Application of CD8 + Tcm in preparation of product for evaluating sensitivity of PD-1 inhibitor to cancer treatment

Abstract

The invention relates to application of CD8 + Tcm in preparation of a product for evaluating sensitivity of a PD-1 inhibitor to cancer treatment. The scRNA-seq is utilized to comprehensively characterize the influence of PD-1 inhibitor treatment on bladder cancer tumor microenvironment, reveal the cytodynamics and molecular characteristics of bladder cancer during PD-1 inhibitor treatment, and highlight the potential of CD8 + Tcm cells as immunotherapy curative effect prediction biomarkers. In addition, SFRP2 + CAF is a key marker for immunotherapy drug resistance, and IFIT1 + neutrophil is a key marker for immunotherapy sensitivity. The invention deepens the understanding of the potential PD-1 treatment drug resistance mechanism of the bladder cancer patient in the prior art, provides a theoretical basis for optimizing the bladder cancer immunotherapy strategy, and provides a new direction for the potential immunotherapy target of the bladder cancer.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and relates to the application of CD8+ Tcm in the preparation of products for evaluating the sensitivity of PD-1 inhibitors to cancer treatment. Background Art

[0002] In recent years, the application of PD-1 / PD-L1 checkpoint inhibitors in bladder cancer has become increasingly widespread. Compared with platinum-based chemotherapy combined with gemcitabine, Enfortumab Vedotin (EV) combined with pembrolizumab has shown significant improvement in clinical outcomes. This new regimen has been recommended for first-line treatment of advanced unresectable or metastatic urothelial carcinoma. If a patient does not meet the criteria for EV or EV is not available, then PD-1 / PD-L1 checkpoint inhibitors still play an important role, such as nivolumab in combination with gemcitabine / cisplatin (GC), followed by maintenance with nivolumab, or the use of immune checkpoint inhibitors as first-line monotherapy in patients with PD-L1-positive and cisplatin-ineligible cases. In the non-metastatic setting, immunotherapy alone or in combination is also used for neoadjuvant and adjuvant treatment. However, a large proportion of patients do not show an obvious response, resulting in disease progression in advanced patients who are unresponsive to immunotherapy-related treatments, or local cases that do not benefit from neoadjuvant and adjuvant treatments. Therefore, it is of great significance to explore the related mechanisms of immunotherapy resistance and improve the efficiency.

[0003] Single-cell RNA sequencing (scRNA-seq) technology has become a powerful tool for studying the tumor microenvironment (TME), which can clearly describe the heterogeneity among tumor cells and the complexity within the TME. Detailed analysis of the TME using this technology will help to understand more deeply the mechanisms behind cancer immune evasion. In addition, scRNA-seq can analyze the heterogeneity, evolutionary path, and cell-cell interactions of individual cells. This provides a detailed understanding of the tumor microenvironment at the single-cell level, which is of great significance for the diagnosis, treatment, and prevention of cancer.

[0004] To identify patients who are most likely to benefit from PD-1 / PD-L1 treatment, various biomarkers have been explored in previous studies, including PD-L1 expression, CD8 +T cells and tumor mutational burden (TMB). However, no molecular biomarker has been proven to guide patient selection. For example, the anti-tumor effect of immunotherapy does not necessarily correlate with the PD-L1 expression level, as tumors with low PD-L1 expression may still respond to immune checkpoint inhibitors (ICIs). One study utilized the tumor inflammation score (TIS), which is based on the expression of 18 genes related to IFN-γ signaling, to classify patients as "hot" or "cold". However, the prospective utility of TIS in patient selection remains uncertain, as the score has not been shown to reliably identify patients more likely to benefit from immunotherapy. Instead, there is evidence that pre-existing activated T cell activity may be the key to predicting treatment response. For example, the high presence of CD8 + T cells is associated with a higher pathologic complete response (pCR) rate (40% vs. 20%, p < 0.05). Additionally, dual staining for CD8 and granzyme B, a mediator of lymphocyte activity, is associated with response in tumors with an inflammatory phenotype (pCR: 80% vs. 30%, p < 0.05). These findings highlight the importance of pre-existing T cell activation as a potential predictor of immunotherapy response. In summary, current predictors are not accurate enough in predicting the efficacy of PD-1 / PD-L1 inhibitors. Therefore, there is an urgent need to identify predictive biomarkers to accurately assist clinicians in selecting treatment strategies and to identify targets associated with the efficacy of PD-1 / PD-L1 inhibitors, thereby improving treatment outcomes and ultimately prolonging patient survival. Summary of the Invention

[0005] The object of the present invention is to solve the problems existing in the prior art. By using scRNA-seq, the present invention comprehensively characterized the impact of PD-1 inhibitor treatment on the bladder cancer tumor microenvironment, revealed the cell kinetics and molecular characteristics of bladder cancer during PD-1 inhibitor treatment, provided the key target CD8 + Tcm cells as a biomarker for predicting the efficacy of immunotherapy; and identified SFRP2 + CAF as a key biomarker for immunotherapy resistance, and IFIT1 + neutrophils as key biomarkers for immunotherapy sensitivity. The present invention deepens the understanding of the potential mechanisms of PD-1 treatment resistance in bladder cancer patients in the prior art, provides a theoretical basis for optimizing bladder cancer immunotherapy strategies, and offers a new direction for potential immunotherapy targets in bladder cancer.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions.

[0007] The first aspect of the present invention provides the use of immune cells and / or cancer-associated fibroblasts in the preparation of a product for detecting the sensitivity of PD-1 inhibitors to cancer treatment, wherein the immune cells are selected from CD8 + Tcm (central memory CD8 + T cells), IFIT1 + neutrophils, or one or more of them; the cancer-associated fibroblasts (CAF) are selected from SFRP2 + CAF. +

[0008] Preferably, the cancer is selected from bladder cancer.

[0009] Preferably, the PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, camrelizumab, penpulimab, sepacavimab, finolimab, and surufatinib; more preferably, the PD-1 inhibitor is selected from toripalimab.

[0010] The second aspect of the present invention provides the use of a reagent for detecting the level of immune cells and / or cancer-associated fibroblasts in the preparation of a product for detecting the sensitivity of PD-1 inhibitors to cancer treatment, wherein the immune cells are selected from CD8 + Tcm, IFIT1 + neutrophils, or one or more of them; the cancer-associated fibroblasts are selected from SF RP2 + CAF.

[0011] Preferably, the cancer is selected from bladder cancer.

[0012] Preferably, the PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, camrelizumab, penpulimab, sepacavimab, finolimab, and surufatinib; more preferably, the PD-1 inhibitor is selected from toripalimab.

[0013] ​It should be understood that, without special instructions, in the context of the present invention, those skilled in the art can understand that the "level of immune cells and / or cancer-associated fibroblasts" refers to an indicator reflecting the number / activity of cells in the body. The specific cells can be directly counted by conventional methods in the art, or the specific markers in the specific cells can be detected, so as to specifically reflect the number / activity of the immune cells and / or cancer-associated fibroblasts, and to distinguish the level differences of the immune cells and / or cancer-associated fibroblasts in different test samples. In the present invention, the level of CD8 + Tcm can be determined by detecting the level of one or more markers among IL7R, ZFP36L2, and CXCR4; the level of IFIT1 + neutrophils can be determined by detecting the level of one or more markers among RSAD2, IFIT1, IFIT2, IFIT3, and MX1; the level of SFRP2 + CAF can be determined by detecting the level of one or more markers among COL11A1, LRRC15, CILP, SFRP2, and COL10A1. The detection of the expression levels of the above-mentioned markers also belongs to the conventional means in the art. At the same time, since it is not the main improvement direction of the present invention, the relevant detection methods and results are not specifically presented in the present invention. Those skilled in the art can detect and verify according to the experimental methods described in the present invention or other conventional methods in the prior art as needed. For example, corresponding primers / primer pairs can be designed based on the sequences of the markers for gene-level detection, or relevant monoclonal / polyclonal antibodies can be designed / purchased for protein-level detection; for IL7R, IL7R Antibody

[0014] (Cy7)(orb1578711, Biorbyt), ZFP36L2 can be detected using ZFP36L2 (E4M9Z) Rabbit mAb (85891, CST), CXCR4 can be detected using CXCR4 (E3Q4B) Rabbit mAb (64837, CST), RSAD2 can be detected using RSAD2 Monoclonal Antibody (OTI4D12) (TA505799, ThermoFisher), IFIT1 can be detected using IFIT1 (D2X9Z) Rabbit mAb (14769, CST), IFIT2 can be detected using IFIT2 Antibody (92633, CST), IFIT3 can be detected using IFIT3 (E8G5Y) Rabbit mAb (47676, CST), MX1 can be detected using MX1 (D3W7I) Rabbit mAb (37849, CST), COL11A1 can be detected using COL11A1 (E6X3Y) Rabbit mAb (96321, CST), LRRC15 can be detected using LRRC15 (E4X8J) Rabbit mAb (50546, CST), CILP can be detected using Anti-CILP antibody [EPR16303] (ab192881, Abcam), SFRP2 can be detected using Anti-SFRP2 antibody [EPR26088-157] (ab318185, Abcam), COL10A1 can be selected from COL10A1 Rabbit Polyclonal Antibody (a13288, Abclonal). The listing of the above antibody information is only for exemplification when detecting relevant biomarkers and at the relevant cellular level in the present invention, so as to facilitate those skilled in the art to better understand the solutions and concepts of the present invention, and is not used to limit the technical solutions and protection scope of the present invention.

[0015] The third aspect of the present invention provides the use of immune cells in the preparation of a drug for promoting the sensitivity of PD-1 inhibitors to cancer treatment, and the immune cells are selected from CD8 + Tcm, IFIT1 + neutrophils, or one or more of them.

[0016] Preferably, the cancer is selected from bladder cancer.

[0017] Preferably, the PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, camrelizumab, penpulimab, sepacilimab, finolimab, and surufatinib; more preferably, the PD-1 inhibitor is selected from toripalimab.

[0018] In the fourth aspect of the present invention, a pharmaceutical composition for treating cancer is provided, comprising immune cells and a PD-1 inhibitor; the immune cells are selected from CD8 + Tcm, IFIT1 + one or more of neutrophils.

[0019] Preferably, the cancer is selected from bladder cancer.

[0020] Preferably, the PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, camrelizumab, penpulimab, sepaclisimab, fenoulizumab, and surufatinib; more preferably, the PD-1 inhibitor is selected from toripalimab.

[0021] In the fifth aspect of the present invention, a kit for evaluating the sensitivity of PD-1 inhibitors to cancer treatment is provided, comprising reagents for detecting the levels of immune cells and / or cancer-associated fibroblasts, the immune cells are selected from CD8 + Tcm, IFIT1 + one or more of neutrophils; the cancer-associated fibroblasts are selected from SFRP2 + CAF.

[0022] Preferably, the cancer is selected from bladder cancer.

[0023] Preferably, the PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, camrelizumab, penpulimab, sepaclisimab, fenoulizumab, and surufatinib; more preferably, the PD-1 inhibitor is selected from toripalimab.

[0024] Preferably, the kit further comprises one or more of PCR enzymes, PCR buffers, dNTPs, and fluorescent substrates.

[0025] Preferably, the fluorescent substrate is selected from Syber Green or fluorescently labeled probes.

[0026] PD-1 inhibitor treatment as a neoadjuvant treatment strategy for bladder cancer has shown great potential. Accurately identifying patients who may benefit from immunotherapy before surgery is crucial for optimizing clinical outcomes and exploring organ preservation options for resectable cases. However, there is still a blank in the development of mechanism-based biomarker-guided precision immunotherapy approaches. T cells are key predictors of immunotherapy efficacy. In bladder cancer, cytotoxic CD4 +T cell subsets kill autologous tumors in an MHC-II-dependent manner and are inhibited by regulatory T cells, and also predict responses to PD-L1 inhibitors. CD103 + CD8 + The presence of TRM cells, which can be quantified by intratumoral CD103 expression, predicts treatment outcomes. In addition, machine learning-based single-cell and bulk RNA sequencing analyses have identified fibroblast and CD8 + T cell fibroblast subtypes that predict prognosis and immunotherapy responses in bladder cancer and have been validated on multiple independent datasets. The antitumor effects of immunotherapy targeting the PD-1 / PD-L1 pathway are generally attributed to the activation of CD8 + T cells. However, not all CD8 + T cells respond to treatment. Recent studies have highlighted the functional heterogeneity within CD8 + T cells, identifying subsets such as precursor exhausted T cells (Tpex) that respond to ICIs and terminally exhausted T cells that do not. Notably, there is a positive correlation between Tpex and successful tumor immunotherapy. In the present invention, single-cell transcriptomic analysis was performed on bladder cancer patients receiving PD-1 inhibitor treatment to identify cell characteristics associated with treatment response. Among them, CD8 + Tcm cells exhibit a phenotype similar to Tpex and gradually differentiate into a terminally exhausted state. In addition, using Tcm gene markers, the efficacy of immune checkpoint inhibitors (ICIs) can be accurately predicted in an external validation cohort. In summary, CD8 + Tcm cells are considered a promising predictive biomarker for the efficacy of PD-1 immunotherapy in bladder cancer.

[0027] Only 20% to 40% of cancer patients have immune checkpoints. Understanding the mechanisms of the immunosuppressive tumor microenvironment (TME) and tracking changes in the immune landscape during treatment are crucial for improving immunotherapy responses. Recent studies have shown that cancer-associated fibroblasts (CAFs) are involved in immunotherapy resistance, but their role in T cell immunomodulation in the human tumor microenvironment remains unclear. Previous studies have shown that CAFs and bladder cancer cells interact through crosstalk mediated by multiple molecular mechanisms. Through single-cell sequencing analysis, the present invention has identified three CAF subsets in bladder cancer tissues: iCAF, mCAF, and SFRP2 + CAF. Notably, SFRP2 +CAF is in the early differentiation stage of iCAF and, in addition to the characteristics of iCAF, also exhibits different functions, including ECM receptor interaction, proteoglycans in cancer, and the AGE-RAGE signaling pathway in diabetic complications. Among all CAF subsets, SFRP2 + The expression of CXCL12 in CAF is the highest. CXCL12 is a chemokine that promotes immune evasion of bladder cancer by inducing CD8 + T cell exhaustion. In addition, the present invention discovers that SFRP2 + There is a close association between CAF and CD8 + T cells, which indicates that they may ultimately affect the efficacy of PD-1 inhibitors by influencing the function of CD8 + T cells.

[0028] In cancer, neutrophils exhibit anti-tumor and tumor-promoting properties. The diversity and plasticity of neutrophils support the dual potential of tumor-associated neutrophils (TAN) in the tumor microenvironment. The present invention uses single-cell sequencing to study the molecular characteristics of highly heterogeneous neutrophils at the cellular level and their key role in the response to PD-1 inhibitors, thereby revealing the molecular mechanism related to the efficacy of PD-1 inhibitors and further studying how the interaction between neutrophils and T cells alters the tumor microenvironment and affects the patient's response to immunotherapy. The present invention discovers an IFIT1 + neutrophil subset related to the response to PD-1 inhibitors in tumor tissues. The research results of the present invention show that IFIT1 + Neutrophils may activate CD8 + T cells through the IFIT1 signaling pathway, thereby enhancing the anti-tumor response and promoting the effect of PD-1 inhibitors. IFIT1 + Neutrophils are in the early differentiation stage and gradually transition from an immune-activated state to an immunosuppressive phenotype.

[0029] The present invention for the first time applies scRNA-seq to characterize the cellular and molecular dynamics of the tumor microenvironment (TME) of bladder cancer patients receiving monotherapy with PD-1 inhibitors. By treating patients with ICIs alone, it ensures that the TME is not affected by other factors, and enables a clearer understanding of the resistance and sensitivity to ICIs treatment. The present invention presents a single-cell gene expression atlas of human bladder cancer related to ICIs, revealing a highly heterogeneous and bladder cancer-specific transcriptomic landscape. In addition, the present invention identifies subtypes related to ICIs response, and reveals immune cell subtypes related to ICIs resistance and sensitivity. Overall, the present invention comprehensively characterizes the impact of PD-1 inhibitor treatment on the tumor microenvironment of bladder cancer using scRNA-seq, reveals the cellular dynamics and molecular characteristics of bladder cancer during PD-1 inhibitor treatment, and highlights the potential of CD8 + Tcm cells as a biomarker for predicting the efficacy of immunotherapy. In addition, the present invention identifies SFRP2 + CAF as a key marker for immunotherapy resistance, and IFIT1 + neutrophils as key markers for immunotherapy sensitivity. The present invention deepens the understanding of the potential mechanisms of resistance to PD-1 treatment in bladder cancer patients in the prior art, provides a theoretical basis for optimizing the immunotherapy strategy for bladder cancer, and offers new directions for potential immunotherapy targets for bladder cancer. Description of the Drawings

[0030] Figure 1 Schematic diagram of the CT detection results for advanced bladder cancer patients.

[0031] Figure 2 Schematic diagram of the immunohistochemical analysis results for advanced bladder cancer patients.

[0032] Figure 3 Schematic diagram of the grouping results for advanced bladder cancer patients.

[0033] Figure 4 Schematic diagram of the results of cell clustering.

[0034] Figure 5 Schematic diagram of the results of labeling each cluster of cells.

[0035] Figure 6 Schematic diagram of the proportions of different cell types before and after treatment in patients who respond or do not respond to PD-1 inhibitors.

[0036] Figure 7 Schematic diagram of the results of the analysis of cell proportion differences among each cell subset.

[0037] Figure 8 Schematic diagram of the immunohistochemical analysis results of the tumor samples of the sequenced patients.

[0038] Figure 9 To characterize and score precursor exhausted T cells in responsive and non-responsive patients after PD-1 inhibitor treatment, the proliferative ability, cytotoxicity of CD8 + Tcm cells against tumor cells, and the schematic diagram of the results of T cell-mediated immune response in the response analysis.

[0039] Figure 10 For the pseudotime analysis results schematic diagram of the CD8 + cell population.

[0040] Figure 11 For the Monocle3 analysis results schematic diagram of the CD8 + cell population.

[0041] Figure 12 For the pseudotime dynamic change analysis results schematic diagram of progenitor cell-related genes and exhaustion-related genes in CD8 + Tcm and CD8 + Tex cells.

[0042] Figure 13 For the prognostic results schematic diagram of CD8 + Tcm cells on the outcome of immunotherapy.

[0043] Figure 14 For the TCIA score results schematic diagram of the high-score group and low-score group of CD8 + Tcm subsets before and after PD-1 or CTLA-4 inhibitor treatment.

[0044] Figure 15 For the mIF analysis results schematic diagram of patients responsive and non-responsive to PD-1 inhibitor treatment.

[0045] Figure 16 For the GO analysis and KEGG analysis results schematic diagram of mCAF.

[0046] Figure 17 For the GO analysis and KEGG analysis results schematic diagram of iCAF.

[0047] Figure 18 For the GO analysis and KEGG analysis results schematic diagram of SFRP2 + CAF.

[0048] Figure 19 For the RNA velocity analysis and pseudotime analysis results schematic diagram of SFRP2 + CAF.

[0049] Figure 20 For the SFRP2 +Schematic diagram of the results of CellChat analysis by CAF.

[0050] Figure 21 Schematic diagram of the results of mIF analysis of bladder cancer tissues.

[0051] Figure 22 For SFRP2 + CAF may affect T cell function by the ligand-receptor interaction between members of the collagen family and CD44 on T cells.

[0052] Figure 23 Schematic diagram of the results of the expression levels of PD-L1 and interferon response-related signature genes in different neutrophils.

[0053] Figure 24 For IFIT1 + Schematic diagram of the results of GO analysis and KEGG analysis of neutrophils.

[0054] Figure 25 Schematic diagram of the results of neutrophil phenotype analysis.

[0055] Figure 26 For IFIT1 + Schematic diagram of the results of the response rate and survival outcomes of patients with high and low infiltration of IFIT1 neutrophils to PD-1 inhibitors.

[0056] Figure 27 For IFIT1 + Schematic diagram of the results of the response rate of patients with high and low infiltration of IFIT1 neutrophils to PD-1 and CTLA-4 inhibitors.

[0057] Figure 28 Schematic diagram of the results of pseudotime and RNA velocity analysis of neutrophil subsets.

[0058] Figure 29 Schematic diagram of the results of gene set analysis related to MDSC.

[0059] Figure 30 For IFIT1 + Schematic diagram of the results of mIF analysis of IFIT1 neutrophils. Detailed implementation manners

[0060] To make the objectives, technical solutions and effects of the present invention clearer and more definite, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] Unless otherwise specified, all reagents used in the context of the present invention are commercially available. For the use of clinical specimens, informed consent forms have been signed with the patients, and the relevant procedures and methods comply with the requirements of medical ethics and the Good Clinical Practice for drug clinical trials. The experimental methods used in the present invention are all conventional methods and techniques in the art. All clinical samples are from the First Affiliated Hospital of Guangzhou Medical University. The advanced bladder cancer patients included in the present invention are only suitable for PD-1 inhibitor treatment due to renal insufficiency or physiological intolerance to other treatments. These patients have not received anti-tumor treatment before and have received neoadjuvant immunotherapy or advanced treatment, including 4 cycles of the PD-1 inhibitor toripalimab. The experimental methods used in the present invention, such as single-cell isolation, single-cell sequencing, single-cell RNA statistical analysis, pseudotime analysis, RNA velocity analysis, CellChat analysis, survival analysis, prediction of immunotherapy response, immunohistochemistry, multiplex immunofluorescence (mIF), bioinformatics analysis, etc., are all conventional methods and techniques in the art. Representative results were selected in the biological experiment replicates and presented in the context drawings, and the data are shown as mean±SD and mean±SEM as specified in the drawings. All p-values are two-sided, and values less than 0.05 are considered statistically significant.

[0062] Example 1

[0063] Samples of advanced bladder cancer patients who were unable to use platinum-based chemotherapy drugs due to renal insufficiency (creatinine clearance rate < 60 mL / min) and poor physical condition (ECOG > 1) were collected, and each sample was analyzed before and after treatment with the PD-1 inhibitor (toripalimab). According to the RECIST criteria and pathological response, the patient samples were divided into a PD-1 inhibitor treatment response group (P001, P002, P003) and a PD-1 inhibitor treatment non-response group (P004, P005) (see Figure 1-3 ). After single-cell RNA sequencing, standard data quality control, statistical analysis, and screening, 49,773 cells were obtained for further analysis.

[0064] These cells were divided into 20 clusters after removing batch effects, and these clusters were classified into 10 cell types using conventional methods in the prior art, including T cells (CD8 + T cells, CD4 + T cells), B cells, plasma cells, mast cells, neutrophils, monocytes, epithelial cells, endothelial cells, and fibroblasts (see Figure 4 ). To observe the distribution of each cell type, the clusters were labeled according to cell type, sample ID, treatment outcome, etc., where Figure 5 A-5B is the proportion of subtype cells before and after treatment of different patients,Figure 5 C is the proportion of cell subtypes before and after treatment and prognosis of patients, Figure 5 D-5E is the proportion of subtype cells before and after treatment of patients (see Figure 5 ).

[0065] To further clarify which cell type distributions change during PD-1 inhibitor treatment and to determine which cell types can be used as predictive indicators for PD-1 inhibitors or as targets to enhance the sensitivity of PD-1 inhibitors, the proportions of 8 annotated cell types under different PD-1 inhibitor treatment conditions were analyzed. First, the cell types that changed before and after PD-1 inhibitor treatment were studied to show the impact of PD-1 inhibitors on the tumor microenvironment of patients. The results showed that there was no significant change in the proportions of the 8 cell types. Second, the proportions of different cell types before and after treatment of patients who responded or did not respond to PD-1 inhibitors were analyzed. The results showed that after PD-1 inhibitor treatment, the proportion of T cells in patients who responded to treatment was significantly higher than that in non-responders (see Figure 6 ). This result indicates that PD-1 inhibitors may enhance the recruitment or proliferation of T cells in the tumor microenvironment. A similar phenomenon has also been reported in non-small cell lung cancer and may be related to the "clonal revival" effect. The above results indicate that patients with good clinical outcomes tend to establish a more T cell-rich immune microenvironment after PD-1 inhibitor treatment. Subsequently, these cell types were further re-clustered and annotated to determine which subsets showed changes in proportion and function before and after PD-1 inhibitor treatment. Among them, T cells were divided into 6 subgroups, B cells and plasma cells were divided into 4 subgroups, fibroblasts were divided into 3 subgroups, monocytes were divided into 3 subgroups, epithelial cells were divided into 4 subgroups, endothelial cells were divided into 4 subgroups, and neutrophils were divided into 8 subgroups, and the cell proportion difference analysis was performed on these subgroups. The results are as Figure 7 shown. These results emphasize the heterogeneity and complexity of the tumor microenvironment (TME) in response to PD-1 inhibitor treatment.

[0066] Example 2

[0067] CD8 + Since CD8 + T cells play a central role in anti-tumor immunity, they play a key role in mediating the response to PD-1 inhibitor treatment. It has been found through research that in scRNA-seq data, the infiltration of CD8 Figure 8 T cells in responders increased after PD-1 inhibitor treatment compared to non-responders. To further verify this finding, immunohistochemistry was performed on the tumor samples of the sequenced patients, and the results were consistent with the scRNA-seq data (see +T cell infiltration into tumors can predict survival rate and indicates that scRNA-seq can reflect the true infiltration level of cell populations to a certain extent. To further investigate their functional diversity and contribution to treatment outcomes, CD8 + T cells were divided into 6 different subtypes: central memory CD8 + T cells (CD8 + Tcm), CD8 + T cells expressing the 4-1BB receptor (CD8 + 41BB), CD8 + T cells expressing interferon-stimulated genes (CD8 + ISG), exhausted CD8 + T cells (CD8 + Tex), and effector CD8 + T cells (CD8 + Teff), and these cell subtypes were annotated according to their specific markers. Statistical analysis of patients in the responder and non-responder groups before PD-1 inhibitor treatment showed significant differences in the proportions of CD8 + Tcm and CD8 + Teff cells between the two groups. In patients who were non-responsive before treatment, the proportion of CD8 + Teff cells infiltrating the TME was higher; however, these cells did not show an effective response to PD-1 inhibitors. This observation suggests that either the function of CD8 + Teff cells is lost or they may not be the main targets of PD-1 inhibitors. Evidence from previous studies further indicates that this phenomenon may be related to the cell exhaustion state.

[0068] To investigate whether these cells also exist in bladder cancer, gene signatures related to T cell function were used to determine their functional status. The results showed that the signatures of precursor-exhausted T cells were mainly enriched in CD8 + Tcm cells, while the signatures of terminally exhausted T cells were identified in CD8 + Tex cells. The results also found that after PD-1 inhibitor treatment, compared with non-responsive patients, responsive patients showed an increased signature score of precursor-exhausted T cells; in addition, the proliferative ability, cytotoxicity, and T cell-mediated immune response of CD8 + Tcm cells against tumor cells were better in responsive patients than in non-responsive patients (see Figure 9 ).

[0069] Subsequently, pseudotime analysis was performed on all CD8 + cell populations, and it was found that the differentiation trajectory of CD8 + Tcm cells preceded that of CD8+ Tex cells (see Figure 10 , CD8 on the left in the figure + Tcm cells, CD8 on the right in the figure + Tex cells). Analysis using Monocle3 revealed that CD8 + Tcm cells not only differentiated into CD8 + Tex cells, but also into 4-1BB + CD8 + T cells (see Figure 11 ). Existing technologies have shown that 4-1BB is related to the proliferation of T cells and the enhancement of the efficacy of PD-1 inhibitors. Next, the pseudotemporal dynamic changes of progenitor cell-related genes and exhaustion-related genes in CD8 + Tcm and CD8 + Tex cells were analyzed. The results showed that the expression of progenitor cell-related genes (GZMK, IL7R, REL, TBX21, TCF7) first increased and then decreased, while the expression of exhaustion-related genes (CTLA4, EOMES, LAG3, PDCD1, RSG1) first decreased and then increased (see Figure 12 ). This indicates that CD8 + Tcm cells are transitioning to terminal exhaustion, and it is speculated that this may be due to the gradual weakening of the efficacy of PD-1 treatment on T cells over time. Further, bladder cancer patient samples were used to verify whether CD8 + Tcm cells can be used as a predictor of the response to PD-1 inhibitor treatment. The results showed that CD8 + Tcm cells showed strong predictive ability for the prognosis of immunotherapy outcomes (see Figure 13 ).

[0070] To verify the role of CD8 + Tcm in PD-1 inhibitor treatment, the TCGA bladder cancer (BLCA) dataset was analyzed. Patients were divided into high and low groups according to the percentage score of the CD8 + Tcm subset, and the immunotherapy efficacy prediction scores provided by the Cancer Immunome Atlas (TCIA) were used to compare the scores between the high and low groups. The results showed that there was no significant difference between the two groups without PD-1 or CTLA-4 inhibitor treatment; after PD-1 inhibitor treatment, the TCIA score of the high-scoring group was significantly higher than that of the low-scoring group (p < 0.001) (see Figure 14 ). According to the response of patients to PD-1 inhibitors, patients were divided into responder and non-responder groups to evaluate the infiltration of CD8 + Tcm cells in tumors. The mIF analysis results showed that compared with non-responder patients, the CD8 +The infiltration level of Tcm cells was higher (see Figure 15 ). The above results highlighted the progenitor-like phenotype of CD8 + Tcm cells, jointly indicating that the percentage level of CD8 + Tcm cells can be used as a biomarker for predicting the efficacy of immunotherapy in patients.

[0071] Example 3

[0072] Increasing evidence indicates that cancer-associated fibroblasts (CAFs) promote tumor resistance to immunotherapy through multiple mechanisms, including inducing the expression of tumor cell resistance genes, inhibiting the activity of immune cells, and producing immunosuppressive molecules. In addition, CAFs play a key role in remodeling the tumor microenvironment in bladder cancer, promoting tumor stemness, chemoresistance, and immunotherapy resistance. Three subsets of fibroblasts, namely myofibroblasts (mCAFs), inflammatory fibroblasts (iCAFs), and SFRP2 + CAFs, were identified in the samples of the present invention. The mCAF and iCAF subsets were consistent with the markers found in current studies, while SFRP2 + CAFs not only expressed several inflammation-related markers but also specific markers including SFRP2 and LRRC15. SFRP2 + CAFs were similar to the known extracellular matrix CAFs and collagen-expressing CAFs. To further investigate the functions of these subsets, gene ontology (GO) functional analysis and KEGG pathway enrichment analysis were performed. GO analysis found that mCAFs were mainly related to focal adhesion, contractile fiber, and focal adhesion, while KEGG analysis showed associations with focal adhesion, regulation of the actin cytoskeleton, and vascular smooth muscle contraction, all of which are functions related to myofibroblasts (see Figure 16 ). In GO analysis, iCAFs were related to collagen-containing extracellular matrix, extracellular matrix organization, and extracellular structure organization. KEGG analysis further revealed that iCAFs were related to inflammation-related functions, including the TGF-β signaling pathway, PI3K-Akt signaling pathway, and IL-17 signaling pathway (see Figure 17 ). In GO analysis, SFRP2 + CAFs exhibited functions similar to those of iCAFs, such as collagen-containing extracellular matrix, extracellular matrix organization, and extracellular structure organization; while in KEGG analysis, they showed unique functions, including ECM receptor interaction, proteoglycans in cancer, and AGE-RAGE signaling pathway in diabetic complications (see Figure 18 ).

[0073] To further confirm SFRP2 +The relationship between CAF and iCAF was analyzed by RNA velocity analysis and pseudotime analysis, and the results showed that SFRP2 + CAF is in the early stage of iCAF differentiation and has a tendency to differentiate into iCAF (see Figure 19 ). Research has shown that CAF can inhibit CD8 + T cell infiltration by secreting CXCL12 and is involved in the resistance to immune checkpoint inhibitors. In this regard, by detecting the expression of CXCL12 in CAF subsets, it was found that SFRP2 + CAF has the highest expression of CXCL12, indicating that SFRP2 + CAF may be the cell population that hinders the action of PD-1 inhibitors on T cells. To further study the relationship between SFRP2 + CAF and T cells, CellChat analysis was performed, and the results found that SFRP2 + CAF interacts with various T cell subsets, and the CXCL signaling pathway is mainly initiated by SFRP2 + CAF cross-linked with T cells (see Figure 20 ). Through mIF analysis, it was found that SFRP2 + CAF exists in bladder cancer tissues; in addition, compared with responsive patients, non-responsive patients showed a higher enrichment of SFRP2 + CAF subsets, and higher levels of CXCL12 were detected around these cells. The spatial proximity between the SFRP2 + CAF subset and CD8 + Tcm cells indicates that SFRP2 + CAF may affect the function of CD8 + T cells by secreting CXCL12 (see Figure 21 ). In addition to the CXCL signaling pathway, the present invention also found through experiments that SFRP2 + CAF may affect T cell function through the interaction of ligand receptors that are interdependent between collagen family members (COL1A1, COL1A2, COL4A1, COL4A2, COL6A1, COL6A2, COL6A3) and CD44 on T cells (see Figure 22 ). In summary, SFRP2 + CAFs can interact with T cells in multiple ways to inhibit the action of PD-1 inhibitors.

[0074] Example 4

[0075] Tumor-associated neutrophils (TANs) have become a key component of the tumor microenvironment. TANs can promote tumor progression by driving angiogenesis, metastasis, and immunosuppression, while also mediating antitumor responses by directly killing tumor cells and participating in the regulation of antitumor resistance. Both tumors and the host have neutrophil functional responses to conventional therapies and immunotherapies. To investigate the role of neutrophils in PD-1 inhibitor treatment of bladder cancer, neutrophils were classified, and a total of 8 subgroups were identified. Among all subgroups, Neu2 exhibited the highest level of PD-L1 expression and expressed characteristic genes related to interferon response (IFIT1, IFIT2, IFIT3, RSAD2) (see Figure 23 ), and it was defined as IFIT1 + neutrophils. Subsequently, functional enrichment analysis was performed on IFIT1 + neutrophils. The results showed that IFIT1 + neutrophils were mainly related to antiviral and innate immune responses, such as defense responses to viruses, defense responses to symbionts, and regulation of innate immune responses. KEGG analysis showed that in addition to pathways related to innate immunity, IFIT1 + neutrophils were also related to the NOD-like receptor signaling pathway, necrosis, and TNF signaling pathway (see Figure 24 ). In addition, phenotypic analysis of neutrophils in all other subgroups found that IFIT1 + neutrophils were related to type I interferon (IFN) signaling pathway, neutrophil activation, neutrophil chemotaxis, phagocytosis, and secretory vesicles (see Figure 25 ). These studies suggest that IFIT1 + neutrophils are related to immune activation.

[0076] To investigate the potential role of IFIT1 + neutrophils in PD-1 inhibitor treatment, multiple external validation datasets were used for evaluation and validation. The results found that patients with high infiltration of IFIT1 + neutrophils had significantly higher response rates to PD-1 inhibitors and improved survival outcomes (see Figure 26 ). In addition, analysis of the TCIA database showed that patients with high infiltration of IFIT1 + neutrophils had higher response rates to both PD-1 and CTLA-4 inhibitors (see Figure 27 ). The above results suggest that IFIT1 + neutrophils are related to the sensitivity of bladder cancer patients to PD-1 inhibitor treatment.

[0077] The latest research shows that the type I interferon signaling pathway plays a crucial role in anti-cancer immunity. Many traditional chemotherapy drugs, targeted anti-cancer drugs, and immune adjuvants can achieve complete efficacy only in the presence of intact type I interferon signaling. Therefore, this invention studied the type I interferon signaling pathway score in neutrophil subsets and found that this score was mainly elevated in IFIT1 + neutrophils. In addition, after treatment with PD-1 inhibitors, the pathway score increased in patients with good prognosis, while it decreased in patients with poor prognosis. By performing pseudotime and RNA velocity analyses on these subsets, the differentiation trajectory of neutrophils was further studied, and it was found that IFIT1 + neutrophils existed along multiple differentiation pathways, and IFIT1 + neutrophils were mainly located in the early stage of pseudotime (see Figure 28 ). Since neutrophils include myeloid-derived suppressor cell (MDSC) subsets, which are closely related to tumor immune escape and resistance to immunosuppressive agents, in order to explore whether such subsets exist in these neutrophils, 9 gene sets related to MDSCs were analyzed, and it was found that gene sets 1, 2, 4, and 5 were related to MDSCs and were most highly expressed in Neu4 (MDSC-like Neu) (see Figure 29 ). Based on this, it was presumed that IFIT1 + neutrophils might gradually differentiate from a functional state into an immunosuppressive MDSC-like Neu state, thus leading to resistance to PD-1 inhibitors.

[0078] Existing research shows that type I interferon plays a crucial role in promoting the response of CD8 + T cells to cancer. Therefore, it is necessary to study the relationship between IFIT1 + neutrophils and CD8 + T cells. Both CellChat and CellphoneDB analyses showed that there was an interaction between IFIT1 + neutrophils and CD8 + T cell subsets. In addition, mIF analysis showed that IFIT1 + neutrophils were spatially adjacent to T cells; and compared with responder patients, the infiltration of IFIT1 + T cells was lower in non-responder patients (see Figure 30 ). In the CellphoneDB analysis, MDSC-like Neu also interacted with CD8 + T cells. This may indicate that immunosuppressive MDSC-like Neu hinders the response of CD8 + T cells to PD-1 inhibitors, while CD8 +Tex cells can also affect IFIT1 + Neutrophils and MDSC-like Neu, indicating that terminally exhausted T cells may shape the immune microenvironment and further affect the efficacy of PD-1 inhibitors.

[0079] PD-1 inhibitor therapy plays a crucial role in the treatment of advanced bladder cancer. However, approximately 70% of advanced patients do not respond to this treatment. To identify the major molecular and cellular features associated with resistance to PD-1 inhibitor therapy, the present invention comprehensively characterized the impact of PD-1 inhibitor therapy on the tumor microenvironment of bladder cancer using scRNA-seq, revealing the cell dynamics and molecular features of bladder cancer during PD-1 inhibitor therapy, highlighting the potential of CD8 + Tcm cells as biomarkers for predicting the efficacy of immunotherapy. In addition, the present invention discovered SFRP2 in cancer-associated fibroblasts (CAFs) + CAF subsets, which may affect T cell function through the CXCL signaling pathway and members of the collagen family, thereby promoting immunosuppression and contributing to patient resistance to PD-1 inhibitors. In the immune microenvironment, the present invention discovered a neutrophil subset, IFIT 1 + Neu, which can express interferon-related genes and is associated with favorable outcomes of PD-1 inhibitor therapy. In addition, this neutrophil subset may gradually transform into neutrophils with a myeloid-derived suppressor cell (MDSC) phenotype, thereby mediating T cell resistance to PD-1 inhibitors. The studies of the present invention indicate that PD-1 inhibitor therapy remodels the bladder cancer microenvironment and alters the interactions of cancer immune cells; the present invention deepens the understanding of the potential mechanisms of PD-1 treatment resistance in bladder cancer patients in the prior art, provides a theoretical basis for optimizing bladder cancer immunotherapy strategies, and offers new directions for potential immunotherapy targets in bladder cancer.

[0080] The above specific embodiments section specifically introduced the analysis methods involved in the present invention. It should be noted that the above introduction is only to help those skilled in the art better understand the methods and ideas of the present invention, rather than a limitation of the relevant content. Without departing from the principles of the present invention, those skilled in the art can also make appropriate adjustments or modifications to the present invention, and such adjustments and modifications should also fall within the protection scope of the present invention.

Claims

1. Use of immune cells and / or cancer-associated fibroblasts in the preparation of a product for detecting the sensitivity of cancer treatment to a PD-1 inhibitor, characterized in that, The immune cells are selected from one or more of CD8 + Tcm, IFIT1 + neutrophils; the cancer-associated fibroblasts are selected from one or more of SFRP2 + CAF.

2. The application according to claim 1, characterized in that The cancer is selected from bladder cancer.

3. The application according to claim 1, wherein The PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, carrelizumab, penampalimab, sepalimab, fenolizumab, and slulizumab.

4. Use of a reagent for detecting the level of immune cells and / or cancer-associated fibroblasts in the preparation of a product for detecting the sensitivity of a PD-1 inhibitor to cancer treatment, characterized in that, The immune cells are selected from one or more of CD8 + Tcm, IFIT1 + neutrophils; the cancer-associated fibroblasts are selected from one or more of SFRP2 + CAF.

5. The application according to claim 4, characterized in that, The cancer is selected from bladder cancer.

6. The application according to claim 4, characterized in that, The PD-1 inhibitor is selected from one or more of pembrolizumab, nivolumab, toripalimab, tislelizumab, sintilimab, carrelizumab, penampalimab, sepalimab, fenolizumab, and slulizumab.

7. Use of immune cells in the preparation of a drug for promoting the sensitivity of a PD-1 inhibitor to cancer treatment, characterized in that, The immune cells are selected from CD8 + Tcm, IFIT1 + one or more of neutrophils.

8. The application according to claim 7, wherein The cancer is selected from bladder cancer.

9. A pharmaceutical composition for treating cancer, characterized in that, Comprising immune cells, and a PD-1 inhibitor; the immune cells are selected from one or more of CD8 + Tcm, IFIT1 + neutrophils.

10. A kit for evaluating the sensitivity of PD-1 inhibitors to cancer treatment, characterized in that, Comprising reagents for detecting the levels of immune cells and / or cancer-associated fibroblasts, wherein the immune cells are selected from CD8 + Tcm, IFIT1 + one or more of neutrophils; the cancer-associated fibroblasts are selected from SFRP2 + CAF.

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