CN116449009A

CN116449009A - Autoantibody marker for predicting immune neoadjuvant therapeutic effect of patients with lung cancer in third stage

Info

Publication number: CN116449009A
Application number: CN202310445788.6A
Authority: CN
Inventors: 孙苏彭; 康美华; 吴建虎; 阴亮; 朱得坤; 周兴宇; 孙立平
Original assignee: SHANGHAI HENGXIN BIOTECHNOLOGY CO Ltd
Current assignee: SHANGHAI HENGXIN BIOTECHNOLOGY CO Ltd
Priority date: 2023-01-30
Filing date: 2023-01-30
Publication date: 2023-07-18
Also published as: CN115825441A; CN115825441B

Abstract

The invention provides an autoantibody marker for predicting the lung cancer immune new adjuvant therapy effect of a patient in the third stage of lung cancer, a series of autoantibody marker molecules are screened to have great correlation with the lung cancer immune new adjuvant therapy effect prediction by detecting autoantibodies aiming at different antigen targets in the blood of the patient in the third stage of lung cancer, and 3 autoantibody biomarkers with better lung cancer immune new adjuvant therapy effect prediction efficacy of the patient in the third stage of lung cancer are further screened and obtained by combining with a CART decision tree strategy; the combination of the autoantibody biomarkers can be used for efficiently predicting whether the lung cancer immune new auxiliary treatment of the lung cancer patients in the third period is effective, provides a reference basis for a clinician to determine a treatment scheme, provides a new prediction means for the lung cancer immune new auxiliary treatment effect of the lung cancer patients in the third period, and has important scientific significance and clinical application value.

Description

Autoantibody marker for predicting immune neoadjuvant therapeutic effect of patients with lung cancer in third stage

This application is a divisional application of the parent application 202310044847.9.

Technical Field

The invention relates to the technical field of biology, in particular to a biomarker related to a lung cancer immune novel auxiliary treatment effect, and especially relates to an autoantibody marker for predicting a lung cancer immune novel auxiliary treatment effect of a lung cancer patient in third-stage lung cancer.

Background

Excision of the primary tumor is an essential treatment for lung cancer to heal, but surgery itself may also promote postoperative recurrence by: inducing micro-metastasis and spreading in perioperative period, eliminating anti-angiogenesis signals from tumor, inducing secretion of tumor growth factors, and inducing postoperative cell-mediated immunosuppression. Therefore, the tumor cell vitality in the micrometastasis range is reduced, and the early intervention type new auxiliary treatment becomes an attractive treatment strategy, and the strategy can improve the complete control rate of tumor patients before operation, so that the long-term survival and cure rate of the patients can be improved to a great extent. The activation of T cell viability by immune checkpoint inhibitors in neoadjuvant therapy is of great significance, and can lead regulatory T cells to increase, natural killer cells to decrease and the like in non-small cell lung cancer (NSCLC) tumor tissues to form an immunosuppressive tumor microenvironment. The antitumor effect generated by the preoperative immunity novel adjuvant therapy can not only shrink the tumor but also maximize the antitumor effect of the activated organism before the surgical lymph node cleaning. After the main focus is resected, the activated T cells can still eliminate the potential transfer lesions by 'memory' fixed points, thereby improving the cure rate.

The conference of the European oncology medical Association (European Society for Medical Oncology, ESMO) of 2016 has reported the results of a clinical study of 21 resectable NSCLC patients using an immunological neoadjuvant for the first time. The article published in the journal of new england medicine in 2018 shows good therapeutic safety. For NSCLC patients who can be treated by surgery, no matter whether the driver gene mutation exists or not, 3mg/kg (1 times every 2 weeks and 2 times) of PD-1 inhibitor Nivolumab is used for new adjuvant therapy before surgery, the tolerance is good, unexpected toxic and side effects do not occur, and the patients are not found to delay the treatment time of surgery due to the use of immune checkpoint inhibitor. The study found that the treatment-related adverse effect was 23% and that there were only 1 case of pneumonia with adverse effects exceeding grade 3. In terms of efficacy, 10% (2/20) of patients achieved Partial Response (PR), 86% (18/20) of patients achieved Stable Disease (SD), disease control rate (disease control rate, DCR) up to 96%, and postoperative major pathology-relieving MPR (surviving cells < 10%) up to 45% (9/20), with complete pathology-relieving (pCR) of 5% (3/20). The world lung cancer conference and the ESMO conference in 2018 report on a plurality of clinical test results of NEOSTAR, NADIM, LCMC, MAC and the like on application of immune checkpoint inhibitors to NSCLC neoadjuvant therapy. The research of the novel adjuvant immune therapy of the lung cancer which can be subjected to surgical excision shows relatively optimistic data initially, and shows that the novel adjuvant immune therapy of the lung cancer has good application prospect for improving prognosis of patients.

However, although lung cancer immunoneoadjuvant therapy has achieved remarkable results, there are data that indicate that some lung cancer patients still have no benefit from it. For example, a substantial proportion of lung cancer patients do not respond to anti-PD-1/PD-L1 antibodies. Therefore, the lung cancer immune new adjuvant therapy also has beneficiary groups and non-beneficiary groups. The current data show that the overall efficacy of lung cancer immune neoadjuvant therapy varies greatly, and it is not clear which populations will benefit from lung cancer immune neoadjuvant therapy. Therefore, the effective biomarker has important significance for selecting lung cancer immune new adjuvant therapy crowd.

Immunotherapeutic markers in general cannot be used for the prediction of the efficacy of immune neoadjuvant therapy of lung cancer. The CheckMate159 study included 21 resectable NSCLC patients receiving the naive adjuvant therapy with nal Wu Liyou mab. The results suggest that tumor MPR was observed at the initial diagnosis regardless of tumor cell PD-L1 expression. 181 resectable NSCLC patients were included in LCMC3 (NCT 02927301) study, and no baseline/intraoperative Tumor Mutational Burden (TMB) correlation with MPR was found in patients receiving 2 cycles of atilizumab neoadjuvant therapy, and further study of TMB with a cutoff value of 10 or 16, nor TMB correlation with MPR was found. In the NADIM study, NSCLC patients can be resected to receive 2 cycles of naive Wu Liyou mab in combination with chemotherapy to obtain complete pathology remission with higher tumor biopsy PD-L1 expression at baseline. However, no PD-L1 expression or TMB was observed to be associated with the benefit of long-term survival (PFS) PFS/OS. The predictive value of PD-L1 is to be validated for further data. The expert consensus that tumor mutation load is applied to lung cancer immunotherapy is not recommended for predicting the curative effect of novel adjuvant therapy of lung cancer immunity.

Moreover, the lung cancer immune neoadjuvant therapy is a very expensive drug, and is effective for some patients, but may cause serious adverse reactions, and may cause delayed or no surgery for the patients, unlike conventional chemotherapy. Therefore, if each lung cancer patient can be predicted in advance to be effective in the stage of adopting the lung cancer immune new auxiliary treatment, a doctor can be effectively helped to predict in advance whether the stage of adopting the lung cancer immune new auxiliary treatment is needed, so that adverse reactions are avoided, and the lung cancer patient is enabled to really benefit.

Clinically, lung cancer is divided into four different stages according to the symptoms and stages of lung cancer. The third stage lung cancer refers to the middle and late stages of lung cancer onset, in which the cancer cells have begun to spread and metastasize. After the second stage of diffusion and metastasis, the third stage of lung cancer is reached, and cancer cells invade the mediastinum tissue and the cervical lymphoid tissue and cause obvious symptoms of lung pain and hemoptysis. Some patients also experience blood metastases at this stage. By the third stage of lung cancer, the opportunity of operation treatment is almost lost, and the tumor has the phenomena of central necrosis and invasion of peripheral blood vessels and bronchus, so that the lung cancer can be treated only by radiotherapy and chemotherapy, and simultaneously, the targeted drug treatment or the lung cancer immune new auxiliary treatment method can be used for the combined treatment.

The patients in the third stage of lung cancer have a larger proportion in the patients with lung cancer, so that a biomarker capable of effectively predicting the treatment effect of the lung cancer immune novel adjuvant therapy is necessary to be found for the patients in the third stage of lung cancer.

Autoantibodies refer to antibodies produced by the body against an organ, cell or cellular component of the body. Currently, autoantibodies to certain proteins have become potential markers for tumor prognosis. For example, the presence of anti-XAGE 1 (GAGED 2 a) antibodies in tumor patients, whether EGFR is mutated or not, is a powerful predictor of increased survival in tumor patients that are positive for XAGE1 (GAGED 2 a) antigens. In addition, studies have been conducted to suggest that anti-p 53 autoantibodies, anti-PGP 9.5 autoantibody levels, and the like, can be used as a tool for predicting lung cancer recurrence. The Yoshihiro Ohue et al study showed that serum antibodies against NY-ESO-1 and/or XAGE1 tumor-testis antigens could predict immune checkpoint inhibitor efficacy and patient survival for both initial and postline NSCLC, regardless of PD-L1 expression, infiltration of TMB and CD8+ T cells. The university of the same university affiliated with Shanghai's department of pneumology hospital Su Chunxia teaching team, the third army medical university affiliated with new bridge hospital Zhu Bo teaching team and the university of Huazhong's science affiliated with the same Ji hospital Qian teaching team began in 2019, and after two years, the real world study of the system was carried out on the samples of the immune checkpoint inhibitor follow-up of NSCLC patients, and the detection of the tumor-related autoantibody combination positive molecules has good prediction value. Thus, autoantibodies may be a potential marker molecule for predicting the efficacy of immune neoadjuvant therapy for lung cancer.

Therefore, for patients with lung cancer in the third stage, it is also highly demanded to find an autoantibody marker which has higher accuracy in predicting the effect of lung cancer immune new adjuvant therapy, is easy to use, has low cost and is easy to popularize and apply, and an application means thereof, and develop an antigen for detection for the autoantibody marker so as to provide a new prediction means for predicting the effect of lung cancer immune new adjuvant therapy for patients with lung cancer in the third stage.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides an autoantibody marker for predicting the treatment effect of lung cancer immune new adjuvant treatment of a patient in the third stage of lung cancer and a combination thereof, and finally discovers that a group of autoantibody markers have great correlation with the treatment effect prediction of lung cancer immune new adjuvant treatment by detecting autoantibodies aiming at different antigen targets in blood of the patient in the third stage of lung cancer, develops an antigen for detection aiming at the group of autoantibody markers, can be used for efficiently predicting whether the patient in the third stage of lung cancer is effective for lung cancer immune new adjuvant treatment or not, provides a reference basis for a clinician to determine a treatment scheme, provides a new prediction means for the treatment effect of lung cancer immune new adjuvant treatment, and has important scientific significance and clinical application value.

The marker is an autoantibody marker, and a novel prediction means for predicting the curative effect of the lung cancer immune novel adjuvant therapy is provided by recognizing a novel autoantibody marker which can be used for predicting the curative effect of the lung cancer immune novel adjuvant therapy and developing an antigen for detecting the autoantibody biomarker.

In one aspect, the invention provides a use of an autoantibody marker in the preparation of a reagent for predicting whether an immune neoadjuvant treatment for lung cancer is effective in a patient with third stage lung cancer, wherein the autoantibody marker is one or more autoantibodies selected from the group consisting of: CIP2A, CTAG, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, VCX1.

Autoantibodies to the above antigens are respectively: anti-CIP2A, anti-CTAG2, anti-GNA11, anti-SS18, anti-NPM1, anti-MAGEB1, anti-CDK2, anti-PBRM1, anti-S100B, anti-TRIM21, anti-TXNDC2, anti-RASSF7, anti-LIN28B, anti-P62, anti-Livin-1, anti-14-3-3ζ, anti-BARD1, anti-PAGE3, anti-CT47A, anti-VCX1.

According to the invention, by detecting the autoantibodies aiming at purified antigen proteins existing in a lung cancer patient, simultaneously synthesizing a large amount of public data, comparing the population with the lung cancer patient to obtain positive treatment effects on lung cancer immune neoadjuvant treatment and the population without positive treatment effects, searching the autoantibody which can indicate the lung cancer immune neoadjuvant treatment effects by aiming at the autoantibody content of different antigen targets in blood, and through preliminary screening, finding 20 autoantibody biomarkers which can distinguish the lung cancer patient to have positive effects and not positive effects on lung cancer immune neoadjuvant treatment.

The lung cancer patients in the third stage are the larger population of the lung cancer patients, and the autoantibody biomarker aiming at all lung cancer patients cannot be used for predicting the curative effect of the lung cancer immune new adjuvant therapy of the lung cancer patients in the third stage with high efficiency; also, biomarkers for patients with primary and secondary lung cancer are not necessarily effective in predicting the efficacy of lung cancer immune neoadjuvant therapy in patients with tertiary lung cancer. Therefore, for patients with third-stage lung cancer, more proper autoantibody markers need to be further found to predict the curative effect of the lung cancer immune novel adjuvant therapy.

In some modes, the immune neoadjuvant therapy of lung cancer according to the invention is: xindi Li Shan was given against 200mg intravenously, once every three weeks, for two cycles.

The autoantibody biomarker provided by the invention can be used for predicting or judging whether a subject, such as a lung cancer patient, can benefit from lung cancer immune neoadjuvant therapy (the curative effect evaluation index of the current neoadjuvant therapy mainly comprises clinical RECIST evaluation standard and pathology evaluation standard, namely, pathology main remission rate (major pathological rate, MPR) is generally defined as that the surviving tumor cells in tumor tissues are less than or equal to 10 percent, and the surviving tumor cells containing complete pathology remission (pCR) are 0 percent.

Clinical efficacy evaluation indexes for lung cancer immune novel adjuvant therapy of the invention comprise PD (progressive disease), PR (partial response), SD (stable disease) and CR (complet response). Wherein PD (progressive disease): the sum of all target lesion diameters increases by at least 20% and the absolute value of the sum increase must also be greater than 5mm, compared to the minimum of the sum of all target lesion diameters prior to treatment; or new lesions appear. PR (partial response): the sum of the diameters of all target lesions is reduced by at least 30% compared to the sum of the diameters of all target lesions prior to treatment. SD (stable disease): the reduction of the target lesions is not in Partial Remission (PR) and the increase is not in disease Progression (PD) compared to the minimum sum of all target lesion diameters prior to treatment, a condition intermediate PR and PD. CR (complet response): all target lesions disappear and the short axis value of any pathological lymph node (whether or not the target lesion) must be <10mm.

In some embodiments, the autoantibody markers are selected from one or more of autoantibodies against the following antigens: PBRM1, SS18, TRIM21.

Because of the limitation of the performance of the lung cancer immune new adjuvant therapy of a patient with the third period of lung cancer, which is predicted by one autoantibody marker alone, the accuracy of distinguishing or predicting is improved to a certain extent by combining a plurality of autoantibody markers. However, the human and material resources required in the detection and analysis process of the combination of multiple autoantibody markers are obviously improved, so that the combination needs to be found, the performance of better predicting the lung cancer immune new adjuvant therapy of the lung cancer patients in the third stage can be achieved, and the number of the autoantibody markers can be reduced as much as possible.

In order to simplify the prediction process of the curative effect of the lung cancer immune new adjuvant therapy of the lung cancer patients in the third stage as much as possible, the invention expects to obtain a precise curative effect prediction level by using fewer detection markers as much as possible. By detecting a large number of clinical lung cancer patients in three phases for evaluating the curative effect of the lung cancer immune novel adjuvant therapy and combining with CART decision tree strategies, the invention finds 3 autoantibody biomarkers which can particularly sensitively and specifically distinguish the lung cancer immune novel adjuvant therapy from the lung cancer patients in three phases, and the autoantibody markers of crowds which do not obtain the positive therapeutic effect, and constructs a lung cancer immune novel adjuvant therapy effect prediction model consisting of three autoantibody molecules of anti-PBRM1, anti-SS18 and anti-Trim 21. The serial numbers of antigen uniprot databases corresponding to the 3 autoantibodies are respectively as follows: PBRM1: Q86U86; SS18: q15532; trim21: p19474. The Uniprot database has a website address www.uniprot.org.

Further, the autoantibody marker is a combination comprising TRIM21 and PBRM1, or a combination comprising PBRM1 and SS18, or a combination comprising TRIM21 and SS 18.

Further, the autoantibody markers include a combination of PBRM1, SS18, and TRIM 21.

The data of serum samples of patients in the third stage of clinical lung cancer are detected, and only 3 autoantibody biomarkers are adopted to predict ICI curative effect, so that very good prediction performance can be achieved; for the patients with the lung cancer in the third period, the probability of obtaining positive effects by receiving the lung cancer immune new adjuvant therapy is more than 70%; for the lung cancer patients with negative detection results, the possibility that the positive effect cannot be obtained by the lung cancer immune new adjuvant therapy is more than 80%, so that the effect of the lung cancer immune new adjuvant therapy can be effectively judged by adopting fewer autoantibody markers.

In some embodiments, the autoantibody marker is selected from one of the following combinations:

(1) anti-Trim21 and anti-PBRM1;

(2) anti-Trim21 and anti-SS18;

(3) anti-PBRM1 and anti-SS18;

(4) anti-PBRM1, anti-SS18 and anti-Trim21.

The invention evaluates the curative effect of lung cancer immune new adjuvant therapy of patients in the third stage of lung cancer through autoantibody biomarkers, specifically, scores one by one according to the concentration level of each autoantibody, and further can be used for judging according to the scoring result of the autoantibody combination: the effect of good or poor lung cancer immunoneoadjuvant therapy in a subject; the subject may or may not benefit from lung cancer immunoneoadjuvant therapy; the lung cancer immune new adjuvant therapy is effective or ineffective; or, the tumor of the subject is sensitive or insensitive to the immune neoadjuvant therapy of lung cancer.

Further, the reagent is used for detecting an autoantibody marker in blood, interstitial fluid, cerebrospinal fluid or urine samples of patients with the third stage of lung cancer; the autoantibody markers in the detection sample are as follows: detecting whether the autoantibody marker is positive.

In some embodiments, the autoantibody is an autoantibody in serum, plasma, or blood prior to receiving tumor immunity neoadjuvant therapy in the subject; in some embodiments, the autoantibodies in the serum, plasma, or blood are in the form of IgA (e.g., igA1, igA 2), igM, or IgG (e.g., igG1, igG2, igG3, igG 4).

In another aspect, the invention provides a kit for predicting whether an immune neoadjuvant therapy for lung cancer is effective in a patient with third stage lung cancer, the kit comprising a detection reagent for an autoantibody marker as described above.

The detection reagent for detecting the autoantibody marker is an antigen protein and comprises one or more selected from CIP2A, CTAG, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3 and CT47A, VCX 1.

In some embodiments, the detection reagent comprises one or more antigenic proteins selected from the group consisting of PBRM1, SS18, and TRIM 21.

In some embodiments, the detection reagent is selected from one of the following combinations:

(1) Trim21 and PBRM1;

(2) Trim21 and SS18;

(3) PBRM1 and SS18;

(4) PBRM1, SS18 and Trim21.

In some embodiments, the kit is an enzyme-linked immunosorbent assay (ELISA) detection kit. Namely, the kit is used for detecting whether the autoantibody marker in the sample of the subject is positive or not through an enzyme-linked immunosorbent assay.

In some embodiments, the kit further comprises additional components required for ELISA detection of autoantibody markers, all as is well known in the art. For detection purposes, for example, the antigen protein in the kit may be linked to a tag peptide, such as His tag, streptavidin tag, myc tag; for another example, the kit may include a solid support, such as a support having microwells to which antigen proteins can be immobilized, such as an elisa plate; and can also comprise adsorption proteins for fixing antigen proteins on a solid carrier, diluents of blood such as serum, washing liquid, secondary antibodies with enzyme labels, chromogenic liquid, stop solution and the like.

The kit can be used for detecting the concentration level of the corresponding autoantibody marker in a sample (such as blood plasma or serum or blood sample) of a subject, such as a patient suffering from lung cancer, so as to further realize the prediction or judgment of the clinical effect of administering the lung cancer immune neoadjuvant therapy.

In yet another aspect, the invention provides a detection reagent for an autoantibody marker for predicting whether an immune neoadjuvant treatment for lung cancer is effective in a patient with third stage lung cancer, wherein the detection reagent for detecting the autoantibody marker is an antigen protein and comprises one or more selected from CIP2A, CTAG2, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3, and CT47A, VCX1.

The detection reagent can be used for detecting the concentration level of the autoantibody marker in a lung cancer patient sample (such as blood, serum or plasma sample), so as to predict or judge whether the effect of the lung cancer immune novel adjuvant therapy on the lung cancer patient is curative or non-curative.

In yet another aspect, the present invention provides a system for predicting whether lung cancer immune neoadjuvant therapy is effective in a patient with third stage lung cancer, the system comprising a data analysis module; the data analysis module is used for analyzing the detection condition of an autoantibody marker, wherein the autoantibody marker is one or more selected from autoantibodies of the following antigens: CIP2A, CTAG, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, VCX1.

Further, the autoantibody markers are one or more selected from autoantibodies against the following antigens: PBRM1, SS18, and TRIM21.

Further, the analysis method of the data analysis module comprises the following steps: detecting whether an autoantibody marker in a blood sample of a patient in the third stage of lung cancer is positive; the data analysis module is used for evaluating whether the lung cancer immune new adjuvant therapy of the lung cancer patients in the third period is effective by analyzing whether the autoantibody marker is positive.

Further, the analysis method of the data analysis module further comprises the following steps: when one or more of the autoantibody marker combinations is positive, the autoantibody marker combination is positive, and the lung cancer immune new adjuvant treatment effect of the lung cancer patient in the third period is predicted; when all autoantibodies in the autoantibody marker combination are negative, the autoantibody biomarker combination is negative, and the lung cancer immune new adjuvant therapy of the lung cancer patient in the third period is predicted to have no effect.

Further, when one or more of the 3 autoantibody markers are positive, the 3 autoantibody markers are combined to be positive, and the lung cancer patient in the third stage is predicted to have an effect of treating the lung cancer by adopting the lung cancer immune new adjuvant therapy; when all the 3 autoantibody markers are negative, the 3 autoantibody marker combinations are negative, and the lung cancer patient in the third stage is predicted to have no effect on the treatment of the lung cancer immune novel adjuvant therapy.

In yet another aspect, the invention provides an autoantibody marker combination for predicting whether an immune neoadjuvant therapy is effective in a patient with lung cancer in stage three, the autoantibody marker combination comprising an autoantibody combination against: PBRM1, SS18, and TRIM21.

The lung cancer immune neoadjuvant therapy is single administration of immune checkpoint inhibitor therapy or combined therapy of immune checkpoint inhibitor and chemotherapy, radiotherapy, anti-vascular therapy, targeted therapy or other tumor treatment means. Wherein the immune checkpoint inhibitor is an immune checkpoint inhibitor against PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or CD160, preferably an anti-PD-1 antibody or an anti-PD-L1 antibody.

According to specific embodiments of the present invention, the anti-PD-1 antibody or anti-PD-L1 antibody may be nivolumab, pamizumab, bedi Li Shan antibody, terlipressin Li Shan antibody, and domestic immune checkpoint inhibitors (e.g., bedi Li Shan antibody, tirelimumab).

The autoantibody marker provided by the invention can be used for predicting or judging whether a subject, such as a patient in the third stage of lung cancer, can benefit from lung cancer immune new adjuvant therapy (the immune therapy effect is good or poor, whether the immune therapy is effective or not, or whether the lung cancer of the subject is sensitive or insensitive to the immune therapy), and can be at least used for corresponding adjuvant judgment.

In the present invention, "presence" or "absence" of an autoantibody marker is used interchangeably with "positive" or "negative"; this is judged as conventional in the art.

In yet another aspect, the invention provides the use of the autoantibody marker in the manufacture of a product for predicting or judging the therapeutic effect of a patient in stage three lung cancer on the immune neoadjuvant treatment of lung cancer.

The autoantibody marker for predicting whether the lung cancer immune new adjuvant therapy of the lung cancer patient in the third period is effective has the following beneficial effects:

1. screening a series of brand-new autoantibody markers which can predict whether the lung cancer immunity new adjuvant therapy of the lung cancer patients in the third stage is effective;

2. further screening to obtain 3 autoantibody markers with better efficacy of predicting the lung cancer immune new adjuvant therapy; the 3 autoantibody markers are adopted to predict the curative effect of the lung cancer immune new adjuvant therapy, so that very good prediction performance can be achieved, and the probability of obtaining positive effects of the lung cancer immune new adjuvant therapy on patients with the lung cancer in the third period with positive detection results is more than 70%; for the patients with lung cancer in the third period, the probability that the positive effect cannot be obtained by the lung cancer immune new adjuvant therapy is more than 80 percent;

3. Based on the prediction result of the autoantibody marker, the patient or the clinician can better decide whether the patient needs to be subjected to lung cancer immune new adjuvant therapy, thereby avoiding excessive medical treatment, reducing the treatment cost and reducing or avoiding adverse reaction.

Detailed Description

(1) Diagnosis or detection

Diagnostic or test herein refers to the detection or assay of a biomarker in a sample, or the level of the biomarker of interest, such as absolute or relative, and then indicating whether the individual providing the sample is likely to have or suffer from a disease, or the likelihood of having a disease, by the presence or amount of the biomarker of interest. The diagnostic and detection meanings are interchangeable herein. The result of such detection or diagnosis is not directly as a direct result of the disease, but is an intermediate result, and if a direct result is obtained, it is also necessary to confirm that the patient has a disease by other auxiliary means such as pathology or anatomy. For example, the present invention provides a variety of novel biomarkers that are directly related to whether lung cancer immunotherapy is effective in patients with third stage lung cancer, and whether changes in the levels of these markers are directly related to whether lung cancer immunotherapy is effective in patients with third stage lung cancer.

(2) Effective connection of markers or biomarkers and lung cancer immune new adjuvant therapy of patients with third-phase lung cancer

Markers and biomarkers have the same meaning in the present invention. The term "associated with" is used herein to refer to the presence or amount of a marker in a sample that has a direct correlation with the efficacy of a particular therapeutic method, e.g., a relative increase or decrease in the amount, indicating a higher or lower likelihood of having a beneficial effect for that therapeutic method.

The likelihood of having a beneficial effect of such a treatment method is also higher if multiple different markers are present in the sample at the same time or in the presence of a relative change in the amount. That is, some markers are strongly associated with the treatment, some markers are weakly associated with the treatment, or some markers are not even associated with the treatment. One or more of those markers with strong association can be used as markers for predicting whether the treatment is effective, and those markers with weak association can be combined with the strong markers to predict whether the treatment is effective, so that the accuracy of the prediction result is improved.

Aiming at a plurality of autoantibody biomarkers in a patient with the third-stage lung cancer, the increase or decrease of the existence or the content of the autoantibody biomarkers is effectively and directly related to whether the patient with the third-stage lung cancer adopts lung cancer immune neoadjuvant therapy.

Drawings

FIG. 1 shows the results of Wilcoxon test antibody detection and corresponding efficacy assessment analysis of PBRM1 of example 1;

FIG. 2 is a flow chart of analysis of the CART decision tree strategy in example 2;

FIG. 3 is a ROC curve showing the use of 3 autoantibody molecule combinations in example 4 to distinguish whether a patient with third stage lung cancer has positive efficacy in lung cancer immunotherapy;

FIG. 4 is a ROC curve showing the discrimination of whether a lung cancer patient has obtained positive therapeutic effects in the novel adjuvant treatment of lung cancer using 3 combinations of autoantibody molecules in example 4;

FIG. 5 is a graph showing the results of the predictive model in example 4 to distinguish whether an adenocarcinoma patient has acquired positive therapeutic effects in lung cancer immunotherapy;

FIG. 6 is a graph showing the result of the predictive model in example 4 to distinguish whether a cancer patient has acquired positive effects in the adjuvant treatment of lung cancer;

FIG. 7 is a graph showing the results of the predictive model in example 4 to distinguish whether a patient with small cell carcinoma has obtained positive effects in the adjuvant treatment of lung cancer.

Detailed Description

In the present invention, the term "antigen" or the term "antigenic protein" is used interchangeably.

The terms "antibody" and "autoantibody" are interchangeable in the present invention.

Furthermore, the present invention is directed to the following experimental operations or definitions, and it should be noted that the present invention may also be implemented using other conventional techniques in the art, and is not limited to the following experimental operations.

Preparation of recombinant antigen proteins

The cDNA fragment of the antigen protein was cloned into PET28 (a) expression vector containing the 6XHIS tag. At the N-or C-terminus of the antigen, streptavidin or the like (biotin-binding tag protein) is introduced. The obtained recombinant expression vector is transformed into escherichia coli for expression. The protein expressed by the supernatant was purified by Ni-NTA affinity column and ion column. When the protein is expressed in inclusion bodies, the protein is denatured by 6M guanidine hydrochloride, renaturated and folded in vitro according to a standard method, and then purified by a Ni-NTA affinity column through a 6XHIS tag, so that antigen protein is obtained.

(II) preparation and preservation of serum or plasma

Serum or plasma of gastric cancer patients is collected when the patients are initially diagnosed as gastric cancer and have not received any radiotherapy and chemotherapy or surgical treatment. Plasma or serum was prepared according to standard clinical procedures and stored in a-80 ℃ refrigerator for long periods of time.

(III) ELISA detection

The concentration of autoantibody markers in the sample was quantified by enzyme-linked immunosorbent assay (ELISA). The purified tumor antigen is immobilized to the microwell surface by its tag streptavidin or the like. Microwells were pre-coated with biotin-labeled Bovine Serum Albumin (BSA). Serum or plasma samples were diluted 1:110 fold with phosphate buffer and reacted by adding microwells (50 ml/well). After washing unbound serum or plasma components with wash solution, horseradish peroxidase (HRP) -conjugated anti-human IgG was added to each well for reaction. Then, TMB (3, 3', 5' -tetramethylbenzidine) as a reaction substrate was added for color development. Stop solution (1 NHCl) was added and absorbance was read by a microplate reader (OD) with a single spectrum at 450 nm. Serum autoantibody concentrations were quantified using a standard curve.

The concentration of the antigen marker in the sample is quantified by a sandwich enzyme-linked immunosorbent assay. Connecting the specific antibody with a solid phase carrier to form a solid phase antibody, and washing to remove unbound antibody and impurities; and (3) adding a sample to be tested, namely diluting a serum or plasma sample by 1:110 times by using phosphate buffer, adding micropores to react (50 ml/hole), enabling the sample to contact and react with the solid-phase antibody for a period of time, and combining the antigen in the sample with the antibody on the solid-phase carrier to form a solid-phase antigen complex. Washing to remove other unbound material. Horseradish peroxidase (HRP) -conjugated anti-human IgG was added for reaction. Then, TMB (3, 3', 5' -tetramethylbenzidine) as a reaction substrate was added for color development. Stop solution (1N HCl) was added and the absorbance was read by a microplate reader (OD) using a single spectrum at 450 nm. The amount of enzyme carried on the solid support is now positively correlated with the amount of test substance in the sample. The enzyme in the sandwich complex catalyzes the substrate to a colored product. Qualitative or quantitative determination of the antigen is performed according to the degree of color reaction.

Critical value (cutoff value) of autoantibody

The cutoff value of the autoantibody level was defined as being equal to the average of the healthy control cohort in the control group (the control group being the group confirmed to have no cancer by physical examination) plus 2 Standard Deviations (SDs).

(V) determination of the positivity and negativity of autoantibodies

For the determination of each autoantibody, positive reaction is defined as that after quantifying the level of the autoantibody in a sample, comparing it with a cutoff value, and determining that the value is more than or equal to the cutoff value is positive; accordingly, a negative response is defined as < cutoff value negative.

The cutoff value of anti-CIP2A was 20, the cutoff value of anti-CDK2 was 3, the cutoff value of anti-Trim21 was 13, the cutoff value of anti-TXNDC2 was 12, the cutoff value of anti-CTAG2 was 40, the cutoff value of anti-GNA11 was 38, the cutoff value of anti-ss18 was 10, the cutoff value of anti-npm1 was 3.5, the cutoff value of anti-mageb1 was.5, the cutoff value of anti-pbrm1 was 20, the cutoff value of anti-s100b was 15, the cutoff value of anti-rassf7 was 3.5, the cutoff value of anti-lin28b was 16, the cutoff value of anti-p62 was 15, the cutoff value of anti-livin-1 was 35, the cutoff value of anti-14-3-3 was 3, the cutoff value of anti-3 was 25, the cutoff value of anti-1 was 3, the cutoff value of anti-6 was 15, the cutoff value of anti-3-6 was 1, the cutoff value of anti-7 was 3, the cutoff value of anti-7 was 3, and the cutoff value of anti-7 was 12.

Positive determination of autoantibody combinations

Since the single autoantibody has a low positive rate, the result is analyzed by combining the results of a plurality of autoantibodies to determine the predictive effect in order to increase the positive rate of autoantibody detection. The rules are: detecting a plurality of autoantibodies in a patient sample, and judging that the combined result of the antibodies is positive as long as one or more autoantibodies show positive; if all the autoantibodies are negative, the antibody combination result is judged to be negative.

(seventh) clinical efficacy assessment index

PD (progressive disease): the sum of all target lesion diameters increases by at least 20% and the absolute value of the sum increase must also be greater than 5mm, compared to the minimum of the sum of all target lesion diameters prior to treatment; or new lesions appear.

PR (partial response): the sum of the diameters of all target lesions is reduced by at least 30% compared to the sum of the diameters of all target lesions prior to treatment.

SD (stable disease): the reduction of the target lesions is not in Partial Remission (PR) and the increase is not in disease Progression (PD) compared to the minimum sum of all target lesion diameters prior to treatment, a condition intermediate PR and PD.

CR (complet response): all target lesions disappear and the short axis value of any pathological lymph node (whether or not the target lesion) must be <10mm.

(eighth) statistical analysis method

Both groups were statistically analyzed using GraphPad Prism v.6 (GraphPad Prism software, san diego, california) and IBM SPSS Statistics for Windows (IBM, new york) using the Mann-Whitney U test. In analyzing the relationship between each parameter, a Spearman correlation analysis was performed.

The invention will be described in further detail below with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate an understanding of the invention and are not intended to limit the invention in any way. The reagents used in this example were all known products, and were all commercially available products unless otherwise specified.

Example 1 screening of autoantibody markers related to the Effect of Lung cancer Immunity New adjuvant treatment in patients with third stage Lung cancer

This example was performed by aggregating 169 antigen proteins that were statistically combined with a large number of public data. The serum of 90 patients diagnosed with lung cancer is subjected to autoantibody detection aiming at purified antigen protein, and the curative effect of the patients after lung cancer immune neoadjuvant therapy (Xindi Li Shan anti 200mg intravenous injection, once every three weeks and two cycle periods) is evaluated according to the curative effect evaluation standard 1.1 edition (Response Evaluation Criteria in Solid Tumors RECIST Version 1.1, recist v 1.1) of solid tumors, so that an autoantibody biomarker which has correlation with the curative effect of the lung cancer immune neoadjuvant therapy of the lung cancer patients is hoped to be found. Through preliminary screening (by searching for positive correlation and negative correlation antigens and searching for correlation antigens with good and poor prediction effects on the curative effect of the lung cancer immune neoadjuvant treatment), antigen proteins of 20 autoantibodies shown in table 1 are found, and the 20 autoantibodies are correlated with the curative effect of the lung cancer immune neoadjuvant treatment of a lung cancer patient, wherein the website of the Uniprot database is www.uniprot.org.

Table 1 antigen proteins of 20 autoantibodies obtained by preliminary screening

90 cases of lung cancer patients participating in the study, 56 cases of which are patients with three stages of lung cancer, have the highest proportion. 20 (Table 1) candidate autoantibody markers in serum of patients with lung cancer stage III were tested. Meanwhile, according to the treatment effect evaluation standard of the solid tumor version 1.1 (Response Evaluation Criteria in Solid Tumors RECIST Version.1, recist v 1.1), the treatment effect of the lung cancer immune neoadjuvant treatment of the patients in the third stage of lung cancer is evaluated (the image result after two treatment periods). The CR and PR evaluations were defined as "positive therapeutic effect was obtained", while the SD and PD evaluations were defined as "positive therapeutic effect was not obtained". The results of the antibody detection and corresponding efficacy assessment were analyzed using the Wilcoxon test and are shown in table 2. Wherein a lower p-value represents a more pronounced difference between the two groups (positive and non-positive) and is statistically significant.

TABLE 2 correlation of 20 autoantibodies obtained by preliminary screening with efficacy of adjuvant treatment of lung cancer immunization in patients with third stage lung cancer

Autoantibodies	P value	q(BH)	q(fdr)
				anti-CIP2A	0.744	0.7533	0.7533
anti-CTAG2	0.2491	0.7151	0.7151
				anti-NA11	0.6087	0.7367	0.7367
anti-SS18	0.4403	0.7151	0.7151
				anti-NPM1	0.5006	0.7151	0.7151
anti-MAGEB1	0.4299	0.7151	0.7151
				anti-CDK2	0.4776	0.7151	0.7151
anti-Trim21	0.2523	0.7151	0.7151
				anti-S100B	0.7534	0.7533	0.7533
anti-PBRM1	0.0083	0.1658	0.1658
				anti-TXNDC2	0.1885	0.7151	0.7151
anti-RASSF7	0.4905	0.7151	0.7151
				anti-LIN28B	0.4776	0.7151	0.7151
anti-P62	0.4905	0.7151	0.7151
				anti-Livin-1	0.6433	0.7367	0.7367
anti-14-3-3ζ	0.6631	0.7367	0.7367
				anti-BARD1	0.1357	0.7151	0.7151
anti-PAGE3	0.2684	0.7151	0.7151
				anti-CT47A	0.4571	0.7151	0.7151
anti-VCX1	0.4406	0.7151	0.7151

As can be seen from table 2, the P value of PBRM1 is particularly low, 0.0083, indicating that the difference between the positive and non-positive efficacy groups is particularly pronounced for PBRM1 autoantibodies, and thus the significance of PBRM1 autoantibody levels for efficacy assessment is probably greatest, followed by BARD1, and again TXNDC2.

The analysis result of the PBRM1 is shown in figure 1, which shows that the PBRM1 level in the patient with the lung cancer in the third period has higher positive curative effect than the patient with the lung cancer in the third period, which has no positive curative effect, shows that the PBRM1 has a correlation with the effect of predicting the lung cancer immunity new adjuvant therapy of the patient with the lung cancer in the third period, and the P value is 0.0083, which proves that the correlation has a very good statistical significance.

However, when autoantibodies with low P values are actually used for prediction, the efficacy of lung cancer immune neoadjuvant therapy for predicting patients with lung cancer in the third stage is not necessarily the best, and further verification is required. Moreover, the predictive efficacy of a single autoantibody is very limited, and several autoantibodies need to be combined to improve the predictive efficacy. When the obtained autoantibody marker combination is used for predicting the curative effect of lung cancer immunity new adjuvant therapy of a patient in the third period of lung cancer, good prediction efficiency can not be obtained necessarily, and a more proper autoantibody marker combination can be found only by combining other evaluation means and combining clinical prediction results for verification.

Example 2 construction of an autoantibody marker combination related to the efficacy of Lung cancer Immunity New adjuvant treatment in patients with third stage Lung cancer

The more the number of the markers in the autoantibody marker combination is, the more labor and material resources are required to be consumed in the detection and analysis process, so that the combination needs to be found, the better performance of predicting the lung cancer immune new adjuvant therapy of the lung cancer patients in the third stage can be achieved, and the fewer the number of the autoantibody markers can be contained.

According to the results obtained in example 1, class and Regression Tree (Card) was constructed by R Package rpart version 4.1.16, and the antibody detection results and post-treatment evaluation of the three-phase subjects were fitted using default parameters, and the input data type was numeric, and the analysis flow is shown in fig. 2. The specific flow is as follows: firstly, selecting a detection result of an autoantibody PBRM1 as a single primary screening antigen for analyzing lung cancer immunity new adjuvant therapy of a patient in a third period of lung cancer, smoothly separating 43% of positive population (obtaining positive curative effect), and remaining 57% of positive population which fails to successfully predict lung cancer immunity new adjuvant therapy curative effect; then through fitting calculation (any one autoantibody is selected from the remaining 19 autoantibodies to be combined with PBRM1, the autoantibody SS18 with the largest crowd can be successfully separated is selected), the autoantibody SS18 is selected to be further predictive analysis, 18% of positive crowd with positive curative effect is successfully separated from the remaining 57%, and the remaining 39% of negative crowd is not successfully separated; and then through fitting calculation (any one autoantibody is selected from the remaining 18 autoantibodies and combined with PBRM1+SS18 respectively, the autoantibody Trim21 with the largest crowd can be successfully separated out is selected), the autoantibody Trim21 is selected for further predictive analysis, 14% of positive crowd with positive curative effect and 25% of negative crowd without positive curative effect are successfully separated out from the remaining 39%. Finally, a lung cancer immune new adjuvant therapy treatment effect prediction model consisting of three autoantibody molecules of anti-Trim21, anti-SS18 and anti-PBRM1 is constructed.

In the embodiment, CART decision tree strategy is applied to fit the antibody detection results of the three-phase subjects and the post-treatment evaluation, so that a lung cancer immune new adjuvant therapy treatment effect prediction model consisting of three autoantibody molecules of anti-PBRM1, anti-SS18 and anti-Trim21 is constructed.

In this example, the prediction performance of three autoantibody molecule prediction models of anti-PBRM1, anti-SS18 and anti-Trim21 constructed in this example was verified by further comparing the prediction performance of the therapeutic effect of the novel adjuvant therapy for lung cancer immunization with the different autoantibody marker combinations shown in Table 3.

TABLE 3 comparison of Performance of different autoantibody marker combinations

As can be seen from table 3, when only two autoantibodies pbrm1+ss18 are contained, the positive population has a positive efficacy probability of 71.4% and the negative population has no positive efficacy probability of 93.2%; when the three autoantibodies PBRM1+SS18+TRIM21 are contained, the positive treatment probability of the positive population is increased to 75%, and the negative population does not obtain the positive treatment probability to be 95%; further, on the basis of PBRM1+SS18+TRIM21, an autoantibody RASSF7 or S100B is added, or two autoantibodies RASSF7+S100deg.B are added, so that the probability of positive treatment effect of positive people is predicted to be slightly increased, and the probability of negative people not being positive treatment effect is predicted to be obviously reduced. And the number of one or more autoantibodies is increased, more manpower and material resources are needed to be increased, the prediction cost is increased, the effect is not increased, and even the trend is reduced, so that a prediction model consisting of three autoantibody molecules of anti-PBRM1, anti-SS18 and anti-Trim21 is preferably adopted to predict the curative effect of the lung cancer immune novel adjuvant therapy of the patient in the third stage of lung cancer.

Example 3 Properties of combinations of different autoantibodies to predict the efficacy of novel adjuvant treatment of lung cancer in patients with different lung cancer

For 90 cases of lung cancer patients, 56 cases are lung cancer patients in three phases, 20 cases are lung cancer patients in two phases, 14 cases are lung cancer patients in one phase, and the present example further predicts lung cancer patients in two phases and lung cancer patients in one phase by using various combinations as shown in table 4.

TABLE 4 comparison of the performance of novel adjuvant therapy for the combined prediction of different autoantibodies for lung cancer in different lung cancer patients

As can be seen from Table 4, the performance of predicting the effect of the lung cancer immune novel adjuvant therapy is different for the lung cancer patients in different periods by adopting different combinations, and the performance of the lung cancer immune novel adjuvant therapy for predicting the lung cancer patients in two periods or the lung cancer patients in one period is better by adopting the combination of five autoantibodies of PBRM1, SS18, TRIM21, RASSF7 and S100B, while the prediction model consisting of three autoantibody molecules of PBRM1, SS18 and Trim21 is more preferable for predicting the lung cancer patients in three periods, so that the prediction performance is improved, the detection method is simpler, and the cost is reduced.

EXAMPLE 4 prediction of the Effect of novel adjuvant treatment on lung cancer by Using the autoantibody combinations constructed according to the present invention

The three autoantibody molecule combinations of anti-PBRM1, anti-SS18 and anti-Trim21 of the invention are used for distinguishing whether a patient with third-phase lung cancer obtains positive curative effect in lung cancer immune adjuvant therapy or not, and ROC curve is obtained, as shown in figure 3. As can be seen from fig. 3, the autoantibody molecule combination of the present invention can effectively predict the effect of the immune neoadjuvant therapy on patients in the third stage, and the probability of obtaining a positive effect of the immune neoadjuvant therapy on patients in the third stage who have positive detection results and who have received the immune neoadjuvant therapy on lung cancer is 75%; for patients with lung cancer in three phases, the detection result is negative, and the probability that the positive effect cannot be obtained by the lung cancer immune new adjuvant therapy is 95%.

The combined prediction model of three autoantibody molecules of anti-PBRM1, anti-SS18 and anti-Trim21 is used for all lung cancer patients to be tested, and ROC curves are drawn to show the prediction efficacy of the model according to the model detection results and post-treatment evaluation, as shown in figure 4. The autoantibody molecule combination can effectively predict the effect of the lung cancer immune new adjuvant therapy of a patient, and the probability of obtaining positive effect of the lung cancer immune new adjuvant therapy of the patient with positive detection result is 75%; for lung cancer patients with negative detection results, the probability that the positive effect cannot be obtained by the lung cancer immune new adjuvant therapy is 81.4%.

The present example further subdivided 90 lung cancer patients into 27 adenocarcinomas, 34 squamous cell carcinomas, 29 small cell lung carcinomas according to the pathological subtypes of lung cancer. The ratio of positive evaluation after treatment of patients with positive autoantibody detection in any subtype is found to be higher than that of negative patients, especially in small cell lung cancer and squamous cell carcinoma patients (objective remission: 89% vs 80%, 92% vs 68% and 100% vs 69%) as shown in fig. 5-7, wherein fig. 5 is a graph showing the positive effect of the predictive model for distinguishing whether the adenocarcinoma patients obtain positive effect in the lung cancer immune neoadjuvant therapy, fig. 6 is a graph showing the positive effect of the predictive model for distinguishing the squamous cell carcinoma patients obtain positive effect in the lung cancer immune neoadjuvant therapy, and fig. 7 is a graph showing the positive effect of the predictive model for distinguishing the small cell carcinoma patients in the lung cancer immune neoadjuvant therapy.

The invention can predict the curative effect of the crowd receiving the immune new adjuvant therapy by detecting the level of the autoantibody combination in the serum of the lung cancer patient: if the autoantibody level detection is positive, the effect of the received treatment is expected to be better; if the autoantibody level detection is negative, the effect of the treatment is not expected to be ideal. Can be used for providing effective basis for patients to take relevant therapeutic measures or decisions, and has good clinical application prospect.

Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.

Claims

1. A system for predicting whether lung cancer immune neoadjuvant therapy is effective in a patient with a third stage lung cancer, the system comprising a data analysis module; the data analysis module is used for analyzing the detection condition of an autoantibody marker, wherein the autoantibody marker is one or more selected from autoantibodies of the following antigens: CIP2A, CTAG, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, VCX1.

2. The system of claim 1, wherein the autoantibody markers are selected from one or more of autoantibodies against the following antigens: PBRM1, SS18, TRIM21.

3. The system of claim 2, wherein the autoantibody marker is a combination comprising TRIM21 and PBRM1, or a combination comprising PBRM1 and SS18, or a combination comprising TRIM21 and SS 18.

4. The system of claim 3, wherein the autoantibody markers comprise a combination of PBRM1, SS18, and TRIM 21.

5. The system of claim 4, wherein the data analysis module analyzes the data by: detecting whether an autoantibody marker in a blood sample of a patient in the third stage of lung cancer is positive; the data analysis module is used for evaluating whether the lung cancer immune new adjuvant therapy of the lung cancer patients in the third period is effective by analyzing whether the autoantibody marker is positive.

6. The system of claim 5, wherein the analysis method of the data analysis module further comprises: when one or more of the autoantibody marker combinations is positive, the autoantibody marker combination is positive, and the lung cancer immune new adjuvant treatment effect of the lung cancer patient in the third period is predicted; when all autoantibodies in the autoantibody marker combination are negative, the autoantibody biomarker combination is negative, and the lung cancer immune new adjuvant therapy of the lung cancer patient in the third period is predicted to have no effect.

7. Use of an autoantibody marker for the preparation of a reagent for predicting whether an immune neoadjuvant treatment for lung cancer is effective in a patient with stage three lung cancer, wherein the autoantibody marker is one or more autoantibodies selected from the group consisting of anti-: CIP2A, CTAG, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM, TXNDC2, RASSF7, LIN28B, P62, livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, VCX1.

8. The use according to claim 7, wherein the autoantibody markers are selected from one or more of autoantibodies against the following antigens: PBRM1, SS18, TRIM21.

9. A kit for predicting whether an immune neoadjuvant treatment for lung cancer is effective in a patient with a third stage of lung cancer, comprising the detection reagent for an autoantibody marker for use according to any one of claims 7 to 8.

10. An autoantibody marker combination for predicting whether an immune neoadjuvant treatment for lung cancer is effective in a patient with a third stage of lung cancer, wherein the autoantibody marker combination is a combination comprising TRIM21 and PBRM1, or a combination comprising PBRM1 and SS18, or a combination comprising TRIM21 and SS 18.