Application of reagent for detecting B cell surface marker in peripheral blood in preparation of diagnostic kit

Abstract

The invention discloses an application of a reagent for detecting B cell surface markers in peripheral blood in preparation of a diagnostic kit, which is characterized in that on the basis of spectral flow data of B lymphocytes in peripheral blood, a chronic graft versus host disease patient characteristic subgroup is found, and eight surface markers of the subgroup are clearly identified; wherein three surface markers can be used for accurately defining a B cell characteristic subgroup of the chronic graft versus host disease patient; establishing a monitoring system for distinguishing a chronic graft versus host disease patient and a non-chronic graft versus host disease patient: cGPS; the cGPS system can be used for dynamically monitoring the condition of a chronic graft versus host disease patient and accurately judging whether the patient is in the active period of the disease or not; meanwhile, the three surface markers can also be used for diagnosing the health state of a human body and effectively distinguishing health from sub-health.

Description

Technical Field

[0001] The present invention relates to the technical field of graft-versus-host disease diagnosis, and in particular to the use of a reagent for detecting B cell surface markers in peripheral blood in the preparation of a diagnostic kit. Background Art

[0002] Hematopoietic stem cell transplantation (HSCT) has become the most effective means of clinical treatment of hematological malignancies and congenital diseases of the hematopoietic system. Graft-versus-host disease (GVHD) is the main complication of allogeneic hematopoietic stem cell transplantation and one of the main causes of non-relapse mortality after transplantation. GVHD is a series of clinical manifestations caused by the recognition of host (receptor) antigens by donor lymphocytes, the occurrence of allogeneic immune responses, and the attack of host tissues and organs. GVHD is divided into acute GVHD (aGVHD) and chronic GVHD (cGVHD) according to its occurrence time and pathological characteristics. aGVHD is mainly caused by inflammatory damage to recipient tissues caused by radiotherapy and chemotherapy pretreatment. After the activation of donor T lymphocytes, a large number of inflammatory factors (IFN-γ, TNF-α, IL-1, etc.) are produced, attacking the host target tissues (skin, mucosa, liver, intestines). Unlike aGVHD, which is mainly caused by donor T cells, the pathogenesis of cGVHD is more complicated. Antigen-presenting cells migrate to secondary lymphoid organs, causing activation and differentiation of various T helper cells (such as Th1 / Tc1 cells, Th17 cells, Tfh cells) and B cells that produce autoantibodies, thereby leading to damage and fibrosis of target tissues such as the skin, mucous membranes, gastrointestinal tract, and glands, showing immunopathological characteristics similar to autoimmune diseases.

[0003] The pathogenesis of cGVHD is complex, the clinical manifestations of patients are diverse, the individual differences are large, and the course of the disease is prolonged. If timely and appropriate intervention is not given, it will seriously affect the patient's quality of life and even endanger his life. Although the emergence of a variety of new immunosuppressants has provided an effective treatment for cGVHD, the treatment effect is not stable because it is often not intervened in the early stages of the disease. Therefore, effective measures are urgently needed to diagnose the disease early, or to subdivide the patients and formulate targeted treatment plans. Only in this way can the clinical treatment effect of cGVHD be improved, and the key to forming effective measures is to find indicators that accurately and timely reflect the condition of cGVHD.

[0004] Unfortunately, the current diagnosis and evaluation of cGVHD mainly rely on the patient's clinical manifestations and pathological indicators, which leads to delayed and inaccurate monitoring of the disease. The current disease evaluation system cannot meet the clinical needs of early diagnosis and subdivision of cGVHD patients. Therefore, exploring sensitive and specific cell or molecular markers and establishing a monitoring system that dynamically reflects the development of the disease is a key link in solving the dilemma of cGVHD diagnosis and treatment. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned deficiencies of the prior art and to provide a reagent for detecting B cell surface markers in peripheral blood for use in preparing a diagnostic kit.

[0006] The first object of the present invention is to provide a reagent for detecting B cell surface markers in peripheral blood for use in preparing a diagnostic kit for graft-versus-host disease.

[0007] The second object of the present invention is to provide a reagent for detecting B cell surface markers in peripheral blood for use in preparing a diagnostic kit for graft-versus-host disease.

[0008] The third object of the present invention is to provide a diagnostic kit.

[0009] The fourth object of the present invention is to provide a composition of reagents for diagnosing B cell surface markers in peripheral blood.

[0010] In order to achieve the above object, the present invention is implemented by the following scheme:

[0011] The invention discloses a reagent for detecting B cell surface markers in peripheral blood in the preparation of a diagnostic kit for graft-versus-host disease, wherein the B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

[0012] That is, patients who do not express CD20 and CD268 on the surface of B cells and express CD5, CD27, CD38, CD86, CD269 and CD319 on the surface of B cells are cGVHD and non-GVHD patients.

[0013] Patients with cGVHD are those who have developed chronic graft-versus-host disease.

[0014] Non-GVHD patients are those who have not yet developed chronic graft-versus-host disease but may develop chronic graft-versus-host disease in the future.

[0015] Preferably, the B cell surface markers are the negative surface marker CD20, and the positive surface markers: CD27 and / or CD86.

[0016] Preferably, the diagnosis of GVHD is to distinguish chronic GVHD patients from non-chronic GVHD patients, wherein chronic GVHD patients include patients who have developed the disease and those who have received hematopoietic stem cell transplantation but have not developed the disease.

[0017] More preferably, the chronic graft-versus-host disease patients include patients who have developed the disease and have not developed the disease within 6 months after receiving hematopoietic stem cell transplantation.

[0018] More preferably, the diagnosis of GVHD is to determine whether treatment for a chronic GVHD patient is effective.

[0019] Further preferably, the number of CD45 + and CD19 + In double positive cells, CD20 - Cell proportion and CD27 + Cell proportion and CD86 + The product of the cell proportions multiplied by 100 is taken as the cGPS score. The probability of not developing graft-versus-host disease after transplantation in patients with a cGPS score less than 1.15 is lower than the probability of not developing graft-versus-host disease after transplantation in patients with a cGPS score greater than or equal to 1.15.

[0020] Further preferably, the number of CD45 + and CD19 + In double positive cells, CD20 - Cell proportion and CD27 + Cell proportion and CD86 + The product of the cell percentages multiplied by 100 is taken as the cGPS score. The probability of disease progression after treatment in cGVHD patients with a cGPS score less than 1.505 is lower than that in cGVHD patients with a cGPS score greater than or equal to 1.505.

[0021] The present invention also seeks to protect the use of a reagent for detecting peripheral blood B cell surface markers in the preparation of a kit for diagnosing human health status, wherein the B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

[0022] Preferably, the B cell surface markers are the negative surface marker CD20, and the positive surface markers: CD27 and / or CD86.

[0023] Preferably, the diagnosis of human health status is to differentiate human health, and the human health status includes: health and / or sub-health.

[0024] More preferably, the number of CD45 + and CD19 + In double positive cells, CD20 - Cell proportion and CD27 + Cell proportion and CD86 + The product of the cell percentages multiplied by 100 is the hGPS score; an hGPS score of less than or equal to 0.54 is considered a healthy state; an hGPS score greater than 0.54 is considered a sub-healthy state.

[0025] The present invention also claims a diagnostic kit containing reagents for detecting B cell surface markers in peripheral blood, wherein the B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

[0026] Preferably, the B cell surface markers are the negative surface marker CD20, and the positive surface markers: CD27 and CD86.

[0027] Preferably, the reagent for detecting B cell surface markers in peripheral blood is a flow cytometry-based analysis reagent.

[0028] Preferably, the flow cytometry-based analysis reagent is a spectral flow cytometry-based reagent.

[0029] The present invention also claims a composition of reagents for detecting B cell surface markers in peripheral blood, wherein the B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention finds the cGVHD characteristic subpopulation based on the spectral flow cytometry data of peripheral blood B lymphocytes, and clearly identifies 8 surface markers of the subpopulation; and further finds that only 3 of the surface markers are needed to accurately define the cGVHD B cell characteristic subpopulation; according to the flow cytometric data of the 3 surface markers in normal and disease states, digital conversion is performed to establish a dynamic monitoring system for distinguishing the progression of cGVHD: cGPS (cGVHD Progress Score); the cGPS system can dynamically monitor the condition of patients with chronic graft-versus-host disease and accurately determine whether the patient is in the active stage of the disease; at the same time, the above 3 surface markers can also be used to diagnose the health status of the human body and effectively distinguish between healthy and sub-healthy states. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 cGVHD characteristic B cell subsets: A: Peripheral blood B cells of cohort 1 samples can be divided into three mechanical subgroups; B: The specific proportion of cells in the three mechanical subgroups; horizontal axis: mechanical subgroups, vertical axis: the proportion of cells from different sample sources in each subgroup (red: cGVHD patients, gray: healthy controls, blue: non-cGVHD); The red frame marks the cGVHD characteristic B cell subsets;

[0033] Figure 2 Surface markers of cGVHD characteristic B cell subsets: green boxes indicate negative surface markers; red boxes indicate positive surface markers;

[0034] Figure 3 The cGVHD characteristic B cell subsets perfectly distinguish cGVHD / non-GVHD from healthy controls: The line graph is a schematic diagram of the area under the curve, the ordinate represents the true positive rate, the abscissa represents the false positive rate, and the area under the blue line is the AUC area;

[0035] Figure 4 The AUC scores, specificity and sensitivity evaluation results of the final six groups of candidate surface marker combinations are shown in Figure 1. A: ROC curves and AUC scores of the six groups of candidate surface marker combinations; the horizontal axis represents the false positive rate (1-specificity), and the vertical axis represents the true positive rate (sensitivity); B: Detailed parameters of the above surface marker combinations, including AUC scores, and 95% confidence intervals, specificity and sensitivity corresponding to the AUC scores;

[0036] Figure 5There are statistical differences in cGPS scores between groups of samples: box plot results of cGPS scores; the horizontal axis represents the classification of healthy controls, non-GVHD, and cGVHD samples; each point in the figure represents the corresponding value of the sample, the length of the long vertical line in the box plot represents the fluctuation range of the data in this group, and the three horizontal lines in the box plot represent the upper quartile, median, and lower quartile of the data in this group from top to bottom; the color represents the source of the sample: gray represents healthy controls, blue represents non-GVHD patients, and red represents cGVHD patients; ** represents p<0.01, **** represents p<0.0001, and ns represents no significant difference;

[0037] Figure 6 cGPS threshold for non-GVHD patients: A: ROC curve for distinguishing whether non-GVHD will develop; B: Specific indicators corresponding to ROC include AUC, score threshold, specificity, sensitivity, and accuracy; including the best value and 95% confidence interval;

[0038] Figure 7 The cGPS threshold is effective for determining whether non-GVHD patients will develop the disease: the horizontal axis represents the cGPS score, and the vertical axis represents the sample frequency corresponding to the cGPS score; the blue dotted line represents the cGPS threshold of 1.150;

[0039] Figure 8 cGPS threshold for distinguishing whether cGVHD patients will deteriorate after treatment: A: ROC curve for distinguishing whether cGVHD patients will deteriorate after treatment; B: Specific indicators corresponding to ROC include AUC, score threshold, specificity, sensitivity, accuracy; including the best value and 95% confidence interval;

[0040] Fig. 9 The cGPS threshold is used to effectively determine whether a GVHD patient will deteriorate after treatment: the horizontal axis represents the cGPS score, and the vertical axis represents the sample frequency corresponding to the cGPS score; the blue dotted line represents the cGPS threshold of 1.505;

[0041] Fig.10 The box plots corresponding to the hGPS scores of 51 healthy people in Example 7 are summarized in the box plots. Each point represents the hGPS score corresponding to the sample. The length of the long vertical line in the box plot represents the fluctuation range of the data. The three horizontal lines in the box plot are, from top to bottom, the upper quartile line, the median line, and the lower quartile line.

[0042] Fig.11It is a box plot of the hGPS scores of 12 healthy participants before and after COVID-19 infection in Example 7; the horizontal axis represents day 0: before COVID-19 infection, day 1: after COVID-19 infection; each point in the figure represents the corresponding numerical value of the sample, the length of the long vertical line in the box plot represents the fluctuation range of this group of data, and the three horizontal lines of the box plot represent the upper quartile, median and lower quartile of this group of data from top to bottom; the color represents whether it is infected: blue represents before COVID-19 infection, and red represents after COVID-19 infection; * indicates p<0.05. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples of the specification. The examples are only used to explain the present invention and are not used to limit the scope of the present invention. The test methods used in the following examples are conventional methods unless otherwise specified; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

[0044] Example 1 Analysis of B cell subsets with cGVHD characteristics

[0045] 1. Sample Source

[0046] With informed consent, 15 samples were enrolled, including 5 healthy controls (no hematopoietic stem cell transplantation), 5 non-cGVHD patients (hematopoietic stem cell transplantation and chronic GVHD has not yet occurred) and 5 cGVHD patients (hematopoietic stem cell transplantation and chronic GVHD has already occurred), as the research cohort-1. Detailed information is shown in Table 1:

[0047] Table 1 Basic statistics of patients in study cohort-1

[0048]

[0049]

[0050] Among them, Source of graft: graft source, PBSCT: peripheral blood, BMT+PBSCT: bone marrow + peripheral blood, PBSCT+UCBT: peripheral blood + umbilical cord blood, Source of donor: donor source, Sibling: sibling, Unrelated: unrelated, Parents: parents, Children: children, Relatives: related relatives, HLA matching: human leukocyte antigen matching, Identical matched: fully matched, Haploid matched: half matched, Non-identical: completely unmatched, GVHD Prophylaxis: GVHD prophylaxis, CSA: cyclosporine A, MTX: methotrexate, MMF: mycophenolate mofetil, ATG: antithymocyte globulin, PT-Cy: post-transplant cyclophosphamide, Initial disease: primary disease, AML: acute myeloid leukemia, ALL: acute lymphocytic leukemia, CML: chronic myeloid leukemia, MDS: myelodysplastic syndrome.

[0051] 2. Experimental Methods

[0052] 1. Spectral flow cytometry

[0053] Spectral flow analysis was performed on 15 samples. The specific analysis method is as follows:

[0054] (1) Peripheral blood mononuclear cells (PBMCs) were isolated according to standard procedures;

[0055] (2) According to the dye spectral parameters such as stain index, cross-stain index, and similarity index provided by the Cytek Aurora spectral flow analyzer, the 20 selected classical B cell surface markers were matched with fluorescein. The information of B cell detection surface markers is shown in Table 2;

[0056] Table 2 Spectral flow cytometry detection of B cell surface marker combinations

[0057]

[0058]

[0059] (3) Analyze PBMCs according to the standard process provided by Cytek Aurora spectral flow cytometer to obtain spectral flow cytometric raw data.

[0060] 2. Determination of cGVHD-specific B cell subsets

[0061] It is divided into three steps: processing of raw spectral data (1-2), determination of cGVHD characteristic B cell subpopulations (3), and screening of surface markers of the subpopulations (4). The detailed methodology is introduced as follows:

[0062] (1) The raw data of spectral flow cytometry were normalized and downsampled using FlowJo v10.6.2 to obtain spectral flow cytometry data;

[0063] (2) According to the standard process of processing single-cell data by Seurat (v4.0.5), the spectral flow data obtained in step (1) were subjected to dimensionality reduction and clustering to obtain cell subpopulations;

[0064] (3) Among the cell subpopulations obtained in step (2), the cell subpopulation in which cGVHD accounts for more than 70% is used as a cGVHD characteristic B cell subpopulation;

[0065] (4) Using the plotting function VlnPlot, the expression of the surface markers in the cGVHD characteristic B cell subpopulation obtained in step (3) is displayed, and based on this, the surface markers that can specifically identify the characteristic subpopulation are screened out.

[0066] 3. Experimental Results

[0067] The results are as follows Figure 1 As shown, according to the data of spectral flow cytometry analysis, 15 samples were divided into 3 mechanical subgroups; among them, subgroup-2 cGVHD-derived cells accounted for 74.3% (2577 cells), non-GVHD-derived cells accounted for 17.7% (578 cells), and cells from the healthy control group accounted for only 9% (313 cells); Based on this, subgroup-2 may be a cGVHD characteristic B cell subgroup;

[0068] like Figure 2 As shown, 8 of the 20 surface markers used for spectral flow analysis can effectively identify this characteristic subpopulation, including two negative surface markers (CD20 and CD268) and six positive surface markers (CD5, CD27, CD38, CD86, CD269 and CD319). Negative surface markers are not expressed on the surface of B cells, and positive surface markers are expressed on the surface of B cells.

[0069] That is, for the cGVHD characteristic B cell subset (subset-2), CD20 and CD268 are not expressed on the B cell surface, and CD5, CD27, CD38, CD86, CD269 and CD319 are not expressed on the B cell surface.

[0070] Example 2 Analysis of B cell subsets with cGVHD characteristics

[0071] 1. Sample Source

[0072] With informed consent, 17 healthy controls (who had not undergone hematopoietic stem cell transplantation), 15 non-cGVHD patients (who had undergone chronic hematopoietic stem cell transplantation and had not yet developed GVHD), and 32 cGVHD patients (who had undergone chronic hematopoietic stem cell transplantation and had already developed GVHD) were enrolled as the study cohort-2. Detailed information is shown in Table 3 (SD: stable after treatment, which means that the patient did not experience worsening or improvement of the disease after drug treatment; DP: worsening after treatment, which means that after drug treatment, the patient experienced worsening of the disease in one or more organs. PR: partial remission, which means that after drug treatment, one or more organs of the patient showed improvement in the disease, but it did not completely disappear. CR: complete remission, which means that after drug treatment, all disease symptoms of the patient disappeared. Note: If the patient's condition improves in some organs and worsens in other organs at the same time after drug treatment, it is still counted as worsening after treatment):

[0073] Table 3 Basic statistics of patients in study cohort-2

[0074]

[0075]

[0076] Among them, SD, non-CVHD: no disease after transplantation; DP, non-GVHD: disease progression to cGVHD; PR / CR, cGVHD: complete / partial remission; SD, cGVHD: stable disease; DP, cGVHD: worsening of the disease. Source of graft: graft source, PBSCT: peripheral blood, BMT+PBSCT: bone marrow + peripheral blood, PBSCT+UCBT: peripheral blood + umbilical cord blood, Source of donor: donor source, Sibling: sibling, Unrelated: unrelated, Parents: parents, Children: children, Relatives: related relatives, HLA matching: human leukocyte antigen matching, Identical matched: fully matched, Haploid matched: half matched, Non-identical: completely unmatched, GVHD Prophylaxis: GVHD prophylaxis, CSA: cyclosporine A, MTX: methotrexate, MMF: mycophenolate mofetil, ATG: antithymocyte globulin, PT-Cy: post-transplant cyclophosphamide, Initial disease: primary disease, AML: acute myeloid leukemia, ALL: acute lymphocytic leukemia, CML: chronic myeloid leukemia, MDS: myelodysplastic syndrome.

[0077] According to the protocol of Example 1, the samples were analyzed.

[0078] 3. Experimental Results

[0079] The results are as follows Figure 3 As shown, based on Example 1, 8 surface markers (two negative surface markers (CD20 and CD268) and five positive surface markers (CD5, CD27, CD38, CD86, CD269 and CD319) were obtained. Figure 2 As shown in the figure, the spectral flow cytometry data obtained can effectively distinguish whether the patient has cGVHD or not, indicating that this characteristic subpopulation is commonly found in cGVHD patients and can be used as a biomarker to identify the disease.

[0080] Example 3 Optimization of cGVHD characteristic B cell subset surface markers

[0081] 1. Experimental Methods

[0082] (1) Using the software package sklearn (v 0.23.0), the function combinations for calculating permutations and combinations was introduced according to the module itertools. This function was used to randomly extract 2, 3, 4, 5, 6, and 7 of the 8 surface markers that identify cGVHD characteristic B cell subsets, and all combinations of these surface markers were obtained (a total of 246 groups);

[0083] (2) Based on each combination of surface markers, the fluorescence intensity of the above surface markers was extracted from the spectral data of study cohort-1 (5 healthy controls, 5 non-GVHD and 5 cGVHD patients) for subsequent machine learning analysis;

[0084] (3) Machine learning parameter setting: First, 70% of the samples of the research cohort-1 were used as training samples and the remaining 30% as test samples through the function train_test_split module;

[0085] (4) Machine learning parameter setting: Build a random forest model through the function RandomForestClssfier;

[0086] (5) Under the parameters set in step (3) and step (4), the rfc score corresponding to each group of surface marker combinations is obtained by the function rfc.score (the rfc score can be a judgment accuracy score); the larger the rfc score, the stronger the ability of the corresponding surface marker combination to distinguish between healthy controls and patients (patients with cGVHD);

[0087] (6) All surface marker combinations were ranked from high to low in terms of their accuracy scores for the test samples, and the top 10 combinations with the highest accuracy scores of 2, 3, 4, 5, 6, and 7 surface markers were selected as candidate surface marker combinations (a total of 58 groups) for further analysis to confirm their ability to distinguish healthy controls from patients (patients with cGVHD);

[0088] (7) Further expand the sample size, and enroll 17 healthy controls, 15 non-GVHD and 32 cGVHD patients (research cohort-2) with informed consent. For the 58 groups of surface marker combinations obtained in step (6), use the area under the curve (AUC) to quantify their ability to distinguish healthy controls from patients (patients with cGVHD); the AUC value can simultaneously reflect the sensitivity and specificity of the surface marker combination in distinguishing healthy controls from patients (patients with cGVHD). AUC equals 1, indicating that the surface marker combination has perfect discrimination ability;

[0089] (8) Among the combinations of 2 or 3 surface markers, the combination with an AUC value greater than 0.9 was selected as the optimal surface marker combination that can accurately identify the cGVHD characteristic B cell subsets.

[0090] 2. Experimental Results

[0091] Based on the data of the research cohort-1, among the combinations of 2, 3, 4, 5, 6, and 7 surface markers, the top 10 with the largest accuracy scores were selected as candidate surface marker combinations, totaling 58 groups. The results are shown in Figure 4.

[0092] Table 4 List of candidate surface marker combinations

[0093]

[0094] Based on the data of the study cohort-2, the AUC scores of these candidate combinations for distinguishing cGVHD patients from healthy controls were calculated. There were 2 or 3 marker combinations with AUC scores greater than 0.9 in the top 6 groups, indicating that these 6 groups of surface marker combinations were sufficient to accurately identify cGVHD characteristic B cell subsets and effectively distinguish between patients with and without cGVHD.

[0095] The AUC scores of the above six groups of surface marker combinations were compared in detail. The results are as follows Figure 4 As shown in the figure, a combination of three surface markers (CD20, CD27 and CD86) had the highest AUC, specificity and sensitivity scores (AUC score: 0.9583, specificity: 0.871, sensitivity: 1.000), which was significantly better than other combinations, indicating that the combination of three surface markers can accurately identify cGVHD characteristic B cell subsets and is the optimal combination.

[0096] Example 4 A cGVHD disease monitoring system: cGPS

[0097] 1. Experimental Methods

[0098] 17 healthy controls, 15 non-GVHD and 32 cGVHD patients were enrolled, and the cell percentage data of each surface marker in the optimal surface marker combination (CD20, CD27 and CD86) of the samples were obtained according to the standard process of conventional flow cytometry analysis;

[0099] The above data are digitally converted to obtain the cGPS score of each sample. The formula is as follows:

[0100] Score=P CD20- ×P CD27+ ×P CD86+ .

[0101] (ie: P CD20- Indicates CD20 in B lymphocytes (i.e. CD45+CD19+) in peripheral blood - The proportion of cells, P CD27+ CD27 + The proportion of cells, P CD86+ CD86 + This formula describes the ratio of peripheral blood B lymphocytes that meet the CD20 - CD27 + CD86 + The condition of the cells.)

[0102] 2. Experimental Results

[0103] The results are as follows Figure 5 As shown, for 17 healthy controls, 15 non-GVHD and 32 cGVHD patients, the distribution of cGPS scores of each sample was statistically analyzed, and it was found that cGPS can effectively distinguish healthy controls, non-GVHD and cGVHD patients.

[0104] Example 5 Clinical study on the cGPS threshold for determining whether non-GVHD occurs

[0105] 1. Experimental Methods

[0106] The 15 non-GVHD patients enrolled in Example 2 (Study Cohort-2) were used as research subjects, and their disease progression after 3 months was statistically analyzed. The cGPS scores of the research subjects were obtained according to the method of Example 4;

[0107] Non-GVHD patients were divided into two groups according to whether they developed the disease or not; the cGPS score with the largest AUC value was determined through ROC (Receiver Operating Characteristic) analysis, and the cGPS score was the threshold for judging whether non-GVHD patients would develop the disease; the specific score threshold was confirmed; and the bootstrap was used to perform 10,000 resamplings to obtain the 95% confidence interval of the AUC value corresponding to the cGPS threshold.

[0108] 2. Experimental Results

[0109] The results are as follows Figure 6 As shown, through ROC analysis combined with the Bootstrap resampling method, it was determined that when the cGPS score was 1.150, the efficacy of judging whether non-GVHD would develop was the best (AUC value was 0.9773, specificity was 0.818, sensitivity was 1, accuracy was 0.8667, 95% confidence interval was 1.075-2.64);

[0110] The results are as follows Figure 7 As shown in the figure, non-GVHD patients were divided into two groups according to the cGPS score of 1.150, and the cGPS score distribution curves were drawn for each group. It was found that none of the non-GVHD patients with a cGPS score less than 1.15 developed the disease; on the contrary, 4 patients with a score higher than 1.150, or 80%, had their disease progress to cGVHD. This result once again proves that the cGPS score of 1.150 is the threshold for judging whether non-GVHD patients will develop the disease.

[0111] Example 6 Retrospective clinical study of the cGPS threshold for determining whether cGVHD patients will deteriorate after treatment

[0112] 1. Experimental Methods

[0113] The 32 cGVHD patients enrolled in Example 2 (Research Cohort-2) were used as research subjects, and their cGPS scores were obtained according to the method of Implementation 4. The patients were divided into two groups according to whether they would deteriorate after treatment. The cGPS score with the largest AUC value was determined by ROC analysis, and the cGPS score was the threshold for judging whether the cGVHD patient would deteriorate after treatment; the specific score threshold was confirmed; and 10,000 resamplings were performed by bootstrap to obtain the 95% confidence interval of the AUC value corresponding to the cGPS threshold.

[0114] 2. Experimental Results

[0115] The results are as follows Figure 8As shown in the figure, through ROC analysis combined with the Bootstrap resampling method, it was determined that when the cGPS score was 1.505, the efficacy of judging whether cGVHD would worsen after treatment was the best (AUC value was 0.8704; specificity was 1.0000, sensitivity was 0.7778, accuracy was 0.8330, and 95% confidence interval was 1.4-1.84); cGVHD patients were divided into two groups according to the cGPS score of 1.505, and the cGPS score distribution curves were drawn for each group. It was found that no cGVHD patients with a cGPS score less than 1.505 had worsening of their condition after treatment;

[0116] On the contrary Fig. 9 As shown in the figure, 6 patients (55%) with a score higher than 1.505 had worsening symptoms after treatment. The above results prove that a cGPS score of 1.505 is the threshold for judging whether a cGVHD patient will worsen after treatment.

[0117] Example 7 A system for distinguishing human health status: hGPS

[0118] 1. Experimental methods

[0119] 51 healthy people were enrolled (referring to those with normal routine physical examination indicators including blood routine examination and no chronic underlying diseases)

[0120] According to the standard process of conventional flow cytometry sorting, the cell percentage data of each surface marker in the optimal surface markers (CD20, CD27 and CD86) of the volunteer samples were obtained:

[0121] The above data are digitally converted to obtain the hGPS score of each sample. The formula is as follows:

[0122] Score=P CD20- ×P CD27+ ×P CD86+ .

[0123] (ie: P CD20- Indicates CD20 in B lymphocytes (i.e. CD45+CD19+) in peripheral blood - The proportion of cells, P CD27+ CD27 + The proportion of cells, P CD86+ CD86 + This formula describes the ratio of peripheral blood B lymphocytes that meet the CD20 - CD27 + CD86 + The condition of the cells.)

[0124] The box plot was drawn using the built-in drawing function geom_boxplot in the ggplot2 software package, and the 95% quantile of each group was calculated to visually analyze the distribution of hGPS scores in healthy people.

[0125] 2. Experimental results

[0126] The box plot of 51 healthy people (referring to people with normal routine physical examination indicators including blood routine examination and no chronic underlying diseases) is as follows Fig.10 shown.

[0127] The results show that 95% of healthy people have hGPS scores no higher than 0.54, and only 5% of people have hGPS scores higher than 0.54.

[0128] Example 8 Changes in hGPS scores before and after infection with COVID-19

[0129] 1. Experimental methods

[0130] Twelve healthy volunteers (referring to persons with normal various routine physical examination indicators including blood routine tests and no chronic underlying diseases) were enrolled in the group. The 12 healthy volunteers were tested according to the method described in Example 7, and the corresponding hGPS scores were recorded (recorded as day 0); after the 12 healthy volunteers were infected with the new coronavirus and recovered, they were tested again according to the method described in Example 7, and the corresponding hGPS scores were recorded (recorded as day 1).

[0131] 2. Experimental results

[0132] The score changes before and after COVID-19 infection are as follows Fig.11 ; The results showed that the overall hGPS mean of the volunteers increased after being infected with the new coronavirus, and the score was significantly different from that before the infection with the new coronavirus (p<0.05); in addition, there were 3 samples after being infected with the new coronavirus, or 25% of the healthy volunteers whose scores exceeded 0.54.

[0133] The results show that the health status of healthy volunteers is prone to change after being infected with the new coronavirus; and some people are in a sub-healthy state after recovering from the new coronavirus infection.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above descriptions and ideas. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting B cell surface markers in peripheral blood in the preparation of a diagnostic kit for graft-versus-host disease, characterized in that: The B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

2. The use according to claim 1, characterized in that: The B cell surface markers are negative surface marker CD20, and positive surface markers: CD27 and / or CD86.

3. The use according to claim 1, characterized in that: The diagnosis of GVHD is to distinguish chronic GVHD patients from non-chronic GVHD patients. Chronic GVHD patients include patients who have developed the disease and those who have received hematopoietic stem cell transplantation but have not developed the disease.

4. The use according to claim 3, characterized in that: The diagnosis of graft-versus-host disease is to determine whether the treatment for a chronic graft-versus-host disease patient is effective.

5. The use according to any one of claims 3 to 4, characterized in that: Count the CD45 + and CD19 + In double positive cells, CD20 - The product of the cell proportion and the proportion of CD27 cells and CD86 double-positive cells, multiplied by 100, is taken as the cGPS score; the probability of patients who do not develop graft-versus-host disease after transplantation with a cGPS score less than 1.15 is lower than the probability of patients who do not develop graft-versus-host disease after transplantation with a cGPS score greater than or equal to 1.

15.

6. Use of a reagent for detecting B cell surface markers in peripheral blood in the preparation of a kit for diagnosing human health status, characterized in that: The B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

7. The use according to claim 6, characterized in that: The diagnosing of the health status of a human body is to distinguish the health status of a human body, and the health status of a human body includes: health and / or sub-health.

8. The use according to claim 7, characterized in that: Count the CD45 + and CD19 + In double positive cells, CD20 - Cell proportion and CD27 + and CD86 + The product of the percentage of double positive cells multiplied by 100 is taken as the hGPS score; an hGPS score less than or equal to 0.54 is considered a healthy state; an hGPS score greater than 0.54 is considered a sub-healthy state.

9. A diagnostic kit, characterized in that Contains reagents for detecting B cell surface markers in peripheral blood, wherein the B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

10. A composition of reagents for detecting B cell surface markers in peripheral blood, characterized in that: The B cell surface markers are two negative surface markers: CD20 and / or CD268, and six positive surface markers: CD5, CD27, CD38, CD86, CD269 and / or CD319.

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