Method and system for monitoring patient immune response in clinical research of cell-based drugs

Abstract

The present invention discloses a method and system for monitoring the immune response of patients for clinical research on cell drugs, and the method includes the following steps: Step 1, obtaining a peripheral blood sample from the patient, and pre-treating the peripheral blood sample to obtain the patient sample components. The present invention comprehensively reflects the complex immune response system by comprehensively utilizing multiple technologies to detect multi-dimensional immune indicators and cell drug-specific immune responses, covering immune cell subpopulations, cytokines, immunoglobulins and T cell responses, etc. At the same time, according to the mechanism of action and half-life of the cell drug, peripheral blood samples are collected at multiple time points to effectively track the changes in the immune response throughout the process, thereby accurately grasping the development trend of the immune response; by constructing 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, assist doctors in adjusting treatment in a timely manner, thereby improving the safety and effectiveness of cell drug therapy.

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, cellular drugs have shown great potential in the treatment of various diseases. However, the mechanism of action of cellular drugs in the body is complex, and they may trigger varying degrees of immune responses in patients. These immune responses not only affect the therapeutic effect but may also lead to adverse reactions. Therefore, in the clinical research of cellular drugs, accurate and comprehensive monitoring of patients' immune responses is extremely important. However, existing immune response monitoring methods have many shortcomings.

[0003] Existing immune response monitoring methods have single detection indicators, focusing only on the number of a certain type of immune cells or the concentration of a certain 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, reducing 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-based drugs, so as to solve one of the problems raised in the above-mentioned background technology.

[0005] To solve the above technical problems, the present application adopts a technical solution: a method for monitoring patient immune responses in clinical research of cell-based drugs, comprising the following steps:

[0006] Step 1: Obtain a peripheral blood sample from the patient and pre-treat the peripheral blood sample to obtain the patient sample components;

[0007] 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;

[0008] Step 3: Analyze and detect the components of patient samples based on cell-based drug-specific immune response detection technology to build a cell-based drug-specific immune response detection database;

[0009] 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 cluster analysis is used to construct the patient immune response map;

[0010] Step 5: Based on the patient's immune response profile, generate evaluation and prediction results by comparing it with the immune data of healthy people and analyzing the data before and after treatment;

[0011] Step 6: Provide monitoring reports and treatment recommendations to clinicians based on the assessment and prediction results.

[0012] As a further preferred embodiment of this technical solution: the specific steps of constructing the immune index detection database are as follows:

[0013] S201. Based on flow cytometry detection technology, use sorting fluid to detect the number and proportion of immune cell subsets in patient sample components and the expression changes of their surface markers to obtain immune cell subset analysis data;

[0014] S202. Detecting cytokine concentrations in patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid phase chip technology, and obtaining cytokine detection data by analyzing changes in cytokine concentrations;

[0015] S203. Determine the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluate changes in the patient's immune function, and obtain immunoglobulin test data;

[0016] S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of the T cell receptors, and obtaining T cell receptor analysis data;

[0017] 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.

[0018] As a further preferred embodiment of this technical solution: the specific steps of constructing a cell drug specific immune response detection database are as follows:

[0019] S301. Detecting specific antibodies against the cellular drug in the patient sample components based on a specific immunoassay method to obtain specific antibody detection data;

[0020] S302. Detect specific T cell responses to cellular drugs in patient sample components based on enzyme-linked immunosorbent assay combined with flow cytometry sorting technology to obtain cellular drug-specific T cell response detection data;

[0021] S303. Integrate 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.

[0022] As a further preferred embodiment of the present invention, the method for constructing a patient immune response profile comprises the following steps:

[0023] S401. Clean the data in the immune index detection database and the cell drug specific immune response detection database to remove abnormal values ​​and missing values;

[0024] S402, extracting main characteristic components using principal component analysis algorithm, and solving principal component calculation using singular value decomposition algorithm;

[0025] S403, clustering the extracted principal components using hierarchical cluster analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the inter-class distance using the average linkage method, and obtaining dimensionality reduction and clustering processing results through multiple cross-validations;

[0026] S404. Based on the dimensionality reduction and clustering processing results, a multidimensional space coordinate system is constructed using the principal components as coordinate axes, and the positions of the sample points are determined in the multidimensional space coordinate system to obtain a patient immune response map framework;

[0027] S405: Attach the patient's clinical characteristic information to the corresponding sample points, and optimize and display the patient's immune response map framework using graphics rendering technology to construct the patient's immune response map.

[0028] 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 the concentration thereof is 1.5-2.0 mg / ml, and the pretreatment of the peripheral blood sample includes a centrifugal separation operation.

[0029] As a further preferred embodiment of the present technical solution: 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.

[0030] As a further preferred embodiment of the present technical solution: the separation solution is a phosphate buffer solution, to which 2% fetal bovine serum and 1 mM ethylenediaminetetraacetic acid are added.

[0031] 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 imputation method.

[0032] As a further preferred embodiment of the present technical solution: the method further 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.

[0033] 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-based drugs, comprising: a sample collection module, a sample pretreatment module, a multi-dimensional immune index detection module, a cell-based 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;

[0034] The sample collection module is used to obtain peripheral blood samples from the patient at multiple predetermined time points before and after 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;

[0035] The sample preprocessing module is used to preprocess the collected peripheral blood sample to obtain patient sample components;

[0036] The multidimensional immune index detection module is used to analyze and detect patient sample components based on multidimensional immune index detection technology and build an immune index detection database;

[0037] 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 build a cell-drug specific immune response detection database;

[0038] 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;

[0039] 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;

[0040] 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;

[0041] 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.

[0042] Advantages of the present invention:

[0043] 1. This invention comprehensively reflects the complex immune response system by comprehensively utilizing multiple technologies to detect multi-dimensional immune indicators and cellular drug-specific immune responses, covering immune cell subsets, cytokines, immunoglobulins, T cell receptors, drug-specific antibodies, and T cell responses. At the same time, peripheral blood samples are collected at multiple time points based on the mechanism of action and half-life of cellular drugs to effectively track the changes in the immune response throughout the entire process, thereby accurately grasping the development trend of the immune response;

[0044] 2. The present invention constructs a patient immune response map to intuitively present characteristics, assist in evaluation and prediction, and provides 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 cell drug therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0046] Figure 1 Schematic diagram of the process of the patient immune response monitoring method for clinical research of cell medicines of the present invention;

[0047] Figure 2 A schematic diagram of the process of constructing an immune index detection database according to the present invention;

[0048] Figure 3 A schematic diagram of the process of constructing a cell-drug specific immune response detection database according to the present invention;

[0049] Figure 4 A schematic diagram of the process of constructing a patient immune response profile according to the present invention;

[0050] Figure 5 Schematic diagram of the functional modules of the patient immune response monitoring system used in clinical research of cell medicines according to the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0052] Example

[0053] Figure 1 Schematic diagram of the process of monitoring the patient immune response in a cell-based drug clinical study according to an embodiment of the present invention. Figure 1 The process sequence shown is limited. Figure 1-Figure 4 Shown: A method for monitoring patient immune responses in clinical studies of cellular drugs, comprising the following steps:

[0054] Step 1: Obtain a peripheral blood sample from the patient and pre-treat the peripheral blood sample to obtain the patient sample components;

[0055] Specifically, peripheral blood samples are collected from patients by venous sampling before administration of the cell-based drug and at multiple predetermined time points after administration (e.g., 24 hours, 72 hours, one week, one month after administration, etc., with the specific time determined based on the drug characteristics and study design). The collection process is ensured to be aseptic and contamination-free, and the sampling time is accurately recorded.

[0056] 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;

[0057] 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 further analysis of specific types of cells (such as T cells, B cells, etc.) is required, 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 cell drugs; white blood cells and other cellular components obtained by cell separation can be used to analyze the number, activity, differentiation status and gene expression of cells, so as to understand the impact of cell drugs on the immune system.

[0058] Step 2: Analyze and test the components of patient samples based on multi-dimensional immune index detection technology to build an immune index detection database:

[0059] Step 3: Analyze and detect the components of patient samples based on cell-based drug-specific immune response detection technology to build a cell-based drug-specific immune response detection database:

[0060] 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 cluster analysis is used to construct the patient immune response map;

[0061] Step 5: Based on the patient's immune response profile, generate evaluation and prediction results by comparing it with the immune data of healthy people and analyzing the data before and after treatment;

[0062] Specifically: First, collect immune data from healthy people as a reference standard. This data can come from large-scale immune testing projects for healthy people or relevant 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 the patient and the healthy population in terms of immune cell subsets, cytokine concentrations, immunoglobulin content, T cell receptor diversity, and cell-drug specific immune responses. For example, compare whether the patient's T cell subset ratio deviates from the normal range of healthy people, and whether the cytokine secretion concentration is abnormally increased or decreased.

[0063] Then, the patient's immune response data before and after cell drug treatment are analyzed to observe the dynamic changes in the patient's immune response during the treatment process. 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.

[0064] Step 6: Provide monitoring reports and treatment recommendations to clinicians based on the assessment and prediction results;

[0065] The report content includes the patient's basic information, cellular drug treatment plan, detailed analysis results of immune response monitoring data (including comparison with healthy subjects and specific data and charts of 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 after treatment, and explain the possible impact of such changes on treatment efficacy and patient health;

[0066] 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 the treatment plan according to the patient's immune response and improve the safety and effectiveness of cellular drug therapy.

[0067] In this embodiment, the specific steps of constructing an immune index detection database are as follows:

[0068] S201. Based on flow cytometry detection technology, use sorting fluid to detect the number and proportion of immune cell subsets in patient sample components and the expression changes of their surface markers to obtain immune cell subset analysis data;

[0069] Prepare the flow cytometer, ensuring proper calibration and sufficient reagents. Take an appropriate amount of processed patient sample components and add fluorescently labeled antibodies specific for 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). Incubate at an appropriate temperature and time to allow the antibodies to fully bind to the corresponding immune cell surface antigens.

[0070] The incubated samples were tested on a flow cytometer, and the signal intensity of different fluorescence channels was detected by flow cytometry to distinguish different immune cell subsets and obtain their quantity and proportion information. At the same time, the changes in the fluorescence intensity 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 subset analysis data were recorded.

[0071] S202. Detecting cytokine concentrations in patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid phase chip technology, and obtaining cytokine detection data by analyzing changes in cytokine concentrations;

[0072] Specific embodiments of enzyme-linked immunosorbent assay (ELISA):

[0073] Select an appropriate ELISA kit, add the coated antibody (targeting a specific cytokine) to the ELISA plate, and incubate at an appropriate temperature for a certain period of time to allow the antibody to be fixed to the bottom of the plate;

[0074] 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;

[0075] Wash again, add enzyme-labeled secondary antibody (specifically binds to cytokines and is enzyme-labeled), incubate and wash, and add substrate solution;

[0076] The enzyme-catalyzed substrate reaction produces a color change, and the absorbance value is detected by a microplate reader. The cytokine concentration in the sample is calculated according to the standard curve to obtain the cytokine detection data. For cytokine concentration detection, the above steps can be repeated in sequence or a multi-channel ELISA kit can be used.

[0077] Specific implementation of Luminex liquid phase chip technology:

[0078] Capture antibodies for different cytokines are coupled to microspheres, each with a specific fluorescent code to distinguish between different antibodies;

[0079] After mixing the microspheres, patient sample components are added and incubated in liquid phase to allow cytokines to bind to the corresponding capture antibodies;

[0080] 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;

[0081] The type of cytokine detected was determined by detecting the fluorescence coding of the microspheres using a Luminex instrument. The fluorescence intensity of the antibody was also detected. The cytokine concentration was calculated based on the standard curve to obtain the cytokine detection data.

[0082] S203. Determine the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluate changes in the patient's immune function, and obtain immunoglobulin test data;

[0083] 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.

[0084] As the immune complex forms, the turbidity of the solution changes, and the absorbance change of the solution at a specific wavelength is detected by a turbidimeter;

[0085] 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.

[0086] S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of the T cell receptors, and obtaining T cell receptor analysis data;

[0087] Extract total RNA or genomic DNA from patient sample components, amplify T cell receptor genes using specific primers, and construct a sequencing library;

[0088] After the library is quality-checked and quantified, it is loaded onto a high-throughput sequencing platform (such as an Illumina sequencer) for sequencing;

[0089] 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 T cell receptor diversity indicators (such as Shannon diversity index, clonality index, etc.) are calculated to obtain T cell receptor analysis data.

[0090] 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;

[0091] Design the database structure and create tables to store different types of immune indicator data, including the data source (patient number, sampling time, etc.), specific test indicator values, and related notes (such as test methods, instrument parameters, etc.);

[0092] Enter the various data obtained in steps S201-S204 into the corresponding tables in sequence according to the format designed for the database to ensure the accuracy and completeness of the data;

[0093] Conduct preliminary verification and review of the entered data to check for missing data, abnormal values, etc. If any problems are found, review and correct them in a timely manner;

[0094] Establish data indexes to facilitate subsequent data query, retrieval and analysis operations, and complete the construction of the immune index detection database.

[0095] In this embodiment, the specific steps of constructing a cell-drug specific immune response detection database are as follows:

[0096] S301. Detecting specific antibodies against the cellular drug in the patient sample components based on a specific immunoassay method to obtain specific antibody detection data;

[0097] Select an appropriate specific immunoassay method. Here, we use the commonly used enzyme-linked immunosorbent assay (ELISA) as an example. First, prepare an ELISA kit. The coating antigen in the kit should be a conjugate of a cell-drug-associated antigen and a carrier protein, and the conjugation ratio must meet specific requirements (e.g., 1:5-1:10).

[0098] Prepare other required reagents, such as enzyme-labeled secondary antibodies and substrate solutions, and ensure that laboratory instruments and equipment (such as microplate readers and pipettes) are functioning properly and are calibrated;

[0099] 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 an appropriate 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;

[0100] After incubation, wash the plate thoroughly with washing solution to remove unbound substances, then add enzyme-labeled secondary antibody, which should be able to specifically recognize and bind to the specific antibody bound to the coated antigen, and incubate again under appropriate conditions (e.g., 37°C, 30-60 minutes);

[0101] After the incubation, the ELISA plate is washed again to remove 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 a microplate reader. 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.

[0102] S302. Detect specific T cell responses to cellular drugs in patient sample components based on enzyme-linked immunosorbent assay combined with flow cytometry sorting technology to obtain cellular drug-specific T cell response detection data;

[0103] First, the capture antibody used to capture 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 must be controlled within a specific range (e.g., 1:3-1:6).

[0104] Coat the labeled capture antibody onto the bottom of the ELISPOT plate wells and incubate under appropriate conditions (e.g., overnight at 4°C) to allow for fixation.

[0105] Prepare other required reagents, such as detection antibodies, substrate solutions, etc., and ensure that instruments and equipment such as ELISPOT readers are functioning properly;

[0106] Take an appropriate amount of pre-treated patient sample components and add them to the ELISPOT plate wells coated with capture antibodies. Simultaneously, add stimulators (such as the cell-based drug itself or its related antigen fragments) to activate specific T cells in the sample and cause them to secrete relevant cytokines. Incubate at an appropriate temperature (such as 37°C) and time (such as 16-24 hours).

[0107] After incubation, wash the ELISPOT plate thoroughly with washing solution to remove unbound substances, then add the detection antibody, which should be able to specifically recognize and bind to the cytokines secreted by specific T cells and captured by the capture antibody, and incubate again under appropriate conditions (e.g., 37°C, 1-2 hours);

[0108] After the incubation, the ELISPOT plate is washed again to remove unbound detection antibodies, and then the substrate solution is added. Under the catalytic action of the enzyme carried by the detection antibody, the substrate undergoes a color reaction to form visible spots. The spots are counted using an 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.

[0109] Flow cytometry sorting technology operation:

[0110] Prepare the flow cell sorter, ensuring that the instrument is functioning properly and that all relevant reagents (such as sorting fluid, which in this case is phosphate buffered saline supplemented with 2% fetal bovine serum and 1 mM EDTA) are fully prepared.

[0111] 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;

[0112] The incubated samples are placed on a flow cytometer for flow cytometry sorting. By setting appropriate sorting parameters (such as fluorescence intensity, cell size, etc.), specific T cells are separated from the sample, 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.

[0113] 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;

[0114] Design a reasonable database structure based on the characteristics of the data to be stored, and create tables to store specific antibody test data and cellular drug-specific T cell response test data, including the data source (patient number, sampling time, etc.), specific test indicator values, and related notes (such as test methods, instrument parameters, etc.);

[0115] 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;

[0116] Conduct preliminary verification and review of the entered data to check for missing data, abnormal values, etc. If any problems are found, review and correct them in a timely manner;

[0117] Establishing a data index facilitates subsequent query, retrieval, and analysis of the data, thereby completing the construction of a database for cell-based drug-specific immune response detection.

[0118] In this embodiment, the method for constructing a patient immune response profile includes the following steps:

[0119] S401. Clean the data in the immune index detection database and the cell drug specific immune response detection database to remove abnormal values ​​and missing values;

[0120] First, the required data is extracted from the established immune index detection database and the cell-based drug-specific immune response detection database. This data covers various information on patient immune indicators and cell-based drug-specific immune responses obtained through various detection technologies and is the basis for constructing the immune response map.

[0121] Use an appropriate outlier determination method, such as the Grubbs criterion. For each data metric, 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 is deleted from the dataset. Outliers may be caused by errors in the detection process, improper sample collection or processing, etc. Retaining them may interfere with subsequent data analysis and map construction.

[0122] 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;

[0123] S402, extracting main characteristic components using principal component analysis algorithm, and solving principal component calculation using singular value decomposition algorithm;

[0124] Before performing principal component analysis, the cleaned dataset usually needs to be standardized. 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), thus avoiding the impact of dimensional differences on the principal component analysis results. Common standardization methods include Z-score standardization, which performs corresponding mathematical transformations on each data point to ensure that it meets the standardization requirements.

[0125] Principal component analysis (PCA) is a dimensionality reduction technique that aims to find a set of new variables (i.e., principal components) that preserve as much of the variance of the original data as possible. A standardized dataset is fed into the PCA algorithm, which determines the principal components based on the eigenvalues ​​and eigenvectors of the data's covariance matrix (or correlation matrix). Specifically, the algorithm solves the characteristic equation of the covariance matrix to obtain a series of eigenvalues ​​and corresponding eigenvectors. The eigenvalues ​​represent the amount of variance explained by each principal component, while the eigenvectors determine the direction of each principal component in the original data space.

[0126] The singular value decomposition (SVD) algorithm is a matrix decomposition technique closely related to principal component analysis (PCA). It can be used to more efficiently solve some key calculations in PCA, such as the eigenvalues ​​and eigenvectors of the covariance matrix. In practice, the SVD algorithm is used to decompose the matrix corresponding to a dataset 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 achieving the solution and calculation of the principal components.

[0127] Singular value decomposition algorithm calculation steps:

[0128] Let matrix for Matrix , first calculate ( The transpose of ).

[0129] Solution The eigenvalue of and the corresponding eigenvector .

[0130] The eigenvalue Sort from large to small, and the corresponding eigenvectors are also sorted accordingly.

[0131] Calculate singular values .

[0132] calculate ,get Matrix (The column vector is ).

[0133] Build The diagonal matrix of , the elements on the diagonal are singular values .

[0134] make , then the matrix The singular value decomposition of .

[0135] 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 map construction.

[0136] S403, clustering the extracted principal components using hierarchical cluster analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the inter-class distance using the average linkage method, and obtaining dimensionality reduction and clustering processing results through multiple cross-validations;

[0137] 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 constructs a cluster hierarchy by continuously calculating the similarity between samples and the distance between clusters.

[0138] 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 Euclidean distance is calculated as follows:

[0139] ;

[0140] in, and Respectively represent and The samples in the The values ​​on the principal components, represents the number of principal components;

[0141] 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.

[0142] 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 pairs of samples in the two clusters, and then take the average of these distances as the distance between the two clusters;

[0143] Average linkage method formula (used to calculate inter-class distance):

[0144] ;

[0145] in, and There are two classes, and are their sample sizes, It is a sample and The distance between them (Euclidean distance is used here).

[0146] This method is relatively robust and can better reflect the true distance relationship between clusters, which helps to form a reasonable clustering structure;

[0147] In order to improve the reliability and accuracy of the 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). Each time, one of the parts is selected as the validation set, and the remaining parts are used as the training set.

[0148] Multiple cross-validation is a technique used to evaluate model performance and improve model stability. The core idea is to divide the dataset into multiple different parts, train and evaluate the model on each part, and then combine the results to obtain more reliable model performance evaluation and more stable model parameters.

[0149] The calculation steps are as follows:

[0150] The dataset Randomly divided into subsets of similar size (usually Take 5 or 10), record it as .

[0151] For the cross validation :

[0152] Will As a validation set, the remaining subsets As a training set;

[0153] In the training set Perform hierarchical clustering analysis on the cluster to obtain clustering results (such as cluster labels ).

[0154] In the validation set Evaluate the quality of clustering results and calculate evaluation indicators (such as silhouette coefficient ).

[0155] Repeat the steps to Cross-validation, we get Evaluation index value ;

[0156] Calculate the average evaluation metric value:

[0157] ;

[0158] As the final evaluation result;

[0159] Select the optimal model parameters or clustering results based on the average evaluation index value;

[0160] Silhouette coefficient calculation formula (used to evaluate the quality of clustering results): For each sample ,set up For samples The average distance to other samples of the same category, For samples The minimum value of the average distance to other categories of samples, then the sample The silhouette coefficient is:

[0161] ;

[0162] Silhouette coefficient for the entire dataset:

[0163] ;

[0164] in, is the total number of samples;

[0165] The singular value decomposition algorithm was used to achieve dimensionality reduction in principal component analysis, and hierarchical cluster analysis was used to cluster the data. During the clustering process, Euclidean distance was used to measure the similarity between samples, and the average linkage method was used to calculate the distance between clusters. Multiple cross-validations were performed to improve the reliability of the results. These methods jointly constructed a patient immune response map, providing strong support for comprehensive and accurate monitoring of patient immune responses.

[0166] Perform hierarchical clustering analysis on the training set. After obtaining the clustering results, evaluate the quality of the clustering results on the validation set. For example, calculate some evaluation indicators (such as silhouette coefficient) to judge the rationality and accuracy of the clustering.

[0167] 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.

[0168] S404. Based on the dimensionality reduction and clustering processing results, a multidimensional space coordinate system is constructed using the principal components as coordinate axes, and the positions of the sample points are determined in the multidimensional space coordinate system to obtain a patient immune response map framework;

[0169] Based on the results of dimensionality reduction and clustering, the number of principal components to be retained is determined. 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.

[0170] 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;

[0171] 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 positioned in the multidimensional space coordinate system to obtain the immune response map framework.

[0172] S405: attaching the patient's clinical characteristic information to the corresponding sample points, and optimizing and displaying the patient's immune response map framework using graphics rendering technology to construct the patient's immune response map;

[0173] Collect clinical characteristic information of patients, which may include 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 relevant information about the immune response, but also be combined with the patient's clinical condition.

[0174] Graphics rendering techniques, such as specialized drawing software or programming libraries (e.g., Matplotlib in Python), can be used to optimize the display of immune response maps. Display parameters such as color, line thickness, and font size can be customized to make the maps clearer, more aesthetically pleasing, and easier to understand. This approach transforms the immune response map from a simple coordinate framework into a visualization tool that intuitively displays the relationship between a patient's immune response and clinical characteristics, ultimately constructing a complete patient immune response map.

[0175] In this embodiment, specifically: multiple predetermined time points are determined based on 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 where 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 scientifically plan around the drug's mechanism of action and half-life so as to systematically monitor changes in the patient's immune response over time.

[0176] The peripheral blood samples are collected and treated with an anticoagulant, which is ethylenediaminetetraacetate at 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 need to be used 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 from coagulating and 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 effectively anticoagulate, causing the blood to coagulate during the collection process or before subsequent processing, affecting the integrity and detectability of the sample; while 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 EDTA concentration 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 testing and analysis.

[0177] Pretreatment of peripheral blood samples includes centrifugation. After the 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 the different components in the blood sample according to density differences, thereby achieving preliminary separation of the sample components. In this process, the collected peripheral blood sample is placed in a suitable centrifuge tube, placed in the 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 without causing excessive damage to blood cells, for example, it may be set to 1500-2000rpm ( The centrifugation time is usually determined by factors such as sample volume, 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 immune cells such as lymphocytes and monocytes), 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 analytical tests based on multi-dimensional immune index detection technology and cell-based drug-specific immune response detection technology, thereby more accurately understanding the patient's immune response status under the action of cell-based drugs.

[0178] 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;

[0179] CD4+ T helper cells play a key supporting 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. They are crucial for initiating and regulating the body's immune response.

[0180] CD8+ T killer cells: Their primary function is to identify and kill target cells, such as virus-infected cells or tumor cells. They recognize antigenic peptides presented by MHC class I molecules on the surface of target cells. Once successful, they release cytotoxic substances such as perforin and granzymes, causing the target cells to lyse and die. Thus, they play a vital role in clearing pathogen-infected cells and in tumor immune surveillance.

[0181] CD19+ B lymphocytes are the primary cell type responsible for producing specific antibodies. Upon antigen stimulation, B lymphocytes undergo activation, proliferation, and differentiation, ultimately becoming plasma cells that secrete large quantities of specific antibodies. These antibodies can bind to the corresponding antigens and eliminate pathogens through various mechanisms, such as neutralization and opsonophagocytosis. They are the core executors of the humoral immune response.

[0182] NK cells: Part of the innate immune system, they can rapidly identify and kill certain abnormal cells, such as tumor cells and virus-infected cells, without prior sensitization. They initiate their killing mechanism by recognizing features such as the absence of normal MHC class I expression or abnormal expression of MHC class I-associated proteins on the target cell surface. They primarily exert their effects through the release of perforins, granzymes, and secretion of cytokines (such as interferon-γ), playing a crucial role in the body's early immune defense and tumor immune surveillance.

[0183] NKT cells: A specialized lymphocyte subset with some characteristics of both T and NK cells, they recognize lipid antigens presented by CD1d molecules and, upon activation, rapidly secrete large quantities of cytokines (such as interleukin-4 and interferon-γ). These cells regulate innate immune responses and influence the initiation and development of adaptive immune responses, playing a unique role in immune regulation, anti-infection, and anti-tumor activities.

[0184] Monocytes are large white blood cells in the blood. After circulating in the blood for a period of time, they migrate to tissues and differentiate into antigen-presenting cells such as macrophages and dendritic cells. Monocytes have phagocytic functions, capable of ingesting and processing foreign antigens such as pathogens and presenting them to T cells, thereby initiating adaptive immune responses. They also participate in inflammatory responses and immune regulation through the secretion of cytokines.

[0185] Surface markers include CD28, CTLA-4, CD69, CD16, and CD56;

[0186] CD28: An important co-stimulatory molecule, primarily expressed on the surface of T cells (including CD4+ T helper cells and CD8+ T killer cells). It binds to B7 molecules (such as CD80 and CD86) on the surface of antigen-presenting cells, providing important co-stimulatory signals for T cell activation, promoting T cell proliferation, differentiation, and function. Without CD28 co-stimulatory signals, T cells may enter an anergic or apoptotic state.

[0187] CTLA-4: Also expressed on the surface of T cells, it shares a similar structure to CD28 but functions differently. CTLA-4 is primarily upregulated upon T cell activation. Its affinity for the B7 molecule is higher than that of CD28, negatively regulating T cell activation. By competing with CD28 for binding to the B7 molecule, it inhibits overactivation of T cells, maintains a balanced immune response, and prevents damage to the body's own tissues caused by an overactive immune response.

[0188] CD69: An early activation marker, it is rapidly expressed on the surface of various immune cells (such as T cells, B cells, and NK cells). Its expression is detected within a short period of time after immune cell stimulation (such as antigen stimulation or cytokine stimulation). It serves as an important indicator for determining whether immune cells are in an activated state. For example, CD69 expression increases significantly in the early stages of T cell activation and may change as activation progresses.

[0189] CD16: Mainly expressed on the surface of NK cells and certain macrophages, it is an Fc receptor that can recognize and bind to the Fc region of antibodies. Through this binding, NK cells can kill target cells through antibody-dependent cell-mediated cytotoxicity (ADCC). That is, when target cells are coated with specific antibodies, NK cells bind to the Fc region of the antibody through CD16, and then release substances such as perforin and granzymes to kill the target cells, enhancing the body's ability to eliminate pathogens and tumor cells.

[0190] CD56: One of the key markers of NK cells, its expression on the surface of NK cells is relatively stable and can be used to identify NK cells. Meanwhile, the expression concentration of CD56 in NK cells of different tissues may vary, and its expression may also be related to the functional status of NK cells. For example, in some cases, the expression intensity of CD56 may affect the cytotoxic activity and cytokine secretion capacity of NK cells.

[0191] Cytokines include interleukin-2, interleukin-4, interleukin-6, interleukin-10, interleukin-17, tumor necrosis factor-α, and interferon-γ;

[0192] Interleukin-2 (IL-2): Secreted primarily by activated T cells (particularly CD4+ T helper cells), it is an important cytokine with multiple functions in the immune system. It promotes T cell proliferation and differentiation, maintains T cell survival, and stimulates the proliferation and enhances the cytotoxic activity of natural killer (NK) cells. It is a key factor in regulating the intensity and duration of the body's immune response and holds significant research value in areas such as autoimmune diseases and tumor immunotherapy.

[0193] Interleukin-4 (IL-4): Secreted primarily by activated T cells (such as CD4+ T helper cells and NKT cells), it plays a crucial role in humoral immune responses. It promotes B cell proliferation, differentiation, and antibody class switching, prompting B cells to produce more IgE antibodies. It also regulates the functions of other immune cells (such as macrophages and NK cells), playing a crucial role in immune responses to allergic diseases and parasitic infections.

[0194] Interleukin-6 (IL-6): Secreted by various cell types (such as monocytes, macrophages, T cells, and B cells), it is a multifunctional cytokine. It plays an important role in inflammatory responses, immune response regulation, and tissue repair. In the early stages of an inflammatory response, IL-6 levels rise rapidly, stimulating the liver to synthesize acute phase proteins. It also promotes B cell proliferation and differentiation and influences T cell function. Abnormal fluctuations in IL-6 levels are common in some autoimmune diseases (such as rheumatoid arthritis) and infectious diseases.

[0195] Interleukin-10 (IL-10): Secreted primarily by regulatory T cells, B cells, macrophages, and other cells, it is an important immunoregulatory cytokine. Its primary function is to inhibit immune cell activation and inflammatory responses. By suppressing the functions of macrophages and T cells, it reduces the secretion of inflammatory factors, thereby maintaining a balanced immune response and preventing excessive immune reactions. It has attracted considerable attention in the treatment of autoimmune diseases and inflammatory bowel disease.

[0196] Interleukin-17 (IL-17): Secreted primarily by Th17 cells (a specialized subset of CD4+ T helper cells), IL-17 plays a crucial role in bridging the innate and adaptive immune responses and in inflammatory responses. It stimulates epithelial and endothelial cells, among other cells, to secrete inflammatory factors and chemokines, attracting inflammatory cells such as neutrophils to sites of infection or inflammation, contributing to the formation of an inflammatory environment. IL-17 plays a crucial role in autoimmune diseases (such as psoriasis and rheumatoid arthritis) and antibody immunity.

[0197] Tumor necrosis factor-α (TNF-α): Secreted by various cell types (such as macrophages, T cells, and NK cells), it is a cytokine with potent inflammatory effects. It activates inflammatory cells (such as macrophages and neutrophils), promoting phagocytosis and cytotoxicity, while also inducing apoptosis. It plays a vital role in the body's fight against infection, tumor immune surveillance, and autoimmune diseases. However, excessive TNF-α concentrations can also lead to excessive inflammatory responses and damage the body.

[0198] Interferon-γ (IFN-γ): Secreted primarily by activated T cells (such as CD4+ T helper cells, CD8+ T killer cells, and NKT cells), it is an important immunoregulatory cytokine. It activates macrophages, enhancing their phagocytic and bactericidal abilities, promoting cellular immune responses, and inhibiting viral replication. It plays a vital role in the body's fight against viral infections, tumor immune surveillance, and autoimmune diseases.

[0199] Immunoglobulins include IgG, IgA, IgM, and IgE;

[0200] IgG is the most abundant immunoglobulin in the human body and has multiple functions. It can neutralize pathogens by binding to antigens, preventing their invasion and infection. It can also assist phagocytes in engulfing pathogens by modulating phagocytosis. IgG also possesses immune memory, enabling a rapid immune response upon encountering the same antigen again, making it a crucial component of the body's long-term immune protection.

[0201] IgA is primarily found on mucosal surfaces, such as those in the respiratory, digestive, and genitourinary tracts. It is a key player in mucosal immunity. It prevents pathogens from colonizing and invading the mucosal surface. By forming immune complexes, it binds to pathogens and excretes them from the body, playing a crucial role in preventing infections in the respiratory and digestive tracts.

[0202] IgM is the primary immunoglobulin secreted by the body during the early stages of the primary immune response, typically present as a pentamer. It rapidly binds to antigens and initiates an immune response, but its affinity is relatively low. As the immune response progresses, IgM secretion gradually decreases, while that of other immunoglobulins, such as IgG, increases. IgM is valuable in the early diagnosis of certain infectious diseases.

[0203] IgE: Primarily associated with allergic reactions, IgE antibodies are produced by B cells when the body is exposed to allergens. IgE antibodies can bind to Fc receptors on the surfaces of mast cells and basophils. Upon re-exposure to the same allergen, the allergen binds to the IgE on the cell surface, triggering the release of bioactive substances such as histamine, leading to an allergic reaction. This plays a key role in the development and progression of allergic diseases.

[0204] In this embodiment, specifically: the sorting fluid used in the flow cytometry sorting technology is phosphate buffered saline supplemented with 2% fetal bovine serum and 1 mM ethylenediaminetetraacetic acid; wherein, the sorting fluid used in the flow cytometry sorting technology, which is composed of phosphate buffered saline supplemented with 2% fetal bovine serum and 1 mM ethylenediaminetetraacetic acid, can provide a suitable cell environment for the flow cytometry sorting process through the synergistic cooperation of their respective mechanisms of action, thereby ensuring the accuracy of sorting, cell viability, and the reliability of the sorting results.

[0205] In this embodiment, specifically, when removing outliers and missing values, the Grubbs criterion is used to determine outliers, and the multiple imputation method is used to fill missing values. The Grubbs criterion is a statistically based method used to determine outliers in a set of data. Its basic concept 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 based on certain statistical laws, thereby determining whether it is an outlier. The multiple imputation method is an effective method for handling missing data values. Based on the data distribution law and information about other related variables, it generates reasonable imputation values ​​through multiple simulations to replace the missing data points. Its core concept is that data missingness is not completely random, but rather 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.

[0206] 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 operational procedures for establishing a patient immune response database, it is possible to effectively store and scientifically manage important data related to patient immune responses in clinical research on cell drugs, providing strong support for clinical practice and scientific research.

[0207] Figure 5 This is a functional module diagram of a patient immune response monitoring system for clinical research on cell-based drugs according to an embodiment of the present application. Figure 5 As shown, the patient immune response monitoring system for clinical research of cell-based drugs includes: a sample collection module, a sample pretreatment module, a multi-dimensional immune index detection module, a cell-based 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;

[0208] The sample collection module is used to obtain peripheral blood samples from patients at multiple predetermined time points before and after the administration of the cell drug, and treat them with anticoagulants. 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;

[0209] A sample preprocessing module is used to preprocess the collected peripheral blood samples to obtain patient sample components;

[0210] 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;

[0211] Cellular drug-specific immune response detection module, used to analyze and detect patient sample components based on cellular drug-specific immune response detection technology and build a cellular drug-specific immune response detection database;

[0212] The data integration and analysis module is used to perform dimensionality reduction and clustering 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;

[0213] 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;

[0214] Report and suggestion module, which is used to provide clinicians with real-time monitoring reports and treatment suggestions based on the assessment and prediction results;

[0215] 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.

[0216] 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.

[0217] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For system-related embodiments, since they are generally similar to method-related embodiments, their description is relatively simple. For relevant details, refer to the description of the method-related embodiments.

[0218] 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 principles of the present invention should be included in the scope of protection 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-processing the peripheral blood sample to obtain patient sample components; Analyze and detect patient sample components based on multi-dimensional immune index detection technology to build an immune index detection database. The immune index detection database is used to analyze immune cell subsets, cytokines, immunoglobulins, and T cell receptors to reflect the patient's basic immune status; The specific steps of constructing the immune index detection database are as follows: S201. Based on flow cytometry detection technology, use sorting fluid to detect the number and proportion of immune cell subsets in patient sample components and the expression changes of their surface markers to obtain immune cell subset analysis data; S202. Detecting cytokine concentrations in patient sample components based on enzyme-linked immunosorbent assay or Luminex liquid phase chip technology, and obtaining cytokine detection data by analyzing changes in cytokine concentrations; S203. Determine the content of immunoglobulin in the patient sample components based on immunoturbidimetry, evaluate changes in the patient's immune function, and obtain immunoglobulin test data; S204. Sequencing the T cell receptor genes in the patient sample components based on high-throughput sequencing technology, analyzing the diversity changes of the T cell receptors, and obtaining T cell receptor analysis data; 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; Analyze and detect patient sample components based on cellular drug-specific immune response detection technology to build a cellular drug-specific immune response detection database, which is used to analyze specific antibodies and specific T cell responses against cellular drugs and evaluate specific immune responses triggered by cellular drugs; The specific steps of constructing a cell drug specific immune response detection database are as follows: S301. Detecting specific antibodies against the cellular drug in the patient sample components based on a specific immunoassay method to obtain specific antibody detection data; S302. Detect specific T cell responses to cellular drugs in patient sample components based on enzyme-linked immunosorbent assay combined with flow cytometry sorting technology to obtain cellular 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; 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 cluster analysis was used to construct the patient immune response map; The method for constructing a patient immune response profile comprises the following steps: S401. Clean 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 principal component analysis algorithm, and solving principal component calculation using singular value decomposition algorithm; S403, clustering the extracted principal components using hierarchical cluster analysis, measuring the similarity between samples using Euclidean distance during the clustering process, calculating the inter-class distance using the average linkage method, and obtaining 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, and the positions of the sample points are determined in the multidimensional space coordinate system to obtain a patient immune response map framework; S405: attaching the patient's clinical characteristic information to the corresponding sample points, and optimizing and displaying the patient's immune response map framework using graphics rendering technology to construct the patient's 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; Provide monitoring reports and treatment recommendations to clinicians based on assessment and prediction results; Establish a patient immune response database to store and manage patient immune response maps, evaluation and prediction results, as well as monitoring reports and treatment recommendations.

2. The method for monitoring patient immune responses for clinical research on cell-based drugs according to claim 1, wherein: 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.

3. The method for monitoring patient immune responses for clinical research on cell-based drugs according to claim 1, wherein: 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.

4. The method for monitoring patient immune responses for clinical research on cell-based drugs according to claim 1, wherein: The separation solution is phosphate buffered saline supplemented with 2% fetal bovine serum and 1 mM ethylenediaminetetraacetic acid.

5. The method for monitoring patient immune responses for clinical research on cell-based drugs according to claim 1, wherein: When removing outliers and missing values, outliers are determined using the Grubbs criterion, and missing values ​​are filled using the multiple imputation method.

6. A patient immune response monitoring system for cell-based drug clinical research, applied to the patient immune response monitoring method for cell-based drug clinical research according to any one of claims 1 to 5, 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 from the patient at multiple predetermined time points before and after 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 sample to obtain patient sample components; The multidimensional immune index detection module is 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 patient sample components based on the cell-drug specific immune response detection technology and 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 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.

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