Application of DL-endopeptidase of intestinal flora as marker in predicting curative effect of immune checkpoint inhibitor

Abstract

The invention discloses application of DL-endopeptidase of intestinal flora as a marker in predicting the curative effect of an immune checkpoint inhibitor, and the DL-endopeptidase has an amino acid sequence as shown in SEQ ID NO.1 or has a DNA sequence as shown in SEQ ID NO.2. The invention also discloses application of the DL-endopeptidase in predicting the curative effect of the immune checkpoint inhibitor. According to the application, DL-endopeptidase of intestinal flora is taken as a biomarker, and compared with a traditional species marker, the DL-endopeptidase is more accurate and more stable in ICB curative effect prediction and higher in repeatability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical microbiology, in particular, the present application relates to the application of DL-endopeptidase of intestinal flora as a marker in predicting the efficacy of immune checkpoint inhibitors. BACKGROUND

[0002] Immune checkpoint blockade (ICB) therapy is a revolutionary therapy that blocks key immune checkpoints such as PD-1 / PD-L1 to release the "brake" effect on immune cells, effectively activates the anti-tumor ability of the body's immune system, and significantly improves the treatment pattern of patients with various malignant tumors. However, there are significant individual differences in patient response to ICB, more than half of the patients show primary or secondary drug resistance, and some patients may also have immune-related adverse reactions and high treatment costs. Therefore, it is of positive significance to develop more accurate and robust biomarkers for efficacy prediction to achieve precise screening of the benefiting population and individualized treatment.

[0003] At present, the markers approved for ICB efficacy prediction in clinical practice mainly include PD-L1 expression, tumor mutation burden (TMB) and high microsatellite instability (MSI-H). However, these markers have certain limitations, such as non-uniform detection standards and thresholds, large influence of tissue heterogeneity, dependence on invasive sampling for detection, and difficulty in fully reflecting the overall immune status, etc., which limits their predictive efficiency in actual clinical application.

[0004] The prior art believes that the diversity and abundance of intestinal flora and specific genera are closely related to ICB response, and the homeostasis of intestinal flora is crucial for ICB efficacy. However, traditional microbial markers based on species classification have limited reproducibility and generalizability in different cohorts, mainly due to the complexity of the microbial ecosystem, functional redundancy, and functional heterogeneity between strains. These problems make it difficult to obtain robust efficacy prediction markers solely relying on species classification. SUMMARY

[0005] The present application proposes the application of DL-endopeptidase of intestinal flora as a marker in predicting the efficacy of immune checkpoint inhibitors to solve the technical problems of inaccurate, unstable and low reproducibility in related technologies.

[0006] The application of DL-endopeptidase of intestinal flora as a marker in predicting the efficacy of immune checkpoint inhibitors, wherein the DL-endopeptidase has an amino acid sequence as shown in SEQ ID NO. 1, or has a DNA sequence as shown in SEQ ID NO. 2.

[0007] Optionally, the marker further comprises a product of the DL-endopeptidase, the product comprising a muramyl dipeptide.

[0008] Further optionally, the immune checkpoint inhibitor is used to intervene melanoma and / or non-small cell lung cancer.

[0009] Optionally, the detection of the marker is based on a fecal sample of the patient.

[0010] Further, the screening process of the marker is based on an intestinal immune functional module database, each functional module of the intestinal immune functional module database is related to a specific biochemical process, which is related to a specific molecule synthesis or degradation related to the genome, metabolic pathway, immune module and / or disease related pathway performed by the intestinal flora.

[0011] Further optionally, the construction of the intestinal immune functional module database is based on baseline fecal metagenomic data of multiple independent clinical cohorts for cross-cohort analysis.

[0012] Optionally, the multiple independent clinical cohorts include cohorts related to pan-cancer, non-small cell lung cancer and melanoma respectively.

[0013] Further, the marker can be associated with good response consistency of immune checkpoint inhibitors.

[0014] Further, different intestinal types are divided according to the similarity of the abundance of the marker, for predicting the response of individuals to immune checkpoint inhibitors.

[0015] The technical scheme provided by the embodiments of the present application has the following beneficial technical effects: (1) The present application takes the DL-endopeptidase of intestinal flora as a biomarker, which is more accurate, more stable and more reproducible in predicting the efficacy of ICB than traditional species markers.

[0016] (2) The process of screening the marker of the present application constructs a special intestinal immune functional module database, which includes baseline fecal metagenomic data of multiple independent clinical cohorts, and can realize efficient and accurate cross-regional individualized prediction.

[0017] (3) The present application can also use the stratification of DL-endopeptidase intestinal type to associate with good response of ICB and enhanced T cell function, and screen non-small cell lung cancer patients with better response to ICB.

[0018] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description, taken in conjunction with the following drawings of which: Figure 1 For the ICB treatment cohort information incorporated in the present application; Figure 2 For the cross-cohort meta-analysis of the gut microbiota immune function module based on the ICB treatment cohort information incorporated in the present application; Figure 1 Figure 3 For the meta-analysis of the cross-cohort consistency with ICB response based on the results incorporated in the present application; Figure 2 Figure 4 For the Kaplan-Meier curve of overall survival (OS) stratified by DL-endopeptidase level based on the results incorporated in the present application; Figure 2 Figure 5 For the curve of the area under the curve (AUC) value of the DL-endopeptidase feature matching model in the present embodiment changing with the increase of JSD distance; Figure 6 For the clustering analysis diagram according to the DL-endopeptidase feature in the present embodiment. DETAILED DESCRIPTION

[0020] The embodiments of the present application will be described below in conjunction with the accompanying drawings in the present application. It should be understood that the embodiments described below in conjunction with the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0021] Those skilled in the art can understand that, unless specifically stated, "the" and "that" used herein can also include plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or components exist, but does not exclude other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the present art. The term "and / or" used herein means at least one of the items defined by the term, for example, "A and / or B" can be implemented as "A", or as "B", or as "A and B".

[0022] In order to make the purposes, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0023] ​​​The influence of gut microbiota on the efficacy of ICB is mainly derived from the functional genes of gut microbiota, which regulate the tumor immune microenvironment by producing key metabolites, thereby affecting immune response. Therefore, focusing on functionally conserved and key genes between species, especially those closely related to immune regulation, is expected to break through the limitations of traditional taxonomic markers and develop more stable and clinically valuable markers for predicting the efficacy of ICB.

[0024] The screening method of the biomarker of the present application, the verification process and the application prospect are further described below.

[0025] The present embodiment aims to illustrate the screening and identification process of the biomarker proposed by the present application, the verification of its association with the efficacy of ICB, and the specific methods and effects in clinical application.

[0026] 1.1 Screening and identification of key gut immune functional modules Firstly, a database containing 77 Gut-Immune Modules (GIMs) was constructed through systematic literature review and manual annotation. Each GIM represents a specific biochemical process performed by gut microbiota that can affect host immunity and thus affect the efficacy of ICB, such as the synthesis or degradation of specific molecules. The construction process of the GIMs database in this embodiment is as follows: (1) Target function screening and definition: Through extensive and in-depth literature research and integration, the key compounds (such as metabolites, signaling molecules) or biological processes currently known to be produced or metabolized by gut microbiota and to have a regulatory effect on the host immune system (particularly in relation to the efficacy of ICB) were systematically identified and summarized.

[0027] (2) Core metabolic reaction extraction: For each identified immune-regulating compound or process, all known reaction steps in the relevant biosynthetic or degradation metabolic pathways were retrieved and extracted mainly using the MetaCyc database. Emphasis was placed on pathways present in prokaryotes (bacteria and archaea).

[0028] (3) Pathway variant differentiation and modularization: When there are multiple different metabolic pathways for the synthesis or utilization of the same compound (e.g., using different enzymes or intermediates), a separate GIM is constructed for each major, known alternative pathway. This ensures the ability to distinguish and quantify the potential of different functional implementations.

[0029] (4) Manual annotation and construction of missing reactions: For the key reaction steps that are reported in the literature but not included in the MetaCyc database, manual annotation was performed based on the original research papers. The reaction equation, substrates, products, and enzymes (usually represented by EC number) catalyzing the reaction were defined strictly following the syntax of the MetaCyc database.

[0030] (5) Enzyme-gene family mapping: For all reaction steps included in each GIMs, the EC numbers of enzymes catalyzing these reactions were extracted. Subsequently, these EC numbers were used to query the UniRef90 database to obtain all UniRef90 protein family IDs corresponding to the function of each enzyme. This step links abstract enzyme functions to specific, countable gene families in the genome / metagenome.

[0031] (6) Structured integration of GIMs: The structured information collected and annotated above (including MetaCyc reaction equations and associated UniRef90 ID lists) was integrated to construct the final GIMs definition. Each GIMs was defined as a functional unit, following the syntax of the MetaCyc module, consisting of a series of structured MetaCyc reactions, and each reaction was composed of one or more necessary UniRef90 gene family IDs.

[0032] This self-defined database is more specific and interpretable than general databases.

[0033] To determine the GIMs most relevant to the efficacy of ICB, the GIMs analysis framework was applied to 11 independent clinical cohorts from different regions around the world (see Figure 1 ), with a total of 1104 tumor patients (mainly non-small cell lung cancer and melanoma) receiving ICB treatment at baseline fecal metagenome data. Through meta-analysis across cohorts, among the 77 GIMs, the "NOD2 ligand generation" module (NOD2 ligands generation) showed the most robust positive correlation with positive response (Responders) to ICB treatment (FDR q-value < 0.05), with the lowest heterogeneity among different cohorts ( I 2 = 0) ( Figure 2 ).

[0034] NOD is: an intracellular nucleotide-binding oligomerization domain-like receptor (NLR), mainly expressed in monocytes, macrophages, dendritic cells, and intestinal epithelial cells, which not only triggers innate immune response, regulates the production of pro-inflammatory factors and antibacterial peptides, but also participates in the regulation of adaptive immunity.

[0035] 1.2 Identification of key DL-endopeptidase sequences associated with ICB response concordance Further focusing on the core key enzyme in the “NOD2 ligand generation” module, DL-endopeptidases. The abundance of hundreds of different DL-endopeptidase gene sequences were precisely quantified in the aforementioned 1104 clinical samples by targeted quantification methods (ShortBred). DL-endopeptidases are a class of enzymes that can hydrolyze specific peptide bonds of bacterial peptidoglycan (PGN), generating PGN fragments such as Muramyl Dipeptide (MDP).

[0036] To exclude confounding factors (such as age, gender), this application uses 12 difference analysis algorithms (ANCOMBC, MAST, scde, corncob LRT, corncob wald, metagenomeSeq2, DESeq2 TMM, DESeq2poscounts zinbwave, DESeq2 poscounts, limma voom TMM zinbwave, limma voom TMM, Maaslin2), covering commonly used tools for macro-genome analysis (ANCOMBC, Maaslin2, MAST, corncob, metagenomeSeq), RNA sequencing analysis tools (DeSeq2, limma) and single-cell sequencing analysis tools (scde). When applying each analysis method, appropriate data preprocessing or standardization steps (poscounts, zinbwave, TMM, CSS, TSS) and statistical tests (such as Wald test, likelihood ratio test LRT) are used. Meta-analysis is performed after multiple difference analysis. Meta-analysis is a statistical method for systematically integrating data from multiple independent studies, which can improve the reliability of conclusions by quantifying and combining results, and is commonly used in medical and scientific fields to evaluate the effectiveness or relevance of interventions. By performing cross-cohort meta-analysis on the effect values of different difference analysis methods, DL-endopeptidase features associated with ICB response cross-cohort concordance are identified. If a feature can be identified by at least three different difference analysis methods, and each method performs a random effect meta-analysis in all cohorts Figure 3 , the feature is considered significant. Referring to , the purple cells in the left annotation of the heatmap represent the random effect model of each method meta-analysis . The purple cells with asterisks represent (Wilcoxon test). The color of the heatmap cell represents the average difference in relative abundance: red indicates higher average abundance in responders, and blue indicates higher average abundance in non-responders. Figure 3 The upper right panel of FIG. 1 shows the number of DL-endopeptidase or species features that were consistently upregulated in responders. The number of features that were statistically significant in the cross-cohort meta-analysis and simultaneously present in > 90%, > 80%, or > 70% of the cohorts were counted and compared. The cohorts included in the database construction included pan-cancer, non-small cell lung cancer, and melanoma, in which the total abundance of secreted, NLPC_P60 domain type DL-endopeptidase, and the abundance of a specific DL-endopeptidase sequence A0A174BWQ9 (BWQ9) were consistently found to be significantly higher in responders. This demonstrates that DL-endopeptidase as a biomarker is more stable and consistent across cohorts and cancer types than species markers. The amino acid sequence of BWQ9 is shown in SEQ ID NO. 1, and the DNA sequence is shown in SEQ ID NO. 2.

[0037] Reference Figure 4 Kaplan-Meier curves of overall survival (OS) stratified by DL-endopeptidase level in non-small cell lung cancer and melanoma datasets. Patients were divided into high or low level groups according to the median of DL-endopeptidase level (the corresponding scatter plot is on the left side of each Kaplan-Meier curve). The Log-rank test was used to assess the survival difference between high and low DL-endopeptidase groups, and it was found that high baseline abundance of the above DL-endopeptidase markers (such as secreted DL-endopeptidase, NLPC_P60 domain type, i.e., total DL-endopeptidase) was significantly associated with longer overall survival (OS) of patients.

[0038] 1.3 Construction and verification of DL-endopeptidase-based ICB efficacy prediction model To address the issue of regional heterogeneity, the present embodiment uses a matching prediction method based on feature similarity (Jensen-Shannon Divergence, JSD). The JSD (Jensen-Shannon Divergence) distance is a commonly used distance measure for probability distribution in microbial data analysis, which is improved based on KL divergence, has symmetry and boundedness (0-1 range), and is suitable for comparing the differences in microbial community composition between samples. This method first trains the model on the reference cohort, and then applies it to the predicted individual with similar DL-endopeptidase features as any individual in the reference cohort. Reference Figure 5Figure 6 shows the curve of the area under the curve (AUC) value of the DL-endopeptidase signature matching model as a function of the increase in JSD distance. Each point represents the predicted AUC value in a specific prediction set of patients with a JSD distance lower than the given JSD threshold to any individual in the training set. The green arrows indicate the optimal JSD threshold in the non-small cell lung cancer and melanoma cohorts, i.e. 0.020 and 0.035, respectively. The results show that the predicted AUC value can be as high as 0.88 for individuals with a high degree of signature matching in the non-small cell lung cancer cohort, and as high as 0.91 in the melanoma cohort. This demonstrates that the combination of the present marker and the specific algorithm can achieve accurate individualized prediction.

[0039] 1.4 Identification of DL-endopeptidases enterotypes (DE) and their clinical significance To simplify clinical application, this embodiment identifies four new "DL-endopeptidases enterotypes (DE)" in non-small cell lung cancer patients based on the abundance spectrum of DL-endopeptidases by non-negative matrix factorization (NMF) clustering method. Referring to Figure 6 , according to DL-endopeptidase signature clustering, the proportion of responders and non-responders is counted in the combination of cluster 1 and cluster 2 (cluster 1 & 2) and the combination of cluster 3 and 4 (cluster 3 & 4), respectively. Cluster 1 and 2 are defined as enterotypes DE1 and DE2, respectively, which are combined into the responsive DL-endopeptidase enterotype (DE / R), while cluster 3 and 4 are defined as enterotypes DE3 and DE4, respectively, which are combined into the non-responsive enterotype (DE / NR). The P value is calculated by chi-square test. Figure 6 The right panel of Figure 2 shows the difference in T cell cytotoxicity score and T cell exhaustion score between DE / R and DE / NR enterotypes, which are calculated by the ssGSEA method, and the P value for comparison between groups is calculated by Wilcoxon test, .

[0040] Among them, two enterotypes DE1 and DE2 are defined as "responsive enterotypes (DE / R)", and patients belonging to this enterotype have significantly higher response rate to ICB treatment and significantly longer overall survival. By analyzing the matched tumor tissue transcriptome data, it is found that the cytotoxicity of T cells in the tumor microenvironment of DE / R patients is significantly stronger. This directly links the DL-endopeptidase marker in the gut to the anti-tumor immune status.

[0041] Based on the above results, the following conclusions are drawn: (1) Using the gut microbiota DL-endopeptidase gene as a biomarker, compared with traditional species markers, it is more accurate, more stable, and more reproducible in predicting ICB efficacy.

[0042] (2) The feature matching model constructed based on the marker of the application can realize efficient and accurate cross-regional individualized prediction.

[0043] (3) DL-endopeptidase enterotype can stratify non-small cell lung cancer patients with good response to ICB and enhanced T cell function.

[0044] Those skilled in the art can understand that the steps, measures and schemes in various operations, methods and processes discussed in the present application can be alternated, changed, combined or deleted. Further, other steps, measures and schemes in various operations, methods and processes discussed in the present application can also be alternated, changed, rearranged, decomposed, combined or deleted. Further, the steps, measures and schemes in various operations, methods and processes in the related art can also be alternated, changed, rearranged, decomposed, combined or deleted.

[0045] The terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0046] In the description of the present application, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0047] The above is only part of the embodiments of the present application, and it should be pointed out that, for those skilled in the art, without departing from the technical concept of the present application, other similar implementation means based on the technical idea of the present application also belong to the protection scope of the embodiments of the present application.

Claims

1. Application of DL-endopeptidase of intestinal flora as a marker for predicting the efficacy of immune checkpoint inhibitors, characterized in that: The DL-endopeptidase has an amino acid sequence as shown in SEQ ID NO.1, or a DNA sequence as shown in SEQ ID NO.

2.

2. The use according to claim 1, characterized in that The marker also includes a product of the DL-endopeptidase, the product including muramyl dipeptide.

3. The use according to claim 1, characterized in that The immune checkpoint inhibitor is used to intervene in melanoma and / or non-small cell lung cancer.

4. The use according to claim 1, wherein The detection of the markers is based on stool samples from patients.

5. The use according to claim 1, characterized in that The marker screening process is based on the intestinal immune function module database, each functional module of the intestinal immune function module database is about a specific biochemical process, which is associated with the genome, metabolic pathway, immune module and / or disease-related pathway related to the synthesis or degradation of specific molecules performed by the intestinal flora.

6. The use according to claim 5, characterized in that The intestinal immune function module database was constructed based on baseline fecal metagenomic data from multiple independent clinical cohorts to facilitate cross-cohort analysis.

7. The use according to claim 6, characterized in that The multiple independent clinical cohorts include cohorts involving pan-cancer, non-small cell lung cancer and melanoma.

8. The use according to claim 5, characterized in that These markers are consistently associated with good responses to immune checkpoint inhibitors.

9. The use according to claim 5, characterized in that Different enterotypes are divided according to the similarity in the abundance of the markers, which is used to predict an individual's response to immune checkpoint inhibitors.

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