Application of lymphotoxin beta in preparation of medicine for triple negative breast cancer

Abstract

The invention belongs to the technical field of biology, and particularly relates to application of lymphotoxin beta in preparation of a medicine for treating triple-negative breast cancer, triple-negative breast cancer patients in a high immune infiltration group are screened out by adopting unsupervised clustering analysis, and remarkable good prognosis is shown. Five key genes (LTB, IRF8, IDO1, ITM2A and LCP1) for determining prognosis of triple negative breast cancer patients in a high immune infiltration group are screened out, and a corresponding triple negative breast cancer prognosis prediction model is constructed; through research on immune infiltration key gene lymphotoxin beta (LTB), LTB expression is screened out to be a key gene for determining prognosis of TNBC patients, related biological functions of LTB participating in enhancement of killing activity of T cells are clarified, a key role of immune infiltration in prognosis of the TNBC patients is clarified, and an important basis and a potential target are provided for improvement of TNBC immunotherapy effects.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to the application of lymphotoxin beta in the preparation of triple-negative breast cancer drugs. Background Art

[0002] Triple-negative breast cancer (TNBC) is the type of breast cancer with the worst prognosis. Due to the lack of specific molecular therapeutic targets, chemotherapy remains the main treatment option for TNBC. In recent years, immunotherapy has made significant progress in TNBC. Compared with other types of breast cancer, TNBC exhibits higher PD-L1 expression levels and a higher tumor mutation burden. Immunotherapy directly targets infiltrating immune cells, and high numbers of tumor-infiltrating lymphocytes (TILs) are often observed in TNBC. However, significant individual variability in treatment response exists. Therefore, the type of immune infiltration in TNBC is important for optimizing treatment options and analyzing the prognosis of TNBC patients.

[0003] Previous studies on the types of immune cell infiltration in TNBC have shown some morphological classifications, and these immune infiltration types are significantly correlated with patient prognosis. High levels of lymphocyte infiltration are often associated with a good prognosis in early-stage TNBC patients. However, certain triple-negative breast cancer (TNBC) subtypes have high levels of immunosuppressive cells, which are associated with a poor prognosis. In addition, the immunological characteristics of triple-negative breast cancer (TNBC) tumors are closely related to their metabolic phenotype, which ultimately affects immune cell function and infiltration. These findings highlight the research value of triple-negative breast cancer immune infiltration subtypes and personalized immunotherapy strategies.

[0004] Therefore, studying the immunophenotypic differences of TNBC is of great significance for the personalized treatment and prognostic evaluation of TNBC. Summary of the Invention

[0005] In order to solve the problems raised by the background technology, the present invention provides the use of lymphotoxin β in the preparation of triple-negative breast cancer drugs.

[0006] The technical solutions of the present invention are as follows: The present invention provides an application of lymphotoxin beta in preparing triple-negative breast cancer drugs and / or markers for prognosis assessment.

[0007] Furthermore, the expression of lymphotoxin β was used to construct a prognostic prediction model for triple-negative breast cancer and perform prognostic evaluation.

[0008] In addition, the prognostic prediction model for triple-negative breast cancer also includes the expression of IRF8, IDO1, ITM2A and LCP1.

[0009] Lymphotoxin beta overexpression as a biomarker for treatment and / or prognostic assessment of triple-negative breast cancer.

[0010] The prognostic assessment includes the prediction of the patient's overall survival and progression-free survival after surgery for triple-negative breast cancer.

[0011] Furthermore, lymphotoxin beta can be used as a drug for treating triple-negative breast cancer and / or a marker for prognostic assessment by enhancing immune response.

[0012] Furthermore, when used as a drug to treat triple-negative breast cancer, lymphotoxin beta can promote T cell proliferation, reduce T cell exhaustion, induce T cells to differentiate into cytotoxic T lymphocytes, and increase the expression levels of INF-γ and TNF-α cytokines.

[0013] Beneficial effects The present invention uses unsupervised cluster analysis to screen out patients with triple-negative breast cancer in the high immune infiltration group, which shows a significantly good prognosis. It also screens out five key genes (LTB, IRF8, IDO1, ITM2A and LCP1) that determine the prognosis of patients with triple-negative breast cancer in the high immune infiltration group, and constructs a corresponding triple-negative breast cancer prognosis prediction model. Through the study of the key immune infiltration gene lymphotoxin beta (LTB), it is screened that LTB expression is a key gene that determines the prognosis of TNBC patients, and the relevant biological functions of LTB in enhancing the killing activity of T cells are clarified, which clarifies the key role of immune infiltration in the prognosis of TNBC patients, providing important basis and potential targets for improving the immunotherapy effect of TNBC.

[0014] The expression of LTB in triple-negative breast cancer tissue mainly comes from T cells. Overexpression of LTB in T cells can promote T cell proliferation, reduce T cell exhaustion, induce T cells to differentiate into cytotoxic T lymphocytes, and increase the expression levels of INF-γ and TNF-α cytokines. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 GSVA enrichment score based on 29 immune-related gene sets.

[0016] Figure 2 This is a volcano plot of differentially expressed genes between the high-immunity and low-immunity subtypes in the GEO dataset.

[0017] Figure 3 This is a volcano plot of differentially expressed genes between the high-immunity and low-immunity subtypes in the METABRIC dataset.

[0018] Figure 4 This is the Venn diagram of differentially expressed genes in the GEO and METABRIC datasets.

[0019] Figure 5 Figure 2 shows the LASSO coefficient distribution of the 402 significant genes selected for overall survival.

[0020] Figure 6 Random forest analysis.

[0021] Figure 7 Figure 2 is a heat map of LTB, IRF8, IDO1, ITM2A, and LCP1 gene expression.

[0022] Figure 8 Kaplan-Meier curves for survival analysis between high-risk and low-risk LTB genes.

[0023] Figure 9 PFS analysis between high-risk and low-risk LTB genes.

[0024] Figure 10 The correlation between LTB expression and 22 immune cells.

[0025] Figure 11 Correlation between LTB expression and CD8+ T cells (A), activated CD4+ memory T cells (B), resting CD4+ memory T cells (C), and regulatory T cells (D).

[0026] Figure 12 The correlation between LTB expression and immune checkpoints.

[0027] Figure 13 The mRNA changes of LTB before and after T cell activation.

[0028] Figure 14 This is the electrophoresis diagram of LTB overexpression protein.

[0029] Figure 15 The CD8 / CD4 cell ratio on the third day after LTB overexpression.

[0030] Figure 16 The CD8 / CD4 cell ratio on the 7th day after LTB overexpression.

[0031] Figure 17 Comparison of LAG-3 protein expression levels on the surface of total T lymphocytes (A), CD4 T cells (B), and CD8 T cells (C) in the LTB overexpression group and the control group (with virus packaged with empty plasmid) on days 3 and 7.

[0032] Figure 18 Comparison of PD-1 protein expression levels on the surface of total T lymphocytes (A), CD4 T cells (B), and CD8 T cells (C) in the LTB overexpression group and the control group (with virus packaged with empty plasmid) on days 3 and 7.

[0033] Figure 19 Comparison of TIM-3 protein expression levels on the surface of total T lymphocytes (A), CD4 T cells (B), and CD8 T cells (C) in the LTB overexpression group and the control group (with virus packaged with empty plasmid) on days 3 and 7.

[0034] Figure 20 EdU was used to detect cell proliferation on day 7 after LTB overexpression.

[0035] Figure 21 The changes of cytokines on day 3 after LTB overexpression.

[0036] Figure 22 The changes of cytokines on day 7 after LTB overexpression. DETAILED DESCRIPTION

[0037] The following examples are intended to illustrate the present invention rather than to further limit the present invention.

[0038] Experimental materials and methods 1. Acquisition and preprocessing of TNBC dataset The gene chip data of the GEO cohort were obtained from the Gene Expression Omnibus (https: / / www.ncbi.nlm.nih.gov / geo / ). Three triple-negative breast cancer datasets, GSE58812, GSE167213, and GSE76124, were selected and merged using the R software package InSilicoMerging. The batch effect was removed using the method of Johnson WE et al., and the pre- and post-processing data were compared using boxplots, density plots, and UMAP plots. Finally, a matrix was obtained after removing the batch effect, which contained gene expression data for 429 patients.

[0039] RNA-seq data for the METABRIC cohort were downloaded from cBioPortal (http: / / www.cbioportal.org / ). This data includes patient transcriptome data, ER (estrogen receptor), PR (progesterone receptor), and HER2 (human epidermal growth factor receptor 2) expression, as well as patient survival and status. Gene expression data for 298 TNBC patients were extracted based on ER, PR, and HER2 expression.

[0040] 2. Identification and validation of immune subpopulations To evaluate the immune status of each sample, a 29-gene set was selected from previously published literature. Unsupervised clustering of TNBC was performed based on the ssGSEA (single-sample gene set enrichment analysis) scores of the 29 immune-related gene sets, and two distinct immune subgroups were identified. ssGSEA scores were calculated using the "GSVA" package in R. Immune score, stromal score, and ESTIMATE score were analyzed.

[0041] 3. Comparison of immune cell infiltration scores The abundance of 22 immune cell types was estimated based on gene expression profiles using a CIBERSORT-based deconvolution method, and samples significant for CIBERSORT (P < 0.05) were selected for further comparison. The proportions of the 22 immune cell types in the three immune subgroups were compared using the Mann-Whitney U test.

[0042] 4. Construction of prognostic model Survival time, survival status, and gene expression data were integrated, and signature genes were screened using both lasso regression and random forest algorithms. Lasso regression was performed using the glmnet package to select the optimal lambda value, and genes were screened by plotting lambda and lasso regression coefficients. Random forest analysis was performed using the randomForestSRC package, and OBB and VIP plots were plotted to screen genes based on variable importance. Finally, the intersection of signature genes was determined using a Venn diagram.

[0043] 5. T lymphocyte culture, lentiviral infection and proliferation detection The collected peripheral blood mononuclear cells (PBMCs) were incubated in 1640 culture medium (C11875500bt, Gibco) containing 10% fetal bovine serum (FBS) (C04001-500, Vivacell) for 24 h. The cells were then transferred to 24-well plates coated with 1 mg / ml CD3 antibody (130-122-282, Miltenyi) and 0.5 mg / ml CD28 antibody (130-122-350, Miltenyi) for 48 h of activation. The cells were then expanded and cultured in 1640 complete culture medium containing 300 IU / mL IL-2 (200-02-50UG, Peprotech).

[0044] An appropriate amount of LTB-overexpressing lentivirus and Polybrene reagent (40804ES76, Yeasen) were added to the activated T lymphocytes, and the plate was centrifuged at 4000 rpm at 32°C for 1 hour. The plate was then cultured in a cell culture incubator at 37°C with 5% CO2 for 48 hours for subsequent proliferation and cytokine detection.

[0045] Proliferation assays were performed on the seventh day after lentiviral infection. EdU proliferation assay kit was used to label newly generated cells. The specific steps were the same as the official website instructions (C0071S, Beyotime). After staining, proliferation results were analyzed by flow cytometry.

[0046] 6. Western blot and real-time quantitative polymerase chain reaction (RT-qPCR) analysis Proteins were extracted using RIPA medium lysis buffer (P0013C, Beyotime) containing protease inhibitors (P1005, Beyotime). Protein concentration was determined using the BCA assay (ZJ101L, Epizyme). Equal amounts of protein (20 μg) were separated by electrophoresis on a 10% polyacrylamide gel (PG112, Epizyme). The separated proteins were transferred to a nitrocellulose membrane (NC membrane) (10600001, Cytiva) and blocked with 5% nonfat milk for 1 hour. The membranes were incubated with monoclonal antibodies against FLAG (F1804, Sigma-Aldrich) and β-ACTIN (A5441, Sigma-Aldrich) overnight at 4°C. The membranes were then incubated with secondary antibodies for 2 hours at room temperature and immunoblotted using ECL detection reagent (SQ201L, Epizyme).

[0047] Total RNA was extracted using the Trizol method (9109, Takara), and mRNA was reverse transcribed using a reverse transcription kit (11141ES10, Yeasen). cDNA was analyzed in 20 µl of quantitative PCR reaction system (11201ES03, Yeasen) containing 10 µM primers. Expression data were normalized to the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) using the delta-delta CT method (2-ΔΔCT). The specific primer sequences used for quantification are as follows: qPCR-LTB-F 5-GAGGACTGGTAACGGAGACG-3; qPCR-LTB-R 5-AGAAACGCCTGTTCCTTCGT-3; qPCR-GAPDH-F 5-AGAAACGCCTGTTCCTTCGT-3; qPCR-GAPDH-R 5-ACCACCCTGTTGCTGTAGCCAA-3.

[0048] 7. Flow cytometry For FACS analysis, T cells were resuspended in PBS containing 2% FBS. Cells were incubated with antibodies against CD3, CD4, CD8, PD-1, LAG-3, and TIM-3 cell surface markers at room temperature for 30 minutes. After washing twice, cells were resuspended in PBS containing 2% FBS and analyzed by flow cytometry. All flow cytometry results were quantified using FlowJo software to quantify the proportion of positively stained cells or MFI within a specific cell population.

[0049] 8. Cytokine release detection The culture supernatants of T cells from the control group and overexpression group were collected, and cytokines were detected using a ten-factor detection kit (Raisecare) according to the manufacturer's protocol, and the amount of cytokines was quantified by flow cytometry.

[0050] 9. Statistical analysis Statistical analysis was performed using GraphPad Prism 8.0. Data are presented as mean ± SEM. Statistical significance between two groups was determined by unpaired t-test or Mann-Whitney test, as appropriate. Comparisons between groups were performed using one-way analysis of variance or two-way analysis of variance, and the relevant P values ​​were determined using the Bonferroni post-hoc test. * represents P ≤ 0.05, ** represents P ≤ 0.01, and *** represents P ≤ 0.001. In all analyses, P ≤ 0.05 was considered statistically significant.

[0051] Experimental results 1. Unsupervised cluster analysis and identification of immune infiltration phenotypes in triple-negative breast cancer First, an unsupervised cluster analysis was performed on the transcriptome dataset of the TNBC combined cohort (n=429) from the GEO database to identify its immune infiltration phenotype. Based on the ssGSEA score of 29 validated immune-related gene sets, the present invention identified two significantly different immune subgroups: low immune group (n=357) and high immune group (n=72). Compared with the low immune group, the high immune group showed a higher level of immune-related gene set enrichment and a significantly lower tumor purity score than the low immune group subtype ( Figure 1 ).

[0052] 2. Differential expression analysis of immune subgroups The present invention analyzed the differentially expressed genes between the high-immunity group and the low-immunity group. 874 differentially expressed genes (|logFC|>0.585 and FDR<0.05) were identified from the GEO cohort ( Figure 2 ). 568 differentially expressed genes (|logFC|>0.585 and FDR<0.05) were identified from the METABRIC cohort ( Figure 3 Using Venn diagram analysis, the present invention screened out 391 and 11 overlapping DEGs, i.e. 402 differentially expressed genes, from the up-regulated and down-regulated gene groups of the GEO cohort and the METABRIC cohort, respectively ( Figure 4 ).

[0053] 3. Construction of a prognostic model based on TNBC immune phenotype The present invention introduces the L1 regularization term to achieve the dual goals of feature selection and model simplification. The optimal λ value is determined by 10-fold cross validation, and 5 characteristic genes significantly associated with patient survival prognosis are finally screened out from the 402 gene sets obtained above ( Figure 5 ), and shows the coefficient change trajectory of each feature under different λ values, highlighting the five genes retained under the optimal λ value (lymphotoxin-β (LTB), interferon regulatory factor 8 (IRF8), indoleamine 2,3-dioxygenase 1 (IDO1), integral membrane protein 2A (ITM2A) and lymphocyte cytoplasmic protein 1 (LCP1), ranked in order).

[0054] The random forest algorithm, based on its built-in feature importance evaluation mechanism, screened out 80 potential prognosis-related genes from 402 differentially expressed genes ( Figure 6 ), which overlapped with the 5 genes obtained from LASSO regression.

[0055] Afterwards, we further explored the relationship between the expression patterns of these five genes and the patient's prognosis. The survival heat map clearly showed that high expression of these five genes was associated with a lower risk of death in patients ( Figure 7 ).

[0056] A prognostic prediction model for triple-negative breast cancer was constructed using five key genes (LTB, IRF8, IDO1, ITM2A, and LCP1). This model demonstrated strong prognostic accuracy. Importantly, the data analysis is based on tumor tissue expression profiling results, independent of tissue morphology, making its potential for clinical translation even more significant.

[0057] To further validate the prognostic value of these genes, the lymphotoxin beta (LTB) gene, ranked first in LASSO regression, was independently validated in the TCGA breast cancer database. Kaplan-Meier survival analysis showed that patients with high LTB expression had a significantly longer overall survival (OS, hazard ratio HR = 0.65, 95% confidence interval (95% CI): 0.49–0.86, p = 0.00041, Figure 8 ) and progression-free survival (PFS, HR = 0.58, 95% CI: 0.42–0.80, p = 0.00086, Figure 9These results highlight the potential importance of LTB in the prognosis of TNBC.

[0058] 4. High LTB expression is associated with enhanced immune response This study investigated the relationship between LTB gene expression and immunotherapy potential. Patients in TCGA were divided into two groups: high LTB expression and low LTB expression. It was observed that the composition of immune cells between the two groups of patients showed significant differences. The high LTB expression group was present in immune-promoting cells such as naive B cells, CD8 T cells, CD4 memory activated T cells, follicular helper T cells, gamma delta T cells, and activated NK cells. Figure 10 ).

[0059] Moreover, LTB expression levels were positively correlated with CD8+ T cells and activated CD4+ memory T cells, but negatively correlated with resting CD4+ memory T cells and regulatory T cells (Treg). Figure 11 ).

[0060] In addition, LTB expression is positively correlated with the expression of multiple immune checkpoint molecules, such as CD274 (PD-L1), PDCD1 (PD-1), CTLA4 (CTLA-4), TIM-3 (HAVCR2), TIGIT, etc. ( Figure 12 ), this finding suggests that patients with high LTB expression will be more likely to benefit from immune checkpoint therapy.

[0061] 5. LTB is a key gene affecting T cell function Based on the above research, the present invention explored the effect of LTB expression on the biological function of T cells. First, the expression changes within 96 hours after T cell activation were detected. It was found that LTB levels decreased by about 50% after activation, returned to the pre-activation level after 72 hours, and remained stable until 96 hours and beyond ( Figure 13 Therefore, the present invention overexpresses the LTB gene in T cells ( Figure 14 ), and analyzed its effects on T cell function.

[0062] On day 3, it was observed that the proportion of CD8 positive T cells in the LTB overexpression group (denoted as ov-LTB) was significantly increased ( Figure 15 Similarly, a significant increase in the proportion of CD8-positive T cells was observed on day 7 ( Figure 16 ).

[0063] On the third day, the expression level of LAG-3 on the surface of total T lymphocytes, CD4 T cells and CD8 T cells in the LTB overexpression group (denoted as ov-LTB) was significantly lower than that in the control group (added with virus packaged with empty plasmid, denoted as Ctrl) ( Figure 17 ). Similarly, the expression level of PD-1 protein was significantly lower than that of the control group on day 3 ( Figure 18 By day 7, a low LAG-3 expression level could still be detected ( Figure 17 ), and lower PD-1 expression levels ( Figure 18 On day 3, the TIM-3 protein levels of total T lymphocytes and CD8 T cells in the LTB overexpression group were higher than those in the control group, while those of CD4 T cells were lower than those in the control group ( Figure 19 ), but by day 7, total T lymphocytes and CD4+T cells were lower than those in the control group, and there was no significant difference in CD8+T cell subsets ( Figure 19 The results showed that LTB expression helps to alleviate T cell exhaustion. EdU proliferation assay also showed that the proliferation rate of LTB overexpression group was significantly higher than that of control group on day 7 ( Figure 20 ).

[0064] In addition, the present invention also found that the secretion levels of IFN-γ and TNF-α cytokines of T cells were increased on the 3rd day after LTB overexpression ( Figure 21 ), on the 7th day, it was observed that the secretion levels of IFN-γ, TNF-α and IL5 cytokines of T cells increased ( Figure 22 ).

[0065] In summary, in vitro experiments confirmed that LTB overexpression can promote T cell proliferation, alleviate T cell exhaustion, induce T cells to differentiate into cytotoxic T lymphocytes, and increase the expression levels of INF-γ and TNF-α cytokines.

[0066] The present invention uses unsupervised cluster analysis to screen out patients with triple-negative breast cancer in the high immune infiltration group, which shows a significantly good prognosis. It also screens out five key genes (LTB, IRF8, IDO1, ITM2A and LCP1) that determine the prognosis of patients with triple-negative breast cancer in the high immune infiltration group, and constructs a corresponding triple-negative breast cancer prognosis prediction model. Through the study of the key immune infiltration gene lymphotoxin beta (LTB), it is screened that LTB expression is a key gene that determines the prognosis of TNBC patients, and the relevant biological functions of LTB in enhancing the killing activity of T cells are clarified, which clarifies the key role of immune infiltration in the prognosis of TNBC patients, providing important basis and potential targets for improving the immunotherapy effect of TNBC.

Claims

1. Use of lymphotoxin beta in the preparation of triple-negative breast cancer drugs and / or markers for prognosis assessment.

2. The use according to claim 1, characterized in that The expression of lymphotoxin β was used to construct a prognostic prediction model for triple-negative breast cancer and perform prognostic evaluation.

3. The use according to claim 2, characterized in that The prognostic prediction model for triple-negative breast cancer also includes the expression of IRF8, IDO1, ITM2A, and LCP1.

4. The use according to claim 1, characterized in that Lymphotoxin beta overexpression as a biomarker for treatment and / or prognostic assessment of triple-negative breast cancer.

5. The use according to claim 1, characterized in that The prognostic assessment includes the prediction of the patient's overall survival and progression-free survival after surgery for triple-negative breast cancer.

6. The use according to claim 1, characterized in that Lymphotoxin beta may serve as a therapeutic agent and / or a biomarker for prognostic assessment in triple-negative breast cancer by enhancing immune responses.

7. The use according to claim 6, characterized in that When used as a drug to treat triple-negative breast cancer, lymphotoxin beta can promote T cell proliferation, reduce T cell exhaustion, induce T cells to differentiate into cytotoxic T lymphocytes, and increase the expression levels of INF-γ and TNF-α cytokines.

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