Markers for predicting serious adverse reaction of immune combined chemotherapy and application of markers

Abstract

The invention belongs to the technical field of biological medicines, and particularly relates to a group of markers for predicting serious adverse reactions of wide-period small cell lung cancer immune combined chemotherapy and application thereof. The biomarker combination is easy and convenient to sample, and biopsy tissue does not need to be provided. The total T cell percentage is CD3 + / CD45 +, the T cell toxic cell percentage is CD3 + / CD8 + / CD45 +, and the combined combination of the two can predict the occurrence of treatment-related adverse reactions greater than or equal to level 3 of a wide-period small cell lung cancer patient treated by ICI combined chemotherapy, and the combined prediction efficiency is superior to that of a single index. In the training set, the area under the AUC curve of the combined prediction model is 0.852, the optimal Cut-off value is 0.39, the sensitivity is 87.5%, and the specificity is 83.3%. In the constructed prediction model by applying independent external verification, the AUC of the combined prediction model is 0.847, the sensitivity of the model is 85.2%, and the specificity is 73.9%. The method has practicability, and provides precise treatment guidance for clinical medicine.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a group of predictive biomarkers for severe adverse reactions to immunotherapy combined with chemotherapy for extensive-stage small cell lung cancer and their applications. Background Technology

[0002] Small cell lung cancer (SCLC) is the second most common malignant tumor of the chest, accounting for 10-15% of all lung cancers. It is characterized by high invasiveness, rapid proliferation, and easy metastasis. Extensive-stage small cell lung cancer (ES-SCLC) accounts for as much as 60-70% of all cases. This type of tumor is characterized by rapid growth, strong metastatic ability, and extremely poor clinical prognosis. [1] In recent years, based on the IMpower133 and CASPIAN phase III clinical trials, the survival benefit of immunotherapy combined with chemotherapy has been fully validated, increasing the median overall survival (OS) of ES-SCLC from 10.3-10.5 months in the chemotherapy group to 12.3-12.9 months. [2-4] This has led to the adoption of chemotherapy regimens combining programmed cell death ligand 1 (PD-L1) inhibitors, such as atezolizumab and durvalumab, with platinum-based etoposide as the new standard of care for first-line treatment, representing a significant advancement in the treatment of ES-SCLC.

[0003] However, compared to non-small cell lung cancer (NSCLC) patients, SCLC exhibits an "immune desert" phenotype characterized by insufficient immune cell infiltration, low PD-L1 expression levels, and antigen presentation defects, making tumor cells more susceptible to evading the body's immune system surveillance and attack. This results in ES-SCLC patients receiving more limited benefits from immune checkpoint inhibitor (ICI) therapy, shorter duration of sustained response, survival benefits typically only appearing after 6 months of treatment, and significant heterogeneity in clinical responses (PAZ-ARES L, CHEN Y, REINMUTH N, et al. Durvalumab, with or without tremelimumab, plus platinum-etoposide in first-line treatment of extensive-stage small-cell lung cancer: 3-year overall survival update from CASPIAN [J]. ESMO Open, 2022, 7(2): 100408.). Furthermore, while immunotherapy brings benefits, it also carries significant immune-related toxicities. Some patients experience severe adverse events such as severe liver damage, myocarditis, and immune-related pneumonia after only one ICI treatment. Therefore, there is an urgent clinical need to develop effective biomarkers that can predict the prognosis and adverse events of immunotherapy before treatment, thereby promoting precision and personalized treatment for small cell lung cancer.

[0004] While immunotherapy combined with chemotherapy exerts its anti-tumor effects, the treatment-related adverse reactions it induces have become a key factor affecting patients' treatment tolerance and quality of life. Clinical data shows that in ES-SCLC patients receiving immunotherapy combined with chemotherapy, the incidence of any grade of treatment-related adverse reaction is as high as 96.3%, with a ≥ grade 3 serious adverse reaction rate of 58.8%. These mainly include hematologic toxicities such as neutropenia (52.5%), thrombocytopenia (21.3%), and anemia (7.5%), as well as non-hematologic toxicities such as gastrointestinal reactions, myocarditis, and abnormal liver enzymes. More alarmingly, some serious adverse reactions can be life-threatening. One patient died due to severe liver injury complicated by myocarditis, and 6.3% of patients were forced to discontinue treatment due to adverse reactions, not only interrupting the anti-tumor treatment process but also significantly increasing the patients' medical burden and mortality risk. Therefore, how to accurately identify patients at high risk of serious adverse reactions before treatment and take early intervention measures has become a core issue that urgently needs to be addressed in the clinical application of immunotherapy combined with chemotherapy in ES-SCLC.

[0005] Previous studies have attempted to explore the association between some biomarkers and adverse reactions to immunotherapy, but a unified and effective prediction system has not yet been established. For example, some studies have suggested that inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) may be related to immune-related toxicity. However, these markers are easily affected by various factors such as infection and anemia, resulting in insufficient stability and specificity. Meanwhile, tumor-related markers such as PD-L1 expression level and tumor mutational burden (TMB) mainly focus on predicting treatment efficacy, and their predictive value for adverse reactions has not yet been confirmed (HORN L, MANSFIELD AS, SZCZESNA A, et al. First-line atezolizumab plus chemotherapy in extensive-stage small-cell lung cancer [J]. N Engl J Med, 2018, 379(23): 2220-2229.;PAZ-ARES L, DVORKIN M, CHEN Y, et al. Durvalumab plus platinum-etoposide versus platinum-etoposide in first-line treatment of extensive-stage small-cell lung cancer (CASPIAN): a randomised, controlled, Open-label, phase 3 trial [J]. The Lancet, 2019, 394(10212): 1929-1939.). Furthermore, existing research largely focuses on exploring single biomarkers, lacking in-depth studies on the combined prediction of multiple biomarkers, resulting in predictive accuracy that fails to meet clinical needs. New biomarkers are urgently needed to provide precise guidance for clinical medicine.

[0006] Total T cell percentage (CD3) + / CD45 + ) and percentage of T-cell cytotoxic cells (CD3) + CD8 + / CD45 +The percentage of lymphocytes is a core indicator reflecting the body's immune function. From a biological perspective, total T cells are a core component of the body's adaptive immune system, including multiple subsets such as helper T cells, regulatory T cells, and T cell toxic cells. Their percentage directly reflects the overall number and immune response potential of the body's T cell population (BASU A, RAMAMOORTHI G, ALBERT G, et al. Differentiation and Regulation of TH Cells: A Balancing Act for Cancer Immunotherapy [J]. Front Immunol, 2021, 12: 669474.). T cell toxic cells (mainly CD8) + T cells are key effector cells that mediate anti-tumor immunity. By recognizing the antigenic peptide-MHC complex on the surface of tumor cells, they release cytotoxic molecules such as perforin and granzymes, directly lysing tumor cells. They are the core force for the anti-tumor effect of immunotherapy combined with chemotherapy (KATO T, TOMIYAMA E, KOH Y, et al. A Potential Mechanism of Anticancer Immune Response Coincident With Immune-related Adverse Events in Patients With Renal Cell Carcinoma [J]. Anticancer Res,2020, 40(9): 4875-4883.).

[0007] Existing predictive methods generally suffer from a "lag effect," with most adverse reactions only gradually appearing after treatment has begun. By this time, patients have already suffered toxic damage, and even adjustments to the treatment plan are unlikely to completely reverse the damage. Therefore, there is an urgent clinical need for a predictive tool that can be implemented before treatment, is highly specific, and accurate, capable of identifying high-risk patients in advance, providing a scientific basis for individualized adjustments to treatment plans, and reducing the risk of serious adverse reactions from the outset. Summary of the Invention

[0008] This invention discovered the percentage of total T cells with CD3 + / CD45 + T cell cytotoxicity percentage CD3 + CD8 + / CD45 +， Furthermore, the combination of these two indicators can predict grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy, and the combined predictive efficacy is superior to that of a single indicator. Based on this, the present invention was completed.

[0009] In a first aspect, the present invention provides a set of biomarkers for predicting grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy, wherein the biomarkers are selected from the percentage of total T cells and CD3+. + / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + .

[0010] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patients experienced a grade ≥3 serious adverse reaction.

[0011] Furthermore, when the marker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patients experienced a grade ≥3 serious adverse reaction.

[0012] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors is used to calculate the P-value using a regression model, as shown in the following formula: Logit (P) = -9.077 + 0.104 * (CD3 + / CD45 + ) + 0.063 * (CD3) + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + The unit is % When P is greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction.

[0013] Secondly, the present invention provides the use of a set of biomarkers as described in the first aspect of the present invention in the preparation of reagents for predicting grade ≥3 treatment-related adverse reactions in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy; said biomarkers are selected from total T cell percentage CD3+ / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + .

[0014] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patients experienced a grade ≥3 serious adverse reaction.

[0015] Furthermore, when the marker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patients experienced a grade ≥3 serious adverse reaction.

[0016] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors is used to calculate the P-value using a regression model, as shown in the following formula: Logit (P) = -9.077 + 0.104 * (CD3 + / CD45 + ) + 0.063 * (CD3) + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + The unit is % When P is greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction.

[0017] Thirdly, the present invention provides a kit for predicting grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy, the kit containing reagents for detecting the expression levels of the set of biomarkers described in the first aspect; the biomarkers are selected from total T cell percentage CD3. + / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 +.

[0018] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patients experienced a grade ≥3 serious adverse reaction.

[0019] Furthermore, when the marker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patients experienced a grade ≥3 serious adverse reaction.

[0020] Furthermore, when the marker is the percentage of total T cells CD3 + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors is used to calculate the P-value using a regression model, as shown in the following formula: Logit (P) = -9.077 + 0.104 * (CD3 + / CD45 + ) + 0.063 * (CD3) + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + The unit is % When P is greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction.

[0021] Beneficial effects The biomarker combination proposed in this invention is easy to sample, does not require biopsy tissue, and only requires peripheral blood for testing.

[0022] The biomarker combination proposed in this invention has high accuracy: in the training set, the area under the AUC curve of the joint prediction model is 0.852, the optimal cut-off value is 0.39, the sensitivity is 87.5%, and the specificity is 83.3%. Total T cell percentage (CD3+) + / CD45 + The predicted AUC area under the curve was 0.807, the optimal cut-off value was 72.25%, the sensitivity was 81.3%, and the specificity was 75.0%. The percentage of T-cell cytotoxic cells (CD3+) was also measured.+ CD4 + / CD45 + The area under the AUC curve was 0.790, the optimal cut-off value was 28.8%, the sensitivity was 82.6%, and the specificity was 71.5%. The combined prediction was better than the prediction of a single indicator.

[0023] Independent external validation was applied to the constructed prediction model. The combined prediction model had an AUC of 0.847, a sensitivity of 85.2%, and a specificity of 73.9%. Total T cell percentage (CD3+) + / CD45 + The AUC was 0.801, the sensitivity was 77.8%, and the specificity was 65.2%. The percentage of T-cell cytotoxic cells (CD3+) was also measured. + CD8 + / CD45 + The accuracy was 0.773, the sensitivity was 74.1%, and the specificity was 60.9%. The combined prediction was better than the prediction of a single indicator.

[0024] This invention is practical and provides guidance for precision treatment in clinical medicine. Attached Figure Description

[0025] Figure 1 The ROC curves for the training set of single indicators and joint prediction models.

[0026] Figure 2 The ROC curves for a single indicator and a joint prediction model on an independent external validation set. Detailed Implementation

[0027] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the embodiments described below can be combined with each other as long as they do not conflict with each other.

[0028] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the experimental materials used in the following embodiments are all available through conventional commercial channels.

[0029] Example 1: Test Samples and Displacement Standards 1. Patient inclusion and exclusion criteria Inclusion criteria: (1) Histopathological diagnosis of SCLC; (2) Imaging examination confirms clinical stage as ES-SCLC; (3) First-line anti-tumor treatment with atezolizumab combined with etoposide + platinum-based chemotherapy regimen; (4) Measurable lesions; (5) Complete clinical pathology and laboratory data; (6) Signed informed consent form.

[0030] Exclusion criteria: (1) No clear histopathological diagnosis; (2) Previous anti-tumor treatment; (3) Active autoimmune disease; (4) Use of immunosuppressants or long-term use of hormones; (5) Comorbidity of other primary malignant tumors; (6) No efficacy evaluation or loss to follow-up.

[0031] 2. Test Samples and Materials One day prior to the first antitumor treatment, 6 ml of fasting peripheral venous blood was collected from the patient in the morning. Of this, 3 ml of anticoagulated whole blood was used for lymphocyte subset detection, and 3 ml of anticoagulated whole blood was used for plasma cytokine detection.

[0032] Lymphocyte subsets were detected by staining leukocytes, T cells, B cells, and NK cells with fluorescently labeled monoclonal antibodies. Flow cytometry was then used to analyze the expression of cell differentiation antigens, and cell size, intracellular granules, and cell classification were determined. The monoclonal antibodies, staining reagents, flow cytometer, and result analysis software used were all products of BD Biosciences, Inc. (USA). The CD3 clone number was SK7, CD16 was B73.1, CD56 was NCAM16.2, CD45 was 2D1 (HLe-1), CD4 was SK3, CD19 was SJ25C1, and CD8 was SK1.

[0033] Plasma cytokines were quantitatively determined using flow cytometry multiplex protein quantification technology. The levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17A, IL-17F, IL-22, TNF-α, TNF-β, and interferon-γ (IFN-γ) in human plasma were measured. The reagent kits were from Tianjin Kuangbo Tongsheng Biotechnology Co., Ltd., and the flow cytometer and result analysis software were products of BD Biosciences, Inc., USA.

[0034] Treatment-related adverse events were classified according to Medical Dictionary for Regulatory Activities (MedDRA) version 27.1 and graded according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.

[0035] 3. Patient clinical characteristics and grouping Screening test:Of the 80 ES-SCLC patients who received ICI combined with chemotherapy, 62 were male (77.5%) and 18 were female (22.5%); the median age was 64.5 years, with 24 patients (30.0%) being younger than 60 years and 56 patients (70.0%) being older than 60 years; 58 patients (72.5%) were smokers and 22 patients (27.5%) were non-smokers; 16 patients (20.0%) had an ECOG score of 0, 60 patients (75.0%) had a score of 1, and 4 patients (5.0%) had a score of 2; 41 patients (51.3%) had only intrapleural metastases at diagnosis, and 39 patients (49.7%) had distant organ metastases (Table 1).

[0036] Table 1. Baseline data of patients in the screening trial Note: ECOG: Eastern Cooperative Oncology Group.

[0037] Training experiment: The 80 patients in cohort 1 were randomly divided into a training set (40 patients) and a validation set (40 patients). The training set was used for model training, and the validation set was used to validate the model's performance. There were no differences in clinicopathological information between the training and validation sets.

[0038] Independent external validation tests: Fifty patients who met the inclusion and exclusion criteria but were different from the screening and training trials were selected, and samples were collected to form an independent external validation set. The clinicopathological characteristics are shown in Table 2.

[0039] There were no significant differences in the patients' clinicopathological information.

[0040] Table 2. Baseline data of patients in independent external validation trials Note: ECOG: Eastern Cooperative Oncology Group.

[0041] Example 2 Screening Test 1. Test Methods All adverse reactions were recorded during ICI combined with chemotherapy in 80 ES-SCLC patients; the relationship between lymphocyte subsets, plasma cytokine levels and the occurrence of treatment-related serious adverse reactions was analyzed; the significance of the correlation of all factors was analyzed using statistical methods.

[0042] 2. Test Results As shown in Table 3, the incidence of any grade of treatment-related adverse reaction was 96.3%, with adverse reactions having an incidence greater than 30% being neutropenia, gastrointestinal reactions, thrombocytopenia, and anemia. The incidence of grade ≥3 serious adverse reactions was 58.8%, with neutropenia and thrombocytopenia having an incidence greater than 10%. Five patients (6.3%) discontinued treatment due to adverse reactions, and one patient (1.3%) died due to severe liver injury complicated by myocarditis.

[0043] Table 3. Adverse reactions of first-line ICI combined with chemotherapy in ES-SCLC patients Note: ES-SCLC: Extensive-stage small cell lung cancer; ICI: Immune checkpoint inhibitor.

[0044] As shown in Table 4, when analyzing the relationship between patient lymphocyte subsets, plasma cytokine levels, and the occurrence of serious treatment-related adverse reactions, the baseline total T cell percentage (CD3+) was found to be significant. + / CD45 + Patients with a high percentage of T cells (76.59%) had a significantly higher rate of ≥ grade 3 treatment-related adverse events than patients with a low percentage of T cells (69.40%). P =0.007); Baseline T cell cytotoxicity percentage (CD3+) + CD8 + / CD45 + Patients with a high percentage of T-cell cytotoxicity (30.28%) had a significantly higher rate of ≥ grade 3 treatment-related adverse events than patients with a low percentage of T-cell cytotoxicity (23.64%). P =0.026).

[0045] Table 4. Comparison of lymphocyte subsets and plasma cytokine levels among patient groups who experienced grade ≥3 serious adverse reactions. (x-±s) / (x-±s,%) Note: AE: Adverse Event; P <0.05* The difference is statistically significant.

[0046] Table 5 shows the influencing factors of grade ≥3 adverse events (AEs) in ES-SCLC patients receiving ICI combined with chemotherapy: whether grade ≥3 AEs occurred in ES-SCLC patients receiving ICI combined with chemotherapy was used as the dependent variable, and the two groups were compared. P Factors <0.05 (total T cell percentage CD3) + / CD45 + T cell cytotoxicity percentage CD3+ CD8 + / CD45 + Logistic regression analysis was performed using CD3 as a covariate. The results showed that the total percentage of CD3+ T cells... + / CD45 + T cell cytotoxicity percentage CD3 + CD8 + / CD45 + An elevated level of [a certain criterion] is an independent risk factor for grade ≥3 adverse events in patients with ES-SCLC receiving ICI combined with chemotherapy. P <0.05).

[0047] Table 5. Multivariate analysis of factors influencing grade ≥3 adverse events (AEs) in ES-SCLC patients after first-line ICI combined with chemotherapy. Note: ES-SCLC: Extensive-stage small cell lung cancer; ICI: Immune checkpoint inhibitor; AE: Adverse events; B: Regression coefficient; HR: Hazard ratio; CI: Confidence interval; P <0.05* The difference is statistically significant.

[0048] Example 3 Training Experiment 1. Model training Analysis showed that both total T cell percentage and T cell cytotoxicity percentage are independent risk factors for grade ≥3 serious adverse reactions. Both can be used individually to predict the occurrence of grade ≥3 serious adverse reactions. However, when the two are combined to construct a predictive model, the predictive efficacy will be greater than that of the individual indicators.

[0049] In the training set, after applying logistic regression analysis to combine the total T cell percentage and the T cell cytotoxicity percentage, the prognostic prediction probability calculated by the stepwise logistic regression model is as follows: ： Logit (P) = -9.077 + 0.104 * (CD3 + / CD45 + ) + 0.063 * (CD3) + CD8 + / CD45 + ); Among them, the percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + (Unit: %)

[0050] When P is greater than 0.39, the patient experiences a grade ≥3 serious adverse reaction.

[0051] like Figure 1 As shown, the percentage of total T cells (CD3) + / CD45 + The predicted AUC area under the curve was 0.807, the optimal cut-off value was 72.25%, the sensitivity was 81.3%, and the specificity was 75.0%. The percentage of T-cell cytotoxic cells (CD3+) was also measured. + CD4 + / CD45 + The area under the AUC curve for the first model was 0.790, with an optimal cut-off value of 28.8%, a sensitivity of 82.6%, and a specificity of 71.5%. The area under the AUC curve for the combined prediction model was 0.852, with an optimal cut-off value of 0.39, a sensitivity of 87.5%, and a specificity of 83.3%.

[0052] 2. Model Validation The joint prediction model was further validated using a validation set. With 0.39 as the critical value, the validation set data was substituted into the above formula. Compared with the actual results, the predicted results showed 27 true positives, 2 false positives, 7 true negatives, and 4 false negatives. The sensitivity was calculated as: true positives / (true positives + false negatives) * 100% = 87.10%; the specificity was calculated as: true negatives / (true negatives + false positives) * 100% = 77.78%.

[0053] Further application of the validation set to the percentage of total T cells (CD3) + / CD45 + The predictive efficacy was validated using 72.25% as the cutoff value. Compared with the actual results, the predicted results showed 25 true positives, 3 false positives, 6 true negatives, and 6 false negatives. The sensitivity was calculated as (true positives / (true positives + false negatives)) * 100% = 80.65%; the specificity was calculated as (true negatives / (true negatives + false positives)) * 100% = 66.67%.

[0054] Further validation set analysis was conducted on the percentage of cytotoxic T cells (CD3+). + CD4 + / CD45 + The predictive efficacy was validated using 28.8% as the cutoff value. Compared with the actual results, the predicted results showed 24 true positives, 3 false positives, 6 true negatives, and 7 false negatives. The sensitivity was calculated as (true positives / (true positives + false negatives)) * 100% = 77.42%; the specificity was calculated as (true negatives / (true negatives + false positives)) * 100% = 66.67%.

[0055] Example 4 Independent External Validation Test As shown in Table 6, the treatment-related adverse reactions that occurred in 50 patients during treatment were statistically analyzed. The incidence of grade ≥3 serious adverse reactions was 54.0%, among which grade ≥3 serious adverse reactions with an incidence greater than 10% were neutropenia and thrombocytopenia. Two patients (4.0%) discontinued treatment due to adverse reactions, and no patients died due to adverse reactions.

[0056] Table 6. Adverse reactions of ICI combined with chemotherapy in ES-SCLC patients in an independent external validation set Note: ES-SCLC: Extensive-stage small cell lung cancer; ICI: Immune checkpoint inhibitor.

[0057] As shown in Table 7, the baseline total T cell percentage (CD3+) in patients who experienced grade ≥3 treatment-related adverse events is shown. + / CD45 + The baseline total T cell percentage (CD3+) was 77.23 ± 10.26 in patients who did not experience grade ≥ 3 treatment-related adverse events. + / CD45 + The baseline percentage of cytotoxic T cells (CD3+) was 68.72 ± 10.72. This was the baseline percentage of cytotoxic T cells in patients who experienced grade ≥3 treatment-related adverse events. + CD8 + / CD45 + The baseline percentage of cytotoxic T cells (CD3+) in patients who did not experience grade ≥3 treatment-related adverse events was 31.34 ± 11.28. + CD8 + / CD45 + The value was 24.89 ± 11.96.

[0058] Table 7. Lymphocyte subset levels between groups in the independent external validation set who experienced grade 3 or higher serious adverse reactions. ( x -± s ， % ) Note: AE: adverse events; sample size is 50; P <0.05* The difference is statistically significant.

[0059] The aforementioned predicted probabilities and formulas based on the training set of patients: Logit (P) = -9.077 + 0.104 * (CD3 + / CD45 + ) + 0.063 * (CD3) + CD8 + / CD45 + ); Among them, the percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + (Unit: %)

[0060] When P is greater than 0.39, the patient experiences a grade ≥3 serious adverse reaction.

[0061] like Figure 2 As shown, the predictive model constructed by applying independent external validation set patients was validated, and the total T cell percentage (CD3) was calculated. + / CD45 + The AUC was 0.801, the sensitivity was 77.8%, and the specificity was 65.2%. The percentage of T-cell cytotoxic cells (CD3+) was also measured. + CD8 + / CD45 + The AUC of the combined prediction was 0.773, with a sensitivity of 74.1% and a specificity of 60.9%. The AUC of the combined prediction was 0.847, with a model sensitivity of 85.2% and a specificity of 73.9%.

Claims

1. The use of a set of biomarkers in the preparation of a reagent for predicting grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy; said biomarkers are selected from total T cell percentage CD3 + / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + .

2. The application as described in claim 1, where the biomarker is the percentage of total T cells and CD3+. + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of total T cells and CD3... + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors was used to calculate the P-value using a regression model. When P was greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction. The formula is as follows: Logit(P)= -9.077 + 0.104 * (CD3 + / CD45 + )+ 0.063 *(CD3 + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + (Unit: %) 3. A set of biomarkers for predicting grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy, wherein the biomarkers are selected from the percentage of total T cells and CD3+. + / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + .

4. The set of biomarkers as described in claim 3, wherein the biomarker is the percentage of total T cells CD3 + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of total T cells and CD3... + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors was used to calculate the P-value using a regression model. When P was greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction. The formula is as follows: Logit(P)= -9.077 + 0.104 * (CD3 + / CD45 + )+ 0.063 *(CD3 + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + (Unit: %) 5. A kit for predicting grade ≥3 treatment-related adverse events in patients with extensive-stage small cell lung cancer treated with ICI combined with chemotherapy, said kit comprising reagents for detecting the expression levels of a set of biomarkers as described in claim 3; said biomarkers being selected from total T cell percentage CD3 + / CD45 + and / or percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + .

6. The kit of claim 5, wherein the biomarker is the percentage of total T cells and CD3+. + / CD45 + Furthermore, when the percentage is greater than 72.25%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of T-cell cytotoxic cells and CD3... + CD8 + / CD45 + Furthermore, when the percentage is greater than 28.8%, the patient experiences a grade ≥3 serious adverse reaction; when the biomarker is the percentage of total T cells and CD3... + / CD45 + and percentage of T-cell cytotoxic cells CD3 + CD8 + / CD45 + The combination of factors was used to calculate the P-value using a regression model. When P was greater than 0.39, the patient experienced a grade ≥3 serious adverse reaction. The formula is as follows: Logit(P)= -9.077 + 0.104 * (CD3 + / CD45 + )+ 0.063 *(CD3 + CD8 + / CD45 + ); The percentage of total T cells (CD3) + / CD45 + ), percentage of cytotoxic T cells (CD3) + CD4 + / CD45 + (Unit: %) 7. The kit according to claim 5, wherein the kit is selected from ELISA detection kits, colloidal gold detection kits and / or flow cytometry detection kits.

8. The kit according to claim 5, wherein the diagnostic methods of the kit include direct methods, indirect methods, double-antibody sandwich methods, and competitive methods.

Images