Intestinal typing method based on flora transplantation receptor and application of intestinal typing method in prediction of treatment effect of immune checkpoint inhibitor

Abstract

The invention discloses an intestinal typing method based on a flora transplantation receptor and application of the intestinal typing method in prediction of the treatment effect of an immune checkpoint inhibitor, and the intestinal typing method comprises the following steps: carrying out DNA sequencing on an excrement sample from the flora transplantation receptor to obtain the proportion of the relative abundance value of a characteristic flora in a total flora; when the proportion of the relative abundance value of the characteristic flora 1) in the total flora is the highest, the intestinal type is judged as an ERumin intestinal type; when the ratio of the relative abundance value of the characteristic flora 2) is the highest, the intestinal type is determined to be an EEsch intestinal type; when the ratio of the relative abundance value of the characteristic flora 3) is the highest, the intestinal type is determined to be the EBact intestinal type; when the proportion of the relative abundance value of the characteristic flora 4) is the highest, the intestinal type is judged as an EClos intestinal type; and when the relative abundance value proportion of the characteristic flora 5) is the highest, determining that the intestinal type is the EPrev intestinal type. The treatment effect of the flora transplantation receptor on the immune checkpoint inhibitor can be predicted according to the intestinal type typing, and it is verified that when the intestinal type typing is an ERumin intestinal type or an EBact intestinal type, the predicted treatment effect of the immune checkpoint inhibitor is good.

Description

Technical Field

[0001] The present invention relates to the technical field of immunotherapy, and in particular to an enterotype typing method based on flora transplantation receptors and its application in predicting the therapeutic efficacy of immune checkpoint inhibitors. Background Art

[0002] Cancer immunotherapy has shown great potential in cancer treatment by harnessing the power of the immune system. Different strategies have been applied, including antibodies targeting immune checkpoints such as cytotoxic T lymphocyte antigen 4 (CTLA-4), programmed death protein 1 (PD-1), and programmed death ligand 1 (PD-L1). Although immune checkpoint blockade (ICB) therapy, especially combination therapy such as anti-PD-1 and anti-CTLA-4, has shown significant anti-tumor effects, only a subset of patients (approximately 20-40% in different cancer types) show significant treatment responses. Therefore, it is crucial to enhance the understanding of the predictors of clinical benefit of ICB therapy. This will not only help optimize the selection of tumor types and patient populations that may benefit, but also reveal novel immunotherapy mechanisms and provide a basis for decision-making on monotherapy and various combination therapies.

[0003] Although some biomarkers such as tumor mutation burden (TMB), mismatch repair deficiency (MMR), and PD-L1 expression have shown some value in prediction, their predictive effects are still limited. Therefore, finding highly predictive biomarkers to evaluate clinical benefits remains an important and challenging task.

[0004] In our early studies, we have demonstrated that the composition of the gut microbiota can predict the efficacy and related adverse events of ICB therapy. In addition, studies on multiple tumor types have further shown that the gut microbiota has a regulatory effect on the host response to different immunotherapies. Notably, studies have shown that fecal microbiota transplantation (FMT) from treatment responders can restore the response of patients with PD-1-resistant melanoma to anti-PD-1; at the same time, in a phase I clinical trial, FMT from healthy donors also showed certain therapeutic potential for patients with newly diagnosed melanoma. In addition, metabolites associated with the gut microbiota (such as short-chain fatty acids SCFAs, inosine, and trimethylamine oxide TMAO) enhance anti-tumor responses by affecting host immunity. It should be noted that the mechanisms of different ICB drugs may vary due to the influence of the gut microbiota or metabolites. For example, Gunjur et al. found that microbiota-based features showed predictive efficacy only in consistent ICB treatment regimens. Therefore, specific ICB strategies must be considered when studying the interaction between the gut microbiota, its metabolites, and the host immune response.

[0005] Enterotype classification based on intestinal microbiota has been regarded as a key approach to understand the symbiotic relationship between host and microbiota. The concept was first proposed by Arumugam et al. in 2011, including three enterotypes dominated by Bacteroides, Prevotella, and Ruminococcus. Recently, Frioux et al. identified five enterosignatures through the analysis of 5230 metagenomic data, which were dominated by Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia, and were associated with different metabolic characteristics. However, the biological association between intestinal microbiota and metabolites in different enterotypes has not been clarified, which provides a new research direction for exploring the ICB response mechanism mediated by intestinal microbiota. Summary of the invention

[0006] In view of the existing technical problems, the purpose of the present invention is to provide an intestinal typing method based on microbiota transplantation receptors and its application in predicting the therapeutic efficacy of immune checkpoint inhibitors.

[0007] The objective of the present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a method for intestinal typing based on a microbiota transplantation recipient, characterized in that it comprises the following steps:

[0009] S1. Collect the stool sample to be typed from the microbiota transplant recipient and extract the total microbial genomic DNA in the sample;

[0010] S2, subjecting the extracted DNA to ultrasonic shearing treatment to obtain fragmented DNA;

[0011] S3, construct a library of fragmented DNA and then perform double-end sequencing;

[0012] S4. According to the sequencing results, the relative abundance values ​​of the following characteristic bacterial communities in the total bacterial community were obtained:

[0013] Characteristic flora 1): including Ruminococcus, Anaerostipes hadrus, Blautiaobeum / wexlerae, Akkermansia muciniphila and Faecalibacterium prausnitzii;

[0014] Characteristic flora 2): including Escherichia coli, Alistipes finegoldii / onderdonkii and Klebsiella;

[0015] Characteristic flora 3): including Bacteroides, Parabacteroides distasonis and Roseburiahominis;

[0016] Characteristic flora 4): including Clostridium and Methanobrevibacter smithii;

[0017] Characteristic flora 5): including Prevotella;

[0018] S5. Classification is performed according to the following criteria:

[0019] When the relative abundance value of characteristic flora 1) accounts for the highest proportion in the total flora, it is determined to be E_Rumin enterotype;

[0020] When the relative abundance value of characteristic flora 2) accounts for the highest proportion in the total flora, it is determined to be E_Esch enterotype;

[0021] When the relative abundance value of characteristic flora 3) accounts for the highest proportion in the total flora, it is determined to be E_Bact enterotype;

[0022] When the relative abundance value of characteristic flora 4) accounts for the highest proportion in the total flora, it is determined to be E_Clos enterotype;

[0023] When the relative abundance value of characteristic flora 5) accounts for the highest proportion in the total flora, it is determined to be E_Prev enterotype.

[0024] As a preferred solution, in step S2, the specific working conditions for the ultrasonic interruption treatment are: ultrasonication for 30 seconds at a water temperature of 4°C, with an interval of 30 seconds, taking out the sample after 3 cycles, and vortexing and mixing;

[0025] The length of the obtained fragmented DNA was 250 to 300 bp each.

[0026] As a preferred solution, in step S3, the specific steps of constructing a library of fragmented DNA include:

[0027] a1. Fill the ends of each fragmented DNA, phosphorylate the 5' end and add a dA tail to the 3' end;

[0028] a2. Connecting the Adapter to the end of the product obtained in step a1;

[0029] a3, purifying the reaction product obtained in step a2;

[0030] a4. PCR enrichment and purification of the library.

[0031] In a second aspect, the present invention provides a prediction model for identifying each intestinal type constructed according to the aforementioned intestinal type classification method.

[0032] In a third aspect, the present invention provides an application of the aforementioned intestinal typing method or the prediction model described in claim 4 in the preparation of a product for predicting the therapeutic efficacy of immune checkpoint inhibitors.

[0033] As a preferred embodiment, the specific criteria for predicting the therapeutic efficacy of immune checkpoint inhibitors are as follows:

[0034] When the enterotype is E_Rumin enterotype or E_Bact enterotype, it is predicted that the immune checkpoint inhibitor treatment will have a good effect;

[0035] When the enterotype is E_Esch enterotype, E_Clos enterotype, or E_Prev enterotype, it is predicted that the efficacy of immune checkpoint inhibitor treatment is poor.

[0036] As a preferred embodiment, the immune checkpoint inhibitor includes one or more combinations of blockers that act on T cell negative co-stimulatory molecules and / or their respective ligands;

[0037] As a preferred embodiment, the immune checkpoint inhibitor is selected from at least one of an anti-PD-1 inhibitor, an anti-PD-L1 inhibitor, an anti-CTLA-4 inhibitor, an anti-LAG-3 inhibitor, an anti-TIGIT inhibitor or an anti-TIM-3 inhibitor.

[0038] In a second aspect, the present invention provides an application of a microbial product rich in characteristic flora in the preparation of a product for improving the therapeutic efficacy of immune checkpoint inhibitors in patients, wherein the characteristic flora includes at least one of the genera Ruminococcus, Anaerostipes hadrus, Blautia obeum / wexlerae, Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides, Parabacteroides distasonis and Roseburia hominis.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention proposes a new typing method to type the intestinal type of microbiota transplant recipients, and finds that there are obvious differences in the response of each intestinal type to immune checkpoint inhibitors. Therefore, the therapeutic efficacy of microbiota transplant recipients (i.e. patients) to immune checkpoint inhibitors can be predicted based on the typing of the intestinal type.

[0041] The present invention has been verified to find that when the enterotype of the patient sample is E_Rumin enterotype or E_Bact enterotype, it is predicted that the immune checkpoint inhibitor treatment has a good effect. It indicates that adjusting the intestinal microbiota of the recipient to an enterotype that is more conducive to response to treatment (such as E_Rumin / E_Bact) may restore its responsiveness to anti-PD-1 treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0043] Figure 1 The PCoA principal component analysis results obtained for enterotype cluster analysis;

[0044] Figure 2 The results of the evaluation of microbial alpha diversity levels in patients with different enterotypes;

[0045] Figure 3 The results of the correlation analysis between different intestinal types and the efficacy of immune checkpoint therapy; in the figure, NR is a non-responder and N is a responder;

[0046] Figure 4 To evaluate the disease-free survival analysis results of patients with different intestinal types in the validation cohort; Figure 4 A is the analysis result of five enterotypes; Figure 4 B is the analysis result of the intestinal type after integration of E_Rumin intestinal type and E_Bact intestinal type (E_Rumin / E_Bact) and the intestinal type after integration of E_Esch intestinal type, E_Clos intestinal type and E_Prev intestinal type (E_Others);

[0047] Figure 5 To validate the results of the correlation between enterotype and immunotherapy efficacy for an external data cohort;

[0048] Figure 6 To use intestinal type grouping as an outcome for FMT recipient efficacy assessment (mouse data);

[0049] Figure 7 To use intestinal type grouping as an outcome measure for FMT recipient efficacy assessment (population clinical trial data). DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0051] The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, etc. used in the following examples are all commercially available unless otherwise specified.

[0052] Example 1

[0053] 1. Patient recruitment and sample collection

[0054] A total of 303 cancer patients receiving anti-PD-1 / PD-L1 treatment were recruited, of which cohort 1 (N = 165) was the discovery cohort and cohort 2 (N = 138) was the validation cohort. The specific information of the cohorts is shown in Tables 1 and 2. Fecal samples were collected from patients before they started immunotherapy. The relevant protocols for patient recruitment and sample collection were approved by the Medical Ethics Committee of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (ID: LY2020-067-B), Xuzhou Central Hospital Affiliated to Xuzhou Medical University (ID: XZXY-LJ-20200110-090), the First Affiliated Hospital of Soochow University (ID: 2023084), Shanghai Chest Hospital (ID: KS23026), and Jiangsu Cancer Hospital (ID: 2021617). Inclusion criteria include adult patients with pathologically confirmed advanced solid tumors (such as non-small cell lung cancer, gastric cancer, renal cancer, and colorectal cancer), suitable for anti-PD-1 or PD-L1 as standard treatment, and patients who can provide stool samples. In addition, the selected patients must be about to start anti-cancer treatment, be able and willing to comply with the research process, and have social security or equivalent protection plans. Informed consent must be obtained before any research procedures are carried out. Exclusion criteria include pregnant or breastfeeding women, patients under legal guardianship or judicial / administrative detention, or patients who are unable to provide informed consent.

[0055] The patient's standard treatment will continue until disease progression or unacceptable adverse reactions occur. Baseline clinical characteristics, including gender, age, BMI, immune checkpoint inhibitor (ICB) drug type, cancer type, and antibiotic use, were recorded, and concomitant drugs used within 3 months before the start of ICB, follow-up time (PFS), and treatment outcomes were listed in detail.

[0056] All participants signed written informed consent before enrollment in the study. The patients' treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1. The primary endpoint was the investigator-assessed objective response rate (ORR), defined as the number and proportion of patients who achieved a confirmed complete response (CR) or partial response (PR). The best overall response (BOR) was the best response from the first treatment to tumor progression recorded according to RECIST v1.1 criteria or the initiation of subsequent treatment, whichever occurred first. BOR was mainly evaluated at intervals of 6 months, with complete response (CR), partial response (PR) or stable disease (SD) considered as responders (R), while progressive disease (PD) was considered as non-responders (NR). For patients who did not have documented disease progression or did not initiate subsequent treatment, all available response data were used for BOR assessment. For patients who were still alive at the time of database lock, follow-up ended until the last recorded contact date.

[0057] Table 1. Summary of clinical information of cohort 1

[0058]

[0059]

[0060] Table 2 Summary of clinical information of cohort 2

[0061]

[0062] The method for collecting stool samples is as follows: To prevent the collected stool from being contaminated by urine, the subjects were asked to urinate before collecting the stool. The stool sample weight was at least 5-10 g. The collected stool samples were stored in disposable sterile containers in a cool place (avoid light or high temperature), and immediately frozen in a -80°C refrigerator for subsequent metagenomic sequencing and metabolomics analysis (depending on the situation).

[0063] 2. Metagenomic Sequencing of Fecal Samples

[0064] 2.1 Fecal DNA extraction

[0065] HiPure Stool DNA Mini Kit (China) was used to extract total microbial genomic DNA from 100-200 mg of fecal samples. The DNA extract was stored in a -80°C refrigerator.

[0066] 2.2 DNA fragmentation (ultrasonic fragmentation of genomic DNA)

[0067] 1) Genomic DNA quantification; 1-500ng genomic DNA (50ul system)

[0068] 2) Ultrasonic shearing of genomic DNA; 50ul genomic DNA was ultrasonically sheared to 250-300bp, and the working conditions of the ultrasonic shearing instrument were: 4°C water temperature, ultrasonication for 30s with intervals of 30s, and the sample was taken out after 3 cycles, and vortexed to mix; 50ul was taken and purified using 1XVAHTSDNA Clean Beads (washed twice with 70% ethanol), and 54ul of water was added to elute 50ul.

[0069] 2.3 DNA library construction

[0070] Step 1: End Preparation

[0071] This step will make the ends of the Input DNA blunt, phosphorylate the 5' end and add a dA tail to the 3' end. The specific method is as follows:

[0072] 1) Thaw the reagent components in Table 3 below, mix them by inversion, and prepare the reaction system shown in Table 3 below in a sterile PCR tube:

[0073] Table 3

[0074]

[0075] 2) Use a pipette to gently pipette to mix (do not oscillate to mix), and briefly centrifuge to collect the reaction solution at the bottom of the tube.

[0076] 3) Place the PCR tube in a PCR instrument and perform the reaction shown in Table 4 below:

[0077] Table 4

[0078]

[0079]

[0080] Step 2: Adapter Ligation

[0081] In this step, connect the Adapter to the end of the End Preparation product obtained in step 1. The specific method is as follows:

[0082] 1) Dilute the Adapter to the appropriate concentration according to the amount of Input DNA as shown in Table 4.

[0083] 2) Thaw the Rapid Ligation buffer, mix thoroughly by inversion, and place on ice for later use.

[0084] 3) Prepare the reaction system shown in Table 5 in the PCR tube in the End Preparation step:

[0085] Table 5

[0086] Components volume End Preparation products 60μl Rapid Ligation Buffer* 30μl PCR-grade water* 5μl DNA Ligase* 10μl DNA Adapter X 5μl total 110μl

[0087] 4) Use a pipette to gently pipette to mix (do not oscillate to mix), and briefly centrifuge to collect the reaction solution at the bottom of the tube.

[0088] 5) Place the PCR tube in a PCR instrument and perform the reaction shown in Table 6 below:

[0089] Table 6

[0090] temperature time Heating cover 105℃ On 20℃ 15min 4℃ Hold

[0091] 6) Purify the reaction product using VAHTSDNA Clean Beads:

[0092] i. After the beads are equilibrated to room temperature, vortex and mix the VAHTSDNA Clean Beads. ii. Pipette 60ul of VAHTSDNA Clean Beads into 110ul of Adapter Ligation product, vortex or use a pipette to gently pipette 10 times to mix thoroughly. iii. Incubate at room temperature for 5min. iv. Centrifuge the PCR tube briefly and place it on a magnetic rack to separate the beads and liquid. After the solution is clear (about 5min), carefully remove the supernatant. v. Keep the PCR tube in the magnetic rack, add 200ul of freshly prepared 80% ethanol to rinse the beads, incubate at room temperature for 30s, and carefully remove the supernatant. vi. Repeat step v for a total of two rinses. Keep the PCR tube in the magnetic rack, open the lid and air dry the beads for 5mins until no ethanol remains. vii. Add 23ul of water to elute 20ul of Adapter Ligation product.

[0093] Step 3: PCR enrichment library

[0094] 1) Thaw KAPA Library Amplification Primer Mix and KAPA HiFi HotStart ReadyMix, mix by inversion, and prepare the reaction system as shown in Table 7 in a sterile PCR tube:

[0095] Table 7

[0096]

[0097] 2) Use a pipette to gently pipette to mix (do not oscillate to mix), and briefly centrifuge to collect the reaction solution at the bottom of the tube.

[0098] 3) Carry out the reaction shown in Table 8 below. After the reaction is completed, take 3 ul and run agarose gel electrophoresis to identify the bands.

[0099] Table 8

[0100]

[0101] 4) Purify the reaction product with VAHTS DNA Clean Beads: i. After the magnetic beads are equilibrated to room temperature, vortex and oscillate to mix the VAHTS DNA Clean Beads. ii. Pipette 45ul VAHTSDNA Clean Beads into 50ul LibraryAmplification product, vortex or use a pipette to gently blow 10 times to mix thoroughly; iii. Incubate at room temperature for 5min; iv. Centrifuge the PCR tube briefly and place it on a magnetic rack to separate the magnetic beads and liquid. After the solution is clarified (about 5min), carefully remove the supernatant; v. Keep the PCR tube in the magnetic rack, add 200ul freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30s, and carefully remove the supernatant; vi. Repeat step v for a total of two rinses; vii. Keep the PCR tube in the magnetic rack, open the lid and air-dry the magnetic beads for 5mins 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. Quantify the library and store it in a -20 degree refrigerator.

[0102] 2.4 Quality Inspection

[0103] Qualified standards for use: nucleic acid quality ≥ 100ng, concentration greater than 2ng / μl, volume greater than 10μl. Agarose electrophoresis band fragment size ≥ 500bp.

[0104] 2.5 Illumina Nova6000 sequencing

[0105] Nova6000 (illumnia, USA) was used for operation; the original data were converted into Fastq format.

[0106] 2.6 Illumina Novo-seq 6000 sequencing and raw data processing and analysis

[0107] Shanghai Sier Biotechnology Co., Ltd. was commissioned to perform double-end sequencing on all metagenomic libraries using the Illumina Novaseq 6000 platform. The average amount of sequencing data per sample was 11.9GB.

[0108] The specific processing of the sequencing results is as follows:

[0109] 1) Preprocessing of sequencing data: filtering low-quality data from sequencing to ensure the accuracy of subsequent information analysis results;

[0110] 2) Metagenomic assembly: After obtaining valid data, assemble them according to the samples, and then mix and assemble the unmatched reads to obtain information on all species in the sample as much as possible;

[0111] 3) Gene prediction: The assembled contigs are subjected to CDS prediction, and then filtered and de-redundant according to the prediction results; the corresponding abundance is calculated; Unigenes are obtained after filtering low-abundance expressions;

[0112] 4) Species annotation: Compare Unigenes with the NR_mate library to obtain species annotation information;

[0113] 5) Functional annotation: Unigenes were compared with GO, KEGG, eggNOG, CAZy, CARD, and PHI databases for functional annotation and abundance analysis.

[0114] 3. Public intestinal microbiome data collection

[0115] This study used four metagenomic datasets on anti-PD-1 therapy (n = 568) as a public cohort, which was derived from the following studies: McCulloch et al. (PRJNA762360) (n = 63); Peng et al.

[0116] (PRJNA615114) (n=40); Derosa et al. (PRJNA751792) (n=338); Routy et al.

[0117] (PRJEB22863) (n=127). Additional validation data sources included Davar et al. (PRJNA672867); Baruch et al. (PRJNA678737). Another 16S rDNA sequencing data source was Zhu et al. (PRJCA023305, https: / / ngdc.cncb.ac.cn / ). Raw data were obtained from the European Nucleotide Archive (ENA, using the ENA identifier), the Sequence Read Archive (SRA, using the SRA identifier), or the China National Genome Data Center. Clinical information was extracted from the original published articles.

[0118] 4. Download and process public intestinal microbial data

[0119] Download according to the SRA sequence number and analyze through bioinformatics methods to obtain the composition information of microorganisms, specifically, quality control and species annotation of intestinal flora sequencing data to obtain the relative abundance information of sample intestinal flora, including: quality control of metagenomic sequencing data to remove the connectors of metagenomic sequencing data and filter low-quality bases in metagenomic sequencing data; remove the host sequence in the metagenomic sequencing data after quality control to obtain the metagenomic DNA sequence; species annotate the metagenomic DNA sequence; correct the annotation results to obtain the absolute abundance information of intestinal flora; extract the absolute abundance information of intestinal flora at the genus level and normalize it to obtain the abundance information of intestinal flora.

[0120] Specifically, the metagenomic data of the samples were quality controlled, and fastp software was used to automatically find and remove the connectors of the metagenomic data and filter the low-quality bases; kneaddata software was used to remove the host sequence; mOTU (https: / / github.com / motu-tool / mOTUs) software was used to annotate the species of the metagenomic DNA sequences after quality control and host removal to obtain the absolute abundance information of the intestinal flora; the absolute abundance information of the intestinal flora at the species level was extracted and normalized to obtain the relative abundance information of the intestinal flora.

[0121] In view of the fact that the population characteristics (including gender ratio, age and BMI, etc.) of the research subjects included in different research institutes may be different, and the sequencing time, sequencing platform, sequencing depth, etc. of different research cohorts may be different, the study treats the many factors that may affect the research purpose as confounding factors, and corrects the confounding factors with greater influence. In this embodiment, among the many confounding factors, the difference from the research cohort has the greatest impact on the microbial differentiation, so we used the Combat algorithm to correct the microbial abundance under the premise of considering the cohort difference as the main main variable and taking age, gender and BMI as covariates, and finally obtained the corrected relative abundance data.

[0122] 5. Identification of intestinal types associated with response and prognosis to anti-PD-1 / PD-L1 therapy

[0123] The human gut microbiome consists of diverse and unique ecological communities that are highly robust but can be influenced by factors such as diet, medications, and lifestyle. We have previously identified two enterotypes that are associated with patient age and efficacy of immune checkpoint inhibitors (ICBs). Recent studies have shown that bacteria-based prediction models are highly robust in cohorts receiving the same ICB regimen, highlighting the importance of considering treatment regimens in immunotherapy-related studies, including prediction models, potential mechanisms, and enterotype analysis.

[0124] McCulloch et al. identified enterotypes associated with response to anti-PD-1 therapy in melanoma patients. However, due to limited sample size, only two rough enterotypes were identified. Given that five more prominent enterotypes have recently been identified in healthy people, we sought to determine whether similar enterotypes exist in patients receiving anti-PD-1 therapy. To this end, we used the grouped around central point (PAM) clustering method to perform cluster analysis of bacterial enterotypes. A total of 733 samples from the internal cohort 1 and the previously published public cohorts treated with anti-PD-1 / PD-L1 antibodies were integrated and analyzed. We further analyzed the corrected microbial data by first filtering out bacterial data that were 0 in all samples, and then clustering them using the PAM algorithm to obtain the classification information of the samples, that is, classifying the samples into different enterotypes. The chi-square test was further used to compare the distribution differences between responders and non-responders in people with different enterotypes, and finally the most enriched bacterial communities in each enterotype were obtained by differential analysis. The results are shown in Figure 1 and Figure 2 As shown, we identified five major enterotypes ( Figure 1 ), and there were significant differences in the microbial α diversity level (Shannon index) among different enterotypes (e.g. Figure 2 We then performed differential microbial analysis on each enterotype and found that the abundance of certain specific bacterial genera in different enterotypes was different, as shown below:

[0125] E_Rumin enterotype: enriched with Ruminococcus (such as R.bromii, R.bicirculans), Anaerostipes hadrus, Blautia obeum / wexlerae, Akkermansia muciniphila and Faecalibacterium prausnitzii, that is, the relative abundance values ​​of the aforementioned characteristic bacterial communities account for the highest proportion in the total bacterial community.

[0126] E_Esch enterotype: enriched with Escherichia coli, Alistipes finegoldii / onderdonkii and Klebsiella (such as K.pneumoniae, K.michiganensis / oxytoca), that is, the relative abundance values ​​of the aforementioned characteristic bacterial communities account for the highest proportion in the total bacterial community.

[0127] E_Bact enterotype: enriched with Bacteroides (such as B.dorei / vulgatus, B.uniformis, B.sartorii, etc.), Parabacteroides distasonis and Roseburia hominis, that is, the relative abundance values ​​of the aforementioned characteristic bacterial communities account for the highest proportion in the total bacterial community.

[0128] E_Clos enterotype: enriched with Clostridium and one Archaea (Methanobrevibacter smithii), that is, the relative abundance values ​​of the aforementioned characteristic bacterial communities accounted for the highest proportion in the total bacterial community.

[0129] E_Prev enterotype: enriched with Prevotella (especially Prevotella copri), that is, the relative abundance value of the aforementioned characteristic flora accounts for the highest proportion in the total flora.

[0130] We further analyzed the correlation between different intestinal types and the efficacy of immune checkpoint therapy. Figure 3 As shown, the E_Rumin enterotype and E_Bact enterotype accounted for a higher proportion in responders (R).

[0131] We further performed metagenomic sequencing analysis on the feces of the recruited patients receiving immunotherapy. By analyzing the patient enterotype based on the dominant flora and combining it with the public cohort, we used the patient enterotype and immunotherapy progression-free survival to associate the 228 patients with survival data out of 773 patients. We found that the progression-free survival (PFS) of patients with E_Rumin and E_Bact enterotypes was also significantly longer than that of other enterotypes. The results are as follows: Figure 4 A. The E_Rumin and E_Bact intestinal types were further merged into the E_Rumin / E_Bact intestinal type, and the E_Prev, E_Clos, and E_Esch intestinal types were merged into the E_Others intestinal type. Figure 4 As shown in B, the progression-free survival (PFS) of patients in the E_Rumin / E_Bact intestinal type group was significantly longer than that in the E_Others intestinal type group.

[0132] Example 2 Intestinal type function verification

[0133] Our study suggests that even without interventions such as fecal microbiota transplantation (FMT), a patient's inherent enterotype may affect their response to anti-PD-1 therapy. Based on previous studies showing that fecal microbiota transplantation of a specific enterotype can enhance the efficacy of anti-PD-1 therapy by altering the intestinal microbiota and tumor microenvironment, we further explored the association between artificial induction of enterotypes by FMT and treatment response.

[0134] The relative abundance of microorganisms in the feces of the microbiota transplant recipients was extracted, and PAM cluster prediction was performed according to the method in Example 1 to define the specific enterotype of the recipients (enterotype with better prognosis after immunotherapy: E_Rumin / E_Bact enterotype group; enterotype with worse prognosis after immunotherapy: E_Others enterotype group). Subsequently, the chi-square test was performed to analyze the enterotype of the recipients and the clinical outcomes of the recipients (the efficacy related to immunotherapy).

[0135] 1. External pan-cancer immunotherapy cohort validation: We used the newly collected cohort 2 (cohort information is shown in Table 2) for external validation. Based on the intestinal flora abundance matrix of the patient's metagenomic sequencing results, we predicted the enterotype of patients in the validation cohort and performed a chi-square test analysis on the relationship between the enterotype of the recipient and the clinical outcome of the recipient (immunotherapy-related efficacy). The results showed that patients predicted to have E_Rumint enterotype or E_Bact enterotype in the validation cohort (E_Rumint / E_Bact enterotype group) had a significantly higher response to immune checkpoint inhibitor treatment than patients predicted to have E_Esch enterotype, E_Clos enterotype or E_Prev enterotype (E_others enterotype group) ( Figure 5 ).

[0136] 2. Intestinal type evaluation and verification of the mouse model fecal microbiota transplantation recipient cohort: The cohort information was derived from a previously published literature (PMID: 38513657, n = 20). The recipients were classified into intestinal types based on the metagenomic sequencing results of fecal microbiota transplantation recipient mice. The results showed that the recipient mice with E_Rumin / E_Bact intestinal type were more sensitive to fecal microbiota transplantation combined with anti-PD1 / PDL1 immune checkpoint inhibitors ( Figure 6 ).

[0137] 3. Validation of enterotype assessment in the fecal microbiota transplant recipient cohort in clinical trials: Cohort information comes from previously published literature

[0138] Based on the clinical data of melanoma in Davar et al. (PRJNA672867); Baruch et al. (PRJNA678737); n = 25), the recipient enterotype was classified based on the metagenomic sequencing results of fecal microbiota transplant recipients. The results showed that the recipients of E_Rumin / E_Bact enterotype were responsive to fecal microbiota transplantation combined with anti-PD1 / PDL1 immune checkpoints.

[0139] More sensitive to inhibitor therapy (eg Figure 7 shown).

[0140] Based on the results of the above validation cohort, it can be seen that the patient's enterotype may be used as an indicator of response to anti-PD-1 therapy, whether it is natural or adjusted through FMT. For patients who show resistance to anti-PD-1 therapy, FMT is a potential strategy to restore their responsiveness to anti-PD-1 therapy by adjusting the intestinal microbiota to the E_Rumin / E_Bact enterotype that is more conducive to response to treatment.

[0141] The present invention has many specific application paths, and the above is only a preferred embodiment of the present invention. It should be pointed out that the above embodiments are only used to illustrate the present invention, and are not used to limit the protection scope of the present invention. For those of ordinary skill in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A method for intestinal typing based on microbiota transplantation receptors, characterized in that: The following steps are involved: S1. Collect the stool sample to be typed from the microbiota transplant recipient and extract the total microbial genomic DNA in the sample; S2, subjecting the extracted DNA to ultrasonic shearing treatment to obtain fragmented DNA; S3, construct a library of fragmented DNA and then perform double-end sequencing; S4. According to the sequencing results, the relative abundance values ​​of the following characteristic bacterial communities in the total bacterial community were obtained: Characteristic flora 1): including Ruminococcus, Anaerostipes hadrus, Blautia obeum / wexlerae, Akkermansia muciniphila and Faecalibacterium prausnitzii; Characteristic flora 2): including Escherichia coli, Alistipes finegoldii / onderdonkii and Klebsiella; Characteristic flora 3): including Bacteroides, Parabacteroides distasonis and Roseburia hominis; Characteristic flora 4): including Clostridium and Methanobrevibacter smithii; Characteristic flora 5): including Prevotella; S5. Classification is performed according to the following criteria: When the relative abundance value of characteristic flora 1) accounts for the highest proportion in the total flora, it is determined to be E_Rumin enterotype; When the relative abundance value of characteristic flora 2) accounts for the highest proportion in the total flora, it is determined to be E_Esch enterotype; When the relative abundance value of characteristic flora 3) accounts for the highest proportion in the total flora, it is determined to be E_Bact enterotype; When the relative abundance value of characteristic flora 4) accounts for the highest proportion in the total flora, it is determined to be E_Clos enterotype; When the relative abundance value of characteristic flora 5) accounts for the highest proportion in the total flora, it is determined to be E_Prev enterotype.

2. The intestinal typing method based on microbiota transplantation recipient according to claim 1, characterized in that: In step S2, the specific working conditions for the ultrasonic interruption treatment are: ultrasonication for 30 seconds at a water temperature of 4°C, with an interval of 30 seconds, taking out the sample after 3 cycles, and vortexing and mixing; The length of each of the obtained fragmented DNA was 250 to 300 bp.

3. The intestinal typing method based on microbiota transplantation recipient according to claim 1, characterized in that: In step S3, the specific steps of constructing a library of fragmented DNA include: a1. Fill the ends of each fragmented DNA, phosphorylate the 5' end and add a dA tail to the 3' end; a2. Connecting the Adapter to the end of the product obtained in step a1; a3, purifying the reaction product obtained in step a2; a4. PCR enrichment and purification of the library.

4. A prediction model for identifying each intestinal type constructed according to the intestinal type classification method according to any one of claims 1 to 3.

5. Use of the intestinal typing method according to any one of claims 1 to 3 or the prediction model according to claim 4 in the preparation of a product for predicting the therapeutic efficacy of immune checkpoint inhibitors.

6. The use according to claim 5, characterized in that: The specific criteria for predicting the efficacy of immune checkpoint inhibitors are as follows: When the enterotype is E_Rumin enterotype or E_Bact enterotype, it is predicted that the immune checkpoint inhibitor treatment will have a good effect; When the enterotype is E_Esch enterotype, E_Clos enterotype, or E_Prev enterotype, it is predicted that the efficacy of immune checkpoint inhibitor treatment is poor.

7. The use according to claim 5, characterized in that: The immune checkpoint inhibitors include one or more combinations of blockers acting on T cell negative co-stimulatory molecules and / or their respective ligands.

8. The use according to claim 7, characterized in that: The immune checkpoint inhibitor is selected from at least one of an anti-PD-1 inhibitor, an anti-PD-L1 inhibitor, an anti-CTLA-4 inhibitor, an anti-LAG-3 inhibitor, an anti-TIGIT inhibitor or an anti-TIM-3 inhibitor.

9. Use of a microbial product rich in characteristic flora in the preparation of a product for improving the therapeutic efficacy of immune checkpoint inhibitors in patients, characterized in that: The characteristic bacterial community includes at least one of the genera Ruminococcus, Anaerostipes hadrus, Blautia obeum / wexlerae, Akkermansia muciniphila, Faecalibacteriumprausnitzii, Bacteroides, Parabacteroides distasonis and Roseburia hominis.

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