Screening technique for fecal microbiota transplantation donor based on five bacterial combinations and application thereof in tumor immunotherapy
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- RENJI HOSPITAL AFFILIATED TO SHANGHAI JIAO TONG UNIV SCHOOL OF MEDICINE
- Filing Date
- 2026-01-19
- Publication Date
- 2026-05-29
Smart Images
Figure CN122104462A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of immunotherapy technology, specifically to a microbial transplantation donor screening technology based on a combination of five bacteria and its application in tumor immunotherapy. Background Technology
[0002] Immune checkpoint inhibitors (ICIs) restore the body's anti-tumor immune response by blocking immunosuppressive pathways such as programmed death protein 1 (PD-1), programmed death ligand 1 (PD-L1), or cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), and have been widely used in the treatment of various malignant tumors. However, in clinical application, the overall efficacy of immune checkpoint inhibitors remains limited, and there are significant differences in efficacy among different patients. In addition to the molecular characteristics of the tumor itself, factors such as host immune status, metabolic status, and gut microbiota composition are all considered to be closely related to the efficacy of immunotherapy. Recent studies have shown that the gut microbiota can participate in influencing the therapeutic effect of immune checkpoint inhibitors through multiple mechanisms, such as regulating antigen presentation, effector T cell activation, and the tumor immune microenvironment. Based on the above understanding, fecal microbiota transplantation (FMT), as a technique for holistic remodeling of the gut microbiota, is considered to have potential value as an adjunct strategy for immunotherapy.
[0003] However, in the current technology, the application of microbial transplantation in tumor immunotherapy still faces many problems, including: (1) There are significant differences in the effect of different donor-derived microbiota on enhancing the efficacy of immunotherapy; (2) Donor screening relies heavily on empirical judgment or macroscopic health indicators, and lacks objective screening methods based on the characteristics of functionally related microbiota; (3) Differences in host metabolic status may affect the degree of association between gut microbiota characteristics and immunotherapy response, but the existing technology has not provided a gut microbiota transplantation donor selection scheme that takes into account different metabolic statuses without limiting the scope of patients. Summary of the Invention
[0004] To address the existing technical problems, the purpose of this invention is to provide a microbial transplantation donor screening technology based on a combination of five bacteria and its application in tumor immunotherapy.
[0005] The objective of this invention is achieved through the following technical solution: In a first aspect, the present invention provides an intestinal flora, which includes one or more of Eubacterium rectale, Adlercreutzia equolifaciens, Megamonashypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae.
[0006] As a preferred embodiment, the sum or weighted sum of the relative abundance of the gut microbiota is greater than or equal to 8.51%.
[0007] Secondly, the present invention provides the use of gut microbiota in the preparation of products for predicting the efficacy of immune checkpoint inhibitor therapy in cancer patients, wherein the gut microbiota includes one or more of Eubacterium rectale, Adlercreutziaequolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae.
[0008] As a preferred option, the cancer patient is an obese cancer patient.
[0009] As a preferred option, the obese cancer patient has a BMI ≥ 25.
[0010] As a preferred embodiment, the immune checkpoint inhibitor includes at least one of anti-PD-1 inhibitors, anti-PD-L1 inhibitors, and anti-CTLA-4 inhibitors.
[0011] Thirdly, the present invention provides the use of gut microbiota in the preparation of products for improving the response to immune checkpoint inhibitor therapy in cancer patients, said gut microbiota including one or more of Eubacterium rectale, Adlercreutziaequolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae.
[0012] As a preferred embodiment, the immune checkpoint inhibitor comprises one or more combinations of blockers that act on negative co-stimulatory molecules of T cells and / or their respective ligands; further comprising at least one of anti-PD-1 inhibitors, anti-PD-L1 inhibitors, and anti-CTLA-4 inhibitors.
[0013] As a preferred embodiment, the total relative abundance or weighted sum of gut microbiota in the product is greater than or equal to 8.51%.
[0014] As a preferred embodiment, the product includes a microbial transplantation preparation, a microecological preparation, or a pharmaceutically acceptable microbial composition.
[0015] As a preferred embodiment, the gut microbiota is derived from fecal samples; the relative abundance or weighted sum of the gut microbiota in the fecal samples is greater than or equal to 8.51%.
[0016] As a preferred option, the cancer patient is an obese cancer patient.
[0017] As a preferred option, the obese cancer patient has a BMI ≥ 25.
[0018] As a preferred option, the tumors treated by the immune checkpoint inhibitors include colorectal cancer and melanoma.
[0019] Fourthly, the present invention provides a method for screening gut microbiota transplantation donors based on the aforementioned gut microbiota, comprising the following steps: S1. After collecting fecal samples, extract microbial genomic DNA; S2. Perform metagenomic sequencing on microbial genomic DNA to obtain information on the composition of the gut microbiota; S3. Based on the gut microbiota composition information, calculate the relative abundance of the gut microbiota as described in claim 1 or 2; S4. When the relative abundance or weighted sum of the gut microbiota is greater than or equal to 8.51%, the fecal sample is used as a microbiota transplantation donor.
[0020] Fifthly, the present invention provides the use of gut microbiota obtained by screening according to the foregoing method from a microbiota transplantation donor in the preparation of a product for improving the response to immune checkpoint inhibitor therapy in cancer patients.
[0021] As a preferred option, the cancer patient is an obese cancer patient.
[0022] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention screened five characteristic gut microbiota, represented by Lachnospiraceae bacterium (5_1_63FAA), which are associated with the efficacy of immune checkpoint inhibitor therapy in obese cancer patients. The abundance of these microbiota was significantly increased in patients responding to immune checkpoint inhibitor therapy. Therefore, the relative abundance of these five characteristic gut microbiota can be used to predict the efficacy of immune checkpoint inhibitor therapy in cancer patients.
[0023] 2) This invention further utilizes obese cancer patients with a total relative abundance of five characteristic gut microbiota, represented by Lachnospiraceae bacterium (5_1_63FAA), greater than or equal to 8.51%, as fecal microbiota transplantation donors. The resulting microbiota transplantation preparation, when combined with an immune checkpoint inhibitor, significantly enhances the efficacy of tumor immunotherapy in DIO mice. This demonstrates that the five characteristic gut microbiota, represented by Lachnospiraceae bacterium (5_1_63FAA), can improve the response to immune checkpoint inhibitor therapy in cancer patients and enhance tumor treatment efficacy. Attached Figure Description
[0024] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 is a bar chart showing the relationship between immune checkpoint inhibitor response and BMI in cancer patients; in the figure, Response represents responders and Non-Response represents non-responders. Figure 2 shows the results of the microbial community difference analysis between responders and non-responders of immune checkpoint inhibitor treatment in the screening cohort (cohort 1) of obese cancer patients; in the figure, R represents responders and NR represents non-responders; Figure 3 shows the results of the differential microbiota analysis of non-obese cancer patients in the screening cohort (cohort 1) who responded to and did not respond to immune checkpoint inhibitor treatment; in the figure, R represents responders and NR represents non-responders. Figure 4 shows the results of the differential microbiota analysis of the response and non-response of immune checkpoint inhibitor treatment in obese cancer patients in the validation cohort (cohort 2); in the figure, R represents responders and NR represents non-responders; Figure 5 shows the results of the differential microbiota analysis of immune checkpoint inhibitor treatment response and non-response in non-obese cancer patients in the validation cohort (cohort 2); in the figure, R represents responders and NR represents non-responders; Figure 6 shows the efficacy evaluation results of FMT in MC38-induced tumors using patients with high abundance of gut microbiota, represented by Lachnospiraceae bacterium, as donors (mouse data, the mean sum of the relative abundance of the five key bacteria used in the FMT-high abundance group was 8.51%). Figure 7 shows the efficacy evaluation results of FMT in B16-F0 induced tumors using patients with high abundance of gut microbiota, represented by Lachnospiraceae bacterium, as donors (mouse data, the mean sum of the relative abundance of the 5 key bacteria used in the FMT-high abundance group was 8.51%). Detailed Implementation
[0025] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.
[0026] In the following embodiments, the method for screening to obtain five characteristic gut microbiota, represented by Lachnospiraceae bacterium, is as follows: S1. Collect baseline stool samples from obese (BMI≥25) and non-obese (BMI<25) patients treated with immune checkpoint inhibitors, and extract total microbial genomic DNA from the samples. S2. The extracted DNA is subjected to ultrasonic fragmentation to obtain fragmented DNA; S3. Construct a library from the fragmented DNA and then perform paired-end sequencing; S4. Based on the sequencing results, obtain the relative abundance values of characteristic gut microbiota in obese and non-obese patients who respond to or do not respond to immune checkpoint inhibitor treatment.
[0027] In one specific embodiment, the specific working conditions for performing ultrasonic interruption in step S2 are as follows: ultrasonic treatment at 4°C for 30 seconds, with a 30-second interval, for 3 cycles, after which the sample is taken out and vortexed to mix. The resulting fragmented DNA fragments were each 250–300 bp in length.
[0028] In one specific embodiment, step S3, the specific steps of constructing the library from the fragmented DNA include: a1. Fill in the ends of each fragmented DNA, and phosphorylate the 5' end and add a dA tail to the 3' end; a2. Connect the Adapter to the end of the product obtained in step a1; a3. Purify the reaction product obtained in step a2; a4. PCR enrichment and purification of the library.
[0029] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.
[0030] Example 1 1. Patient recruitment and sample collection To explore the correlation between BMI and cancer prognosis and treatment response, we included clinical data from eight pan-cancer studies, some of which were based on clinical data generated by the TCGA research network: https: / / www.cancer.gov / tcga. Patient characteristics and response phenotypes (cohort 1) were derived from original publications (clinical information summary is shown in Table 1). Survival analysis included 4,509 samples with available BMI and prognostic information. Microbiome analysis included 291 samples, selected based on the following criteria: available BMI and metagenomic data, no antibiotic use, and stool samples collected before immunotherapy.
[0031] Cohort 2 was an internal validation cohort. Stool samples were collected from patients before they began immune checkpoint inhibitor therapy. Patient recruitment and sample collection protocols were approved by the Medical Ethics Committee of Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine (ID: LY2020-067-B), Xuzhou Central Hospital affiliated with Xuzhou Medical University (ID: XZXY-LJ-20200110-090), Fudan University Cancer Hospital, Fudan University Shanghai Medical College (ID: 050432-4-1911D), and Shanghai Chest Hospital affiliated with Shanghai Jiao Tong University School of Medicine (ID: KS23026). All participants provided written informed consent for sample collection and subsequent analysis. Inclusion criteria included patients with pathologically confirmed cancer and BMI data, pathologically confirmed advanced solid tumors (such as non-small cell lung cancer, gastric cancer, renal cancer, and colorectal cancer), and who were eligible for anti-PD-1 or PD-L1 therapy as standard treatment and could provide stool samples. All samples were collected before the initiation of immune checkpoint inhibitor therapy. All patients had no history of autoimmune diseases or cancer, and were treatment-naïve, having not received chemotherapy, radiotherapy, or other anti-tumor treatments prior to surgery. Patient characteristics are summarized in Table 2. A total of 183 usable stool samples were obtained for metagenomic analysis (119 samples from patients with a BMI <25 and 64 samples from patients with a BMI ≥25). Furthermore, enrolled patients were required to be about to begin anti-cancer treatment, be able and willing to comply with the study procedures, and have social security or an equivalent protection plan. Informed consent must be obtained before any study procedure. Exclusion criteria included pregnant or breastfeeding women, patients under legal guardianship or judicial / administrative detention, or patients unable to provide informed consent.
[0032] Standard treatment for patients will continue until disease progression or unacceptable adverse events occur. Baseline clinical characteristics should be recorded, including sex, age, BMI, type of immune checkpoint inhibitor (ICB), cancer type, and antibiotic use. A detailed list of concomitant medications used in the 3 months prior to ICB initiation, follow-up duration, and treatment outcomes should also be provided.
[0033] All participants signed written informed consent prior to inclusion in the study. Treatment response was assessed according to the RECIST v1.1 criteria for evaluating the efficacy of treatment in solid tumors. The primary endpoint was investigator-assessed objective response rate (ORR), defined as the number and proportion of patients achieving a confirmed complete response (CR) or partial response (PR). Best overall response (BOR) was defined as the best response from initial treatment to the onset of tumor progression or initiation of subsequent treatment according to RECIST v1.1 criteria (whichever occurs first). BOR was primarily assessed at 6-month intervals. Complete response (CR), partial response (PR), or stable disease (SD) were all considered responders (R), while disease progression (PD) was considered a non-responder (NR). For patients who did not have recorded disease progression or initiation of subsequent treatment, all available response data were used for BOR assessment. For patients still alive at the time the database was locked, follow-up ended at the last recorded contact date.
[0034] The stool sample collection method is as follows: To prevent the collected stool from being contaminated by urine, the subjects were instructed to urinate completely before collecting the stool. The stool sample should weigh at least 5-10g. After collection, the stool sample should be stored in a disposable sterile container in a cool place (avoiding light or high temperature) and immediately frozen at -80°C for subsequent use in metagenomic sequencing and metabolomics analysis (depending on the situation).
[0035] Table 1. Summary of Clinical Information in Cohort 1 Table 2. Summary of Clinical Information in Cohort 2 2. Metagenomic sequencing of fecal samples. 2.1 Fecal DNA Extraction Total microbial genomic DNA was extracted from 100-200 mg of fecal samples using the HiPure Stool DNA Mini Kit (China). The DNA extract was stored at -80°C.
[0036] 2.2 DNA fragmentation (ultrasound-induced disruption of genomic DNA) 1) Genomic DNA quantification; 1-500 ng genomic DNA (50 μL system).
[0037] 2) Sonication to break down genomic DNA; 50 μL of genomic DNA was sonicated to break down to 250-300 bp. The sonication instrument was operated under the following conditions: 4℃ water temperature, sonication for 30 seconds with a 30-second interval, and after 3 cycles, the sample was removed and vortexed to mix. 50 μL of the sample was purified using 1X VAHTS DNA Clean Beads (washed twice with 70% ethanol), and 54 μL of water was added to wash away 50 μL of the ion.
[0038] 2.3 DNA Library Construction Step 1: End Preparation This step involves closing the ends of the input DNA, phosphorylating the 5' end, and adding a dA tail to the 3' end. The specific method is as follows: 1) Thaw each component reagent in Table 3 below, invert and mix well, and prepare the reaction system shown in Table 2 below in a sterile PCR tube: Table 3 2) Use a pipette to gently mix the mixture (do not shake to mix), and briefly centrifuge to collect the reaction solution to the bottom of the tube.
[0039] 3) Place the PCR tubes in the PCR instrument and perform the reactions shown in Table 4 below: Table 4 Step Two: Adapter Ligation This step involves connecting an adapter to the end of the End Preparation product obtained in step one. The specific method is as follows: 1) Dilute the Adapter to the appropriate concentration according to the amount of Input DNA as shown in Table 5.
[0040] 2) After thawing the Rapid Ligation buffer, invert it to mix well and place it on ice for later use.
[0041] 3) Prepare the reaction system shown in Table 5 in the PCR tubes used in the End Preparation step: Table 5 4) Use a pipette to gently mix the mixture (do not shake to mix), and briefly centrifuge to collect the reaction solution to the bottom of the tube.
[0042] 5) Place the PCR tubes in the PCR instrument and perform the reactions shown in Table 6 below: Table 6 6) Purify the reaction products using VAHTS DNA Clean Beads: i. After the magnetic beads have equilibrated to room temperature, vortex to mix the VAHTS DNA Clean Beads.
[0043] ii. Add 60 μL of VAHTS DNA Clean Beads to 110 μL of Adapter Ligation product and vortex or gently pipette 10 times to mix thoroughly.
[0044] iii. Incubate at room temperature for 5 min.
[0045] iv. Briefly centrifuge the PCR tube and place it in a magnetic rack to separate the magnetic beads and liquid. After the solution becomes clear (about 5 minutes), carefully remove the supernatant.
[0046] v. Keep the PCR tube in the magnetic rack at all times, add 200 μL of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30 seconds, and carefully remove the supernatant.
[0047] vi. Repeat step v, rinsing a total of two times. Keep the PCR tubes in the magnetic rack at all times, and air-dry the magnetic beads for 5 minutes until no ethanol residue remains.
[0048] vii. Add 23 μL of water and elute 20 μL of Adapter Ligation product.
[0049] Step 3: PCR enrichment of libraries 1) Thaw the KAPA Library Amplification Primer Mix and KAPA HiFi HotStart ReadyMix, then invert and mix thoroughly. Prepare the reaction system shown in Table 7 below in a sterile PCR tube: Table 7 2) Use a pipette to gently mix the mixture (do not shake to mix), and briefly centrifuge to collect the reaction solution to the bottom of the tube.
[0050] 3) Perform the reactions shown in Table 8 below. After the reaction is complete, take 3 μL and run agarose gel electrophoresis to identify the bands.
[0051] Table 8 In Table 8, the cycle number X varies depending on the amount of Input DNA at a yield of 100 ng: 1 ng: 13-15; 2.5 ng: 11-13; 5 ng: 9-11; 10 ng: 7-9; 25 ng: 5-7; 50 ng: 3-5; 100 ng: 2-3; 250 ng: 0-2; 500 ng: 0.
[0052] 4) VAHTS DNA Clean Beads are used to purify the reaction products: i. After the magnetic beads have equilibrated to room temperature, vortex to mix the VAHTS DNA Clean Beads; ii. Add 45 μL of VAHTS DNA Clean Beads to 50 μL of Library Amplification product and vortex or gently pipette 10 times to mix thoroughly. iii. Incubate at room temperature for 5 min; iv. Briefly centrifuge the PCR tube and place it in a magnetic rack to separate the magnetic beads and liquid. After the solution becomes clear (about 5 minutes), carefully remove the supernatant. v. Keep the PCR tube in the magnetic rack at all times, add 200ul of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30 seconds, and carefully remove the supernatant; vi. Repeat step v, rinsing a total of two times; vii. Keep the PCR tubes in the magnetic rack at all times, and open the caps to air dry the magnetic beads for 5 minutes until there is no ethanol residue; viii. Remove the PCR tube from the magnetic rack, add 33ul of water to wash and elute 30ul; ix. The library was quantified and stored frozen at -20 degrees Celsius.
[0053] 2.4 Quality Inspection The criteria for successful execution are as follows: nucleic acid mass ≥100ng, concentration ≥2 ng / μl, volume ≥10 μl. Agarose gel electrophoresis band size ≥500bp.
[0054] 2.5 Illumina Nova6000 sequencing The Nova 6000 (Illumnia, USA) was used for the initial setup; the system ran for two days before being shut down; the raw data was converted to Fastq format.
[0055] 2.6 Metagenomic Sequencing and Raw Data Processing and Analysis All metagenomic libraries were sequenced using the Illumina Novaseq 6000 platform by Shanghai Nier Biotechnology Co., Ltd. The average sequencing data volume per sample was 11.9 GB.
[0056] The specific processing of the sequencing results is as follows: 1) Preprocessing of sequencing data: Filtering out low-quality data from the raw sequencing data to ensure the accuracy of subsequent information analysis results; 2) Metagenomic assembly: After obtaining valid data, assemble the data according to the samples, and then mix and assemble the unmatched reads to obtain as much species information as possible from the samples; 3) Gene prediction: The assembled contigs are subjected to CDS prediction, followed by filtering and redundancy removal based on the prediction results; and the corresponding abundance is calculated; after filtering out low abundance expression, unigenes are obtained. 4) Species annotation: Unigenes are compared with the NR_mate database to obtain species annotation information; 5) Functional annotation: Unigenes were compared with the GO, KEGG, eggNOG, CAZy, CARD, and PHI databases to perform functional annotation and abundance analysis.
[0057] 3. Identification of gut microbiota associated with immune checkpoint inhibitor responses in obese patients Recent research suggests that obese cancer patients may exhibit a stronger response to certain types of immune checkpoint inhibitor therapy. This may be due to obesity selectively inducing PD-1 expression on tumor-associated macrophages, leading to accelerated immunosenescence and PD-1-mediated T cell dysfunction. However, given the promoting effect of obesity on tumorigenesis and progression, identifying the key factors contributing to higher immune checkpoint inhibitor response efficiency in obese individuals and effectively translating the observed advantages of immune checkpoint inhibitor therapy in obese patients into clinical practice remains a significant challenge. Cross-sectional studies have revealed significant differences in gut microbiota composition between lean and obese individuals, accompanied by a unique inflammatory state partially driven by adipose tissue (as a metabolically active organ). Nevertheless, the specific role of gut microbiota in determining the outcome of immunosuppressive therapy in obese cancer patients, and its potential interactions with adipose tissue, remain poorly understood and require further investigation.
[0058] By meta-analyzing 2381 patients treated with immune checkpoint inhibitors from previously published public cohorts (Cohort 1), patients were divided into two groups based on BMI, and differences in their gut microbiota were compared. We found a significant difference in the response to immune checkpoint inhibitor therapy between the BMI <25 group and the BMI ≥25 group of cancer patients. Figure 1 Furthermore, significant taxonomic differences were observed in the gut microbiota composition of fecal samples. To identify bacterial species specifically associated with a good response to immunotherapy in an obese context, we first used LEfSe analysis to screen for enriched species in individuals with a BMI ≥ 25. Within this enriched subset, we then compared the relative abundance of these species between responders and non-responders in the BMI ≥ 25 subgroup of the cohort. We found a significant increase in the relative abundance of five bacteria in obese patients who responded to immune checkpoint inhibitor therapy: *Eubacterium rectale* (P value = 0.02), *Adlercreutziaequolifaciens* (P value = 0.0004), *Megamonas hypermegale* (P value = 0.02), *Lachnospiraceae bacterium* (5_1_63FAA) (P value = 0.0002), and *Gordonibacterpamelaeae* (P value = 0.0006). This was not observed in non-obese patients (between responders and non-responders in the BMI < 25 subgroup of the cohort). Figure 2 and Figure 3 (As shown).
[0059] To assess whether the obesity-associated, immunotherapy-responsive bacterial species identified in the discovery cohort (cohort 1) could be validated in another independent cohort, we collected fecal samples from patients with BMI data before immunotherapy. A total of 183 samples (cohort 2) underwent metagenomic sequencing and analysis. LEfSe analysis showed a significant increase in the relative abundance of five bacteria in obese patients (BMI ≥ 25) who responded to immune checkpoint inhibitor therapy: *Eubacterium rectale* (P value = 0.0029), *Adlercreutzia equolifaciens* (P value = 0.0041), *Megamonas hypermegale* (P value = 0.0078), *Lachnospiraceae bacterium(5_1_63FAA)* (P value = 0.0013), and *Gordonibacter pamelaeae* (P value = 0.0304), a phenomenon not observed in non-obese patients (BMI < 25). Figure 4 and Figure 5 (As shown).
[0060] Example 2 Based on the experimental results of Example 1, the efficacy of the obtained specific obesity-related bacteria in immunotherapy of DIO mice was further verified. The specific steps are as follows: 1. Experimental mice were randomly assigned to groups and housed in a laminar flow biosafety cabinet under specific pathogen-free (SPF) conditions of 20-25℃, 30-70% humidity, and a 12-hour light / 12-hour dark cycle. Mice were housed in ventilated cages, with a maximum of 5 mice per cage, and had free access to food and water. High-fat DIO (T002040) mice were obtained through a dietary intervention experiment. Specifically, 6-week-old male C57BL / 6J mice (purchased from GemPharmatech / Jiangsu Jicui Yaokang Biotechnology Co., Ltd.) were randomly assigned to cages (5 mice per cage) and fed either a 60% fat diet (ResearchDiets, D12492) or a control diet with 10% fat and sucrose content matched (Research Diets, D12492J). Mice continued to receive the corresponding dietary treatment for 20 weeks before initiating the subcutaneous tumor injection experiment, resulting in DIO mice. Metabolic parameters were assessed at weeks 19–20 of the dietary intervention. All mouse experiments were conducted in accordance with the National Institutes of Health's Guidelines for the Care and Use of Laboratory Animals. All experimental procedures in this study were approved by the Laboratory Animal Ethics Committee of Renji Hospital, affiliated with Shanghai Jiao Tong University School of Medicine.
[0061] 2. Fecal samples were collected from 10 cancer patients in the validation cohort (pair 2) before the start of ICB treatment as fecal microbiota transplantation (FMT) donors, and metagenomic sequencing was performed as described above. Based on the relative abundance of the five bacteria associated with immunotherapy response in obese patients as identified in Example 1, FMT donors were divided into two groups: a high-abundance FMT group (high abundance of bacteria associated with ICB response in obese patients, 5 donors) and a low-abundance FMT group (low abundance of bacteria associated with ICB response in obese patients, 5 donors), with a median relative abundance cutoff of 0.71%. The mean sum of the relative abundances of the five bacteria associated with immunotherapy response in obese patients in the high-abundance FMT group was 8.51%, and the mean sum of the relative abundances of the five bacteria associated with immunotherapy response in obese patients in the low-abundance FMT group was 0.07%.
[0062] 3. Add 0.9% physiological saline and 15% glycerol to the fecal samples of the FMT-high abundance group and the FMT-low abundance group at a sample-to-solution ratio of 1:2, respectively, and then shake or homogenize after stirring.
[0063] 4. After shaking or homogenizing, filter the sample through a funnel using five layers of moistened sterile gauze (or use GenFMTer for elution microbial transplantation), repeating several times to remove particulate matter. Centrifuge at 3000 rpm for 10 minutes and discard the supernatant.
[0064] 5. Collect the fecal precipitate and resuspend it in 20% glycerol at 0.1 g / mL, or in 0.9% physiological saline at a ratio of fecal sample to solution of 2:1, to obtain a fecal microbiota suspension.
[0065] 6. Before tumor transplantation, the DIO mice from step 1 were treated with a combination of broad-spectrum antibiotics (ABX) for 2 weeks to eliminate the intestinal flora. The antibiotic mixture used contained ampicillin (1 g / L), streptomycin (0.5 g / L), and polymyxin (1 g / L), and was added to sterile drinking water.
[0066] 7. After antibiotic treatment, before subcutaneous tumor inoculation, each experimental group mouse that had been pretreated with antibiotics was given 200 μL of fecal microbiota suspension orally via gavage using an animal feeding needle (the FMT-high abundance group mice were given fecal microbiota suspension obtained from the FMT-high abundance group donor, and the FMT-low abundance group mice were given fecal microbiota suspension obtained from the FMT-low abundance group donor).
[0067] 8. Subsequently, the mice were inoculated with either MC38 tumor cells (purchased from Beijing Medical Cell Bank (BMCR)) or B16-F0 tumor cells (purchased from BNCC). Both the MC38 tumor model mice and the B16-F0 tumor model mice obtained after inoculation received anti-PD-1 treatment.
[0068] 9. MC38 tumor model mice were treated with an anti-mouse PD-1 monoclonal antibody (RMP1-14, BioXCell) at days 0, 5, 10, 15, and 20. Mouse body weight, tumor length (A), and tumor width (B) were measured. Tumor volume was calculated using the formula (A × B²) / 2 to obtain the average tumor volume of the FMT-high abundance group ((ABX)FMT(High) + anti-PD1) and the FMT-low abundance group ((ABX)FMT(Low) + anti-PD1). The results are shown below. Figure 6 As shown.
[0069] 10. In B16-F0 tumor model mice, body weight, tumor length (A), and tumor width (B) were measured at days 0, 5, 10, 15, and 20 after treatment with the anti-mouse PD-1 monoclonal antibody (RMP1-14, BioXCell). Tumor volume was calculated using the formula (A × B²) / 2 to obtain the average tumor volume of the FMT-high abundance group and the FMT-low abundance group. The results are shown below. Figure 7 As shown.
[0070] Figure 6 and Figure 7 The results showed that in the MC38 and B16-F0 xenograft models, the tumor growth rate of DIO mice treated with FMT-high abundance donors was significantly lower than that of DIO mice treated with FMT-low abundance donors.
[0071] Based on the results of the aforementioned validation cohort, fecal microbiota transplantation (FMT) using five gut bacteria, represented by Lachnospiraceae bacterium (5_1_63FAA), and donors selected based on their abundance, may serve as a complementary therapy to enhance the efficacy of immune checkpoint inhibitors in obese cancer patients. This involves adjusting the gut microbiota to a more favorable mode for response to treatment, thereby enhancing its responsiveness to immune checkpoint inhibitors.
[0072] Based on the above validation results, a method for screening gut microbiota transplantation donors based on five intestinal bacteria, represented by Lachnospiraceae bacterium (5_1_63FAA), can be obtained from healthy individuals to enhance the therapeutic response of immune checkpoint inhibitors. The screening method includes the following steps: S1. After collecting fecal samples, extract microbial genomic DNA; S2. Perform metagenomic sequencing on microbial genomic DNA to obtain information on the composition of the gut microbiota; S3. Based on the gut microbiota composition information, calculate the relative abundance of five gut bacteria: Eubacterium rectale, Adlercreutzia equolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae. S4. When the sum or weighted sum of the relative abundance of the five intestinal bacteria is greater than or equal to 8.51%, the fecal sample is used as a donor for microbial transplantation.
[0073] This invention has many specific applications, and the above description is only a preferred embodiment. It should be noted that the above embodiments are for illustrative purposes only and are not intended to limit the scope of protection of this invention. For those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.
Claims
1. A gut microbiota, characterized in that, The gut microbiota includes one or more of Eubacterium rectale, Adlercreutzia equolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae.
2. The intestinal flora according to claim 1, characterized in that, The sum or weighted sum of the relative abundance of the gut microbiota is greater than or equal to 8.51%.
3. The use of gut microbiota in the preparation of products for predicting the efficacy of immune checkpoint inhibitor therapy in cancer patients, characterized in that, The gut microbiota includes one or more of Eubacterium rectale, Adlercreutzia equolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacterpamelaeae.
4. The use of a gut microbiota in the preparation of a product for improving the response to immune checkpoint inhibitor therapy in cancer patients, characterized in that, The gut microbiota includes one or more of Eubacterium rectale, Adlercreutziaequolifaciens, Megamonas hypermegale, Lachnospiraceae bacterium (5_1_63FAA), and Gordonibacter pamelaeae.
5. The use according to claim 4, characterized in that, The immune checkpoint inhibitors include at least one of anti-PD-1 inhibitors, anti-PD-L1 inhibitors, and anti-CTLA-4 inhibitors.
6. The use according to claim 4, characterized in that, In the product, the total relative abundance of gut microbiota is greater than or equal to 8.51%.
7. The use according to claim 4, characterized in that, The products include microbial transplantation preparations, microecological preparations, or pharmaceutically acceptable microbial compositions.
8. The use according to claim 4, characterized in that, The total relative abundance or weighted sum of gut microbiota in the fecal sample was greater than or equal to 8.51%.
9. A method for screening gut microbiota transplantation donors based on the gut microbiota described in claim 1 or 2, characterized in that, Includes the following steps: S1. After collecting fecal samples, extract microbial genomic DNA; S2. Perform metagenomic sequencing on microbial genomic DNA to obtain information on the composition of the gut microbiota; S3. Based on the gut microbiota composition information, calculate the relative abundance of the gut microbiota as described in claim 1 or 2; S4. When the relative abundance or weighted sum of the gut microbiota is greater than or equal to 8.51%, the fecal sample is used as a microbiota transplantation donor.
10. Use of gut microbiota from a microbiota transplantation donor obtained by the method of claim 9 in the preparation of a product for improving the response to immune checkpoint inhibitor therapy in cancer patients.