Patient immune response monitoring method and system for cell drug clinical research

Abstract

The invention discloses a patient immune response monitoring method and system for cell drug clinical research, and the method comprises the following steps: 1, obtaining a peripheral blood sample of a patient, and preprocessing the peripheral blood sample to obtain patient sample components; multi-dimensional immune indexes and cell drug specific immune response are detected by comprehensively utilizing multiple technologies, immune cell subsets, cell factors, immune globulin, T cell response and the like are covered, and therefore a complex immune response system is comprehensively reflected; peripheral blood samples are collected at multiple time points according to a cell drug action mechanism and a half-life period, and the whole-course change of the immunoreaction is effectively tracked, so that the development trend of the immunoreaction is accurately grasped; the method has the advantages that the characteristics are visually presented by constructing the immune response atlas of the patient, evaluation and prediction are assisted, real-time monitoring reports and treatment suggestions are provided according to evaluation and prediction results, doctors are assisted to adjust treatment in time, and accordingly the safety and effectiveness of cell drug treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cell drug clinical research, and in particular to a method and system for monitoring patient immune responses used in cell drug clinical research. Background Art

[0002] With the rapid development of cell therapy technology, cell drugs have shown great potential in the treatment of various diseases. However, the mechanism of action of cell drugs in the body is complex, and they may trigger different degrees of immune responses in patients. These immune responses not only affect the treatment effect, but may also lead to adverse reactions. Therefore, in the clinical research of cell drugs, it is extremely important to accurately and comprehensively monitor the immune response of patients. However, existing immune response monitoring methods have many defects.

[0003] The existing immune response monitoring methods have a single detection indicator, focusing only on the number of a certain type of immune cells or the concentration of a certain type of immune cytokine, and cannot fully reflect the complex immune response system. They cannot dynamically monitor the continuous changes of the immune response during the treatment process, making it difficult to accurately grasp the development trend of the immune response, which reduces the safety and effectiveness of cell drug therapy. Therefore, a patient immune response monitoring method and system for clinical research on cell drugs are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for monitoring patient immune responses for clinical research on cell medicines, so as to solve one of the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, a technical solution adopted in this application is: a method for monitoring the immune response of patients for clinical research of cell drugs, comprising the following steps: Step 1: obtaining a peripheral blood sample from a patient, and pre-treating the peripheral blood sample to obtain the patient sample components; Step 2: Analyze and detect the components of patient samples based on multi-dimensional immune index detection technology to build an immune index detection database; Step 3: Analyze and detect the components of patient samples based on the cell-based drug-specific immune response detection technology, and build a cell-based drug-specific immune response detection database; Step 4: Based on the immune index detection database and the cell drug specific immune response detection database, a data integration model combining principal component analysis algorithm and hierarchical clustering analysis is used to construct a patient immune response map; Step 5: Generate evaluation and prediction results based on the patient's immune response profile by comparing it with the immune data of healthy people and analyzing the data before and after treatment; Step 6: Provide monitoring reports and treatment recommendations to clinicians based on the evaluation and prediction results.

[0006] As a further preferred embodiment of the present technical solution: the specific steps of constructing the immune index detection database are as follows: S201. Based on flow cytometry detection technology, the number and proportion of immune cell subsets in the patient sample components and the expression changes of their surface markers are detected using sorting fluid to obtain immune cell subset analysis data; S202. Detect the cytokine concentration in the patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid chip technology, and obtain cytokine detection data by analyzing the changes in cytokine concentration; S203, determining the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluating the changes in the patient's immune function, and obtaining immunoglobulin detection data; S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of T cell receptors, and obtaining T cell receptor analysis data; S205. Integrate the immune cell subset analysis data, cytokine detection data, immunoglobulin detection data, and T cell receptor analysis data to construct an immune index detection database.

[0007] As a further preferred embodiment of the present technical solution: the specific steps of constructing a cell drug specific immune response detection database are as follows: S301, based on a specific immunoassay method, detecting specific antibodies against the cellular drug in the patient sample components to obtain specific antibody detection data; S302. Based on the enzyme-linked immunosorbent assay combined with flow cytometry sorting technology, the specific T cell response to the cell drug in the patient sample components is detected to obtain the cell drug specific T cell response detection data; S303, integrating the specific antibody detection data and the cellular drug-specific T cell response detection data to construct a cellular drug-specific immune response detection database.

[0008] As a further preferred embodiment of the present technical solution: the method for constructing a patient immune response profile comprises the following steps: S401, cleaning the data in the immune index detection database and the cell drug specific immune response detection database to remove abnormal values ​​and missing values; S402, extracting main characteristic components using a principal component analysis algorithm, and calculating the principal components using a singular value decomposition algorithm; S403, clustering the extracted principal components using hierarchical clustering analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the distance between classes using the average linkage method, and obtaining the dimensionality reduction and clustering processing results through multiple cross-validations; S404, based on the dimensionality reduction and clustering processing results, a multidimensional space coordinate system is constructed using the principal components as coordinate axes, the positions of the sample points are determined in the multidimensional space coordinate system, and a framework of the patient's immune response map is obtained; S405, attaching the clinical characteristic information of the patient to the corresponding sample point, and optimizing and displaying the patient immune response map framework using graphic rendering technology to construct the patient immune response map.

[0009] As a further preferred embodiment of the present technical solution: the peripheral blood sample is collected and treated with an anticoagulant, the anticoagulant is ethylenediaminetetraacetate, and its concentration is 1.5-2.0 mg / ml, and the pretreatment of the peripheral blood sample includes a centrifugal separation operation.

[0010] As a further preferred embodiment of the present technical scheme: the immune cell subpopulations include CD4+T helper cells, CD8+T killer cells, CD19+B lymphocytes, NK natural killer cells, NKT cells and monocytes, the surface markers include CD28, CTLA-4, CD69, CD16, CD56; the cytokines include interleukin-2, interleukin-4, interleukin-6, interleukin-10, interleukin-17, tumor necrosis factor-α, interferon-γ; the immunoglobulins include IgG, IgA, IgM, and IgE.

[0011] As a further preferred embodiment of the present technical solution: the separation solution is a phosphate buffer solution, and 2% fetal bovine serum and 1 mm ethylenediaminetetraacetic acid are added.

[0012] As a further preferred embodiment of the present technical solution: when removing outliers and missing values, outliers are determined using the Grubbs criterion, and missing values ​​are filled using a multiple filling method.

[0013] As a further preferred embodiment of the present technical solution: the method also includes establishing a patient immune response database to store and manage the patient immune response maps, evaluation and prediction results, monitoring reports and treatment recommendations.

[0014] To solve the above technical problems, another technical solution adopted in this application is: a patient immune response monitoring system for clinical research of cell drugs, including: a sample collection module, a sample pretreatment module, a multi-dimensional immune index detection module, a cell drug specific immune response detection module, a data integration and analysis module, an evaluation and prediction module, a report and suggestion module and a database management module; The sample collection module is used to obtain peripheral blood samples of the patient at multiple predetermined time points before and after the administration of the cell drug, and treat the samples with an anticoagulant, wherein the multiple predetermined time points are determined according to the mechanism of action and half-life of the cell drug; The sample preprocessing module is used to preprocess the collected peripheral blood samples to obtain patient sample components; The multidimensional immune index detection module is used to analyze and detect the patient sample components based on the multidimensional immune index detection technology and build an immune index detection database; The cell drug specific immune response detection module is used to analyze and detect patient sample components based on the cell drug specific immune response detection technology and to build a cell drug specific immune response detection database; The data integration and analysis module is used to perform dimensionality reduction and clustering processing based on the immune index detection database and the cell drug specific immune response detection database using a data integration model that combines the principal component analysis algorithm with the hierarchical clustering analysis to construct a patient immune response map; The evaluation and prediction module is used to generate evaluation and prediction results based on the patient's immune response profile, by comparing it with the immune data of healthy people and analyzing the data before and after treatment; The reporting and suggestion module is used to provide real-time monitoring reports and treatment suggestions to clinicians based on the evaluation and prediction results; The database management module is used to establish a patient immune response database to store and manage patient immune response maps, evaluation and prediction results, monitoring reports, and treatment recommendations.

[0015] Advantages of the present invention: 1. The present invention comprehensively utilizes multiple technologies to detect multi-dimensional immune indicators and cell-drug-specific immune responses, covering immune cell subpopulations, cytokines, immunoglobulins, T cell receptors, drug-specific antibodies, and T cell responses, thereby comprehensively reflecting the complex immune response system. At the same time, the peripheral blood samples are collected at multiple time points according to the mechanism of action and half-life of cell-drugs, effectively tracking the changes in the immune response throughout the process, thereby accurately grasping the development trend of the immune response; 2. The present invention constructs a patient immune response map to intuitively present characteristics, assist in evaluation and prediction, and provide real-time monitoring reports and treatment recommendations based on the evaluation and prediction results, assisting doctors to adjust treatment in a timely manner, thereby improving the safety and effectiveness of cellular drug therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The figure is a schematic diagram of the process of the method for monitoring the immune response of patients used in clinical research of cell medicines of the present invention; Figure 2 A schematic diagram of the process of constructing an immune index detection database according to the present invention; Figure 3 A schematic diagram of the process of constructing a cell drug specific immune response detection database according to the present invention; Figure 4 A schematic diagram of the process of constructing a patient immune response profile according to the present invention; Figure 5 This is a schematic diagram of the functional modules of the patient immune response monitoring system used in the clinical research of cell medicines of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example Figure 1 is a flow chart of a method for monitoring patient immune responses in a clinical study of cell-based drugs according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 1 The process sequence shown is limited. Figure 1-Figure 4 Shown: A method for monitoring patient immune responses for clinical studies of cell-based drugs, comprising the following steps: Step 1: obtaining a peripheral blood sample from a patient, and pre-treating the peripheral blood sample to obtain the patient sample components; Specifically, first, peripheral blood samples are collected from patients by venous blood sampling before and at multiple predetermined time points after administration of the cell drug (such as 24 hours, 72 hours, one week, one month, etc. after administration, and the specific time is determined according to the characteristics of the drug and the research design), ensuring that the collection process follows the principles of sterility and non-contamination, and accurately records the sampling time; Then, an appropriate amount of anticoagulant is added to the collected peripheral blood sample to prevent blood coagulation and ensure the integrity and usability of the sample; Finally, the anticoagulated peripheral blood sample is centrifuged to separate components such as plasma, white blood cells, and red blood cells. This step is a key step in obtaining the patient sample components. If specific types of cells (such as T cells, B cells, etc.) need to be further analyzed, specific cell separation techniques (such as magnetic bead separation, flow cytometry, etc.) can be used to separate and purify the cells; plasma contains a variety of biomarkers, such as cytokines, antibodies, etc., which can be used to evaluate the efficacy and safety of cellular drugs; white blood cells and other cellular components obtained by cell separation can be used to analyze the number, activity, differentiation state, and gene expression of cells, so as to understand the impact of cellular drugs on the immune system.

[0020] Step 2: Analyze and detect the components of patient samples based on multi-dimensional immune index detection technology to build an immune index detection database: Step 3: Analyze and detect the components of patient samples based on the cell-based drug-specific immune response detection technology to build a cell-based drug-specific immune response detection database: Step 4: Based on the immune index detection database and the cell drug specific immune response detection database, a data integration model combining principal component analysis algorithm and hierarchical clustering analysis is used to construct a patient immune response map; Step 5: Generate evaluation and prediction results based on the patient's immune response profile by comparing it with the immune data of healthy people and analyzing the data before and after treatment; Specifically: First, collect the immune data of healthy people as a reference standard. These data can come from large-scale immune detection projects for healthy people or related medical research databases. Compare the data in the patient's immune response map with the immune data of healthy people, and analyze the differences between patients and healthy people in terms of immune cell subpopulations, cytokine concentrations, immunoglobulin content, T cell receptor diversity, and cell drug-specific immune responses; for example, compare whether the proportion of the patient's T cell subpopulation deviates from the normal range of healthy people, whether the secretion concentration of cytokines is abnormally increased or decreased, etc.; Then, the patient's immune response data before and after cell drug treatment are analyzed to observe the dynamic changes of the patient's immune response during the treatment. For example, whether the activation state of immune cells changes after treatment, whether a cytokine storm occurs, whether specific antibodies and specific T cell responses are enhanced or weakened, etc. Through these analyses, evaluation and prediction results are generated to evaluate the patient's immune response type to cell drugs (such as immune activation, immunosuppression, immune tolerance, etc.), the intensity and duration of the immune response, and predict the patient's possible immune-related adverse reactions (such as allergic reactions, autoimmune diseases, etc.) or treatment effects (such as disease remission, recurrence, etc.) during subsequent treatment.

[0021] Step 6: Provide monitoring reports and treatment recommendations to clinicians based on the evaluation and prediction results; The report includes the patient's basic information, cellular drug treatment plan, detailed analysis results of immune response monitoring data (including specific data and charts of comparison with healthy people and changes before and after treatment), summary and explanation of evaluation and prediction results, etc. For example, the report can list the trend of changes in the concentration of specific cytokines of patients after treatment, and explain the possible impact of such changes on the treatment effect and patient health; Based on the information in the monitoring report, treatment recommendations are provided to clinicians. If the patient has an excessive immune activation response, it may be recommended to adjust the cellular drug dose, add immunosuppressants, or take other symptomatic treatment measures. If the patient's immune response is weak, it may be recommended to optimize the treatment plan, such as increasing the cellular drug dose, combining other immunomodulators, etc. If it is predicted that the patient may have specific adverse reactions, it is recommended to take preventive measures in advance, such as the preventive use of anti-allergic drugs, etc. These treatment recommendations are designed to help clinicians personalize treatment plans based on the patient's immune response and improve the safety and effectiveness of cellular drug therapy.

[0022] In this embodiment, the specific steps of constructing an immune index detection database are as follows: S201. Based on flow cytometry detection technology, the number and proportion of immune cell subsets in the patient sample components and the expression changes of their surface markers are detected using sorting fluid to obtain immune cell subset analysis data; Prepare the flow cytometer and ensure that the instrument is calibrated properly and the reagents are sufficient; take an appropriate amount of processed patient sample components, add specific fluorescent-labeled antibodies targeting different immune cell subsets (such as CD4+T helper cells, CD8+T killer cells, CD19+B lymphocytes, NK natural killer cells, NKT cells, and monocytes, etc.), and incubate at appropriate temperature and time conditions to allow the antibodies to fully bind to the corresponding immune cell surface antigens; The incubated samples were tested on the machine, and the signal intensity of different fluorescence channels was detected by flow cytometry to distinguish different immune cell subpopulations and obtain their quantity and proportion information. At the same time, the fluorescence intensity changes of related surface markers (such as CD28, CTLA-4, CD69, CD16, CD56, etc.) were detected to determine the functional status and activation degree of immune cells, and the immune cell subpopulation analysis data were recorded.

[0023] S202. Detect the cytokine concentration in the patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid chip technology, and obtain cytokine detection data by analyzing the changes in cytokine concentration; Specific embodiments of enzyme-linked immunosorbent assay (ELISA): Select a suitable ELISA kit, add the coated antibody (targeting a specific cytokine) to the ELISA plate, and incubate at a suitable temperature for a certain period of time to fix the antibody to the bottom of the plate; After washing the ELISA plate, add the patient sample components and incubate to allow the cytokines in the sample to bind to the coated antibodies; Wash again, add enzyme-labeled secondary antibody (specifically binds to cytokines and is enzyme-labeled), incubate and wash, and add substrate solution; The enzyme catalyzes the substrate reaction to produce a color change, and the absorbance value is detected by an enzyme reader. The cytokine concentration in the sample is calculated according to the standard curve to obtain the cytokine detection data. For the cytokine concentration detection, the above steps can be repeated in sequence or a multi-channel ELISA kit can be used.

[0024] Specific implementation of Luminex liquid phase chip technology: Capture antibodies for different cytokines are coupled to microspheres, each with a specific fluorescent code to distinguish between different antibodies; After mixing the microspheres, patient sample components are added and incubated in liquid phase to allow cytokines to bind to the corresponding capture antibodies; After washing, the detection antibody (which binds to another antigenic site of the cytokine and is fluorescently labeled) is added, and the cells are incubated and washed again; The type of cytokine detected was determined by detecting the fluorescence coding of the microspheres using the Luminex instrument, and the fluorescence intensity of the antibody was detected at the same time. The cytokine concentration was calculated based on the standard curve to obtain the cytokine detection data.

[0025] S203, determining the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluating the changes in the patient's immune function, and obtaining immunoglobulin detection data; The patient sample components are added to a buffer containing specific antibodies (for immunoglobulins such as IgG, IgA, IgM, and IgE). Under specific temperature and stirring conditions, the immunoglobulins bind to the antibodies to form immune complexes; As the immune complex forms, the turbidity of the solution changes, and the change in absorbance of the solution at a specific wavelength is detected by a turbidimeter; According to the pre-established standard curve (made from immunoglobulin standards of known concentration), the measured absorbance value is converted into the immunoglobulin content to obtain the immunoglobulin detection data.

[0026] S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of T cell receptors, and obtaining T cell receptor analysis data; Extract total RNA or genomic DNA from patient sample components, amplify T cell receptor genes using specific primers, and construct a sequencing library; After the library is quality tested and quantified, it is loaded onto a high-throughput sequencing platform (such as an Illumina sequencer) for sequencing; After sequencing is completed, bioinformatics analysis is performed on the large amount of sequencing data obtained, including sequence alignment, splicing, T cell receptor gene family classification, clonal type identification and other operations, and the diversity indicators of T cell receptors (such as Shannon diversity index, clonality index, etc.) are calculated to obtain T cell receptor analysis data.

[0027] S205, integrating the immune cell subset analysis data, cytokine detection data, immunoglobulin detection data, and T cell receptor analysis data to construct an immune index detection database; Design the database structure and create tables to store different types of immune index data, including the source of the data (patient number, sampling time, etc.), specific test index values, and related notes (such as test methods, instrument parameters, etc.); Enter the various data obtained in steps S201-S204 into the corresponding tables in sequence according to the format designed by the database to ensure the accuracy and completeness of the data; Conduct preliminary verification and review of the entered data to check whether there are any missing data, abnormal values, etc. If there are any problems, review and correct them in a timely manner; Establish data indexes to facilitate subsequent data query, retrieval and analysis operations, and complete the construction of the immune index detection database.

[0028] In this embodiment, the specific steps of constructing a cell drug specific immune response detection database are as follows: S301, based on a specific immunoassay method, detecting specific antibodies against the cellular drug in the patient sample components to obtain specific antibody detection data; Select an appropriate specific immunoassay method. Here we take the commonly used enzyme-linked immunosorbent assay (ELISA) as an example. First, prepare the ELISA kit. The coating antigen in the kit should be a conjugate of a cell drug-related antigen and a carrier protein, and the conjugation ratio must meet specific requirements (for example, 1:5-1:10); Prepare other necessary reagents, such as enzyme-labeled secondary antibodies, substrate solutions, etc., and ensure that laboratory instruments and equipment (such as microplate readers, pipettes, etc.) are working properly and are calibrated; Take an appropriate amount of pre-treated patient sample components and add them to the wells of the ELISA plate coated with the cell drug-related antigen-carrier protein conjugate. Usually incubate at a suitable temperature (such as 37°C) and time (such as 1-2 hours) to allow the specific antibodies in the sample to fully bind to the coated antigen; After the incubation is completed, the ELISA plate is washed thoroughly with washing solution to remove unbound substances, and then the enzyme-labeled secondary antibody is added. The enzyme-labeled secondary antibody should be able to specifically recognize and bind to the specific antibody that has been bound to the coated antigen, and incubated again under appropriate conditions (such as 37°C, 30-60 minutes); After the incubation, the ELISA plate is washed again to remove the unbound enzyme-labeled secondary antibody, and then the substrate solution is added. Under the catalytic action of the enzyme carried by the enzyme-labeled secondary antibody, the substrate will undergo a color reaction. The absorbance value is detected at a specific wavelength by an ELISA instrument. According to the pre-established standard curve (made from specific antibody standards of known concentrations), the absorbance value is converted into the concentration of the specific antibody, thereby obtaining the specific antibody detection data.

[0029] S302. Based on the enzyme-linked immunosorbent assay combined with flow cytometry sorting technology, the specific T cell response to the cell drug in the patient sample components is detected to obtain the cell drug specific T cell response detection data; First, the capture antibody used to capture the cell drug-specific T cell secretion products (such as cytokines such as interferon-γ) is pre-labeled with biotin, and the molar ratio of biotin to antibody needs to be controlled within a specific range (e.g., 1:3-1:6); Coat the labeled capture antibody to the bottom of the ELISPOT plate wells and incubate under appropriate conditions (e.g., overnight at 4°C) to fix it; Prepare other required reagents, such as detection antibodies, substrate solutions, etc., and ensure that instruments and equipment such as ELISPOT readers are functioning properly; Take an appropriate amount of pre-treated patient sample components and add them to the ELISPOT plate wells coated with capture antibodies. At the same time, add stimulants (such as the cell drug itself or its related antigen fragments, etc.) to activate specific T cells in the sample to secrete related cytokines, and incubate at an appropriate temperature (such as 37°C) and time (such as 16-24 hours); After the incubation is completed, the ELISPOT plate is washed thoroughly with washing solution to remove unbound substances, and then the detection antibody is added. The detection antibody should be able to specifically recognize and bind to the cytokines secreted by specific T cells and captured by the capture antibody, and incubated again under appropriate conditions (such as 37°C, 1-2 hours); After the incubation, the ELISPOT plate is washed again to remove the unbound detection antibody, and then the substrate solution is added. Under the catalytic action of the enzyme carried by the detection antibody, the substrate will undergo a color reaction to form visible spots. The spots are counted by the ELISPOT reader. Each spot represents a specific T cell that secretes a specific cytokine, thereby obtaining the cell drug-specific T cell response detection data. After completing the ELISPOT test, it is necessary to combine flow cytometry sorting technology to further analyze the characteristics of specific T cells.

[0030] Flow cytometry sorting technology operation: Prepare the flow cell sorter, make sure the instrument is working properly and the relevant reagents (such as sorting solution, which is phosphate buffered saline with 2% fetal bovine serum and 1mM ethylenediaminetetraacetic acid) are ready; Take an appropriate amount of pre-treated patient sample components, add fluorescently labeled antibodies targeting specific T cell surface markers, and incubate under appropriate temperature and time conditions to allow the antibodies to fully bind to specific T cell surface antigens; The incubated samples are put on the machine for flow cytometry sorting. By setting appropriate sorting parameters (such as fluorescence intensity, cell size, etc.), specific T cells are separated from the samples, and their cell surface marker expression, cell activity and other characteristics are further analyzed to improve the cell drug-specific T cell response detection data.

[0031] S303, integrating the specific antibody detection data and the cellular drug-specific T cell response detection data to construct a cellular drug-specific immune response detection database; According to the characteristics of the data to be stored, a reasonable database structure is designed, and tables are created to store specific antibody detection data and cell drug-specific T cell response detection data, including the source of the data (patient number, sampling time, etc.), specific detection index values, and related remarks (such as detection methods, instrument parameters, etc.); Enter the specific antibody detection data obtained in step S301 and the cell drug specific T cell response detection data obtained in step S302 into the corresponding tables in sequence according to the format designed by the database to ensure the accuracy and completeness of the data; Conduct preliminary verification and review of the entered data to check whether there are any missing data, abnormal values, etc. If there are any problems, review and correct them in a timely manner; Establishing a data index facilitates subsequent data query, retrieval and analysis operations, thereby completing the construction of a cell drug-specific immune response detection database.

[0032] In this embodiment, specifically, the method for constructing a patient immune response profile comprises the following steps: S401, cleaning the data in the immune index detection database and the cell drug specific immune response detection database to remove abnormal values ​​and missing values; First, the required data are extracted from the established immune index detection database and cell drug specific immune response detection database. These data cover various information on the patient's immune indexes and cell drug specific immune responses obtained through various detection technologies, and are the basis for constructing the immune response map. Use appropriate outlier determination methods, such as the Grubbs criterion. For each data indicator, calculate whether each data point is an outlier based on its data distribution characteristics and pre-set Grubbs criterion parameters (usually determined based on statistical principles); once a data point is determined to be an outlier, it will be deleted from the data set. Outliers may be caused by errors in the detection process, improper sample collection or processing, etc. Keeping them may interfere with subsequent data analysis and map construction; For missing values ​​in the data set, appropriate filling methods are used, such as the multiple filling method. The multiple filling method generates reasonable filling values ​​through multiple simulations based on the information of other related variables in the data set and the overall distribution of the data to replace the missing data points. This can ensure the integrity of the data, so that subsequent data analysis can be carried out on the basis of relatively complete data, thereby improving the accuracy and reliability of the analysis results; S402, extracting main characteristic components using a principal component analysis algorithm, and calculating the principal components using a singular value decomposition algorithm; Before principal component analysis, it is usually necessary to standardize the cleaned data set. This is because different immune indicators and cell drug-specific immune response indicators may have different dimensions and value ranges. Standardization can convert them into values ​​with the same mean (usually 0) and standard deviation (usually 1) to avoid the impact of dimension differences on the results of principal component analysis. Common standardization methods include Z-score standardization, which performs corresponding mathematical transformations on each data point to make it meet the standardization requirements. Principal component analysis is a dimensionality reduction technique that aims to find a set of new variables (i.e., principal components) that can retain as much variance information of the original data as possible. The standardized data set is input into the principal component analysis algorithm, and the algorithm determines the principal components based on the eigenvalues ​​and eigenvectors of the data's covariance matrix (or correlation matrix). Specifically, by solving the characteristic equation of the covariance matrix, a series of eigenvalues ​​and corresponding eigenvectors are obtained. The eigenvalue represents the variance explained by each principal component, and the eigenvector determines the direction of each principal component in the original data space. The singular value decomposition algorithm is a matrix decomposition technology closely related to principal component analysis. It can be used to more efficiently solve some key calculations in principal component analysis, such as the eigenvalues ​​and eigenvectors of the covariance matrix. In actual operation, the singular value decomposition algorithm is used to decompose the matrix corresponding to the data set to obtain its singular values, left singular vectors and right singular vectors. These decomposition results can help accurately determine the number of principal components and their specific composition, thereby realizing the solution calculation of the principal components. Singular value decomposition algorithm calculation steps: Let matrix for The matrix , first calculate ( The transpose of ).

[0033] Solution The eigenvalue of and the corresponding eigenvector .

[0034] The eigenvalue Sort from large to small, and the corresponding eigenvectors are also sorted accordingly.

[0035] Calculate singular values .

[0036] calculate ,get The matrix (The column vector is ).

[0037] Build The diagonal matrix , the elements on the diagonal are singular values .

[0038] make , then the matrix The singular value decomposition of .

[0039] In this way, the main characteristic components can be extracted. These characteristic components are the most important information parts retained after the original data is processed by dimensionality reduction. They will play a key role in subsequent clustering analysis and graph construction.

[0040] S403, clustering the extracted principal components using hierarchical clustering analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the distance between classes using the average linkage method, and obtaining the dimensionality reduction and clustering processing results through multiple cross-validations; Hierarchical cluster analysis is a method that gradually merges or splits data points to form a cluster structure based on the similarity between clusters. It does not require the number of clusters to be specified in advance, but gradually builds a cluster hierarchy by continuously calculating the similarity between samples and the distance between classes. In this step, Euclidean distance is used to measure the similarity between samples represented by the extracted principal components. Euclidean distance is a common distance measurement method. For two sample points (in the space composed of principal components), the calculation formula of the Euclidean distance is: ; in, and Respectively represent and The samples in the The values ​​on the principal components, represents the number of principal components; By calculating the Euclidean distance, we can intuitively understand the distance between different samples in the principal component space. The closer the distance, the higher the similarity between the samples, and vice versa. When merging two clusters, the distance between them needs to be calculated. The average linkage method is used here. The calculation method of the average linkage method is as follows: first calculate the Euclidean distance between all sample pairs in the two clusters, and then take the average of these distances as the distance between the two clusters; Average linkage method formula (used to calculate inter-class distance): ; in, and There are two classes. and are their sample sizes, It is a sample and The distance between (Euclidean distance is used here).

[0041] This method is relatively robust and can better reflect the real distance relationship between clusters, which helps to form a reasonable clustering structure; In order to improve the reliability and accuracy of clustering results, multiple cross-validation methods are used to divide the data set into several parts (for example, the common K-fold cross-validation divides the data set into K parts), and each time one of them is selected as the validation set, and the rest are used as the training set; Multiple cross-validation is a technique used to evaluate model performance and improve model stability. The core idea is to divide the data set into multiple different parts, train and evaluate the model on each part, and then combine multiple results to obtain more reliable model performance evaluation and more stable model parameters. The calculation steps are as follows: The dataset Randomly divide into subsets of similar size (usually Take 5 or 10), recorded as .

[0042] For Cross validation : Will As the validation set, the remaining subset As a training set; In the training set Perform hierarchical clustering analysis on the clusters to obtain clustering results (such as cluster labels ).

[0043] In the validation set Evaluate the quality of clustering results and calculate evaluation indicators (such as silhouette coefficient ).

[0044] Repeat steps to proceed Cross-validation, we get Evaluation index value ; Calculate the average evaluation metric value: ; As the final evaluation result; Select the optimal model parameters or clustering results based on the average evaluation index value; Silhouette coefficient calculation formula (used to evaluate the quality of clustering results): For each sample ,set up For sample The average distance to other samples of the same category, For sample The minimum value of the average distance to other categories of samples, then the sample The silhouette coefficient is: ; Silhouette coefficient for the entire dataset: ; in, is the total number of samples; The singular value decomposition algorithm is used to achieve dimensionality reduction calculations in principal component analysis, and hierarchical clustering analysis is used to cluster data. In the clustering process, the Euclidean distance is used to measure the similarity between samples, and the average linkage method is used to calculate the distance between classes. Multiple cross-validations are used to improve the reliability of the results. These methods together construct the patient immune response map, providing strong support for comprehensive and accurate monitoring of the patient's immune response. After performing hierarchical clustering analysis on the training set and obtaining the clustering results, the quality of the clustering results is evaluated on the validation set. For example, some evaluation indicators (such as silhouette coefficient) are calculated to judge the rationality and accuracy of the clustering. By repeating this process multiple times, changing the division of the validation set and the training set each time, and combining the results of multiple times, we can obtain more stable and accurate dimensionality reduction and clustering processing results.

[0045] S404, based on the dimensionality reduction and clustering processing results, a multidimensional space coordinate system is constructed using the principal components as coordinate axes, the positions of the sample points are determined in the multidimensional space coordinate system, and a framework of the patient's immune response map is obtained; According to the results of dimensionality reduction and clustering, the number of principal components to be retained is determined, and these principal components are used as coordinate axes to construct a multidimensional space coordinate system. For example, if three principal components are retained after the previous processing, a three-dimensional space coordinate system is constructed, and each principal component corresponds to a coordinate axis, which are marked as PC1, PC2, PC3, etc.; For each patient sample, it has a corresponding score on each principal component (these scores are calculated during the principal component analysis process). These scores are used as coordinate values ​​to determine the position of the sample point in the constructed multidimensional space coordinate system; For example, if a patient sample scores 0.5 on PC1, 0.3 on PC2, and 0.2 on PC3, then the coordinates of the patient sample in this three-dimensional space coordinate system are (0.5, 0.3, 0.2). In this way, all patient samples are located in the multidimensional space coordinate system to obtain the immune response map framework.

[0046] S405, attaching the clinical characteristic information of the patient to the corresponding sample point, and optimizing and displaying the patient immune response map framework using graphic rendering technology to construct the patient immune response map; Collect clinical characteristic information of patients, which may include the patient's age, gender, disease type, treatment stage, medication dosage, etc., and attach these clinical characteristic information to the corresponding sample points in the form of labels or annotations; for example, in a multidimensional space coordinate system, when the mouse hovers over a sample point, a window can pop up to display the relevant clinical characteristic information of the patient, so that the atlas can not only display the relevant information of the immune response, but also be combined with the patient's clinical condition; By using graphics rendering technology, such as professional drawing software or programming libraries (such as matplotlib in Python), the immune response map can be optimized for display; the color, line thickness, font size and other display parameters of the map can be set as needed to make the map clearer, more beautiful and easier to understand. In this way, the immune response map is transformed from a simple coordinate framework into a visualization tool that can intuitively display the relationship between the patient's immune response and clinical characteristics, and a complete patient immune response map is constructed.

[0047] In this embodiment, specifically: multiple predetermined time points are determined according to the mechanism of action and half-life of the cell drug; in clinical research on cell drugs, accurately grasping the time point of collecting peripheral blood samples is crucial for comprehensively monitoring the patient's immune response. The multiple predetermined time points here are carefully determined based on the mechanism of action and half-life of the cell drug; after the cell drug enters the human body, its action process in the body is a dynamic process, and different stages may trigger different degrees and types of immune responses. By understanding the mechanism of action of the cell drug, such as how it interacts with human cells and which physiological processes it affects, it is possible to preliminarily estimate at which time points more critical immune response changes may occur; and the half-life is an important indicator to measure the metabolic rate of the cell drug in the body, which indicates that the drug concentration in the body is reduced by half. The time required to determine the sampling time point based on the half-life can ensure that samples can be collected at all stages when the drug concentration changes significantly, thereby more comprehensively capturing the entire process of the drug's impact on the immune system; for example, if the half-life of a cell drug is 24 hours, then according to the setting, a sample may be collected once before administration as a basic control, and peripheral blood samples may be collected at 6 hours (1 / 4 of the half-life), 12 hours (1 / 2 of the half-life), 24 hours (1 times the half-life), 48 hours (2 times the half-life), 72 hours (3 times the half-life) and other time points after administration. Of course, the specific time point settings will vary according to the characteristics of different cell drugs, but the overall principle is to make scientific plans around the drug's mechanism of action and half-life so as to systematically monitor changes in the patient's immune response over time.

[0048] The peripheral blood samples are collected and treated with an anticoagulant, which is ethylenediaminetetraacetate, with a concentration of 1.5-2.0 mg / ml. When collecting peripheral blood samples, in order to prevent blood coagulation and ensure the quality of the samples for subsequent testing and analysis, anticoagulants are required for treatment. The anticoagulant selected here is ethylenediaminetetraacetate (EDTA), and its concentration is set in the range of 1.5-2.0 mg / ml. EDTA combines with calcium ions in the blood to form a stable complex, thereby preventing the blood coagulation process from relying on calcium ions. The activation of coagulation factors allows the blood to remain in a liquid state; the appropriate concentration range is crucial. A concentration that is too low may not be able to effectively anticoagulate, causing the blood to coagulate during the collection process or before subsequent processing, affecting the integrity and detectability of the sample; and a concentration that is too high may potentially interfere with some test indicators, or cause unnecessary effects on components such as blood cells. Therefore, controlling the concentration of EDTA in the range of 1.5-2.0 mg / ml can not only ensure a good anticoagulation effect, but also minimize the adverse effects on subsequent detection and analysis.

[0049] Pretreatment of peripheral blood samples includes centrifugation. After peripheral blood samples are collected, they need to be pretreated, and one of the key pretreatment operations is centrifugation. Centrifugation uses the centrifugal force generated by the centrifuge to separate different components in the blood sample according to density differences, thereby achieving preliminary separation of sample components. In this process, the collected peripheral blood sample is placed in a suitable centrifuge tube, placed in a centrifuge, and a specific centrifugation speed and time are set. Specifically, the centrifugation speed is generally set within a range that can effectively achieve stratification but does not cause excessive damage to blood cells, etc., for example, it may be set to 1500-2000rpm ( The centrifugation time is usually determined by factors such as sample size, centrifuge performance and the desired separation effect, and is generally 10-15 minutes. Through such centrifugation, the blood sample will be roughly divided into an upper plasma layer, a middle white blood cell layer (including lymphocytes, monocytes and other immune cells) and a lower red blood cell layer. These separated components can be further extracted and processed according to subsequent specific testing needs, providing suitable sample materials for analysis and testing based on multi-dimensional immune index detection technology and cell drug-specific immune response detection technology, thereby more accurately understanding the patient's immune response status under the action of cell drugs.

[0050] In this embodiment, specifically: immune cell subsets include CD4+T helper cells, CD8+T killer cells, CD19+B lymphocytes, NK natural killer cells, NKT cells and monocytes; CD4+T helper cells: They play a key auxiliary role in the immune system. They can recognize antigenic peptides presented by major histocompatibility complex (MHC) class II molecules on the surface of antigen presenting cells (APCs), and regulate the functions of other immune cells by secreting cytokines, such as activating B cells to produce antibodies and enhancing the killing activity of CD8+T killer cells, etc. They are crucial to the initiation and regulation of the body's immune response.

[0051] CD8+T killer cells: The main function is to identify and kill target cells such as virus-infected cells or tumor cells. They can recognize antigenic peptides presented by MHC class I molecules on the surface of target cells. Once the recognition is successful, they will release cytotoxic substances such as perforin and granzyme, causing the target cells to lyse and die, thus playing an important role in clearing pathogen-infected cells and tumor immune surveillance.

[0052] CD19+B lymphocytes: They are the main cell type that produces specific antibodies. When stimulated by antigens, B lymphocytes undergo activation, proliferation, and differentiation, and eventually differentiate into plasma cells, secreting a large number of specific antibodies. These antibodies can bind to the corresponding antigens and eliminate pathogens in a variety of ways (such as neutralization, opsonization, etc.), and are the core executors of humoral immune responses.

[0053] NK natural killer cells: part of the innate immune system, they can quickly identify and kill certain abnormal cells such as tumor cells and virus-infected cells without prior sensitization. They initiate the killing mechanism by recognizing the lack of normal MHC class I molecule expression or abnormal expression of MHC class I molecule-related proteins on the surface of target cells, and mainly exert their effects by releasing perforins, granzymes, and secreting cytokines (such as interferon-γ), playing an important role in the body's early immune defense and tumor immune surveillance.

[0054] NKT cells: They are a special lymphocyte subset that has some characteristics of both T cells and NK cells. They can recognize lipid antigens presented by CD1d molecules and can rapidly secrete a large number of cytokines (such as interleukin-4, interferon-γ, etc.) after activation. They can regulate the natural immune response and affect the initiation and development of the adaptive immune response, playing a unique role in immune regulation, anti-infection and anti-tumor.

[0055] Monocytes: They are large white blood cells in the blood. After circulating in the blood for a period of time, they can migrate to tissues and differentiate into antigen-presenting cells such as macrophages and dendritic cells. Monocytes have phagocytic functions and can ingest and process foreign antigens such as pathogens and present them to T cells, thereby initiating adaptive immune responses. They can also participate in inflammatory responses and immune regulation processes by secreting cytokines.

[0056] Surface markers include CD28, CTLA-4, CD69, CD16, and CD56; CD28: is an important co-stimulatory molecule, mainly expressed on the surface of T cells (including CD4+T helper cells and CD8+T killer cells, etc.). It binds to B7 molecules (such as CD80 and CD86) on the surface of antigen-presenting cells, providing important co-stimulatory signals for the activation of T cells, promoting the proliferation, differentiation and function of T cells. If there is a lack of CD28 co-stimulatory signals, T cells may enter an unresponsive or apoptotic state.

[0057] CTLA-4: Also expressed on the surface of T cells, it has a similar structure to CD28, but its functions are different. CTLA-4 is mainly upregulated after T cell activation. Its affinity with B7 molecules is higher than that of CD28. Its function is to negatively regulate the activation of T cells. By competing with CD28 for binding to B7 molecules, it inhibits the overactivation of T cells, maintains the balance of the body's immune response, and prevents excessive immune response from causing damage to the body's own tissues.

[0058] CD69: It is an early activation marker that can be rapidly expressed on the surface of a variety of immune cells (such as T cells, B cells, NK cells, etc.). Its expression can be detected in a short time after the immune cells are stimulated (such as antigen stimulation, cytokine stimulation, etc.). It can be used as an important indicator to determine whether the immune cells are in an activated state. For example, in the early stage of T cell activation, the expression of CD69 will increase significantly, and its expression may change as the activation process progresses.

[0059] CD16: Mainly expressed on the surface of NK cells and some macrophages, it is an Fc receptor that can recognize and bind to the Fc segment of antibodies. Through this binding, NK cells can kill target cells with the help of antibody-dependent cell-mediated cytotoxicity (ADCC), that is, when target cells are coated with specific antibodies, NK cells can bind to the Fc segment of antibodies through CD16, and then release perforin, granzyme and other substances to kill target cells, thereby enhancing the body's ability to clear pathogens and tumor cells.

[0060] CD56: It is one of the important markers of NK cells. Its expression on the surface of NK cells is relatively stable and can be used to identify NK cells. At the same time, the expression concentration of CD56 in NK cells of different tissues may vary, and its expression may also be related to the functional state of NK cells. For example, in some cases, the expression intensity of CD56 may affect the killing activity and cytokine secretion ability of NK cells.

[0061] Cytokines include interleukin-2, interleukin-4, interleukin-6, interleukin-10, interleukin-17, tumor necrosis factor-α, and interferon-γ; Interleukin-2 (IL-2): Mainly secreted by activated T cells (especially CD4+T helper cells), it is an important cytokine with multiple functions in the immune system. It can promote the proliferation and differentiation of T cells, maintain the survival of T cells, stimulate the proliferation of NK cells and enhance their killing activity. It is one of the key factors in regulating the intensity and duration of the body's immune response. It also has important research value in the fields of autoimmune diseases and tumor immunotherapy.

[0062] Interleukin-4 (IL-4): Mainly secreted by activated T cells (such as CD4+T helper cells, NKT cells, etc.), it plays an important role in humoral immune response. It can promote the proliferation, differentiation and antibody class conversion of B cells, prompting B cells to produce more IgE antibodies, and also regulate the functions of other immune cells (such as macrophages, NK cells, etc.), and plays an important role in immune responses such as allergic diseases and parasitic infections.

[0063] Interleukin-6 (IL-6): Secreted by a variety of cell types (such as monocytes, macrophages, T cells, B cells, etc.), it is a multifunctional cytokine. It plays an important role in inflammatory response, immune response regulation, and tissue repair. In the early stages of inflammatory response, IL-6 can increase rapidly, stimulating the liver to synthesize acute phase proteins, while also promoting the proliferation and differentiation of B cells and affecting the function of T cells. In some autoimmune diseases (such as rheumatoid arthritis) and infectious diseases, the concentration of IL-6 often changes abnormally.

[0064] Interleukin-10 (IL-10): It is mainly secreted by regulatory T cells, B cells, macrophages and other cells, and is an important immunoregulatory cytokine. Its main function is to inhibit the activation of immune cells and inflammatory response. By inhibiting the functions of macrophages and T cells, it reduces the secretion of inflammatory factors, thereby maintaining the balance of the body's immune response and preventing excessive immune response. It has attracted much attention in the treatment and research of autoimmune diseases and inflammatory bowel disease.

[0065] Interleukin-17 (IL-17): Mainly secreted by Th17 cells (a special subset of CD4+T helper cells), it plays an important role in the connection between innate and adaptive immune responses and inflammatory responses. It can stimulate epithelial cells, endothelial cells and other cells to secrete inflammatory factors and chemokines, attract inflammatory cells such as neutrophils to the site of infection or inflammation, participate in the formation of an inflammatory environment, and play an important role in autoimmune diseases (such as psoriasis, rheumatoid arthritis, etc.) and antibody immunity.

[0066] Tumor necrosis factor-α (TNF-α): secreted by a variety of cell types (such as macrophages, T cells, NK cells, etc.), it is a cytokine with a strong inflammatory effect. It can activate inflammatory cells (such as macrophages, neutrophils, etc.), promote their phagocytosis and cytotoxicity, and also induce cell apoptosis. It plays an important role in the body's fight against infection, tumor immune surveillance, and autoimmune diseases. However, excessive TNF-α concentrations may also lead to excessive inflammatory responses and damage the body.

[0067] Interferon-γ (IFN-γ): It is mainly secreted by activated T cells (such as CD4+T helper cells, CD8+T killer cells, NKT cells, etc.) and is an important immunoregulatory cytokine. It can activate macrophages, enhance their phagocytic and bactericidal abilities, promote cellular immune responses, and inhibit viral replication. It plays an important role in the body's fight against viral infections, tumor immune surveillance, and autoimmune diseases.

[0068] Immunoglobulins include IgG, IgA, IgM, and IgE; IgG: It is the most abundant immunoglobulin in the human body and has multiple functions. It can neutralize pathogens and other antigens by binding to them, preventing them from invading and infecting. It can also assist phagocytes in engulfing pathogens by regulating phagocytosis. In addition, IgG also has immune memory function, which means it can quickly initiate an immune response when encountering the same antigen again. It is an important component of the body's long-term immune protection.

[0069] IgA: It is mainly found on the mucosal surface, such as in the mucosal secretions of the respiratory tract, digestive tract, urogenital tract, etc. It is an important executor of mucosal immunity. It can prevent the colonization and invasion of pathogens on the mucosal surface, and by forming immune complexes, it binds to pathogens and excretes them from the body, playing an important role in preventing infections in the respiratory tract, digestive tract, etc.

[0070] IgM: It is the main immunoglobulin secreted by the body in the early stage of the primary immune response, usually in the form of pentamers. It can quickly bind to antigens and initiate immune responses, but its affinity is relatively low. As the immune response develops, the secretion of IgM will gradually decrease, while the secretion of other immunoglobulins such as IgG will gradually increase. IgM is of great value in the early diagnosis of certain infectious diseases.

[0071] IgE: Mainly related to allergic reactions. When the body comes into contact with allergens, it will prompt B cells to produce a large amount of IgE antibodies. IgE antibodies can bind to Fc receptors on the surface of mast cells and basophils. When the same allergen is exposed again, the allergen will bind to the IgE bound to the cell surface, triggering the cell to release biologically active substances such as histamine, thereby leading to the occurrence of allergic reactions. It plays a key role in the occurrence and development of allergic diseases.

[0072] In this embodiment, specifically: the sorting fluid used in the flow cell sorting technology is a phosphate buffer, and 2% fetal bovine serum and 1mM ethylenediaminetetraacetic acid are added; wherein, the sorting fluid used in the flow cell sorting technology, which is composed of a phosphate buffer and 2% fetal bovine serum and 1mM ethylenediaminetetraacetic acid, can provide a suitable cell environment for the flow cell sorting process through the synergistic cooperation of their respective mechanisms of action, thereby ensuring the accuracy of sorting, the vitality of the cells and the reliability of the sorting results.

[0073] In this embodiment, specifically: when removing outliers and missing values, the outliers are determined using the Grubbs criterion, and the missing values ​​are filled using the multiple imputation method, wherein the Grubbs criterion is a method based on statistical principles for determining outliers in a set of data. Its basic idea is to calculate the mean and standard deviation of the data, and then determine whether a data point deviates too far from the overall data distribution according to certain statistical laws, so as to determine whether it is an outlier; the multiple imputation method is an effective method for dealing with missing data values. It is based on the distribution law of the data and the information of other related variables, and generates reasonable filling values ​​through multiple simulations to replace the missing data points. Its core idea is that the missing data is not completely random, but has some correlation with other observed data. Therefore, by analyzing the relationship patterns of existing data, these relationships can be used to infer the possible values ​​of the missing values.

[0074] In this embodiment, specifically: the method also includes establishing a patient immune response database to store and manage patient immune response maps, evaluation and prediction results, monitoring reports and treatment recommendations. Through the above detailed planning and operation procedures for establishing a patient immune response database, it is possible to achieve effective storage and scientific management of important data related to patient immune responses in clinical research on cell drugs, providing strong support for clinical practice and scientific research.

[0075] Figure 5 Schematic diagram of the functional modules of the patient immune response monitoring system for clinical research of cell medicines according to the embodiment of the present application. Figure 5 As shown, the patient immune response monitoring system used for clinical research of cell drugs includes: sample collection module, sample pretreatment module, multi-dimensional immune index detection module, cell drug specific immune response detection module, data integration and analysis module, evaluation and prediction module, report and suggestion module and database management module; The sample collection module is used to obtain peripheral blood samples of patients at multiple predetermined time points before and after the administration of the cell drug, and to process them with an anticoagulant. The multiple predetermined time points are determined according to the mechanism of action and half-life of the cell drug, that is, a peripheral blood sample is collected once a day before administration, and peripheral blood samples are collected at 1 / 4, 1 / 2, 1 times, 2 times, and 4 times the half-life of the cell drug after administration; A sample preprocessing module is used to preprocess the collected peripheral blood samples to obtain patient sample components; Multidimensional immune index detection module, used to analyze and detect patient sample components based on multidimensional immune index detection technology and build an immune index detection database; The cell drug specific immune response detection module is used to analyze and detect the components of patient samples based on the cell drug specific immune response detection technology and to build a cell drug specific immune response detection database; The data integration and analysis module is used to perform dimension reduction and clustering processing based on the immune index detection database and the cell drug specific immune response detection database using a data integration model that combines the principal component analysis algorithm with hierarchical clustering analysis to construct a patient immune response map; The evaluation and prediction module is used to generate evaluation and prediction results based on the patient's immune response profile, by comparing it with the immune data of healthy people and analyzing the data before and after treatment; The reporting and recommendation module is used to provide clinicians with real-time monitoring reports and treatment recommendations based on the evaluation and prediction results; The database management module is used to establish a patient immune response database, store and manage patient immune response maps, evaluation and prediction results, monitoring reports and treatment recommendations.

[0076] For other details about the technical solutions for implementing each module in the system of the above embodiment, please refer to the description of the patient immune response monitoring method for clinical research of cell drugs in the above embodiment, which will not be repeated here.

[0077] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring patient immune responses for clinical research on cell-based drugs, characterized in that: The following steps are involved: Obtaining a peripheral blood sample from a patient, and pre-treating the peripheral blood sample to obtain patient sample components; Analyze and test the components of patient samples based on multi-dimensional immune index detection technology, and build an immune index detection database; Analyze and detect the components of patient samples based on cell-based drug-specific immune response detection technology, and build a cell-based drug-specific immune response detection database; Based on the immune index detection database and the cell drug specific immune response detection database, a data integration model combining principal component analysis algorithm and hierarchical clustering analysis was used to construct the patient immune response map; Based on the patient's immune response profile, by comparing it with the immune data of healthy people and analyzing the data before and after treatment, we can generate evaluation and prediction results; Based on the evaluation and prediction results, monitoring reports and treatment recommendations are provided to clinicians.

2. The method for monitoring patient immune responses for clinical research of cell medicines according to claim 1, characterized in that: The specific steps of constructing the immune index detection database are as follows: S201. Based on flow cytometry detection technology, the number and proportion of immune cell subsets in the patient sample components and the expression changes of their surface markers are detected using sorting fluid to obtain immune cell subset analysis data; S202. Detect the cytokine concentration in the patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid chip technology, and obtain cytokine detection data by analyzing the changes in cytokine concentration; S203, determining the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluating the changes in the patient's immune function, and obtaining immunoglobulin detection data; S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of T cell receptors, and obtaining T cell receptor analysis data; S205. Integrate the immune cell subset analysis data, cytokine detection data, immunoglobulin detection data, and T cell receptor analysis data to construct an immune index detection database.

3. The method for monitoring patient immune responses for clinical research of cell medicines according to claim 1, characterized in that: The specific steps of constructing a cell drug specific immune response detection database are as follows: S301, based on a specific immunoassay method, detecting specific antibodies against the cellular drug in the patient sample components to obtain specific antibody detection data; S302. Based on the enzyme-linked immunosorbent assay combined with flow cytometry sorting technology, the specific T cell response to the cell drug in the patient sample components is detected to obtain the cell drug specific T cell response detection data; S303, integrating the specific antibody detection data and the cellular drug-specific T cell response detection data to construct a cellular drug-specific immune response detection database.

4. The method for monitoring patient immune responses for clinical research of cell medicines according to claim 1, characterized in that: The method for constructing a patient immune response profile comprises the following steps: S401, cleaning the data in the immune index detection database and the cell drug specific immune response detection database to remove abnormal values ​​and missing values; S402, extracting main characteristic components using a principal component analysis algorithm, and calculating the principal components using a singular value decomposition algorithm; S403, clustering the extracted principal components using hierarchical clustering analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the distance between classes using the average linkage method, and obtaining the dimensionality reduction and clustering processing results through multiple cross-validations; S404, based on the dimensionality reduction and clustering processing results, a multidimensional space coordinate system is constructed using the principal components as coordinate axes, the positions of the sample points are determined in the multidimensional space coordinate system, and a framework of the patient's immune response map is obtained; S405, attaching the clinical characteristic information of the patient to the corresponding sample point, and optimizing and displaying the patient immune response map framework using graphic rendering technology to construct the patient immune response map.

5. The method for monitoring patient immune response for clinical research of cell medicine according to claim 1, characterized in that: The peripheral blood sample is collected and treated with an anticoagulant, wherein the anticoagulant is ethylenediaminetetraacetate, and the concentration thereof is 1.5-2.0 mg / ml. The pretreatment of the peripheral blood sample includes a centrifugal separation operation.

6. The method for monitoring patient immune response for clinical research of cell medicine according to claim 2, characterized in that: The immune cell subpopulations include CD4+T helper cells, CD8+T killer cells, CD19+B lymphocytes, NK natural killer cells, NKT cells and monocytes; the surface markers include CD28, CTLA-4, CD69, CD16, and CD56; the cytokines include interleukin-2, interleukin-4, interleukin-6, interleukin-10, interleukin-17, tumor necrosis factor-α, and interferon-γ; the immunoglobulins include IgG, IgA, IgM, and IgE.

7. The method for monitoring patient immune responses for clinical research of cell medicines according to claim 2, characterized in that: The sorting solution is a phosphate buffer, and 2% fetal bovine serum and 1 mm ethylenediaminetetraacetic acid are added.

8. The method for monitoring patient immune responses for clinical research of cell medicines according to claim 4, characterized in that: When removing outliers and missing values, outliers are determined using the Grubbs criterion, and missing values ​​are filled using the multiple imputation method.

9. The method for monitoring patient immune response for clinical research of cell medicine according to claim 1, characterized in that: The method also includes establishing a patient immune response database to store and manage patient immune response maps, evaluation and prediction results, monitoring reports, and treatment recommendations.

10. A patient immune response monitoring system for cell drug clinical research, applied to a patient immune response monitoring method for cell drug clinical research according to any one of claims 1 to 9, characterized in that: include: Sample collection module, sample pretreatment module, multi-dimensional immune index detection module, cell drug specific immune response detection module, data integration and analysis module, evaluation and prediction module, report and suggestion module and database management module; The sample collection module is used to obtain peripheral blood samples of the patient at multiple predetermined time points before and after the administration of the cell drug, and treat the samples with an anticoagulant, wherein the multiple predetermined time points are determined according to the mechanism of action and half-life of the cell drug; The sample preprocessing module is used to preprocess the collected peripheral blood samples to obtain patient sample components; The multidimensional immune index detection module is used to analyze and detect the patient sample components based on the multidimensional immune index detection technology and build an immune index detection database; The cell drug specific immune response detection module is used to analyze and detect patient sample components based on the cell drug specific immune response detection technology and to build a cell drug specific immune response detection database; The data integration and analysis module is used to perform dimensionality reduction and clustering processing based on the immune index detection database and the cell drug specific immune response detection database using a data integration model that combines the principal component analysis algorithm with the hierarchical clustering analysis to construct a patient immune response map; The evaluation and prediction module is used to generate evaluation and prediction results based on the patient's immune response profile, by comparing it with the immune data of healthy people and analyzing the data before and after treatment; The reporting and suggestion module is used to provide real-time monitoring reports and treatment suggestions to clinicians based on the evaluation and prediction results; The database management module is used to establish a patient immune response database to store and manage patient immune response maps, evaluation and prediction results, monitoring reports, and treatment recommendations.

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