checkpoint inhibitors.
  • the anti-PD-1 antibody was effective for 1 year or more, almost no exacerbation of the disease was observed after that, and a state close to cure was obtained. This suggests that there are three different subgroups of clinical efficacy, such as "ineffective group”, “significantly effective group”, and "intermediate group”.
  • Patent Documents 1 to 5 a method for predicting a response to an immune checkpoint inhibitor has been sought, and a method using intestinal bacteria as a biomarker has been proposed.
  • An object of the present invention is to predict the response to an immune checkpoint inhibitor in a subject suffering from cancer.
  • the present inventors have surprisingly found that the abundance of a specific bacterial species or a specific genomic pathway that has not been known so far in the fecal or intestinal contents in a subject. Score, the abundance of a particular metabolite in blood, serum or plasma in a subject, the expression of a particular gene in blood, serum or plasma in a subject, or the presence or absence of a particular single nucleotide polymorphism (SNP) in a subject. By doing so, it was found that the response to the immune checkpoint inhibitor in the subject can be predicted, and the present invention has been completed.
  • SNP single nucleotide polymorphism
  • the present invention may include the following aspects.
  • [1] A method for predicting the response to an immune checkpoint inhibitor in a subject suffering from cancer.
  • (I) Abundance of microorganisms in the feces or intestinal contents of the subject;
  • (Iii) Abundance of metabolites in the blood, serum or plasma of the subject;
  • (Iv) Gene expression levels in the subject's blood, serum or plasma; and
  • SNPs Single nucleotide polymorphisms
  • the microorganism is one or more selected from the group consisting of the genus Geobacillus, the genus Gordonibacter, the genus Odoribacter, the genus Veillonella, the genus Corynebacterium, the genus Polychromonas, and the genus Arthrobacter.
  • the genomic pathways include bacterial invasion of epithelial cells, fatty acid metabolism, flagellar metabolism, fatty acid biosynthesis (Fatty acid metabolism), fatty acid biosynthesis (Fatty acid metabolism), and fatty acid biosynthesis (Fatty acid metabolism).
  • pathway peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism)
  • the metabolite is one or more selected metabolites from the group consisting of lactic acid, pyruvic acid, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid.
  • the gene is one or more selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway, TCR signal pathway-related gene, and BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7.
  • the SNP is one or a plurality of SNPs selected from the group consisting of the following rs numbers: rs2228145.1; rs71900199; and rs7185320.
  • step (1) is (ii) a step of determining a genomic pathway score in the fecal or intestinal content of the subject.
  • step (1) is (iii) a step of determining the abundance of metabolites in the blood, serum or plasma of the subject.
  • step (1) is (iii) a step of determining the abundance of metabolites in the blood, serum or plasma of the subject.
  • the abundance of the lactic acid, the pyruvic acid, the glucose, the glyceric acid, the octanic acid, the 2-hydroxybutyric acid and / or the 2-oxobutyric acid is higher than a predetermined threshold value, and / or.
  • step (1) is (iv) a step of determining the expression level of the gene in the blood, serum or plasma of the subject.
  • step (1) is (iv) a step of determining the expression level of the gene in the blood, serum or plasma of the subject.
  • the expression level of one or more genes selected from the group consisting of BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7 is higher than a predetermined threshold, immunity in the subject.
  • the expression level of the gene related to the pathway selected one or more from the group consisting of the MAPK pathway, the Type I IFN receptor complex pathway, and the gene related to the TCR signal pathway is lower than a predetermined threshold value.
  • the method of item 1 or 20 wherein the response to the checkpoint inhibitor is predicted to be poor.
  • the item (1) is a step of (v) determining the presence or absence of a single nucleotide polymorphism (SNP) of one or a plurality of genes selected from the group consisting of the IL6R and the NLRC5 in the subject. The method according to 1.
  • SNP single nucleotide polymorphism
  • the immune checkpoint inhibitors are CTLA-4, PD-1, PD-L1, PD-L2, LAG-3, TIM3, BTLA, B7H3, B7H4, 2B4, CD160, A2aR, KIR, VISTA, IDO1. , Arginase I, TIGIT, and the method of any one of items 1-26, which is an inhibitor of an immune checkpoint molecule selected from the group consisting of CD115.
  • the immune checkpoint inhibitor is an anti-PD-1 antibody.
  • [29] It is characterized in that it is administered to a subject (cancer patient) suffering from cancer whose response to an immune checkpoint inhibitor is determined (predicted) to be good by the method according to any one of items 1 to 28.
  • a cancer therapeutic agent containing an immune checkpoint inhibitor [30] (i) suffering from cancer in which the abundance of microorganisms of the genus Geobacillus, Gordonibacter, and / or Veillonella is higher than a predetermined threshold, and / or the abundance of microorganisms of the genus Odoribacter is lower than a predetermined threshold.
  • the cancer is characterized by administration to a subject (preferably, the abundance of Veillonella microorganisms is higher than a predetermined threshold, and / or the abundance of Geobacter microorganisms is lower than a predetermined threshold. It is characterized by administration to affected subjects); (Ii) Bacterial invasion of epithelial cells, fatty acid degradation, Grandellar assembly, PPAR signal pathway (PPAR signaling gene) Scores below a predetermined threshold and / or scores for fatty acid biosynthesis, Nucleotide metabolism and / or peptide glycan biosynthesis are higher than a predetermined threshold.
  • a subject suffering from cancer preferably, administration to a subject suffering from cancer having a score of bacterial invasion of epithelial cells in epithelial cells lower than a predetermined threshold. do
  • administration to a subject suffering from cancer preferably, administration to a subject suffering from cancer having a score of bacterial invasion of epithelial cells in epithelial cells lower than a predetermined threshold. do
  • the abundance of lactic acid, pyruvate, glucose, glyceric acid, octanoic acid, 2-hydroxybutyric acid and / or 2-oxobutyric acid is below a predetermined threshold, and / or the abundance of pipecholinic acid and / or citrulin.
  • the expression level of a gene associated with a plurality of selected pathways is higher than a predetermined threshold, and / or a gene one or more selected from the group consisting of BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7. It is characterized in that it is administered to a subject suffering from cancer whose expression level is higher than a predetermined threshold.
  • a cancer therapeutic agent containing an immune checkpoint inhibitor. Administered to subjects suffering from cancer in which the abundance of microorganisms of the genus Geobacillus, Gordonibacter, and / or Veillonella is higher than a predetermined threshold and / or the abundance of microorganisms of the genus Odoribacter is lower than a predetermined threshold.
  • the abundance of microorganisms of the genus Geobacillus is higher than a predetermined threshold, and / or the abundance of microorganisms of the genus Geobacter is lower than a predetermined threshold.
  • Bacterial invasion of epithelial cells, Fatty acid degradation, Flagellar assembly, PPAR signal pathway or PPAR signaline metabolism (PPAR sine) ) Is lower than a predetermined threshold, and / or a fatty acid biosynthesis, a Nucleotide metabolism and / or a peptide glycan biosynthesis score is a predetermined threshold.
  • a cancer therapeutic agent containing an immune checkpoint inhibitor [33] The abundance of lactic acid, pyruvate, glucose, glyceric acid, octanoic acid, 2-hydroxybutyric acid and / or 2-oxobutyric acid is below a predetermined threshold, and / or the abundance of pipecolic acid and / or citrulin.
  • a cancer therapeutic agent comprising an immune checkpoint inhibitor which is administered to a subject suffering from cancer having a glucose higher than a predetermined threshold.
  • a method of treating a subject suffering from cancer (1) below: (I) Abundance of microorganisms in the feces or intestinal contents of the subject; (Ii) Genomic pathway score in the fecal or intestinal contents of the subject; (Iii) Abundance of metabolites in the blood, serum or plasma of the subject; (Iv) Gene expression levels in the subject's blood, serum or plasma; and (v) Single nucleotide polymorphisms (SNPs) of one or more genes selected from the group consisting of IL6R and NLRC5 in the subject.
  • SNPs Single nucleotide polymorphisms
  • a step of determining the presence or absence of one or more selected values or SNPs from a group consisting of (2) A step of predicting a response to an immune checkpoint inhibitor in the subject using the value obtained in the step (1) or the presence or absence of an SNP as an index. (3) A step of administering the immune checkpoint inhibitor to the subject determined to have a good response to the immune checkpoint inhibitor and / or not to induce an adverse skin event.
  • the microorganism is one or more selected from the group consisting of the genus Geobacillus, the genus Gordonibacter, the genus Odoribacter, the genus Veillonella, the genus Corynebacterium, the genus Polychromonas, and the genus Arthrobacter.
  • the genomic pathways include bacterial invasion of epithelial cells, fatty acid metabolism, flagellar metabolism, fatty acid biosynthesis (Fatty acid metabolism), fatty acid biosynthesis (Fatty acid metabolism), and fatty acid biosynthesis (Fatty acid metabolism).
  • pathway peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism)
  • the metabolite is one or more selected metabolites from the group consisting of lactic acid, pyruvic acid, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid.
  • the gene is one or more selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway, TCR signal pathway-related gene, and BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7.
  • the SNP is one or a plurality of SNPs selected from the group consisting of the following rs numbers: rs2228145.1; rs71900199; and rs7185320. Method.
  • a system for determining the responsiveness to an immune checkpoint inhibitor in a subject having cancer including a storage unit, an input unit, a data processing unit, and an output unit.
  • the memory is (1-i) Abundance of microorganisms in feces or intestinal contents; (1-ii) Genomic pathway scores in fecal or intestinal contents; (1-iii) Abundance of metabolites in blood, serum or plasma; (1-iv) Gene expression levels in blood, serum or plasma; and (1-v) Single nucleotide polymorphisms (SNPs) of one or more selected genes from the group consisting of IL6R and NLRC5.
  • SNPs Single nucleotide polymorphisms
  • Memorize the cutoff value for determining responsiveness to one or more selected immune checkpoint inhibitors from the group consisting of The storage unit is from the input unit.
  • SNP Single nucleotide polymorphism
  • a value selected from one or more of the group consisting of SNPs or the presence or absence of SNP is input and stored in the storage unit.
  • the data processing unit compares the stored value or the presence or absence of the SNP with the cutoff value to determine the responsiveness to the immune checkpoint inhibitor in the subject.
  • the output unit outputs the determination result of responsiveness to the immune checkpoint inhibitor of cancer in the subject. It is a system characterized by that The microorganism is one or more selected from the group consisting of the genus Geobacillus, the genus Gordonibacter, the genus Odoribacter, the genus Veillonella, the genus Corynebacterium, the genus Polychromonas, and the genus Arthrobacter.
  • the genomic pathways include bacterial invasion of epithelial cells, fatty acid metabolism, flagellar metabolism, fatty acid biosynthesis (Fatty acid metabolism), fatty acid biosynthesis (Fatty acid metabolism), and fatty acid biosynthesis (Fatty acid metabolism).
  • pathway peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism)
  • the metabolite is one or more selected metabolites from the group consisting of lactic acid, pyruvic acid, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid.
  • the gene is one or more selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway, TCR signal pathway-related gene, and BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7.
  • the SNP is one or a plurality of SNPs selected from the group consisting of the following rs numbers: rs2228145.1; rs71900199; and rs7185320. system. [36] Further including an analysis and measurement unit The analysis and measurement unit determines the abundance of microorganisms and / or the score of the genomic pathway in the fecal or intestinal contents of the subject. The analysis and measurement unit determines the abundance of metabolites in the blood, serum or plasma of the subject.
  • the analysis and measurement unit determines the expression level of the gene in the blood, serum or plasma of the subject, and / or the analysis and measurement unit determines one or more genes selected from the group consisting of IL6R and NLRC5 in the subject. Determine the presence or absence of single nucleotide polymorphism (SNP), The abundance of the microorganism, the score of the genomic pathway, the abundance of the metabolite, the expression level of the gene, and / or the abundance of the microorganism determined by the analysis and measurement unit on behalf of or through the input unit.
  • SNP single nucleotide polymorphism
  • the present invention it becomes possible to predict the response to an immune checkpoint inhibitor in a subject suffering from cancer.
  • the immune checkpoint inhibitor can be applied to a subject having a good response to the immune checkpoint inhibitor, and improvement in the therapeutic effect can be expected.
  • FIG. 1 shows the relationship between bacterial species diversity in feces and the effect of anti-PD-1 antibody.
  • A First half (Training cohort) Genus data
  • B Second half (Validation cohort) Genus data.
  • Ace and Chao1 represent a diversity index.
  • FIG. 2 shows a list of bacterial species (genus) that showed significant differences as a result of metagenome analysis in feces of PD (progressive disease: tumor growth) group and non-PD (non-tumor growth) group.
  • FIG. 3 shows a list of KEGG pathways that showed significant differences as a result of metagenomic analysis in feces of the PD group and the non-PD group.
  • FIG. 1 shows the relationship between bacterial species diversity in feces and the effect of anti-PD-1 antibody.
  • FIG. 4 shows the results of analysis in the latter half of the patient group (PD group and non-PD group) for the bacterial species in which a significant difference was observed in the fecal metagenome analysis of the first half of the patient group.
  • FIG. 5 shows the results of analysis in the latter half of the patient group (PD group and non-PD group) for the bacterial species (Geobacillus genus, Gordonibacter genus) in which a significant difference was observed in the fecal metagenome analysis of the first half patient group. ..
  • FIG. 6 shows the results of analysis of the KEGG pathway, which showed a significant difference in fecal metagenomic analysis of the first half patient group, in the second half patient group (PD group and non-PD group).
  • FIG. 7 shows the results of analysis of the KEGG pathway (Fatty acid biosynthesis) in the fecal metagenome analysis of the first half of the patient group in the second half of the patient group (PD group and non-PD group).
  • A Comparison of scores of fatty acid biosynthesis pathways.
  • B Comparison of fatty acid decomposition pathway scores.
  • C Schematic diagram of fatty acid biosynthesis pathway.
  • FIG. 8 shows the results of analysis of the KEGG pathway (PPAR signaling pathway) in the fecal metagenome analysis of the first half of the patient group in the second half of the patient group (PD group and non-PD group).
  • A Comparison of PPAR signaling pathway scores in the first half of the patient group.
  • FIG. 9 shows the results of analysis by narrowing down to bacterial species (0.01% or more) that frequently appear in the metagenomic analysis in feces of the first half and the second half patient groups (PD group and non-PD group). ..
  • A patient group in the first half
  • B patient group in the second half
  • FIG. 10 shows the scores of the genus Odoribacter (A) and the genus Veillonella (B) in the feces of the first half and the second half patient groups (PD group and non-PD group).
  • FIG. 11 shows the profile of the entire bacterial species by fecal metagenomic analysis of the first half patient group.
  • FIG. 12 shows the profile of the entire bacterial species by fecal metagenomic analysis of the latter half of the patient group.
  • FIG. 13 shows the results of plasma metabolome analysis of the patient group (PD group and non-PD group).
  • FIG. 14-1 shows the results of plasma metabolome analysis of the first half and the second half patient groups (PD group and non-PD group).
  • FIG. 14-2 shows the results of plasma metabolome analysis of the first half and the second half patient groups (PD group and non-PD group).
  • FIG. 15 shows the results of association analysis of fecal metagenomics and plasma metabolome.
  • FIG. 16 shows the results of whole blood gene expression analysis (RNA-Seq analysis) in the patient group (PD group and non-PD group).
  • FIG. 17-1 shows the results of whole blood gene expression analysis (RNA-Seq analysis) in the patient group (PD group and non-PD group).
  • 17-2 and 17-3 show the results (MAPK signing pathway) of whole blood gene expression analysis (RNA-Seq analysis) of the first half and the second half patient groups (ratio of PD group and non-PD group). This pathway is enhanced in the effective group (non-PD group).
  • 17-4 and 17-5 show the results (TCR signing pathway) of whole blood gene expression analysis (RNA-Seq analysis) of the first half and the second half patient groups (ratio of PD group and non-PD group). This pathway is enhanced in the effective group (non-PD group).
  • MAPK signing pathway RNA-Seq analysis of the first half and the second half patient groups
  • FIG. 17-6 and 17-7 show the results (Type I IFN receptor Complex) of whole blood gene expression analysis (RNA-Seq analysis) of the first half and second half patient groups (ratio of PD group and non-PD group). show. This pathway is enhanced in the effective group (non-PD group).
  • FIG. 18 shows the results of drug effect prediction of a patient group (PD group and non-PD group) using a combination of a plurality of markers (genus Odoribacter and genus Verionella) as an index.
  • FIG. 19 shows the results of drug effect prediction of a patient group (PD group and non-PD group) using a combination of a plurality of markers (Genus Gordonibacter, Geobacillus, Odoribacter and Verionella) as an index.
  • FIG. 18 shows the results of drug effect prediction of a patient group (PD group and non-PD group) using a combination of a plurality of markers (Genus Gordonibacter, Geobacillus, Odoribacter and Verionella) as an index.
  • FIG. 20-1 shows the first half patient group (PD group and non-PD group) using a combination of multiple markers (pyruvic acid, pipecolic acid, glyceric acid) as an index.
  • FIG. 20-2 shows the latter half of the patient group (PD group and non-PD group) using a combination of multiple markers (pyruvic acid, pipecolic acid, glyceric acid) as an index.
  • FIG. 20-3 shows the first half of the patient group (PD group and non-PD group) using a combination of multiple markers (pyruvic acid, glyceric acid, lactic acid) as an index. The result of drug effect prediction is shown.
  • FIG. 20-2 shows the latter half of the patient group (PD group and non-PD group) using a combination of multiple markers (pyruvic acid, pipecolic acid, glyceric acid) as an index.
  • FIG. 20-3 shows the first half of the patient group (PD group and non-PD group
  • FIG. 20-4 shows the latter half of the patient group (PD group and non-PD group) using a combination of multiple markers (pyruvic acid, glyceric acid, lactic acid) as an index.
  • the result of drug effect prediction is shown.
  • FIG. 21-1 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (18 genes) as an index.
  • FIG. 21-2 shows the results of drug effect prediction of the latter half patient group (PD group and non-PD group) using a combination of a plurality of markers (18 genes) as an index.
  • FIG. 22-1 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (15 factors) as an index.
  • FIG. 21-1 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (18 genes) as an index.
  • FIG. 22-2 shows the results of drug effect prediction of the latter half patient group (PD group and non-PD group) using a combination of a plurality of markers (15 factors) as an index.
  • FIG. 23 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (12 factors) as an index.
  • FIG. 24 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (7 factors) as an index.
  • FIG. 25 shows the results of drug effect prediction of the first half patient group (PD group and non-PD group) using a combination of a plurality of markers (4 factors) as an index.
  • FIG. 26 shows a block diagram of the system of the present invention.
  • FIG. 27 shows bacterial species and KEGG pathways that showed significant differences by overall survival (OS) analysis using fecal metagenomic analysis data from the first and second half patient groups. *: Items for which a significant difference was found by the Bonferroni method.
  • FIG. 28 shows bacterial species and KEGG pathways that showed significant differences by progression-free survival (PFS) analysis using metagenomic analysis data in feces of the first half and second half patient groups. *: Items for which a significant difference was found by the Bonferroni method.
  • FIG. 29 shows the scores of the Nucleotide metabolism pathway, which showed a significant difference in OS analysis using fecal metagenome analysis data of the first half and the second half of the patient group, divided into the survival and death patient groups during the observation period. show.
  • FIG. 29 shows the scores of the Nucleotide metabolism pathway, which showed a significant difference in OS analysis using fecal metagenome analysis data of the first half and the second half of the patient group, divided into the survival and death patient groups during the
  • FIG. 30 shows the survival curves of the patient group (first half and second half) classified according to the expression level (score) of the Nucleotide metabolism pathway of 3.4 or more or less than 3.4 shown by metagenome analysis in feces.
  • FIG. 31 shows patients in the Nucleotide metabolism pathway, which showed a significant difference in PFS analysis using fecal metagenome analysis data of the first half and the second half of the patient group, when they were classified into the group with / without exacerbation of cancer during the observation period. Show its score.
  • FIG. 32 shows the progression-free survival (PFS) curve of the patient group when the expression level (score) of the Nucleotide metabolism pathway shown by metagenome analysis in feces is classified as 3.4 or more or less than 3.4.
  • PFS progression-free survival
  • FIG. 33 shows the score of bacterial species diversity (Chao1) shown by metagenomic analysis in feces of patients who survived or died during the observation period (first half and second half).
  • FIG. 34 shows the survival curves of the patient groups (first half and second half) classified with a strain diversity (Chao1) score of 606 or more or less than 606, as shown by metagenomic analysis in feces.
  • FIG. 35 shows the score of bacterial species diversity (Chao1) shown by metagenomic analysis in feces of patients (first half and second half) classified into the cancer exacerbation / no exacerbation group during the observation period.
  • FIG. 34 shows the survival curves of the patient groups (first half and second half) classified with a strain diversity (Chao1) score of 606 or more or less than 606, as shown by metagenomic analysis in feces.
  • FIG. 35 shows the score of bacterial species diversity (Chao1) shown by metagenomic analysis in feces of patients (
  • FIG. 36 shows the progression-free survival (PFS) curves of the patient groups (first half and second half) classified with a strain diversity (Chao1) score of 606 or greater or less than 606, as shown by metagenomic analysis in feces.
  • FIG. 37 shows the scores of the Peptidoglycan biosynthesis pathways that showed significant differences in OS analysis using fecal metagenomic analysis data of the first half and the second half of the patient groups, divided into the survival and death patient groups during the observation period. show.
  • FIG. 38 shows the survival curves of the patient group (first half and second half) classified according to the expression level (score) of the Peptidoglycan biosynthesis pathway shown by metagenome analysis in feces of 1.32 or more or less than 1.32.
  • FIG. 39 shows the metabolome analysis score of 2-Hydroxybutyric acid amount (normalized by the internal standard (2-Isopropanolmatic acid)) in plasma of patients who survived or died during the observation period (first half and second half).
  • FIG. 40 shows a group of patients (first half and second half) classified with a metabolome analysis score of 0.76 or more or less than 0.76 for the amount of 2-Hydroxybutyric acid in plasma (normalized by the internal standard (2-Isopropanolmatic acid)). The survival curve of is shown.
  • FIG. 40 shows a group of patients (first half and second half) classified with a metabolome analysis score of 0.76 or more or less than 0.76 for the amount of 2-Hydroxybutyric acid in plasma (normalized by the internal standard (2-Isopropanolmatic acid)). The survival curve of is shown.
  • FIG. 41 shows the metabolome analysis score of 2-Oxovolytic acid amount (normalized by the internal standard (2-Isopropanolmatic acid)) in plasma of patients who survived or died during the observation period (first half and second half).
  • FIG. 42 shows a group of patients (first half and second half) classified with a metabolome analysis score (Median) of 0 or more or less than 0 for the amount of 2-Oxovolytic acid in plasma (normalized by the internal standard (2-Isopropanolmatic acid)). Shows a survival curve.
  • FIG. 43 shows the markers reproduced in the first half and the second half of the patient group as a result of comparing the whole blood gene expression analysis (RNA-Seq analysis) data obtained in the patient group that survived or died during the observation period.
  • RNA-Seq analysis whole blood gene expression analysis
  • FIG. 44 shows the sum of RNA-Seq analysis expression scores of the 7 genes shown in FIG. 43 in patients who survived or died during the observation period (first half and second half).
  • FIG. 45 shows the survival curves of the patient groups (first half and second half) classified by the sum of the RNA-Seq analysis expression scores of the seven genes shown in FIG. 43 being 0.5 or more or less than 0.5.
  • FIG. 46 shows patients in the Phenylalane metabolism pathway, which showed a significant difference in PFS analysis using fecal metagenome analysis data of the first half and the second half of the patient group, when they were classified into the cancer exacerbation / no cancer exacerbation group during the observation period. Show its score.
  • FIG. 44 shows the sum of RNA-Seq analysis expression scores of the 7 genes shown in FIG. 43 in patients who survived or died during the observation period (first half and second half).
  • FIG. 45 shows the survival curves of the patient groups (first half and second half) classified by the sum of the RNA-S
  • FIG. 47 shows the progression-free survival (PFS) curve of the patient group when the expression level (score) of the Phenylanaline metabolism pathway shown by metagenome analysis in feces is classified as 0.16 or more or less than 0.16.
  • FIG. 48 shows the metabolome analysis score of the plasma Glyceric acid amount (normalized by the internal standard (2-Isopropanolmatic acid)) of patients with / without cancer exacerbation (first half and second half) during the observation period.
  • FIG. 49 shows a group of patients (first half and second half) classified with a metabolome analysis score of -0.00033 or higher or less than -0.00033 for the amount of Glyceric acid in plasma (normalized by the internal standard (2-Isopropanolmatic acid)).
  • FIG. 50 shows the scores of markers (Arthrobacter genus, Fatty acid metabolism pathway) extracted by intergroup comparative analysis of fecal metagenome data in patients with / without adverse events in the skin.
  • FIG. 51 shows SNPs markers for immune-related genes extracted by intergroup comparative analysis of whole blood genomic data from patients with / without adverse events in the skin.
  • the present invention presents in patients with cancer to whom an immune checkpoint inhibitor has been applied, with tumor growth (PD group) and non-tumor growth group (non-PD group), survival group and death group, or exacerbation of cancer.
  • PD group tumor growth
  • non-PD group non-tumor growth group
  • survival group and death group or exacerbation of cancer.
  • the present invention is a method of predicting a response to an immune checkpoint inhibitor in a subject suffering from cancer.
  • SNPs Single nucleotide polymorphisms
  • the microorganism is one or more selected from the group consisting of the genus Geobacillus, the genus Gordonibacter, the genus Odoribacter, the genus Veillonella, the genus Corynebacterium, the genus Polychromonas, and the genus Arthrobacter.
  • the genomic pathways include bacterial invasion of epithelial cells, fatty acid metabolism, flagellar metabolism, fatty acid biosynthesis (Fatty acid metabolism), fatty acid biosynthesis (Fatty acid metabolism), and fatty acid biosynthesis (Fatty acid metabolism).
  • pathway peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism)
  • the metabolite is one or more selected metabolites from the group consisting of lactic acid, pyruvic acid, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid.
  • the gene is one or more selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway, TCR signal pathway-related gene, and BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7.
  • the SNP is one or a plurality of SNPs selected from the group consisting of the following rs numbers: rs2228145.1; rs71900199; and rs7185320. Provide a method.
  • cancer is not particularly limited, and is, for example, leukemia (eg, acute myeloid leukemia, chronic myeloid leukemia, acute lymphocytic leukemia, chronic lymphocytic leukemia), malignant lymphoma (hodgkin lymphoma, non-cancer).
  • leukemia eg, acute myeloid leukemia, chronic myeloid leukemia, acute lymphocytic leukemia, chronic lymphocytic leukemia
  • malignant lymphoma hodgkin lymphoma, non-cancer.
  • Hodgkin lymphoma eg, adult T-cell leukemia, follicular lymphoma, diffuse large B-cell lymphoma), multiple myeloma, myelodystrophy syndrome, head and neck cancer, esophageal cancer, esophageal adenocarcinoma, gastric cancer, colon Cancer, colon cancer, rectal cancer, liver cancer (eg, hepatocellular carcinoma), bile sac / bile duct cancer, biliary tract cancer, pancreatic cancer, thyroid cancer, lung cancer (eg, non-small cell lung cancer (eg, squamous epithelial non-small cell lung cancer), Non-flat epithelial non-small cell lung cancer), small cell lung cancer), breast cancer, ovarian cancer (eg, serous ovarian cancer), cervical cancer, uterine body cancer, endometrial cancer, vaginal cancer, genital cancer, renal cancer (eg, serous ovarian cancer) For example, renal cell cancer), urinar
  • cancer (malignant tumor) treatment refers to, for example, (i) reducing the growth of cancer, (ii) reducing symptoms caused by cancer, and (iii) improving the quality of life of cancer patients. Includes treatments performed to (iv) reduce the dose of other anticancer drugs or cancer treatment aids already administered, and / or (v) prolong the survival of the cancer patient. Treatment also includes suppression of recurrence. "Relapse suppression” means prophylactically suppressing cancer recurrence in patients whose cancer lesions have been completely or substantially eliminated or removed by cancer treatment or cancer resection surgery.
  • examples of the "other anticancer drug” include alkylating agents, platinum preparations, antimetabolites (eg, antimetabolites, pyridine metabolism inhibitors, purine metabolism inhibitors), ribonucleotide reductase inhibitors, and the like. Included are nucleotide analogs, topoisomerase inhibitors, microtube polymerization inhibitors, microtube depolymerization inhibitors, antitumor antibiotics, cytokine preparations, antihormonal agents, molecular targeting agents and cancer immunotherapeutic agents.
  • antimetabolites eg, antimetabolites, pyridine metabolism inhibitors, purine metabolism inhibitors
  • ribonucleotide reductase inhibitors include alkylating agents, platinum preparations, antimetabolites (eg, antimetabolites, pyridine metabolism inhibitors, purine metabolism inhibitors), ribonucleotide reductase inhibitors, and the like. Included are nucleotide analogs, topoisomerase inhibitors, microtube polymerization
  • the therapeutic agents of the present invention are (1) reduced doses of other agents used in combination and / or (3) other agents used in combination to enhance the therapeutic effect of cancer.
  • it may be used in combination with one or more other drugs (mainly anticancer drugs) used for the therapeutic purpose of the above-mentioned cancers.
  • the dosage form when prescribing in combination with other drugs may be a combination drug form in which both components are mixed in one preparation, or an administration form as separate preparations. good.
  • the therapeutic agent or the like of the present invention and another drug are separately administered, the therapeutic agent or the like of the present invention may be administered first, and then the other agent may be administered, or the other agent may be administered.
  • each drug may be administered first and the therapeutic agent of the present invention or the like may be administered later, or in the above administration, there may be a period during which both agents are simultaneously administered for a certain period of time.
  • the administration method of each drug may be the same or different.
  • the drug can also be provided as a kit containing the therapeutic agent of the present invention and other drugs.
  • the dose of the other drug can be appropriately selected based on the clinically used dose.
  • other drugs may be administered in combination of any two or more at an appropriate ratio.
  • the other drugs include not only those found so far but also those found in the future.
  • immune checkpoint inhibitor means an agent that exerts an immunosuppressive function by inhibiting an immune checkpoint molecule and transmitting an inhibitory co-signal.
  • immune checkpoint molecules include CTLA-4, PD-1, PD-L1 (programmed cell death-ligand 1), PD-L2 (programmed cell date-ligand 2), LAG-3 (Lymphometry activationTime3).
  • T cell immunoglobulin and mucin-3 T cell immunoglobulin and mucin-3
  • BTLA B and T lympho-cyte attenator
  • B7H3, B7H4, 2B4 CD160
  • A2aR adenosine A2a receptor
  • IR Contining support of T cell activation IDO1 (Indoreamine 2,3-dioxygene), ArginaseI, TIGIT (T cell immunoglobulin and ITIM doman), CD115, etc. 2012, Cancer Cell, 27, 450-461, 2015), and the molecule is not particularly limited as long as it has a function consistent with the definition.
  • the applicable immune checkpoint inhibitor is not particularly limited as long as it is a substance capable of suppressing the function (signal) of the immune checkpoint molecule.
  • the immune checkpoint inhibitor is preferably an inhibitor of a human immune checkpoint molecule, and more preferably a neutralizing antibody against the human immune checkpoint molecule.
  • Immune checkpoint inhibitors consist of, for example, CTLA-4, PD-1, PD-L1, PD-L2, LAG-3, TIM3, BTLA, B7H3, B7H4, 2B4, CD160, A2aR, KIR, VISTA and TIGIT. Included are inhibitors of immune checkpoint molecules selected from the group. Examples of immune checkpoint inhibitors are given below, but immune checkpoint inhibitors are not limited to these.
  • immune checkpoint inhibitors examples include anti-CTLA-4 antibody (eg, Ipilimumab (YERVOY®), Tremerimumab, AGEN-1884), anti-PD-1 antibody (eg, nivolumab (registered trademark)).
  • anti-CTLA-4 antibody eg, Ipilimumab (YERVOY®), Tremerimumab, AGEN-1884
  • anti-PD-1 antibody eg, nivolumab (registered trademark)
  • Antibodies comprising the heavy and light chain complementarity determining regions (CDRs) or variable regions (VR) of the known antibodies are also aspects of immune checkpoint inhibitors.
  • further embodiments of anti-PD-1 antibodies include antibodies comprising, for example, nivolumab heavy and light chain complementarity determining regions (CDRs) or variable regions (VR).
  • the immune checkpoint inhibitor applicable in the present invention is preferably anti-CTLA-4 antibody, anti-PD-1 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody, PD-L1 fusion protein, PD-L2 fusion protein.
  • it is an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-PD-L2 antibody, a PD-L1 fusion protein, and a PD-L2 fusion protein.
  • Particularly preferred is an anti-PD-1 antibody.
  • the anti-PD-1 antibody is preferably an antibody (including nivolumab) containing the heavy and light chain complementarity determining regions (CDRs) or variable regions (VR) of nivolumab, and more preferably nivolumab.
  • CDRs heavy and light chain complementarity determining regions
  • VR variable regions
  • An antibody or fusion protein of any one of these immune checkpoint inhibitors or any plurality of types can be applied in the present invention.
  • the dose of the immune checkpoint inhibitor that can be used for a subject determined to have a good response to the immune checkpoint inhibitor depends on age, body weight, symptoms, therapeutic effect, administration method, treatment time, and the like. Different, but adjusted to produce the optimum desired effect.
  • one aspect of the dose when using an anti-PD-1 antibody, one aspect of the dose is 0.1 to 20 mg / kg body weight. Also, when using an antibody (eg, nivolumab) containing nivolumab heavy and light chain complementarity determining regions (CDRs) or variable regions (VR), one aspect of the dose is 0.3-10 mg / kg body weight. Yes, preferably, for adults, (1) 1 mg / kg (body weight) once at 3 week intervals, (2) 3 mg / kg (body weight) once at 2 week intervals, (3) once as nivolumab.
  • an antibody eg, nivolumab
  • CDRs light chain complementarity determining regions
  • VR variable regions
  • a single dose of 480 mg can be administered by intravenous drip infusion at 4-week intervals.
  • feces refers to those excreted from the intestinal tract to the outside of the body
  • intestinal contents refers to the contents before being excreted from the intestinal tract to the outside.
  • the "step of determining the value of the abundance of microorganisms" may be to determine the presence or absence of a desired microorganism, and to determine the absolute abundance of a desired microorganism. It may be, or it may be to determine the relative abundance of the desired microorganism.
  • the step of determining the value of the abundance of microorganisms can be determined by using a known method for identifying microorganisms, and for example, the abundance of microorganisms can be determined by metagenomic analysis.
  • Metagenomic analysis refers to analysis of the entire genome of the microbial flora without going through the process of culturing, and metagenomic analysis applicable in the present invention is a known method, for example, a method and analysis using a next-generation sequencer. It is possible to carry out by.
  • Identification of microorganisms by metagenome analysis may be, for example, a method of identification by classification of microorganisms by comparing the base sequences of 16S rRNA genes in small ribosome subunits, and the bacterial species may be identified by the sequences of other genomic regions of the microorganisms. It may be an identification method and is not particularly limited. It is also possible to determine the abundance of the corresponding microorganism by performing metagenomic analysis.
  • the present inventors have found that the response to an immune checkpoint inhibitor in a subject can be predicted by using the abundance as an index.
  • the abundance of Geobacillus, Gordonibacter, or Veillonella microorganisms is an immune checkpoint inhibitor in patients suffering from cancer that responds well to immune checkpoint inhibitors.
  • the abundance of Geobacillus, Gordonibacter, or Veillonella microorganisms in the feces or intestinal contents of the subject is lower than a predetermined threshold, the response to the immune checkpoint inhibitor in the subject is Although it can be predicted to be defective, it is more preferable to use the abundance of microorganisms of the genus Veillonella as an index.
  • the abundance of microorganisms of the genus Odoribacter as an index among the microorganisms in the feces or intestinal contents of the subject. They found it for the first time. For example, we have shown that the abundance of microorganisms of the genus Odoribacter shows a good response to immune checkpoint inhibitors in patients suffering from cancer who show a good response to immune checkpoint inhibitors. It was found to be significantly lower than patients with no cancer.
  • the immune checkpoint inhibitor will induce skin adverse events in the subject. can do.
  • the specific value of the "predetermined threshold” or “cutoff value” is not limited because it is changed depending on the analysis method, measurement conditions, etc., but for example, for an immune checkpoint inhibitor.
  • a "genome pathway” is a pathway (inter-molecular interaction network in a metabolic or signaling system) that can be identified by a base sequence obtained from a fecal or intestinal content in a subject, eg, KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAY database (https://www.genome.jp/kegg/pathway.html) is a pathway registered. Genome pathways can be identified using the above KEGG PATHWAY database from fecal or intestinal contents in the subject, for example from metagenomic datasets that can be obtained by methods similar to the metagenomic analysis described above.
  • the present inventors By analyzing the metagenome datasets that can be obtained from the feces or intestinal contents of the subject, the present inventors have bacterial invasion of epithelial cells, fatty acid degradation, and fluff aggregation.
  • the score of one or more genomic pathways selected from the group consisting of (Flagellar assembly), fatty acid biosynthesis, and PPAR signal pathway (PPAR signing pathway) is good for immune checkpoint inhibitors.
  • PPAR signing pathway PPAR signing pathway
  • peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism) and metabolism of fatty acids (selected from Phenelylanine metabolism) and metabolism of fatty acids Metabolic pathway scores show good responses to cancer-affected patients (survival group or no exacerbation group) who respond well to immune checkpoint inhibitors and to immune checkpoint inhibitors. We found that there was a significant difference in the group of patients with no cancer (death group or group with exacerbations).
  • the genomic pathways in the feces or intestinal contents of the subject bacterial invasion of epithelial cells, fatty acid metabolism, and flagellar assembly PPAR signals.
  • the score of PPAR signing passage and / or phenylalanine metabolism is lower than a predetermined threshold, it can be predicted that the response to the immune checkpoint inhibitor in the subject is good.
  • the genomic pathways in the feces or intestinal contents of the subject bacterial invasion of epithelial cells, fatty acid metabolism, and flagellar assembly PPAR signals. If the PPAR signing passage and / or phenylalanine metabolism scores are higher than a predetermined threshold, it can be predicted that the response to the immune checkpoint inhibitor in the subject is poor.
  • fatty acid biosynthesis Fatty acid metabolism
  • nucleotide metabolism Nucleotide metabolism
  • peptide glycan biosynthesis Peptidoglycan biosynthesis
  • fatty acid metabolism (Fatty acid metabolism) score of the genomic pathway in the feces or intestinal contents of the subject is higher than a predetermined threshold, the subject is subjected to an immune checkpoint inhibitor. It can be predicted to induce adverse skin events.
  • the genomic pathway score used in the present invention can be determined by performing metagenomic analysis on the fecal or intestinal contents of the subject and inputting the resulting metagenomic dataset into the KEGG PHATWAY database, eg, References: Abubucker et al. PLoS Computational Biology 2012, (8) 6 e1002588. It can be implemented by referring to.
  • the invention makes it possible to predict a response to an immune checkpoint inhibitor in a subject by determining the abundance of metabolites in the subject's blood, serum or plasma.
  • the present inventors consist of metabolites, particularly lactic acid, pyruvate, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid in the target blood, serum or plasma. It has been found that the response to an immune checkpoint inhibitor in a subject can be predicted by using the abundance of one or more selected metabolites as an index.
  • the abundance of lactic acid, pyruvate, glucose, glyceric acid, octanoic acid, 2-hydroxybutyric acid or 2-oxobutyric acid among the metabolites present in the target blood, serum or plasma is predetermined. If it is lower than the threshold of, it can be predicted that the response to the immune checkpoint inhibitor in the subject is good. Further, in another embodiment, the abundance of lactic acid, pyruvate, glucose, glyceric acid, octanoic acid, 2-hydroxybutyric acid or 2-oxobutyric acid among the metabolites present in the blood, serum or plasma of the subject is determined. If it is higher than a predetermined threshold, it can be predicted that the response to the immune checkpoint inhibitor in the subject is poor.
  • the immune checkpoint inhibitor in the subject when the abundance of pipecolic acid and / or citrulline among the metabolites present in the blood, serum or plasma of the subject is higher than a predetermined threshold value, the immune checkpoint inhibitor in the subject is referred to. It is possible to predict that the response will be good. In still another embodiment, if the abundance of pipecolic acid and / or citrulline among the metabolites present in the blood, serum or plasma of the subject is lower than a predetermined threshold, the immune checkpoint inhibitor in the subject. It is possible to predict that the response to is poor.
  • the method for determining the abundance of metabolites present in blood, serum or plasma is not particularly limited, but may be determined using a known metabolome analysis method, for example, mass spectrometry or nuclear magnetic resonance. It may be determined by a resonance method or the like.
  • mass spectrometry a blood, serum or plasma sample is converted into gaseous ions using an ion source (ionization), and the blood is ionized by moving it in a vacuum in a vacuum and using electromagnetic force or by a flight time difference in the analysis unit.
  • a measurement method using a mass spectrometer that can separate and detect serum or plasma samples according to the mass-to-charge ratio.
  • Methods of ionization using an ion source include electron ionization (EI) method, chemical ionization (CI) method, electrospray ionization (FD) method, fast atom bombardment (FAB) method, and matrix-assisted laser desorption ionization (MALDI).
  • EI electron ionization
  • CI chemical ionization
  • FD electrospray ionization
  • FAB fast atom bombardment
  • MALDI matrix-assisted laser desorption ionization
  • ESI electrospray ionization
  • the method for separating the ionized blood, serum, or plasma sample in the analysis unit is a magnetic field deflection type or a quadrupole type.
  • Ion trap type flight time (TOF) type, Fourier transformed ion cyclotron resonance type and the like can be appropriately selected.
  • tandem mass spectrometry which is a combination of two or more mass spectrometry methods, can be used.
  • the metabolites may be separated / purified from the contaminants and analyzed by gas chromatography (GC), liquid chromatography (LC) or high performance liquid chromatography (HPLC).
  • the step (1) may be (iv) a step of determining the expression level of the gene in the blood, serum or plasma of the target.
  • the gene to be determined is preferably a gene related to one or a plurality of pathways selected from the group consisting of MAPK pathway, Type I IFN receptor compact pathway and TCR signal pathway.
  • the present inventors relate to one or a plurality of pathways selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway and TCR signal pathway.
  • Non-PD group Patients with cancer whose gene expression responds favorably to immune checkpoint inhibitors (non-PD group) and cancers that do not respond favorably to immune checkpoint inhibitors
  • PD group the patient group
  • the present inventors showed that the expression of the following genes: BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7 inhibited immune checkpoints.
  • Significant difference between the group of patients with cancer who responded well to the drug (survival group) and the group of patients with cancer who did not respond well to immune checkpoint inhibitors (death group) I found that there is.
  • the "MAPK pathway (also referred to as MAPK signal pathway)” refers to a signal pathway in which MAP kinase (mitogen-activated proteininase) is involved, and MAPK refers to metabolism, proliferation, division, motility, and apoptosis. It is a serine / threonine kinase that is involved in various functions of cells.
  • the MAPK pathway-related genes that can be used to predict the response to an immune checkpoint inhibitor are selected, for example, one or more from MAPKAPK5, MAPKAPK5-AS1, MAP3K14, MAPK3K7, MAP3K1, MAPK9, MAP3K5 and MAPK14. Genes can be mentioned.
  • the genes related to "Type I IFN receptor complex pathway" are PIK3R1, PTPN11, STAT1, FYN, EIF4B, RAC1, MAP3K1, RPS6KB1, PDCD4, REL, MAPK14, RPS6KA5, ST. RAPGEF1 and the like can be mentioned.
  • the genes related to the "TCR signal pathway” are MAP3K14, CD4, FYN, DLG1, PDK1, NFKB1, MALT1, LAT, SOS1, MAP3K7, IL10, GRAP2, CD40LG, PIK3CA, PRKCQ, NFATC2, CALM1. And so on.
  • the expression level of a gene associated with one or more pathways selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway and TCR signal pathway eg, an expression score calculated by any method.
  • a predetermined threshold it can be predicted that the response to the immune checkpoint inhibitor is good.
  • the expression score is lower than a predetermined threshold, it can be predicted that the response to the immune checkpoint inhibitor is poor.
  • the expression level of one or more genes selected from the group consisting of BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7 is predetermined. If it is higher than the threshold, it can be predicted that the response to the immune checkpoint inhibitor is good. Further, in another embodiment, the expression level of one or a plurality of genes selected from the group consisting of BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7 (for example, the calculated expression score measured by an arbitrary method) is determined. If it is lower than a predetermined threshold, it can be predicted that the response to the immune checkpoint inhibitor is poor.
  • the step of determining the expression level of a gene in the blood, serum or plasma of a subject may be determined by known transcriptome analysis.
  • step (1) may be (v) determine the presence or absence of a single nucleotide polymorphism (SNP) in one or more selected genes from the group consisting of IL6R and NLRC5 in the subject.
  • the SNP here may be one or more selected SNPs from the group consisting of the following rs numbers: rs2228145.1; rs7190199; and rs7185320.
  • the present inventors performed specific SNP and skin adverse events. We found that there was a correlation with the occurrence of. That is, if the subject has one or more SNPs selected from the group consisting of rs2228145.1; rs7190199; and rs7185320, it can be predicted that immune checkpoint inhibitors will induce skin adverse events.
  • the method of predicting the response to an immune checkpoint inhibitor in a subject suffering from cancer is described in (i) the abundance of microorganisms in the fecal or intestinal contents of the subject; (Ii) Genomic pathway score in the fecal or intestinal contents of the subject; (Iii) Abundance of metabolites in the blood, serum or plasma of the subject; (Iv) Gene expression levels in the subject's blood, serum or plasma; and (v) Single nucleotide polymorphisms (SNPs) of one or more genes selected from the group consisting of IL6R and NLRC5 in the subject.
  • a value selected from one or more of the group consisting of SNPs or an index combining the presence or absence of SNPs may be used to predict the response to an immune checkpoint inhibitor in a subject. By combining the above indicators, the correct diagnosis rate will be higher.
  • the present invention is a method of treating a subject suffering from cancer.
  • a step of determining the presence or absence of one or more selected values or SNPs from a group consisting of (2) A step of predicting a response to an immune checkpoint inhibitor in the subject using the value obtained in the step (1) or the presence or absence of an SNP as an index. (3) A step of administering the immune checkpoint inhibitor to the subject determined to have a good response to the immune checkpoint inhibitor.
  • the microorganism is one or more selected from the group consisting of the genus Geobacillus, the genus Gordonibacter, the genus Odoribacter, the genus Veillonella, the genus Corynebacterium, the genus Polychromonas, and the genus Arthrobacter.
  • the genomic pathways include bacterial invasion of epithelial cells, fatty acid metabolism, flagellar metabolism, fatty acid biosynthesis (Fatty acid metabolism), fatty acid biosynthesis (Fatty acid metabolism), and fatty acid biosynthesis (Fatty acid metabolism).
  • pathway peptide glycan biosynthesis (Peptidoglycan biosynthesis), nucleotide metabolism (Nucleotide metabolism), purine metabolism (Purine metabolism), phenylalanine metabolism (Phenylalanine metabolism)
  • the metabolite is one or more selected metabolites from the group consisting of lactic acid, pyruvic acid, glucose, 2-oxobutyric acid, glyceric acid, octanoic acid, citrulin, 2-hydroxybutyric acid and pipecholinic acid.
  • the gene is one or more selected from the group consisting of MAPK pathway, Type I IFN receptor complex pathway, TCR signal pathway-related gene, and BCL11B, ERCC3, ERCC6, FCRL1, FCRL3, MS4A1 and TCF7.
  • the SNP is one or a plurality of SNPs selected from the group consisting of the following rs numbers: rs2228145.1; rs71900199; and rs7185320. It may provide a method.
  • the present invention is for determining the responsiveness to an immune checkpoint inhibitor in a subject having cancer, including a storage unit 11, an input unit 12, a data processing unit 14, and an output unit 15.
  • System 10 The storage unit 11 (1-i) Abundance of microorganisms in feces or intestinal contents; (1-ii) Genomic pathway scores in fecal or intestinal contents; (1-iii) Abundance of metabolites in blood, serum or plasma; (1-iv) Gene expression levels in blood, serum or plasma; and (1-v) Single nucleotide polymorphisms (SNPs) of one or more selected genes from the group consisting of IL6R and NLRC5.
  • SNPs Single nucleotide polymorphisms
  • a value selected one or more from the group consisting of SNPs or the presence or absence of SNP is input and stored in the storage unit 11.
  • the data processing unit 14 compares the presence or absence of the stored value or SNP with the cutoff value to determine the responsiveness to the immune checkpoint inhibitor in the subject.
  • the output unit 15 outputs a determination result of responsiveness to the immune checkpoint inhibitor of cancer in the subject.
  • the system 10 may be characterized in that (FIG. 26).
  • the system 10 may further include an analytical measurement unit 13, which determines the abundance of microorganisms or the genomic pathway score in the fecal or intestinal content of the subject.
  • the analysis and measurement unit 13 determines the abundance of metabolites in the blood, serum or plasma of the subject.
  • the analysis and measurement unit 13 determines the expression level of the gene in the blood, serum or plasma of the subject, and / or the analysis and measurement unit 13 is one or a plurality of genes selected from the group consisting of IL6R and NLRC5.
  • Determine single nucleotide polymorphisms (SNPs) The abundance of the microorganism, the score of the genomic pathway, the abundance of the metabolite, the expression level of the gene determined by the analysis / measurement unit 13 in place of or through the input unit 12. And / or the presence or absence of the SNP may be input (FIG. 26).
  • the storage unit 11 has a memory device such as RAM, ROM, and flash memory, a fixed disk device such as a hard disk drive, or a portable storage device such as a flexible disk and an optical disk.
  • the storage unit 11 is a computer program used for various processes of the information processing device, such as data measured by the analysis and measurement unit 13, data and instructions input from the input unit 12, calculation processing results performed by the data processing unit 14, and the like. , Database etc. are stored.
  • the computer program may be installed via a computer-readable recording medium such as a CD-ROM or a DVD-ROM, or via the Internet.
  • the computer program is installed in the storage unit 11 using a known setup program or the like.
  • the input unit 12 is an interface or the like, and includes an operation unit such as a keyboard and a mouse. As a result, the input unit 12 can input the data measured by the analysis and measurement unit 13, the instruction of the arithmetic processing performed by the data processing unit 14, and the like. Further, for example, when the analysis / measurement unit 13 is located outside, the input unit 12 may include an interface unit capable of inputting measured data or the like via a network or a storage medium, in addition to the operation unit.
  • the analysis and measurement unit 13 performs the above-mentioned steps of measuring the abundance of microorganisms, the score of the genome pathway, the abundance of metabolites, the expression level of genes and / or SNP. Therefore, the analysis / measurement unit 13 has a configuration that enables measurement of the abundance of microorganisms, the score of the genomic pathway, the abundance of metabolites, the expression level of genes and / or SNP.
  • the analysis and measurement unit 13 is not intended to be limited to the following, but for example, a next-generation sequencer capable of performing metagenome analysis, a mass spectrometer capable of determining the abundance of metabolites, and the like. Known devices such as nuclear magnetic resonance devices can be used alone or in combination.
  • the analysis / measurement unit 13 may be configured separately from the system 10, and the measured data or the like may be input via the input unit 12 using a network or a storage medium.
  • the data processing unit 14 uses the value input from the input unit 12 or the analysis measurement unit 13 and the cutoff value for determining the responsiveness to the immune checkpoint inhibitor to determine the immune checkpoint inhibitor of the cancer in the subject. Responsiveness can be determined.
  • the data processing unit 14 executes various arithmetic processes on the data measured by the analysis and measurement unit 13 and stored in the storage unit 11 according to the program stored in the storage unit 11.
  • the arithmetic processing is performed by the CPU included in the data processing unit 14.
  • This CPU includes a functional module that controls an analysis measurement unit 13, an input unit 12, a storage unit 11, and an output unit 15, and can perform various controls. Each of these parts may be composed of an independent integrated circuit, a microprocessor, firmware, or the like.
  • the output unit 15 is configured to output the result of performing arithmetic processing in the data processing unit.
  • the output unit 15 may be a display device such as a liquid crystal display that directly displays the result of arithmetic processing, an output means such as a printer, or an interface unit for outputting to an external storage device or outputting via a network. There may be.
  • the present invention relates to the endpoints of the invention ((1-i) abundance of microorganisms in feces or intestinal contents; (1-ii) scores of genomic pathways in feces or intestinal contents; (1-iii) blood, Abundance of metabolites in serum or plasma; (1-iv) expression of genes in blood, serum or plasma; or (1-v) one or more genes selected from the group consisting of IL6R and NLRC5. Also includes inventions relating to inspections or measurement kits for measuring type (SNP)).
  • the "first half” means a training cohort
  • the "second half” means a validation cohort
  • Example 1 ⁇ Materials and analysis methods> 1.
  • Fecal metagenomic analysis 1-1 Sample collection From 478 cases (196 samples in the first half + 282 samples in the second half), feces about the size of azuki beans were collected with a special spoon and immediately placed in a container containing a guanidine solution. After shaking the container 5 to 6 times to mix the contents, the container was submitted to a medical institution within 1 week and stored at ⁇ 20 ° C. or ⁇ 80 ° C.
  • sequencing library was prepared with 1 ⁇ g of genomic DNA in the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, USA).
  • the APIPure XP system was used for library purification, and the quality of the sequence library was confirmed by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA).
  • the sequence of the sequence library was performed using NovaSeq6000 (Illumina, Inc., San Diego, CA) with a 150 bp pair end.
  • RNA-Seq Blood cell RNA expression analysis
  • Sequencing Sequencing libraries use Total RNA for NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs, MA) and NEBNextRan (NEBNextRanb. ..
  • the quality of the sequence library was confirmed by Agilent 2200 TapeStation High Sensitivity D1000 (Agilent Technologies, Santa Clara, CA), and the concentration was measured by KOD SYBR qPCR Mix (TOYOBO).
  • the sequence of the sequence library was performed using NovaSeq6000 (Illumina, Inc., San Diego, CA) with a 50 bp pair end.
  • Plasma blood collected from 489 cases was collected in a blood collection tube containing EDTA-2K, immediately miscible 10 times or more, and centrifuged at 1200 xg for 10 minutes.
  • the separated supernatant (plasma) was separated, dispensed into a dispensing tube, and stored at ⁇ 20 ° C. or ⁇ 80 ° C.
  • -PD group A group of cases in which tumor growth is observed in the first image evaluation after starting treatment-Non-PD group: Tumor size is maintained or reduced in the first image evaluation after treatment is started Case group
  • Geobacillus For bacterial species, Geobacillus, Gordonibacter, Sebaldella, etc. were extracted as markers at the genus level, and for the functional KEGG pathway, Bacterial invasion of epithelial cells, Grandellar fatty acid, etc. were extracted.
  • RNA-Seq Whole blood gene expression analysis
  • the MAPK pathway As a result of investigating whether or not the gene group having a difference between the PD group vs. non-PD group is biased to a specific GO-Term by functional annotation information (GO-Term) from the gene expression data, the MAPK pathway. , TCR signal pathway, NF-kB, cytokine signaling (for example, Type I IFN receptor complex), etc. were found to have different expression levels in the PD group vs non-PD group. (Fig. 17-1 to Fig. 17-7)
  • the frequency information (expression level) of each of the two genera, Odoribacter and Verionella is normalized and scored by adding the values, and the PD group vs non-PD group.
  • the AUC was 0.571 to 0.584 and the p value was 0.020 to 0.036.
  • the AUC was 0.571 to 0.634 and the p value was 0.001 as a result of scoring with a combination of four species (Gordonibacter, Geobacillus, Odoribacter, Veillonella) and predicting the drug effect.
  • the correct diagnosis rate was up to 60% for the prediction with the effect and up to 70% for the prediction without the effect.
  • AUC was 0.708 as a result of scoring using three metabolites of pyruvic acid (Pyruvic acid), pipecolic acid (Pipecolic acid), and glyceric acid (Glyceric acid).
  • the correct diagnosis rate was 51% for the prediction that there was an effect and 84% for the prediction that there was no effect.
  • genes contained in 18 genes TCR signing pathway, MAPK signing pathway, IF typeI signing pathway, etc .: CD247, STAT1, CCR5, CB, BK3, BP1, BP, BP, BP, BP, and CFB,
  • AUC was 0.598, the prediction with effect was 43%, and the prediction without effect was 73%. It was the correct diagnosis rate.
  • biomarkers The following 15 types were used for the combination. Bacterial Invasion of epithelial cells, Fatty acid degradation, Flagellar assembly, Fatty acid biosynthesis, PPAR signaling pathway, Geobacillus, Gordonibacter, Odoribacter, Veillonella, Lactic acid, Pyruvic acid, Glucose, 2-Oxobutyric acid, Glyceric acid, Pipecolinic acid
  • the AUC was 0.631 to 0.682
  • the p-value was 0.00003 to 0.0025
  • the prediction of effectiveness was up to 67%
  • there was no effect. was predicted to have a correct diagnosis rate of up to 74%. (Figs. 22 to 25)
  • OS overall survival
  • PFS progression-free survival
  • RNA-Seq blood cell RNA expression analysis
  • metabolome analysis was performed to obtain data, thereby resulting in overall survival (OS) and progression-free survival. (PFS) analysis was performed.
  • the survival group and the death group were defined as follows.
  • the group with exacerbation and the group without exacerbation were defined as follows.
  • Group with exacerbation Group confirmed to have exacerbation during the observation period
  • Group without exacerbation Group confirmed to have no exacerbation during the observation period
  • Figures 27 and 28 show the markers that showed a significant difference in the expression level between the two groups from the OS analysis comparing the stool metagenomics of the surviving group and the dead group and the PFS analysis comparing the exacerbation group and the exacerbation non-exacerbation group.
  • RNA-Seq RNA expression analysis
  • Example 3 Adverse event analysis by fecal metagenomics Using the same sample as in Example 1, fecal metagenomic analysis was performed, and a comparison between 56 samples with skin adverse events and other samples was performed, and the first half sample and the second half sample were compared. The marker to be reproduced with was extracted. A marker having a Median difference of 0.01 or more and a t-test P value of 0.05 or less was searched for.
  • Group with adverse events Group with any adverse events of acne-like rash, dry skin, or pruritus during the observation period
  • Group without adverse events Group with acne-like rash, skin during the observation period Group without any adverse events of dryness or pruritus
  • Example 4 Adverse event analysis by fecal metagenomics Genome single nucleotide polymorphisms (SNPs) array analysis was performed from whole blood samples using the same sample as in Example 1. Genomic DNA was extracted from each of the 175 samples in the first half and the 265 samples in the second half and labeled. SNPs analysis was performed on them by a microarray (Illumina: Infinium Screening ArrayBedChip ( ⁇ 600,000 markers). GenomeStudio Software was used for data analysis.
  • SNPs single nucleotide polymorphisms

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Abstract

L'invention concerne un procédé de prédiction d'une réponse à un inhibiteur de point de contrôle immunitaire pour un sujet atteint d'un cancer, le procédé comprenant les étapes suivantes : une étape (1) pour déterminer une ou plusieurs valeurs ou la présence ou l'absence d'un polymorphisme mononucléotidique (SNP), choisi dans le groupe constitué par : (I) l'abondance de micro-organismes dans les selles ou le contenu intestinal du sujet, (ii) le score de la voie du génome dans les selles ou la teneur en intestin du sujet, (iii) l'abondance de produits métaboliques dans le sang, le sérum ou le plasma du sujet, (iv) l'expression génique dans le sang, le sérum ou le plasma du sujet, et (v) le SNP d'un ou de plusieurs gènes choisis dans le groupe Constitué par IL6R et NLRC5 du sujet ; et une étape (2) pour prédire une réponse du sujet à un inhibiteur de point de contrôle immunitaire sur la base de la valeur obtenue à l'étape (1).
PCT/JP2021/034399 2020-09-18 2021-09-17 Procédé pour prédire la réponse à un inhibiteur de point de contrôle immunitaire Ceased WO2022059785A1 (fr)

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WO2025048570A1 (fr) * 2023-08-31 2025-03-06 의료법인 성광의료재단 Marqueur pour prédire une réponse thérapeutique à un inhibiteur de point de contrôle immunitaire dans un cancer du rein, et méthode de prédiction de réponse thérapeutique associée à son utilisation
EP4617665A1 (fr) * 2024-03-14 2025-09-17 Centre National de la Recherche Scientifique Procédé in vitro pour établir le pronostic d'un sujet diagnostiqué atteint ou souffrant d'un lymphome diffus à grandes cellules b
WO2025191110A1 (fr) * 2024-03-14 2025-09-18 Centre National De La Recherche Scientifique Procédé in vitro pour établir le pronostic d'un sujet diagnostiqué comme souffrant ou ayant souffert d'un lymphome diffus à grandes cellules b

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