Application of markers in the preparation of products for predicting the efficacy of rheumatoid arthritis drugs

Abstract

The present invention discloses the use of a marker in the preparation of a product for predicting the efficacy of a rheumatoid arthritis drug, wherein the marker is at least one of FGF-19, IL-7 and CXCL1, and the product is used to detect the content of at least one of FGF-19, IL-7 and CXCL1 in a biological sample of a rheumatoid arthritis patient. The marker of the present invention can accurately predict the efficacy of RA patients on TNF-α inhibitors, thereby realizing accurate individual medication for RA, which can effectively reduce resource waste and avoid treatment delays.

Description

Technical Field

[0001] The present invention relates to the technical field of drug efficacy evaluation, and in particular to the application of markers in the preparation of products for predicting the efficacy of rheumatoid arthritis drugs. Background Art

[0002] Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with erosive arthritis as its main clinical feature. The pathological characteristics of RA are erosive synovitis and the resulting destruction of articular cartilage and bone, which ultimately leads to joint deformities. Without formal treatment, the disability rate is very high. Therefore, early and accurate treatment of RA is crucial.

[0003] Studies have found that tumor necrosis factor alpha (TNF-α) is one of the cytokines that plays a key role in the pathogenesis of RA. Currently, the use of TNF-α inhibitors can significantly improve the condition of RA patients. However, TNF-α inhibitors are not universal, and their effectiveness is less than 70%. Coupled with the high price, this limits the widespread use of TNF-α inhibitors in RA. Therefore, accurately screening out RA patients who may benefit from TNF-α inhibitors can not only reduce the waste of social resources, but also reduce the adverse prognosis of patients caused by delayed treatment.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an application of a marker in the preparation of a product for predicting the efficacy of a rheumatoid arthritis drug, to predict in advance the efficacy of RA patients on TNF-α inhibitors, so as to achieve accurate individualized medication for RA.

[0006] The technical solution of the present invention is described in detail as follows:

[0007] In the first aspect, a marker is used in the preparation of a product for predicting the efficacy of a rheumatoid arthritis drug, wherein the marker is at least one of FGF-19, IL-7 and CXCL1, and the product is used to detect the content of at least one of FGF-19, IL-7 and CXCL1 in a biological sample of a rheumatoid arthritis patient.

[0008] The FGF-19 (fibroblast growth factor 19) subfamily includes three proteins: FGF19, FGF21, and FGF23, which are active on FGFR1c, 2c, 3c, and 4 receptors, but cannot bind to FGFR1b, 2b, and 3b receptors. Studies have shown that FGF-19 has weak or no heparin binding affinity, and its main function is to participate in the metabolic regulation of the human body.

[0009] IL-7 (interleukin 7) is a cytokine of the chemokine family. The target cells of IL-7 are mainly lymphocytes. It has growth-promoting activity on B progenitor cells from the bone marrow, thymocytes and peripheral mature T cells.

[0010] CXCL1 (Chemokine (CXC motif) ligand 1), also known as GRO-α (Growth-Related Oncogene-alpha), NAP-3 (Neutrophil Activating Protein 3) or MGSA (Melanoma Growth Stimulating Activity), belongs to the CXC chemokine subfamily and is involved in the development of many inflammatory diseases, including the induction of angiogenesis and the recruitment of neutrophils.

[0011] Optionally or preferably, the rheumatoid arthritis therapeutic drug is a TNF-α inhibitor.

[0012] The first TNF-α inhibitor to be launched in China is Yisaipu (recombinant human type 2 tumor necrosis factor receptor antibody fusion protein for injection), which is used to treat autoimmune diseases such as rheumatoid arthritis and ankylosing spondylitis.

[0013] Optionally or preferably, the AU value of FGF-19 in the biological sample of the rheumatoid arthritis patient is greater than 0.78, the AU value of IL-7 is greater than 0.71, and the AU value of CXCL1 is greater than 0.75, indicating that drug treatment for rheumatoid arthritis will be effective.

[0014] Olink data is displayed as normalized protein expression (NPX). NPX is the relative quantitative level of Olink after taking log2 of protein expression. The calculation formula is: NPX = correction factor - (Ct 待测血清 -Ct 延伸对照 -Ct 板间对照 ). The correction factor is calculated by Olink in the panel validation. The ROC curve is drawn with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa. The AU value at the maximum Youden index is selected as the cutoff value. The AU value in the patient's biological sample is higher than the cutoff value. That is, if the AU value of FGF-19 is greater than 0.78, the AU value of IL-7 is greater than 0.71, and the AU value of CXCL1 is greater than 0.75, the diagnosis result is positive, and the treatment of rheumatoid arthritis drugs will be effective.

[0015] Optionally or preferably, the product comprises at least one of a reagent for detecting FGF-19, a reagent for detecting IL-7 and a reagent for detecting CXCL1.

[0016] Optionally or preferably, the biological sample is blood, plasma, serum or tissue fluid.

[0017] In a second aspect, the present invention provides a product for predicting the efficacy of a rheumatoid arthritis drug, the product comprising at least one of a reagent for detecting FGF-19, a reagent for detecting IL-7, and a reagent for detecting CXCL1.

[0018] The present invention has the following beneficial effects:

[0019] Currently, there is a lack of biomarkers for predicting the efficacy of rheumatoid arthritis drugs. The present invention provides the use of at least one of the three markers FGF-19, IL-7 and CXCL1 in the preparation of a product for predicting the efficacy of rheumatoid arthritis drugs, which can accurately predict the efficacy of RA patients on TNF-α inhibitors.

[0020] Yisaipu is the first TNF-α inhibitor to be marketed in China and is used to treat rheumatoid arthritis and other diseases. However, there is currently a lack of biomarkers to predict the efficacy of Yisaipu in treating RA. The markers screened in this study have been proven to be able to predict the efficacy of Yisaipu, thereby achieving precise individualized medication for RA, which can effectively reduce resource waste and avoid treatment delays. The combined effect of the three is the best. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the research method for the research object in the embodiment.

[0022] Figure 2 The figure is a flow chart of screening in the embodiment.

[0023] Figure 3 The expression heat map of 184 proteins in RA patients who are effective (ETN-AR) and ineffective (ETN-IR) to Etanercept.

[0024] Figure 4 The weight of each protein expression in the model among the 184 proteins, ETN-IR on the left and ETN-AR on the right.

[0025] Figure 5 The nine proteins with the highest weights in the prediction model, with ETN-IR on the left and ETN-AR on the right.

[0026] Figure 6 Scatter plot and confidence ellipse for the prediction model.

[0027] Figure 7This is a heat map analysis of the expression of MMP1, SCF, TPSAB1, IL-7, EN-RAGE, CLEC4G, CXCL1, EDAR and FGF-19 in RA patients in the ESN effective group (ETN-AR) and ineffective group (ETN-IR).

[0028] Figure 8 This is the difference analysis of the expression of MMP1, SCF, TPSAB1, IL-7, EN-RAGE, CLEC4G, CXCL1, EDAR and FGF-19 in RA patients between the ESN effective group (ETN-AR) and the ineffective group (ETN-IR). * indicates P < 0.05, ** indicates P < 0.01.

[0029] Fig. 9 ROC curves for FGF-19, IL-7 and CXCL1 to predict the efficacy of Yisaipu in RA patients, respectively. P<0.05 indicates that the difference is statistically significant.

[0030] Fig.10 This is the ROC curve of combined detection of FGF-19, IL-7 and CXCL1 to predict the efficacy of Yisaipu in RA patients. P<0.05 indicates that the difference is statistically significant.

[0031] In the above figures, ETN-AR: patients who responded to Yisaipu treatment; ETN-IR: patients who did not respond to Yisaipu treatment. DETAILED DESCRIPTION

[0032] In order to make the technical personnel in this field better understand the scheme of this application, the present application will be clearly and completely described below in conjunction with the embodiments and drawings. Obviously, the described embodiments are only embodiments of a part of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of this application. The instruments and reagents used in the embodiments are all derived from commercial channels unless otherwise specified.

[0033] Example 1 Screening and verification of biomarkers for predicting the efficacy of rheumatoid arthritis drugs

[0034] 1. Research subjects

[0035] The enrolled patients were male or female patients aged 18 to 70 years (inclusive), diagnosed with RA at the time of screening (according to the classification criteria revised by the American College of Rheumatology in 2010), and had used traditional synthetic slow-acting antirheumatic drugs (DMARDs) for at least 12 weeks before enrollment. After evaluation by rheumatologists, they planned to add Yisaipu for treatment. All subjects received Yisaipu combined with traditional synthetic DMARDs from week 0 to week 12. From week 13, all subjects who received trial treatment and met the ACR20 criteria will receive Yisaipu combined with traditional DMARDs until week 24 (effective group, ETN-AR); subjects who did not meet the ACR20 criteria were considered out of the group (ineffective group, ETN-IR) and would no longer continue to receive Yisaipu treatment. The research methods are as follows: Figure 1 shown.

[0036] ACR20 remission criteria: refers to the following indicators improved by ≥ 20%:

[0037] 1) Joint swelling (28 joints) and joint tenderness counts (28 joints);

[0038] 2) Among the following five evaluation indicators, three of them improved by ≥20%:

[0039] a) Patients’ evaluation of pain (VAS score);

[0040] b) patients’ global assessment of disease activity (VAS score);

[0041] c) physician's global assessment of disease activity (VAS score);

[0042] d) patients’ evaluation of physical function (HAQ score);

[0043] e) C-reactive protein.

[0044] 2. Screening of biomarkers related to the efficacy of Yisaipu

[0045] Baseline serum samples of RA patients in the ESN effective group (ETN-AR) and the ineffective group (ETN-IR) were collected. Olink proteomics technology was used to detect immune-related proteins (92 types) and inflammation-related (92 types) protein markers in the serum. The differences in serum markers between the effective group (ETN-AR) and the ineffective group (ETN-IR) were compared to screen and verify biomarkers that can predict the efficacy of ESN in RA.

[0046] The screening process is as follows:

[0047] The RA patients in the effective group (ETN-AR) and the ineffective group (ETN-IR) for Etanercept treatment obtained in 1 (research subjects) were selected, including 17 and 15 patients respectively, and the serum of all patients was collected, 20 μl for each patient.

[0048] Olink proteomics technology was used to detect the expression levels of the following immune and inflammation-related proteins (a total of 184 proteins) in the sera of different patients.

[0049] Olink uses PEA (Proximity Extension Assay) technology to connect protein-specific antibodies to DNA-encoded tags, converting protein information into amplifiable signals to obtain large amounts of protein abundance, which is an existing technology.

[0050] A. Immune-related proteins: 92 in total

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] B. Inflammation-related proteins: 92 in total

[0057]

[0058]

[0059]

[0060] 3. Use machine learning methods to screen biomarkers related to the efficacy of Yisaipu and establish an efficacy prediction model

[0061] 3.1 Screening process (for details, see Figure 2 )

[0062] A. Unsupervised cluster analysis was used to analyze the differences in the expression of immune and inflammation-related proteins in the serum of RA patients who were effective (ETN-AR) and ineffective (ETN-IR) to ESN.

[0063] B. The sparse partial least squares discriminant analysis (sPLS-DA) algorithm was used to identify the differences in the expression of each protein, screen the differential proteins that may predict the efficacy of Yisaipu, and establish a classification model to distinguish the effective group from the ineffective group.

[0064] C. The t-test was used to verify the differences between the effective (ETN-AR) and ineffective (ETN-IR) proteins screened in B, and P < 0.05 was considered statistically significant.

[0065] D. Logistic regression analysis was used to screen the independent risk proteins in B that predicted the ineffectiveness of RA patients to Yisaipu, and a prediction model was established. The difference was considered statistically significant when P < 0.05.

[0066] 3.2 The analysis results are as follows:

[0067] A. Unsupervised cluster analysis was used to compare the differences in the expression of 184 proteins in the serum of the effective group (ETN-AR) and the ineffective group (ETN-IR). The heat map showed that there were differences in the expression of immune and inflammation-related proteins in the serum of RA patients who were effective (ETN-AR) and ineffective (ETN-IR) to Yisaipu (see Figure 3 , the darker the color, the higher the expression level of the protein).

[0068] B. Applying the sPLS-DA algorithm, we screened differential proteins that may predict the efficacy of ESNAP by identifying the differences in the expression of each protein, and established a classification model to distinguish the effective group from the ineffective group. The results showed that the nine proteins, MMP1, SCF, TPSAB1, IL-7, EN-RAGE, CLEC4G, CXCL1, EDAR, and FGF-19, had the largest weight in distinguishing the effective group (ETN-AR) and the ineffective group (ETN-IR) of ESNAP (see Figure 4 , Figure 5 The PLS-DA model based on the expression of the above 9 immune and inflammation-related proteins has good predictive efficacy in distinguishing those who respond to ESN (ETN-AR) and those who do not respond to ESN (ETN-IR) (see Figure 6 ).

[0069] C. The heat map shows that the expression of nine proteins, including MMP1, SCF, TPSAB1, IL-7, ENRAGE, CLEC4G, CXCL1, EDAR and FGF-19, was different between the RA patients in the effective group and the ineffective group of Yisaipu (see Figure 7 ).

[0070] D. The t-test was used to further verify the differences in the expression of immune and inflammatory-related proteins between the ETN-AR group and the ETN-IR group. The results showed that the expressions of MMP1, SCF, TPSAB1, IL-7, ENRAGE, CLEC4G, CXCL1, EDAR, and FGF-19 were significantly different between the ETN-AR group and the ETN-IR group (P < 0.05) (see Figure 8 ).

[0071] E. Logistic regression analysis results showed that FGF-19, IL-7 and CXCL1 were independent risk factors affecting the efficacy of Yisaipu in RA patients (see Table 1 below).

[0072] Table 1. Analysis of risk factors for predicting RA patients’ ineffectiveness to Yisaipu

[0073]

[0074]

[0075] Note: P<0.05 indicates that the difference is statistically significant.

[0076] The detection of F, FGF-19, IL-7 and CXCL1 alone can predict the efficacy of RA patients to Yisaipu, and the areas under the ROC curve were 0.78, 0.71 and 0.75, respectively (see Fig. 9 ).

[0077] The combined detection of G, FGF-19, IL-7 and CXCL1 can significantly improve the predictive value of the efficacy of Yisaipu in RA patients, with a ROC curve area of ​​0.973, a sensitivity of 93.3%, and a specificity of 94.6% (see Fig.10 ).

[0078] In summary, the combined detection of fibroblast growth factor 19 (FGF-19), interleukin-7 (IL-7) and chemokine ligand 1 (CXCL1) can predict the efficacy of RA patients on Yisaipu, with excellent sensitivity (93.3%) and specificity (94.6%). It can accurately predict the efficacy of RA patients on TNF-α inhibitors and realize the precise individualized medication of RA.

[0079] The invention concept is described in detail using specific examples herein, and the description of the above embodiments is only used to help understand the core idea of ​​the invention. It should be pointed out that any obvious modification, equivalent substitution or other improvement made by a person of ordinary skill in the art without departing from the invention concept should be included in the protection scope of the present invention.

Claims

1. Application of markers in the preparation of products for predicting the efficacy of rheumatoid arthritis drugs, wherein the markers are FGF-19, IL-7 and CXCL1, and the products are used to detect the content of FGF-19, IL-7 and CXCL1 in biological samples of patients with rheumatoid arthritis; The rheumatoid arthritis therapeutic drug is a TNF-α inhibitor.

2. The use according to claim 1, characterized in that: The biological sample is blood, plasma, serum or tissue fluid.

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