PD-1 treatment responsiveness-oriented gastric cancer patient tumor microenvironment targeting cell enrichment method

Abstract

The invention discloses a PD-1 treatment responsiveness-oriented tumor microenvironment targeting cell enrichment method for a gastric cancer patient. On the basis that a ligand-receptor shows more significant correlation to the change of the expression level of VEGFA-FLT1 in the tumor tissue of the gastric cancer and the responsiveness of immunotherapy, a sufficient number of Mast cells and Endothelial cells are separated and obtained from the tumor tissue of a patient with the gastric cancer, and the expression level change of VEGFA-FLT1 in the tumor tissue of the gastric cancer and the responsiveness of the Endothelial cells in the tumor tissue of the patient with the gastric cancer show more significant correlation. The method can be used for making up for the deficiency of a single marker in improving the accuracy of immunotherapy responsiveness evaluation, so that the realization of future precise individualized immunotherapy of gastric cancer is better promoted.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and relates to the mining and application of gastric cancer-related gene signaling pathways, and specifically to the identification and separation of gastric cancer immunotherapy (ICI)-responsive targeted cells. Background Art

[0002] Gastric cancer is one of the most common malignant tumors in the world. The latest research shows that its incidence and mortality rate are both ranked fifth. In the past few decades, chemotherapy has played a cornerstone role in the treatment of gastric cancer. However, no matter how the chemotherapy regimen changes, the survival of patients with advanced gastric cancer is always unsatisfactory. In 2010, the ToGA study brought the treatment of gastric cancer into the targeted era, but targeted therapy can only benefit patients with specific subtypes. With the vigorous development of molecular biology, the subtle relationship between the immune system and tumors has been gradually revealed. The role of immune checkpoints such as CTLA-4 and PD-1 / PD-L1 in tumor immune escape has been discovered, providing new targets for the treatment of tumors. For example, PD-1 / PD-L1 inhibitors significantly prolong the survival of some gastric cancer patients by blocking the binding of PD-1 to PD-L1 and restoring the anti-tumor activity of T cells.

[0003] Due to the heterogeneity of tumors, the effect of PD-1 treatment shows significant differences between different patients. The tumor microenvironment (TME) is composed of multiple cell types, including cancer cells, fibroblasts, endothelial cells, and various immune cells. The complex and dynamic interactions between these cells and the surrounding system make the tumor microenvironment extremely heterogeneous. The microenvironment characteristics of the tumor are usually associated with the therapeutic response to immune checkpoint inhibitors. For example, the expression of PD-L1 in the tumor microenvironment is associated with the efficacy of anti-PD-1 / PD-L1 therapy for gastric cancer. In addition, the tumor microenvironment may also limit the infiltration of effector T cells into tumor cells, thereby weakening the expansion of T cells, or directly reducing the survival of infiltrating lymphocytes (TILs).

[0004] For some biomarkers currently used to predict the response to gastric cancer immunotherapy, multiple clinical trials have shown that these biomarkers have inconsistent efficacy results at the individual level, and some even produce contradictory conclusions. For example, the tumor genome biomarkers TMB and MMR / MSI have large heterogeneity in TMB prediction values ​​between different tumor types and high detection costs; the incidence of digestive system MSI-H is low, and the quality of the test samples is high. In addition, the detection of PD-L1 expression status has the problems of strong subjectivity of immunohistochemical scoring, large intratumor heterogeneity, and inconsistent critical value standards. In terms of blood markers, although peripheral blood collection is convenient, the sampling time point and analysis method still need to be standardized; liquid biopsy technology has limited research data in the field of gastric cancer immunotherapy and high detection costs.

[0005] Single cell sequencing technology can perform high-throughput sequencing analysis on genetic information such as genome, transcriptome, and epigenome at the level of a single cell. Compared with traditional tissue sample sequencing, single cell sequencing technology can capture different body tissues and different types of cells at different developmental times, and reveal the gene structure and gene expression status of each cell, reflecting heterogeneous information and genetic information of rare cells that cannot be obtained by sequencing mixed samples. Single cell sequencing technology plays a vital role in the study of tumor microenvironment. Single cell sequencing technology can reveal the gene expression profile and genetic variation of single cells, thereby helping people better understand the heterogeneity of the tumor microenvironment. For example, Professor Hiroyoshi Nishikawa studied the factors affecting the steady-state balance between effector T cells and regulatory T cells (Tregs) expressing PD-1 in TME from a metabolic perspective, and found that lactate can serve as a metabolic checkpoint for Treg cells and control the immune response in TME, affecting the efficiency of PD-1 blockade. However, the mechanism of action of lactate as a metabolite in the tumor microenvironment is quite complex. It not only involves the metabolic reprogramming of T cells, but may also affect the function of other immune cells and the growth of tumor cells. This intricate mechanism makes it difficult to clearly define the direct causal relationship between lactate and immunotherapy responsiveness. At the same time, as a single marker, lactate may have limitations in fully reflecting the complexity of gastric cancer immunotherapy. In order to more accurately evaluate the expected effects of immunotherapy, lactate markers may need to be used in combination with multiple other biomarkers (such as PD-L1 expression levels, tumor mutation load, etc.).

[0006] Given that little is known about the mechanism by which immune checkpoint inhibitors regulate tumor mutation distribution and affect the tumor microenvironment by mediating immune activation, and that the responsiveness of tumor immunotherapy is often affected by multiple factors, including the genetic characteristics of tumor cells, the infiltration of immune cells, and the metabolic state of the tumor microenvironment, and these factors have affected the research and revelation of molecular markers regulating the responsiveness of gastric cancer immunotherapy to a certain extent, there is currently a lack of effective auxiliary means in the clinic to evaluate the expected effects of gastric cancer immunotherapy, especially the lack of reliable and universal biomarkers for guiding the prediction of immunotherapy response and drug selection in gastric cancer, which increases the treatment burden on patients. Therefore, it is urgent to isolate and identify target cells related to gastric cancer immunotherapy response from the tumor microenvironment. Summary of the invention

[0007] The purpose of the present invention is to provide a method for enriching targeted cells in the tumor microenvironment of gastric cancer patients that are responsive to PD-1 therapy.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, a method for enriching targeted cells in the tumor microenvironment of a gastric cancer patient is provided, wherein the targeted cell enrichment comprises the following steps:

[0010] Step 1: Sample preparation

[0011] Cut the tumor tissue samples from gastric cancer patients into 2-4 mm tissue fragments;

[0012] Step 2 Tissue digestion

[0013] Cut the tissue fragments into pieces; wash the tissue fragments with PBS or saline, and then incubate with trypsin for 15 to 48 hours;

[0014] Step 3: Preparation of cell suspension

[0015] After digestion is complete, the cells are dispersed; a cell suspension is obtained by filtering using a cell sieve; the cells are separated by sedimentation and then washed with a buffer; the cells are resuspended in a culture medium;

[0016] Step 4 Flow cytometry sorting

[0017] The cells are labeled using the fluorescent labeling function of the flow cytometer; the cell suspension is sorted by the flow cytometer to obtain mast cells and endothelial cells respectively;

[0018] Step 5: Verification of cell purity

[0019] The sorted mast cells and endothelial cells were tested for purity by flow cytometry to ensure the purity of the cell population.

[0020] Preferably, the targeted cell enrichment method further comprises the following steps:

[0021] Step 6 Cell culture

[0022] The sorted mast cells and endothelial cells were inoculated into culture medium for culture.

[0023] Preferably, the targeted cell enrichment method further comprises the following steps:

[0024] Step 7: Detection of endogenous gene expression in cells

[0025] For the cultured mast cells and / or endothelial cells, the expression levels of VEGFA and FLT1 genes in the corresponding cells are detected.

[0026] Preferably, the method for detecting gene expression is selected from immunohistochemistry, RNA sequencing or proteomics technology.

[0027] Preferably, in step 1, the gastric cancer patient is a gastric cancer patient who has received ≤7 PD-1 cycles (stages) of treatment (including gastric cancer patients who have not received relevant immunotherapy).

[0028] Preferably, the tumor tissue is obtained by biopsy of the same gastric cancer patient before each stage of treatment.

[0029] Preferably, in step 4, the marker genes of mast cells are TPSAB1 and GATA2, and the marker genes of endothelial cells are VWF and CD34, and the fluorescent markers of the corresponding cells are determined according to the marker genes. The cell populations (mast cells) expressing TPSAB1 and GATA2 and the cell populations (endothelial cells) expressing VWF and CD34 are sorted out respectively by the precise sorting function of the flow cytometer.

[0030] In a second aspect, the invention provides the use of mast cells and / or endothelial cells separated from gastric cancer tissue (gastric cancer tumor microenvironment) in preparing drug screening preparations.

[0031] Preferably, the drug is selected from VEGFA-FLT1 pathway inhibitors.

[0032] Preferably, the drug is selected from immune checkpoint inhibitors.

[0033] The beneficial effects of the present invention are embodied in:

[0034] Based on the fact that the changes in the expression levels of the ligand-receptor pair VEGFA-FLT1 in gastric cancer tumor tissues show a more significant correlation with the responsiveness to immunotherapy, and are closely related to the core processes of angiogenesis, growth and metastasis of gastric cancer patients' tumors, the present invention proposes a technical solution for isolating and obtaining a sufficient number of mast cells and endothelial cells (e.g., sufficiently accurate identification of their VEGFA and FLT1 expression levels) from the tumor tissues of gastric cancer patients. This solution can be used to make up for the shortcomings of a single marker in improving the accuracy of immunotherapy responsiveness assessment, thereby better promoting the realization of precise individualized immunotherapy for gastric cancer in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the distribution of various quality control indicators before cell filtration; among them, pCR1~3 are the sample numbers of three patients in the pCR group, and non_pCR1~3 are the sample numbers of three patients in the non_pCR group.

[0036] Figure 2 is the distribution of quality control indicators after cell filtration; among them, pCR1~3 are the sample numbers of three patients in the pCR group, and non_pCR1~3 are the sample numbers of three patients in the non_pCR group.

[0037] Figure 3 To identify the results.

[0038] Figure 4 Cluster distribution t-SNE diagram for each sample.

[0039] Figure 5 Cluster distribution UMAP map for each sample.

[0040] Figure 6 The percentage of cells in each Cluster in each sample.

[0041] Figure 7 It is the correlation heat map and PCA map between Clusters.

[0042] Figure 8 UMAP diagram of prediction results for different groups of cells.

[0043] Fig. 9 Bubble chart showing the correlation between predicted cell type and marker genes.

[0044] Fig.10 The percentage of each cell type in each sample.

[0045] Fig.11Comparison of the number of different types of cells between different groups: the left column from top to bottom are T cells & NK cells (pCR vs nopCR P = 0.91), Myeloid cells (pCR vs nopCR P = 0.56), PlasmaCells (pCR vs nopCR P = 0.37), Mast cells (pCR vs nopCR P = 0.28), B cells (pCR vsnopCR P = 0.24); the right column from top to bottom are Fibroblasts (pCR vs nopCR P = 0.91), Epithelial cells (pCR vs nopCR P = 0.14), Endothelial cells (pCR vs nopCR P = 0.37), Endocrine cells (pCR vs nopCR P = 0.89), Neutrophils (pCR vs nopCR P = 0.42).

[0046] Fig.12 To re-cluster the B cell results.

[0047] Fig.13 Pseudo-temporal analysis of B cells.

[0048] Fig.14 The results for re-clustering T / NK cells.

[0049] Fig.15 Pseudo-time analysis of T / NK cells.

[0050] Fig.16 This is the interaction result of non_pCR group cells.

[0051] Fig.17 This is the result of cell interaction in the pCR group. DETAILED DESCRIPTION

[0052] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The embodiments are only used to explain the present invention, but not to limit the scope of protection of the present invention.

[0053] (I) Using single-cell sequencing technology to study and reveal the tumor microenvironment response mechanism of gastric cancer related to the difference in PD-1 efficacy

[0054] 1.1 Research Scope and Objectives

[0055] This study intends to use single-cell sequencing technology to conduct in-depth analysis of clinical samples of gastric cancer patients receiving PD-1 treatment, comprehensively reveal the gene expression characteristics, heterogeneity and interaction networks of various immune cells, tumor cells and stromal cells in the tumor microenvironment, and construct a high-precision single-cell atlas for gastric cancer immunotherapy. Based on the constructed single-cell atlas, the composition, functional state and dynamic changes of the tumor microenvironment between patients who are sensitive and resistant to immunotherapy are compared and analyzed, key cell subpopulations, molecular markers and signaling pathways that affect the efficacy of immunotherapy are identified, and the core regulatory mechanism of the tumor microenvironment between different efficacy of immunotherapy is clarified. In-depth exploration of the functional transformation, metabolic characteristics and regulatory networks of specific immune cell subpopulations in the tumor microenvironment will be carried out to clarify how they regulate the responsiveness of gastric cancer immunotherapy by affecting immune responses and promoting tumor escape, providing a theoretical basis for overcoming immunotherapy resistance.

[0056] 1.2 Research ideas and overall plan

[0057] In this study, tumor tissues of 6 gastric cancer patients who received PD-1 neoadjuvant therapy were collected (samples were collected in Xi'an, Shaanxi Province, from March to May 2024, by biopsy). Among them, 3 patients achieved pathological complete response (pCR) after treatment, indicating that their tumor tissue was completely cleared after treatment; while the other 3 patients failed to meet this standard, and the tumor still remained after treatment, and were classified as non-pathological complete response (non_pCR or nopCR). The collected tumor samples were made into single-cell suspensions, and the obtained single-cell suspensions were strictly quality inspected and counted to ensure that the cell survival rate was not less than 85%, so as to ensure the accuracy of subsequent experiments. The qualified cells were washed, resuspended, and other treatments for 10X Genomics Chromium TM The system is ready for operation. After the sequencing library is constructed and the sequencing is completed, the supporting analysis software Cell Ranger provided by 10X Genomics and the software Seurat, Monocle, SingleR, etc., which are widely used in single-cell sequencing technology, are used to perform cell filtering, dimensionality reduction clustering, cell type prediction, differential gene analysis, pseudo-time trajectory and other analyses.

[0058] 1.3 Single-cell sequencing and quality control

[0059] Tumor tissues from gastric cancer patients who have received 4 to 6 cycles of PD-1 treatment were selected and placed in a culture dish on ice to prevent tissue degradation. Use sterile scissors or a scalpel to cut the tissue into 2 to 4 mm pieces. Add the minced tissue to an appropriate buffer or balanced salt solution (such as PBS or saline) on ice and wash 2 to 3 times to remove excess blood stains and impurities. Add an appropriate amount of enzyme (such as trypsin) to decompose the intercellular matrix and extracellular matrix. Incubate at the optimal temperature (usually 37°C) for an appropriate time (generally 15 to 48 hours, depending on the density of the tissue and the activity of the enzyme). Mix intermittently during incubation to promote digestion. After digestion is complete, gently disperse the cells by pipetting or grinding to avoid excessive cell damage. Filter the cell suspension using a suitable filter (such as a 30-70 μm cell sieve) to remove undigested tissue fragments and cell clumps. Allow the cells to settle and pour out excess enzyme-containing liquid. Wash the cells 2 to 3 times with PBS to remove residual enzymes and other impurities. Resuspend the cells in PBS to prepare a cell suspension with a concentration of 700-1200 cells / μl for subsequent experiments. TM The system loads the Gel Beads with sequence tags, sample and reagent premixes, and oil into their respective injection channels, and finally forms a single-cell micro-reaction system GEMs wrapped by oil droplets through a "double cross" cross system formed by a microfluidic channel network. The cells complete the lysis, barcode labeling, and reverse transcription processes in GEMs. After that, the oil-in-water is broken to purify the first-strand cDNA and amplify the cDNA. For cDNA that passes the quality inspection, the sequencing library is constructed and sequenced on the machine.

[0060] After the sequencing data is downloaded from the machine, FastQC software is used for quality control analysis. 10X Chromium single-cell transcriptome sequencing can be divided into capture of the region near the 3-end polyA and capture and library construction sequencing near the 5-end transcription start position. Among them, 5-end transcript sequencing is usually sequenced and analyzed together with T cell and B cell receptor gene TCR / BCR sequences. The library structure of different kits (including 3-end vs 5-end, and different versions) is slightly different: for example, for samples processed by the 10X single cell 3-end V3 kit, Read 1 consists of a 16bp 10X Cell Barcode and a 12bp UMI sequence. In the analysis, the 10XCell Barcode in Read1 is used to split single cells, and the UMI is used to remove PCR amplification bias and distinguish different transcripts; Read2 is a 151bp cDNA sequence, and the first 91bp are generally used for downstream analysis of Cell Ranger.

[0061] 1.4 Cell Identification

[0062] The first step of the analysis is to obtain the expression matrix of cell X gene from Fastq Reads, one of the key steps of which is to identify valid cells from background signals. Cell Ranger software is used to perform data quality statistics, alignment to the reference genome of the Ensembl database, cell splitting, PCR duplication removal, and cell identification on the original Fastq data. The software quantifies the high-throughput single-cell transcriptome results by identifying the Barcode sequence used to distinguish cells in the sequence and the UMI markers of different mRNA molecules in each cell. The number of high-quality cells is determined based on the distribution of the number of UMIs of the cells. The evaluation method is to sort the number of UMIs of each Barcode in descending order, and the threshold is greater than or equal to 10% of the 99% quantile of the UMI number distribution in the pre-set number of cells to screen cells with high RNA abundance, and identify cells with low RNA abundance through the background model, thereby obtaining high-quality cells (see Figure 1 , Figure 2 ).

[0063] The process of identifying cells by Cell Ranger is to identify oil-in-water droplets containing cells from empty droplets, but the identified cells may be low-quality cells, double cells, or unwanted cells (such as red blood cells). Before downstream analysis such as clustering, cells need to be filtered to remove low-quality cells and retain high-quality cells. To this end, cell quality control is performed from five perspectives: the number of transcripts detected in each cell (nFeature_RNA), the number of genes (nCount_RNA), the proportion of mitochondrial genes (percent.mt), the proportion of ribosomal genes (percent.ribo), and the proportion of red blood cell genes (percent.redcell). The default cell filtering indicators are: cells that simultaneously meet 200 < number of genes < 10000, mitochondrial gene proportion < 10%, and red blood cell marker gene proportion < 10%, and use Scrublet to remove possible double cells.

[0064] Finally, 68,943 cells were screened and divided into 37 cell clusters (i.e., Cluster, see Figure 3 ).

[0065] 1.5 Cluster analysis

[0066] The expression profiles after cell filtration were subjected to downstream analysis to obtain cell clustering and dimensionality reduction results. Specifically, the widely used Seurat package was used to normalize the expression matrix, find genes with high variation, perform PCA principal component analysis, cluster analysis, t-SNE and UMAP dimensionality reduction.

[0067] (1) Standardization: Calculate the ratio of the total number of UMIs of each cell to the Scaling Factor of 10,000 to correct the cell sequencing depth. (2) Find highly variable genes: Measure genes with high variability based on the diffusion coefficient. (3) PCA principal component analysis: Use highly variable genes to perform PCA linear dimensionality reduction. (4) Cluster analysis: Select the first 30 PCA principal components, construct the nearest neighbor KNN graph based on the Euclidean distance in the PCA space, and then use the Louvain Modularity optimization algorithm to cluster the cell populations, with the resolution set to 0.9. (5) Dimensionality reduction: Select the first 30 PCA principal components, and use two dimensionality reduction methods, t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) (see Figure 4 , Figure 5 ), visualize single cell clustering (see Figure 6 , Figure 7 ).

[0068] 1.6 Cell type prediction and differential gene analysis

[0069] Cell type annotation is an important step in single-cell transcriptome data analysis. There are two main approaches: one is manual annotation, which is to determine the annotation based on the expression of each type of marker gene through professional knowledge; the other is automatic annotation by software, which is to use annotated data sets or cell type marker gene lists for annotation. This study combined the gastric cancer cell markers included in CellMarker 2.0 and the data of published articles to determine the marker genes corresponding to each cell type. See Table 1, the marker genes corresponding to PlasmaCells are MZB1 and JCHAIN; the marker genes corresponding to Mast cells are TPSAB1 and GATA2; the marker genes corresponding to Myeloid cells are MS4A7 and CD14; the marker genes corresponding to Bcells are CD79A and MS4A1; the marker genes corresponding to T cells & NK cells (T / NK cells) are CD3D, CD3E and CD3G; the marker genes corresponding to Endocrine cells are PCSK1N, CHGA and SCG5; the marker genes corresponding to Epithelial cells are MUC1, CDH1 and AGR2; Endothelial The marker genes corresponding to cells (endothelial cells) are VWF and CD34; the marker genes corresponding to fibroblasts (fibroblasts) are DCN, COL1A1, COL1A2, CAV1 and LUM; the marker gene corresponding to neutrophils (neutrophils) is S100A8 (see Figure 8 , Fig. 9 , Fig.10 , Fig.11 ).

[0070] Differential gene analysis can be used to annotate cell types or test the results of automatic predictions by software based on marker genes. On the other hand, marker genes can be used to characterize the biological functions of each cluster. Seurat's FindAllMarkers function was used to screen differentially expressed genes to obtain the marker gene set for each Cluster.

[0071] Table 1. Marker genes corresponding to each cell type

[0072]

[0073] 1.7 Re-clustering of cells and pseudo-time series analysis

[0074] In single-cell sequencing, re-clustering cells can provide a deeper understanding of the functions and interactions of cell subsets. Even cells of the same type (such as B cells, T / NK cells) may have significant heterogeneity. This heterogeneity may be manifested in gene expression, functional status, etc., so they need to be re-clustered. The expression pattern of marker genes of cells may change at different developmental stages or in different physiological states. Therefore, clustering cells based on known marker genes may not accurately reflect their true situation. Through re-clustering, new marker genes can be discovered and new relationships between cell subsets can be revealed.

[0075] Pseudo-time analysis is an effective method to infer the differentiation and development paths between cells by using transcriptome similarity, and it can also calculate the changes in the expression of the underlying driving genes. The pseudo-time analysis in the standard analysis combines all cells together, and uses the DDRTree method to reduce the dimension and construct the minimum spanning tree based on the marker genes of each cluster detected by Seurat, and then searches for the optimal sorting of single cells in high-dimensional and low-dimensional space to fit the best pseudo-time trajectory curve of cell development or differentiation.

[0076] 1.7.1 B cells

[0077] B cells were re-clustered into B_cell:CXCR4-_centrocyte, B_cell:Memory, Class-switched memory B-cells, Memory B-cells, and naive B-cells for pseudo-time series analysis (see Fig.12 , Fig.13 ).

[0078] 1.7.2 T / NK cells

[0079] T / NK cells were re-clustered into CD4+Tcm, CD4+Tem, CD8+Tcm, CD8+Tem, NK_cell, NK_cell:CD56hiCD62L+, T cells,CD4+,memory TREG, T_cell:gamma-delta, Terminal effectorCD8 T cells for pseudo-time series analysis (see Fig.14 , Fig.15 ).

[0080] 1.8 Cell interaction analysis of non-pCR group and pCR group

[0081] Cell interaction analysis focuses on the molecular interactions within and between cells and their regulatory mechanisms. These molecular interactions constitute the basic processes of life, such as cell proliferation, differentiation, apoptosis, and signal transduction. In cell-to-cell interaction analysis, the unique gene expression signature of each cell type is first obtained in the single-cell data. Next, the bulk expression profile data is used to infer the relative abundance of various cell types. In order to further explore the mutual influence between cell types, a correlation matrix of gene expression and cell type abundance is constructed, which can reveal how changes in the abundance of one cell type are associated with changes in gene expression in other cell types. In order to ensure the accuracy of the analysis results, the correlation matrix needs to be corrected to exclude the possible interference of the gene expression of the cell type itself on the results. Subsequently, cell type enrichment analysis is performed, which aims to discover the enrichment pattern of genes closely related to the abundance of a specific cell type in other cell types, thereby revealing the potential mechanism of cell-to-cell communication. Based on these enrichment analysis results, an interaction network between cell types is further constructed to intuitively show how different cell types influence and coordinate each other through changes in gene expression. Finally, the expression levels of key ligand-receptor pairs (such as VEGFA-FLT1, etc.) can be selectively detected to assess their role and strength in intercellular communication (see Fig.16 , Fig.17 ).

[0082] 1.9 Main Research Findings

[0083] Through the study, we compared and analyzed the composition, functional status and dynamic changes of the tumor microenvironment between patients who were sensitive to immunotherapy (pCR group) and those who were resistant to immunotherapy (non-pCR group). We identified key cell subpopulations (specifically mast cells and endothelial cells), signaling pathways (specifically VEGFA-FLT1) and candidate molecular markers that affect the efficacy of immunotherapy.

[0084] We have deeply explored the functional transformation, metabolic characteristics and regulatory networks of specific immune cell subsets (such as B cells, T / NK cells) in the tumor microenvironment.

[0085] Cell interaction analysis revealed the interaction mechanism between different cell types in the tumor microenvironment. In the non-pCR group, VEGFA-FLT1 was significantly enriched between mast cells-endothelial cells, and the expression levels of VEGFA and FLT1 were significantly increased, while in the pCR group, the expression levels of VEGFA and FLT1 were not significantly increased.

[0086] The regulatory mechanism of the tumor microenvironment in different therapeutic effects of immunotherapy was elucidated. After binding to FLT1, VEGFA can activate the signaling pathway in endothelial cells, promote the proliferation, migration and lumen formation of endothelial cells, and thus induce tumor angiogenesis. The VEGFA-FLT1 pathway not only acts directly on endothelial cells, but also indirectly participates in tumor immune response by affecting the function of immune cells. For example, VEGFA can inhibit the maturation and function of dendritic cells through the FLT1 signaling pathway, thereby reducing T cell activation and anti-tumor immune response. In addition, the VEGFA-FLT1 pathway may also promote the proliferation and function of tumor-infiltrating Treg cells, further inhibiting the anti-tumor immune response.

[0087] (II) Isolation and gene expression detection of mast cells and endothelial cells in gastric cancer tissues of gastric cancer patients to be evaluated (referring to gastric cancer patients other than the above 6 samples)

[0088] 2.1Mast cells and Endothelial cells separation process

[0089] S1. Sample preparation

[0090] Take tumor tissue samples from gastric cancer patients before PD-1 treatment. Quickly cut the tissue samples into 2-4 mm pieces on ice to reduce cell damage and death.

[0091] S2. Tissue Digestion

[0092] Use sterile scissors or a scalpel to cut the tissue into smaller pieces. In a culture dish on ice, add the tissue fragments to a centrifuge tube containing PBS or saline, and shake gently to wash the tissue fragments and remove excess blood and impurities. Add an appropriate amount of trypsin and incubate at 37°C for 15 to 48 hours. The specific time is adjusted according to the density of the tissue and the activity of the enzyme.

[0093] S3. Preparation of cell suspension

[0094] After digestion is complete, gently disperse the cells to avoid excessive cell damage. Filter the cell suspension through a 30-70 μm cell sieve to remove undigested tissue debris and cell clumps. Allow the cells to settle and pour off excess enzyme-containing liquid. Wash the cells 2-3 times with PBS or other appropriate buffer to remove residual enzymes and other impurities. Resuspend the cells in DMEM medium and prepare for flow cytometry sorting.

[0095] S4. Flow Cytometry

[0096] The cell suspension is sorted by flow cytometry. The cells are labeled using the fluorescent labeling function of the flow cytometer. According to the marker genes TPSAB1 and GATA2 of Mast cells, and the marker genes VWF and CD34 of Endothelial cells, the corresponding fluorescent markers are set. Through the precise sorting function of the flow cytometer, the cell population expressing TPSAB1 and GATA2 (mast cells) and the cell population expressing VWF and CD34 (endothelial cells) are sorted out respectively.

[0097] S5. Cell Purity Verification

[0098] The sorted cells are tested for purity by flow cytometry to ensure that the purity of the cell population meets the requirements of subsequent experiments (such as >90%). The cell surface markers of the sorted cells are tested to verify their specificity.

[0099] S6. Cell Culture

[0100] The sorted mast cells and endothelial cells were inoculated into DMEM culture medium for culture, respectively, so as to carry out subsequent experiments or functional studies.

[0101] S7 Result Recording

[0102] Record key parameters during the experiment, including enzyme concentration, incubation time, purity after cell sorting, etc., in order to reproduce and verify the experimental results.

[0103] 2.2 VEGFA and FLT1 gene expression level detection / immunohistochemistry scoring process

[0104] The expression levels of VEGFA and FLT1 in tumor tissues (specifically, mast cells and endothelial cells isolated from tissue samples) are evaluated by immunohistochemistry, RNA sequencing or proteomics techniques. These expression levels can be used to predict the efficacy of immunotherapy.

[0105] Immunohistochemistry scoring: Immunohistochemistry was used to detect the expression of VEGFA and FLT1 in tumor tissues, and scores were given based on the staining intensity and range to evaluate their correlation with immunotherapy response.

[0106] (III) Drug screening application

[0107] 3.1 Cell type selection

[0108] In the above studies, the focus was on mast cells and endothelial cells in the tumor microenvironment of gastric cancer, because the interaction between them (such as the VEGFA-FLT1 signaling pathway) is closely related to the immunotherapy responsiveness of gastric cancer.

[0109] 3.2 Experimental design

[0110] Combined / interactive culture system: In order to simulate the cell-cell interactions in the tumor microenvironment, Mast cells and Endothelial cells are co-cultured to observe the cell response under a near-physiological state.

[0111] Separate culture system: In addition to co-culture, Mast cells and Endothelial cells can also be cultured separately to more clearly observe the independent response of each cell type to a specific drug.

[0112] 3.3 Drug administration

[0113] In the above two culture systems, known or newly synthesized drug molecules can be added for treatment. These drug molecules can be inhibitors or activators designed based on the key signaling pathways obtained (such as the VEGFA-FLT1 signaling pathway). By observing indicators such as cell growth, proliferation, apoptosis, and gene expression changes before and after drug treatment, the effectiveness of the drug and its effect on a specific signaling pathway can be evaluated.

[0114] 3.4 Drug Screening

[0115] Based on the experimental results of the above drug administration treatment, drug molecules that have significant effects on specific cell types or signaling pathways can be screened and used as candidate drugs. The candidate drugs can be further verified and optimized to determine the potential clinical application value of the drug molecules, thereby helping to promote the research and development of gastric cancer immunotherapy drugs.

[0116] (IV) Other applications

[0117] 4.1 Activity evaluation of signaling pathways

[0118] VEGFA-FLT1 signaling pathway activity: The activity of the VEGFA-FLT1 signaling pathway was evaluated by detecting the phosphorylation status of downstream signaling molecules (such as AKT, ERK, etc.). The experimental design based on the evaluation results is as follows:

[0119] (1) Angiogenesis and immune cell infiltration

[0120] The VEGFA-FLT1 signaling pathway promotes tumor angiogenesis, which is a prerequisite for immune cell infiltration into tumors. Therefore, by evaluating the activity of the VEGFA-FLT1 signaling pathway, it is possible to predict the infiltration of immune cells in the tumor microenvironment and thus the effect of immunotherapy.

[0121] (2) Immunosuppressive microenvironment

[0122] The VEGFA-FLT1 signaling pathway may be involved in creating an immunosuppressive microenvironment, for example, by promoting the proliferation and function of regulatory T cells (Tregs), thereby inhibiting anti-tumor immune responses. Therefore, the activity of the VEGFA-FLT1 signaling pathway can serve as a basis for discovering biomarkers of immunotherapy response.

[0123] (3) Regulation of immune cell function

[0124] The VEGFA-FLT1 signaling pathway may affect the function of immune cells, including the maturation and function of dendritic cells, thereby affecting the activation of T cells. Therefore, by analyzing the activity of the VEGFA-FLT1 signaling pathway, the effect of immunotherapy can be predicted.

[0125] (4) Immunotherapy sensitivity

[0126] Therefore, the activity of the VEGFA-FLT1 signaling pathway may be used to predict patient response to PD-1 / PD-L1 inhibitor therapy, including the extent to which inhibition of this pathway is associated with increased sensitivity to immune checkpoint inhibitors.

[0127] (5) Combined treatment strategy

[0128] Targeted drugs of the VEGFA-FLT1 signaling pathway (such as anti-VEGF antibodies or VEGFR tyrosine kinase inhibitors, or) may be used in combination with immune checkpoint inhibitors to enhance the effect of immunotherapy. Therefore, the application of this combined treatment strategy can be guided by analyzing the activity of the VEGFA-FLT1 signaling pathway.

[0129] 4.2 Functional Experiments

[0130] In vitro co-culture experiment: In the in vitro experiment, tumor cells were co-cultured with immune cells, and VEGF-FLT1 signaling pathway inhibitors were added to observe whether the killing ability of immune cells against tumor cells was enhanced.

[0131] In vivo animal models: In vivo experiments were conducted using mouse models to evaluate the effects of VEGF-FLT1 signaling pathway inhibitors on tumor growth and their enhancement of immunotherapy effects.

[0132] In summary, the present invention uses single-cell sequencing technology to deeply analyze clinical samples of gastric cancer patients receiving PD-1 treatment, comprehensively revealing the gene expression characteristics, heterogeneity and interaction networks of various immune cells, tumor cells and stromal cells in the tumor microenvironment, and constructing a high-precision single-cell atlas for gastric cancer immunotherapy. Based on the constructed single-cell atlas, the composition, functional state and dynamic changes of the tumor microenvironment between patients sensitive to immunotherapy and those resistant to immunotherapy are compared and analyzed, key cell subpopulations, signaling pathways and candidate molecular markers that affect the efficacy of immunotherapy are identified, and the regulatory mechanism of the tumor microenvironment between different immunotherapy effects is clarified, providing a theoretical basis for overcoming immunotherapy resistance. At the same time, based on the above-mentioned mechanism research, candidate drug molecules for key targets are screened and verified, and new therapies are designed to regulate the tumor microenvironment, reverse the immunosuppressive state, enhance the anti-tumor immune response, and provide new treatment options for gastric cancer patients.

Claims

1. A method for enriching targeted cells in the tumor microenvironment of gastric cancer patients, characterized by: The targeted cell enrichment includes the following steps: Step 1: Sample preparation Cut the tumor tissue samples from gastric cancer patients into 2-4 mm tissue fragments; Step 2 Tissue digestion Cut the tissue fragments into pieces; wash the tissue fragments with PBS or saline, and then incubate with trypsin for 15 to 48 hours; Step 3: Preparation of cell suspension After digestion is complete, the cells are dispersed; a cell suspension is obtained by filtering using a cell sieve; the cells are separated by sedimentation and then washed with a buffer; the cells are resuspended in a culture medium; Step 4 Flow cytometry sorting The cells are labeled using the fluorescent labeling function of the flow cytometer; the cell suspension is sorted by the flow cytometer to obtain mast cells and endothelial cells respectively; Step 5: Verification of cell purity The sorted mast cells and endothelial cells were tested for purity by flow cytometry to ensure the purity of the cell population.

2. A method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to claim 1, characterized in that: The targeted cell enrichment method further comprises the following steps: Step 6 Cell culture The sorted mast cells and endothelial cells were inoculated into culture medium for culture.

3. A method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to claim 2, characterized in that: The targeted cell enrichment method further comprises the following steps: Step 7: Detection of endogenous gene expression in cells For the cultured mast cells and / or endothelial cells, the expression levels of VEGFA and FLT1 genes in the corresponding cells are detected.

4. A method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to claim 3, characterized in that: The method for detecting gene expression is selected from immunohistochemistry, RNA sequencing or proteomics technology.

5. The method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to any one of claims 1 to 4, characterized in that: In step 1, the gastric cancer patient is a gastric cancer patient who has received ≤7 PD-1 cycles of treatment.

6. The method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to claim 5, characterized in that: The tumor tissue is obtained by biopsy of the same gastric cancer patient before each stage of treatment.

7. The method for enriching targeted cells in the tumor microenvironment of gastric cancer patients according to any one of claims 1 to 4, characterized in that: In step 4, the marker genes of mast cells are TPSAB1 and GATA2, and the marker genes of endothelial cells are VWF and CD34. The fluorescent markers of the corresponding cells are determined according to the marker genes.

8. Use of mast cells and / or endothelial cells isolated from gastric cancer tissue in the preparation of drug screening preparations.

9. The use according to claim 8, characterized in that: The drug is selected from VEGFA-FLT1 pathway inhibitors.

10. The use according to claim 8, characterized in that: The drug is selected from immune checkpoint inhibitors.

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