Use of an immunoinhibitory receptor gene TIGIT as a therapeutic target in the preparation of a drug for preventing and / or treating pregnancy-associated breast cancer

Abstract

The application relates to application of an immunosuppressive receptor gene TIGIT as a therapeutic target in preparation of a drug for preventing and / or treating pregnancy-related breast cancer, and belongs to the technical field of biological medicine. The immunosuppressive receptor gene TIGIT is used as a therapeutic target of the pregnancy-related breast cancer, and an inhibitor thereof is used for preparing a drug for preventing and / or treating the pregnancy-related breast cancer. The inhibitor of the immunosuppressive receptor gene TIGIT is TIGIT monoclonal antibody. The application firstly finds that the TIGIT path is a key immune checkpoint for distinguishing the PABC from common BC, proves that the TIGIT antibody has a specific treatment advantage on the PABC, and thus improves the survival rate and treatment benefit of a patient with the pregnancy-related breast cancer.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of the immunosuppressive receptor gene TIGIT as a therapeutic target in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer. Background Technology

[0002] Breast cancer is the most common malignant tumor among women worldwide, and its incidence and mortality rates have long been the leading cause of cancer death among women. According to the latest global cancer statistics (GLOBOCAN), breast cancer has surpassed lung cancer to become the most diagnosed cancer globally, posing a serious threat to women's health. Although advances in early screening and comprehensive treatment methods (such as surgery, chemotherapy, endocrine therapy, and targeted therapy) have significantly improved the overall survival rate of breast cancer patients, treatment for specific high-risk subgroups still faces enormous challenges.

[0003] Pregnancy-associated breast cancer (PABC) is generally defined as breast cancer diagnosed during pregnancy or within one year postpartum, and it is one of the most common malignant tumors in pregnant and lactating women. Statistics show that the incidence of PABC in pregnancy is approximately 1 in 3,000 to 1 in 10,000, and its incidence is showing a significant upward trend as modern women postpone childbearing age.

[0004] However, the clinical treatment of PABC is extremely challenging. Current treatment strategies primarily rely on the standard regimen for non-pregnant breast cancer, namely surgical resection supplemented by anthracycline or taxane cytotoxic chemotherapy. This approach has significant limitations: First, many targeted therapies (such as the anti-HER2 drug trastuzumab) and endocrine therapies (such as tamoxifen), which are effective in conventional breast cancer, are strictly contraindicated during pregnancy due to their clear teratogenic risks, leaving PABC patients with far fewer treatment options than those in general. Second, the unique physiological changes during pregnancy (such as increased blood volume and elevated glomerular filtration rate) significantly alter the pharmacokinetic characteristics of chemotherapy drugs, making it difficult to achieve the expected killing concentration with standard doses, often leading to inadequate treatment or recurrence.

[0005] Furthermore, due to the unique immune tolerance and tissue remodeling microenvironment of the breast during pregnancy and postpartum, PABC often exhibits stronger invasiveness and immune escape capabilities, resulting in poor prognosis for conventional treatment regimens in this population. Moreover, there is still a lack of sufficient clinical evidence to support the application of novel immunotherapies (such as PD-1 / L1 inhibitors) in PABC.

[0006] Therefore, overcoming the shortcomings of existing technologies is an urgent problem to be solved in the field of biomedicine. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies and provide an application of the immunosuppressive receptor gene TIGIT as a therapeutic target in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The first aspect of this invention provides the use of the immunosuppressive receptor gene TIGIT as a therapeutic target in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer. Furthermore, preferably, the immunosuppressive receptor gene TIGIT serves as a therapeutic target for pregnancy-related breast cancer, and its inhibitor is used to prepare drugs that have preventive and / or therapeutic effects against pregnancy-related breast cancer.

[0009] Furthermore, preferably, the inhibitor of the immunosuppressive receptor gene TIGIT is a TIGIT monoclonal antibody.

[0010] A second aspect of the present invention provides the use of TIGIT monoclonal antibody in the preparation of medicaments for the prevention and / or treatment of pregnancy-related breast cancer.

[0011] A third aspect of the present invention provides a medicament for the prevention and / or treatment of pregnancy-related breast cancer, the active ingredient of which includes TIGIT monoclonal antibody.

[0012] Furthermore, preferably, the drug dosage form is an injection, powder for injection, tablet, oral liquid, capsule, granule or powder.

[0013] A fourth aspect of the present invention provides a kit for treating or diagnosing PABC, comprising an inhibitor of the immunosuppressive receptor gene TIGIT.

[0014] Furthermore, preferably, the inhibitor of the immunosuppressive receptor gene TIGIT is a TIGIT monoclonal antibody.

[0015] This invention provides an application of the immunosuppressive receptor gene TIGIT (T cell immunoreceptor with Ig and ITIM domains) as a target in the preparation of drugs for the specific treatment of pregnancy-associated breast cancer (PAC), providing guidance for precise clinical medication for PABC patients, thereby improving the survival rate and treatment efficacy of PABC patients.

[0016] 1. Target discovery and screening: First, bioinformatics analysis revealed the unique characteristics of PABC compared to BC, namely, the immunosuppressive microenvironment of T cell depletion.

[0017] Then, by using flow cytometry to detect the T cell status in mouse tumors, it was verified that the proportion of exhausted T cells was significantly increased in the pregnant group.

[0018] Given the background of T cell depletion, bioinformatics analysis was used to analyze the expression of immune checkpoints in mice. TIGIT was identified as a key checkpoint driving PABC immunosuppression. Therefore, immunotherapy targeting TIGIT may have special efficacy for this patient population.

[0019] 2. Drug application strategy: Based on the above mechanism, it is proposed to use TIGIT inhibitors (TIGIT monoclonal antibodies) as specific drugs for the treatment of PABC.

[0020] 3. Animal model validation: A PABC mouse model was constructed, and a standard spontaneous breast cancer mouse model was used as a control.

[0021] After the tumors in the PABC group and the control group reached a certain size, the mice were randomly divided into the TIGIT monoclonal antibody group and the IgG group, and were treated with the same dose and frequency.

[0022] 4. Result Verification: In the PABC group, compared with its IgG group, the TIGIT monoclonal antibody group showed significantly slower tumor growth and significantly longer survival time.

[0023] In the control group, there were no significant differences in tumor growth and survival time between the TIGIT monoclonal antibody group and the IgG group.

[0024] Compared with the prior art, the beneficial effects of this invention are as follows: (1) High specificity: This invention is the first to discover that the TIGIT pathway is the key immune checkpoint that distinguishes PABC from ordinary BC, proving that TIGIT antibody has specific therapeutic advantages for PABC; (2) Great potential for translation: TIGIT drugs already have a clinical research foundation, and this invention opens up new indications for them (PABC), especially providing new hope for PABC patients who are unresponsive to conventional immunotherapy; (3) Supporting experimental data: The efficacy difference of TIGIT monoclonal antibody in PABC and ordinary BC was rigorously verified through in vivo animal experiments, and the data are true and reliable. Attached Figure Description

[0025] Figure 1A UMAP dimensionality-reduced visualization of the single-cell transcriptome atlas 1; where each point represents a cell, and the color indicates the lineage annotated by labeled genes: epithelial cells (blue), immune cells (red), and stromal cells (green); the data were dimensionality-reduced using UMAP and clustered according to gene expression; Figure 2 This is a bubble diagram showing the relative expression levels of characteristic genes in different cell types in Single Cell Transcriptome Atlas 1. The diagram illustrates the relative expression levels of selected characteristic genes in epithelial cells, stromal cells, and immune cells. The shades of blue represent the expression range of the marker genes, and the bubble size represents the relative proportion of the marker genes expressed in the cells. Figure 3 A UMAP dimensionality-reduced visualization of Single Cell Transcriptome Atlas 2; immune cell (Immune) clusters derived from Single Cell Transcriptome Atlas 1; further dimensionality-reduced clustering to show the distribution of different immune cell types (B cells, myeloid cells, NK cells, T cells) in two-dimensional space; each point represents a cell, and the color indicates the cluster to which the cell belongs; Figure 4 Further annotations for the UMAP dimensionality reduction visualization of Single Cell Transcriptome Atlas 2 show the further cell type clustering and annotation of tumor-infiltrating immune cells in human immune cells. Figure 5 The percentage stacked bar chart for cell types in Single Cell Transcriptome Atlas 2 shows the percentage composition of various immune cells (CD4 Tfh, CD8 Exhausted, CD4 Treg, CD4 Memory, CD8 Activated, CD8 Memory, Natural killer cells, CD4 Effector, B cells, CD8 Cytotoxic, Myeloid cells) in the BC and PABC groups. Figure 6 This is a faceted UMAP dimension reduction plot of TIGIT expression in Single Cell Transcriptome Atlas 2; the horizontal axis represents the first dimension of UMAP (umap_1), and the vertical axis represents the second dimension of UMAP (umap_2); where (A) is the common breast cancer (BC) group and (B) is the pregnancy-associated breast cancer (PABC) group. This plot is used to show the expression distribution characteristics of TIGIT in the low-dimensional space in these two groups. Figure 7 The flow cytometry analysis begins with lymphocytes, followed by single-cell screening (twice), live cells, and CD45. + Immune cells, CD3 + T cells, up to CD8 +Gating logic diagram of cytotoxic T cells; Figure 8 The scatter plots are from flow cytometry, showing the expression distribution of PD-1 and Tim-3 in cells under different conditions; (A) to (C) represent three independent samples from the non-pregnant group (NO-P), and (D) to (F) represent three independent samples from the pregnant group (P); the numbers in the figure represent the percentages in each quadrant. Figure 9 CD8 levels in the non-pregnant group (NO-P) and the pregnant group (P) + Comparison of the number of PD-1 and Tim-3 double-positive cells on T cells; Figure 10 This figure shows the expression profiles of immune checkpoint genes in humans and mice. It displays the percentage (Per.Exp) and average expression level (AveExp) of a specified gene in humans and mice, with color gradients reflecting expression intensity and proportion. Figure 11 Tumor growth curves were displayed for mice in the TIGIT treatment group and the IgG control group under pregnant and non-pregnant conditions; where Pregnancy TIGIT was the pregnant TIGIT group, Non-pregnancy TIGIT was the non-pregnant TIGIT group, Pregnancy IgG control was the pregnant IgG control group, and Non-pregnancy IgG control was the non-pregnant IgG control group. Figure 12 The image shows the tumor volume analysis of pregnant and non-pregnant tumor-bearing mice; (A) is a photograph of representative tumors in each group at week 5; (B) is a bar chart comparing tumor weight in each group (left: non-pregnant, right: pregnant; in each group, the left column is the IgG control group and the right column is the TIGIT monoclonal antibody group). Figure 13 Survival curves for the TIGIT treatment group and the IgG control group under pregnant and non-pregnant conditions are shown, with day 0 of drug administration and death as the time endpoint; where Pregnancy TIGIT is the pregnant TIGIT group, Non-pregnancy TIGIT is the non-pregnant TIGIT group, Pregnancy IgG control is the pregnant IgG control group, and Non-pregnancy IgG control is the non-pregnant IgG control group. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the embodiments.

[0027] Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed in accordance with the techniques or conditions described in the literature in the field or according to the product instructions. Materials or equipment whose manufacturers are not specified are all conventional products that can be obtained by purchase.

[0028] I. Screening of PABC-specific targets 1. Preprocessing and integration of single-cell transcriptome data This invention performed single-cell transcriptome sequencing on tumor tissue samples from patients with pregnancy-associated breast cancer (PABC) and ordinary breast cancer (BC). Cell Ranger software was used for alignment, quantification, and initial quality control of the raw sequencing data. Subsequently, the expression matrices of each sample were imported into R software for downstream analysis. The Seurat package was used for data integration and batch effect correction. Quality control criteria included: removing cells with excessively high mitochondrial gene expression (>20%) and cells with too few (<200) or too many (>6000) genes detected.

[0029] 2. Dimensionality reduction, clustering, and cell type annotation After logarithmic normalization, the integrated data were used to identify hypervariable genes using the FindVariableFeatures function. The integrated data and the data identifying hypervariable genes were standardized using ScaleData, and then linear dimensionality reduction was performed using Principal Component Analysis (PCA). Significant principal components were determined based on ElbowPlot, and cell clustering was then performed using the FindNeighbors and FindClusters functions (resolution set to 0.8). Finally, nonlinear dimensionality reduction visualization was performed using the Uniform Manifold Approximation and Projection (UMAP) algorithm. Figure 1 ).

[0030] To identify the cell types of each cluster, we used the FindAllMarkers function to screen for genes (marker genes) that are specifically highly expressed in each cluster relative to other clusters. We then annotated the major cell groups using classic marker genes reported in the literature (such as EPCAM for epithelial cells, PTPRC for immune cells, and DCN and COL1A2 for stromal cells). The expression levels of key characteristic genes in each cell type were visualized using the DotPlot function. Figure 2 ).

[0031] This study included surgical specimens from 6 breast cancer patients (3 with conventional breast cancer (BC) and 3 with pregnancy-associated breast cancer (PABC)). Tumor tissue was used to prepare single-cell suspensions for 10×Genomics single-cell transcriptome sequencing. After quality control, dimensionality reduction (UMAP / t-SNE), and unsupervised clustering, the raw data were used to obtain a global clustering visualization of all live cell populations, i.e., the single-cell transcriptome atlas 1.

[0032] Building upon this foundation, in order to gain a deeper understanding of the immune components in the tumor microenvironment, this study used bioinformatics methods to digitally sort and extract immune cell clusters (based on immune marker genes such as CD45 / PTPRC) from the single-cell transcriptome atlas 1. Subsequently, the extracted immune cells were individually subjected to secondary clustering, subgroup subdivision, and functional annotation to obtain a higher resolution immune cell subtype classification atlas, namely the single-cell transcriptome atlas 2.

[0033] 3. Subgrouping of immune cells To gain a deeper understanding of the immune microenvironment, we extracted immune cells (primarily based on PTPRC expression) from the data and performed secondary re-clustering. This subset was then subjected to the same normalization, PCA dimensionality reduction, clustering, and UMAP visualization process described above. Figure 3 Based on classic markers of T cells, B cells, myeloid cells, and NK cells, immune systems were initially classified into major categories. Subsequently, a third, more refined re-clustering was conducted, focusing on the T cell population.

[0034] Based on naïve T cells, effector T cells, memory T cells, regulatory T cells (Treg), follicular helper T cells (Tfh), and CD8... + Characteristic gene expression profiles of various functional subsets of T cells (such as exhaustion, cytotoxicity, and activation) are used to accurately annotate T cell subsets. Figure 4 ).

[0035] 4. Cell ratio difference analysis To compare the differences in cell composition between groups PABC and BC, we calculated the relative proportion of each cell subpopulation in each sample. Specifically, we counted the number of cells belonging to a specific cell subpopulation in each sample and divided it by the total number of cells in that sample to obtain the percentage of that subpopulation. We used the ggplot2 package to create stacked bar plots or group comparison plots to show the frequency changes of the dominant cell type (such as CD8+ exhausted T cells) between the two groups. Figure 5 ).

[0036] 5. Analysis of differential expression of key genes For the specific immunomodulatory molecule TIGIT, we extracted its expression levels in different cell subpopulations across each group. We used the VlnPlot function to create violin plots to compare the expression distribution of TIGIT in specific cell populations (e.g., T cells) between the BC and PABC groups. The statistical significance of the expression differences between the two groups was assessed using a nonparametric test (Wilcoxon rank-sum test). Figure 6 ).

[0037] In summary, this invention collected single-cell transcriptome data from patients with pregnancy-associated breast cancer (PABC) and ordinary breast cancer (BC). After integration and batch correction, three major cell types were successfully annotated ( Figure 1 And the characteristic marker genes of each cell subpopulation were displayed using bubble charts. Figure 2 Further focus on immune cell populations ( Figure 3 We performed fine annotation on T cell subsets ( Figure 4 By comparing the differences in the proportion of cell types between the two groups, it was found that PABC patients exhibited an immunosuppressive microenvironment characterized by CD8+ T cell depletion. Figure 5 This may affect the treatment outcome in PABC patients. It is noteworthy that there was a significant difference in TIGIT expression between the two groups ( Figure 6 In summary, through bioinformatics analysis, we identified the unique characteristics of PABC compared to BC, namely, an immunosuppressive microenvironment characterized by T cell depletion, and suggested that TIGIT may serve as a potential immunotherapeutic target.

[0038] 6. Flow cytometry detection of T cell exhaustion markers To verify the T cell exhaustion phenotype, we used multicolor flow cytometry to detect the expression of the co-inhibitory molecules PD-1 and Tim-3 on the surface of CD8+ T cells in tumor-infiltrating lymphocytes (TILs).

[0039] (1) Preparation of single-cell suspension: Mice with spontaneous breast cancer were divided into pregnant and non-pregnant groups according to their pregnancy status. After tumor formation, the tumor volume reached 100 mm. 3 At that time, the in situ tumor was removed. The in situ tumor tissue was minced and then ground using a tumor tissue dissociation kit (Miltenyi Biotec) or mechanical methods. The tissue was filtered through a 70μm cell sieve, and red blood cells were removed using red blood cell lysis buffer. The cells were then resuspended in PBS (staining buffer) containing 2% (v / v) fetal bovine serum, counted, and the cell density adjusted to 1×10⁶ cells / mL. 7 single-cell suspensions were prepared by dispersing cells / mL.

[0040] (2) Staining of live and dead cells: First, staining was performed to eliminate interference from dead cells. 100 μL of single-cell suspension (containing 1 × 10⁶ cells) was taken. 6 (Number of cells) were placed in a flow cytometry tube. Following the Zombie Dyes instructions: the Zombie NIR dye stock solution (dissolved in 100 μL DMSO) was diluted 1:500 with PBS to obtain the working solution of the live / dead dye. 100 μL of the diluted working solution was added to the flow cytometry tube, vortexed to mix, and incubated at room temperature in the dark for 15 minutes. After incubation, 2 mL of PBS (8.0 g NaCl, 0.2 g KCl, 1.44 g Na₂HPO₄, 0.24 g KH₂PO₄, and distilled water to a final volume of 1000 mL) was added, and the tube was centrifuged at 350 × g for 5 minutes. The supernatant was discarded.

[0041] (3) Fc receptor blocking: To reduce non-specific staining, the cells remaining after discarding the supernatant in (2) were resuspended in 100 μL of staining buffer, and 5 μL of human Fc receptor blocking solution (BioLegend, Cat#422301) was added. The cells were incubated at room temperature for 10 minutes. After blocking, the cells were directly subjected to the next step of surface staining without washing.

[0042] (4) Surface antibody staining: According to the experimental grouping design, the corresponding combination of surface antibodies was added to each tube.

[0043] Antibodies include: CD45-PerCP / Cyanine 5.5 (BioLegend, clone number HI30), recommended dosage 1.25 μL / 1×10 6 cells; CD3-FITC (Elabscience, clone number OKT3) recommended dosage: 5 μL / 1×10 6 cells; CD8a-PE / Cyanine7 (BioLegend, clone number SK1), recommended dosage 1.25 μL / 1×10 6 cells; PD-1-APC (BioLegend, clone number EH12.2H7), recommended dosage 5 μL / 1×10 6 cells; Tim-3-PE (BioLegend, clone number F38-2E2), recommended dosage 5μL / 1×10 6 cells.

[0044] After adding the antibody, vortex to mix and incubate at 4°C in the dark for 30 minutes. After incubation, add 2 mL of staining buffer, centrifuge at 350×g for 5 minutes, wash twice, and discard the supernatant.

[0045] Group design: 1. Blank control: No antibodies are added and no procedures are performed; 2. Single staining tubes with live and dead dyes: Stain live and dead cells according to step (2), and add Fc receptor blocking solution according to step (3); 3. CD45 single staining tube: Add CD45-PerCP / Cyanine 5.5 antibody and Fc receptor blocking solution; 4. CD3 single staining tube: Add CD3-FITC antibody and Fc receptor blocking solution; 5. CD8 single staining tube: Add CD8a-PE / Cyanine7 antibody and Fc receptor blocking solution; 6. PD-1 single staining tube: Add PD-1-APC antibody and Fc receptor blocking solution; 7. TIM03 single staining tube: Add Tim-3-PE antibody and Fc receptor blocking solution; 8. PD-1-FMO tubes: Add all antibodies except PD-1-APC antibody (i.e., CD45-PerCP / Cyanine5.5, CD3-FITC, CD8a-PE / Cyanine7, Tim-3-PE), and add Fc receptor blocking solution; 9. TIM-3-FMO tubes: Add all antibodies except Tim-3-PE antibody (i.e., CD45-PerCP / Cyanine5.5, CD3-FITC, CD8a-PE / Cyanine7, PD-1-APC), and add Fc receptor blocking solution; 10. Experimental tubes: Add all antibodies (i.e., CD45-PerCP / Cyanine5.5, CD3-FITC, CD8a-PE / Cyanine7, PD-1-APC, Tim-3-PE), and add Fc receptor blocking solution.

[0046] (5) On-machine testing: Cells stained in step (4) were resuspended in 200 μL of staining buffer and analyzed using a flow cytometer (SONY ID7000 full-spectrum ultra-multicolor flow cytometer). Compensation was adjusted and a positive grouping gate was set using single-stain tubes and FMO control tubes. Among them, single-stain tubes refer to all single-stain tubes (inactivated / dead dye single-stain tubes, CD45 single-stain tubes, CD3 single-stain tubes, etc.), and FMO control tubes refer to PD-1-FMO tubes and TIM-3-FMO tubes.

[0047] (6) Data Analysis and Gating Strategy: FlowJo software (v10.8.1) was used to analyze the streaming data. Specific gating strategies ( Figure 7)as follows: First, circle the lymphocyte populations in the scatter plots of FSC-A and SSC-A; Adhesions were excluded and single cells were screened by using scatter plots of FSC-A and SSC-A; In the scatter plot of the live and dead dye channel (Zombie BV510) and FSC-A, circle the live cell population that is negative for live and dead dyes; From the live cell population, circle out CD45-positive immune cells; Circle the CD3-positive T cells from the CD45⁺ cells; Circle CD8a-positive cytotoxic T cells from CD3⁺ T cells. In the CD8a⁺ T cell population, gating by PD-1 and Tim-3, double-positive cells (Q2 quadrant) are considered exhausted CD8a⁺ cells. + T cells.

[0048] The T cell status in mouse tumors was detected by flow cytometry, resulting in six two-parameter scatter plots, such as... Figure 8 (A) – Figure 8 (F), the horizontal axis represents PD-1 expression intensity (APC-A), and the vertical axis represents TIM-3 expression intensity (PE-A). The first quadrant (PD-1) + TIM-3 + () represents terminally exhausted CD8⁺ T cells. Figure 8 (A) Figure 8 (B) Figure 8 (C) represents three replicates in the non-pregnant group, i.e., the control group, with the proportions of cells in the first quadrant being 3.11%, 5.85%, and 2.72%, respectively. Figure 8 (D) Figure 8 (E) Figure 8 (F) represents the three replicates of PABC in the pregnancy group, with proportions of 7.49%, 9.87%, and 12.60%, respectively.

[0049] based on Figure 8 First Quadrant (PD-1) + TIM-3 + CD8 + Statistical analysis was performed on the proportion of T cells. First, the Levin test for equivalence of variance was used to assess the homogeneity of variance between the two groups. The results showed F=0.245, P=0.646, indicating that the variances of the two populations were homogeneous. Therefore, a two-tailed independent samples t-test (assuming equal variances) was used for inter-group comparisons. The test results showed that the PD-1 ratio in the pregnancy group... + TIM-3 + Double positive CD8 +The proportion of T cells (9.99% ± 2.56%) was significantly higher in the group than in the non-pregnant group (3.89% ± 1.71%), and the difference was statistically significant (t = –3.43, df = 4, P = 0.026). Figure 9 This result indicates that, compared to the control group, the tumor microenvironment of PABC in the pregnancy group was in a state of immune exhaustion, thus verifying the significantly increased proportion of exhausted T cells in the pregnancy group. 7. To assess the conservation of expression of key immunomodulatory molecules in human and mouse breast cancer, we performed a meta-analysis of single-cell transcriptome data from human PABC and BC samples and corresponding mouse breast tumor models (such as MMTV-PyMT or orthotopic transplantation models).

[0050] (1) Homologous gene identification: Use the biomaRt R package or the Ensembl database to convert human genes (such as PDCD1, LAG3, TIGIT, CTLA4) into their corresponding homologous mouse genes. This ensures the accuracy of gene annotation when making cross-species comparisons.

[0051] (2) Definition of cell subsets: In human and mouse data, target cell subsets (e.g., T cells or exhausted CD8 cells) were defined based on characteristic marker genes (e.g., human CD3E, mouse Cd3e). + (T cells). Use the AddModuleScore function of the Seurat package or a manual gating strategy to ensure consistency in subpopulation definitions.

[0052] (3) Calculation of the proportion of positive cells: For each target gene, we calculated its expression percentage in a specific cell subpopulation. The specific method was as follows: In the standardized expression matrix, an expression threshold is set (usually log-normalized expression level > 0).

[0053] Calculate the proportion of cells expressing this gene to the total number of cells in this subpopulation.

[0054] Use the scale parameter of the DotPlot function or calculate it manually to generate percentage data as shown in the table (e.g., PDCD1 expression is 10% in a human subpopulation and 1% in mice).

[0055] (4) Cross-species comparison: The expression percentages of corresponding genes in humans and mice were compared side-by-side. Figure 10The expression patterns of immunomodulatory molecules (including co-inhibitory molecules PDCD1, CTLA4, LAG3, and TIGIT; co-stimulatory molecule TNFRSF9; effector molecules GZMB and IFNG; and exonucleases NT5E and ENTPD1) across species were visualized (e.g., bubble charts or heatmaps). This analysis aimed to assess the extent to which mouse models could reproduce the immune microenvironment characteristics of human breast cancer.

[0056] The specific significance of this experiment is as follows: Through cross-species (human and mouse) single-cell transcriptome sequencing analysis, this invention revealed that the tumor immune microenvironment of pregnancy-associated breast cancer (PABC) exhibits a significant state of T-cell exhaustion. This provides a potential immunological explanation for the poor response to conventional treatments or the tendency to develop drug resistance in PABC patients.

[0057] Phenomenon: Breast cancer samples from pregnancy exhibited an immunosuppressive microenvironment, with T cells in a state of depletion.

[0058] Consequence: It renders conventional treatments ineffective.

[0059] Mechanism: TIGIT was found to be a highly expressed immune checkpoint conserved across species.

[0060] Solution: Anti-TIGIT therapy is proposed as a precision treatment strategy.

[0061] Given that reversing T cell exhaustion is the core mechanism of immune checkpoint inhibitors, we screened all known immune checkpoint molecules through cross-species (human and mouse) single-cell transcriptome sequencing analysis. The results showed that among all candidate molecules, only TIGIT exhibited consistently high expression in exhausted T cells of both human and mouse PABC models. This cross-species conservation further suggests that TIGIT may be a key checkpoint driving PABC immunosuppression; therefore, TIGIT-targeted immunotherapy may have specific therapeutic effects on this patient population.

[0062] II. Construction and Drug Intervention of the PABC Mouse Model 1. PABC Model Construction This experiment used MMTV-PyMT transgenic mice to construct a PABC (polyacrylamide adenocarcinoma) model. Pathological examination showed that MMTV-PyMT female mice had a high incidence of mammary tumors at 6 weeks of age, and tumor masses could be palpated on the body surface of mice aged 8-10 weeks. After genotyping of MMTV-PyMT female mice born in the same batch at 3 weeks of age, we randomly divided them into two groups: one group served as the control group, and the other group was mated with age-appropriate male mice at 6 weeks of age to complete pregnancy during the period of high incidence of mammary tumors, thus completing the construction of the PABC mouse model, which served as the PABC group.

[0063] 2. Dosing regimen The PABC group and the control group were fed simultaneously until the mouse tumors grew to approximately 50 mm. 3 When the mice were large, they were divided into TIGIT monoclonal antibody subgroup and IgG subgroup within their respective groups, and administered the drugs accordingly. Each mouse was injected twice a week via intraperitoneal injection, with each injection consisting of 200 μg of TIGIT monoclonal antibody (TIGIT monoclonal antibody group) or 200 μg of IgG (IgG group), for three consecutive weeks.

[0064] 3. Monitoring Indicators: Day 0 was recorded as the start of drug administration. Tumor growth in mice was continuously monitored, and tumor volume was recorded twice a week until the mice died, with the time of death also recorded. In the fifth week after drug administration, mice were randomly selected, their tumors were removed, weighed, photographed, and their lungs were removed to observe lung metastasis.

[0065] 4. Observation indicators and sample collection During the course of treatment, the major diameter (a) and minor diameter (b) of the tumor were measured twice a week using calipers, and the results were calculated using the formula V = 0.52 × a × b. 2 Calculate the tumor volume and plot the tumor growth curve.

[0066] Observe the general condition and survival status of the mice daily, record the survival time of each group of mice, and plot the survival curve.

[0067] As required by the ethics committee, mice are euthanized when the tumor diameter exceeds 12 mm or the tumor volume reaches the humanitarian endpoint. In this experiment, the largest tumor volume was observed to be close to the institution's humanitarian endpoint by week 3, so all mice in all groups were euthanized, the tumor tissue was completely dissected, and the final volume and weight were measured.

[0068] As shown in Figure 11, in the PABC group, tumor growth was significantly slowed in the TIGIT monoclonal antibody group compared to the IgG group; while in the control group (non-pregnant), there was no significant difference in tumor growth between the TIGIT monoclonal antibody group and the IgG group.

[0069] like Figure 12 As shown, by week five, the maximum tumor volume in the tumor-bearing mice was close to the humane endpoint set by the institutional ethics committee. Therefore, the mice were euthanized, and the tumors were dissected for volume measurement (A). We observed that in the PABC group, the tumor volume in the TIGIT monoclonal antibody group was significantly smaller than that in the IgG group; while in the control group (non-pregnant), there was no significant difference in tumor size between the TIGIT monoclonal antibody group and the IgG group (B).

[0070] like Figure 13As shown, in the PABC group, the survival time of mice in the TIGIT monoclonal antibody group was significantly prolonged compared to the IgG group; while in the control group (non-pregnant group), there was no significant difference in survival time between the mice in the TIGIT monoclonal antibody group and the IgG group. This indicates that the TIGIT inhibitor (TIGIT monoclonal antibody) can effectively prolong the lifespan of PABC mice.

[0071] comprehensive Figure 11 , Figure 12 , Figure 13 We found that TIGIT monoclonal antibody could slow tumor growth and prolong the survival time of tumor-bearing mice in the pregnancy group.

[0072] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. The application of the immunosuppressive receptor gene TIGIT as a therapeutic target in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer.

2. The application of the immunosuppressive receptor gene TIGIT as a therapeutic target according to claim 1 in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer, characterized in that: The immunosuppressive receptor gene TIGIT is used as a therapeutic target for pregnancy-related breast cancer, and its inhibitors are used to prepare drugs that have preventive and / or therapeutic effects on pregnancy-related breast cancer.

3. The application of the immunosuppressive receptor gene TIGIT as a therapeutic target according to claim 1 in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer, characterized in that: The inhibitor of the immunosuppressive receptor gene TIGIT is a TIGIT monoclonal antibody.

4. Application of TIGIT monoclonal antibody in the preparation of drugs for the prevention and / or treatment of pregnancy-related breast cancer.

5. A drug for the prevention and / or treatment of pregnancy-related breast cancer, characterized in that, The active ingredient includes TIGIT monoclonal antibody.

6. The medicament for the prevention and / or treatment of pregnancy-related breast cancer according to claim 1, characterized in that, The drug dosage forms are injections, powder injections, tablets, oral liquids, capsules, granules, or powders.

7. A kit for treating or diagnosing PABC, characterized in that, Inhibitors that include the immunosuppressive receptor gene TIGIT.

8. The kit for treating or diagnosing PABC according to claim 7, characterized in that, The inhibitor of the immunosuppressive receptor gene TIGIT is a TIGIT monoclonal antibody.

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