WO2021164713A1 - Biomarker relating to effect of tumor immunotherapy and application thereof - Google Patents

Abstract

Disclosed is a biomarker for predicting the effect of tumor immunotherapy. The biomarker is a group of autoantibodies against tumor-associated antigens. The biomarker comprises at least one of the following autoantibodies against the tumor-associated antigen: Trim21, BRCA2, Annexin1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, and PRAME. The biomarker can be detected in samples from patients with tumors to predict the clinical effect of immunotherapy, thereby helping to determine whether the immunotherapy benefits the patients with tumors. A combination of antigen and protein for detecting the biomarker, a kit containing the combination of antigen and protein, and a corresponding detection or diagnosis method are further provided.

Description

Biomarkers related to the effect of tumor immunotherapy and their applications

This patent application claims the priority rights of the Chinese invention patent application with the application number CN2020101066590 filed on February 21, 2020, and the entire content of which is hereby incorporated by reference.

Technical field

The present invention relates to the field of biotechnology. Specifically, the present invention relates to a biomarker related to the effect of tumor immunotherapy, a protein combination for detecting the biomarker, and the corresponding application in predicting the effect of tumor immunotherapy.

Background technique

Immunotherapy is currently the most promising research direction in the field of cancer treatment. One of the hot spots is the use of immune checkpoint inhibitors (immune checkpoint inhibitor, ICI) immune blocking therapy, such as blocking programmed death factor-1 (programmed death factor-1). death-1, PD-1)/programmed death ligand-1 (PD-L1) immune checkpoint pathway therapy.

PD-1/PD-L1 immune checkpoint pathway blocking therapy usually refers to injecting specific antibodies against PD-1 or PD-L1 into the tumor patient so that the tumor no longer has the ability to evade the immune system. Thereby promoting the body's immune system to eliminate tumor cells. This therapy has achieved a significant effect of inhibiting tumor growth and removing tumors in some patients, and has been approved for melanoma, Hodgkin’s lymphoma, lung cancer, head and neck squamous cell carcinoma, liver cancer, esophageal cancer, breast cancer, Various indications for gastric cancer, nasopharyngeal cancer, lymphoma, etc.

However, although ICI (such as anti-PD-1/PD-L1 antibodies) has made remarkable achievements in tumor treatment, there are data showing that most patients receiving ICI do not benefit from it. For example, a considerable proportion of tumor patients do not respond to anti-PD-1/PD-L1 antibodies. Therefore, it is urgent to identify and develop biomarkers that can predict the efficacy of ICI, so as to accurately identify patients who benefit from ICI.

A number of clinical studies have shown that patients with tumors with elevated PD-L1 expression benefit more from ICI treatment. Therefore, some studies regard PD-L1 expression as the main evaluation target and are based on PD-L1 expression levels in some tumors. Regarding the good distinguishing effect of ICI curative effect prediction, it has become the only accompanying diagnosis of ICI treatment at present. However, some studies have found that there is no correlation between PD-L1 expression in some patients and ICI treatment response or overall survival (OS). For example, PD-L1 is highly expressed (the expression level of PD-L1 is 50% or higher), and the response rate of immune checkpoint molecular inhibitor drugs is only between 30% and 40%; but in addition, it is found that 10% of PD -L1 low expression (PD-L1 expression <50%) or PD-L1 negative cases are response cases, and even many patients whose PD-L1 expression is so low that it is undetectable have obtained lasting clinical benefit from ICI treatment . In addition, the use of PD-L1 expression as a predictor of ICI curative effect has the following drawbacks: a considerable proportion of patients with advanced tumors cannot provide enough tumor tissue for the detection of PD-L1 expression; PD-L1 expression varies in different stages of tumor development. There will be heterogeneity in the region, and the detection results may be affected by the sampling time and sampling site; in addition, differences in detection methods, evaluation standards, and the definition of positive cut-off values can also lead to different results judgments, which all lead to PD -L1 expression cannot be used as a comprehensive enough independent biomarker for clinical treatment decision-making. In addition, the most commonly used predictors of ICI efficacy include tumor mutation burden (tumor mutation burden, TMB), neoantigen burden, mismatch repair (MMR)/microsatellite instability (microsatellite instability). high, MSI-H), human leukocyte antigen (HLA) genotype, etc., but these predictive factors cannot accurately distinguish between responders and non-responders, cannot provide clear sensitivity or specificity, poor predictability, or detection requirements such as biopsy Defects such as high sampling or cost.

Therefore, the clinical methods used to distinguish patients who respond to ICI treatment are still very limited, and it is necessary to provide biomarkers and their application methods with higher accuracy, easier use, lower cost, and easier promotion and application. , To improve the therapeutic effect of immune checkpoint blocking therapy and tumor immunotherapy.

As early as the 1960s, Robert W. Baldwin confirmed that in the very early stage of tumorigenesis and development, the human immune system can already produce autoantibodies against tumor cells, laying the foundation for tumor-associated antigen (TAA) research. Base. The human body's autoantibodies produced in the process of tumor immune surveillance have been used in the early diagnosis of tumors, and they are considered to be a potential biomarker for predicting the efficacy of tumor treatment. For example, existing studies have shown that the presence of anti-XAGE1 (GAGED2a) antibodies in tumor patients is a strong predictor of OS prolongation in tumor patients with XAGE1 (GAGED2a) antigen-positive regardless of EGFR mutation. In addition, some studies have proposed that the levels of anti-p53 autoantibodies and anti-PGP9.5 autoantibodies can be used as tools to predict the recurrence of lung cancer.

Tumor antigen expression is also related to the cellular activity of tumor infiltrating immune cells. Therefore, autoantibodies against tumor-associated antigens may also become targets or predictive markers for highly specific immunotherapy. There have been related research reports, including Sachet a. Shukla has identified a subclass of tumor testis antigen MAGE-A, which is located in a narrow 75kb region of Xq28 chromosome, which can be used to predict the efficacy of anti-CTLA-4 antibodies . Other studies have shown that serum antibodies against NY-ESO-1 and/or XAGE1 tumor testis antigen can be used to predict the ICI efficacy and patient survival of initial and subsequent non-small cell lung cancer (NSCLC). In addition, in an evaluation of 81 NSCLC patients who were treated with anti-PD-1/PD-L1 antibodies for subsequent treatment, the predictive value of autoantibodies against more than ten tumor-associated antigens was evaluated for anti-PD-1 therapy. It shows that autoantibodies against IMP2 are significantly related to tumor progression. However, it is worth noting that, so far, as far as the relationship between the expression of autoantibodies against tumor-associated antigens and ICI response is concerned, relevant reports are still very limited; and these autoantibodies are all single biomarkers, which are predicted It may not be accurate, and its actual forecasting effect still needs further verification.

Immune checkpoint inhibitors are very expensive drugs. Unlike conventional chemotherapy, although immune checkpoint inhibitor drugs are effective for some patients, they may also cause serious adverse reactions, especially the development of systemic autoimmune diseases. Therefore, there is still a need to identify new autoantibody biomarkers that can be used to predict the efficacy of ICI, and to develop detection antigens for the autoantibody biomarkers, especially antigen combinations, in order to provide new predictive methods for tumor immunotherapy effects .

Summary of the invention

In order to solve the above technical problems, the present invention detects autoantibodies against different antigen targets in the blood of lung cancer patients, and finally identifies a group of immune checkpoint inhibitors (ICI) that can be used to predict or judge the therapeutic effect of tumors, especially lung cancer. Autoantibody biomarkers.

Therefore, an object of the present invention is to provide a combination of autoantibody biomarkers for predicting or judging the effect of tumor immunotherapy.

Based on the identification of autoantibody biomarkers, another object of the present invention is to provide reagents for detecting the autoantibody markers, such as an antigen protein combination, which can be used to detect the self in a tumor patient sample (such as blood) Whether the antibody biomarker is positive, so as to predict or judge the immunotherapy effect of tumor patients; and provide a kit containing the detection reagent, which can be used for corresponding detection.

Another object of the present invention is to provide the use of the autoantibody biomarker combination or antigen protein combination in the preparation of products for predicting or judging the effect of tumor immunotherapy.

Another object of the present invention is to provide a method for predicting or judging the effect of immunotherapy of tumor patients or a method for treating tumors.

The technical scheme of the present invention is as follows.

In one aspect, the present invention provides a biomarker for predicting or judging the effect of tumor immunotherapy in a subject. The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes anti-tumor-related antigens selected from the group consisting of: At least one of autoantibodies: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.

With regard to the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the autoantibody combination may include at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY -ESO-1, P53, IMP2. The biomarker can be used to predict or judge: the subject's good tumor treatment effect; the subject benefits from tumor immunotherapy; the treatment is effective; or, the subject's tumor is sensitive to immunotherapy.

Preferably, the autoantibody combination includes two, three or four autoantibodies selected from the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD; more preferably, the autoantibody combination includes anti- Tumor-associated antigen autoantibodies: Trim21 and BRCA2; further preferably, the autoantibody combination also includes one or two of the following autoantibodies against the following tumor-associated antigens: Annexin 1, HUD.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(A) Trim21, BRCA2, IMP2;

(B) Trim21, BRCA2, NY-ESO-1;

(C) Trim21, BRCA2, NY-ESO-1, IMP2;

(D) Trim21, BRCA2, P53;

(E) Trim21, BRCA2, Annexin 1;

(F) Trim21, BRCA2, Annexin 1, P53;

(G) Trim21, BRCA2, Annexin 1, NY-ESO-1, IMP2;

(H) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, IMP2;

(I) Trim21, BRCA2, Annexin 1, NY-ESO-1, P53, IMP2;

(R) Trim21, BRCA2, Annexin 1, HUD;

(RN) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1;

(RP) Trim21, BRCA2, Annexin 1, HUD, P53; or

(RNP) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53.

Most preferably, the present invention provides a biomarker for predicting or judging the effect of tumor immunotherapy in a subject, the biomarker is a combination of autoantibodies, and the combination of autoantibodies includes anti-tumor-associated antigen Trim21, BRCA2 , Annexin 1, HUD autoantibodies, namely anti-Trim21 autoantibody, anti-BRACA2 autoantibody, anti-Annexin 1 autoantibody and anti-HUD autoantibody.

Alternatively, in terms of the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the autoantibody combination may include at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, PRAME. The biomarkers can be used to predict or judge: the subject's poor tumor treatment effect; the subject does not benefit from tumor immunotherapy; the treatment is ineffective; or the subject's tumor is not sensitive to immunotherapy.

Preferably, the autoantibody combination includes autoantibodies against the following tumor-associated antigens: HSP105, or HSP105 and AKAP4.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(K)HSP105;

(L)HSP105, AKAP4;

(M) HSP105, MAGE-A3, AKAP4; or

(P) HSP105, AKAP4, PRAME.

Most preferably, the present invention provides a biomarker for predicting or judging a subject's immune tumor treatment effect, the biomarker is a combination of autoantibodies, and the combination of autoantibodies includes anti-tumor-associated antigens HSP105, AKAP4 Anti-HSP105 autoantibodies and anti-AKAP4 autoantibodies.

According to the present invention, the autoantibody is the autoantibody in serum, plasma, interstitial fluid, cerebrospinal fluid or urine before the subject receives tumor immunotherapy; preferably, the autoantibody is IgA (for example, IgA1, IgA2), IgM or IgG (e.g. IgG1, IgG2, IgG3, IgG4).

According to the present invention, the subject is a mammal, preferably a primate mammal, more preferably a human. And, preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodge King’s lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer.

According to the present invention, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy. Treatment or other tumor treatment methods, wherein the immune checkpoint inhibitor is for PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or The CD160 immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody. According to a specific embodiment of the present invention, the antibodies are nivolumab, pambrolizumab, sintilizumab, teriprizumab, and domestic immune checkpoint inhibitors, especially anti-PD-1 antibodies Or anti-PD-L1 antibody.

According to the present invention, the biomarker, that is, the combination of autoantibodies, can be detected in a sample (such as plasma or serum) of a subject, such as a tumor patient, to predict or judge the efficacy of tumor immunotherapy for the subject. The data indicates that when these autoantibody biomarkers are positive in the blood of a subject, they are more likely or less likely to benefit from immunotherapy such as ICI therapy. Therefore, the autoantibody biomarkers provided by the present invention can be used to predict or judge whether a subject, such as a tumor patient, can benefit from immunotherapy (whether the immunotherapy effect is good or poor; whether the immunotherapy is effective; or whether the subject is effective The patient’s tumor is sensitive or insensitive to immunotherapy), at least for the corresponding auxiliary judgment. In the present invention, "presence" or "absence" and "positive" or "negative" of autoantibody biomarkers can be used interchangeably; judging this is a conventional technique in the art. According to specific embodiments of the present invention, the level of autoantibodies in a sample can be quantified by referring to a standard curve, and then the cutoff value is used to determine the presence (≥cutoff value, positive) or absence of autoantibody biomarkers (<cutoff value) , Is negative) judgment. The cutoff value of the autoantibody level can be a reference level from a healthy person or a healthy person; for example, it can be defined as the average value of a person who is confirmed to have no cancer through a physical examination plus 2 standard deviations.

The autoantibody biomarker provided by the present invention can be detected by a variety of methods, for example, it can be detected by the antigen-antibody specific reaction between the tumor-associated antigen that causes the autoantibody to appear and the antigen-antibody specific reaction. Therefore, correspondingly, the present invention also provides a reagent for detecting the autoantibody biomarker.

Depending on the specific technical means, the reagents may be reagents used in detection methods such as enzyme-linked immunosorbent assay (ELISA), protein/peptide chip detection, immunoblotting, bead immunoassay, microfluidic immunoassay, etc. Preferably, the reagent is used to detect the autoantibody biomarker of the present invention through an antigen-antibody reaction, for example, by ELISA.

In this aspect, the reagent may be an antigen protein combination for detecting the autoantibody combination, and the antigen protein combination includes at least one selected from the following antigen proteins: Trim21, BRCA2, Annexin 1, HUD, NY- ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.

The reagent can be used to detect whether the corresponding autoantibody biomarker in a sample (such as plasma or serum) of a subject, such as a tumor patient, is positive, so as to realize the prediction or clinical effect of the administration of tumor immunotherapy as described above. judge.

In the present invention, for example, the amino acid sequence contained in the tumor-associated antigen or antigen protein is as follows:

Trim21 includes the amino acid sequence shown in SEQ ID NO:1;

BRCA2 includes the amino acid sequence shown in SEQ ID NO: 2;

Annexin 1 includes the amino acid sequence shown in SEQ ID NO: 3;

HUD includes the amino acid sequence shown in SEQ ID NO: 4;

NY-ESO-1 includes the amino acid sequence shown in SEQ ID NO: 5;

P53 includes the amino acid sequence shown in SEQ ID NO: 6;

IMP2 includes the amino acid sequence shown in SEQ ID NO: 7;

HSP105 includes the amino acid sequence shown in SEQ ID NO: 8;

MAGE-A3 includes the amino acid sequence shown in SEQ ID NO: 9;

AKAP4 includes the amino acid sequence shown in SEQ ID NO: 10;

PRAME includes the amino acid sequence shown in SEQ ID NO: 11.

In another aspect, the present invention provides the use of the biomarker or reagent in the preparation of a product for predicting or judging the effect of tumor immunotherapy in a subject. As mentioned above, the tumor immunotherapy effects include good tumor immunotherapy effects and poor tumor immunotherapy effects, respectively, depending on the corresponding biomarkers or reagents.

According to the present invention, the subject is a mammal, preferably a primate mammal, more preferably a human. And, preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodge King’s lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer.

According to the present invention, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy. Treatment or other tumor treatment methods, wherein the immune checkpoint inhibitor is for PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or The CD160 immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody. According to a specific embodiment of the present invention, the antibodies are nivolumab, pambrolizumab, sintilizumab, teriprizumab, and domestic immune checkpoint inhibitors, especially anti-PD-1 Antibody or anti-PD-L1 antibody.

In another aspect, the present invention provides a kit comprising the reagent of the present invention.

Depending on the specific technical means, the kit can be used for enzyme-linked immunosorbent assay (ELISA), protein/peptide chip detection, western blotting, microbead immunoassay, microfluidic immunoassay, etc. to the autoantibody biomarker Kits for detection of substances. Preferably, the kit is used to detect the autoantibody biomarker of the present invention through an antigen-antibody reaction, for example, by ELISA.

Therefore, preferably, the kit is an enzyme-linked immunosorbent assay (ELISA) detection kit. That is, using this kit, the enzyme-linked immunosorbent assay is used to detect whether the autoantibody biomarker in the subject's sample is positive. Correspondingly, the kit can also include other components required for ELISA detection of autoantibody biomarkers, all of which are well known in the art. For detection purposes, for example, the antigen protein in the kit can be linked with a tag peptide, such as His tag, streptavidin tag, Myc tag; for another example, the kit can include a solid phase carrier, such as with immobilized antigen Microporous protein carrier, such as an enzyme-labeled plate; it may also include adsorbed protein for immobilizing antigen protein on a solid carrier, dilution of blood such as serum, washing solution, enzyme-labeled secondary antibody, color development Liquid, stop liquid, etc.

In another aspect, the present invention provides a method for predicting or judging the effect of tumor immunotherapy in a subject. Alternatively, the present invention provides a method for predicting or judging the sensitivity of a subject's tumor to immunotherapy.

The above methods include testing whether the following biomarkers in a sample from a subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.

With regard to the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the method may include detecting whether the following biomarkers in a sample from a subject are positive:

The biomarker is an autoantibody combination, and the autoantibody combination includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2. When the biomarker is positive, it is predicted or judged that the subject has a good tumor immunotherapy effect; the subject benefits from the tumor immunotherapy; the treatment is effective; or the subject's tumor is sensitive to the immunotherapy.

Preferably, the autoantibody combination includes two, three or four autoantibodies selected from the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD; more preferably, the autoantibody combination includes anti- Tumor-associated antigen autoantibodies: Trim21 and BRCA2; further preferably, the autoantibody combination also includes one or two of the following autoantibodies against the following tumor-associated antigens: Annexin 1, HUD.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(A) Trim21, BRCA2, IMP2;

(B) Trim21, BRCA2, NY-ESO-1;

(C) Trim21, BRCA2, NY-ESO-1, IMP2;

(D) Trim21, BRCA2, P53;

(E) Trim21, BRCA2, Annexin 1;

(F) Trim21, BRCA2, Annexin 1, P53;

(G) Trim21, BRCA2, Annexin 1, NY-ESO-1, IMP2;

(H) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, IMP2;

(I) Trim21, BRCA2, Annexin 1, NY-ESO-1, P53, IMP2;

(R) Trim21, BRCA2, Annexin 1, HUD;

(RN) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1;

(RP) Trim21, BRCA2, Annexin 1, HUD, P53; or

(RNP) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53.

Most preferably, the present invention provides a method for predicting or judging the effect of a subject’s tumor immunotherapy, or a method for predicting or judging the sensitivity of a subject’s tumor to immunotherapy, the method comprising detecting from Whether the following biomarkers in the subject’s sample are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes autoantibodies against tumor-associated antigens Trim21, BRCA2, Annexin 1, and HUD, namely anti-Trim21 autoantibody, anti-BRACA2 autoantibody, anti-Annexin 1 autoantibody, and anti-tumor related antigens. HUD autoantibodies.

Alternatively, in terms of the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the method may include detecting whether the following biomarkers in a sample from the subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, and PRAME. When the biomarker is positive, it is predicted or judged that the subject has poor tumor immunotherapy effect; the subject does not benefit from tumor immunotherapy; the treatment is ineffective; or the subject's tumor is not sensitive to immunotherapy.

Preferably, the autoantibody combination includes autoantibodies selected from the following tumor-associated antigens: HSP105, or HSP105 and AKAP4.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(K)HSP105;

(L)HSP105, AKAP4;

(M) HSP105, MAGE-A3, AKAP4; or

(P) HSP105, AKAP4, PRAME.

Most preferably, the present invention provides a method for predicting or judging the effect of a subject’s tumor immunotherapy, or a method for predicting or judging the sensitivity of a subject’s tumor to immunotherapy, the method comprising detecting from Whether the following biomarkers in the subject’s sample are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes autoantibodies against tumor-associated antigens HSP105 and AKAP4, that is, anti-HSP105 autoantibodies and anti-AKAP4 autoantibodies.

Wherein, the detection can be carried out using the reagent of the present invention, for example, an antigen-protein combination or a kit containing the reagent.

According to the present invention, the subject is a mammal, preferably a primate mammal, more preferably a human. And, preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodge King's lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer.

According to the present invention, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy. Treatment or other tumor treatment methods, wherein the immune checkpoint inhibitor is for PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or The CD160 immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody. According to a specific embodiment of the present invention, the antibodies are nivolumab, pambrolizumab, sintilizumab, teriprizumab, and domestic immune checkpoint inhibitors, especially anti-PD-1 Antibody or anti-PD-L1 antibody.

According to the present invention, the sample is serum, plasma, interstitial fluid, cerebrospinal fluid, or urine before the subject receives tumor immunotherapy; preferably, the autoantibody is IgA (for example, IgA1, IgA2), IgM or IgG ( For example, IgG1, IgG2, IgG3, IgG4).

For example, the method includes the following steps:

(1) Obtain a sample from the subject;

(2) Detecting whether the autoantibody biomarker of the present invention in the sample is positive;

(3) When the autoantibody biomarker in the sample is positive, predict or judge: the subject has a good or poor tumor immunotherapy effect; the subject benefits or does not benefit from tumor immunotherapy; the treatment is effective Or ineffective; or, the subject's tumor is sensitive or insensitive to immunotherapy.

In yet another aspect, the present invention provides a method for treating tumors in a subject, the method comprising detecting whether the following biomarkers in a sample from the subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.

With regard to the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the method may include detecting whether the following biomarkers in a sample from a subject are positive:

The biomarker is an autoantibody combination, and the autoantibody combination includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2. When the biomarker is positive, the subject is subjected to tumor immunotherapy.

Preferably, the autoantibody combination includes two, three or four autoantibodies selected from the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD; more preferably, the autoantibody combination includes anti- Autoantibodies against tumor-associated antigens: Trim21 and BRCA2; further preferably, the autoantibody combination also includes one or two of the following autoantibodies against the following tumor-associated antigens: Annexin 1, HUD.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(A) Trim21, BRCA2, IMP2;

(B) Trim21, BRCA2, NY-ESO-1;

(C) Trim21, BRCA2, NY-ESO-1, IMP2;

(D) Trim21, BRCA2, P53;

(E) Trim21, BRCA2, Annexin 1;

(F) Trim21, BRCA2, Annexin 1, P53;

(G) Trim21, BRCA2, Annexin 1, NY-ESO-1, IMP2;

(H) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, IMP2;

(I) Trim21, BRCA2, Annexin 1, NY-ESO-1, P53, IMP2;

(R) Trim21, BRCA2, Annexin 1, HUD;

(RN) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1;

(RP) Trim21, BRCA2, Annexin 1, HUD, P53; or

(RNP) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53.

Most preferably, the present invention provides a method for treating tumors in a subject, the method comprising detecting whether the following biomarkers in a sample from the subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes autoantibodies against tumor-associated antigens Trim21, BRCA2, Annexin 1, and HUD, namely anti-Trim21 autoantibody, anti-BRACA2 autoantibody, anti-Annexin 1 autoantibody, and anti-tumor related antigens. HUD autoantibodies.

Alternatively, in terms of the tumor immunotherapy effect indicated by the biomarkers provided by the present invention, the method may include detecting whether the following biomarkers in a sample from the subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, and PRAME. When the biomarker is positive, the subject is prevented from undergoing tumor immunotherapy.

Preferably, the autoantibody combination includes autoantibodies selected from the following tumor-associated antigens: HSP105, or HSP105 and AKAP4.

According to a specific embodiment of the present invention, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

(K)HSP105;

(L)HSP105, AKAP4;

(M) HSP105, MAGE-A3, AKAP4; or

(P) HSP105, AKAP4, PRAME.

Most preferably, the present invention provides a method for treating tumors in a subject, the method comprising detecting whether the following biomarkers in a sample from the subject are positive:

The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes autoantibodies against tumor-associated antigens HSP105 and AKAP4, that is, anti-HSP105 autoantibodies and anti-AKAP4 autoantibodies.

Wherein, the detection can be carried out using the reagent of the present invention, for example, an antigen-protein combination or a kit containing the reagent.

According to the present invention, the subject is a mammal, preferably a primate mammal, more preferably a human. And, preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodge King’s lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer.

According to the present invention, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy. Treatment or other tumor treatment methods, wherein the immune checkpoint inhibitor is for PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or The CD160 immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody. According to a specific embodiment of the present invention, the antibodies are nivolumab, pambrolizumab, sintilizumab, teriprizumab, and domestic immune checkpoint inhibitors, especially anti-PD-1 Antibody or anti-PD-L1 antibody.

According to the present invention, the sample is serum, plasma, interstitial fluid, cerebrospinal fluid, or urine before the subject receives tumor immunotherapy; preferably, the autoantibody is IgA (for example, IgA1, IgA2), IgM or IgG ( For example, IgG1, IgG2, IgG3, IgG4).

For example, the method includes the following steps:

(1) Obtain a sample from the subject;

(2) Detect whether the autoantibody biomarker in the sample is positive; wherein the enzyme-linked immunosorbent method is preferably used for detection.

When the autoantibody biomarker in the sample is positive, the subject is allowed to undergo tumor immunotherapy, or the subject is not allowed to undergo tumor immunotherapy.

Compared with the prior art, the present invention provides a biomarker for predicting or judging the effect of tumor immunotherapy, and the biomarker is a combination of autoantibodies. And the autoantibody combination of the present invention includes two combinations that predict good tumor immunotherapy effect and poor tumor immunotherapy effect. The former can be called a positive prediction of tumor immunotherapy effect, and the latter can be called tumor immunity. Negative prediction of treatment effect.

Experiments have shown that regardless of PD-L1 expression level and TMB level, whether it is first-line or post-line immunotherapy, the combination of autoantibodies is positively predicted, and the effective rate of immune checkpoint blocking therapy for tumor patients with positive detection is significantly higher. For tumor patients who tested negative for the autoantibody combination (P<0.05); similarly, whether it is immune monotherapy or immune combination chemotherapy, the effective rate of immune checkpoint blocking therapy in these tumor patients is also significantly higher than that of the autoantibody combination Tumor patients with negative tests (P<0.05), especially when using immune monotherapy, the former is more effective. Regarding the combination of negatively predicted autoantibodies, whether it is immune first-line therapy or post-line therapy, whether it is immune monotherapy or immune combination chemotherapy, the effective rate of immune checkpoint blocking therapy for tumor patients who test positive is more significant. Lower than the autoantibody combination test negative tumor patients (P<0.05).

Therefore, whether tumor patients can benefit from immunotherapy, especially immune checkpoint inhibitor therapy, the autoantibody biomarkers provided by the present invention can provide accurate prediction or judgment results. Based on the prediction or judgment result, the patient or clinician can better decide whether the patient needs immunotherapy, so as to avoid excessive medical treatment, reduce treatment costs, and reduce or avoid adverse reactions.

Moreover, the two autoantibody combinations provided by the present invention can also be used alone or in combination as required. For example, when used in combination, the positive conditions of the autoantibody biomarkers predicted in the positive direction and the negative conditions of the autoantibody biomarkers predicted in the negative direction can be combined.

Description of the drawings

Hereinafter, the embodiments of the present invention will be described in detail with reference to the accompanying drawings, in which:

Figure 1 shows the tumor response to immunotherapy after the treatment of patients who showed positive or negative autoantibody combination before treatment, where Figure 1-A: P_Ab. combination, Figure 1-B: N_Ab. combination.

Figure 2 shows the survival curve of patients who showed positive or negative autoantibody combination before treatment after treatment, where Figure 2-A: training set and Figure 2-B: validation set.

Figure 3 shows the survival curve of patients who showed positive or negative autoantibody combination before treatment after treatment, wherein Figure 3-A to Figure 3-RNP respectively show the results of corresponding autoantibody combination A to autoantibody combination RNP.

Figure 4 shows the survival curve of patients who showed positive or negative autoantibody combination before treatment after treatment, wherein Figure 4-K to Figure 4-P show the results of corresponding autoantibody combination K to autoantibody combination P, respectively.

Figure 5 shows the survival curve of patients who showed positive or negative autoantibody or autoantibody combination before treatment after treatment, where Figure 5-A: IMP2, Figure 5-B: anti-XAGE-1 and anti-NY-ESO-1.

Figure 6 shows the survival curve of patients who showed positive or negative autoantibody combination before treatment after treatment, where Figure 6-A: first-line immunotherapy, Figure 6-B: posterior-line immunotherapy, and Figure 6-C: immune single agent Treatment), Figure 6-D: Immunotherapy combined with chemotherapy.

The best way to implement the invention

In the present invention, the term "antigen" or the term "antigenic protein" can be used interchangeably. In addition, the following experimental operations or definitions are involved in the present invention. It should be noted that the present invention can also be implemented using other conventional techniques in the field, and is not limited to the following experimental operations.

(1) Preparation and fixation of antigen protein

The cDNA of the tumor-associated antigen (TAA) was cloned into the 6XHis-labeled PET28(a) expression vector. At the N-terminus or C-terminus of the antigen, a streptavidin protein or the like (biotin-binding tag protein) is introduced. The obtained recombinant expression vector was transformed into Escherichia coli for expression. After the protein was expressed in the inclusion body, the protein was denatured with 6M guanidine hydrochloride, and renatured in vitro according to standard methods, and then Ni-NTA affinity was carried out by 6XHis tag Column purification to obtain antigen protein.

(2) Preparation of plasma

Venous blood was collected in EDTA-treated or citric acid-treated blood collection tubes one week to one day before immunotherapy. Then centrifuge at 1000-2000RCF at room temperature for 15 minutes; after centrifugation, gently transfer the supernatant to another clean centrifuge tube at room temperature, and place it in a refrigerator at -80°C for long-term storage.

(3) ELISA detection and quantification of autoantibodies

The prepared antigen protein is coated on the surface of the microwell of a 96-well solid phase plate. Using indirect coating: a 96-well solid phase plate was coated overnight with 5-10ug/ml biotin-labeled bovine serum albumin; on the second day, the uncoated bovine serum albumin in the microwells of the solid phase plate was washed away. Add 300uL of blocking solution containing BSA to block at room temperature for 1h; add antigen protein and incubate for 1.5h, then wash off unadsorbed antigen protein. After coating the antigen protein, add 300ul of the stabilized solution containing BSA to the microwells, incubate for 1h, and then use it or dry it under vacuum for later use.

As above, the purified antigen protein is indirectly coated on the surface of the solid phase plate through the specific reaction between biotin and streptavidin. The diluted plasma sample is added to the microwells coated with the antigen protein, and the autoantibodies in the plasma sample are specifically combined with the antigen protein on the surface of the solid phase plate after incubation. Wash off unbound autoantibodies, add horseradish peroxidase-labeled anti-human IgG antibody, and incubate for the second time so that the enzyme-labeled anti-human IgG antibody binds to the autoantibody adsorbed on the surface of the solid phase plate to form an antigen-antibody -Enzyme-labeled antibody complex, then wash away the unbound enzyme-labeled anti-human IgG antibody, add color reagent substrate to react and measure the absorbance with a microplate reader at 450nm wavelength. Finally, it is judged whether the autoantibody test result is negative or positive by comparing with the cutoff value. The detection steps are as follows:

1. Preparation steps

1. Place the reagents for detection at room temperature for at least 30 minutes to restore the reagents to room temperature.

2. Dilute the plasma sample to be tested: Add 545ul of the sample diluent (PBS, containing 1% BSA) into the 1.5ml EP tube, and then add 5ul of the plasma sample to be tested to the sample diluent (the sample can be adjusted according to the required amount The volume ratio of the plasma sample to the sample diluent is 1:109), turn it upside down 5-6 times and mix gently.

3. After diluting and mixing each plasma sample to be tested, transfer 530ul to a clean deep hole tank.

4. Prepare the working solution of lotion: Dilute the 10 times PBST lotion with purified water or distilled water 10 times to make the original lotion and set aside.

5. PBS buffer: prepare your own, with a pH of 7.6.

Second, the detection steps

1. Add primary antibody: After washing the microtiter plate with 270ul/well of PBS buffer once, add 50ul/well of the diluted plasma to the microtiter plate, and react on the microwell shaker for 1 hour at room temperature.

2. Add secondary antibody: prepare secondary antibody before use (horseradish peroxidase-labeled anti-human IgG antibody concentrated solution returned to room temperature: enzyme conjugate dilution solution = 1:19, the dilution solution is PBS containing 1% BSA ). Wash the plate 3 times with 1*PBST washing solution 270ul/well, pat dry each time, then add the secondary antibody diluent at 50ul/well, stick the membrane, and react on the microwell shaker at room temperature for 0.5h.

3. Add color developer: prepare color developer (liquid developer A: liquid developer B=1:19) before use. Wash the plate 3 times with 1*washing solution 270ul/well, pat dry each time, then add the color developer at 100ul/well, add the first line to start timing, stick the film, and react on the microporous shaker at room temperature for 15 minutes.

4. Stop and read: Add 50ul/well of stop solution according to the order of adding color reagent, and read at 450nm in the microplate reader.

5. Judgment of negative and positive results: The OD value is compared with the cutoff value to determine the test result.

(4) Cutoff value of autoantibody (cutoff value)

The cutoff value of the autoantibody level is defined as equal to the average value of the healthy control cohort in the control group (people confirmed to have no cancer through physical examination) plus 2 standard deviations (SD).

(5) The positive judgment of a single autoantibody

After quantifying the level of autoantibodies in the sample, compare it with the cutoff value. ≥cutoff value is positive, and <cutoff value is negative.

(6) Positive judgment of autoantibody combination

Because the positive rate of a single autoantibody is low, in order to increase the positive rate of autoantibody detection, the results of multiple autoantibodies are combined to judge the predictive effect when analyzing the results. The rule is: multiple autoantibodies are detected in a patient sample. As long as one or more of the autoantibodies are positive, the antibody combination is judged to be positive; and if all autoantibodies are negative, the antibody combination is judged to be negative .

(7) Clinical efficacy evaluation indicators

According to the response evaluation criteria for solid tumors Version 1.1 (Response Evaluation Criteria in Solid Tumors RECIST Version 1.1, RECIST v1.1), the target lesion at baseline (before treatment) is evaluated, and the baseline sum of the longest diameter of the target lesion is recorded to determine the objective reaction.

BOR: The best curative effect refers to the record of the best curative effect from the beginning of the treatment study to the end of the treatment, which is confirmed after considering various factors.

PD: Compared with the minimum sum of the diameters of all target lesions before treatment, the sum of the diameters of all target lesions is increased by at least 20% and the absolute value of the total increase in diameter must be greater than 5mm; or new lesions appear.

PR: Compared with the sum of the diameters of all target lesions before treatment, the sum of the diameters of all target lesions is reduced by at least 30%.

SD: Compared with the minimum sum of the diameters of all target lesions before treatment, the shrinkage of the target lesion does not meet the partial remission (PR), and the increase does not meet the disease progression (PD). It refers to a type between PR and PD. The state of the time.

CR: All target lesions disappear, and the short axis value of any pathological lymph node (whether it is a target lesion or not) must be less than 10mm.

PFS: Progression-free survival time, that is, the time from the beginning of randomization to the recurrence of the disease or the death of the patient due to various reasons.

mPFS: Median progression-free survival time, that is, the median time from randomization to disease recurrence or death due to various reasons.

PD-L1 expression level: Use immunohistochemistry to evaluate the percentage of tumor cells stained with PD-L1 membrane of any intensity in all tumor cells. The test results are divided into four groups, namely negative (less than 1%) , Low expression group (1%-49%), high expression group (≥50%), unknown.

(8) Statistical analysis methods

Use GraphPad Prism v.6 (Graphpad Prism software, San Diego, California) and IBM SPSS Statistics 23 for Windows (IBM, New York, New York), and use Mann-Whitney U test to perform statistical analysis on the two groups. When analyzing the relationship between each parameter, Spearman's correlation analysis was performed. The Kaplan-Meier method was used to analyze the median Progression Free Survival (mPFS). The log-rank test was used to analyze differences in mPFS between patient subgroups.

Hereinafter, the present invention will be explained with reference to specific embodiments. Those skilled in the art can understand that these embodiments are only used to illustrate the present invention, and they do not limit the scope of the present invention in any way. The sample collection has been informed by the patient's consent and approved by the regulatory authority (Shanghai Pulmonary Hospital Review Committee).

The experimental methods in the following examples, unless otherwise specified, are all conventional methods. The medicinal materials, reagent materials, etc. used in the following examples are all commercially available products unless otherwise specified.

Example 1

In order to find autoantibodies that can indicate the effect of immunotherapy, first use the plasma of 47 normal healthy people (healthy control group) and 47 patients diagnosed with lung cancer (lung cancer group) to detect whether there are autoantibodies against purified antigen protein in lung cancer patients. . The healthy control group are those who have not been or are not diagnosed with cancer among the medical examiners. The lung cancer group includes 10 patients with small cell lung cancer, 12 patients with lung squamous cell carcinoma, 19 patients with lung adenocarcinoma, and 6 patients with other subtypes of lung cancer. Plasma was collected before treatment. The information of the experimental population is shown in Table 1.

Table 1. Information on the initial screening test population

In order to compare the concentration of autoantibodies corresponding to each antigen in the healthy control group and the lung cancer group in parallel, the detection specificity of each antigen is set to ≥93.6% (in the healthy control group of 47 cases, there are ≤6.4 % Positive rate); and the sensitivity in the lung cancer group (positive rate, that is, the proportion of autoantibody positive in the total number of lung cancer patients in 47 cases) is similar to that found in other literature, the positive detection rate of a single autoantibody Low, usually between 5-20%. Therefore, in the preliminary screening of lung cancer-related antigens, antigen proteins are divided into four groups based on sensitivity. The screened antigen protein and the sensitivity and specificity determined by the test are shown in Table 2.

Table 2. Results of preliminary screening of tumor-associated antigens

Example 2

Autoantibodies were detected in the baseline (pre-treatment) plasma of immunotherapy patients (Table 3). The antigen proteins of the first three groups (sensitivity> 5%) in Example 1 and Table 2 were used to screen for autoantibodies related to the therapeutic efficacy of lung cancer patients. .

In order to be able to screen for predictive markers of immunotherapy suitable for clinical application, this study tries to include various scenarios of immunotherapy for patients with lung cancer. Thirty-eight lung cancer patients used immunotherapy as the first-line treatment, while 40 lung cancer patients received one or more treatments such as chemotherapy and targeted therapy, and then chose immunotherapy (post-line treatment). In addition, the immunotherapy used by the enrolled patients included immune checkpoint inhibitors, including imported nivolumab and pembrolizumab, as well as domestic immune checkpoint inhibitors (Table 3). Regardless of whether it is first-line or second-line immunotherapy, the treatment plan for lung cancer patients includes two situations: immunotherapy is monotherapy (25 patients), or immunotherapy combined with chemotherapy (53 patients).

First, use the lung cancer-related antigens initially screened as the antigen protein, and use the baseline plasma of first-line immunotherapy patients as the "training set" for autoantibody ELISA testing, and then determine the positive autoantibody results (refer to the above The cutoff value) is compared with the tumor response to immunotherapy (BOR). The results show that the relationship between positive autoantibody test results and immunotherapy efficacy is classified into three categories (see Table 4):

Some tumor-associated antigens belong to "positively related" antigens. In the present invention, autoantibodies against this antigen are named "P_Ab.". Patients showing positive signals of such autoantibodies have basically achieved the "PR" and "SD" in BOR. ", that is, a good curative effect, and the patient population as a whole meets the percentage of "PR"/"PD" ≥2. This type of autoantibody is preliminarily determined as a positive predictive antibody, which has a positive predictive effect of good immunotherapy curative effect.

Some tumor-associated antigens are "negatively related" antigens. In the present invention, autoantibodies against this antigen are named "N_Ab.". Patients showing positive signals of such autoantibodies all have "PD" and "SD" in BOR. , And the patient population as a whole meets the "PD" percentage/"PR" percentage ≥2. Determining such autoantibodies as negative predictive antibodies has the negative predictive effect of poor immunotherapy efficacy. It is preliminarily determined that both the positive predictive antibody and the negative predictive antibody can be used to predict the effect of immunotherapy, and have good and poor predictive effects of immunotherapy respectively.

Another part of the autoantibodies of tumor-associated antigens are "insignificantly correlated" antigens, which show that they cannot be used to predict therapeutic effects.

Table 3. Baseline characteristics of patients

Table 4. Correlation between antigenic proteins used and detected autoantibody positivity and tumor response to immunotherapy

After using the first-line immunotherapy patients as the training set and discovering potential markers that can be used to guide immunotherapy, the combined subsequent-line immunotherapy is then used. The treatment methods include immune monotherapy patients and immune-combined chemotherapy patients (Table 3)) as a "validation set" to verify whether the discovered autoantibodies still have the characteristics of efficacy prediction.

Because the positive rate of a single autoantibody is low, in order to increase the positive rate of autoantibody detection, the results of multiple autoantibodies are combined to judge the predictive effect when analyzing the results. The rule is: if multiple autoantibodies are detected in a patient, as long as one or more of the autoantibodies are positive, the antibody combination is judged to be positive; and if all autoantibodies are negative, the antibody combination is judged to be negative. For this test, anti-Trim21 autoantibodies, anti-BRACA2 autoantibodies, anti-Annexin 1 autoantibodies and anti-HUD antibodies were selected as a combination of autoantibodies.

In the training set and validation set, treat with PD-1 inhibitors (the immune checkpoint inhibitors used include imported nivolumab and pembrolizumab, as well as domestic immune checkpoint inhibitors, which are any of them A) The percentage of patients showing tumor response to immunotherapy is shown in Figure 1. Among them, "P_Ab. positive" in Figure 1-A (P_Ab. combination) refers to patients who are positive for any one of anti-Trim21 autoantibodies, anti-BRACA2 autoantibodies, anti-Annexin 1 autoantibodies, and anti-HUD antibodies (ie, antibody combination Positive), "P_Ab. Negative" refers to patients whose anti-Trim21 autoantibody, anti-BRACA2 autoantibody, anti-Annexin 1 autoantibody and anti-HUD antibody are all negative (ie, the antibody combination is negative); Figure 1-B (N_Ab. combination) "N_Ab. positive" refers to patients whose anti-HSP105 autoantibody and anti-AKAP4 antibody are positive (that is, the combination of antibodies is positive), "N_Ab. negative" refers to both anti-HSP105 autoantibody and anti-AKAP4 antibody negative The patient (ie, the antibody combination is negative).

Figure 1-A in Figure 1 shows that in the training set (first-line), 47.6% of patients with positive correlation autoantibodies have a treatment effect of "PR" after treatment, and 42% of patients have a treatment effect of "SD". , 9.5% of the patients had a treatment effect of "PD"; while the positively correlated autoantibody combination was negative, 28.6% of the patients had a treatment effect of "PR", 50% of the patients had a treatment effect of "SD", and 21.4% of the patients The therapeutic effect is "PD". In the validation set (post-line), 50% of the patients with positive correlation autoantibody combination had a treatment effect of "PR", 37.5% of patients had a treatment effect of "SD", and 12.5% of patients had a treatment effect of "PR". PD"; and for patients with a negative combination of positively correlated autoantibodies, after treatment, 50% of patients have a treatment effect of "PD" and 50% of patients have a treatment effect of "SD".

Figure 1-B in Figure 1 shows that in the training set (first-line), 50% of patients with positive negative autoantibody combinations have a treatment effect of "PD" after treatment, and 50% of patients have a treatment effect of "SD" , 0% of patients have a treatment effect of "PR". In the validation set (post-line), after treatment, 42.9% of patients with positive negative autoantibodies have a treatment effect of "PD", 42.9% of patients have a treatment effect of "SD", and 14.3% of patients have a treatment effect of " PR".

Therefore, it is proved that the combined detection of positively related autoantibody combinations can effectively predict good immunotherapy effects, while the combined detection of negatively related autoantibody combinations can effectively predict poor immunotherapy effects.

The Kaplan-Meier method was used to analyze the progression-free survival of patients in the training set and the validation set, and to draw a survival curve. The results found that the positive and negative groups of autoantibody combinations showed great differences in the progression-free survival curve. In the training set (first-line), the median progression-free survival time of patients with positive antibody combinations was greater than 10 Month, and the antibody combination negative patient is 5.52 months, the p-value is 0.0512, see Figure 2-A in Figure 2. In the validation set (back line), the same group of positively correlated autoantibody combination positive patients The median progression-free time was 7.56 months, while the antibody combination negative patient was 2.43 months, and the p-value was less than 0.005, as shown in Figure 2-B in Figure 2.

Example 3

Since the same lung cancer patient may detect several antibodies with positive results at the same time, it is necessary to determine whether to use a smaller number of autoantibodies in combination, which can also predict the efficacy of immunotherapy and even achieve better results.

Based on the correlation between the positive test results of a single autoantibody and BOR, it is predicted that the corresponding autoantibodies of the seven tumor-associated antigens are most likely to be correlated with good immunotherapy effects, which may have the best tumor immunotherapy response prediction effect. Therefore, seven different combinations of autoantibodies were analyzed, including the positive ratio (sensitivity) of the antibody combination in lung cancer patients, and the two different groups of patients with positive and negative antibody combination test results on the progression-free survival curve. Differences, list the median progression-free time and p-value to find the best combination of autoantibodies. The different combinations of autoantibodies and the analysis results are shown in Table 5.

Table 5. Autoantibody combinations and their positive results and the median progression-free time of patients

Correspondingly, the median progression-free time (Kaplan-Meier method) of patients after treatment with PD-1 inhibitors is shown in Figure 3 in Figure 3-A to Figure 3-RNP, respectively.

Example 4

Based on the correlation between the positive test results of a single autoantibody and BOR, it is predicted that the corresponding autoantibodies of the four tumor-associated antigens are most likely to be related to poor immunotherapy effects, which may have the best tumor immunotherapy response prediction effect. Therefore, four different combinations of autoantibodies were analyzed, including their positive antibody ratio (sensitivity) in lung cancer patients, and the difference in the progression-free survival curve of two different groups of patients with positive and negative antibody combination test results. , List the median progression-free time and p-value to find the best autoantibody combination. The different combinations of autoantibodies and the analysis results are shown in Table 6.

Table 6. Autoantibody combinations and their positive results and the median progression-free time of patients

Correspondingly, the median progression-free time (Kaplan-Meier method) of patients after treatment with PD-1 inhibitors is shown in Figure 4-K to Figure 4-P in Figure 4, respectively.

The results show that the combination of anti-HSP105 and AKAP4 autoantibodies can best predict poor immunotherapy effects, which is statistically significant.

Example 5

The PD-L1 expression level and IMP2, XAGE-1 and NY-ESO-1 autoantibodies were used as prediction methods, and the patient's response to immunotherapy was observed. The results are shown in Table 7.

In clinical applications, PD-L1 is used as a common marker for predicting the effect of immunotherapy. Because fluorescence detection of PD-L1 tissue expression requires the use of qualified tumor tissue samples from lung cancer patients, the source of the samples is not easy to obtain, and for other reasons, in the immunotherapy patients studied in the present invention, about 50% of patients There is no information on PD-L1 markers.

As shown in Table 7, among patients with PD-L1 expression, patients with high PD-L1 expression (>1%) developed 45.5% PR, 14.3% SD, and 15% PD after treatment. Patients with low PD-L1 expression (<1%) had 4.5% PR, 28.6% SD, and 30% PD after treatment. If the first-line treatment and the back-line treatment are distinguished, the data results are similar.

For comparison, the antibody combination R-positive patients had 68.2% PR, 22.9% SD and 10% PD after treatment; the antibody combination RPN-positive patients had 72.7% PR, 28.6% SD and 15% after treatment. PD. These autoantibody combinations can predict the efficacy of immunotherapy as well as PD-L1.

As a comparison, the combination of anti-XAGE-1 autoantibody and anti-NY-ESO-1 autoantibody and anti-IMP2 antibody alone are also analyzed for the predictive effect of immunotherapy. As shown in Table 7, patients with a combination of anti-XAGE-1 autoantibodies and anti-NY-ESO-1 autoantibodies were predicted to have 31.8% of PR, 17.1% of SD, and 20% of PD; patients with positive IMP2 antibodies were being treated After that, 18.2% of PR, 5.7% of SD, and 10% of PD appeared. At the same time, the combination of anti-XAGE-1 and anti-NY-ESO-1 autoantibodies, and IMP2 antibody were used for Kaplan-Meier survival curve analysis in this study. As shown in Figure 5-A in Figure 5, the median progression-free survival time of the IMP2 antibody positive and negative groups was 10.02 months and 5.52 months, respectively, but the P value was 0.7867, which was not statistically significant. As shown in Figure 5-B in Figure 5, the survival curves of the positive and negative groups of anti-XAGE-1 and anti-NY-ESO-1 autoantibody combinations basically overlap, and the results are the median of the positive and negative groups of patients. There is no difference in progression-free survival time. Therefore, regardless of the combination of anti-XAGE-1 antibody and anti-NY-ESO-1 antibody or anti-IMP2 antibody, the predictive effect of the therapeutic effect of patients with immunotherapy is not particularly ideal.

Table 7. Comparison of tumor autoantibodies with other prediction methods and response predictions to immunotherapy (number of people (%))

Example 6

Take the detection of autoantibody combination RNP as an example. Trim21, BRCA2, Annexin1, HUD, P53 and NY-ESO-1 autoantibodies are combined. If any of the antibody test results is positive, the judgment result is positive, and all antibody test results are negative. , The judgment result is negative. The analysis result is shown in Figure 6.

As shown in Figure 6-A (first-line immunotherapy) and Figure 6-b (later-line immunotherapy), among lung cancer patients treated with first-line immunotherapy, the risk ratio of positive antibody combination (HR: Hazard Ratio) (Mantel- Haenszel) predictive value is 0.2541 (0.0684-0.8786). The median progression-free survival time of the two groups of patients with antibody combination positive and negative is (>10 months) and 5.52 months (P value is 0.0309). Among lung cancer patients, the predictive value of HR for positive antibody combination was 0.2948 (0.1409-0.6167), and the median progression-free survival time of the two groups of positive and negative antibody combination were (8.18 months) and 2.43 months (P value 0.0012) . As shown in Figure 6-C (immune monotherapy) and Figure 6-D (immunotherapy combined with chemotherapy), in all patients undergoing immune monotherapy, the predictive value of HR for a positive antibody combination was 0.1876 (0.0677 -0.520), the median progression-free survival time of the two groups of patients with antibody combination positive and negative were (>10 months) and 2.94 months (P value 0.0013), and for patients with immunotherapy combined with chemotherapy, the antibody combination was positive The HR was 0.3863 (0.1714-0.8705), and the median progression-free survival time of the antibody combination positive and negative groups were (9.36 months) and 4.27 months (P value 0.0218).

The above description of the specific embodiments of the present invention does not limit the present invention. Those skilled in the art can make various changes or modifications according to the present invention, as long as they do not deviate from the spirit of the present invention, they shall fall within the scope of the appended claims of the present invention.

Claims (17)

1. A biomarker for predicting or judging the effect of tumor immunotherapy in a subject, the biomarker being a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens : Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.
2. The biomarker of claim 1, wherein the combination of autoantibodies comprises at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1 , P53, IMP2;

   Preferably, the autoantibody combination includes two, three or four autoantibodies selected from the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD;

   More preferably, the autoantibody combination includes autoantibodies against the following tumor-associated antigens: Trim21 and BRCA2;

   Further preferably, the autoantibody combination also includes one or two of the following autoantibodies against the following tumor-associated antigens: Annexin 1, HUD;

   Even more preferably, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

   (A) Trim21, BRCA2, IMP2;

   (B) Trim21, BRCA2, NY-ESO-1;

   (C) Trim21, BRCA2, NY-ESO-1, IMP2;

   (D) Trim21, BRCA2, P53;

   (E) Trim21, BRCA2, Annexin 1;

   (F) Trim21, BRCA2, Annexin 1, P53;

   (G) Trim21, BRCA2, Annexin 1, NY-ESO-1, IMP2;

   (H) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, IMP2;

   (I) Trim21, BRCA2, Annexin 1, NY-ESO-1, P53, IMP2;

   (R) Trim21, BRCA2, Annexin 1, HUD;

   (RN) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1;

   (RP) Trim21, BRCA2, Annexin 1, HUD, P53; or

   (RNP) Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53.
3. The biomarker according to claim 1 or 2, wherein the combination of autoantibodies comprises at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, PRAME;

   Preferably, the autoantibody combination includes autoantibodies against the following tumor-associated antigens: HSP105, or HSP105 and AKAP4;

   More preferably, the autoantibody combination includes autoantibodies against the following tumor-associated antigens:

   (K)HSP105;

   (L)HSP105, AKAP4;

   (M) HSP105, MAGE-A3, AKAP4; or

   (P) HSP105, AKAP4, PRAME.
4. The biomarker according to any one of claims 1 to 3, wherein the autoantibody is the autoantibody in serum, plasma, interstitial fluid, cerebrospinal fluid, or urine before the subject receives tumor immunotherapy. Antibody;

   Preferably, the autoantibody is IgA, IgM or IgG.
5. The biomarker according to any one of claims 1 to 4, wherein the subject is a mammal, preferably a primate mammal, more preferably a human;

   Preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodgkin’s Lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer;

   Preferably, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy Or other tumor treatment methods, wherein the immune checkpoint inhibitor is against PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or CD160 The immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody.
6. A reagent for detecting the biomarker of any one of claims 1 to 5.
7. The reagent according to claim 6, wherein the reagent is a reagent for enzyme-linked immunosorbent assay (ELISA), protein/peptide chip detection, immunoblotting, bead immunoassay, microfluidic immunoassay ; Preferably, the reagent is used to detect the biomarker through an antigen-antibody reaction, for example, by ELISA;

   More preferably, the reagent is an antigen protein combination for detecting the autoantibody combination, and the antigen protein combination includes at least one selected from the following antigen proteins: Trim21, BRCA2, Annexin1, HUD, NY-ESO-1 , P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.
8. Use of the biomarker according to any one of claims 1 to 5 or the reagent according to claim 6 or 7 in the preparation of a product for predicting or judging the effect of tumor immunotherapy in a subject.
9. The use according to claim 8, wherein the subject is a mammal, preferably a primate mammal, more preferably a human;

   Preferably, the tumor is kidney cancer, liver cancer, ovarian cancer, cervical cancer, head and neck squamous cell carcinoma, nasopharyngeal cancer, urothelial cancer, laryngeal cancer, gastric cancer, melanoma, prostate cancer, Hodgkin’s Lymphoma, bladder cancer, colorectal cancer, lung cancer, especially lung cancer, such as small cell lung cancer, non-small cell lung cancer, lung squamous cell carcinoma, lung adenocarcinoma and other subtypes of lung cancer;

   Preferably, the immunotherapy includes treatment with immune checkpoint inhibitors; preferably, the immunotherapy is the treatment of immune checkpoint inhibitors alone or immune checkpoint inhibitors combined with chemotherapy, radiotherapy, anti-vascular therapy, and targeted therapy Or other tumor treatment methods, wherein the immune checkpoint inhibitor is against PD-1, PD-L1, CTLA-4, BTLA, TIM-3, LAG-3, TIGIT, LAIR1, 2B4 and/or CD160 The immune checkpoint inhibitor is preferably an anti-PD-1 antibody or an anti-PD-L1 antibody.
10. A kit comprising the reagent according to claim 6 or 7.
11. The kit according to claim 10, wherein the kit is used for enzyme-linked immunosorbent assay (ELISA), protein/peptide chip detection, western blotting, bead immunoassay, microfluidic immunoassay The kit; preferably, the kit is used to detect the biomarker through an antigen-antibody reaction;

    More preferably, the kit is an ELISA detection kit.
12. A method for predicting or judging the effect of tumor immunotherapy in a subject, or, a method for predicting or judging the sensitivity of a subject’s tumor to immunotherapy, the method comprising detecting Are the following biomarkers positive in the examinee’s sample:

    The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.
13. The method according to claim 12, wherein the method comprises detecting whether the following biomarkers in a sample from the subject are positive:

    The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2; Moreover, when the biomarker is positive, it is predicted or judged that the subject has a good tumor immunotherapy effect; the subject benefits from tumor immunotherapy; the treatment is effective; or the subject's tumor is sensitive to immunotherapy.
14. The method according to claim 12, wherein the method comprises detecting whether the following biomarkers in a sample from the subject are positive:

    The biomarker is an autoantibody combination, and the autoantibody combination includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, PRAME; and, the biomarker is positive Time prediction or judgment: the subject has a poor tumor immunotherapy effect; the subject does not benefit from the tumor immunotherapy; the treatment is ineffective; or the subject’s tumor is not sensitive to the immunotherapy.
15. A method of treating tumors in a subject, the method comprising detecting whether the following biomarkers in a sample from the subject are positive:

    The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2, HSP105, MAGE-A3, AKAP4, PRAME.
16. The method according to claim 15, wherein the method comprises detecting whether the following biomarkers in a sample from the subject are positive:

    The biomarker is a combination of autoantibodies, and the combination of autoantibodies includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: Trim21, BRCA2, Annexin 1, HUD, NY-ESO-1, P53, IMP2; And, when the biomarker is positive, the subject is subjected to tumor immunotherapy.
17. The method according to claim 15, wherein the method comprises detecting whether the following biomarkers in a sample from the subject are positive:

    The biomarker is an autoantibody combination, and the autoantibody combination includes at least one selected from the group consisting of autoantibodies against the following tumor-associated antigens: HSP105, MAGE-A3, AKAP4, PRAME; and, the biomarker is positive When the subject does not undergo tumor immunotherapy.

Images