Application of MTA3 in the preparation of PD-L1 / PD-1 monoclonal antibody for tumor immunotherapy

Abstract

The present invention belongs to the field of PD-L1 / PD-1 monoclonal antibody tumor immunotherapy. The purpose of the present invention is to provide a new research and development direction for PD-L1 / PD-1 monoclonal antibody tumor immunotherapy detection or immunotherapy adjuvant drugs, and to develop a new mode of combined medication. Specifically, the present invention provides the use of MTA3 in the preparation of a detection kit for treating PD-L1 / PD-1 monoclonal antibody tumor immunotherapy drugs and the use of MTA3 in combination with PD-L1 / PD-1 monoclonal antibody drugs in the preparation of tumor immunotherapy drugs, wherein the tumor is a solid tumor, and further the solid tumor is melanoma.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and specifically to the field of PD-L1 / PD-1 monoclonal antibody tumor immunotherapy. Background Art

[0002] Skin tumors are the most common type of cancer among all tumors. Among them, skin malignant melanoma (SKCM) is a highly malignant skin tumor derived from melanocytes. The incidence of invasive melanoma accounts for only 1% of skin tumor cases, but it leads to an extremely high mortality rate. In the past few decades, the incidence of melanoma has been on the rise. According to statistics, the incidence of melanoma has increased by 4% to 6% per year among Caucasians in North America, Northern Europe, Australia and other regions. The latest statistics from the American Cancer Society show that the incidence of skin melanoma is still rising. In 2020, about 100,350 new cases of melanoma were diagnosed in the United States, and about 6,850 patients died of melanoma. The mortality rate of skin melanoma has dropped significantly in recent years, mainly due to the application of new therapies and new drugs in metastatic melanoma to improve the survival rate of patients. Analysis of the Surveillance, Epidemiology, and End Results Program (SEER) database showed that in 2011, the U.S. Food and Drug Administration (FDA) approved the first cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) immune checkpoint inhibitor. (Ipilimumab) and targeted therapy drugs targeting BRAF V600E gene mutation After the use of vemurafenib in the treatment of advanced melanoma, the 1-year relative survival rate of patients increased from 42% in 2008 to 2010 to 55% in 2013 to 2015.

[0003] Sphingosine kinases (SPHKs) are an important class of rate-limiting enzymes, mainly including SPHK1 and SPHK2 isoforms, which can catalyze and phosphorylate sphingosine (SPH) to generate sphingosine-1-phosphate (S1P). Although the two isoforms have the same substrate and highly similar amino acid sequences, there are significant differences in the expression levels and subcellular localization of SPHK1 and SPHK2. SPHK1 is located in the cytoplasm of various organs, while SPHK2 is mainly distributed in the nucleus and specific organelles. The accumulated evidence shows that S1P synthesized by SPHK1 in the cytoplasm can be exported to the cell membrane through G protein-coupled receptors, regulate cell physiological functions, and participate in inflammation and tumor microenvironment (TME) formation. SPHK1 plays an important role in the intracellular and extracellular signal transmission of S1P. Therefore, targeting SPHK1 may be a key link in blocking tumor progression. Recent studies have elucidated the function of SPHK1 in cancer progression; however, the specific mechanism of SPHK1 in tumor immune escape remains unclear.

[0004] MTA3 is considered to be an important component of the nucleosome remodeling complex (NuRD) and is involved in the formation of the histone deacetylase core subcomplex of NuRD. In addition, some studies have reported the biological characteristics of the MTA3 gene in breast cancer. A large number of studies have shown that estrogen receptor-α (ER-α) directly regulates the transcription of MTA3 by binding to the MTA3 promoter, but its specific mechanism of action in the immune response of melanoma remains unclear.

[0005] Programmed cell death ligand 1 (PD-L1), also known as B7-H1 (B7homolog1) or CD274, is a cell surface glycoprotein and a co-stimulatory molecule belonging to the B7 family. In malignant melanoma, PD-L1 can be induced to be upregulated through innate and adaptive mechanisms. The abnormally high expression of PD-L1 on tumor cells is considered to be an important factor in immune tolerance and escape from immune surveillance. After PD-L1 on the surface of tumor cells binds to the homologous programmed cell death protein 1 (PD-1) expressed on tumor infiltrating lymphocytes, the inhibitory signaling pathway induced by PD-L1 can negatively regulate the activation and proliferation of T cells, inhibit cytokine secretion, promote T cell exhaustion, and enable tumor cells to escape from immune surveillance. Since PD-1 / PD-L1 related inhibitors have a lasting anti-tumor effect, especially in some refractory malignant tumors, since 2014, multiple drugs targeting PD-L1 / PD-1 pathways have been approved by the FDA for the treatment of various malignant tumors. Their indications are wider than those of CTLA-4 inhibitors. However, due to the different etiologies and pathogenesis of various tumors, complex tumor microenvironment, individual differences and other factors, PD-1 / PD-L1 inhibitors can only benefit some patients, and about 80% of patients develop acquired resistance or non-response after using the drugs. Therefore, before formulating a specific treatment plan, it is an urgent problem to predict the applicable population of immune checkpoint inhibitors based on biomarkers and avoid immune-related adverse reactions caused by drugs. Summary of the invention

[0006] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a new research and development direction for PD-L1 / PD-1 monoclonal antibody tumor immune detection or immunotherapy adjuvant drugs, and to develop a new mode of combined drug use.

[0007] The technical solution of the present invention is:

[0008] Technical solution 1 of the present invention:

[0009] Application of SPHK1 detection reagent in the preparation of detection kit for treating PD-L1 / PD-1 monoclonal antibody tumor immunotherapy.

[0010] Application of SPHK1 inhibitors in the preparation of PD-L1 / PD-1 monoclonal antibody tumor immunotherapy drugs.

[0011] Application of SPHK1-MTA3 axis detection reagent in the preparation of PD-L1 / PD-1 monoclonal antibody tumor immunotherapy drug kit.

[0012] Technical solution 2 of the present invention:

[0013] Application of PF543 in the preparation of PD-L1 / PD-1 monoclonal antibody tumor immunoadjuvant therapy drugs.

[0014] The PF543 targets sphingosine kinase SPHK1 to prepare PD-L1 / PD-1 monoclonal antibody tumor immunotherapy drugs.

[0015] The application of PF543 in combination with PD-L1 / PD-1 monoclonal antibody drugs in the preparation of tumor immunotherapy drugs.

[0016] Technical solution three of the present invention:

[0017] The application of MTA3 detection reagent in the preparation of detection kits for treating PD-L1 / PD-1 monoclonal antibody tumor immunotherapy.

[0018] The application of MTA3 inhibitors in combination with PD-L1 / PD-1 monoclonal antibodies in the preparation of tumor immunotherapy drugs.

[0019] The application of inhibitors of SPHK1-MTA3 axis in combination with PD-L1 / PD-1 monoclonal antibody drugs in the preparation of tumor immunotherapy drugs.

[0020] In the above three technical solutions, the tumor is preferably a solid tumor, and the tumor is further preferably a skin malignant melanoma.

[0021] Compared with the prior art, the advantages of the present invention are:

[0022] The detailed structure of the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0023] In Example 1, Figure 1 As shown in Table 1, we used the TCGA public database to perform gene set variation analysis on different types of tumors, and the results showed that SPHK1 and SPHK2 were generally abnormally expressed in tumor tissues. We found that SPHK1 was significantly overexpressed in the vast majority of cancer types. In order to further explore the potential function of SPHK1 in the tumor microenvironment, we conducted a correlation analysis between SPHK1 and immune checkpoint expression levels in 33 types of cancer, and found that the SPHK1 gene was significantly positively correlated with most checkpoints with immunosuppressive functions. In many types of tumors, including cutaneous melanoma, we found that PD-1 (PDCD1) and PD-L1 (CD274), as well-defined immunosuppressive pathways in the tumor microenvironment, were significantly positively correlated with the expression of the SPHK1 gene. The above bioinformatics analysis results show that SPHK1 may be an important immunosuppressive molecule. As Figure 2As shown in the figure, we constructed a subcutaneous transplanted tumor (melanoma) immune mouse model and treated mouse tumors with SPHK1-specific inhibitor PF543. We found that targeted inhibition of sphingosine kinase can significantly inhibit tumor proliferation in mice without significant toxicity. Figure 3 As shown, flow cytometry was used to further analyze the function of SPHK1 in the local microenvironment of melanoma. We found that PF543 treatment could induce the expression of CD4 and CD8 + T cell infiltration. Among them, CD8 secretes granzyme GzmB + The proportion of T lymphocytes (CTL) increased significantly in the drug-treated group. Since GZMB is an important marker that can kill target cells, the above results indicate that PF543 treatment may exert anti-tumor effects by increasing specific CTL cells in the tumor microenvironment. In addition, we found that the expression of PD-L1 membrane protein in tumor cells was significantly reduced in the drug-treated group.

[0024] In Example 2, Figure 4 As shown in Table 4 and Table 5, through the results of mRNA sequencing, we found that inhibiting SPHK1 activity can significantly downregulate the expression of PD-L1. In vitro studies further confirmed that gene knockdown of SPHK1 or PF543 inhibiting sphingosine kinase activity can significantly inhibit downstream MTA3 expression; through bioinformatics analysis of the tumor TCGA database, we also found that in various tumor types, there is a significant positive correlation between the expression of MTA3 and PD-L1. Further through ChIP detection, we found that MTA3 can directly bind to the c-Myc promoter, thereby further transcriptionally regulating the expression of tumor PD-L1. Figure 5 As shown, we found that knocking down MTA3 in vitro can indeed cause downregulation of tumor PD-L1 expression. Downregulating MTA3 expression can inhibit c-Myc-mediated PD-L1 expression, but there are no studies reporting on the direct regulatory effect of MTA3, a transcription factor, on tumor PD-L1. We believe that by targeting and inhibiting MTA3, we can achieve an effect similar to that of PD-L1 / PD-1 monoclonal antibodies, thereby inhibiting the promotion of PD-L1 / PD-1, an immune checkpoint, on tumor immune escape.

[0025] In Example 3, Figure 6 As shown in the figure, we constructed an immune mouse model of subcutaneous transplanted tumor (melanoma) with gene overexpression of transcription factor MTA3, and treated mouse tumors with PD-1 monoclonal antibody. We found that the combined intervention group had stronger anti-mouse tumor proliferation ability than the single drug group or the control group, and had no significant toxicity. Figure 7As shown, flow cytometry was used to further explore the immune characteristics of SPHK1-MTA3 overexpression in the local microenvironment of melanoma. Both SPHK1 and MTA3 can significantly upregulate the expression of PD-L1 membrane protein in tumor cells. At the same time, we found that after PD-1 monoclonal antibody acted on the melanoma model with high expression of SPHK1 or MTA3, the proportion of local GZMB+CD8+ lymphocytes in the tumor was significantly increased. This result shows that PD-1 monoclonal antibody can effectively reverse the tumor immune escape caused by SPHK1 and MTA3-induced PD-L1 expression, thereby slowing down tumor growth. The above results can suggest that our development of targeted SPHK1 inhibitors can provide a new research and development direction for immunotherapy adjuvant drugs for PD-L1 / PD-1 monoclonal antibody tumor immunotherapy, and develop new combination drug models. Figure 8 As shown, in order to explore the prognostic characteristics of MTA3 expression in melanoma patients, we retrieved melanoma patient survival-related data from the TCGA public database and performed survival analysis. In melanoma patients who did not receive immunotherapy, MTA3 gene expression had no significant effect on overall survival. In patients who received immunotherapy, the risk of death in the MTA3 high expression group was significantly reduced. We also observed the histopathological staining results and clinical prognosis of melanoma patients who had received clinical treatment with PD-1 monoclonal antibody (teplizumab). By comparing the expression of SPHK1 and MTA3 in tumors and correlating the patients' progression-free survival time, we found that melanoma patients with higher expression of SPHK1 and MTA3 had better responses to PD-1 monoclonal antibodies and longer progression-free survival times, and there were significant statistical differences. The above results can suggest that we can provide new immune detection targets for PD-L1 / PD-1 monoclonal antibody tumor immunotherapy by targeting the SPHK1-MTA3 axis, develop detection reagents or provide new research and development directions for immunotherapy adjuvant drugs, and develop new combination drug models. As Fig. 9 As shown, our study shows that targeting sphingosine kinase 1 can regulate the c-Myc-mediated tumor PD-L1 transcription level by downregulating the downstream transcription factor MTA3, thereby inhibiting tumor immune escape and blocking tumor growth. This mechanism directly explores the existing problems of the role of PD-1 blockers, provides a new research and development direction for PD-L1 / PD-1 monoclonal antibody tumor immune detection or immunotherapy adjuvant drugs, and develops a new combination drug model.

[0026] Name explanation: The “ / ” in PD-L1 / PD-1 means “and” or “or”. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The expression and function analysis diagram of sphingosine kinase in tumor tissues in Example 1 is shown in FIG. Figure 1A is a graph of the expression levels of SPHK1 and SPHK2 in different cancers analyzed in the TCGA database. Compared with normal samples, the differential expression of SPHK1 and SPHK2 genes in 16 types of cancer. The data are expressed as the logarithm of the gene difference fold (LogFC), and the test level α = 0.05. The pie chart on the right shows the proportion of cancers in which gene expression is not significant (gray) or significantly upregulated (red) or downregulated (blue) in tumor tissues; Figure 1 B is the correlation analysis diagram between SPHK1 gene and inhibitory immune checkpoints in different cancers. The data are expressed by Spearman correlation coefficient (Rs), and the test level α = 0.05; Figure 1 C is a correlation analysis diagram of SPHK1 and PD-L1 (CD274) expression in various cancers. The data are expressed using the Spearman correlation coefficient, and the test level α=0.05.

[0028] Figure 2 This is a graph showing the experimental data of the mouse model of Example 1, wherein Figure 2 A is a schematic diagram of the mouse experiment process; Figure 2 B is a picture of tumors isolated from a melanoma mouse model after 9 days of treatment with different doses (5 mg / kg, 10 mg / kg) of PF543; Figure 2 C is a scatter plot of the in vitro measurement of the volume of subcutaneous transplanted tumors in mice on the day of sacrifice. The data are expressed as mean ± standard error. The results were statistically analyzed using one-way analysis of variance for overall inter-group comparisons, and Tukey HSD test for further comparisons between the two groups, NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; Figure 2 D is the real-time measurement of the volume change curve of the mouse subcutaneous transplanted tumor. From the beginning of the intervention, the mouse tumor volume was measured every two days and the volume curve was drawn. The mice were taken out of the body on the day of sacrifice for in vitro measurement and bar charts were drawn. The data were expressed as mean ± standard error. The results were statistically analyzed by one-way analysis of variance for overall inter-group comparison, and Tukey HSD test was used for further comparison between the two groups. NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; Figure 2 E is the real-time measured curve of mouse weight changes. From the beginning of the intervention, the general condition of the mice was observed and the weight of the mice was measured every two days, and the weight curve was drawn. The data were expressed as mean ± standard error. The results were statistically analyzed using one-way analysis of variance for overall inter-group comparisons, and the Tukey HSD test was used for further comparisons between the two groups.

[0029] Figure 3 Figure 1 is a flow cytometry analysis of cell membrane protein and intracellular marker staining after in vivo inhibition of SPHK1 activity in Example 1, wherein Figure 3A and 3B are representative scatter plots (left) and statistical analysis (right) of the distribution of tumor-local lymphocytes in melanoma mice after PF543 treatment; Figure 3 C and 3D are representative scatter plots (left) and statistical analysis (right) of PD-L1 expression on the surface of tumor cells in melanoma tissues of melanoma mice treated with PF543. Data are expressed as mean ± standard error of the mean, NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001.

[0030] Figure 4 This is a diagram of transcriptome sequencing and bioinformatics mechanism exploration in Example 2, wherein Figure 4 A is the expression levels of candidate transcription factors in different samples screened after transcriptome sequencing analysis, with two groups in total and three independent replicates in each group; Figure 4 B is the expression of PD-L1 and transcription factors in the samples verified by qRT-PCR. Data are expressed as mean ± standard error of the mean. NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; Figure 4 C is the transcriptome data of 52 melanoma cell lines obtained from the GDSC public database. The Pearson product-moment correlation coefficient and significance test were used to analyze the correlation between SPHK1 and candidate transcription factors, with the test level α = 0.05; Figure 4 D is the use of Pearson product-moment correlation coefficient and significance test to analyze and evaluate the correlation between MTA3 gene expression and MsigDB cancer marker-related pathways, with a test level of α = 0.05; Figure 4 E is the GSEA enrichment analysis of differentially expressed genes downstream of SPHK1 in transcriptome sequencing; Figure 4 F is the detection of Flag-MTA3 transfection efficiency in MTA3 stably overexpressing melanoma cell lines by Western blot; Figure 4 G is the primer sequence design map of the predicted MTA3 binding site in the c-Myc promoter region; Figure 4 H: In melanoma cell lines, anti-Flag antibody was used, SYBR RT-PCR used c-Myc primers, and chromatin immunoprecipitation assay (ChIP) was used to prove that MTA3 had obvious binding to the c-Myc promoter.

[0031] Figure 5 This is an in vitro study experimental data diagram of SPHK1-MTA3 axis regulating tumor PD-L1 expression level in Example 2, wherein Figure 5A and 5B are melanoma cells treated with different concentrations of PF543 and IFN-γ (200 ng / mL) for 24-36 hours, and proteins were extracted and Western blot was used to detect the expression levels of MTA3 and c-Myc. Figure 5 A is a representative picture of Western blot; Figure 5 B is the statistical analysis of the gray value of protein bands after three repeated experiments; Figure 5 C and 5D are Western blots used to detect the knockdown efficiency and MTA3 and c-Myc expression levels after siSPHK1 interference silencing of the SPHK1 gene in melanoma cells. Figure 5 C is a representative picture of Western blot; Figure 5 D is the statistical analysis of the gray value of protein bands after three repeated experiments. Figure 5 E and 5F are melanoma cell lines that silenced the MTA3 gene by siMTA3 interference, and Western blot was used to detect the knockdown efficiency, c-Myc and PD-L1 expressions. Figure 5 E is a representative picture of Western blot; Figure 5 F is the statistical analysis of the gray value of protein bands after three repeated experiments. All data are expressed as mean ± standard error of the mean. NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

[0032] Figure 6 This is a graph showing the experimental data of the mouse model of Example 3, wherein Figure 6 A is a schematic diagram of the mouse experiment process; Figure 6 B is a picture of tumors isolated from a melanoma mouse model 14 days after treatment with PD-1 monoclonal antibody; Figure 6 C is a scatter plot of the in vitro measurement of the volume of subcutaneous transplanted tumors in mice on the day of sacrifice. The data are expressed as mean ± standard error. The results were statistically analyzed using one-way analysis of variance for overall inter-group comparisons, and Tukey HSD test for further comparisons between the two groups, NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; Figure 6 D is the real-time measurement of the volume change curve of the mouse subcutaneous transplanted tumor. From the beginning of the intervention, the mouse tumor volume was measured every two days and the volume curve was drawn. The mice were taken out of the body on the day of sacrifice for in vitro measurement and bar charts were drawn. The data were expressed as mean ± standard error. The results were statistically analyzed by one-way analysis of variance for overall inter-group comparison, and Tukey HSD test was used for further comparison between the two groups. NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; Figure 6E is the real-time measured curve of mouse weight changes. From the beginning of the intervention, the general condition of the mice was observed and the weight of the mice was measured every two days, and the weight curve was drawn. The data were expressed as mean ± standard error. The results were statistically analyzed using one-way analysis of variance for overall inter-group comparisons, and the Tukey HSD test was used for further comparisons between the two groups.

[0033] Figure 7 This is a flow cytometric analysis of the effect of high SPHK1 or MTA3-expressing melanoma cells treated with PD-1 monoclonal antibody on the tumor microenvironment in Example 3, wherein Figure 7 A and 7B are representative scatter plots and statistical analysis of the proportion of tumor-infiltrating lymphocytes and PD-L1 membrane protein expression in tumor cells in melanoma tissues of melanoma mouse models with high expression of SPHK1 or MTA3 genes after treatment with PD-1 monoclonal antibodies. Data are expressed as mean ± standard error of the mean, NS, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001.

[0034] Figure 8 For Example 3, public data bioinformatics analysis and immunofluorescence staining were performed on tumor tissues of patients with melanoma treated with immunotherapy to mark the expression levels of SPHK1 and MTA3 in tumors. The overall survival of patients with skin melanoma was obtained from the TCGA public database, and the patients were divided into ( Figure 8 A) Group without immunotherapy and ( Figure 8 B) In the immunotherapy group, patients were divided into high expression (first 65%) and low expression (last 35%) groups based on the expression of MTA3 gene. The product limit method (Kaplan-Meier, KM) was used to perform survival analysis on the overall survival time of melanoma patients, and the Log-rank test was used to compare the statistical differences between the groups; Figure 8 C and 8D respectively divided the patients into groups according to the median expression levels of SPHK1 and MTA3 genes. The KM method was used to perform survival analysis on the progression-free survival time of melanoma patients, and the Log-rank test was used to compare the statistical differences between the groups.

[0035] Fig. 9 This is a schematic diagram of how the SPHK1-MTA3 signaling axis promotes tumor immune escape by regulating melanoma PD-L1. DETAILED DESCRIPTION

[0036] Example 1

[0037] 1. Expression and functional analysis of sphingosine kinase in tumor tissues.

[0038] 1.1 Experimental methods: Gene transcriptome data of 33 types of cancer (including 10,205 tumor samples) were obtained from the Cancer Genome Atlas (TCGA) database (http: / / gdac.broadinstitute.org / ) using the FPKM (Fragments Per Kilobase per Million) algorithm. The fold change (FC) of genes in tumors and corresponding normal tissues was analyzed using limma1.4.5, with a screening threshold of |Log2(FC)|>0.58, and the Benjamini-Hochberg (BH) method was used to correct the hypothesis test probability P value. The Spearman correlation coefficient and its significance test were used to analyze the correlation between genes in tumor tissues, with the test level α=0.05.

[0039] 1.2 Experimental results:

[0040] Gene set variation analysis was performed by comparing tumors and normal tissues of different cancer types in the public database of The Cancer Genome Atlas (TCGA), and the relative expression levels of SPHKs were calculated. The results showed that SPHK1 and SPHK2 were significantly differentially expressed in multiple types of cancer, and both were commonly expressed abnormally in tumor tissues. Figure 1 As shown in Figure A, SPHK1 was significantly overexpressed in 12 cancer types, including breast invasive carcinoma (BRCA, LogFC=0.52, P<0.001), esophageal cancer (ESCA, LogFC=2.38, P<0.001), head and neck squamous cell carcinoma (HNSC, LogFC=1.30, P<0.001), hepatocellular carcinoma (LIHC, LogFC=1.23, P=0.002), lung adenocarcinoma (LUAD, LogFC=0.68, P=0.001), rectal adenocarcinoma (READ, LogFC=1.24, P=0.02), gastric cancer (STAD, LogFC=1. 32, P < 0.001), colon cancer (COAD, LogFC = 1.19, P < 0.001), renal clear cell carcinoma (KIRC, LogFC = 1.35, P < 0.001), renal papillary cell carcinoma (KIRP, LogFC = 2.31, P < 0.001), lung squamous cell carcinoma (LUSC, LogFC = 1.32, P < 0.001), thyroid cancer (THCA, LogFC = 1.17, P < 0.001). In order to further explore the potential function of SPHK1 in the tumor microenvironment, the correlation analysis between SPHK1 and the expression levels of inhibitory immune checkpoints in 33 types of cancer was performed. Figure 1As shown in Figure B, the SPHK1 gene is associated with a variety of immune checkpoints with immunosuppressive functions. We found that PD-1 (PDCD1) and PD-L1 (CD274), as well-defined immunosuppressive pathways in the tumor microenvironment, have a significant positive correlation with the expression of the SPHK1 gene in a variety of tumors including cutaneous malignant melanoma (see Figure 1 C).

[0041] 2. In vivo study on targeting sphingosine kinase SPHK1 to inhibit the proliferation of mouse transplanted tumors (melanoma).

[0042] 2.1 Group settings:

[0043] Vehicle (Solvent control group)

[0044] PF543 (5 mg / kg) (PF543 low concentration group)

[0045] PF543 (10 mg / kg) (PF543 high concentration group)

[0046] 2.2 Experimental process, see Figure 1 A:

[0047] About 6 days before the experiment, 8*10 B16F10 melanoma cells were injected subcutaneously into the right dorsal wing of 6-8 week old C57BL / 6 mice. 5 There were 10 mice in each group.

[0048] Day 0 of the experiment: Record the weight and tumor volume of mice. When the tumor volume grows to 50-100mm 3 Initially, tumor-bearing mice were intraperitoneally injected with PF543 (5 mg / kg) / mouse / 2 days or PF543 (10 mg / kg) / mouse / 2 days, and a solvent control group was set up, with a total of three groups, each with 10 mice.

[0049] Days 2, 4, 6, and 8 of the experiment: The mice were weighed and the tumor volume was calculated every two days using the formula volume = (length × width × width × π) / 6.

[0050] Day 8 of the experiment: Half of the mice were killed, tumors were photographed, and tissue samples were preserved for subsequent data analysis.

[0051] 2.3 Experimental results:

[0052] Depend on Figure 2 As shown in B, 2C, and 2D, compared with the solvent control group, the tumor volume of the drug-treated group was significantly lower than that of the solvent control group, and the tumor volume of the high-dose administration group (10 mg / kg) was lower than that of the low-dose group (5 mg / kg);

[0053] Depend on Figure 2As shown in E, there was no significant fluctuation in the body weight of mice in each group during the administration period, and there was no statistical difference in body weight gain among the groups, indicating that PF543 had no obvious toxic side effects on tumor-bearing mice.

[0054] The above results suggest that the use of sphingosine kinase inhibitor PF543 can inhibit melanoma growth.

[0055] 3. In vivo study on targeting SPHK1 to promote the cytotoxic activity of tumor-infiltrating lymphocytes.

[0056] 3.1 Experimental methods: staining of cell membrane proteins and intracellular markers.

[0057] 3.2 Experimental results:

[0058] like Figure 3 As shown in A and 3B, PF543 drug treatment can significantly induce tumor local CD4 + T cells and CD8 + T cell infiltration. Among them, CD8 + The proportion of cytotoxic T lymphocytes (CTL) in the drug-treated group increased significantly. Since GZMB is an important biomarker that can kill target cells, the above results indicate that PF543 drug treatment may increase specific CD8 in the tumor microenvironment. + CTL cells exert anti-tumor effects;

[0059] like Figure 3 As shown in C and 3D, we found that the expression of PD-L1 surface protein in tumor cells was significantly reduced in the drug-treated group.

[0060] The above results suggest that SPHK1 may play an immunosuppressive function in the local microenvironment of cutaneous melanoma, and inhibiting SPHK1 enzyme activity may reverse the dysfunctional state of tumor-infiltrating lymphocytes by downregulating the expression of PD-L1 membrane protein in tumor cells, thereby promoting the immune response of the tumor.

[0061] 4. Retrospective observation of the correlation between tumor PD-L1 and SPHK1 expression levels in melanoma patients

[0062] The experimental results are shown in Table 1. The expression levels of tumor PD-L1 and SPHK1 in melanoma patients were significantly positively correlated, with statistical differences (P<0.05).

[0063]

[0064] Notes on Appendix 1: Table 1 shows the expression levels of PD-L1 and SPHK1 in tumors by immunofluorescence staining of paraffin sections of tumor tissue samples from melanoma patients. Table 1 summarizes the number of patients in each category. Based on these data, the Pearson chi-square test (two-sided test) was used to analyze the association between variables and evaluate the significance of the correlation between the two.

[0065] Example 2

[0066] 1. In vitro study on the regulation of tumor PD-L1 expression level by SPHK1-MTA3 axis.

[0067] 1.1 Experimental methods: transcriptome sequencing, TCGA database bioinformatics analysis, gene editing, cell gene intervention, PF543 drug intervention, Western blot, ChIP detection

[0068] The target gene siRNA Oligo sequences are shown in Table 2:

[0069]

[0070]

[0071] The primer specificity was determined based on the amplification curve, and the gene primers with poor specificity needed to be redesigned and synthesized. The optimized gene primer sequences are shown in Table 3 below:

[0072]

[0073] 1.2 Experimental results:

[0074] See Figure 4 A, Among the 4182 significantly down-regulated genes detected in the drug-treated group compared with the control group, nine candidate transcription factors were identified based on the gene differential expression fold;

[0075] See Figure 4 B, To evaluate the reliability of the sequencing analysis results, we used the same batch of RNA samples to perform qRT-PCR to detect the expression of candidate transcription factors;

[0076] See Figure 4 C, Through the melanoma cell-related transcriptome data obtained from the GDSC public database, we found that the SPHK1 gene had a significant positive correlation with MTA3 among the nine candidate transcription factors;

[0077] See Figure 4 D, MTA3 expression is associated with cancer-promoting targets, such as G2M and c-Myc;

[0078] See Figure 4E, Through gene enrichment analysis, we found that the downregulated genes downstream of SPHK1 were significantly enriched in the c-Myc-related pathway.

[0079] See Figure 4 F, 4G, and 4H, ChIP was used to detect whether MTA3 binds to the c-Myc promoter. Compared with the control group, MTA3 significantly bound to the c-Myc promoter, and there was a statistical difference.

[0080] The above results suggest that MTA3, as a downstream gene of SPHK1, may be involved in the expression of PD-L1 mediated by c-Myc.

[0081] See Figure 5 A, 5B, 5C, 5D, melanoma cells treated with SPHK1-specific inhibitors or knocking down the SPHK1 gene can significantly downregulate the expression of MTA3 and c-Myc;

[0082] See Figure 5 E, 5F, Knockdown of the MTA3 gene in melanoma cell lines can significantly reduce the expression of c-Myc and IFN-γ-induced PD-L1.

[0083] The above results suggest that SPHK1 can positively regulate PD-L1 expression through MTA3.

[0084] 2. Retrospective observation of the correlation between tumor MTA3 and SPHK1 expression levels in melanoma patients

[0085] The experimental results are shown in Table 4. The expression levels of tumor MTA3 and SPHK1 in melanoma patients were significantly positively correlated, with a statistically significant difference (P<0.05).

[0086]

[0087] Notes on Appendix Table 4: Table 4 shows the expression levels of MTA3 and SPHK1 in tumors by immunofluorescence staining of paraffin sections of tumor tissue samples from melanoma patients. Table 4 summarizes the number of patients in each category. Based on these data, the Pearson chi-square test (two-sided test) was used to perform correlation analysis between variables and evaluate the significance of the correlation between the two.

[0088] 3. Retrospectively observe the correlation analysis between tumor PD-L1 and MTA3 expression levels in melanoma patients.

[0089] The experimental results are shown in Table 5. The expression levels of tumor PD-L1 and MTA3 in melanoma patients were significantly positively correlated, with statistical differences (P<0.05).

[0090]

[0091] Notes on Appendix 5: Table 5 shows the expression levels of PD-L1 and MTA3 in tumors by immunofluorescence staining of paraffin sections of tumor tissue samples from melanoma patients. Table 5 summarizes the number of patients in each category. Based on these data, the Pearson chi-square test (two-sided test) was used to perform correlation analysis between variables and evaluate the significance of the correlation between the two.

[0092] Example 3

[0093] 1. In vivo study of tumor PD-L1 expression induced by SPHK1 and MTA3.

[0094] 1.1 Group settings:

[0095] CTL+IgG2α isotype control group (control group)

[0096] CTL+PD-1 monoclonal antibody group (PD-1 monoclonal antibody treatment group)

[0097] SPHK1-OE+IgG2α isotype control group (SPHK1 overexpression group)

[0098] SPHK1-OE+PD-1 monoclonal antibody group (SPHK1 overexpression PD-1 monoclonal antibody group)

[0099] MTA3-OE+IgG2α isotype control group (MTA3 overexpression group)

[0100] MTA3-OE+PD-1 monoclonal antibody group (MTA3 overexpression PD-1 monoclonal antibody group)

[0101] 1.2 Experimental process, see Figure 6 A:

[0102] About 6 days before the experiment, 6-8 week old C57BL / 6 mice were subcutaneously injected with 8*10 B16F10 melanoma SPHK1 overexpressing cell lines and MTA3 overexpressing cell lines at the right dorsal wing. 5 There were 20 mice in each group and the same number of CTL cell lines (empty control group).

[0103] The SPHK1 gene overexpression sequence is shown in SEQ ID NO:037.

[0104] The MTA3 gene overexpression sequence is shown in SEQ ID NO:038.

[0105] Day 0 of the experiment: Record the weight and tumor volume of mice. When the tumor volume grows to 50-100mm 3 Initially, tumor-bearing mice were intraperitoneally injected with PD-1 monoclonal antibody at 200 μg / mouse / 3 days, and an IgG2α isotype control group was set up, with a total of six groups, each with 10 mice.

[0106] Days 2, 4, 6, 8, 10, 12, and 14 of the experiment: The mice were weighed and the tumor volume was calculated every two days using the formula volume = (length × width × width × π) / 6.

[0107] Day 14 of the experiment: Half of the mice were killed, tumors were photographed, and tissue samples were preserved for subsequent data analysis.

[0108] 1.3 Experimental results:

[0109] See Figure 6 B, 6C, 6D, the tumor volume of the PD-1 monoclonal antibody-treated group was significantly lower than that of the isotype control IgG2α group, and PD-1 monoclonal antibody treatment could significantly inhibit tumor growth mediated by SPHK1 and MTA3;

[0110] See Figure 6 E, There was no significant fluctuation in the body weight of mice in each group during the administration period, and there was no statistical difference in body weight gain among the groups, indicating that the PD-1 monoclonal antibody and the IgG2α isotype control had no obvious toxicity to tumor-bearing mice.

[0111] The above results suggest that the tumor volume in the PD-1 monoclonal antibody-treated group was significantly lower than that in the isotype control IgG2α group, and that PD-1 monoclonal antibody treatment could significantly inhibit tumor growth mediated by SPHK1 and MTA3.

[0112] 2. In vivo study on the effect of PD-1 monoclonal antibody treatment on the tumor microenvironment of melanoma cells with high expression of SPHK1 or MTA3.

[0113] 2.1 Experimental methods: staining of cell membrane proteins and intracellular markers.

[0114] 2.2 Experimental results:

[0115] See Figure 7 A, 7B, SPHK1 and MTA3 can significantly upregulate PD-L1 protein on the surface of tumor cells. At the same time, compared with the IgG2a control group, we found that after PD-1 monoclonal antibody acted on SPHK1 or MTA3 high-expressing melanoma models, the local GZMB + CD8 + The proportion of lymphocytes was significantly upregulated, indicating that PD-1 monoclonal antibodies can reverse tumor immune escape caused by SPHK1 and MTA3-induced PD-L1 expression, thereby slowing down tumor growth.

[0116] 3. Retrospective clinical observation on the correlation between tumor SPHK1 and MTA3 expression levels and patient prognosis (overall survival time or progression-free survival time) in melanoma patients receiving immunotherapy.

[0117] See Figure 8 A, In melanoma patients who did not receive immunotherapy, MTA3 gene expression had no significant effect on overall survival (hazard ratio 1.20, 95% confidence interval 0.87-1.64);

[0118] See Figure 8 B, Among patients receiving immunotherapy, the risk of death was significantly reduced in the MTA3 high expression group (hazard ratio 0.40, 95% confidence interval 0.19-0.85);

[0119] See Figure 8 C, 8D, Melanoma patients with high expression of SPHK1 (hazard ratio 0.30, 95% confidence interval 0.13-0.72) and MTA3 (hazard ratio 0.44, 95% confidence interval 0.20-0.95) had significantly prolonged progression-free survival after receiving PD-1 monoclonal antibody treatment.

[0120] These results indicate that the SPHK1-MTA3 axis can reverse resistance to PD-1 monoclonal antibody treatment by upregulating PD-L1 expression in tumor cells.

[0121] The above is a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes according to the technical solutions and concepts of the present invention, which should be covered by the protection scope of the claims of the present invention. Sequence Listing <110> Xiangya Hospital of Central South University <120> Application of MTA3 in the preparation of PD-L1PD-1 monoclonal antibody for tumor immunotherapy <141> 2021-12-28 <160> 38 <170> SIPOSequenceListing 1.0 <210> 1 <211> twenty one <212> DNA / RNA <213> null <400> 1 gcgucaugca ucuguucuat t 21 <210> 2 <211> twenty one <212> DNA / RNA <213> null <400> 2 uagaacagau gcaugacgct t 21 <210> 3 <211> 21 <212> DNA / RNA <213> null <400> 3 gugcacccaa acuacuucut t 21 <210> 4 <211> 21 <212> DNA / RNA <213> null <400> 4 agaaguaguu ugggugcact t 21 <210> 5 <211> 21 <212> DNA / RNA <213> null <400> 5 gugcaacaga aacgucuaat t 21 <210> 6 <211> 21 <212> DNA / RNA <213> null <400> 6 uuagacguuu cuguugcact t 21 <210> 7 <211> 21 <212> DNA / RNA <213> null <400> 7 ccaaauagcc cacuuacggt t 21 <210> 8 <211> 21 <212> DNA / RNA <213> null <400> 8 ccguaagugg gcuauuuggt t 21 <210> 9 <211> 21 <212> DNA / RNA <213> null <400> 9 uucuccgaac gugucacgut t 21 <210> 10 <211> 21 <212> DNA / RNA <213> null <400> 10 acgugacacg uucggagaat t 21 <210> 11 <211> 19 <212> DNA / RNA <213> null <400> 11 gcccaatacg accaaatcc 19 <210> 12 <211> 20 <212> DNA / RNA <213> null <400> 12 ctctgctcct cctgttcgac 20 <210> 13 <211> 18 <212> DNA / RNA <213> null <400> 13 tgctgtcacc catgaacc 18 <210> 14 <211> 18 <212> DNA / RNA <213> null <400> 14 cccagacgcc gatacttc 18 <210> 15 <211> 20 <212> DNA / RNA <213> null <400> 15 attggtggtg gtggtcttac 20 <210> 16 <211> 18 <212> DNA / RNA <213> null <400> 16 ccagtctctg aacatgaa 18 <210> 17 <211> 22 <212> DNA / RNA <213> null <400> 17 agcccactta cggatcgaca ga 22 <210> 18 <211> 23 <212> DNA / RNA <213> null <400> 18 caaactaggc tgcctcacag aac 23 <210> 19 <211> 21 <212> DNA / RNA <213> null <400> 19 ctaccagcag cagcagcaga g 21 <210> 20 <211> 21 <212> DNA / RNA <213> null <400> 20 ggtgtgaccg caacgtagga g 21 <210> 21 <211> 22 <212> DNA / RNA <213> null <400> 21 accacaacca cactctggag ga 22 <210> 22 <211> 22 <212> DNA / RNA <213> null <400> 22 tcggttctg gtctggatgc ct 22 <210> 23 <211> 22 <212> DNA / RNA <213> null <400> 23 tcagaagtac tccatggaca ac 22 <210> 24 <211> 21 <212> DNA / RNA <213> null <400> 24 gtcctcgatg atgagttcga t 21 <210> 25 <211> 23 <212> DNA / RNA <213> null <400> 25 ttggcacagt ctgtgaacca tgg 23 <210> 26 <211> 23 <212> DNA / RNA <213> null <400> 26 gtaacatcct ggcggtggta ttc 23 <210> 27 <211> 17 <212> DNA / RNA <213> null <400> 27 gctgttgcc acgtttc 17 <210> 28 <211> 19 <212> DNA / RNA <213> null <400> 28 ctgtccaccc accatatcc 19 <210> 29 <211> 18 <212> DNA / RNA <213> null <400> 29 gccatcctct ccaacacc 18 <210> 30 <211> 18 <212> DNA / RNA <213> null <400> 30 ccgatgtccg tctctcct 18 <210> 31 <211> 22 <212> DNA / RNA <213> null <400> 31 ccgaatgtgg aacgacctgt ct 22 <210> 32 <211> 22 <212> DNA / RNA <213> null <400> 32 gatctcctca gctctttttgg gg 22 <210> 33 <211> 17 <212> DNA / RNA <213> null <400> 33 gcagccattc tccagca 17 <210> 34 <211> 18 <212> DNA / RNA <213> null <400> 34 tcgtcctccc agatgtcc 18 <210> 35 <211> 22 <212> DNA / RNA <213> null <400> 35 ggacctggaa actgaccatc ag 22 <210> 36 <211> 22 <212> DNA / RNA <213> null <400> 36 cagtgaggtc tcatagcgtg ac 22 <210> 37 <211> 1187 <212> DNA / RNA <213> null <400> 37 tctagaatgg aaccagtaga atgccctcga ggactgctcc cacggccatg cagagtgctg 60 gtgctgctga acccccaggg tggcaagggc aaggctctgc agctcttcca gagccgtgtg 120 cagcccttcc tggaggaggc agagataacc tttaaactga tactcaccga acggaagaac 180 catgccaggg agctggtgtg tgcagaggag ttgggtcact gggacgccct ggcagtcatg 240 tccggtgatg gtctgatgca tgaggtggtg aatgggctaa tggaacggcc agactgggag 300 actgccatcc agaaacccct gtgtagcctc cctggaggct ccggcaatgc gctggcagct 360 tctgtgaacc actatgctgg gtacgagcag gtgactaatg aagacctgct catcaactgc 420 acactgctgt tgtgccgccg gcgcctgtca cccatgaacc tgctgtccct gcacactgct 480 tctgggctgc ggctctattc tgtgctcagt ctgtcctggg gctttgttgc tgacgtggac 540 ctcgagagtg agaagtacag gcgcttgggg gagattcgtt tcacagtggg caccttcttt 600 cgcctagcaa gcctgcgcat ctaccaaggc caactggcct accttcctgt aggaactgtg 660 gcctctaaga gacccgcctc tacactggtg cagaagggcc ccgtcgacac acaccttgtt 720 cctctggagg agccagtgcc ttctcattgg actgtggtac cagaacagga cttcgtcctg 780 gtgctggtgc tgctacacac ccacctgagc tccgagctgt ttgcagcacc catgggccgc 840 tgtgaggctg gtgttatgca tctgttctac gtacgtgcgg gggtgtcaag ggctgcgctg 900 ctgcgcctct tcctggccat gcagaagggc aagcatatgg aacttgactg tccatacctg 960 gttcatgtgc ccgtggttgc tttccgcctg gagcccagga gccagagggg cgtgttttct 1020 gtggatggag agctgatggt atgtgaagct gtgcagggcc aagtgcaccc aaactacctt 1080 tggatggtct gtggcagcag agatgcccca tccggccggg actcccggcg ggggccacct 1140 ccagaagaac cagattacaa ggacgacgat gacaagtaag cggccgc 1187 <210> 38 <211> 1799 <212> DNA / RNA <213> null <400> 38 gctagcatgg cggccaacat gtaccgggtc ggagactacg tttattttga gaattcctcc 60 120 gaagcaaaag tcgtctgctt ttacaagaaga cgggacatct ccaacacgct tatcatgctc 180 gccgacaagc atgctaaaga aactgaggag gagtcggagaga cgccagttga ggccgacttg 240 acggagaagc agaagcacca gctgaaacac agggagctct ttctgtcgcg ccagtacgag 300 tcactgcctg cgacacatat cagggggaag tgcagcgtgg ccctgctgaa tgagacagaa 360 tccgtgttgt cataccttga caaggaggat accttcttct actcactggt atacgaccct 420 tcagtgaaaa cattattggc tgacaaaggt gaatcagag tgggcccaaa gtaccaagct 480 gacattccag acatgctacc agaaggagac tcagatgaga gggaacaatc aaaattggaa 540 gttaaggttt gggaccccaa tagcccactt acggatcgac agattgacca gtttttagtt 600 gtagcccgtg ccgtgggaac atttgcccga gccctggact gcagcagctc cgtgaggcaa 660 cccagccttc acatgagtgc agccgcagcc tcccgagaca tcaccttgtt tcatgccatg 720 agamcactgt acaggcacgg ctatgaccctc agcagcgcca tcagtgtgct ggtgccgctc 780 ggagggccag tcctctgcag ggacgagatg gaggagtgt cggcctctga agcctgccta 840 ttcgaagaag cactggaaaa atacggcaaa gatttcaacc acatcgtca ggactttctc 900 ccgtggaaat ccttgactag catcattgaa tattaca tgtggaaac tactgacaga 960 tacgtgcagc agaagcgcct aaggccgca gaagccgaga gcaactgaa acagtgtac 1020 atcccgactt acaaaccaaa tcccaaccaa atctccagca gcaacggcaa ggctggcact 1080 gtgaatggag ctgtggggac ccagtttcag ccccagagcg ccctcctagg gcgagcctgc 1140 gagagctgct acgccacaca gtctcatcag tggtattcct ggggcccacc tacatgcag 1200 tgtagactct gcgcgacctg ttggctt tggaaaagt atggaggtct gaaaatgcct 1260 acccagtcag acgagagaa gtctcccagc ccgactgcag aggaccgcg cgcaagaagc 1320 cacatgtccc ggcaggcctt gcagggcatg ccggtccgga acaccgaag cccaagct 1380 gcagtgaaga cccgccaagc tttctcctt cgtactacgt atttcacaa aattgctcgt 1440 caggtctgca aaagcaccct gcggctgcgg caggcagcga gacggccgtt tgttgctatt 1500 aattatgctg ccattagggc agatatgca gabacatg ccgaactttc tggaagccca 1560 ctgaaaagca gaagcaccag gaaacctttg tcgtgtatca ttggttattt agagatccat 1620 cccgcaaga aacctaatgt aattcggtct ccaccaagcc tgcaactcc agctaccaag 1680 aggatgctcg ccgctccgaa tcacatct ctgagcattc tgggaaag aaactacagc 1740 catcacaatg gtctagatgg tacggattac aaggacgacg atgacaagta agcggccgc 1799

Claims

1. Use of a reagent for detecting the expression amount of MTA3 in the preparation of a detection kit for the efficacy of PD-L1 / PD-1 monoclonal antibody tumor immunotherapy, wherein the tumor is melanoma.

2. Application of an MTA3 inhibitor in combination with a PD-L1 / PD-1 monoclonal antibody in the preparation of a tumor immunotherapy drug, wherein the tumor is melanoma, and the MTA3 inhibitor is si MTA3 The specific sequences are as shown in SEQ ID NO:005-SEQ ID NO:008.

Images