Method for predicting solid tumor immune cell therapy treatment response

Abstract

The present invention relates to a method for predicting a solid tumor immune cell therapy treatment response. The method comprises acquiring a tumor tissue section from a patient, and detecting the presence, number, proportion or density of one or more cell types in the tumor tissue section. A solid tumor immune cell therapy response is predicted by determining cell markers, such as α-SMA, CD3, CD4 or CD8, associated with the cell types. In addition, the present invention also relates to a kit used for predicting a solid tumor treatment response, including a reagent which detects expression levels of one or more cell types and cell markers in tumor tissue sections, and software and / or a user manual used for analyzing detection results. The method and kit provide an effective tool for clinical treatment, so as to optimize dosages and clinical regimens of immune cell therapy, thereby improving treatment response rates.

Description

A method for predicting the treatment response of solid tumor immune cell therapy TECHNICAL FIELD

[0001] The present invention belongs to the field of biological medicine, particularly relates to the application of cell immunotherapy technology in tumor treatment, and the use of tumor microenvironment characteristics to predict and optimize the therapeutic effect of immune cell therapy. BACKGROUND

[0002] With the rapid development of biological medicine, cell immunotherapy has become an important means for the treatment of various solid tumors. Cell immunotherapy, especially gene-modified cell therapy (such as CAR-T, TCR-T, etc.), by modifying immune cells to enable specific recognition and killing of tumor cells, provides new treatment options for cancer patients. However, due to the complexity of the tumor microenvironment and individual differences among patients, different patients respond differently to immune cell therapy, and some patients even develop drug resistance. Therefore, predicting the response of patients to immune cell therapy has become an urgent problem to be solved.

[0003] Traditional tumor efficacy evaluation mainly relies on changes in tumor volume, but this evaluation method often has a lag, and cannot reflect the treatment effect in a timely manner. In recent years, with the in-depth study of tumor microenvironment, it has been found that the cell types, cell-cell interactions and cell marker expression levels in the tumor microenvironment are closely related to the response of immune cell therapy. Therefore, by analyzing the tumor microenvironment, the response of patients to immune cell therapy can be predicted, thereby providing more accurate and personalized guidance for clinical treatment.

[0004] Although there are some studies that attempt to analyze the tumor microenvironment to predict the response of immune cell therapy, these methods often have the problems of complex operation, high cost, and low accuracy. Therefore, it is of great clinical value to develop a simple, fast and accurate prediction method. SUMMARY

[0005] The present invention relates to a method for predicting the treatment response of solid tumor immune cell therapy, particularly suitable for solid tumors such as gastric cancer, pancreatic cancer and colorectal cancer. By evaluating the presence, number, proportion or density of specific cell types in the tumor microenvironment of tumor tissue sections, the present invention provides an effective method for accurately predicting the response of solid tumors to immune cell therapy.

[0006] Specifically, the prediction method of the present application comprises the following steps: first, obtaining tumor tissue sections from patients with solid tumors; second, detecting the tumor microenvironment in the tumor tissue sections, the presence, number, proportion or density of at least one cell type in the tumor-related fibroblasts (CAF), CD4-positive cells, CD8-positive cells, regulatory T cells, B cells, M1 macrophages, M2 macrophages, dendritic cells (DC), natural killer cells (NK), neutrophils, tumor cells and endothelial cells. This process can be achieved by determining the expression level of cell markers related to these cell types, such as at least one of a-SMA, FAP, CD3, CD4, CD8, CD56, CD16, FoxP3, Helios, CD68, CD206, CD163, CD11C, CD31, PD1, PD-L1, PanCK, CD45RO, CD20, CD66b.

[0007] After obtaining the above detection results, the present application uses machine learning algorithms, neural networks or logistic regression models to comprehensively analyze the detected tumor microenvironment parameters to predict the response of solid tumors to immune cell therapy. These models can integrate complex tumor microenvironment information and predict the patient's response to treatment accordingly.

[0008] Further, the present application also includes the step of adjusting the dosage or treatment regimen of immune cells based on the prediction results to achieve more precise and personalized treatment. For genetically modified cell therapy, such as CAR-T or TCR-T, etc., the present application designs a CAR structure capable of targeting multiple tumor antigens, which is characterized by being capable of targeting multiple tumor-associated antigens, including but not limited to oncoviral antigens, cancer testis antigens, carcinoembryonic antigens, tissue differentiation antigens, mutant protein antigens, neoantigens, lipophilic proteins, CD19, BCMA, CD22, CD20, CD123, CD30, CD38, CD138, CD56, CD7, CLL-1, CD10, CD34, CS1, CD16, CD4, CD5, CDH6, CDH17, GUCY2C, IL-1-RAP, ITGB7, k-IgG, TAC1, TRBC1, MUC1, NKG2D, PD-L1, CD133, CD177, LeY, CD70, ROR1, AFP, AXL, CD80, CD86, DLL3, DR5, FAP, FRα, LMP1, MAGE-A1, MAGE-A4, MG7, MUC16, PMEL, ROR1, ROR2, VEGFR2, CD171, Claudin 18.2, Claudin 6, EphA2, ErbB, Fra, PSCA, cMet, IL13Ra2, EPCAM, EGFR, PSMA, EGFRvIII, GPC3, CEA, HER2, GD2, GM1, Mesothelin, Trop2, Trp p8, NY-ESO-1, NECTIN4, and integrin αvβ3 (CD61).

[0009] The above-mentioned tumor antigen CAR has a structure including an extracellular binding region, a hinge region, a transmembrane region, and an intracellular signaling region. The intracellular signaling region thereof is selected from one or more of 2B4, DNAM1, CD28, 4-1BB, OX40, ICOS, CD27, MYD88, KIR2DS2, DAP10, DAP12, CD3ζ, TLRs, CD2, LFA-1, CD8α, CD40, CD80, and CD3ε.

[0010] The above-mentioned genetically modified immune cells include T cells, NK cells, DC cells, macrophages, γδT cells (gdT), and double-negative T cells (dnT).

[0011] The above-mentioned non-genetically modified immune cell therapy is characterized by including TIL cell therapy, NK cell therapy, DC cell therapy, macrophage therapy, γδT cell (gdT) therapy, and double-negative T cell (dnT) therapy, etc.

[0012] In addition, the present application also provides a kit for predicting the response of solid tumor cells to treatment, which comprises reagents for detecting specific cell types and cell marker expression levels in tumor tissue sections, and software and / or user manuals for analyzing the detection results. This kit makes the prediction method of the present application more convenient and easy to use, providing convenience for clinical application.

[0013] In summary, the present application provides a method for accurately predicting the response of solid tumors to immune cell therapy by comprehensively evaluating the expression of immune cell therapy targets in tumor tissue sections and the presence, number, proportion or density of specific cell types in the tumor microenvironment, combined with advanced algorithm models. This method is not only suitable for a variety of solid tumors, but also enables personalized treatment adjustment, providing more precise and effective treatment options for cancer patients.

[0014] Compared with the prior art, the present application provides a more accurate and comprehensive prediction method, which can help doctors select the most suitable immune cell therapy for patients, improve treatment effectiveness, reduce unnecessary exposure to ineffective treatment, and provide a new research direction for the research and optimization of immune cell therapy. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0016] Figure 1 (A) represents the α-SMA staining of a representative patient, (B) statistical analysis of α-SMA staining in different response groups.

[0017] Figure 2 is an analysis of the proportion of cells expressing markers CD4, CD8, FoxP3, CD206, and α-SMA in the tumor microenvironment. DETAILED DESCRIPTION

[0018] The embodiments of the present application will now be provided in detail, and one or more examples thereof are described below. Each example is provided as an explanation rather than a limitation of the present application. In fact, it is obvious to those skilled in the art that various modifications and changes can be made to the present application without departing from the scope or spirit of the present application. For example, features described or illustrated as part of one embodiment can be used in another embodiment to produce further embodiments.

[0019] Unless otherwise indicated, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. By further guidance, the following definitions are set forth to better define the present teachings. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present teachings.

[0020] The alternative ranges of the terms "and / or", "or / and", "and / or" used herein include any one of two or more related listed items, and also include any and all combinations of the related listed items, including any two related listed items, any more related listed items, or all related listed items. It should be noted that when at least two conjunctions selected from "and / or", "or / and", "and / or" are combined to connect at least three items, it should be understood that in this application, the technical solution undoubtedly includes the technical solution connected by "logical and", and also undoubtedly includes the technical solution connected by "logical or". For example, "A and / or B" includes three parallel solutions of A, B, and A+B. For another example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (i.e., the technical solution connected by "logical or"), and also includes any and all combinations of A, B, C, and D, i.e., includes the combination of any two or any three of A, B, C, and D, and also includes the four-item combination of A, B, C, and D (i.e., the technical solution connected by "logical and").

[0021] The terms "containing", "including", and "comprising" used in the present application are synonymous and are inclusive or open-ended, and do not exclude additional, unrecited members, elements, or method steps.

[0022] The numerical ranges in the present application expressed in endpoints include all numbers and fractions subsumed within the range, as well as the recited endpoints.

[0023] In the present application, the descriptions such as "a plurality of", "a plurality of kinds", etc. refer to greater than or equal to 2 in number, unless otherwise specified.

[0024] In the present application, the technical features described in an open-ended manner include both the closed technical solution consisting of the listed features, and the open technical solution including the listed features.

[0025] As used herein, "chimeric antigen receptor (CAR)" refers to a fusion protein comprising an extracellular domain capable of binding an antigen, a transmembrane domain derived from a different polypeptide than the extracellular domain, and at least one intracellular domain. "Chimeric antigen receptor (CAR)" is sometimes referred to as "chimeric receptor" or "chimeric immunoreceptor (CIR)." "Extracellular domain capable of binding an antigen" refers to any oligopeptide or polypeptide that can bind to a specific antigen. "Intracellular domain" refers to any oligopeptide or polypeptide known to function in a cell as a signaling transmitter to cause activation or inhibition of a biological process.

[0026] CAF (tumor-associated fibroblast): Tumor-associated fibroblasts (CAFs) are crucial stromal cells in the tumor microenvironment, which are transformed from normal fibroblasts or mesenchymal stem cells during tumor development. CAFs can secrete a variety of cytokines, chemokines and extracellular matrix components, which are closely related to the biological processes such as growth, invasion and metastasis of tumor cells.

[0027] CD4-positive cells: CD4-positive cells, i.e. helper T cells, are key players in the immune response. They activate and assist B cells to produce antibodies by recognizing and binding to antigens presented by major histocompatibility complex II (MHC II) molecules, while regulating the activity and function of other immune cells, which are essential for maintaining the balance and homeostasis of the immune system.

[0028] CD8-positive cells: CD8-positive cells, i.e. cytotoxic T cells, are effector cells in the immune response. They can recognize and bind to antigens presented by major histocompatibility complex I (MHC I) molecules, and directly kill infected cells or tumor cells by releasing cytotoxic substances, which are an important defense line of the body against pathogens and tumors.

[0029] Regulatory T cells (Tregs): Regulatory T cells are a subset of T lymphocytes with immunosuppressive function, which prevent excessive immune response by suppressing the activity and function of other immune cells, and maintain the homeostasis and balance of the immune system. In tumor immunotherapy, regulating the activity of Tregs is of great significance to improve the therapeutic effect.

[0030] B cells: B cells are responsible for humoral immunity, which can differentiate into plasma cells and produce specific antibodies to neutralize and clear antigens, protecting the body from pathogens. B cells play an important role in adaptive immune response, closely cooperating with immune cells such as CD4-positive cells and CD8-positive cells, and together maintaining the immune defense of the body.

[0031] M1 Macrophages: M1 macrophages are a polarized state of activated macrophages that have strong bactericidal, antiviral, and antitumor effects. M1 macrophages can secrete various pro-inflammatory cytokines, promote inflammatory responses and immune responses, and are important for the clearance of pathogens and tumor cells.

[0032] M2 Macrophages: M2 macrophages are another polarized state of activated macrophages that mainly participate in immune regulation and tissue repair processes. M2 macrophages can secrete various anti-inflammatory cytokines, inhibit inflammatory responses and promote tissue repair, and help maintain the homeostasis and balance of the immune system.

[0033] Dendritic Cells (DCs): Dendritic cells are professional antigen-presenting cells that can efficiently capture, process, and present antigens to T cells. They play a bridge role in the immune response, connecting innate immunity and adaptive immunity, initiating and regulating T cell-mediated immune responses, and are crucial for the clearance of pathogens and tumor cells.

[0034] Natural Killer Cells (NK): Natural killer cells are a type of lymphocyte that can directly kill target cells without prior activation. They recognize and bind to the MHC class I molecule on the surface of target cells, directly killing infected cells or tumor cells, and are an important defense force against pathogens and tumors.

[0035] Tumor Cells: Tumor cells are cells formed from normal cells through genetic mutation and abnormal proliferation, with malignant biological characteristics. They have malignant biological characteristics such as unlimited proliferation, evasion of apoptosis, infiltration, and metastasis, can destroy the structure and function of normal tissues, and cause corresponding clinical symptoms. The presence of tumor cells is the basis for tumor occurrence and development.

[0036] Endothelial Cells: Endothelial cells are the cell layer that makes up the inner wall of blood vessels, which plays an important role in maintaining vascular integrity, regulating vascular tone and permeability, promoting angiogenesis and repair, etc. Endothelial cells are also an important component of the tumor microenvironment, closely related to the growth, invasion and metastasis of tumor cells, and have an important impact on tumor development and metastasis.

[0037] CAR-T Cell Therapy: CAR-T cell therapy is an innovative immunotherapy strategy that involves collecting T cells from patients, and using genetic engineering techniques to express specific chimeric antigen receptors (CARs). These modified T cells (CAR-T cells) are cultured and expanded in vitro, and then reinfused into patients. In the patient's body, CAR-T cells can specifically recognize and attack tumor cells expressing specific antigens, thereby enhancing the patient's anti-tumor immune response.

[0038] Tumor-Associated Antigen (TAA):

[0039] Tumor-Associated Antigens (TAAs), also known as tumor antigens, are a class of antigens that are expressed on the surface of tumor cells or are abnormally increased in tumor tissues. They can be proteins, glycoproteins, lipids or other types of molecules. Because the expression of these antigens on tumor cells is higher than that of normal cells, they become ideal targets for immunotherapy methods such as CAR-T cell therapy. By targeting these antigens, immunotherapy can specifically attack tumor cells while reducing damage to normal cells. Common tumor antigens include: oncogenic viral antigens, cancer-testis antigens, carcinoembryonic antigens, tissue differentiation antigens, mutant protein antigens, neoantigens, lipophilic proteins, CD19, BCMA, CD22, CD20, CD123, CD30, CD38, CD138, CD56, CD7, CLL-1, CD10, CD34, CS1, CD16, CD4, CD5, CDH6, CDH17, GUCY2C, IL-1-RAP, ITGB7, k-IgG, TAC1, TRBC1, MUC1, NKG2D, PD-L1, CD133, CD177, LeY, CD70, ROR1, AFP, AXL, CD80, CD86, DLL3, DR5, FAP, FRα, LMP1, MAGE-A1, MAGE-A4, MG7, MUC16, PMEL, ROR1, ROR2, VEGFR2, CD171, Claudin 18.2, Claudin 6, EphA2, ErbB, Fra, PSCA, cMet, IL13Ra2, EPCAM, EGFR, PSMA, EGFRvIII, GPC3, CEA, HER2, GD2, GM1, Mesothelin, Trop2, Trp-p8, NY-ESO-1, NECTIN4, and integrin αvβ3 (CD61).

[0040] Regulatory T cells (Treg):

[0041] Regulatory T cells (Tregs) are a special subset of T cells in the immune system that play an important role in maintaining immune tolerance and preventing autoimmune diseases. However, in the tumor microenvironment, Treg cells can promote tumor immune escape by inhibiting the activity and function of other immune cells such as cytotoxic T cells and natural killer cells. Therefore, studying how to regulate or inhibit the activity of Treg cells is of great significance to improve the effectiveness of tumor immunotherapy.

[0042] Signaling domain:

[0043] The signaling domain is a crucial component in the chimeric antigen receptor (CAR), which is located in the intracellular segment of CAR. When CAR recognizes a specific antigen, the signaling domain can activate downstream signaling pathways, leading to a series of biological effects such as T cell activation, proliferation, cytotoxicity, etc. These signaling pathways usually involve the interaction and cascade reaction of multiple signaling molecules, ultimately leading to the killing effect of T cells on tumor cells. Therefore, the design and optimization of the signaling domain are essential for improving the therapeutic effect of CAR-T cell therapy.

[0044] CD3Z (CD3 Zeta)

[0045] CD3Z is a transmembrane protein found on T cells and is a component of the TCR-CD3 complex. The TCR-CD3 complex is responsible for recognizing antigens and initiating immune responses. CD3Z is activated after TCR binds to antigens and participates in transmitting APC (antigen-presenting cell)-induced TCR signals, playing a key role in T cell activation.

[0046] CD28

[0047] CD28 is a costimulatory molecule expressed on the surface of T lymphocytes, which plays an important role in T cell activation. It binds to B7 molecules on APCs, mediates T cell costimulation and promotes T cell survival, proliferation and cytokine production. The costimulatory effect of CD28 is essential for the complete activation of T cells, especially in reducing the activation threshold of T cells, promoting the proliferation and differentiation of T cells.

[0048] 4-1BB (also known as CD137 or TNFRSF9)

[0049] 4-1BB is a very important costimulatory receptor on T cells and other immune cells, belonging to the TNRSF protein family. It has multiple effects on T cell activation, including cell proliferation, cytokine release, cytotoxicity, and long-term immune memory formation. 4-1BB has potential applications in tumor immunotherapy, for example, it can be used as a costimulatory domain in CAR-T cell therapy to enhance the anti-tumor activity of CAR-T cells.

[0050] In some embodiments, the chimeric antigen receptor comprises a hinge region, a transmembrane region and an intracellular signaling region.

[0051] As used herein, a "region" or "domain" contained in the chimeric antigen receptor refers to a region of a polypeptide that can fold into a particular structure independently of other regions. These "regions" or "domains" can be murine or other animal sequences, preferably human sequences. Furthermore, "regions" or "domains" when not specifically distinguished or emphasized are understood to be the well-known sequences, either full length or partial active segments.

[0052] "Solid tumors" of the present application include tumors arising in any of the following: bone, bone joints, muscle, lung, trachea, heart, spleen, arteries, veins, capillaries, lymph nodes, lymph vessels, lymph fluid, mouth, pharynx, esophagus, stomach, duodenum, small intestine, colon, rectum, anus, appendix, liver, gall bladder, pancreas, parotid gland, sublingual gland, urinary kidney, ureter, urinary bladder, urethra, ovary, fallopian tube, uterus, vagina, vulva, scrotum, testicle, vas deferens, penis, eye, ear, nose, tongue, skin, brain, brain stem, medulla, spinal cord, cerebrospinal fluid, nerves, thyroid, parathyroid, adrenal gland, pituitary, pineal body, pancreatic islets, thymus, gonads, sublingual gland, and parotid gland.

[0053] Embodiments of the present application will be described in detail with reference to the following Examples. It is to be understood that the Examples are only by way of illustration and are not intended as limitation on the scope of the application. The experimental methods in the following Examples, where no specific conditions are indicated, are preferably performed according to the indications given in the present application, but can also be performed according to the protocols or general conditions known in the art, or according to the conditions suggested by the manufacturer of the reagents used, or according to the conditions known in the art.

[0054] In the following specific examples, the amount of the raw material components are referred to by measurement parameters, which can have slight deviations within the range of weighing accuracy, if not otherwise specified. The temperature and time parameters are allowed to have acceptable deviations caused by the accuracy of the instruments or the accuracy of the operation.

[0055] Examples

[0056] 1. CAF detection in the tumor microenvironment and its correlation with CAR-T treatment response

[0057] To better evaluate the correlation of different tumor microenvironment markers with the efficacy of EpCAM CAR-T treatment of gastric cancer, we divided patients into three groups according to the efficacy: disease progression (PD, N=l), stable disease (SD, N=5), and partial remission (PR, N=3). Multiple immunohistochemistry (mIHC) analysis was performed on tissue sections of the baseline state of the three groups of patients before treatment. By using the Leica Bond III / Bond MAX staining platform, we successfully performed a-SMA (CST, 19245) staining and labeled the nuclei with DAPI. After antigen retrieval, non-specific binding blocking, sequential incubation of primary and secondary antibodies, and labeling of fluorescent dyes, we obtained detailed immunophenotyping information. Finally, the positive areas of CD4 and a-SMA were identified and counted by HALO image analysis software.

[0058] In our study, we investigated the correlation between the tumor microenvironment (TME) and the response to IMC001 drug. The role of the tumor microenvironment, especially interstitial cells and cancer-associated fibroblasts (CAFs); their interaction with immune cells is a major driver of tumor progression and therapeutic response. Notably, a-SMA, a marker of activated CAFs. We performed a-SMA staining on the tissues of nine patients to describe the pattern of CAFs in the TME and classified them as stromal-poor (paucity of stroma) or stromal-rich (abundance of stroma). Stromal-rich was defined as the presence of more than three concentric layers of spindle a-SMA-positive staining fibroblasts around tumor cells, while Stromal-poor was defined as the presence of less than three concentric layers of spindle a-SMA-positive staining fibroblasts around tumor cells. All three patients who achieved partial remission (PR) exhibited Stromal-poor. In contrast, only 1 / 6 of patients with stable disease (SD) or progression (PD) exhibited Stromal-poor, while the majority (5 / 6) showed the Stromal-rich pattern. These findings suggest that the presence of Stromal-rich in the TME is associated with better efficacy of IMC001 treatment.

[0059] 2. TME marker staining and analysis of correlation with tumor response

[0060] To better evaluate the correlation between different tumor microenvironment markers and the efficacy of EpCAM CAR-T treatment of gastric cancer, we analyzed the staining of more markers, including CD4, FoxP3, CD206, CD8, and a-SMA. The number of patients included in this study was 11, according to the progress of the clinical experiment. The patients were divided into three groups according to the efficacy: progressive disease (PD, N=2), stable disease (SD, N=6), and partial remission (PR, N=3). Multiple immunohistochemistry (mIHC) analysis was performed on the tissue sections of the three groups of patients before treatment. Finally, the proportion of positive cells of each marker to the total number of cells was statistically analyzed by HALO image analysis software. The results showed that the proportion of Treg (CD4+FoxP3+) cells in CD4 cells was higher in PD patients than in PR and SD patients, indicating that it was related to poor efficacy. The proportion of CD206 and a-SMA positive cells in PR patients was significantly lower, indicating that fewer M2 macrophages and CAF cells were related to better patient response. The proportion of CD8 cells in PD patients was significantly lower than that in PR and SD patients, indicating that the proportion of CD8 cells was related to better patient response.

Claims

1. A method of predicting a treatment response of a solid tumor immune cell therapy, characterized in that, The method comprises the following steps: a) obtaining a tumor tissue section from a patient with a solid tumor; b) detecting the number, proportion or density of specific cells in the tumor tissue section, including but not limited to at least one of tumor-associated fibroblasts (CAFs), CD4-positive cells, CD8-positive cells, regulatory T cells, B cells, M1 macrophages, M2 macrophages, dendritic cells (DCs), natural killer cells (NKs), neutrophils, tumor cells and endothelial cells; or detecting the staining pattern of CAFs in the tumor tissue section. c) predicting the response of the solid tumor to the immune cell therapy based on the results of step b.

2. The method of claim 1, wherein, The proportion includes the proportion of the number of cells of the above-mentioned type to the total number of cells or the proportion of the number of subtypes of cells of the above-mentioned type to the number of cells of the type.

3. The method of claim 1, wherein, The method for detecting cells in the tumor tissue section comprises determining the expression level of a cell marker related to the cell type, including but not limited to at least one of α-SMA, FAP, CD3, CD4, CD8, CD56, CD16, FoxP3, Helios, CD68, CD206, CD163, CD11C, CD31, PD1, PD-L1, PanCK, CD45RO, CD20, CD66b.

4. The method of claim 1, wherein, The staining pattern of CAFs includes dividing them into interstitial-rich and interstitial-poor types by detecting α-SMA.

5. The method of claim 1, wherein, Detecting the number, proportion or density of CAFs and the other types of cells in the tumor tissue section.

6. The method of claim 5, wherein, The other types of cells include at least one of CD4+ cells, CD8+ cells, Foxp3+ cells, CD4+ Foxp3+ cells and CD206+ cells.

7. The method of claim 1, wherein, The solid tumor is selected from gastric cancer, pancreatic cancer or colorectal cancer.

8. The method of claim 1, wherein, The immune cell therapy includes genetically modified cell therapy and non-genetically modified cell therapy.

9. The method of claim 8, wherein, The genetically modified cell therapy includes immune cell therapy modified by chimeric antigen receptor (CAR), TCR, etc.

10. The method of claim 9, wherein, The CAR or TCR can target at least one of a variety of tumor-associated antigens or tumor-specific antigens, including but not limited to oncoviral antigens, cancer-testis antigens, carcinoembryonic antigens, tissue differentiation antigens, mutan antigens, neoantigens, lipophilin, CD19, BCMA, CD22, CD20, CD123, CD30, CD38, CD138, CD56, CD7, CLL-1, CD10, CD34, CS1, CD16, CD4, CD5, CDH6, CDH17, GUCY2C, IL-1-RAP, ITGB7, k-IgG, TAC1, TRBC1, MUC1, NKG2D, PD-L1, CD133, CD177, LeY, CD70, ROR1, AFP, AXL, CD80, CD86, DLL3, DR5, FAP, FRa, LMP1, MAGE-A1, MAGE-A4, MG7, MUC16, PMEL, ROR1, ROR2, VEGFR2, CD171, Claudin 18.2, Claudin 6, EphA2, ErbB, Fra, PSCA, cMet, IL13Ra2, EPCAM, EGFR, PSMA, EGFRvIII, GPC3, CEA, HER2, GD2, GM1, Mesothelin, Trop2, Trp-p8, NY-ESO-1, NECTIN4, and integrin avb3 (CD61).

11. The method of claim 9, wherein, The CAR structure comprises an extracellular binding region, a hinge region, a transmembrane region, and an intracellular signaling region.

12. The method of claim 11, wherein, The intracellular signaling region is selected from at least one of 2B4, DNAM1, CD28, 4-1BB, OX40, ICOS, CD27, MYD88, KIR2DS2, DAP10, DAP12, CD3zeta, TLRs, CD2, LFA-1, CD8a, CD40, CD80, and CD3e.

13. The method of claim 9, wherein, The immune cells are selected from at least one of T cells, NK cells, DC cells, macrophages, gdT cells (gdT), and double negative T cells (dnT).

14. The method of claim 8, wherein, The non-genetically modified immune cell therapy is selected from at least one of TIL cell therapy, NK cell therapy, DC cell therapy, macrophage therapy, gdT cell (gdT) therapy, and double negative T cell (dnT) therapy.

15. The method of claim 3, wherein, The detection method comprises immunohistochemical staining, flow cytometry analysis, immunofluorescence analysis, or gene expression profiling.

16. The method of claim 1, wherein, The prediction in step c comprises analyzing the detected tumor microenvironment parameters using a machine learning algorithm, a neural network, or a logistic regression model to predict the therapeutic response of the immune cells.

17. The method of any one of claims 1 to 16, wherein, Further comprising adjusting the dosage or treatment regimen of the immune cells based on the prediction result.

18. A kit for predicting the therapeutic response of a solid tumor cell, comprising reagents for detecting the number, proportion or density of at least one of CAFs, CD4-positive cells, CD8-positive cells, Ml macrophages, M2 macrophages, DCs, NKs, neutrophils, tumor cells and endothelial cells in a tumor tissue section or reagents for detecting the staining pattern of CAFs.

19. The kit of claim 18, comprising reagents for determining the expression level of at least one of a-SMA, FAP, CD3, CD4, CD8, CD56, CD16, FoxP3, Helios, CD68, CD206, CD163, CD11C, CD31, PD1, PD-L1, PanCK, CD45RO, CD20, CD66b.

20. The kit of claim 18, wherein The staining pattern of CAFs is classified into interstitial-rich and interstitial-poor types by detection of a-SMA.

21. The kit of claim 18, wherein The number, proportion or density of CAFs and the other types of cells in a tumor tissue section are detected.

22. The kit of claim 21, wherein The other types of cells include at least one of CD4+ cells, CD8+ cells, Foxp3+ cells, CD4+ Foxp3+ cells, CD206+.

23. The method of claim 1, wherein, The solid tumor is selected from gastric cancer, pancreatic cancer or colorectal cancer.

24. The kit of any one of claims 18-23, further comprising software for analyzing the detection results and / or a user manual.

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