Application of autoantibody to prediction of immunotherapy effect of lung cancer

Abstract

The invention relates to the technical field of biological medicine, in particular to application of an autoantibody in predicting the immunotherapy effect of lung cancer. The invention provides a marker for predicting the immunotherapy effect of lung cancer, the marker comprises one or more than two of an anti-MBP autoantibody, an anti-GLRA2 autoantibody, an anti-KRT20 autoantibody, an anti-SAE1 autoantibody, an anti-BIN1 autoantibody, an anti-PLA2R1 autoantibody, an anti-GAD2 autoantibody or an anti-SSB autoantibody, and when the eight autoantibodies are combined for prediction, the immunotherapy effect of lung cancer can be predicted. The kit has high sensitivity and specificity, the AUC value can reach 0.8 or above, and an important basis is provided for clinical curative effect prediction.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to the application of autoantibodies in predicting the immunotherapy effect of lung cancer. Background Art

[0002] As one of the main causes of cancer-related deaths, due to the lack of specific early symptoms, approximately 57% of non-small cell lung cancer patients are diagnosed at an advanced stage, which limits treatment options and reduces the overall survival rate (OS).

[0003] In recent years, immune checkpoint inhibitors (ICIs), especially inhibitors targeting programmed death-1 (PD-1) and its ligand PD-L1, have significantly prolonged the survival of patients with advanced lung cancer. However, the effectiveness of ICIs is limited by the variability of treatment responses, and only about 20% of non-small cell lung cancer (NSCLC) patients respond to ICIs treatment, and the median duration of remission is only about 12 months. Currently, some markers for predicting the treatment effect of ICIs have been disclosed in the prior art. For example, the patent document (CN119510762A) discloses the application of a detection reagent in preparing a kit for predicting the efficacy of anti-PD-1 immunotherapy for melanoma, and the biomarkers involved include CD68, CD3, S100, CD20, PDL1, and PD1. However, there are certain limitations in the autoantibodies for predicting the immunotherapy efficacy of lung cancer, and finding new and more effective biomarkers is crucial for optimizing immunotherapy strategies. Summary of the Invention

[0004] Through screening, the present invention has obtained a group of autoantibodies related to the immunotherapy effect, including one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody. By constructing a prediction model for the immunotherapy effect, the treatment effect of lung cancer patients treated with immunotherapy is predicted, and it has a good effect (the AUC value reaches 0.800). Based on this prediction model, precise intervention in drug selection, dose adjustment, and treatment timing can be achieved.

[0005] In the first aspect of the present invention, a marker for predicting the immunotherapy effect of lung cancer is provided, and the marker includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody.

[0006] In a specific embodiment of the present invention, the markers include anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, and anti-SSB autoantibody.

[0007] Preferably, the marker is one or more of IgG-type autoantibody, IgA-type autoantibody, IgD-type autoantibody, IgE-type autoantibody, or IgM-type autoantibody.

[0008] Preferably, the markers include one or more of anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, or anti-GAD2-IgG.

[0009] In a specific embodiment of the present invention, the markers include anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, and anti-GAD2-IgG.

[0010] In a second aspect of the present invention, there is provided a marker for evaluating the prognosis of lung cancer, and the marker includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody.

[0011] In a specific embodiment of the present invention, the markers include anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, and anti-SSB autoantibody.

[0012] Preferably, the marker is one or more of IgG-type autoantibody, IgA-type autoantibody, IgD-type autoantibody, IgE-type autoantibody, or IgM-type autoantibody.

[0013] Preferably, the markers include one or more of anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, or anti-GAD2-IgG.

[0014] In a specific embodiment of the present invention, the markers include anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, and anti-GAD2-IgG.

[0015] In a specific embodiment of the present invention, the markers are anti-KRT20-IgA and / or anti-GLRA2-IgA.

[0016] In the third aspect of the present invention, there is provided an application of an autoantibody as a marker in the preparation of a product for predicting the immunotherapeutic effect of lung cancer, and the autoantibody includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody.

[0017] Preferably, the autoantibody includes anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, and anti-SSB autoantibody.

[0018] Preferably, the autoantibody is one or more of IgG-type autoantibody, IgA-type autoantibody, IgD-type autoantibody, IgE-type autoantibody, or IgM-type autoantibody.

[0019] In a specific embodiment of the present invention, the autoantibody includes one or more of anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, or anti-GAD2-IgG.

[0020] In a specific embodiment of the present invention, the autoantibody includes anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-PLA2R1-IgG, anti-GAD2-IgG, and anti-SSB-IgA. Among them,

[0021] MBP: Myelin Basic Protein.

[0022] GLRA2: Glycine Receptor Alpha 2.

[0023] KRT20: keratin 20.

[0024] SAE1: SUMO1 activating enzyme subunit 1.

[0025] BIN1: bridging integrator 1.

[0026] PLA2R1: phospholipase A2 receptor 1.

[0027] GAD2: glutamate decarboxylase 2.

[0028] SSB: Sjōgren's syndrome-related antigen B.

[0029] Preferably, the immunotherapy includes treatment with immune checkpoint inhibitors.

[0030] Preferably, the immune checkpoint inhibitors include inhibitors against at least one of PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4, or CD160.

[0031] In a specific embodiment of the present invention, the immune checkpoint inhibitor is an anti-PD-1 monoclonal antibody.

[0032] Preferably, the product includes reagents for detecting autoantibodies.

[0033] Preferably, the product includes one or more of a chip, a kit, a test strip, a membrane strip, or a device.

[0034] Preferably, the product includes a prediction model for the effect of immunotherapy.

[0035] In a specific embodiment of the present invention, the product includes a prediction model for the effect of immunotherapy for lung cancer.

[0036] Preferably, the autoantibodies are autoantibodies in serum or plasma.

[0037] Preferably, the reagents for detecting autoantibodies include reagents for detecting the presence or absence or expression level of autoantibodies.

[0038] Preferably, the reagent for detecting autoantibodies includes, but is not limited to, one or more reagents required for chemiluminescence, chromogenic assay, immunofluorescence, ELISA, protein chip, liquid chromatography or mass spectrometry.

[0039] Preferably, the lung cancer includes small cell lung cancer or non-small cell lung cancer.

[0040] In a specific embodiment of the present invention, the lung cancer is non-small cell lung cancer.

[0041] In a specific embodiment of the present invention, predicting the immunotherapy effect of lung cancer includes comparing the expression level of autoantibodies with a threshold value after obtaining it. The threshold value is obtained from previous experiments, that is, through experiments and data analysis, the difference degree of the expression level of autoantibodies is determined between patients who respond to treatment and patients who do not respond to treatment.

[0042] Patients who respond to treatment refer to patients with CR / PR or SD exceeding 6 months, and patients who do not respond to treatment refer to patients with PD within 6 months after treatment. Among them, complete remission (CR), partial remission (PR), stable disease (SD) or progressive disease (PD) are defined according to the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) for NSCLC.

[0043] When the expression level of one or more of the above autoantibodies has a difference or significant difference compared with the threshold value (the difference is statistically significant, such as p < 0.05, p < 0.01, p < 0.001, p < 0.0001), it can be used to predict the treatment effect. For example:

[0044] 1) When the expression level of anti-MBP autoantibody (preferably anti-MBP-IgA) is higher than or significantly higher than the threshold value, it is predicted that the patient responds to treatment.

[0045] 2) When the expression level of anti-GLRA2 autoantibody (preferably anti-GLRA2-IgA) is higher than or significantly higher than the threshold value, it is predicted that the patient responds to treatment.

[0046] 3) When the expression level of anti-KRT20 autoantibody (preferably anti-KRT20-IgA) is higher than or significantly higher than the threshold value, it is predicted that the patient responds to treatment.

[0047] 4) When the expression level of anti-SAE1 autoantibody (preferably anti-SAE1-IgA) is higher than or significantly higher than the threshold value, it is predicted that the patient responds to treatment.

[0048] 5) When the expression level of anti-BIN1 autoantibody (preferably anti-BIN1-IgA) is higher than or significantly higher than the threshold value, it is predicted that the patient responds to treatment.

[0049] 6) When the expression level of anti-PLA2R1 autoantibody (preferably anti-PLA2R1-IgG) is lower than or significantly lower than the threshold, it is predicted that the patient responds to the treatment.

[0050] 7) When the expression level of anti-GAD2 autoantibody (preferably anti-GAD2-IgG) is lower than or significantly lower than the threshold, it is predicted that the patient responds to the treatment.

[0051] 8) When the expression level of anti-SSB autoantibody (preferably anti-SSB-IgA) is lower than or significantly lower than the threshold, it is predicted that the patient responds to the treatment.

[0052] In a specific embodiment of the present invention, the above 1)-8) can be used alone to predict whether the patient responds to the treatment, or can be used in combination.

[0053] In the fourth aspect of the present invention, there is provided an application of an autoantibody as a marker in the preparation of a product for evaluating the prognosis of lung cancer.

[0054] Preferably, the relevant definitions of the autoantibody and lung cancer are the same as those in the third aspect of the present invention.

[0055] When the expression level of one or more of the autoantibodies of the present invention has a difference or a significant difference compared with the threshold (the difference is statistically significant, for example, p < 0.05, p < 0.01, p < 0.001, p < 0.0001), it can be used for the prognosis evaluation of lung cancer patients. For example:

[0056] 1) When the expression level of anti-MBP autoantibody (preferably anti-MBP-IgA) is higher than or significantly higher than the threshold, it is evaluated that the patient has a good prognosis. 2) When the expression level of anti-GLRA2 autoantibody (preferably anti-GLRA2-IgA) is higher than or significantly higher than the threshold, it is evaluated that the patient has a good prognosis. 3) When the expression level of anti-KRT20 autoantibody (preferably anti-KRT20-IgA) is higher than or significantly higher than the threshold, it is evaluated that the patient has a good prognosis. 4) When the expression level of anti-SAE1 autoantibody (preferably anti-SAE1-IgA) is higher than or significantly higher than the threshold, it is evaluated that the patient has a good prognosis. 5) When the expression level of anti-BIN1 autoantibody (preferably anti-BIN1-IgA) is higher than or significantly higher than the threshold, it is evaluated that the patient has a good prognosis. 6) When the expression level of anti-PLA2R1 autoantibody (preferably anti-PLA2R1-IgG) is lower than or significantly lower than the threshold, it is evaluated that the patient has a good prognosis. 7) When the expression level of anti-GAD2 autoantibody (preferably anti-GAD2-IgG) is lower than or significantly lower than the threshold, it is evaluated that the patient has a good prognosis. 8) When the expression level of anti-SSB autoantibody (preferably anti-SSB-IgA) is lower than or significantly lower than the threshold, it is evaluated that the patient has a good prognosis.

[0057] In a specific embodiment of the present invention, the above 1)-8) can be used alone for the prognostic evaluation of patients, or can be used in combination.

[0058] In a specific embodiment of the present invention, the autoantibodies are anti-KRT20-IgA and / or anti-GLRA2-IgA.

[0059] The fifth aspect of the present invention provides an application of an autoantibody as a biomarker in the preparation of a product for predicting whether a lung cancer patient responds to immunotherapy.

[0060] The sixth aspect of the present invention provides a method for predicting whether a lung cancer patient responds to immunotherapy, and the method includes detecting autoantibodies in a lung cancer patient.

[0061] Preferably, the relevant definitions of autoantibodies and lung cancer are the same as those in the first aspect of the present invention.

[0062] Responding to immunotherapy refers to patients with CR / PR or SD exceeding 6 months, and not responding to treatment refers to patients with PD within 6 months after treatment. Among them, complete remission (CR), partial remission (PR), stable disease (SD) or progressive disease (PD) are defined according to the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) for NSCLC.

[0063] Preferably, predicting whether a lung cancer patient responds to immunotherapy includes, after obtaining the expression level of autoantibodies, comparing it with a threshold value obtained from previous experiments, that is, through experiments and data analysis, the threshold value is determined based on the degree of difference in the expression level of autoantibodies between patients who respond to immunotherapy and those who do not. When the expression level of one or more of the autoantibodies described in the present application is different or significantly different from the threshold value (the difference is statistically significant, such as p < 0.05, p < 0.01, p < 0.001, p < 0.0001), it can be used to predict whether a lung cancer patient responds to immunotherapy. For example: 1) When the expression level of anti-MBP autoantibody (preferably anti-MBP-IgA) is higher than or significantly higher than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 2) When the expression level of anti-GLRA2 autoantibody (preferably anti-GLRA2-IgA) is higher than or significantly higher than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 3) When the expression level of anti-KRT20 autoantibody (preferably anti-KRT20-IgA) is higher than or significantly higher than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 4) When the expression level of anti-SAE1 autoantibody (preferably anti-SAE1-IgA) is higher than or significantly higher than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 5) When the expression level of anti-BIN1 autoantibody (preferably anti-BIN1-IgA) is higher than or significantly higher than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 6) When the expression level of anti-PLA2R1 autoantibody (preferably anti-PLA2R1-IgG) is lower than or significantly lower than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 7) When the expression level of anti-GAD2 autoantibody (preferably anti-GAD2-IgG) is lower than or significantly lower than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy. 8) When the expression level of anti-SSB autoantibody (preferably anti-SSB-IgA) is lower than or significantly lower than the threshold value, it is predicted that the lung cancer patient responds to immunotherapy.

[0064] In a seventh aspect of the present invention, a method for constructing a prediction model for the immunotherapy effect of lung cancer is provided, and the construction method includes:

[0065] i) Collect the detection results of the expression levels of autoantibodies in the lung cancer immunotherapy response group and the lung cancer immunotherapy non-response group;

[0066] ii) Construct a prediction model based on the information collected in step i).

[0067] Alternatively, the construction method includes:

[0068] I) Collect samples from subjects and detect the expression levels of autoantibodies;

[0069] II) Clinically diagnose the subjects and divide the subjects into a lung cancer immunotherapy response group and a lung cancer immunotherapy non-response group;

[0070] III) Construct a prediction model based on the detection results of step I) and the diagnosis results of step II).

[0071] Preferably, the algorithm used for constructing the prediction model is a binary logistic regression model.

[0072] The autoantibodies include one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody.

[0073] Preferably, the autoantibodies include anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, and anti-SSB autoantibody.

[0074] In a specific embodiment of the present invention, the autoantibodies include anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG, and anti-GAD2-IgG.

[0075] Preferably, the lung cancer includes small cell lung cancer or non-small cell lung cancer.

[0076] In a specific embodiment of the present invention, the lung cancer is non-small cell lung cancer.

[0077] In the eighth aspect of the present invention, there is provided a prediction model prepared by the construction method described in the seventh aspect.

[0078] In the ninth aspect of the present invention, there is provided an application of the prediction model obtained by the above construction method in the preparation of a product for predicting the immunotherapy effect of lung cancer.

[0079] In the tenth aspect of the present invention, there is provided a chip, kit, test strip, membrane strip, or device, wherein the chip, kit, test strip, membrane strip, or device contains reagents for detecting autoantibodies, and the autoantibodies include one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody.

[0080] Preferably, the autoantibodies include anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody and anti-SSB autoantibody.

[0081] In a specific embodiment of the present invention, the autoantibodies include anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG and anti-GAD2-IgG.

[0082] Preferably, the chip, kit, test strip, membrane strip or device further contains a reagent for processing the sample.

[0083] The term "subject" in the present invention includes mammals, such as humans or non-human mammals, and can also be cells, tissues or organs of humans or non-human mammals. The non-human animals can be wild animals, zoo animals, economic animals, pets or laboratory animals, etc. Preferably, the non-human animals include but are not limited to pigs, cows, sheep, foxes, donkeys, minks, jackals, camels, dogs, cats, rabbits, rats (such as rats, mice, guinea pigs, hamsters, gerbils, chinchillas or squirrels, etc.) or monkeys.

[0084] The term "prognosis assessment" in the present invention refers to assessing the survival rate of a patient after receiving treatment.

[0085] The term "predicting the immunotherapy effect of lung cancer" in the present invention refers to assessing whether a subject or patient responds to immunotherapy.

[0086] The term "and / or" in the present invention includes all combinations of the items connected by this term, and should be regarded as each combination having been separately listed in the present application. For example, "A and / or B" includes "A", "A and B" and "B". Another example, "A, B and / or C" includes "A", "B", "C", "A and B", "A and C", "B and C" and "A and B and C".

[0087] The "Cut-off Value" in the present invention refers to the critical value for determining the positivity and negativity of a test. For example, in the present application, the cut-off value is used to determine whether the treatment is effective. The most commonly used method for determining the cut-off value is the receiver operating characteristic curve.

[0088] The term "comprising" or "including" in the present invention is an open-ended writing method, including the specified components or steps described, as well as other specified components or steps that will not be substantially affected.

[0089] Advantages of the present invention:

[0090] 1) The present invention screens autoantibodies related to the immunotherapy effect of lung cancer, systematically studies the predictive role of autoantibodies in the immunotherapy effect of lung cancer, and constructs a predictive model for the immunotherapy effect of lung cancer to assist in screening out patient groups sensitive to immunotherapy, thereby achieving precise intervention in drug selection, dose adjustment, and treatment timing.

[0091] 2) The present invention provides a set of autoantibody markers for predicting the immunotherapy effect of lung cancer. The autoantibodies include one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody, or anti-SSB autoantibody. When combined for prediction, the 8 autoantibodies have high sensitivity and specificity, and the AUC value can reach above 0.8, providing an important basis for clinical efficacy prediction.

[0092] 3) The markers of the present invention can also be used for the prognosis assessment of lung cancer patients. In particular, anti-GLRA2 autoantibody and anti-KRT20 autoantibody are significantly correlated with the survival rate of lung cancer patients after receiving immunotherapy. Brief Description of the Drawings

[0093] Hereinafter, the embodiments of the present invention will be described in detail with reference to the drawings, where:

[0094] Figure 1 : Example of the image of the serum test result detected by the AID chip;

[0095] Figure 2 : Volcano plot of the differential analysis between the NR group and the R group;

[0096] Figure 3 : Detection results of 8 differential autoantibodies in the NR group and the R group;

[0097] Figure 4 : Box plot of 8 differential autoantibodies in the NR group and the R group;

[0098] Figure 5 : Prediction performance of the 8-panel in the discovery cohort;

[0099] Figure 6 : Prediction performance of the 8-panel in the validation cohort;

[0100] Figure 7 : Cox regression forest plot to verify the relationship between autoantibodies and prognosis. Detailed Embodiments

[0101] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0102] This study was approved by the Institutional Review Board of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (UHCT-IEC-SOP-016-03-01). All experiments were approved by the Research Ethics Committee and conducted in accordance with the Declaration of Helsinki.

[0103] The AID chip used in the embodiments of this application refers to: the Autoimmune disease autoantigen microarray (AID chip). By combining clinical biomarkers related to autoimmune diseases and literature mining, 125 autoantigens highly related to more than 30 autoimmune diseases (such as rheumatoid arthritis, systemic lupus erythematosus, and autoimmune hepatitis) were mainly studied. These autoantigens were diluted and printed on the surface of a glass substrate to make the AID chip, and then the performance was evaluated. The AID chip showed excellent repeatability (intra-batch correlation: 0.99; inter-batch correlation: 0.97), and had a strong consistency with the clinical chemiluminescence immunoassay (R 2 = 0.86), proving the reliability and clinical applicability of the AID chip.

[0104] Example 1 Screening of autoantibody markers

[0105] In this example, serum samples of 83 NSCLC patients who received anti-PD-1 monoclonal antibody treatment (among them, the drugs of anti-PD-1 monoclonal antibody include Beize'an (23 cases, purchased from BeiGene), Keytruda (29 cases, purchased from MSD Ireland (Carlow)), Toripalimab (21 cases, purchased from Junshi Biosciences), and Darzalex Faspro (10 cases, purchased from Innovent Biologics (Suzhou) Co., Ltd.)) at the Cancer Center of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2019 to March 2024 were collected as the discovery cohort. (Serum was separated by centrifuging EDTA-anticoagulated blood at 16,000 g for 10 minutes at 4°C, the supernatant was transferred to a new tube, transported under cold chain conditions, and stored at -80°C until analysis).

[0106] Table 1

[0107]

[0108]

[0109] According to the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) for NSCLC, clinical response is defined as complete remission (CR), partial remission (PR), stable disease (SD), or progressive disease (PD). To evaluate the potential of autoantibodies in predicting the efficacy of immunotherapy, the serum samples of the discovery cohort were divided into a responder group (R) and a non-responder group (NR) according to the treatment response. The R group included patients with CR / PR or SD for more than 6 months, and the NR group included patients with PD within 6 months after treatment.

[0110] After the clinical diagnosis of 83 patients in the discovery cohort, 44 samples were in the R group and 39 samples were in the NR group.

[0111] Furthermore, the serum samples of 83 NSCLC patients who received anti-PD-1 monoclonal antibody treatment in the discovery cohort were detected using the AID chip.

[0112] Each AID chip detected 14 serum samples. There were 162 probes fixed on the AID chip. The autoantibodies to be detected in the serum bound to the probes on the AID chip. Subsequently, donkey anti-human IgG fluorescently labeled secondary antibody (green fluorescence) and goat anti-human IgA fluorescently labeled secondary antibody (red fluorescence) bound to the antigen-antibody complex, and were scanned by GenePix 4300A chip scanner to scan the fluorescence signals and perform data processing. The specific steps were as follows:

[0113] 1) Blocking: Enclose the AID chip. Slowly add the blocking solution at 100 μL / well to the non-array position of the chip and block at room temperature for 30 min.

[0114] 2) Sample preparation: Centrifuge the serum sample to be detected at 10,000 rpm for 5 min at 4°C. Take 2 μL of the supernatant and dilute it 300-fold with 600 μL of 5% skim milk, and mix evenly.

[0115] 3) Loading: Aspirate the blocking solution and add the diluted serum at 100 μL / well, and incubate at room temperature for 1 h.

[0116] 4) Washing: Add the washing solution (0.05% PBST) at 100 μL / well, wash 3 times, 5 min each time.

[0117] 5) Add fluorescent detection probes: Dilute Cy3-Donkey anti-human IgG(H+L) antibody with an original concentration of 1 mg / mL and Alexa fluor 647-conjugated goat anti-human IgA antibody to a working concentration of 2 μg / mL using 5% skim milk. Take 100 μL / well and add it to the chip, and incubate at room temperature for 30 min;

[0118] 6) Wash in the dark: Wash 3 times with washing solution (0.05% PBST), 5 min each time; then wash 2 times with ddH2O, 2 min each time.

[0119] 7) Chip detection: Dry the AID chip and use GenePix 4300A chip scanner. Based on the added fluorescently labeled secondary antibody, select scanning wavelengths of 532 nm and 635 nm respectively to scan the protein chip.

[0120] An example result of the AID chip detecting serum results is as Figure 1 shown.

[0121] The non-parametric difference analysis method (samr-nonparametric, setting the significance level p < 0.05) was used to compare the samples in the R group and the NR group. The results identified 5 autoantibodies upregulated in the responder group, namely anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, and anti-BIN1-IgA, and 3 autoantibodies downregulated in the responder group, namely anti-PLA2R1-IgG, anti-GAD2-IgG, and anti-SSB-IgA (as Figure 2 ). Figure 3 The detection results of different autoantibodies in the samples of the R group and the NR group are shown by fluorescence signals. The differences in the specific expression levels of these autoantibodies in the responder group (R group) and the non-responder group (NR group) are as Figure 4 shown.

[0122] Furthermore, based on the discovery of 8 differentially expressed autoantibodies in the discovery cohort, binary logistic regression was used to construct a prediction model (8-panel). The sensitivity, specificity, and AUC of the 8 autoantibodies for predicting efficacy alone and in combination were evaluated using SPSS software, and the ROC curve visualization was completed based on the ROC-Curve module of the Hiplot platform.

[0123] The results are as Figure 5As shown, using the prediction model (8-panel) of the present application, the sensitivity (72.73%) and specificity (87.18%) of predicting whether lung cancer patients respond to immunotherapy in the discovery cohort are relatively high. The Cut-off Value is 0.54391, and the AUC value is 0.855.

[0124] Example 2 Verification of Autoantibody Markers

[0125] Collect serum samples of another 83 NSCLC patients who received anti-PD-1 monoclonal antibody treatment (where the drugs of anti-PD-1 monoclonal antibody include Beizetan (25 cases), Keytruda (31 cases), Toripalimab (19 cases), and Dabrafenib (8 cases)) at the Cancer Center of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2019 to March 2024 (serum was separated by centrifuging EDTA-anticoagulated blood at 16,000g for 10 minutes at 4°C, the supernatant was transferred to a new tube, transported under cold chain conditions, and stored at -80°C until analysis), as the validation cohort.

[0126] Table 2

[0127]

[0128] Using the same method as in Example 1, detect the expression levels of 8 autoantibodies in the validation cohort, and use the binary logistic regression model to verify the sensitivity, specificity, and AUC of the 8 autoantibody combinations in predicting whether lung cancer patients respond to immunotherapy in the validation cohort. The results are as Figure 6 shown. In the validation cohort, the sensitivity is 72.73%, the specificity is 84.62%, the Cut-off Value is 0.569135, and the AUC value reaches 0.800.

[0129] The prediction model of the present application can be used to predict whether lung cancer patients respond to immunotherapy, especially the response of lung cancer patients to anti-PD-1 monoclonal antibody treatment, and also shows good generalization ability and excellent discrimination efficacy.

[0130] Example 3 Application of Autoantibody Markers in Prognostic Evaluation

[0131] Furthermore, through Cox regression analysis of the discovery cohort, the present application also found that the autoantibodies of the present application can be used for the survival prognosis evaluation of lung cancer patients. Figure 7Taking anti-GLRA2-IgA (p = 0.02, HR = 0.14) and anti-KRT20-IgA (p = 0.023, HR = 0.52) as examples, it shows that the autoantibody markers of the present application are significantly correlated with the survival of lung cancer patients after treatment and can be used as markers for lung cancer prognosis assessment.

[0132] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0133] In addition, it should be noted that, in the case of no contradiction, the various specific technical features described in the above specific embodiments can be combined in any suitable manner. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

Claims

1. A marker for predicting the immunotherapy effect of lung cancer or evaluating the prognosis of lung cancer, characterized in that, The described marker includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody or anti-SSB autoantibody.

2. Use of autoantibodies as markers in the preparation of products for predicting the immunotherapy effect of lung cancer or evaluating the prognosis of lung cancer, characterized in that, The described autoantibody includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody or anti-SSB autoantibody.

3. The application according to claim 2, characterized in that, The described autoantibody includes anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody and anti-SSB autoantibody.

4. The application according to claim 2 or 3, characterized in that, The described immunotherapy includes treatment with immune checkpoint inhibitors; Preferably, the immune checkpoint inhibitors include inhibitors against at least one of PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 or CD160.

5. The application according to any one of claims 2-4, characterized in that, The described lung cancer includes small cell lung cancer or non-small cell lung cancer.

6. The application according to any one of claims 2-5, characterized in that The described autoantibody is one or more of IgG-type autoantibody, IgA-type autoantibody, IgD-type autoantibody, IgE-type autoantibody or IgM-type autoantibody; Preferably, the autoantibody includes one or more of anti-MBP-IgA, anti-GLRA2-IgA, anti-KRT20-IgA, anti-SAE1-IgA, anti-BIN1-IgA, anti-SSB-IgA, anti-PLA2R1-IgG or anti-GAD2-IgG.

7. The application according to any one of claims 2-6, characterized in that, The described product includes a reagent for detecting autoantibodies; Preferably, the product includes one or more of a chip, a kit, a test strip, a membrane strip or a device; Preferably, the product includes a prediction model for the immunotherapy effect of lung cancer; Preferably, the autoantibody is an autoantibody in serum or plasma.

8. The application according to claim 7, characterized in that The reagent for detecting autoantibodies includes one or more of the reagents required for chemiluminescence, colorimetry, immunofluorescence, ELISA, protein chip, liquid chromatography or mass spectrometry.

9. A chip, kit, test strip, membrane strip or device, characterized in that, The chip, kit, test strip, membrane strip or device contains a reagent for detecting autoantibodies, and the autoantibody includes one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody or anti-SSB autoantibody.

10. A method for constructing a prediction model for the immunotherapy effect of lung cancer, characterized in that, The described construction method includes: i) Collect the detection results of the expression levels of autoantibodies in the lung cancer immunotherapy response group and the lung cancer immunotherapy non-response group; ii) Construct a prediction model according to the information collected in step i); Alternatively, the described construction method includes: I) Collect samples from subjects and detect the expression levels of autoantibodies; II) Clinically diagnose the subjects and divide them into a lung cancer immunotherapy response group and a lung cancer immunotherapy non-response group; III) Construct a prediction model based on the detection results in step I) and the diagnosis results in step II); The autoantibodies include one or more of anti-MBP autoantibody, anti-GLRA2 autoantibody, anti-KRT20 autoantibody, anti-SAE1 autoantibody, anti-BIN1 autoantibody, anti-PLA2R1 autoantibody, anti-GAD2 autoantibody or anti-SSB autoantibody; Preferably, the lung cancer includes small cell lung cancer or non-small cell lung cancer; Preferably, the algorithm used for constructing the prediction model is a binary logistic regression model.

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