Autoantibody markers predictive of immune neoadjuvant efficacy in stage iii lung cancer patients

Abstract

The application provides an autoantibody marker for predicting the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage, a series of autoantibody marker molecules are screened by detecting autoantibodies against different antigen targets in blood of lung cancer patients in the third stage, and the autoantibody marker molecules have great correlation with the prediction of the effect of lung cancer immunneoadjuvant therapy, and three autoantibody biomarkers with better prediction of the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage are further screened by combining with a CART decision tree strategy; the combination of the autoantibody biomarkers can be used for efficiently predicting whether the lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage is effective, providing a reference basis for a clinician to decide a treatment scheme, providing a new prediction means for the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage, and having important scientific significance and clinical application value.

Description

[0001] This application is a divisional application of the parent application 202310044847.9. Technical Field

[0002] This invention relates to the field of biotechnology, and more specifically, to a biomarker related to the efficacy of neoadjuvant immunotherapy for lung cancer, particularly to an autoantibody biomarker for predicting the efficacy of neoadjuvant immunotherapy for lung cancer in stage III patients. Background Technology

[0003] Removal of the primary tumor is essential for the cure of lung cancer. However, surgery itself can also promote postoperative recurrence through: inducing perioperative micrometastasis, clearing anti-angiogenic signals from the tumor, inducing the secretion of tumor growth factors, and inducing postoperative cell-mediated immunosuppression. Therefore, reducing the activity of tumor cells in micrometastases and implementing neoadjuvant therapy at an earlier stage has become an attractive treatment strategy. This strategy can improve the complete control rate of cancer patients before surgery, significantly improving long-term survival and cure rates. Activating T cell activity with immune checkpoint inhibitors is crucial in neoadjuvant therapy. It can increase regulatory T cells and decrease natural killer cells in non-small cell lung cancer (NSCLC) tumor tissue, creating an immunosuppressive tumor microenvironment. The anti-tumor effect of preoperative neoadjuvant immunotherapy not only shrinks the tumor but also maximizes the activation of the body's anti-tumor effect before surgical lymph node dissection. After the primary lesion is removed, activated T cells can still target and eliminate potential metastatic lesions, improving the cure rate.

[0004] A clinical study of 21 patients with resectable NSCLC receiving neoadjuvant immunotherapy for the first time was reported at the 2016 European Society for Medical Oncology (ESMO) Congress. A 2018 article published in the *New England Journal of Medicine* showed a good safety profile. For surgically treatable NSCLC patients, regardless of the presence of driver gene mutations, neoadjuvant therapy with the PD-1 inhibitor nivolumab 3 mg / kg (twice every two weeks) was well-tolerated, with no unexpected toxicities or delays in surgical intervention due to the use of immune checkpoint inhibitors. The study found treatment-related adverse events in 23% of cases, with only one case of pneumonia exceeding grade 3. In terms of efficacy, 10% (2 / 20) of patients achieved partial response (PR), 86% (18 / 20) achieved stable disease (SD), and the disease control rate (DCR) was as high as 96%. The postoperative major pathological response (MPR) (surviving cells <10%) reached 45% (9 / 20), of which pathological complete response (pCR) was 5% (3 / 20). At the 2018 World Conference on Lung Cancer and the ESMO Congress, the results of several clinical trials using immune checkpoint inhibitors, including NEOSTAR, NADIM, LCMC3, and MAC, were reported as neoadjuvant therapy for NSCLC. Preliminary data from studies on neoadjuvant immunotherapy for surgically resectable lung cancer have shown promising potential for improving patient prognosis.

[0005] However, despite the remarkable achievements of neoadjuvant immunotherapy in lung cancer, data indicate that some lung cancer patients still do not benefit from it. For example, a significant proportion of lung cancer patients do not respond to anti-PD-1 / PD-L1 antibodies. Therefore, there are beneficiary and non-beneficial populations for neoadjuvant immunotherapy in lung cancer. Current data show that the overall efficacy of neoadjuvant immunotherapy in lung cancer varies considerably, and it is not yet clear which populations will benefit from it. Therefore, effective biomarkers are of great significance for the selection of patients for neoadjuvant immunotherapy in lung cancer.

[0006] Conventional immunotherapy biomarkers are not used to predict the efficacy of neoadjuvant immunotherapy in lung cancer. The CheckMate 159 study included 21 patients with resectable NSCLC who received neoadjuvant nivolumab. Results suggested that tumor malignancy response (MPR) was observed regardless of PD-L1 expression at initial diagnosis. The LCMC3 (NCT02927301) study included 181 patients with resectable NSCLC. No correlation was found between baseline / surgical tumor mutational burden (TMB) and MPR in patients receiving two cycles of neoadjuvant atezolizumab. Further studies with TMB cutoff values ​​of 10 or 16 also showed no correlation between TMB and MPR. In the NADIM study, patients with resectable NSCLC who received two cycles of neoadjuvant nivolumab combined with chemotherapy and achieved complete pathological remission had higher baseline PD-L1 expression on tumor biopsy. However, no correlation was observed between PD-L1 expression or TMB and long-term survival (progression-free survival, PFS / OS) benefit. The predictive value of PD-L1 needs further data validation. The "Expert Consensus on the Application of Tumor Mutation Burden in Lung Cancer Immunotherapy" does not currently recommend using TMB to predict the efficacy of neoadjuvant immunotherapy in lung cancer.

[0007] Furthermore, neoadjuvant immunotherapy for lung cancer is a very expensive drug. Unlike conventional chemotherapy, while it may be effective for some patients, it can also cause serious adverse reactions and may delay or prevent surgery. Therefore, if it is possible to predict in advance whether each lung cancer patient will benefit from neoadjuvant immunotherapy, it will effectively help doctors determine in advance whether neoadjuvant immunotherapy is necessary, thereby avoiding adverse reactions and ensuring that lung cancer patients truly benefit.

[0008] In clinical medicine, lung cancer is classified into four stages based on its symptoms and progression. Stage III lung cancer refers to the middle to late stage of lung cancer, in which cancer cells have begun to spread and metastasize. After the spread and metastasis of Stage II, in Stage III lung cancer, cancer cells have invaded the mediastinal tissue and cervical lymph nodes, causing significant lung pain and hemoptysis. Some patients also experience hematogenous metastasis at this stage. At Stage III, surgical treatment is almost always lost, as the tumor has undergone central necrosis and invaded surrounding blood vessels and bronchi. Therefore, treatment is limited to radiotherapy and chemotherapy, and targeted therapy or neoadjuvant immunotherapy may also be used in conjunction with these treatments.

[0009] Stage III lung cancer patients account for a large proportion of all lung cancer patients. Therefore, it is necessary to find biomarkers that can effectively predict the efficacy of neoadjuvant immunotherapy for stage III lung cancer patients.

[0010] Autoantibodies are antibodies produced by the body against its own organs, cells, or cellular components. Currently, autoantibodies against certain proteins have become potential prognostic markers for tumors. For example, regardless of EGFR mutation status, the presence of anti-XAGE1 (GAGED2a) antibodies in cancer patients is a strong predictor of prolonged survival in XAGE1 (GAGED2a) antigen-positive cancer patients. Furthermore, studies have suggested that levels of anti-p53 and anti-PGP9.5 autoantibodies can serve as tools for predicting lung cancer recurrence. Research by Yoshihiro Ohue et al. has shown that, regardless of PD-L1 expression, TMB, and CD8+ T cell infiltration, serum antibodies against NY-ESO-1 and / or XAGE1 tumor-testis antigen can predict the efficacy of immune checkpoint inhibitors and patient survival in both initial and subsequent lines of NSCLC. A team led by Professor Su Chunxia at Shanghai Pulmonary Hospital affiliated with Tongji University, a team led by Professor Zhu Bo at Xinqiao Hospital affiliated with the Third Military Medical University, and a team led by Professor Chu Qian at Tongji Hospital affiliated with Huazhong University of Science and Technology conducted a systematic real-world study on immune checkpoint inhibitor follow-up samples from NSCLC patients starting in 2019. The study, which lasted two years, showed that positive results for tumor-related autoantibody combinations all demonstrated good predictive value. Therefore, autoantibodies may be a potential biomarker for predicting the efficacy of neoadjuvant immunotherapy in lung cancer.

[0011] Therefore, for patients with stage III lung cancer, there is an urgent need to find autoantibody biomarkers that are more accurate in predicting the efficacy of neoadjuvant immunotherapy for lung cancer, and that are easy to use, low in cost, and readily applicable. Additionally, there is a need to develop antigens for detecting these autoantibody biomarkers, in order to provide new predictive methods for the efficacy of neoadjuvant immunotherapy for stage III lung cancer patients. Summary of the Invention

[0012] To address the problems existing in the prior art, this invention provides an autoantibody biomarker and its combination for predicting the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients. By detecting autoantibodies against different antigen targets in the blood of stage III lung cancer patients, it was found that a group of autoantibody biomarkers is highly correlated with the efficacy prediction of neoadjuvant immunotherapy in lung cancer. Antigens for detecting this group of autoantibody biomarkers have been developed, which can be used to efficiently predict whether neoadjuvant immunotherapy is effective in stage III lung cancer patients. This provides a reference for clinicians to decide on treatment plans and offers a new predictive tool for the efficacy of neoadjuvant immunotherapy in lung cancer, possessing significant scientific and clinical application value.

[0013] The biomarker described in this invention is an autoantibody biomarker. By identifying new autoantibody biomarkers that can be used to predict the efficacy of neoadjuvant immunotherapy for lung cancer, and by developing antigens for detecting these autoantibody biomarkers, a new predictive means is provided for the efficacy of neoadjuvant immunotherapy for lung cancer.

[0014] On one hand, the present invention provides the use of an autoantibody biomarker in the preparation of a reagent for predicting the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients, characterized in that the autoantibody biomarker is one or more autoantibodies selected from the following antigens: CIP2A, CTAG2, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM21, TXNDC2, RASSF7, LIN28B, P62, Livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, VCX1.

[0015] The autoantibodies against the above antigens are: 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, and anti-VCX1.

[0016] This invention detects autoantibodies against purified antigen proteins in lung cancer patients and, by combining a large amount of public data, compares the levels of autoantibodies against different antigen targets in the blood of lung cancer patients who achieved positive therapeutic effects from neoadjuvant immunotherapy and those who did not. The aim is to identify autoantibodies that can indicate the therapeutic effect of neoadjuvant immunotherapy. After initial screening, 20 autoantibody biomarkers were identified that can distinguish between lung cancer patients who have positive and those who do not have positive effects from neoadjuvant immunotherapy.

[0017] Stage III lung cancer patients constitute a large proportion of all lung cancer patients. However, autoantibody biomarkers for all lung cancer patients are not necessarily effective in predicting the efficacy of neoadjuvant immunotherapy in stage III patients. Similarly, biomarkers for stage I and II lung cancer patients are not necessarily effective in predicting the efficacy of neoadjuvant immunotherapy in stage III patients. Therefore, for stage III lung cancer patients, it is necessary to further identify more suitable autoantibody biomarkers to predict the efficacy of neoadjuvant immunotherapy.

[0018] In some embodiments, the neoadjuvant immunotherapy for lung cancer described in this invention is: sintilimab 200mg intravenously, once every three weeks, for two cycles.

[0019] The autoantibody biomarkers provided by this invention can be used to predict or determine whether subjects, such as lung cancer patients, can benefit from neoadjuvant immunotherapy for lung cancer (currently, the efficacy evaluation indicators for neoadjuvant therapy mainly include the clinical RECIST evaluation criteria and the pathological evaluation criteria, namely the major pathological rate (MPR). MPR is generally defined as ≤10% of surviving tumor cells in the tumor tissue, including 0% of surviving tumor cells in complete pathological response (pCR). Some studies define it as 1% to 10% surviving tumor cells, excluding pCR. Because early immunotherapy is accompanied by a large number of immune cells infiltrating the tumor, the "tumor" may not shrink, and the use of RECIST evaluation criteria may lead to misjudgment of efficacy. The pathological MPR evaluation criteria may be more accurate in assessing the efficacy of immunotherapy than the objective response rate (ORR) of solid tumor efficacy evaluation (RECIST), which is mainly based on imaging methods, and is therefore often used as a surrogate endpoint for neoadjuvant therapy), and can at least be used for corresponding auxiliary judgment.

[0020] The clinical efficacy evaluation indicators for neoadjuvant immunotherapy in lung cancer according to this invention include PD (progressive disease), PR (partial response), SD (stable disease), and CR (complete response). PD (progressive disease): Compared to the minimum sum of the diameters of all target lesions before treatment, the sum of the diameters of all target lesions increases by at least 20%, and the absolute value of the increase must be greater than 5 mm; or new lesions appear. PR (partial response): Compared to the sum of the diameters of all target lesions before treatment, the sum of the diameters of all target lesions decreases by at least 30%. SD (stable disease): Compared to the minimum sum of the diameters of all target lesions before treatment, the degree of shrinkage of target lesions does not meet the criteria for partial remission (PR), and the degree of enlargement does not meet the criteria for disease progression (PD); it refers to a state between PR and PD. CR (complete response): All target lesions disappear, and the short axis value of any pathological lymph node (whether or not a target lesion) must be <10 mm.

[0021] In some embodiments, the autoantibody marker is selected from one or more autoantibodies against the following antigens: PBRM1, SS18, TRIM21.

[0022] Because the performance of a single autoantibody biomarker in predicting neoadjuvant immunotherapy for stage III lung cancer patients is limited, combining multiple autoantibody biomarkers can improve the accuracy of differentiation or prediction to some extent. However, the detection and analysis process for combining multiple autoantibody biomarkers will inevitably increase the manpower and material resources required. Therefore, it is necessary to find a combination that can achieve good predictive performance for neoadjuvant immunotherapy in stage III lung cancer patients while including as few autoantibody biomarkers as possible.

[0023] To simplify the prediction of the efficacy of neoadjuvant immunotherapy for stage III lung cancer patients, this invention aims to achieve a high level of accuracy in predicting efficacy using a minimal number of biomarkers. By evaluating the efficacy of neoadjuvant immunotherapy in a large number of clinical stage III lung cancer patients and incorporating a CART decision tree strategy, this invention identified three autoantibody biomarkers from the aforementioned 20 biomarkers that are particularly sensitive and specific in distinguishing between stage III lung cancer patients who achieved positive treatment results with neoadjuvant immunotherapy and those who did not. A predictive model for the efficacy of neoadjuvant immunotherapy for lung cancer, composed of three autoantibody molecules—anti-PBRM1, anti-SS18, and anti-Trim21—was constructed. The corresponding Uniprot database sequence numbers for these three autoantibodies are: PBRM1: Q86U86; SS18: Q15532; Trim21: P19474. The Uniprot database is available at www.uniprot.org.

[0024] Furthermore, the autoantibody marker is a combination of TRIM21 and PBRM1, or a combination of PBRM1 and SS18, or a combination of TRIM21 and SS18.

[0025] Furthermore, the autoantibody markers include a combination of PBRM1, SS18, and TRIM21.

[0026] Data from serum samples of stage III lung cancer patients showed that using only these three autoantibody biomarkers to predict the efficacy of ICI (immunoadjuvant immunotherapy) achieved very good predictive performance. For stage III lung cancer patients with positive test results, the probability of receiving neoadjuvant immunotherapy for lung cancer is greater than 70%. For stage III lung cancer patients with negative test results, the probability of receiving neoadjuvant immunotherapy for lung cancer is greater than 80%. This approach can effectively assess the efficacy of neoadjuvant immunotherapy for lung cancer using as few autoantibody biomarkers as possible.

[0027] In some embodiments, the autoantibody marker is selected from one of the following combinations:

[0028] (1) anti-Trim21 and anti-PBRM1;

[0029] (2) anti-Trim21 and anti-SS18;

[0030] (3) anti-PBRM1 and anti-SS18;

[0031] (4)anti-PBRM1, anti-SS18 and anti-Trim21.

[0032] This invention evaluates the efficacy of neoadjuvant immunotherapy for stage III lung cancer patients using autoantibody biomarkers. Specifically, it scores each autoantibody based on its concentration level, and further uses the scoring results of the autoantibody combination to determine: whether the subject has a good or poor response to neoadjuvant immunotherapy; whether the subject benefits from or does not benefit from neoadjuvant immunotherapy; whether neoadjuvant immunotherapy is effective or ineffective; or whether the subject's tumor is sensitive or insensitive to neoadjuvant immunotherapy.

[0033] Furthermore, the reagent is used to detect autoantibody markers in blood, interstitial fluid, cerebrospinal fluid, or urine samples from patients with stage III lung cancer; the detection of autoantibody markers in the samples involves determining whether the autoantibody markers are positive.

[0034] In some embodiments, the autoantibody is an autoantibody in the serum, plasma, or blood of the subject prior to receiving neoadjuvant immunotherapy for tumors; in other embodiments, the autoantibody in the serum, plasma, or blood is specifically in the form of IgA (e.g., IgA1, IgA2), IgM, or IgG (e.g., IgG1, IgG2, IgG3, IgG4).

[0035] On the other hand, the present invention provides a kit for predicting the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients, the kit comprising detection reagents for autoantibody biomarkers as described above.

[0036] The detection reagent for detecting autoantibody markers is an antigen protein, including one or more selected from CIP2A, CTAG2, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM21, TXNDC2, RASSF7, LIN28B, P62, Livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, and VCX1.

[0037] In some embodiments, the detection reagent includes one or more antigen proteins selected from PBRM1, SS18, and TRIM21.

[0038] In some embodiments, the detection reagent is selected from one of the following combinations:

[0039] (1) Trim21 and PBRM1;

[0040] (2) Trim21 and SS18;

[0041] (3) PBRM1 and SS18;

[0042] (4) PBRM1, SS18 and Trim21.

[0043] In some embodiments, the kit is an enzyme-linked immunosorbent assay (ELISA) kit. This kit is used to detect whether an autoantibody marker is positive in a subject's sample via ELISA.

[0044] In some embodiments, the kit may also include other components required for ELISA detection of autoantibody markers, all of which are well known in the art. For detection purposes, for example, the antigen protein in the kit may be linked to a tagged peptide, such as a His tag, streptavidin tag, or Myc tag; or the kit may include a solid-phase carrier, such as a carrier with micropores for immobilizing the antigen protein, such as an ELISA plate; it may also include adsorbent protein for immobilizing the antigen protein on the solid-phase carrier, blood diluent such as serum, washing solution, enzyme-labeled secondary antibody, chromogenic solution, stop solution, etc.

[0045] The kit can be used to detect the concentration levels of corresponding autoantibody markers in samples (e.g., plasma, serum, or blood samples) of subjects, such as patients with stage III lung cancer, thereby enabling the prediction or assessment of the clinical efficacy of neoadjuvant immunotherapy for lung cancer.

[0046] Furthermore, this invention provides a detection reagent for autoantibody biomarkers used to predict the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients. The detection reagent for autoantibody biomarkers is an antigen protein, including one or more selected from CIP2A, CTAG2, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM21, TXNDC2, RASSF7, LIN28B, P62, Livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, and VCX1.

[0047] In some embodiments, the detection reagent includes one or more antigen proteins selected from PBRM1, SS18, and TRIM21.

[0048] The detection reagent can be used to detect the concentration level of the autoantibody marker in samples (e.g., blood, serum, or plasma samples) from patients with stage III lung cancer, thereby predicting or determining whether neoadjuvant immunotherapy for lung cancer is effective or ineffective in patients with stage III lung cancer.

[0049] In another aspect, the present invention provides a system for predicting the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients. The system includes a data analysis module. The data analysis module is used to analyze the detection status of autoantibody biomarkers, wherein the autoantibody biomarkers are one or more autoantibodies selected from the following antigens: CIP2A, CTAG2, GNA11, SS18, NPM1, MAGEB1, CDK2, PBRM1, S100B, TRIM21, TXNDC2, RASSF7, LIN28B, P62, Livin-1, 14-3-3ζ, BARD1, PAGE3, CT47A, and VCX1.

[0050] Furthermore, the autoantibody marker is one or more autoantibodies selected from the following antigens: PBRM1, SS18, and TRIM21.

[0051] Furthermore, the analysis method of the data analysis module is as follows: detecting whether the autoantibody markers in the blood samples of stage III lung cancer patients are positive; the data analysis module evaluates the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients by analyzing whether the autoantibody markers are positive.

[0052] Furthermore, the analysis method of the data analysis module also includes: when one or more of the autoantibody biomarkers in the combination are positive, the autoantibody biomarker combination is positive, predicting that the neoadjuvant immunotherapy for the stage III lung cancer patient is effective; when all the autoantibodies in the autoantibody biomarker combination are negative, the autoantibody biomarker combination is negative, predicting that the neoadjuvant immunotherapy for the stage III lung cancer patient is ineffective.

[0053] Furthermore, if one or more of the three autoantibody markers are positive, then the combination of the three autoantibody markers is positive, predicting that the stage III lung cancer patient will respond well to neoadjuvant immunotherapy for lung cancer; if all three autoantibody markers are negative, then the combination of the three autoantibody markers is negative, predicting that the stage III lung cancer patient will not respond well to neoadjuvant immunotherapy for lung cancer.

[0054] In another aspect, the present invention provides a combination of autoantibody biomarkers for predicting the effectiveness of neoadjuvant immunotherapy in patients with stage III lung cancer, the combination of autoantibody biomarkers comprising a combination of autoantibodies against the following antigens: PBRM1, SS18 and TRIM21.

[0055] The neoadjuvant immunotherapy for lung cancer described in this invention is either monotherapy with an immune checkpoint inhibitor or a combination therapy with an immune checkpoint inhibitor and chemotherapy, radiotherapy, anti-angiogenic therapy, targeted therapy, or other tumor treatments. The immune checkpoint inhibitor is a target of 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.

[0056] According to a specific embodiment of the present invention, the anti-PD-1 antibody or anti-PD-L1 antibody may be nivolumab, pembrolizumab, sintilimab, toripalimab, or domestically produced immune checkpoint inhibitors (such as sintilimab and tislelizumab).

[0057] The autoantibody biomarkers provided by this invention can be used to predict or determine whether a subject, such as a stage III lung cancer patient, can benefit from neoadjuvant immunotherapy for lung cancer (whether the immunotherapy is effective or not; whether the immunotherapy is effective; or whether the subject's lung cancer is sensitive or insensitive to immunotherapy), or at least to make the corresponding auxiliary judgments.

[0058] In this invention, the terms "presence" or "absence" of autoantibody markers are interchangeable with "positive" or "negative"; making such judgments is a conventional technique in the field.

[0059] In another aspect, the present invention provides the use of the aforementioned autoantibody biomarker in the preparation of products for predicting or assessing the therapeutic effect of neoadjuvant immunotherapy for lung cancer in stage III lung cancer patients.

[0060] The autoantibody biomarker provided by this invention for predicting the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients has the following beneficial effects:

[0061] 1. A series of novel autoantibody biomarkers were identified that can predict the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients;

[0062] 2. Further screening yielded three autoantibody biomarkers with excellent predictive efficacy for neoadjuvant immunotherapy in lung cancer. Using these three autoantibody biomarkers to predict the efficacy of neoadjuvant immunotherapy in lung cancer achieved very good predictive performance. For stage III lung cancer patients with positive test results, the probability of receiving neoadjuvant immunotherapy is greater than 70%; for stage III lung cancer patients with negative test results, the probability of not receiving neoadjuvant immunotherapy is greater than 80%.

[0063] 3. Based on the predictive results of autoantibody markers, patients or clinicians can better decide whether patients should undergo neoadjuvant immunotherapy for lung cancer, thereby avoiding overtreatment, reducing treatment costs, and minimizing or avoiding adverse reactions.

[0064] Detailed description

[0065] (1) Diagnosis or testing

[0066] Here, diagnosis or testing refers to the detection or analysis of biomarkers in a sample, or the determination of the content of a target biomarker, such as its absolute or relative content. The presence or quantity of the target biomarker then indicates whether the individual providing the sample may have or suffer from a certain disease, or the likelihood of having a certain disease. The meanings of diagnosis and testing are interchangeable here. The results of such testing or diagnosis cannot be directly considered as a direct result of disease; rather, they are intermediate results. If a direct result is obtained, further auxiliary methods such as pathology or anatomy are needed to confirm the presence of a certain disease. For example, this invention provides several new biomarkers that are correlated with the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients. Changes in the levels of these biomarkers are directly correlated with the effectiveness of neoadjuvant immunotherapy for stage III lung cancer patients.

[0067] (2) The relationship between biomarkers or biomarkers and the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients.

[0068] In this invention, biomarkers and biomarkers have the same meaning. Here, "related" refers to a direct correlation between the presence or change in the concentration of a biomarker in a sample and the efficacy of a specific treatment method. For example, a relative increase or decrease in concentration indicates a higher or lower likelihood that the treatment method will have a beneficial effect.

[0069] If multiple different biomarkers appear simultaneously in a sample, or if their relative levels change, it indicates a higher likelihood that the treatment method will have a beneficial effect. In other words, among the various biomarkers, some are strongly associated with the effectiveness of a treatment method, while others are weakly associated, or even unrelated. One or more of the strongly associated biomarkers can be used to predict the effectiveness of the treatment method, and those with weak associations can be combined with the strong biomarkers to increase the accuracy of the prediction.

[0070] The presence or absence, or increase or decrease, of numerous autoantibody biomarkers in stage III lung cancer patients discovered in this invention is directly related to the effectiveness of neoadjuvant immunotherapy for lung cancer in these patients. Attached Figure Description

[0071] Figure 1 The results of the Wilcoxon test for PBRM1 in Example 1 are the antibody detection results and the corresponding efficacy evaluation results.

[0072] Figure 2 This is a flowchart illustrating the analysis process using the CART decision tree strategy in Example 2.

[0073] Figure 3 The ROC curve for Example 4 shows the application of three combinations of autoantibody molecules to differentiate whether stage III lung cancer patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer.

[0074] Figure 4 The ROC curve for Example 4 shows the application of three combinations of autoantibody molecules to differentiate whether lung cancer patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer.

[0075] Figure 5 This is a graph showing the results of the prediction model in Example 4 in distinguishing whether adenocarcinoma patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer.

[0076] Figure 6 This is a graph showing the results of the prediction model in Example 4 in distinguishing whether squamous cell carcinoma patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer.

[0077] Figure 7 The graph shows the results of the prediction model in Example 4 in distinguishing whether small cell carcinoma patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer. Detailed Implementation

[0078] In this invention, the terms "antigen" and "antigen protein" are used interchangeably.

[0079] The terms "antibody" and "autoantibody" are used interchangeably in this invention.

[0080] Furthermore, it should be noted that the present invention involves the following experimental operations or definitions, and that the present invention may also be implemented using other conventional techniques in the field, and is not limited to the following experimental operations.

[0081] (I) Preparation of recombinant antigen protein

[0082] The cDNA fragment of the antigen protein was cloned into the PET28(a) expression vector containing a 6XHis tag. Streptavidin protein or an analogue (a biotin-binding tagged protein) was introduced at the N-terminus or C-terminus of the antigen. The resulting recombinant expression vector was transformed into *E. coli* for expression. The expressed protein in the supernatant was purified using a Ni-NTA affinity column and an ion exchange column. When the protein was expressed in inclusion bodies, it was denatured with 6M guanidine hydrochloride, folded in vitro according to standard methods, and then purified using a Ni-NTA affinity column with a 6XHis tag to obtain the antigen protein.

[0083] (II) Preparation and preservation of serum or plasma

[0084] Serum or plasma from patients with gastric cancer is collected when the patient is initially diagnosed with gastric cancer, before receiving any radiotherapy, chemotherapy, or surgical treatment. Plasma or serum is prepared according to standard clinical procedures and stored long-term at -80°C.

[0085] (III) ELISA Testing

[0086] The concentration of autoantibody markers in samples was quantified by enzyme-linked immunosorbent assay (ELISA). Purified tumor antigens were immobilized onto the surface of microwells using a tag of streptavidin or an analogue. Microwells were pre-coated with biotin-labeled bovine serum albumin (BSA). Serum or plasma samples were diluted 1:110 with phosphate buffer and added to the microwells (50 mL / well). After washing away unbound serum or plasma components with washing buffer, horseradish peroxidase (HRP)-conjugated anti-human IgG was added to each well for reaction. The substrate TMB (3,3',5,5'-tetramethylbenzidine) was then added for color development. Stop solution (1N HCl) was added, and the absorbance was measured at 450 nm using a microplate reader (OD). The concentration of serum autoantibodies was quantified using a standard curve.

[0087] The concentration of antigenic markers in samples was quantitatively determined using a sandwich-type enzyme-linked immunosorbent assay (ELISA). Specific antibodies were conjugated to a solid-phase carrier to form a solid-phase antibody. Unbound antibodies and impurities were washed away. The test sample (serum or plasma) was diluted 1:110 with phosphate buffer and added to each well (50 mL / well) for reaction. The sample was allowed to react with the solid-phase antibody for a period of time, allowing the antigen in the sample to bind to the antibody on the solid-phase carrier, forming a solid-phase antigen complex. Other unbound substances were washed away. Horseradish peroxidase (HRP)-conjugated anti-human IgG was added for further reaction. Then, the substrate TMB (3,3',5,5'-tetramethylbenzidine) was added for color development. A stop solution (1N HCl) was added, and the absorbance was measured at 450 nm using a microplate reader (OD). The amount of enzyme on the solid-phase carrier was positively correlated with the amount of the test substance in the sample. The enzyme in the sandwich complex catalyzed the substrate to form a colored product. The antigen was qualitatively or quantitatively determined based on the degree of color reaction.

[0088] (iv) Cutoff value for autoantibodies

[0089] The cutoff value for autoantibody levels was defined as equal to the mean of the healthy control cohort in the control group (a group of people confirmed by physical examination to be free of cancer) plus 2 standard deviations (SD).

[0090] (v) Determination of positive and negative autoantibodies

[0091] For each type of autoantibody assay, a positive reaction is defined as quantifying the level of the autoantibody in the sample and comparing it with a cutoff value; a value ≥ the cutoff value is considered positive. Correspondingly, a negative reaction is defined as < the cutoff value being negative.

[0092] The cutoff values ​​for anti-CIP2A are 20, anti-CDK2 is 3, anti-Trim21 is 13, anti-TXNDC2 is 12, anti-CTAG2 is 40, anti-GNA11 is 38, anti-ss18 is 10, anti-npm1 is 3.5, anti-mageb1 is 0.5, and anti-pbrm1 is 20. The cutoff values ​​are as follows: ti-s100b: 15; anti-rassf7: 3.5; anti-lin28b: 16; anti-p62: 15; anti-livin-1: 35; anti-14-3-3ζ: 14; anti-BARD1: 25; anti-PAGE3: 9; anti-CT47A: 7.5; and anti-VCX1: 12.

[0093] (vi) Positive interpretation of autoantibody combinations

[0094] Because the positive rate of a single autoantibody is low, the results of multiple autoantibodies are analyzed together to determine the predictive effect in order to increase the positive rate of autoantibody detection. The rule is: when multiple autoantibodies are detected in a patient sample, if one or more autoantibodies show a positive result, the antibody combination result is considered positive; if all autoantibodies are negative, the antibody combination result is considered negative.

[0095] (vii) Clinical efficacy evaluation indicators

[0096] PD (progressive disease): Compared with the minimum sum of the diameters of all target lesions before treatment, the sum of the diameters of all target lesions increases by at least 20%, and the absolute value of the increase in the sum of the diameters must be greater than 5 mm; or new lesions appear.

[0097] 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 before treatment.

[0098] SD (stable disease): Compared with the minimum sum of the diameters of all target lesions before treatment, the reduction in the target lesion does not meet the criteria for partial remission (PR), and the increase in the target lesion does not meet the criteria for disease progression (PD). It refers to a state between PR and PD.

[0099] CR (complete response): All target lesions disappear, and the short axis value of any pathological lymph node (whether or not it is a target lesion) must be <10mm.

[0100] (viii) Statistical Analysis Methods

[0101] Statistical analysis of the two groups was performed using GraphPad Prism v.6 (Graphpad Prism software, San Diego, CA) and IBM SPSS Statistics 23 for Windows (IBM, NY, NY). Spearman correlation analysis was performed when analyzing the relationships between each parameter.

[0102] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate understanding of the present invention and are not intended to limit it in any way. The reagents used in this embodiment are all known products, and unless otherwise specified, they are all commercially available products.

[0103] Example 1: Screening of autoantibody biomarkers related to the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients.

[0104] This embodiment summarizes and synthesizes 169 antigen proteins from a large amount of public data. Autoantibodies against purified antigen proteins were detected in the serum of 90 patients diagnosed with lung cancer. Simultaneously, the efficacy of neoadjuvant immunotherapy (sintilimab 200mg intravenously, every three weeks for two cycles) in these patients was evaluated according to RECIST v1.1 (Response Evaluation Criteria in Solid Tumors). The aim was to identify autoantibody biomarkers correlated with the therapeutic effect of neoadjuvant immunotherapy in lung cancer patients. After initial screening (by searching for positively and negatively correlated antigens to predict good and poor efficacy of neoadjuvant immunotherapy), 20 autoantibody antigen proteins were identified, as shown in Table 1. These 20 autoantibodies were correlated with the therapeutic effect of neoadjuvant immunotherapy in lung cancer patients. The Uniprot database is available at www.uniprot.org.

[0105] Table 1. Antigen proteins of 20 autoantibodies obtained in the initial screening

[0106]

[0107]

[0108] Ninety lung cancer patients participated in the study, with 56 of them being stage III lung cancer patients, representing the largest proportion. Twenty candidate autoantibody biomarkers (Table 1) were detected in the serum of stage III lung cancer patients. Simultaneously, the efficacy of neoadjuvant immunotherapy in these stage III lung cancer patients was evaluated according to the Response Evaluation Criteria in Solid Tumors RECIST Version 1.1 (RECISTv1.1) (imaging results after two treatment cycles). CR and PR were defined as "achieving a positive treatment effect," while SD and PD were defined as "not achieving a positive treatment effect." The Wilcoxon test was used to analyze the antibody detection results and corresponding efficacy assessments, and the results are shown in Table 2. A lower p-value indicates a more significant and statistically significant difference between the two groups (positive efficacy group and non-positive efficacy group).

[0109] Table 2. Correlation between 20 autoantibodies obtained in the initial screening and the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients.

[0110] 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

[0111] As shown in Table 2, the P-value for PBRM1 was particularly low, at 0.0083, indicating that the difference in PBRM1 autoantibody levels between the active treatment group and the non-active treatment group was especially significant. Therefore, the level of PBRM1 autoantibody may be the most significant for efficacy assessment, followed by BARD1, and then TXNDC2.

[0112] The analysis results of PBRM1 can be found in Figure 1 The results indicate that the PBRM1 level in stage III lung cancer patients who achieved positive therapeutic effects was significantly higher than that in stage III lung cancer patients who did not achieve positive therapeutic effects. This demonstrates that PBRM1 is highly correlated with predicting the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients, with a P value of 0.0083, proving that this correlation has very good statistical significance.

[0113] However, autoantibodies with low p-values ​​are not necessarily the best predictors of neoadjuvant immunotherapy efficacy in stage III lung cancer patients, and further validation is needed. Moreover, the predictive efficacy of a single autoantibody is very limited; combining several autoantibodies is necessary to improve predictive efficacy. Simply combining autoantibodies with high p-values ​​to obtain a combination of autoantibody biomarkers for predicting the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients may not yield good predictive efficacy either. Further validation using other evaluation methods and clinical prediction results is needed to find a more suitable combination of autoantibody biomarkers.

[0114] Example 2: Construction of a combination of autoantibody biomarkers related to the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients.

[0115] The more biomarkers in an autoantibody biomarker combination, the more manpower and resources are required for its detection and analysis. Therefore, it is necessary to find a combination that can achieve good predictive performance of neoadjuvant immunotherapy for stage III lung cancer patients, while including as few autoantibody biomarkers as possible.

[0116] Based on the results obtained in Example 1, this embodiment uses R Package rpart version 4.1.16 to construct a Class and Regression Tree (Card) with default parameters to fit the antibody detection results and post-treatment evaluation of the three phase subjects. All input data types are numeric. The analysis process is described in [link to analysis]. Figure 2The specific process is as follows: First, the detection result of autoantibody PBRM1 was selected as the single initial screening antigen for analyzing neoadjuvant immunotherapy in stage III lung cancer patients. 43% of the positive population (achieving positive therapeutic effects) were successfully identified, while the remaining 57% of the positive population could not be successfully predicted for neoadjuvant immunotherapy efficacy. Then, through fitting calculations (selecting any one of the remaining 19 autoantibodies to combine with PBRM1, selecting SS18, the autoantibody that successfully identified the largest number of individuals), SS18 was selected for further predictive analysis. From the remaining 57%, 18% of the positive population with positive therapeutic effects were successfully identified, while the remaining 39% of the negative population could not be successfully separated. Next, through fitting calculations (selecting any one of the remaining 18 autoantibodies to combine with PBRM1+SS18, selecting Trim21, the autoantibody that successfully identified the largest number of individuals), TRIM21 was selected for further predictive analysis. From the remaining 39%, 14% of the positive population with positive therapeutic effects and 25% of the negative population without positive therapeutic effects were successfully identified. Finally, a predictive model for the efficacy of neoadjuvant immunotherapy for lung cancer was constructed, consisting of three autoantibody molecules: anti-Trim21, anti-SS18, and anti-PBRM1.

[0117] In this embodiment, the CART decision tree strategy was applied to fit the antibody test results and post-treatment evaluation of the three phase subjects, and a predictive model for the therapeutic effect of neoadjuvant immunotherapy for lung cancer composed of three autoantibody molecules: anti-PBRM1, anti-SS18, and anti-Trim21 was constructed.

[0118] This embodiment further compares the predictive performance of different combinations of autoantibody biomarkers as shown in Table 3 for neoadjuvant immunotherapy in lung cancer, and verifies the predictive performance of the three autoantibody molecular prediction models constructed in this embodiment: anti-PBRM1, anti-SS18, and anti-Trim21.

[0119] Table 3. Performance comparison of different combinations of autoantibody biomarkers

[0120]

[0121] Table 3 shows that when only two autoantibodies, PBRM1 and SS18, are present, the probability of positive patients receiving a positive therapeutic effect is 71.4%, while the probability of negative patients not receiving a positive therapeutic effect is 93.2%. However, when three autoantibodies, PBRM1, SS18, and TRIM21, are present, the probability of positive patients receiving a positive therapeutic effect increases to 75%, while the probability of negative patients not receiving a positive therapeutic effect is 95%. Further addition of one autoantibody, RASSF7 or S100B, or two autoantibodies, RASSF7 and S100B, to the PBRM1, SS18, and TRIM21 model slightly increases the predicted probability of positive patients receiving a positive therapeutic effect, while significantly decreasing the predicted probability of negative patients not receiving a positive therapeutic effect. Moreover, increasing the number of one or more autoantibodies requires more human and material resources, increasing the prediction cost without increasing the effectiveness, and even showing a downward trend. Therefore, a prediction model composed of three autoantibody molecules, anti-PBRM1, anti-SS18, and anti-Trim21, is preferred for predicting the efficacy of neoadjuvant immunotherapy in stage III lung cancer patients.

[0122] Example 3: Performance of different autoantibody combinations in predicting the efficacy of neoadjuvant immunotherapy for lung cancer in different lung cancer patients.

[0123] This study included 90 lung cancer patients, of whom 56 were stage III, 20 were stage II, and 14 were stage I. In this embodiment, multiple combinations as shown in Table 4 were used to further predict stage II and stage I lung cancer patients.

[0124] Table 4. Comparison of the predictive efficacy of different autoantibody combinations for neoadjuvant immunotherapy in different lung cancer patients.

[0125]

[0126] As shown in Table 4, the performance of different combinations in predicting the efficacy of neoadjuvant immunotherapy for lung cancer patients at different stages varies. The combination of five autoantibodies, PBRM1, SS18, TRIM21, RASSF7, and S100B, is better for predicting the efficacy of neoadjuvant immunotherapy for lung cancer patients in stage II or stage I. For predicting stage III lung cancer patients, the prediction model composed of three autoantibody molecules, PBRM1, SS18, and Trim21, is preferred, as it can improve the prediction performance, simplify the detection method, and reduce costs.

[0127] Example 4: Predicting the efficacy of neoadjuvant immunotherapy for lung cancer using the autoantibody combination constructed in this invention.

[0128] The combination of three autoantibody molecules—anti-PBRM1, anti-SS18, and anti-Trim21—of this invention was used to differentiate whether stage III lung cancer patients achieved positive efficacy in neoadjuvant immunotherapy for lung cancer, and ROC curves were obtained, such as... Figure 3 As shown. By Figure 3 It is evident that the autoantibody molecule combination of the present invention can effectively predict the effect of neoadjuvant immunotherapy on stage III patients. For stage III lung cancer patients with positive test results, the probability of obtaining a positive effect from neoadjuvant immunotherapy is 75%; for stage III lung cancer patients with negative test results, the probability of not obtaining a positive effect from neoadjuvant immunotherapy is 95%.

[0129] Subsequently, the predictive model combining three autoantibody molecules—anti-PBRM1, anti-SS18, and anti-Trim21—of this invention was applied to all lung cancer patients in the trial. Based on the model's detection results and post-treatment evaluation, ROC curves were plotted to demonstrate the predictive efficacy of the model. Figure 4 As shown. The autoantibody molecule combination of the present invention can effectively predict the effect of patients receiving neoadjuvant immunotherapy for lung cancer. For lung cancer patients with positive test results, the probability of obtaining a positive effect from neoadjuvant immunotherapy is 75%; for lung cancer patients with negative test results, the probability of not obtaining a positive effect from neoadjuvant immunotherapy is 81.4%.

[0130] This embodiment further subdivided the 90 lung cancer patients according to their pathological subtypes: 27 cases of adenocarcinoma, 34 cases of squamous cell carcinoma, and 29 cases of small cell lung cancer. It was found that, regardless of the subtype, the proportion of patients with positive autoantibody tests who received positive evaluations after treatment was higher than that of patients with negative tests, especially in small cell lung cancer and squamous cell carcinoma patients (objective response rate: adenocarcinoma 89% vs 80%, squamous cell carcinoma 92% vs 68%, small cell carcinoma 100% vs 69%). Figure 5-7 As shown, where Figure 5 The graph shows the predictive model's ability to differentiate between adenocarcinoma patients and those who achieve positive outcomes in neoadjuvant immunotherapy for lung cancer. Figure 6 The graph shows the outcome of a predictive model to distinguish whether squamous cell carcinoma patients achieve positive efficacy in neoadjuvant immunotherapy for lung cancer. Figure 7 The figure shows the results of a predictive model to distinguish whether small cell carcinoma patients achieve positive efficacy in neoadjuvant immunotherapy for lung cancer.

[0131] This invention predicts the efficacy of neoadjuvant immunotherapy in lung cancer patients by detecting the levels of a combination of autoantibodies in their serum: a positive autoantibody test indicates a better expected treatment outcome, while a negative test indicates a less desirable outcome. This provides a valuable basis for patient treatment decisions and has promising clinical application prospects.

[0132] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A system for predicting the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients, characterized in that, The system includes a data analysis module; the data analysis module is used to analyze the detection status of autoantibody markers, wherein the autoantibody marker is PBRM1, or a combination of TRIM21 and PBRM1, or a combination of PBRM1 and SS18, or a combination of PBRM1, SS18 and TRIM21; the analysis method of the data analysis module is to detect whether the autoantibody marker in the blood sample of a stage III lung cancer patient is positive; The data analysis module assesses the effectiveness of neoadjuvant immunotherapy for stage III lung cancer by analyzing whether autoantibody markers are positive.

2. The system as described in claim 1, characterized in that, The analysis method of the data analysis module further includes: when one or more of the autoantibody biomarkers in the combination are positive, the combination of autoantibody biomarkers is positive, predicting that the neoadjuvant immunotherapy for the stage III lung cancer patient is effective; when all the autoantibodies in the combination of autoantibody biomarkers are negative, the combination of autoantibody biomarkers is negative, predicting that the neoadjuvant immunotherapy for the stage III lung cancer patient is ineffective.

3. The use of autoantibody biomarkers in the preparation of reagents for predicting the effectiveness of neoadjuvant immunotherapy in stage III lung cancer patients, characterized in that... The autoantibody marker is PBRM1.

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