Application of helper T cell detection reagent in preparation of product for predicting ICIs adverse reaction
By detecting the levels of Tph, Tfh, Th1 and Th2 cells in the baseline period of patients, the risk of immune checkpoint inhibitor-related adverse reactions (irAEs) is predicted, which solves the problem of lack of effective prediction methods in the prior art and improves the safety and effectiveness of ICIs treatment.
Patent Information
- Application Number
- CN202411779239.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-05
- Publication Date
- 2025-03-11
AI Technical Summary
The existing technology lacks effective prediction methods and cannot predict the risk of immune checkpoint inhibitor-related adverse reactions (irAEs) in advance, which mainly depends on the doctor's subjective judgment and experience.
The levels of Tph, Tfh, Th1 and Th2 cells in PBMCs at baseline were detected by assistive T cell detection reagents, and their proportion of total CD4+ T cells was calculated to predict the risk of irAEs.
The advance prediction of the risk of irAEs is achieved, providing a basis for clinical decision-making, and improving the safety and effectiveness of ICIs treatment.
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Figure CN119595906A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of early prediction of immune checkpoint inhibitor (ICIs)-related adverse reactions (irAEs), and specifically to the use of a baseline helper T cell detection reagent in the preparation of a product for predicting ICIs adverse reactions. Background Art
[0002] The emergence of immune checkpoint inhibitors (ICIs) has significantly changed the landscape of cancer treatment, but immune checkpoint inhibitor-related adverse reactions (irAEs) caused by ICIs have limited their application. irAEs are a group of autoimmune diseases that affect multiple organs or systems. They have a high incidence (60%-80%) and are complex and diverse, posing challenges to clinicians in managing patients receiving ICIs. At present, there is a lack of effective predictive methods for the management of irAEs in clinical practice. It mainly relies on the subjective judgment and experience of doctors, and diagnosis and treatment are performed after the patient develops symptoms, and the risk of irAEs cannot be predicted in advance. Summary of the invention
[0003] The purpose of the present invention is to address the problem that the risk of irAEs cannot be predicted in advance in clinical practice, and to provide the use of helper T cell detection reagents in the preparation of products for predicting adverse reactions of ICIs, so as to predict the risk of irAEs in advance, provide a basis for clinical decision-making, and improve the safety and effectiveness of ICIs treatment.
[0004] The technical solution of the present invention is described in detail as follows:
[0005] In a first aspect, the present invention provides the use of a helper T cell detection reagent in the preparation of a product for predicting adverse reactions of immune checkpoint inhibitors, wherein the detection reagent is used to detect the level of helper T cells in PBMCs of patients during a baseline period, wherein the helper T cells are total CD4+T cells, Tph, Tfh, Th1 and Th2.
[0006] CD4+T cells, i.e., helper T cells (Th, Helper T cell), have CD4 receptor molecules on the cell membrane surface. They are the largest subpopulation of T cells, with many classifications and different functions. Previous studies have shown that T cells are the main targets of ICIs, and CD4+T cell subpopulations play an important role in anti-tumor immunity and immune homeostasis. Among them, Tfh cells express specific surface molecules and secrete cytokine IL-21, and their increase is associated with a variety of autoimmune diseases. Studies have shown that ICIs treatment can promote the dysregulated response of Tfh cells to cause irAEs, but research on the T cell spectrum of patients with irAEs before receiving ICIs treatment is still limited, and there is a lack of irAEs prediction models before ICIs treatment. After a lot of research and verification, the present invention found that Tph, Tfh, Th1 and Th2 in peripheral blood mononuclear cells (PBMC) of patients can be used as biomarkers to effectively predict the risk of irAEs. Therefore, by detecting the levels of Tph, Tfh, Th1 and Th2 in the patient's PBMC, the risk of irAEs in the patient can be further calculated.
[0007] The baseline period refers to the period before the start of intervention. In the present invention, intervention mainly refers to immunotherapy intervention.
[0008] Optionally or preferably, in the above application, the detection reagent is used for flow cytometry detection.
[0009] Optionally or preferably, in the above application, the phenotypic characteristics of the total CD4+T cells are CD4+, the phenotypic characteristics of Tph are CD3+CD4+CXCR5-ICOS+PD-1+, the phenotypic characteristics of Tfh are CD3+CD4+CXCR5+ICOS+PD-1+, the phenotypic characteristics of Th1 are CD3+CD4+CXCR3+CCR6-, and the phenotypic characteristics of Th2 are CD3+CD4+CXCR3-CCR6-.
[0010] Optionally or preferably, in the above application, the detection reagent comprises:
[0011] Antibody Alexa Fluor 700CD3, supplier Biolegend, clone number: UCHT1, used to recognize the helper T cell surface antigen CD3,
[0012] Antibody FITC CD4, supplier Biolegend, clone number: RM4-5, used to recognize the helper T cell surface antigen CD4,
[0013] Antibody PE-CF594 CXCR3, supplier BD Biosciences, clone number: 1C6, used to recognize the helper T cell surface antigen CXCR3,
[0014] Antibody Brilliant Violet 650CCR6, supplier Biolegend, clone number: 11A9, used to recognize the helper T cell surface antigen CCR6,
[0015] Antibody Alexa Fluor 647CXCR5, supplier BD Biosciences, clone number: 2G8, used to recognize the helper T cell surface antigen CXCR5,
[0016] Antibody PE-Cy7 PD-1, supplier Biolegend, clone number: J43, used to recognize the helper T cell surface antigen PD-1,
[0017] The antibody Brilliant Violet 421ICOS, supplier Biolegend, clone number: C398.4A, is used to recognize the helper T cell surface antigen ICOS.
[0018] In a second aspect, the present invention provides a system for predicting adverse reactions of immune checkpoint inhibitors, the system comprising:
[0019] A data collection unit is used to obtain the patient's age data and the percentage values of Tph, Tfh, Th1 and Th2 in the patient's PBMC in the baseline period to the total CD4+T cells;
[0020] The irAEs prediction unit is used to calculate the prediction results of adverse reactions of immune checkpoint inhibitors based on the percentage value of the quantity obtained by the data acquisition unit. The calculation formula is as follows:
[0021]
[0022]
[0023] Among them, Tph, Tfh, Th1, and Th2 represent the percentage of Tph, Tfh, Th1, and Th2 in the total CD4 + T cells, respectively, and Age represents the patient's age;
[0024] The result display unit is used to display the results calculated by the irAEs prediction unit to the user.
[0025] In a third aspect, the present invention provides a device for predicting adverse reactions of immune checkpoint inhibitors, comprising:
[0026] at least one processor;
[0027] A memory is communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions executing the process executed by the above system.
[0028] In a fourth aspect, the present invention provides a storage medium storing computer executable instructions, wherein the computer executable instructions can execute the process executed by the above-mentioned system.
[0029] Compared with the prior art, the present invention has the following beneficial effects:
[0030] The present invention can calculate the risk of irAEs in the patient by detecting the levels of specific helper T cells in the patient's peripheral blood, including follicular helper T cells (Tfh), peripheral helper T cells (Tph), helper T cell 1 (T helper cell 1, Th1) and helper T cell 2 (T helper cell 2, Th2), and calculating their proportion of total CD4+T cells and other indicators. Therefore, the present invention provides a new biomarker for predicting irAEs.
[0031] The immune checkpoint inhibitor adverse reaction prediction system of the present invention can predict the risk of occurrence of immune checkpoint inhibitor (ICIs) related adverse reactions (immune-related adverse events, irAEs) in advance, which helps clinicians to adjust treatment plans in time and improve the safety and effectiveness of treatment.
[0032] The immune checkpoint inhibitor adverse reaction prediction system of the present invention has high predictive ability and calibration, and can provide valuable reference for clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 This is a staining diagram for flow cytometry detection of T cell subsets. The meanings of the figure symbols are: CD3+ represents T lymphocytes; CD4+ represents helper T cells; CD8+ represents cytotoxic T cells; CXCR5, CCR6, CXCR3, etc. represent different chemokine receptors; ICOS represents induced co-stimulatory molecules; PD-1 represents programmed death receptor 1; Th1, Th2, Th17, etc. represent helper T cell subsets 1, 2, 17; Tfh represents follicular helper T cells; Tph represents peripheral helper T cells.
[0034] Figure 2 The difference in the proportion of PBMC lymphocyte subsets between the AE group and the NAE group at baseline.
[0035] Figure 3 The results of GSVA analysis of differentially expressed genes in PBMCs of patients in the AE group and NAE group at baseline.
[0036] Figure 4 The consistency index (C-index) and area under the ROC curve (AUC) results were calculated using the internal bootstrap validation method, where A is the irAEs consistency index, B is the area under the irAEs ROC curve, C is the consistency index of grade 2-4 irAEs, and D is the area under the ROC curve of grade 2-4 irAEs.
[0037] Figure 5 Graphical user interface for the irAE occurrence prediction model. DETAILED DESCRIPTION
[0038] In order to make the technical personnel in this field better understand the present application scheme, the present application will be clearly and completely described below in conjunction with the embodiments and drawings. Obviously, the described embodiments are only embodiments of a part of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of this application. The instruments and reagents used in the embodiments are all derived from commercial channels unless otherwise specified.
[0039] Example 1
[0040] Patient recruitment
[0041] Recruitment conditions: Patients with unresectable tumors were recruited from the Department of Gastrointestinal Tumors, Peking University Cancer Hospital. They were pathologically diagnosed with invasive gastrointestinal cancer, were about to receive anti-PD-1 / PD-L1 treatment, were ≥18 years old, had an ECOG performance status of 0 or 1, had an expected survival of ≥3 months, and had not received immunotherapy (including anti-PD-1 / PD-L1 treatment). Patients who had received other systemic treatments (such as targeted therapy, radiotherapy, cell therapy), had rheumatic diseases (such as SLE, RA, SS), blood system diseases (such as idiopathic thrombocytopenic purpura, myelodysplastic syndrome), and required the use of corticosteroids or immunomodulators were excluded.
[0042] Peripheral blood sample collection
[0043] EDTA-anticoagulated whole blood was collected, and peripheral blood lymphocytes (PBMCs) were separated by Ficoll-Hypaque density gradient centrifugation and immediately subjected to RNA extraction and flow cytometry analysis. Serum was separated and stored at -80°C for proteomic analysis.
[0044] Collection time points: within 3 days before the first immunotherapy (baseline period), 6 weeks after ICIs treatment (early period), and when irAEs occur during follow-up.
[0045] Patient follow-up and irAEs grading
[0046] The patients were followed up for 6 months, and the status and grade of irAEs were evaluated according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Patients with irAEs were divided into AE group, and those without irAEs were divided into NAE group.
[0047] Flow cytometry
[0048] PBMCs were stained with fluorescein-conjugated monoclonal antibodies for 30 minutes (4°C in the dark), and the relative proportions of T cell subsets were analyzed using Beckman and CytExpert software. Specific cell markers are shown in Table 1, reagents used are shown in Table 2, and flow cytometry detection of T cell subset staining is shown in Figure 1 .
[0049] Table 1 Phenotypic characteristics of T cell subsets detected by flow cytometry
[0050]
[0051]
[0052] Table 2 List of antibody reagents used in flow cytometry
[0053] antigen Fluorescence channels supplier Clone number CD3 Alexa Fluor 700 Biolegend UCHT1 CD4 FITC Biolegend RM4-5 CXCR3 PE-CF594 BD Biosciences 1C6 CCR6 Brilliant Violet 650 Biolegend 11A9 CXCR5 Alexa Fluor 647 BD Biosciences 2G8 PD-1 PE-Cy7 Biolegend J43 ICOS Brilliant Violet 421 Biolegend C398.4A
[0054] The differences in the proportions of PBMC lymphocyte subsets between AE and NAE patients during the baseline period (within 3 days before the first immunotherapy) were shown in Figure 2 ,from Figure 2 The distribution of some lymphocyte subsets in the two groups is shown. It can be seen that the differences in Tph proportion, Tfh proportion, and Th1 / Th2 are most obvious between the two groups.
[0055] RNA sequencing (RNA-Seq) and data analysis
[0056] Total RNA was extracted from PBMCs using TRizol. After mRNA was purified using poly-T oligo-attached magnetic beads, fragmentation, double-stranded cDNA synthesis, RNA degradation, end trimming, 3' end adenylation and adapter ligation were performed. Library fragments were purified using the AMPure XP system, and cDNA fragments with a length of 370 bp-420 bp were selected. After PCR amplification, the final library was purified again. The library was quantified using Qubit 2.0 Fluorometer and qRT-PCR, and the insert size was detected using Agilent 2100 Bioanalyzer. Qualified libraries were sequenced using Illumina NovaSeq 6000 to generate 150 bp paired-end reads.
[0057] The raw data were processed to remove adapters, reads containing N bases, and low-quality reads to obtain clean data. The clean reads were mapped to the reference genome (GRCh38.p13) using Hisat2, and FeatureCounts counted the reads and calculated the FPKM value. DESeq2 was used for differential gene expression analysis. The p value was adjusted using the Benjamini&Hochberg method, and p<0.05 was the threshold for significant differential expression. GO, KEGG, Reactome, and GSEA enrichment analysis were performed using clusterProfiler and GSEA tools. The results of GSVA analysis of differentially expressed genes in PBMCs of patients in the AE group and NAE group at baseline are shown in Tables 3 and Figure 3 .
[0058] Table 3 List of genes analyzed by GSVA
[0059]
[0060] Figure 3 The results showed that the gene sets of Tfh, Th1, Th2, CD4+T Cell, and CD8+T Cell were most significantly differentially expressed in PBMCs of patients in the AE group and NAE group at baseline.
[0061] Prediction model building
[0062] The age and baseline Tph, Tfh, Th1, Th2 and other related indicator data of 38 patients were collected, of whom 18 developed irAE (10 developed grade 2-4 irAE, and 8 developed grade 1 irAE), and 20 did not develop irAE.
[0063] The logistic regression model was constructed using the “lrm” function in the “rms” package in R software.
[0064] The model-related formula is as follows:
[0065]
[0066]
[0067] In the formula, Tph, Tfh, Th1, and Th2 represent the percentage of each cell subset in the total CD4+T cells; Age represents age. The grading standard is based on the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.
[0068] The internal bootstrap validation method was used to calculate the consistency index (C-index) and the area under the ROC curve (AUC): 1) The model for predicting the occurrence of irAEs had a C-index of 0.822 and R 2 The C-index of the model for predicting grade 2-4 irAEs was 0.784, and the AUC was 0.875 (95% confidence interval: 0.765-0.985). 2 is 0.261, and AUC is 0.846 (95% confidence interval: 0.717-0.976), see Figure 4 .
[0069] Based on the prediction model constructed above, we developed a user-friendly application software. Figure 5 The graphical user interface is shown. When using it, input relevant parameter values and click Calculation to get the conclusion.
[0070] For example, when a patient's age is 60, Tph is 2.5%, Tfh is 2.0%, Th1 is 30%, and Th2 is 60%, the input is: 60, 2.5, 2.0, 30, 60. The model calculates that the probability of irAEs in this patient is 82.397%, and the probability of 2-4 irAEs is 35.629%. Based on these prediction results and other clinical conditions of the patient, such as tumor type and treatment plan, doctors can comprehensively judge whether it is necessary to adjust the treatment strategy, such as strengthening monitoring, adjusting drug dosage, or combining other drugs to prevent the occurrence of irAEs.
[0071] The invention concept is described in detail using specific examples herein, and the description of the above embodiments is only used to help understand the core idea of the invention. It should be pointed out that any obvious modification, equivalent substitution or other improvement made by a person of ordinary skill in the art without departing from the invention concept should be included in the protection scope of the present invention.
Claims
1. The use of a helper T cell detection reagent in the preparation of a product for predicting adverse reactions of immune checkpoint inhibitors, characterized in that: The detection reagent is used to detect the level of helper T cells in PBMC of patients during the baseline period, and the helper T cells are total CD4+T cells, Tph, Tfh, Th1 and Th2.
2. The use according to claim 1, characterized in that: The detection reagent is used for flow cytometry detection.
3. The use according to claim 1, characterized in that: The phenotypic characteristics of the total CD4+T cells are CD4+, the phenotypic characteristics of Tph are CD3+CD4+CXCR5-ICOS+PD-1+, the phenotypic characteristics of Tfh are CD3+CD4+CXCR5+ICOS+PD-1+, the phenotypic characteristics of Th1 are CD3+CD4+CXCR3+CCR6-, and the phenotypic characteristics of Th2 are CD3+CD4+CXCR3-CCR6-.
4. The use according to claim 3, characterized in that: The detection reagent includes: Antibody Alexa Fluor 700CD3, supplier Biolegend, clone number: UCHT1, used to recognize the helper T cell surface antigen CD3, Antibody FITC CD4, supplier Biolegend, clone number: RM4-5, used to recognize the helper T cell surface antigen CD4, Antibody PE-CF594 CXCR3, supplier BD Biosciences, clone number: 1C6, used to recognize the helper T cell surface antigen CXCR3, Antibody Brilliant Violet 650CCR6, supplier Biolegend, clone number: 11A9, used to recognize the helper T cell surface antigen CCR6, Antibody Alexa Fluor 647CXCR5, supplier BD Biosciences, clone number: 2G8, used to recognize the helper T cell surface antigen CXCR5, Antibody PE-Cy7 PD-1, supplier Biolegend, clone number: J43, used to recognize the helper T cell surface antigen PD-1, The antibody Brilliant Violet 421ICOS, supplier Biolegend, clone number: C398.4A, is used to recognize the helper T cell surface antigen ICOS.
5. An immune checkpoint inhibitor adverse reaction prediction system, characterized in that: The system comprises: A data collection unit is used to obtain the patient's age data and the percentage values of Tph, Tfh, Th1 and Th2 in the patient's PBMC in the baseline period to the total CD4+T cells; The irAEs prediction unit is used to calculate the prediction results of adverse reactions of immune checkpoint inhibitors based on the percentage value of the quantity obtained by the data acquisition unit. The calculation formula is as follows: Among them, Tph, Tfh, Th1, and Th2 represent the percentage of Tph, Tfh, Th1, and Th2 in the total CD4 + T cells, respectively, and Age represents the patient's age; The result display unit is used to display the results calculated by the irAEs prediction unit to the user.
6. An immune checkpoint inhibitor adverse reaction prediction device, characterized in that: include: at least one processor; A memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions executing the process executed by the system of claim 5.
7. A storage medium storing computer executable instructions, characterized in that: The computer executable instructions are capable of executing the process performed by the system of claim 5 .