Use of gut microbiota or metabolite thereof, or antibody or derivative thereof in preparation of immunotherapeutic drugs for colorectal cancer

Abstract

A use of gut microbiota or a metabolite thereof and / or an antibody or a derivative thereof in preparation of immunotherapeutic drugs for colorectal cancer. The gut microbiota comprises at least one of Alistipes shahii, Butyricimonas virosa, Desulfovibrio piger, Actinomyces humiferus, Odoribacter splanchnicus, Faecalibacterium prausnitzii, Phocaeicola plebeius, Eubacterium rectale, Oscillospiraceae bacterium, and Alistipes onderdonkii. The metabolite of the gut microbiota comprises at least one of O-phosphorylethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, phosphatidylcholine (O-16:0_20:4), and 4-acetamidobutyric acid. The antibody comprises an IL-17A signaling pathway monoclonal antibody and / or a colorectal cancer-targeted monoclonal antibody.

Description

Application of gut microbiota or its metabolites, antibodies or their derivatives in the preparation of immunotherapeutic drugs for colorectal cancer Technical Field

[0001] This application relates to the field of biotechnology, and in particular to the use of gut microbiota or its metabolites, antibodies or their derivatives in the preparation of immunotherapeutic drugs for colorectal cancer. Background Technology

[0002] Colorectal cancer (CRC) is one of the three most common malignant tumors worldwide and the fourth leading cause of cancer death. Microsatellites (MS) are small, repetitive DNA sequences of 1-6 nucleotides in the genome, widely distributed in both coding and non-coding regions of the human genome. The high repetitiveness of microsatellites makes them extremely sensitive to DNA mismatches, reflecting tumor-associated genomic instability. The phenomenon where a new microsatellite allele appears due to the insertion or deletion of a repetitive unit in a tumor microsatellite is called microsatellite instability (MSI); conversely, it is called microsatellite stability (MSS).

[0003] Immunotherapy, as a novel treatment method for malignant tumors, represents a landmark innovation in the history of malignant tumor treatment. T cells are the core executors in the body's anti-tumor immune process; T cell activation requires two key signals: the first signal, from the binding of the major histocompatibility complex-antigen peptide on the surface of antigen-presenting cells to the T cell receptor; and the second signal, generated by the interaction of co-stimulatory molecules on the surfaces of antigen-presenting cells and T cells. Co-stimulatory molecules include positive and negative molecules; positive molecules promote lymphocyte proliferation, differentiation, and cytokine secretion; negative molecules weaken, limit, and terminate the lymphocyte immune response. Immune checkpoints are key factors expressed on immune cells that regulate the degree of immune activation. The immune checkpoint pathway is regulated by the interaction of ligands and receptors. Under physiological conditions, immune checkpoints play an important role in maintaining autoimmune tolerance and regulating the duration of physiological immune responses. Tumor cells exploit this characteristic of the immune system by overexpressing ligands in the immune checkpoint pathway to inhibit T cell activity, thereby escaping immune surveillance and killing effects and promoting tumor cell growth. Currently identified inhibitory receptors include CTLA-4, PD-1 and its ligand PD-L1, lymphocyte activation gene-3 (LAG-3), and T-cell immunoglobulin and mucin-domain-containing molecule-3 (TIM-3).

[0004] Immunotherapy, represented by immune checkpoint inhibitors, has been increasingly demonstrated in clinical studies to be effective in patients with microsatellite instability-high (MSI-H) colorectal cancer, but its response remains poor in patients with microsatellite stability (MSS) cancer, which accounts for up to 90% of cases. MSS-type CRC patients rarely benefit from ICI monotherapy. Currently, clinical explorations for MSS / pMMR non-metastatic CRC mainly focus on immunotherapy combined with chemotherapy, immunotherapy combined with radiotherapy, immunotherapy combined with targeted therapy, and dual immunotherapy, but successful cases are relatively few. The combination of treatment regimens, drug sequence, and dosage still warrant further exploration. Addressing these clinical treatment bottlenecks, the applicant has pioneered a neoadjuvant therapy regimen for MSS-type CRC patients using mFOLFOX6 + bevacizumab + PD-1 monoclonal antibody. This regimen has also been registered for a prospective clinical trial (NCT04895137) and has been granted a domestic invention patent (ZL202210083399.9) and a US invention patent (18099291). The applicant aims to increase the sensitivity of MSS-type CRC patients to immunotherapy through this novel treatment regimen, maximizing R0 resection rate and pathological complete response (pCR) rate while avoiding the toxic side effects of triple-drug chemotherapy and concurrent chemoradiotherapy. Compared to the FOXTROT and OPTICAL clinical studies, the applicant increased the pCR rate of neoadjuvant chemotherapy for colorectal cancer from 4% to 20.5%. Furthermore, up to 64.1% of MSS-type CRC patients were sensitive to this regimen, achieving a major pathological response (MPR). However, a small percentage of MSS-type CRC patients who were not sensitive to the treatment did not achieve MPR. Therefore, immunotherapy for MSS / pMMR-type CRC may require screening of the target population and should be based on targeted combination therapy according to immunophenotype and related molecular subtypes.

[0005] Existing strategies for improving immunotherapy sensitivity may involve complex gene editing, targeted drugs, or chemotherapy, which are invasive and accompanied by significant side effects. Some existing immunomodulatory therapies, such as cell therapy and gene therapy, have complex production processes, high costs, and limited application. Due to the unique anatomy and pathophysiology of chronic rheumatoid arthritis (CRC), the role of the gut microbiota and its metabolites in the development and progression of CRC has attracted widespread attention over the past decade. As the largest immune organ in the body, the gastrointestinal tract contains 60-80% of the host's immune cells and immune-related structures that maintain immune homeostasis. The gut microbiota and its metabolites can influence adaptive and innate immunity through multiple pathways and have intricate immunomodulatory effects on systemic tumor immunity. However, research focusing on improving immunotherapy sensitivity in CRC through gut microbiota has not yet been reported. Summary of the Invention

[0006] This application discloses the use of at least one of intestinal flora or its metabolites, antibodies or their derivatives in the preparation of immunotherapeutic drugs for colorectal cancer. The intestinal flora includes at least one of Allergan salviae, Butymonas putrefactiveis, Desulfovibrio suis, Actinobacillus putrefactiveis, Osmotherium visceratum, Clostridium plasmidonum, Procyonella pluribus, Eubacterium rectum, Escherichia coli, and Allergan anginae; and the antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0007] This application discloses the use of a composition in the preparation of an immunotherapy drug for colorectal cancer, the composition comprising at least one of the gut microbiota or its metabolites, an antibody or its derivative, and a product for treating colorectal cancer.

[0008] In some embodiments, the product for treating colorectal cancer includes at least one of bacterial strains, immune checkpoint inhibitors, immune activators, tumor vaccines, immunotherapy drugs, and cytokine therapy drugs.

[0009] This application discloses the use of at least one of the gut microbiota or its metabolites, antibodies or their derivatives, in the preparation of products that improve or predict the sensitivity of colorectal cancer patients to immunotherapy with immune checkpoint inhibitors.

[0010] This application discloses the use of at least one of the gut microbiota or its metabolites, antibodies or their derivatives in the preparation of drugs that inhibit the IL-17A cytokine signaling pathway.

[0011] This application discloses the use of IL-17A signaling pathway monoclonal antibody combined with immune checkpoint inhibitor in the preparation of colorectal cancer drugs.

[0012] This application discloses a pharmaceutical composition containing at least one of the gut microbiota or its metabolites, an antibody or its derivative, and the product for treating colorectal cancer.

[0013] This application discloses a method for improving the sensitivity of colorectal cancer immunotherapy drugs by applying at least one of the following: gut microbiota or its metabolites, antibodies or their derivatives, and an immune checkpoint inhibitor. In some embodiments, improving the sensitivity of colorectal cancer immunotherapy drugs includes improving the sensitivity to immune checkpoint inhibitors. In some embodiments, improving the sensitivity of colorectal cancer immunotherapy drugs also includes improving the sensitivity to immunocellular therapy drugs and / or cytokine therapy drugs.

[0014] In some embodiments, the intestinal flora includes at least two of the following: *Alternaria sarcopenia*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidon*, *Pleurotus pustulosa*, *Eubacterium rectum*, *Vibrio systolica*, and *Alternaria oncogenes*.

[0015] In some embodiments, the gut microbiota metabolites include at least one of phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, phosphatidylcholine (O-16:0-20:4), and 4-acetaminophen.

[0016] In some embodiments, the metabolites of *Alternaria salicylides* include phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, and phosphatidylcholine (O-16:0-20:4). In some embodiments, the metabolites of *Pseudomonas putida* include 4-acetaminophen.

[0017] In some embodiments, the product for treating colorectal cancer includes an immune checkpoint inhibitor.

[0018] In some embodiments, the immune checkpoint inhibitor includes PD-1 antibodies and / or PD-L1 antibodies.

[0019] In some embodiments, the product for treating colorectal cancer includes a bacterial strain.

[0020] In some embodiments, the strain includes at least one of Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium.

[0021] In some embodiments, the colorectal cancer includes microsatellite stable colorectal cancer and microsatellite unstable colorectal cancer.

[0022] In some embodiments, the dosage form of the drug and / or product is independently selected from one of liquid, gas, solid, and semi-solid dosage forms.

[0023] In some embodiments, the drug and / or product further includes pharmaceutically acceptable excipients. Attached Figure Description

[0024] Figure 1, Figure 1 (continued 1), Figure 1 (continued 2), Figure 1 (continued 3) and Figure 1 (continued 4) together constitute the complete Figure 1, which shows the results of fecal metagenomic sequencing species LEfSe analysis of MSS-type CRC patients before treatment, whether they are sensitive or insensitive to neoadjuvant immunotherapy.

[0025] Figures 2A, 2B, 2C, and 2D show the results of the analysis of the effects of different treatment groups (containing Alistipes shahii) on tumor size, tumor volume, body weight, and tumor weight in a mouse MSI-H type colorectal cancer subcutaneous model in Example 2.

[0026] Figures 3A, 3B, 3C, and 3D show the results of tumor size, tumor volume, body weight, and tumor weight analysis in MSS-type colorectal cancer subcutaneous mouse models using different treatment groups (containing Alistipes shahii) in Example 2.

[0027] Figure 4 shows the intratumoral Th17 cells and effector CD8 T cells (Granzyme B (GZMB)) in the MSS-type colorectal cancer orthotopic model of mice with different treatment groups (containing Alistipes shahii) in Example 2. + Figure showing the results of CD8T cell infiltration analysis;

[0028] Figures 5A and 5B are bar charts showing the infiltration of Th17 cells and effector CD8 T cells (GZMB+CD8 T cells) in the MSS-type colorectal cancer orthotopic model of mice with different treatment groups (containing Alistipes shahii) in Example 2.

[0029] Figure 6 shows the results of the analysis of CD8T cell infiltration in the MSS-type colorectal cancer orthotopic model in mice with different treatment groups (containing Alistipes shahii) in Example 2.

[0030] Figure 7A shows the infiltration of exhausted immune cells (PD-1+ cells) in the MSS-type colorectal cancer orthotopic model in mice with different treatment groups (containing Alistipes shahii) in Example 2. Figure 7B shows the analysis results.

[0031] Figures 8A and 8B are respectively the in vivo imaging results and tumor fluorescence intensity analysis results of mouse MSS type orthotopic colorectal cancer models with different treatment groups (containing Alistipes shahii) in Example 2 (fluorescence intensity is proportional to the number and activity of tumor cells). The number of days is calculated as follows: the first day after tumor bearing is Day 1, and so on.

[0032] Figures 9A, 9B, and 9C show the results of tumor size, tumor volume, and tumor weight analysis in mouse MSS-type orthotopic colorectal cancer models using different treatment groups (containing Alistipes shahii) in Example 2.

[0033] Figures 10A, 10B, and 10C show the results of tumor size, tumor volume, and tumor weight analysis in mouse MSS subcutaneous colorectal cancer models using different treatment groups (including Butyricimonas virosa) in Example 2.

[0034] Figures 11A, 11B, 11C, 11D, and 11E show the results of tumor fluorescence intensity, total flux, tumor size, tumor volume, and tumor weight analysis in mouse MSS-type colorectal cancer orthotopic models using different treatment groups (including Butyricimonas virosa) in Example 2.

[0035] Figure 12A shows the significant enrichment of phosphatidylcholine (O-16:0_20:4) in the intestinal contents of mice in the PBS group and the Alistipes shahii group in Example 3; Figure 12B shows the significant enrichment of phosphatidylcholine (O-16:0_20:4) in the intestinal contents of mice in the PD-1 monoclonal antibody group and the Alistipes shahii combined with PD-1 monoclonal antibody group in Example 3.

[0036] Figures 13A and 13C show the significant enrichment of O-Phosphorylethanolamine in the culture supernatant of the Alistipes shahii group and the Alistipes shahii combined with PD-1 monoclonal antibody group in Example 3, respectively. Figures 13B and 13D show the secondary spectra of O-Phosphorylethanolamine metabolome PLUS detection, respectively.

[0037] Figure 14 shows the significant enrichment of 5,6-dihydroxyindole-2-carboxylic acid in the intestinal contents of the Alistipes shahii combined with PD-1 monoclonal antibody group in Example 3.

[0038] Figure 15 shows the significant enrichment of 4-acetamidobutyric acid in the intestinal contents of mice in the Butyricimonas virosa combined with PD-1 monoclonal antibody group in Example 3.

[0039] Figures 16A and 16B are the results of KEGG enrichment analysis of the ordinary transcriptome of tumor tissue in Example 4 (Figure 16A shows the results of significant downregulation of the IL-17 signaling pathway in tumor tissue of MPR patients before treatment compared to non-MPR patients; Figure 16B shows the results of significant downregulation of the IL-17 signaling pathway and Th17 cell differentiation pathway in tumor tissue of MPR patients after treatment compared to non-MPR patients).

[0040] Figures 17A and 17B are the results of GSEA analysis of the conventional transcriptome of tumor tissue before treatment in Example 4, respectively; Figure 17A shows the results of the IL-17 signaling pathway, and Figure 17B shows the results of the Th17 cell differentiation pathway.

[0041] Figures 18A and 18B are the results of GSEA analysis of the conventional transcriptome of tumor tissue after treatment in Example 4, respectively; Figure 18A shows the results of the IL-17 signaling pathway, and Figure 18B shows the results of the Th17 cell differentiation pathway.

[0042] Figure 19 shows the flow cytometry results of Th17 cell differentiation under different treatment conditions (Figure I).

[0043] Figure 20 shows the flow cytometry results of Th17 cell differentiation under different treatment conditions (Figure II).

[0044] Figure 21A is a bar chart showing the differentiation of Th17 cells before and after induction, and Figure 21B is a bar chart showing the differentiation of Th17 cells under different treatment conditions.

[0045] Figure 22 shows the tumor images of the MSS-type colorectal cancer subcutaneous model mice treated with different groups (containing O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid) in Example 6.

[0046] Figures 23A and 23B show the results of tumor volume and tumor weight analysis in the MSS subcutaneous colorectal cancer mouse model using different treatment groups (containing O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid) in Example 6.

[0047] Figures 24A, 24B, and 24C show the results of tumor infiltration of Th17 cells, CD8 T cells, and effector CD8 T cells (GZMB+CD8 T cells) in a mouse MSS colorectal cancer subcutaneous model using different treatment groups (containing O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-carboxylic acid) in Example 6.

[0048] Figures 25A, 25B, and 25C are bar charts showing the infiltration of Th17 cells, CD8 T cells, and effector CD8 T cells (GZMB+CD8 T cells) within the tumor in a mouse MSS colorectal cancer subcutaneous model using different treatment groups (containing O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-carboxylic acid) in Example 6.

[0049] Figure 26A shows the infiltration of exhausted immune cells (PD-1+ cells) within the tumor in a mouse MSS colorectal cancer subcutaneous model in different treatment groups (containing O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid) in Example 6. Figure 26B shows the analysis results. Detailed Implementation

[0050] This application provides the use of gut microbiota or its metabolites, antibodies or their derivatives in the preparation of immunotherapeutic drugs for colorectal cancer. Using the gut microbiota or its metabolites, antibodies or their derivatives of this application can improve the sensitivity of colorectal cancer patients to immunotherapy, and these gut microbiota or their metabolites, antibodies or their derivatives can also predict the survival, prognosis, and sensitivity to immunotherapy in patients with MSS-type colorectal cancer.

[0051] The technical solution adopted in this application is as follows:

[0052] This application provides the use of at least one of intestinal flora or its metabolites, antibodies or their derivatives in the preparation of immunotherapeutic drugs for colorectal cancer, wherein the intestinal flora includes at least one of Alistipes shahii, Butyricimonas virosa, Desulfovibrio piger, Alistipes putredinis, Odoribacter splanchnicus, Faecalibacterium prausnitzii, Phocaeicola plebeius, Eubacterium rectale, Oscillospiraceae bacterium, and Alistipes onderdonkii;

[0053] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0054] Based on the applicant's prospective clinical trial, this application compared the gut microbiota in the feces of patients with MSS-type colorectal cancer who were sensitive to immunotherapy (MPR) and insensitive to it (non-MPR), and identified and characterized 10 bacterial species enriched in the feces of treatment-sensitive patients. Furthermore, the pathogenicity of these 10 bacteria (Salmonella abscessus, Butymonas putrefactiveis, Desulfovibrio suis, Actinomyces putrefactiveis, Osmotherium viscerans, Clostridium plasmidonum, Probenecidae, Eubacterium rectum, Bacteroides systolicum, and S. abscessus) has not been reported.

[0055] Data from prospective clinical trials have demonstrated that the applicant's novel drug combination (mFOLFOX6 + bevacizumab + PD-1 monoclonal antibody) can significantly improve the sensitivity to immunotherapy in MSS-type colorectal cancer, while maximizing R0 resection rate and pCR rate, and avoiding the toxic side effects of triple chemotherapy and concurrent chemoradiotherapy. Although this regimen has shown excellent performance in significantly improving the sensitivity to immunotherapy, pCR rate, and MPR rate in MSS-type CRC patients, some patients still do not respond to treatment and do not achieve MPR.

[0056] The discovery of these related strains is expected to further improve the efficacy and prognosis of patients who are not sensitive to treatment, and improve the overall survival of colorectal cancer patients (especially MSS type colorectal cancer patients); these strains are expected to serve as targets for clinical prediction and improvement of the sensitivity of colorectal cancer to immunotherapy, enabling patients to receive more precise neoadjuvant therapy.

[0057] In addition, this application also found that the metabolites of the above-mentioned gut microbiota can also enhance the sensitivity of colorectal cancer patients to immunotherapy, and the metabolites of the gut microbiota can also predict the survival, prognosis and sensitivity to immunotherapy of colorectal cancer patients (especially MSS type colorectal cancer patients).

[0058] Furthermore, this application also found that *Alternaria salicylates* in the gut microbiota can inhibit the differentiation of CD4 naïve T cells into Th17 cells by secreting related metabolites, thereby reducing the number of Th17 cells infiltrating colorectal cancer and their effects, thus enhancing the therapeutic effect of PD-1 monoclonal antibody on colorectal cancer; IL-17A is mainly secreted by Th17 cells, and it was also found that monoclonal antibodies against the IL-17A signaling pathway can significantly reduce Th17 cells and the IL-17A they secrete, thereby achieving a therapeutic effect on colorectal cancer.

[0059] Furthermore, gut microbiota (e.g., Alistipes shahii) can significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) within tumors, while increasing the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) within tumors. This effect is further enhanced when gut microbiota (Alistipes shahii) is used in combination with PD-1 monoclonal antibodies.

[0060] The combination of IL-17A monoclonal antibodies and PD-1 monoclonal antibodies can also significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, while increasing the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) in tumors. Therefore, gut microbiota (e.g., Alistipes shahii) may promote the infiltration and function of CD8 T cells in tumors by inhibiting the infiltration of Th17 cells, thereby enhancing the anti-tumor immune response.

[0061] In some embodiments, the intestinal flora includes at least two of the following: *Alternaria sarcopenia*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidon*, *Pleurotus pustulosa*, *Eubacterium rectum*, *Vibrio systolica*, and *Alternaria oncogenes*.

[0062] This application utilizes the above-mentioned gut microbiota species in combination, which can enhance the sensitivity of mice to immunotherapy drugs for colorectal cancer and also achieve significant anti-tumor effects (application for the treatment of colorectal cancer).

[0063] In some specific embodiments, the pairing of intestinal flora (two pairs) may include the following combinations: 1) *Salmonella sarcopeniae* and *Butyrosporum putidae*; 2) *Salmonella sarcopeniae* and *Desulfovibrio suis*; 3) *Salmonella sarcopeniae* and *Actinomyces putidae*; 4) *Salmonella sarcopeniae* and *Vibrio viscerans*; 5) *Butyrosporum putidae* and *Desulfovibrio suis*; 6) *Butyrosporum putidae* and *Actinomyces putidae*; 7) *Clostridium plasmidonum* and *Pleurotus pueraria*; 8) *Clostridium plasmidonum* and *Vibrio osmidonum*; 9) *Vibrio osmidonum* and *Salmonella oncogenes*; 10) *Clostridium plasmidonum* and *Salmonella oncogenes*. In this application, the two pairings are not limited to the above-mentioned examples; any two pairs can be used together. This application provides 45 possible combinations of any two intestinal flora.

[0064] In some specific embodiments, the combination of intestinal flora (three combinations) may include the following combinations: 1) *Salmonella sarcopeniae*, *Butyrosporum putidae*, and *Desulfovibrio suis*; 2) *Salmonella sarcopeniae*, *Butyrosporum putidae*, and *Actinomyces putidae*; 3) *Salmonella sarcopeniae*, *Butyrosporum putidae*, and *Ostomyces viscerans*; 4) *Butyrosporum putidae*, *Desulfovibrio suis*, and *Actinomyces putidae*; 5) *Butyrosporum putidae*, *Desulfovibrio suis*, and *Ostomyces viscerans*; 6) *Desulfovibrio suis*, *Actinomyces putidae*, and *Ostomyces viscerans*; 7) *Clostridium plasmidii*, *Plebusch*, and *Eubacterium rectum*; 8) *Plebusch*, *Clostridium plasmidii*, and *Vibrio oscillatori*; 9) *Clostridium plasmidii*, *Vibrio oscillatori*, and *Salmonella oncogenes*; 10) *Eubacterium rectum*, *Vibrio oscillatori*, and *Salmonella oncogenes*. In this application, the three combinations are not limited to the combinations mentioned above, and any three combinations may be used. This application provides 120 possible combinations of any three types of the gut microbiota.

[0065] In some specific embodiments, the gut microbiota can be arbitrarily combined, such as four, five, six, seven, eight, nine, or ten types of gut microbiota combined together, not just the examples mentioned above.

[0066] In some specific embodiments, the intestinal flora is at least one of Alistipes shahii, Butyricimonas virosa, Desulfovibrio piger, Alistipes putredinis, and Odoribacter splanchnicus (e.g., one, two, three, four, five, at least one, at least two, at least three, at least four, or at least five).

[0067] This application also provides the use of a composition in the preparation of an immunotherapy drug for colorectal cancer, the composition comprising at least one of intestinal flora or its metabolites, an antibody or its derivative, and a product for treating colorectal cancer;

[0068] The intestinal flora includes at least one of the following: Allergy salinae, Butymonas putidae, Desulfuric Vibrio suis, Actinomyces putidae, Osmotherium visceratum, Clostridium plasmidonum, Probenecidae, Eubacterium rectum, Vibrio spirochetes, and Allergy salinae.

[0069] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0070] In some embodiments, the product for treating colorectal cancer is at least one of strains, immune checkpoint inhibitors, immune activators, tumor vaccines, immunotherapy drugs, and cytokine therapy drugs.

[0071] In some embodiments, the strain includes at least one of Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium.

[0072] The gut microbiota or its metabolites, antibodies or their derivatives of this application may also be used in combination with strains used to treat colorectal cancer, or the gut microbiota or its metabolites, antibodies or their derivatives of this application may also be used in combination with products (e.g., drugs) used to treat colorectal cancer, thereby enhancing the patient's sensitivity to colorectal cancer immunotherapy drugs and thus improving the treatment effect of colorectal cancer.

[0073] The strains used to treat colorectal cancer are common strains used in the field for treating colorectal cancer, such as Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium, but are not limited to the above-mentioned species.

[0074] The products used in this application for treating colorectal cancer are not limited to the types mentioned above, but also include other products in the field for treating colorectal cancer, all of which are included within the scope of protection of this application.

[0075] In this application, compared with the use of colorectal cancer immunotherapy drugs alone, the combination of intestinal flora or its metabolites, antibodies or their derivatives with colorectal cancer immunotherapy drugs can significantly inhibit tumor (colorectal cancer) growth.

[0076] This application may also utilize strains of bacteria such as Allergy salpermae, Butymonas putrefactiveis, Desulfuric Vibrio suis, Actinomyces putrefactiveis, Osmotherium visceratum, Clostridium plasmidonum, Probenebus, Eubacterium rectum, Vibrio spirochetes, and Allergy serotoninus to act on colorectal cancer treatment and colorectal cancer immunotherapy products, thereby improving the treatment effect of colorectal cancer.

[0077] This application also provides the use of at least one of gut microbiota or its metabolites, antibodies or their derivatives in the preparation of products that improve or predict the sensitivity of colorectal cancer patients to immunotherapy with immune checkpoint inhibitors, wherein the gut microbiota includes at least one of Salmonella, Butymonas putrefactiveis, Desulfovibrio suis, Actinobacillus putrefactiveis, Osmotherium viscerans, Clostridium plasmidonum, Probenecidus, Eubacterium rectum, Vibrio systolicis, and Salmonella oncolyticus;

[0078] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0079] In some embodiments, the gut microbiota metabolites include at least one of O-Phosphorylethanolamine, 5,6-Dihydroxyindole-2-Carboxylic Acid, Phosphatidylcholine PC (O-16:0_20:4), and 4-Acetamidobutyric Acid.

[0080] This application discovers that the above-mentioned gut microbiota metabolites, antibodies, or their derivatives can effectively inhibit the IL-17A signaling pathway, improve the tumor microenvironment, enhance the anti-tumor effect of immunotherapy, and have the function of regulating the immune microenvironment and improving the sensitivity of tumor immunotherapy.

[0081] The above-mentioned metabolites, used alone or in combination, have shown significant anti-tumor effects.

[0082] The metabolites of the aforementioned *Alternaria salicylate* include phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, and phosphatidylcholine (O-16:0-20:4).

[0083] The metabolites of *Pesticide Butyrosporum* include 4-acetaminophen.

[0084] This application also screened for metabolites of the intestinal flora. O-Phosphorylethanolamine, 5,6-Dihydroxyindole-2-carboxylic acid, and phosphatidylcholine (O-16:0_20:4) were significantly enriched in Allergans sarcopenia, while 4-Acetamidobutyric acid was significantly enriched in Putrefactive butyric acid bacteria.

[0085] In some specific embodiments, O-phosphorylethanolamine and 5,6-dihydroxyindole-2-carboxylic acid inhibit... CD4 + T cell differentiation into Th17 cells reduces IL-17A secretion by Th17 cells, thereby downregulating the activity of the IL-17A signaling pathway. Based on this, *Alistipes shahii* effectively inhibits the IL-17A signaling pathway through its metabolites O-phosphorylethanolamine and 5,6-dihydroxyindole-2-carboxylic acid, improving the tumor microenvironment and enhancing the anti-tumor effect of immunotherapy. This mechanism further illustrates the important role of gut microbiota metabolites in regulating the immune microenvironment and improving the sensitivity of tumor immunotherapy.

[0086] Furthermore, experiments have shown that the above metabolites can significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, and improve the infiltration and function of CD8T cells in tumors.

[0087] This application also provides the use of at least one of intestinal flora or its metabolites, antibodies or their derivatives in the preparation of drugs that inhibit the IL-17A cytokine signaling pathway, wherein the intestinal flora includes at least one of Allergan salviae, Butymonas putrefactiveis, Desulfurovibrio suis, Actinobacillus putrefactiveis, Osmotherium viscerans, Clostridium plasmidonum, Probenecidae, Eubacterium rectum, Vibrio systolicis, and Allergan serovar oz.

[0088] The metabolites include any one or a combination of O-Phosphorylethanolamine, 5,6-Dihydroxyindole-2-Carboxylic Acid, phosphatidylcholine (O-16:0-20:4), and 4-Acetamidobutyric Acid.

[0089] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0090] This application included KEGG enrichment analysis and GSEA analysis of the uncomputed transcriptome of tumor tissues from MPR patients. Compared to non-MPR patients, the IL-17 signaling pathway was significantly downregulated in tumor tissues of MPR patients before treatment. Compared to non-MPR patients, both the IL-17 signaling pathway and the Th17 cell differentiation pathway were significantly downregulated in tumor tissues of MPR patients before treatment; and compared to non-MPR patients, the IL-17A signaling pathway and the Th17 cell differentiation pathway were significantly downregulated in tumor tissues of MPR patients after treatment.

[0091] Experimental results show that the gut microbiota or its metabolites, antibodies or their derivatives of this application can inhibit naïve CD4. + The differentiation of T cells into Th17 cells inhibits the secretion of IL-17, improves the immunosuppressive state in the tumor microenvironment, and thus improves the sensitivity of MSS-type CRC patients to immunotherapy and promotes anti-tumor immune response.

[0092] This application also provides the application of IL-17A signaling pathway monoclonal antibody combined with immune checkpoint inhibitor in the preparation of colorectal cancer drugs.

[0093] This application also demonstrates that the combination of an IL-17A signaling pathway monoclonal antibody with an immune checkpoint inhibitor (which may be, but is not limited to, a PD-1 monoclonal antibody) exhibits significant tumor-suppressive activity.

[0094] In some embodiments, the immune checkpoint inhibitor includes a PD-1 antibody or a PD-L1 antibody.

[0095] In some embodiments, the immune checkpoint inhibitor is a PD-1 antibody.

[0096] The immune checkpoint inhibitors used in this application are not limited to PD-1 antibodies or PD-L1 antibodies, but also include immune checkpoint inhibitors commonly used in the field.

[0097] The gut microbiota or metabolites, antibodies or their derivatives used in this application can improve the sensitivity of colorectal cancer patients to PD-1 antibody therapy. Compared with PD-1 monotherapy, the gut microbiota or metabolites, antibodies or their derivatives used in combination with PD-1 antibodies can significantly inhibit the growth of colorectal cancer.

[0098] In some embodiments, the colorectal cancer includes microsatellite stable colorectal cancer and microsatellite unstable colorectal cancer.

[0099] In some specific embodiments, the colorectal cancer also includes MSI-H type colorectal cancer.

[0100] As used in this article, the term “MSS colorectal cancer” encompasses the common or recognized pathological types of MSS colorectal cancer in this field, including, for example, MSS in situ colorectal cancer.

[0101] In some embodiments, the dosage form of the drug includes one of liquid, gas, solid, and semi-solid dosage forms. The drug dosage forms involved in this application are not limited to the above-mentioned dosage forms, but also include dosage form choices commonly used in the art. The intestinal flora or its metabolites, antibodies or their derivatives of this application can be formulated into products of any pharmaceutically acceptable dosage form, such as capsules, powders, granules, tablets, pellets, oral liquids, lyophilized powders for injection, or injections. Appropriate excipients may be added as needed during product formulation; the pharmaceutical excipients used are one or more combinations of any pharmaceutically acceptable excipients.

[0102] In some embodiments, the drug also includes pharmaceutically acceptable excipients.

[0103] In some specific embodiments, pharmaceutical excipients include at least one of solubilizers, cosolvents, preservatives, flavoring agents, colorants, suspending agents, emulsifiers, wetting agents, foaming agents, defoamers, fillers, absorbents, diluents, and binders. This application may select pharmaceutical excipients according to actual circumstances, and is not limited to the above-mentioned categories.

[0104] This application also provides a pharmaceutical composition comprising intestinal flora or its metabolites, antibodies or their derivatives, and colorectal cancer immunotherapy drugs;

[0105] The intestinal flora includes at least one of the following: Allergy sarcopenia, Butymonas putida, Desulfuric Vibrio suis, Actinomyces putidae, Osmotherium visceratum, Clostridium plasmidonum, Probenebus, Eubacterium rectum, Vibrio spirochetes and Allergy oncogenes.

[0106] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0107] In some embodiments, the colorectal cancer immunotherapy drug includes an immune checkpoint inhibitor.

[0108] In some embodiments, the immune checkpoint inhibitor includes a PD-1 antibody or a PD-L1 antibody.

[0109] In some embodiments, the gut microbiota also includes strains that act on the treatment of colorectal cancer.

[0110] In some embodiments, the strains used to treat colorectal cancer include at least one of Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium.

[0111] This application combines gut microbiota or its metabolites, antibodies or their derivatives, and colorectal cancer immunotherapy drugs to form a pharmaceutical composition that can significantly inhibit tumor growth, especially the growth of colorectal cancer. Furthermore, the gut microbiota or its metabolites, antibodies or their derivatives used in this application can enhance patients' sensitivity to colorectal cancer immunotherapy drugs, thereby improving the treatment efficacy for colorectal cancer.

[0112] In some specific embodiments, the pharmaceutical composition of this application can be prepared into oral formulations or dietary supplements, which are convenient to use. Currently, some existing immune-enhancing therapies, such as cell therapy and gene therapy, have complex production processes, high costs, and limited application. The gut microbiota used in this application can be produced on a large scale through mature fermentation and formulation processes, which has the advantages of low cost and short production cycle, making it easy to promote and popularize.

[0113] In some embodiments, the intestinal flora is prepared into a bacterial agent by solvent mixing, and the mass concentration of the intestinal flora in the bacterial agent is 10. 8 ~10 12 CFU (colony forming unit) / agent.

[0114] In some embodiments, the solvent includes a PBS solution. The solvents used in this application are not limited to PBS solutions; any solvent used to prepare the bacterial agent is within the scope of this application.

[0115] This application also provides a method for improving the sensitivity of colorectal cancer immunotherapy drugs by applying at least one of intestinal flora or its metabolites, antibodies or their derivatives, and immune checkpoint inhibitors.

[0116] The intestinal flora includes at least one of the following: Allergy sarcopenia, Butymonas putida, Desulfuric Vibrio suis, Actinomyces putidae, Osmotherium visceratum, Clostridium plasmidonum, Probenebus, Eubacterium rectum, Vibrio spirochetes and Allergy oncogenes.

[0117] The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

[0118] In some embodiments, the method further includes the application of immune cell therapy drugs and cytokine therapy drugs.

[0119] Existing immunotherapies, such as PD-1 or PD-L1 inhibitors, generally have limited efficacy against MSS-type CRC, and many patients have poor treatment responses. Some existing strategies to improve the sensitivity of immunotherapy may involve complex gene editing, targeted drugs, or chemotherapy, which are invasive and accompanied by significant side effects.

[0120] In some embodiments, the antibodies or derivatives thereof include, but are not limited to, antigen-binding antibody fragments and associated antibody-drug conjugates (ADCs). In some embodiments, the antigen-binding fragments include, but are not limited to, Fab, Fab', F(ab')2, ScFv, BiTE, single-domain antibody sdAbs (e.g., camel / alpaca antibody VHH, shark antibody VNAR, humanized VH / VL), double-chain antibody diabody, minibody, bispecific antibody fragment BiTE, trivalent antibody triabody, and VHH-based nanobodies (e.g., nanobody).

[0121] The gut microbiota or its metabolites, antibodies or their derivatives provided in this application improve the sensitivity of colorectal cancer patients to immunotherapy by regulating the gut microbiota and immune system, especially improving the sensitivity of MSS-type CRC patients to immunotherapy, enabling more patients to benefit from immunotherapy. Furthermore, this application uses safe biological agents, requires no complex surgical or drug intervention, has good tolerability and low side effects, and is suitable for long-term use.

[0122] Compared with the prior art, this application has the following beneficial effects:

[0123] This application provides the use of gut microbiota or its metabolites, antibodies or their derivatives in the preparation of immunotherapy drugs for colorectal cancer. The gut microbiota or its metabolites, antibodies or their derivatives of this application can improve the tumor microenvironment, enhance the anti-tumor effect of immunotherapy, and have the function of regulating the immune microenvironment and improving the sensitivity of tumors to immunotherapy. Furthermore, the gut microbiota used in this application can be produced on a large scale through mature fermentation and formulation processes, which has the advantages of low cost and short production cycle, and is easy to promote and popularize. The gut microbiota or its metabolites of this application can also predict the survival, prognosis and sensitivity to immunotherapy of MSS type colorectal cancer patients.

[0124] In this invention, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions that include the listed features.

[0125] In this invention, numerical ranges are involved. Unless otherwise specified, the numerical ranges are considered continuous and include the minimum and maximum values ​​of the range, as well as every value between the minimum and maximum values. Furthermore, when the range refers to integers, it includes every integer between the minimum and maximum values ​​of the range. Additionally, when multiple ranges are provided to describe features or characteristics, the ranges may be merged. In other words, unless otherwise specified, all ranges disclosed herein should be understood to include any and all subranges to which they are included.

[0126] To better illustrate the purpose, technical solution, and advantages of this application, the following description, in conjunction with the accompanying drawings and specific embodiments, will further explain this application. In the following embodiments, unless otherwise specified, the experimental methods used are conventional methods, and the materials and reagents used are commercially available unless otherwise specified, and the raw materials used in each parallel experiment are the same.

[0127] Example 1: Screening of gut microbiota by fecal metagenomic sequencing

[0128] This application collected pre-treatment stool samples from 17 treatment-sensitive (MPR) patients and 8 pre-treatment stool samples from treatment-insensitive (non-MPR) patients, and performed metagenomic sequencing analysis on all samples.

[0129] Specific steps of metagenomic sequencing:

[0130] 1. Test method:

[0131] The DNA from the total sample was separated and purified using a pre-packaged fecal genomic DNA extraction kit (AU46111-96, Biotech) according to the manufacturer's instructions. The DNA concentration was then measured using the Qubit 1XdsDNA HS Assay Kits (Invitrogen, Q33230). 200 ng of QC-compliant DNA was transferred to a 0.6 mL low-adsorption centrifuge tube (#MCT-060-LC), and water was added to a final volume of 52 μL. The DNA was fragmented according to the required fragment length (generally 200-500 bp) using a Bioruptor TMPico (Diagenode). The fragmented products were purified and recovered using a TruSeq library preparation kit with magnetic beads. The fragmented product size was checked using an Agilent 2100 Bioanalyzer (HighSensitivity DNA Reagents, Agilent, 5067-4627). Samples that did not meet the fragment size requirements were resampled and fragmented. Genomic libraries were constructed from the fragment products using the TruSeq Nano DNA LT Library Preparation Kit (Illumina, FC-121-4001). The process included end repair, adapter ligation, index PCR amplification, and purification. Details can be found in the kit's instruction manual. The library was quantified using the Qubit1X dsDNA HS Assay Kits (Invitrogen, Q33230). Finally, paired-end sequencing was performed using an Illumina Novaseq 6000 (LC Bio Technology CO., Ltd., Hangzhou, China) in PE150 mode, using the NovaSeq 6000XP 4-Lane Kit v1.5 (300 cycles) (Illumina, 20043131).

[0132] 2. Bioinformatics analysis methods:

[0133] The raw data from the sequencing was in FastQ format. FastP (https: / / github.com / OpenGene / fastp, version 0.23.4) software was used for quality control of the raw data, with the parameters -l 100 -gW 6 -5 -q 20 -u 30. Bowtie2 (https: / / www.sbgrid.org / software / titles / bowtie-2, version 2.2.0) was used to align the sequencing data with the host genome. Sequences aligned to the host genome were filtered out (this step is only for projects requiring host sequence filtering, such as human or mouse fecal samples), ensuring that subsequent assembly and analysis results consisted of microbial sequences. After obtaining the valid sequences, the valid data for each sample were assembled using the software MEGAHIT (https: / / github.com / voutcn / megahit, version 1.2.9). The assembly result was a FASTA format file, where each sequence was a contig. This application retained contigs longer than 500 bp and used the software MetaGeneMark (https: / / exon.gatech.edu / GeneMark / meta_gmhmmp.cgi, version 3.26) for CDS (Coding Region) prediction. Sequences with a CDS length less than 100 nt were filtered based on the prediction results. Subsequently, based on the CDS prediction results, redundancy was removed using the software MMseqs2 (https: / / github.com / soedinglab / MMseqs2 / , version 15-6f452) to obtain non-redundant unigenes. Clustering was then performed using 95% identity and 90% coverage, and the longest sequence was selected as the representative sequence to construct the unigenes set. Bowtie2 (https: / / www.sbgrid.org / software / titles / bowtie-2, version 2.2.0) was used to align the valid sequences of each sample to the unigene sequences. The number of reads aligned to each unigene in each sample was calculated, while unigenes with less than or equal to 2 reads aligned to all samples were filtered out. The final set of unigenes used for subsequent analysis was obtained, and the abundance of each unigene was calculated.Using DIAMOND software (https: / / github.com / bbuchfink / diamond, version 0.9.14), Unigenes protein sequences were aligned with the NR_meta database to obtain species annotation information at different taxonomic levels. This information was compared with KEGG (http: / / www.genome.jp / kegg / , version 87.1), GO (http: / / geneontology.org / , version 2018.12.21), eggNOG (http: / / eggnogdb.embl.de / download / , version 5.0), PHI (http: / / www.phi-base.org / , version 4.14), and CAZy (http: / / ww The annotation information for each functional database was obtained by comparing it with functional databases such as w.cazy.org / (version 2022.08.06), CARD (https: / / card.mcmaster.ca / download, version v3.2.5), VFDB (http: / / www.mgc.ac.cn / VFs / , version 2023.03.03), MGEs (http: / / github.com / KatariinaParnanen / MobileGeneticElementDatabase, version 2017.12.28), and BacMet (http: / / bacmet.biomedicine.gu.se / , version 2.0) (see report 2.3 for details). Finally, the abundance information for each species and functional taxonomic level was obtained based on the Unigenes abundance statistics. Simultaneously, statistical analysis of differences between comparison groups was conducted based on species and function. Fisher's exact test was used for differences between samples without biological replicates; the Wilcoxon rank-sum test (or Mann-Whitney U test) was used for differences between two groups with biological replicates; and the Kruskal-Wallis test was used for differences between multiple groups with biological replicates. The difference threshold was p < 0.05 and |log2(fold_change)| > 1. Based on the difference analysis results, box plots were generated using R (version 3.6.0) to display the abundance of differentially expressed species or functional items between groups. Functional enrichment analysis was performed using ReporterScore (https: / / github.com / Asa12138 / ReporterScore).Alpha diversity indices, including Chao1, Observed species, Goods coverage, Shannon, and Simpson, were calculated at the species level using qiime1 (http: / / qiime.org / ) and visualized as dilution curves (R language, version 3.6.0). Intergroup difference analysis was performed on samples with 5 or more biological replicates. Results were presented using violin and box plots. The statistical method used for comparisons between two groups was the Wilcoxon rank-sum test (or Mann-Whitney U test), and the statistical method used for comparisons between multiple groups was the Kruskal-Wallis test. Beta diversity was visualized by calculating Bray-Curtis distance and performing PCoA (Principal coordinates analysis), NMDS (Nonmetric Multidimensional Scaling), UPGMA, Anosim (Analysis of similarities), and Adonis (PermANOVA) analyses (R language, version 3.6.0).

[0134] 3. Species selection:

[0135] LEfSe (Linear Discriminant Analysis Effect Size) analysis was used to assess intergroup differences among species at each taxonomic level, with a selection threshold of LDA > 2.5 and p < 0.05. The results were visualized using clade plots and bar charts. Spearman correlation analysis was performed on species at the species level using the R packages ggplot2 (version 3.2.0) and ggnetwork, and relationships with |rho| > 0.8 were displayed using a network diagram. MetagenomeSeq (version 1.38.0) was used to analyze intergroup differences among species at the species level, and the results were visualized using Manhattan plots (ggplot2, version 3.2.0).

[0136] 4. Analysis Results:

[0137] As shown in Figures 1, 1 (continued 1), 1 (continued 2), 1 (continued 3), and 1 (continued 4), comparing pre-treatment specimens from treatment-sensitive (MPR) patients with pre-treatment specimens from treatment-insensitive (non-MPR) patients, 78 species were found to be significantly enriched in treatment-sensitive patients. From this, ten bacteria were screened: Alistipes shahii, Butyricimonas virosa, Desulfovibriopiger, Alistipes putredinis, Odoribacter splanchnicus, Faecalibacterium prausnitzii, Phocaeicola plebeius, Eubacterium rectale, Oscillospiraceae bacterium, and Alistipes onderdonkii for further research.

[0138] in:

[0139] Alistipes shahii abundance ranking: 23; LDA score ranking: 24;

[0140] Butyricimonas virosa abundance ranking: 25; LDA score ranking: 30;

[0141] Desulfovibriopiger (Porcine Desulfovibriopiger) abundance ranking: 48; LDA score ranking: 46;

[0142] Alistipes putredinis abundance ranking: 11; LDA score ranking: 10;

[0143] Odoribacter splanchnicus abundance ranking: 13; LDA score ranking: 14;

[0144] Abundance ranking of Faecalibacterium prausnitzii: 1; LDA score ranking: 1;

[0145] Phocaeicolaplebeius abundance ranking: 2; LDA score ranking: 2;

[0146] Eubacterium rectale abundance ranking: 5; LDA score ranking: 4;

[0147] Oscillospiraceae bacterium abundance ranking: 6; LDA score ranking: 5

[0148] Alistipes onderdonkii abundance ranking: 8; LDA score ranking: 6.

[0149] Example 2: Intestinal flora validation experiment

[0150] 1. Source of gut microbiota: The gut microbiota was purchased through official channels of the American Test and Control Centre (ATCC) in the United States in the form of lyophilized powder, and stored at a temperature of 2-8℃.

[0151] Alistipes shahii (ATCC No.: BAA-1179, Batch No.: 70061103);

[0152] Desulfovibriopiger (ATCC No.: 29098, Batch No.: 70037201);

[0153] Alistipes putredinis (ATCC No.: 29800, Batch No.: 70051086);

[0154] Odoribacter splanchnicus (ATCC No.: 29572, Lot No.: 70054363);

[0155] Butyricimonas virosa is provided by Beijing Bio-Tech Biotechnology Co., Ltd., and is in the form of lyophilized powder. The storage temperature is 2-8℃.

[0156] 2. Cultivation conditions:

[0157] The culture conditions for Alistipes shahii, Alistipes putredinis, and Odoribacter splanchnicus were as follows: ATCC Medium 1490 medium, culture conditions of 37℃, and anaerobic environment (80% N2, 10% CO2, 10% H2).

[0158] The culture conditions for Desulfovibriopiger were as follows: ATCC Medium 1249 medium, culture conditions of 37℃, and anaerobic environment (80% N2, 10% CO2, 10% H2).

[0159] The culture conditions for Butyricimonas virosa were as follows: the culture medium was Nutrient Broth produced by Solarbio, and the culture conditions were 37°C in an anaerobic environment (80% N2, 10% CO2, 10% H2).

[0160] 3. Initial resuscitation steps:

[0161] (1) Resuscitation of Alistipes shahii, Alistipes putredinis and Odoribacter splanchnicus:

[0162] ① Under anaerobic conditions, use approximately 0.5 mL of anaerobic pretreated sterile ATCC Medium 1490 to dissolve the lyophilized powder and obtain a solution.

[0163] ② Under aseptic conditions, transfer the lysate to 5-6 mL of sterile ATCC Medium 1490 culture medium, and label it as primary broth.

[0164] ③ Take a few drops from the primary broth and inoculate them onto an ATCC Medium 260 plate or ATCC Medium 260 agar slant.

[0165] ④ Incubate in an anaerobic incubator at 37℃ for 2-4 days.

[0166] (2) Desulfovibrio piger recovery:

[0167] ① Under anaerobic conditions, use approximately 0.5 mL of anaerobic pretreated sterile ATCC Medium 1249 to dissolve the lyophilized powder and obtain a solution.

[0168] ② Under aseptic conditions, transfer the lysate to 5-6 mL of sterile ATCC Medium 1249 culture medium, and label it as primary broth.

[0169] ③ Take a few drops from the primary broth and inoculate them onto an ATCC Medium 260 plate or ATCC Medium 260 agar slant.

[0170] ④ Incubate in an anaerobic incubator at 37℃ for 2-4 days.

[0171] (3) Butyricimonas virosa resuscitation:

[0172] ① Using a sterile pipette, take 0.3 mL of anaerobic pretreatment culture medium (or sterile water), add it to the lyophilized strain, and gently shake until completely dissolved to obtain a solution.

[0173] ② Transfer all the solution to liquid culture medium or inoculate it into no more than two plates (if using liquid culture medium, use anaerobic screw-top test tubes and remove oxygen with high-purity nitrogen).

[0174] ③ Immediately place the inoculated culture in an anaerobic environment and incubate at a constant temperature of 37℃ for 2-4 days.

[0175] 4. Validation of a mouse subcutaneous colorectal cancer model:

[0176] (1) Model building:

[0177] Six-week-old C57BL / 6 and BALB / c mice were randomly divided into four groups:

[0178] CTRL group, PD-1 group, Alistipes shahii group and Alistipes shahii+PD-1 group.

[0179] The specific procedures for each group are as follows:

[0180] CTRL group: 200 μL PBS was administered by gavage every other day until the sample was collected;

[0181] Alistipes shahii group: 1×10 gavage per administration 9 CFU Alistipes shahii (dissolved in 200 μL PBS) was administered by gavage every other day until the sample was collected.

[0182] (2) Cell injection and tumor model establishment:

[0183] After four oral bolus treatments with bacteria or PBS, mice were subcutaneously injected with either MSI-H type MC38 cell line (2 million cells / mouse) or MSS type CT26 cell line (2 million cells / mouse) to construct a mouse subcutaneous CRC model.

[0184] Monitor tumor size and animal weight every other day.

[0185] When the tumor volume reaches 80mm 3 At that time, in addition to the Alistipes shahii group, PD-1 monoclonal antibody (100 μg / animal, twice a week) was injected intraperitoneally until the samples were collected, forming the Alistipes shahii+PD-1 group.

[0186] (3) Experimental results: In C57BL / 6 mice (subcutaneous MSI-H colorectal cancer mouse model) and BALB / c mice (MSS colorectal cancer mouse model), Alistipes shahii could improve the sensitivity of mice to PD-1 treatment. Compared with PD-1 monotherapy, Alistipes shahii combined with PD-1 could significantly inhibit tumor growth (as shown in Figures 2A, 2B, 2C, 2D, 3A, 3B, 3C, and 3D).

[0187] Therefore, Alistipes shahii may promote the infiltration and function of CD8 T cells within the tumor by inhibiting the infiltration of Th17 cells, thereby enhancing the anti-tumor immune response.

[0188] 5. Validation of the mouse orthotopic colorectal cancer model:

[0189] (1) Model building:

[0190] Six-week-old BALB / c mice were randomly divided into nine groups, as follows:

[0191] The groups included: PBS group, PD-1 monoclonal antibody group, Alistipes shahii group, Alistipes shahii + PD-1 monoclonal antibody group, PD-1 monoclonal antibody + Alistipes shahii + IgG group, PD-1 monoclonal antibody + IL-17A monoclonal antibody group, PD-1 monoclonal antibody + Alistipes shahii + IL-17A monoclonal antibody group, PD-1 monoclonal antibody + Alistipes shahii + IL-17A cytokine group, and PD-1 monoclonal antibody + Alistipes shahii + CD8 monoclonal antibody group. Specific treatments for each group were as follows:

[0192] (2) Cell injection and tumor model establishment:

[0193] ① One week after gavage administration of PBS, on Day 0, MSS-type CT26-luc cell line (500,000 cells / mouse) was injected into the cecal serosal layer of mice to construct an in situ mouse CRC model. PBS was administered at 200 μL each time via gavage, every other day, until samples were collected, forming the PBS group.

[0194] ②On Day 0, MSS-type CT26-luc cell line (500,000 cells / mouse) was injected into the cecal serosa of mice to construct an orthotopic CRC model. When the tumor volume reached 80 mm... 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) was injected intraperitoneally to form the PD-1 monoclonal antibody group.

[0195] ③ One week after gavage administration of Alistipes shahii, on Day 0, MSS-type CT26-luc cell line (500,000 cells / mouse) was injected into the cecal serosa of mice to construct an orthotopic CRC model. Each gavage administration administered 1×102 9 CFU Alistipes shahii (dissolved in 200 μL PBS) was administered by gavage every other day until the sample was collected, forming the Alistipes shahii group.

[0196] ④ Based on the Alistipes shahii group, when the tumor volume reaches 80mm 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) was injected intraperitoneally to form the Alistipes shahii + PD-1 monoclonal antibody group;

[0197] ⑤ Based on the Alistipes shahii group, when the tumor volume reaches 80mm 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) and IgG antibody (200 μg / animal, twice a week until sample collection) were injected intraperitoneally to form the PD-1 monoclonal antibody + Alistipes shahii + IgG group;

[0198] ⑥On Day 0, MSS-type CT26-luc cell line (500,000 cells / mouse) was injected into the cecal serosa of mice to construct an orthotopic CRC model. When the tumor volume reached 80 mm... 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) and IL-17A monoclonal antibody (200 μg / animal, twice a week until sample collection) were injected intraperitoneally to form the PD-1 monoclonal antibody + IL-17A monoclonal antibody group;

[0199] ⑦ Based on the Alistipes shahii group, when the tumor volume reaches 80mm 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) was injected intraperitoneally, and IL-17A cytokine (0.5 μg / animal, every other day until sample collection) was injected via tail vein to form PD-1 monoclonal antibody + Alistipes shahii + IL-17A cytokine group.

[0200] ⑧ Based on the Alistipes shahii group, when the tumor volume reaches 80mm 3At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) and IL-17A monoclonal antibody (200 μg / animal, twice a week until sample collection) were injected intraperitoneally to form PD-1 monoclonal antibody + Alistipes shahii + IL-17A monoclonal antibody group.

[0201] ⑨ Based on the Alistipes shahii group, when the tumor volume reaches 80mm 3 At that time, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) and CD8 monoclonal antibody (100 μg / animal, twice a week until sample collection) were injected intraperitoneally to form the PD-1 monoclonal antibody + Alistipes shahii + CD8 monoclonal antibody group.

[0202] (3) Experimental results:

[0203] In BALB / c mice (orthotopic MSS colorectal cancer model), Alistipes shahii enhanced the sensitivity of mice to PD-1 monoclonal antibody therapy. Compared with PD-1 monotherapy, Alistipes shahii in combination with PD-1 monoclonal antibody significantly inhibited tumor growth.

[0204] Furthermore, Alistipes shahii significantly reduced the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) within tumors, while increasing the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) within tumors. This effect was further enhanced when Alistipes shahii was used in combination with a PD-1 monoclonal antibody.

[0205] The combination of IL-17A monoclonal antibody and PD-1 monoclonal antibody can also significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, and increase the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) in tumors (as shown in Figures 4, 5A, 5B, 6, 7A, and 7B).

[0206] Further verification revealed that the combination of IL-17A monoclonal antibody and PD-1 monoclonal antibody also exhibited significant tumor-suppressive effects. However, the presence of IL-17A cytokines significantly weakened the sensitizing effect of Alistipes shahii on immunotherapy. Therefore, Alistipes shahii may promote anti-tumor immune responses by inhibiting Th17 cells and IL-17A cytokine-related signaling pathways (as shown in Figures 8A, 8B, 9A, 9B, and 9C).

[0207] 6. Validation of a mouse subcutaneous colorectal cancer model:

[0208] (1) Model building:

[0209] Six-week-old BALB / c mice were randomly divided into four groups:

[0210] The CTRL group, PD-1 group, Butyricimonas virosa group, and Butyricimonas virosa+PD-1 group were included.

[0211] The specific procedures for each group are as follows:

[0212] CTRL group: 200 μL PBS was administered by gavage every other day until the sample was collected;

[0213] Butyricimonas virosa group: 1×10^9 CFU / 200μL PBS was administered by gavage every other day until the sample was collected;

[0214] (2) Cell injection and tumor model establishment:

[0215] After four oral bolus treatments with bacteria or PBS, mice were subcutaneously injected with MSS-type CT26 cell line (2 million cells / mouse) to construct a mouse subcutaneous CRC model.

[0216] When the tumor volume reaches 80mm 3 Treatment begins at that time:

[0217] Based on the Butyricimonas virosa group, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) was injected intraperitoneally to form the Butyricimonas virosa+PD-1 group.

[0218] (3) Experimental results:

[0219] In BALB / c mice (a subcutaneous MSS-type subcutaneous colorectal cancer model), Butyricimonas virosa enhanced the sensitivity of mice to PD-1 monoclonal antibody therapy. Compared with PD-1 monotherapy, Butyricimonas virosa in combination with PD-1 monoclonal antibody significantly inhibited tumor growth. Notably, Butyricimonas virosa alone also showed significant antitumor effects (as shown in Figures 10A, 10B, and 10C).

[0220] 7. Validation of the mouse orthotopic colorectal cancer model:

[0221] (1) Model building:

[0222] Six-week-old BALB / c mice were randomly divided into four groups:

[0223] The groups were CTRL group, PD-1 group, Butyricimonas virosa group, and Butyricimonas virosa + PD-1 monoclonal antibody group. Specific treatments for each group are as follows:

[0224] CTRL group: 200 μL PBS was administered by gavage every other day until the sample was collected;

[0225] Butyricimonas virosa group: 1×10^9 CFU / 200μL PBS was administered by gavage every other day until the sample was collected;

[0226] (2) Cell injection and tumor model establishment:

[0227] After four oral bolus treatments with bacteria or PBS, MSS-type CT26-luc cell line (500,000 cells / mouse) was injected into the cecal serosa of mice to construct an in situ mouse CRC model.

[0228] Monitor tumor size and animal weight every other day.

[0229] When the tumor volume reaches 80mm 3 Treatment begins at that time:

[0230] Based on the Butyricimonas virosa group, PD-1 monoclonal antibody (100 μg / animal, twice a week until sample collection) was injected intraperitoneally to form the Butyricimonas virosa+PD-1 monoclonal antibody group.

[0231] (3) Experimental results:

[0232] In BALB / c mice (MSS-type colorectal cancer orthotopic mouse model), Butyricimonas virosa enhanced the sensitivity of mice to PD-1 therapy. Compared with PD-1 monotherapy, Butyricimonas virosa in combination with PD-1 significantly inhibited tumor growth (as shown in Figures 11A, 11B, 11C, 11D, and 11E).

[0233] In conclusion, it can be concluded that:

[0234] (1) In BALB / c mice (MSS subcutaneous colorectal cancer model), Alistipes shahii enhanced the sensitivity of mice to PD-1 monoclonal antibody therapy. Compared with PD-1 monotherapy, Alistipes shahii combined with PD-1 monoclonal antibody significantly inhibited tumor growth. Furthermore, IL-17A monoclonal antibody combined with PD-1 monoclonal antibody also showed significant tumor-suppressive effects. However, the presence of IL-17A cytokines significantly weakened the sensitizing effect of Alistipes shahii on immunotherapy. Therefore, Alistipes shahii may promote anti-tumor immune responses by inhibiting IL-17A cytokine-related signaling pathways.

[0235] (2) In C57BL / 6 mice (MSI-H type colorectal cancer mouse model) and BALB / c mice (MSS type colorectal cancer orthotopic mouse model), Alistipes shahii could improve the sensitivity of mice to PD-1 treatment. Compared with PD-1 monotherapy, Alistipes shahii combined with PD-1 could significantly inhibit tumor growth.

[0236] (3) In BALB / c mice (MSS subcutaneous colorectal cancer model), Butyricimonas virosa improved the sensitivity of mice to PD-1 treatment. Compared with PD-1 monotherapy, Butyricimonas virosa in combination with PD-1 significantly inhibited tumor growth.

[0237] (4) In BALB / c mice (MSS-type colorectal cancer orthotopic mouse model), Butyricimonas virosa enhanced the sensitivity of mice to PD-1 monoclonal antibody therapy. Compared with PD-1 monotherapy, Butyricimonas virosa in combination with PD-1 monoclonal antibody significantly inhibited tumor growth. Notably, Butyricimonas virosa alone also showed significant antitumor effects.

[0238] Example 3: Identification of gut microbiota-related metabolites

[0239] The screening criteria for gut microbiota-related metabolites are as follows:

[0240] Model selection:

[0241] 1. Screening and testing:

[0242] Mice with MSS-type CRC subcutaneous tumors were collected and divided into the following groups according to different treatment methods:

[0243] PBS gavage group (PBS group);

[0244] PD-1 monoclonal antibody treatment group (PD-1 group);

[0245] Alistipes shahii gavage group (Alistipes shahii group);

[0246] Butyricimonas virosa gavage group (Butyricimonas virosa group);

[0247] Alistipes shahii combined with PD-1 monoclonal antibody group (Alistipes shahii + PD-1 group);

[0248] Butyricimonas virosa combined with PD-1 monoclonal antibody group (Butyricimonas virosa + PD-1 group);

[0249] Three biological replicates were set up in each group. Intestinal contents of mice with MSS-type CRC subcutaneous tumors were collected. The samples were subjected to full-spectrum metabolomics PLUS detection using a combination of non-targeted and broadly targeted techniques to obtain comprehensive metabolite profile data for subsequent analysis.

[0250] 2. In vitro bacterial culture supernatant:

[0251] Prepare four 15mL centrifuge tubes and add the following culture media and treatment materials to each:

[0252] 10 mL ATCC 1490 medium (ATCC 1490 group);

[0253] 10 mL ATCC 1490 medium + Alistipes shahii (Alistipes shahii culture supernatant).

[0254] Centrifuge tubes were incubated at 37°C in an anaerobic environment (80% N2, 10% CO2, 10% H2) for 48 hours. After incubation, the culture supernatant from each group was collected. Two biological replicates were set up for each group, and the samples were subjected to full-spectrum metabolomics PLUS analysis using a combination of non-targeted and broadly targeted techniques to obtain comprehensive metabolite profile data for subsequent analysis. Through metabolomics analysis, it was confirmed that *Alistipes shahii* can produce the following metabolites: O-Phosphorylethanolamine, 5,6-Dihydroxyindole-2-Carboxylic Acid, and Phosphatidylcholine PC (O-16:0-20:4).

[0255] Metabolomics analysis confirmed that Butyricimonas virosa can produce the following metabolite: 4-Acetamidobutyric Acid.

[0256] The principle of qualitative and quantitative analysis of metabolites using the Full Spectrum Metabolomics PLUS method: Equal volumes of extraction solutions from all samples are mixed to form a QC sample. Non-targeted detection is performed on an LC-QTOF-MS / MS platform. Accurate qualitative analysis is performed based on a self-built standard database (including secondary spectra and retention times (RTs)), integrated with the Ds-all public database (including Metlin, HIMDB, KEGG, etc.), AI prediction library, and MetDNA. Multiple ion pairs and retention times (RTs) of identified metabolites are extracted. Combined with the self-built standard database, a new library specific to the project is formed. Finally, in all samples, precise MRM quantification of metabolites in the new library is performed using a Q-Trap analyzer. Metabolite quantification is completed using triple quadruple mass spectrometry (MRM) analysis. In MRM mode, the first quadrupole first selects the precursor ions (parent ions) of the target substance, excluding the precursor ions of other substances to initially eliminate interference. The precursor ions are then broken down by collisional ionization in the second quadrupole, forming a series of fragment ions unique to the substance based on its own structural characteristics. These fragment ions are then passed through the third quadrupole to select a typical characteristic fragment ion, eliminating interference from non-target ions, thus making quantification more accurate and improving repeatability. After obtaining LC-MS data from different samples, the peak area of ​​the extracted ion chromatograms of all metabolites is integrated, and the peaks of the same metabolite in different samples are corrected by integration.

[0257] result:

[0258] By comparing the Alistipes shahii group with the PBS group, and the Alistipes shahii combined with PD-1 monoclonal antibody treatment group with the PD-1 monoclonal antibody treatment group, phosphatidylcholine (O-16:0-20:4) was significantly enriched in the intestinal contents of mice in the Alistipes shahii group and the Alistipes shahii combined with PD-1 monoclonal antibody group (Q1:768.6, Q3:184.1, RT:6.45) (as shown in Figures 12A and 12B).

[0259] By comparing the Alistipes shahii combined with PD-1 monoclonal antibody group with the PD-1 monoclonal antibody treatment group, and the culture supernatant of Alistipes shahii with ATCC1490 blank medium, it was identified that O-Phosphorylethanolamine was significantly enriched in the culture supernatant of the Alistipes shahii combined with PD-1 monoclonal antibody group and the Alistipes shahii group (as shown in Figures 13A, 13B, 13C and 13D).

[0260] By comparing the Alistipes shahii plus PD-1 monoclonal antibody group with the PD-1 monoclonal antibody treatment group, 5,6-dihydroxyindole-2-carboxylic acid was identified as significantly enriched in the intestinal contents of the Alistipes shahii plus PD-1 monoclonal antibody group (Q1: 192.03, Q3: 148, RT: 2.72) (as shown in Figure 14).

[0261] By comparing the Butyricimonas virosa plus PD-1 monoclonal antibody group with the PD-1 monoclonal antibody treatment group, 4-acetamidobutyric acid was found to be significantly enriched in the intestinal contents of mice in the Butyricimonas virosa plus PD-1 monoclonal antibody group (Q1: 146.08, Q3: 86, RT: 1.7) (as shown in Figure 15).

[0262] Example 4: Identification of the mechanism of action of gut microbiota (conventional transcriptomics)

[0263] This embodiment is based on clinical samples from patients enrolled in clinical trials. This application submitted 54 sets of general transcriptome data, including:

[0264] 15 tumor tissue samples from treatment-sensitive (MPR) patients before treatment;

[0265] Tumor tissue samples from 16 treatment-sensitive (MPR) patients after treatment;

[0266] Pre-treatment tumor tissue samples from 11 treatment-insensitive (non-MPR) patients;

[0267] Tumor tissue samples from 12 treatment-insensitive (non-MPR) patients after treatment;

[0268] Research findings:

[0269] 1. KEGG enrichment analysis based on differentially expressed genes:

[0270] Compared to non-MPR patients, the IL-17 signaling pathway was significantly downregulated in tumor tissue of MPR patients before treatment (Figure 16A).

[0271] Compared to non-MPR patients, the IL-17 signaling pathway and Th17 cell differentiation pathway were significantly downregulated in tumor tissues of MPR patients after treatment (Figure 16B).

[0272] 2. GSEA Analysis:

[0273] Compared to non-MPR patients, the IL-17 signaling pathway and Th17 cell differentiation pathway were significantly downregulated in the tumor tissue of MPR patients before treatment (Figures 17A and 17B).

[0274] Compared to non-MPR patients, the IL-17 signaling pathway and Th17 cell differentiation pathway were significantly downregulated in tumor tissues of MPR patients after treatment (Figures 18A and 18B).

[0275] Example 5: Effects of gut microbiota metabolites on the IL-17A signaling pathway

[0276] Experimental methods:

[0277] 1. CD4 isolated from the spleen + T cells:

[0278] (1) Preparation of spleen cell suspension:

[0279] Take the spleen of BALB / c mice, place it in 3-5 mL of PBS, and grind it into a cell suspension.

[0280] Transfer the cell suspension to a centrifuge tube and slowly cover the surface of the suspension with 0.2 mL of RPMI 1640 medium.

[0281] (2) Gradient centrifugation:

[0282] Centrifuge at 800×g for 30 minutes.

[0283] Collect the lymphocyte layer between the culture medium and the lymphocyte separation medium, wash twice with RPMI 1640 medium, and count the cell concentration.

[0284] (3) Sorting CD4 + T cells:

[0285] Adjust cell concentration to 2×10 9 / L, with the addition of FITC-labeled anti-CD4 monoclonal antibody.

[0286] CD4 was sorted using flow cytometry under aseptic conditions. + T cells were analyzed, and the purity of the sorted cells was determined.

[0287] 2. Inducing CD4 + T cells differentiate into Th17 cells:

[0288] (1) Coated culture dish:

[0289] Add anti-mouse CD3 (3 μg / mL) to a 60×15 mm plastic culture dish and incubate at 37°C for 2 hours or at 4°C overnight.

[0290] After incubation, wash three times with sterile PBS.

[0291] (2) Standard culture conditions:

[0292] CD4 + T cells were administered at a rate of 2 × 10 6 Inoculate at a density of / mL into coated culture dishes, and add the following inducing factors:

[0293] anti-mouse CD28 (5μg / mL);

[0294] Recombinant mouse IL-6 (100ng / mL);

[0295] Recombinant mouse TGF-β1 (1ng / mL);

[0296] Recombinant mouse IL-1β(10ng / mL);

[0297] Recombinant mouse IL-23 (5ng / mL);

[0298] anti-mouse IL-4 (10 μg / mL);

[0299] anti-mouse IFN-γ (10μg / mL);

[0300] This application relates to gut microbiota metabolites (O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid);

[0301] Incubate for 4 days at 37℃ and 5% CO2.

[0302] 3. Experimental Results:

[0303] As shown in Figures 19, 20, 21A, and 21B, compared to undifferentiated cells, CD4+ after differentiation induction... + IL-17A+ T cells were significantly increased; compared with the DMSO control group, treatment with 5,6-dihydroxyindole-2-carboxylic acid (0.1 mM) and O-phosphorylethanolamine (5 mM) significantly reduced CD4 counts. + IL-17A + The proportion of T cells; O-phosphorylethanolamine and 5,6-dihydroxyindole-2-carboxylic acid inhibited CD4 + T cells differentiate into Th17 cells, reducing the amount of IL-17A secreted by Th17 cells, thereby downregulating the activity of the IL-17A signaling pathway.

[0304] Based on this, *Alistipes shahii* effectively inhibits the IL-17A signaling pathway through its metabolites O-phosphorylethanolamine and 5,6-dihydroxyindole-2-carboxylic acid, thereby improving the tumor microenvironment and enhancing the antitumor effect of immunotherapy. This mechanism further illustrates the important role of gut microbiota metabolites in regulating the immune microenvironment and improving the sensitivity of tumor immunotherapy.

[0305] Example 6: Validation Experiment of Intestinal Microbiota Metabolites

[0306] 1. Establishment of a mouse subcutaneous colorectal cancer model:

[0307] Six-week-old BALB / c mice were randomly divided into six groups: CTRL (PBS) group, 5,6-dihydroxyindole-2-carboxylic acid (DICA) group, ethanolamine phosphate (OP) group, PD-1 monoclonal antibody group, 5,6-dihydroxyindole-2-carboxylic acid (DICA) + PD-1 monoclonal antibody group, and ethanolamine phosphate (OP) + PD-1 monoclonal antibody group.

[0308] The specific procedures for each group are as follows:

[0309] CTRL(PBS) group: 200 μL of PBS was administered by gavage every other day until the sample was collected;

[0310] 5,6-Dihydroxyindole-2-carboxylic acid (DICA) group: Based on the body weight of mice, each mouse was administered 20 mg / kg of 5,6-dihydroxyindole-2-carboxylic acid by gavage every other day until the samples were collected;

[0311] Ethanolamine phosphate (OP) group: Each mouse was administered 20 mg / kg ethanolamine phosphate by gavage every other day, based on its body weight, until the samples were collected;

[0312] PD-1 monoclonal antibody group: when the tumor volume reaches 80 mm 3 At that time, PD-1 monoclonal antibody was injected intraperitoneally (100 μg / animal, twice a week until sample collection);

[0313] 5,6-Dihydroxyindole-2-carboxylic acid (DICA) + PD-1 monoclonal antibody group: Based on the 5,6-dihydroxyindole-2-carboxylic acid (DICA) group, when the tumor volume reaches 80 mm... 3 At that time, PD-1 monoclonal antibody was injected intraperitoneally (100 μg / animal, twice a week until sample collection);

[0314] OP (Polyethanolamine Phosphate) + PD-1 Monoclonal Antibody Group: Based on the OP group, when the tumor volume reaches 80 mm... 3 At that time, PD-1 monoclonal antibody was injected intraperitoneally (100 μg / animal, twice a week until sample collection);

[0315] 2. Cell injection and tumor model establishment:

[0316] After four gavages with metabolites or PBS, mice were subcutaneously injected with MSS-type CT26 cell line (2 million cells / mouse) to construct a mouse subcutaneous CRC model.

[0317] 3. Experimental Results:

[0318] In BALB / c mice (a subcutaneous MSS-type subcutaneous colorectal cancer model), both O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid enhanced the sensitivity of mice to PD-1 monoclonal antibody therapy.

[0319] Compared with PD-1 monoclonal antibody monotherapy, O-Phosphorylethanolamine or 5,6-Dihydroxyindole-2-Carboxylic Acid in combination with PD-1 monoclonal antibody significantly inhibited tumor growth.

[0320] It is worth noting that O-Phosphorylethanolamine or 5,6-Dihydroxyindole-2-Carboxylic Acid, when used alone, also showed significant antitumor effects (as shown in Figures 22, 23A and 23B).

[0321] O-Phosphorylethanolamine and 5,6-Dihydroxyindole-2-Carboxylic Acid can significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, and increase the infiltration and function of CD8T cells in tumors (as shown in Figures 24A, 24B, 24C, 25A, 25B, 25C, 26A, and 26B).

[0322] The above experiments show that the gut microbiota or its metabolites of this application inhibit naïve CD4. + The differentiation of T cells into Th17 cells inhibits the secretion of IL-17, improves the immunosuppressive state in the tumor microenvironment, and thus improves the sensitivity of MSS-type CRC patients to immunotherapy.

[0323] Furthermore, the gut microbiota of this application can significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, and increase the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) in tumors. This effect is further enhanced when gut microbiota (e.g., Alistipes shahii) is used in combination with PD-1 monoclonal antibodies.

[0324] The combination of IL-17A monoclonal antibodies and PD-1 monoclonal antibodies can also significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, while increasing the infiltration of CD8 T cells and effector CD8 T cells (GZMB+CD8 T cells) in tumors. Therefore, gut microbiota (e.g., Alistipes shahii) may promote the infiltration and function of CD8 T cells in tumors by inhibiting the infiltration of Th17 cells, thereby enhancing the anti-tumor immune response.

[0325] In addition, the above-mentioned metabolites, used alone or alone, have shown significant anti-tumor effects, and all of them can significantly reduce the infiltration of Th17 cells and exhausted immune cells (PD-1+ cells) in tumors, and improve the infiltration and function of CD8T cells in tumors.

[0326] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit the scope of protection of this application. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the substance and scope of the technical solutions of this application.

Claims

1. Use of at least one of intestinal flora or its metabolites, antibodies or derivatives thereof in the preparation of an immunotherapeutic drug for colorectal cancer, characterized in that, The intestinal flora includes at least one of the following: *Alternaria salorne*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidoides*, *Pleurotus erythrocytee*, *Eubacterium rectum*, *Vibrio spirochetes*, and *Alternaria oncogenes*; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

2. Use according to claim 1, wherein The intestinal flora includes at least two of the following: Allergy sarcopenia, Butymonas putida, Desulfurovibrio suis, Actinomyces putidae, Osmotherium visceratum, Clostridium plasmidonum, Probenebus, Eubacterium rectum, Bacteria of the family Treponemaceae, and Allergy oncogenes.

3. Use of a composition for the manufacture of a medicament for the immunotherapy of colorectal cancer, characterized in that, The composition includes at least one of intestinal flora or its metabolites, antibodies or their derivatives, and a product for treating colorectal cancer; The intestinal flora includes at least one of the following: *Alternaria salorne*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidoides*, *Pleurotus erythrocytee*, *Eubacterium rectum*, *Vibrio spirochetes*, and *Alternaria oncogenes*; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

4. The use according to claim 3, wherein the compound is ###0002### The products for treating colorectal cancer include at least one of the following: bacterial strains, immune checkpoint inhibitors, immune activators, tumor vaccines, immunotherapy drugs, and cytokine therapy drugs.

5. The use according to claim 4, wherein the compound is ###0002### The strains include at least one of Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium.

6. The use of at least one of intestinal flora or its metabolites, antibodies or their derivatives, in the preparation of products for improving or predicting the sensitivity of colorectal cancer patients to immunotherapy with immune checkpoint inhibitors, characterized in that, The intestinal flora includes at least one of the following: *Alternaria salorne*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidoides*, *Pleurotus erythrocytee*, *Eubacterium rectum*, *Vibrio spirochetes*, and *Alternaria oncogenes*; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

7. Use according to any one of claims 1 to 6, characterized in that, The intestinal flora metabolites include at least one of phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, phosphatidylcholine (O-16:0-20:4), and 4-acetaminophen.

8. Use according to claim 7, wherein the compound is ###0002### The metabolites of the aforementioned *Alternaria salicylides* include phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, and phosphatidylcholine (O-16:0-20:4); And / or, the metabolites of the putrefactive butyric acid bacteria include 4-acetaminophen.

9. Use of at least one of intestinal flora or metabolite, antibody or derivative thereof in the preparation of a medicament for inhibiting IL-17A cytokine signaling pathway, characterized in that, The intestinal flora includes at least one of the following: Allergy sarcopenia, Butymonas putida, Desulfuric Vibrio suis, Actinomyces putidae, Osmotherium visceratum, Clostridium plasmidonum, Probenebus, Eubacterium rectum, Vibrio spirochetes and Allergy oncogenes. The intestinal flora metabolites include at least one of phosphate ethanolamine, 5,6-dihydroxyindole-2-carboxylic acid, phosphatidylcholine (O-16:0-20:4), and 4-acetaminophen; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

10. Application of monoclonal antibodies against the IL-17A signaling pathway combined with immune checkpoint inhibitors in the preparation of drugs for colorectal cancer.

11. Use according to any one of claims 4, 6, 10, wherein The immune checkpoint inhibitors include PD-1 antibodies and / or PD-L1 antibodies.

12. Use according to any one of claims 1 to 8, 10, wherein The colorectal cancers mentioned include microsatellite stable colorectal cancer and microsatellite unstable colorectal cancer.

13. Use according to any one of claims 1 to 10, wherein The dosage form of the drug and / or product is independently selected from one of the following: liquid, gas, solid, and semi-solid dosage forms.

14. Use according to claim 13, wherein The drug and / or product also includes pharmaceutically acceptable excipients.

15. A pharmaceutical composition comprising, The pharmaceutical composition contains at least one of intestinal flora or its metabolites, antibody or its derivative, and a product for treating colorectal cancer; The intestinal flora includes at least one of the following: *Alternaria salorne*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidoides*, *Pleurotus erythrocytee*, *Eubacterium rectum*, *Vibrio spirochetes*, and *Alternaria oncogenes*; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer.

16. The pharmaceutical composition of claim 15, wherein, The products for treating colorectal cancer include at least one of the following: bacterial strains, immune checkpoint inhibitors, immune activators, tumor vaccines, immunotherapy drugs, and cytokine therapy drugs.

17. The pharmaceutical composition of claim 16, wherein, Products used to treat colorectal cancer include immune checkpoint inhibitors; preferably, the immune checkpoint inhibitors include PD-1 antibodies and / or PD-L1 antibodies.

18. The pharmaceutical composition of claim 15, wherein, The product for treating colorectal cancer includes a bacterial strain; preferably, the bacterial strain includes at least one of Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium.

19. A method of increasing the sensitivity of a colorectal cancer immunotherapeutic agent, comprising, Apply at least one of gut microbiota or its metabolites, antibodies or their derivatives, and immune checkpoint inhibitors; The intestinal flora includes at least one of the following: *Alternaria salorne*, *Butyromonas putida*, *Desulfovibrio suis*, *Actinomyces putida*, *Vibrio viscerans*, *Clostridium plasmidoides*, *Pleurotus erythrocytee*, *Eubacterium rectum*, *Vibrio spirochetes*, and *Alternaria oncogenes*; and The antibodies include monoclonal antibodies targeting the IL-17A signaling pathway and / or monoclonal antibodies targeting colorectal cancer. Preferably, the enhancement of the sensitivity of colorectal cancer immunotherapy drugs includes enhancing the sensitivity of immune checkpoint inhibitors.

20. The method of claim 19, wherein, The aforementioned enhancement of the sensitivity of colorectal cancer immunotherapy drugs also includes enhancing the sensitivity of immunotherapy drugs and / or cytokine therapy drugs.

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