Ankylosing spondylitis curative effect prediction marker of interleukin 17A inhibitor

Abstract

The invention relates to an ankylosing spondylitis curative effect prediction marker of an interleukin 17A inhibitor and application of the ankylosing spondylitis curative effect prediction marker. The marker for predicting the curative effect of the ankylosing spondylitis of the interleukin 17A inhibitor, disclosed by the invention, comprises a lysosome related membrane protein 3 (LAMP3) and 11 beta-hydroxysteroid dehydrogenase (HSD11B1).

Description

Technical Field

[0001] The present invention relates to an interleukin-17A inhibitor therapeutic effect prediction marker for ankylosing spondylitis and application thereof. Background Art

[0002] Ankylosing spondylitis is an immune-mediated inflammatory disease. Interleukin-17A (IL-17A) inhibitors are currently widely used to treat the disease. However, studies have found that 30%-60% of patients still do not respond to these drugs. Therefore, there is an urgent need to identify biomarkers that can predict the efficacy of IL-17A inhibitors in clinical practice. Summary of the Invention

[0003] The present invention determined through Olink proteomics and single-cell transcriptome sequencing analysis that the baseline levels of two interferon and Janus kinase-signal transducer and activator of transcription (JAK-STAT)-related molecules: lysosomal associated membrane protein 3 (LAMP3) and 11β-hydroxysteroid dehydrogenase (HSD11B1) can effectively predict the efficacy of IL-17A inhibitors.

[0004] The present invention first provides predictive or diagnostic markers that can predict the efficacy of interleukin-17A inhibitors for ankylosing spondylitis. The predictive markers for the efficacy of interleukin-17A inhibitors for ankylosing spondylitis of the present invention include lysosomal associated membrane protein 3 (LAMP3) and 11β-hydroxysteroid dehydrogenase (HSD11B1).

[0005] The present invention also provides an isolated protein combination, which is a marker for predicting the efficacy of an interleukin-17A inhibitor on ankylosing spondylitis. The protein combination comprises LAMP3 and HSD11B1 proteins.

[0006] In one or more embodiments, the amino acid sequence of the LAMP3 protein is as shown in SEQ ID NO: 1.

[0007] In one or more embodiments, the amino acid sequence of the HSD11B1 protein is as shown in SEQ ID NO:2.

[0008] The present invention also provides a reagent for detecting the content of LAMP3 protein and HSD11B1 protein in plasma.

[0009] In one or more embodiments, the reagent is a reagent used to detect proteins using any of the following methods: PEA, ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry. Preferably, the detection method is PEA.

[0010] In one or more embodiments, the agent is an antibody.

[0011] In one or more embodiments, the reagent is an oligonucleotide probe used in a PEA method.

[0012] In one or more embodiments, the reagents include fluorescent dyes or fluorescent probes for qPCR detection.

[0013] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0014] The present invention also provides a kit comprising the reagents described in any embodiment herein.

[0015] In one or more embodiments, the kit further comprises a protein combination as described in any embodiment herein.

[0016] The present invention also provides a method for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis, comprising the step of detecting the levels of lysosomal associated membrane protein 3 (LAMP3) and 11β-hydroxysteroid dehydrogenase (HSD11B1) in the subject's plasma.

[0017] In one or more embodiments, the detection method comprises: PEA, ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry.

[0018] In one or more embodiments, PEA is used to detect the protein expression levels of LAMP3 and HSD11B1 in the subject's plasma.

[0019] In one or more embodiments, after detecting and obtaining the protein expression values ​​of LAMP3 and HSD11B1, the protein expression values ​​are substituted into the following formula to calculate the probability value:

[0020] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3

[0021] In one or more embodiments, when the plasma level of the marker is detected using PEA, the threshold value is 0.5. If the probability value is greater than 0.5, the patient responds to the interleukin 17A inhibitor treatment. If the probability value is less than or equal to 0.5, the patient does not respond to the interleukin 17A inhibitor treatment.

[0022] The present invention also provides the use of LAMP3 protein and HSD11B1 protein as biomarkers or detection reagents thereof in the preparation of reagents or kits, wherein the kits are used for: (1) predicting the efficacy of interleukin-17A inhibitors for ankylosing spondylitis, and / or (2) diagnosing ankylosing spondylitis.

[0023] In one or more embodiments, the reagent detects the amount of the protein in plasma in the sample.

[0024] In one or more embodiments, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma level of the marker is:

[0025] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0026] In one or more embodiments, when PEA is used to detect the plasma level of the marker, the threshold value is 0.5.

[0027] In one or more embodiments, the reagent is a reagent used to detect proteins using any of the following methods: PEA, ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry. Preferably, the detection method is PEA.

[0028] In one or more embodiments, the agent is an antibody.

[0029] In one or more embodiments, the reagent is an oligonucleotide probe used in a PEA method.

[0030] In one or more embodiments, the reagents include fluorescent dyes or fluorescent probes for qPCR detection.

[0031] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0032] Use of the reagent described in any embodiment of the present invention in the preparation of a reagent or kit for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis or diagnosing ankylosing spondylitis.

[0033] Use of the reagent described in any embodiment of the present invention and the protein combination described in any embodiment of the present invention in the preparation of a reagent or a kit, wherein the kit is used for: (1) predicting the efficacy of interleukin-17A inhibitors for ankylosing spondylitis, and / or (2) diagnosing ankylosing spondylitis.

[0034] In one or more embodiments, the kit is a PEA detection kit.

[0035] In one or more embodiments, the reagents include:

[0036] (1) using PEA to detect the LAMP3 protein and HSD11B1 protein, such as antibodies, oligonucleotides, or fluorescent dyes or fluorescently labeled probes used in qPCR detection, or

[0037] (2) Optionally, a reagent for separating plasma proteins from plasma, preferably selected from alcohol reagents and reagents used for salting out, such as methanol.

[0038] In one or more embodiments, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma level of the marker is:

[0039] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0040] In one or more embodiments, when PEA is used to detect the plasma level of the marker, the threshold value is 0.5.

[0041] The present invention also provides a method for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis or diagnosing ankylosing spondylitis, comprising:

[0042] (1) Obtaining the content of LAMP3 and HSD11B1 proteins in plasma samples,

[0043] (2) obtaining a probability using the content by constructing a model, and

[0044] (3) To predict the efficacy of interleukin-17A inhibitors for ankylosing spondylitis or to diagnose ankylosing spondylitis based on probability.

[0045] In one or more embodiments, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma level of the marker is:

[0046] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0047] In one or more embodiments, when the plasma level of the PEA marker is detected, the threshold value for predicting the efficacy of interleukin-17A inhibitors for ankylosing spondylitis or diagnosing ankylosing spondylitis is 0.5.

[0048] The present invention further provides a device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented:

[0049] (1) Obtaining the content of LAMP3 and HSD11B1 proteins in plasma samples,

[0050] (2) obtaining a probability using the content by constructing a model, and

[0051] (3) To predict the efficacy of interleukin-17A inhibitors for ankylosing spondylitis or to diagnose ankylosing spondylitis based on probability.

[0052] In one or more embodiments, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma level of the marker is:

[0053] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0054] The present invention also provides a system for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis or diagnosing ankylosing spondylitis, characterized by comprising:

[0055] A collection device for obtaining the content of LAMP3 and HSD11B1 proteins in plasma samples,

[0056] Data processing means for obtaining a probability by using the content by constructing a model,

[0057] A determination device is used to predict the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis or diagnose ankylosing spondylitis based on probability.

[0058] In one or more embodiments, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma level of the marker is:

[0059] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0060] The present invention also provides a method for constructing a model for predicting the efficacy of interleukin-17A inhibitors for ankylosing spondylitis, comprising the steps of:

[0061] (1) Obtaining the levels of LAMP3 and HSD11B1 proteins in plasma samples, and

[0062] (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 : Recursive feature screening for the best feature subset for efficacy prediction;

[0064] Figure 2 : Differential expression of proteins LAMP3 (left) and HSD11B1 (right) between non-responders and responders at baseline;

[0065] Figure 3: ROC curves of LAMP3 and HSD11B1 (discovery cohort);

[0066] Figure 4 : ROC curves of LAMP3 and HSD11B1 (discovery cohort);

[0067] Figure 5 : ROC curves of LAMP3 and HSD11B1 (validation cohort);

[0068] Figure 6 : Prediction model constructed based on baseline levels of LAMP3 and HSD11B1 (validation cohort). DETAILED DESCRIPTION

[0069] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a preferred technical solution.

[0070] The inventors collected plasma from patients with ankylosing spondylitis and identified two interferon and Janus kinase-signal transducer and activator of transcription (JAK-STAT)-related molecules: lysosomal-associated membrane protein 3 (LAMP3) and 11β-hydroxysteroid dehydrogenase (HSD11B1) through Olink proteomics and single-cell transcriptome sequencing analysis. Further studies found that LAMP3 and HSD11B1 can be used as predictive markers for the efficacy of interleukin-17A inhibitors in ankylosing spondylitis. The efficacy of IL-17A inhibitors and / or the diagnosis of ankylosing spondylitis can be effectively predicted by detecting the baseline levels of LAMP3 and HSD11B1.

[0071] Herein, "amount", "content", "concentration" and the like include absolute amount and relative amount. The "amount", "content" and "concentration" herein may be standardized.

[0072] IL-17A inhibitors bind to IL-17A, preventing it from interacting with its receptor. Both marketed and unmarketed IL-17A inhibitors can be used in this application, as long as they work in the same way, i.e., inhibiting the interaction between IL-17A and its receptor, such as secukinumab, ixekizumab, fumarozin, celikizumab, and secukinumab.

[0073] In this article, the clinical definition of "improved" and "unimproved" patients is that the reduction in the ASDAS-CRP score after 4 months of medication compared with the ASDAS-CRP score before medication is greater than 1.1, and the patient is considered unimproved otherwise. Among them, ASDAS-CRP is a key clinical parameter for measuring the disease activity of r-axSpA patients, and the calculation formula is as follows:

[0074] ASDAS-CRP = 0.128 × Patient Global Assessment (PtGA, the patient's subjective assessment of their overall condition, 0-10 points) + 0.330 × Spinal Pain Score (SpA, the degree of spinal pain in the past week, 0-10 points) + 0.048 × Morning Stiffness Duration Score (converting the number of minutes of morning stiffness into 0-10 points) + 0.067 × Physical Function Score (BASFI, assessing the degree of limitation of daily activities, 0-10 points) + 0.310 × ln(serum CRP level + 1)

[0075] CRP is serum C-reactive protein (in mg / L; data must first be natural logarithm-transformed and normalized by adding 1). The weights of each parameter were statistically optimized, and the final total score was used to quantify the disease activity of ankylosing spondylitis. Higher scores indicate more severe inflammatory activity.

[0076] Quantitative detection of proteins is a commonly used method in molecular biology. Through specific technical methods, the content of one or more proteins in a sample is accurately determined. Quantitative detection of proteins is mainly carried out from the perspectives of spectral analysis, chemical analysis, immunological analysis, chromatographic analysis, etc. Specifically, methods for quantitative detection of proteins include but are not limited to: ELISA, suspension chip, MSD electrochemiluminescence technology, biuret method, BCA method, Lowry method, Bradford method, and ultraviolet spectroscopy. In this article, Olink proteomics was used to determine the levels of LAMP3 and HSD11B1 in patient plasma. If other methods are used to detect the levels of LAMP3 and HSD11B1, the original values ​​can be preprocessed with data similar to NPX to obtain normalized protein expression values.

[0077] " Marker " described herein can be a nucleic acid molecule or its protein expression product. Methods for detecting protein levels are well known in the art, such as ELISA, suspension chip, MSD electrochemiluminescence technology, mass spectrometry, immunoturbidimetry, chemiluminescence immunoassay (CLIA), radioimmunoassay (RIA), immunofluorescence, protein blotting (Western Blot), mass spectrometry (MS), surface plasmon resonance (SPR), capillary electrophoresis (CE), immunoelectrophoresis, nephelometry, turbidimetry, immunoprecipitation (IP), immunohistochemistry (IHC), flow cytometry, multiple reaction monitoring (MRM), proximity ligation technology (PLA), proximal extension technology (PEA), isoelectric focusing (IEF) or two-dimensional gel electrophoresis (2D-PAGE). In certain embodiments, the present invention relates to detecting the level of marker protein from a sample derived from blood.

[0078] Therefore, the present invention relates to reagents for detecting protein expression levels. Reagents used in the above-mentioned methods for detecting protein levels are well known in the art, such as specific antibodies. In one or more embodiments, the reagent is a fluorescent dye or fluorescent probe used for qPCR detection. The reagent for detecting protein levels can also be an antibody, such as specific antibodies for LAMP3 and HSD11B1.

[0079] In an exemplary embodiment, the present invention uses PEA to detect plasma proteins. Proximity Extension Assay (PEA) is a highly sensitive protein detection method based on the proximity effect, which amplifies the signal by the proximity extension reaction of oligonucleotide-coupled antibodies. The steps and required reagents of conventional PEA methods are known in the art, including immunohybridization reaction reagents and nucleic acid amplification reagents, such as oligonucleotide-coupled antibodies (PEA probes), extension reaction mixtures (DNA polymerase, dNTPs, buffer). PEA probes include monoclonal antibody pairs for target proteins, which are coupled to different DNA oligonucleotide sequences (called "proximity probes") respectively. After knowing the target protein, those skilled in the art can easily design and obtain PEA probes (e.g., commissioned synthesis). PEA reagents can also optionally include amplification detection reagents, such as primer pairs, fluorescent probes (such as TaqMan probes or SYBR Green dyes), sequencing adapters (such as Illumina sequencing platforms).

[0080] The present invention also relates to a method for pre-treating a sample. Samples derived from blood include, but are not limited to, whole blood, plasma, and serum. Those skilled in the art are aware of methods for pre-treating a sample to obtain components (e.g., nucleic acids, proteins) containing markers to be tested, such as plasma protein extraction kits, proteograph workstations, and the like. Therefore, the reagents herein also include reagents for separating proteins from plasma. Typically, the reagents used for pre-treatment include, but are not limited to, lysis buffer, dilution buffer, and purification magnetic beads / columns. For example, Tris-HCl, NaCl, Triton X-100 and protease inhibitors, PBS, binding buffer (containing high salt and detergent), and elution buffer (acidic solution or high concentration imidazole).

[0081] Olink Proteomics

[0082] Olink proteomics technology is a high-throughput protein analysis platform based on the proximity extension assay (PEA), capable of simultaneously detecting the expression levels and interactions of multiple proteins. The core principle of Olink proteomics technology is based on PEA. A pair of monoclonal antibodies is designed for each target protein, each labeled with a unique oligonucleotide sequence (i.e., "proximity probes"), with complementary hybridization regions at the ends of the two sequences. When the pair of antibodies binds to the target protein simultaneously (i.e., the two antigen epitopes are spatially adjacent), the labeled oligonucleotide sequences undergo specific hybridization due to the proximity of the antibodies, forming a complete DNA template (containing universal amplification primer binding sites). The hybridized DNA template is amplified and detected by real-time quantitative PCR (qPCR) or next-generation sequencing (NGS). The fluorescence signal intensity or the number of sequencing reads is positively correlated with the abundance of the target protein. After calibration with a standard curve, it can be converted into relative protein expression (semi-quantitative) or absolute quantification using multiple internal controls.

[0083] Herein, in the Olink proteomics detection method, the target protein comprises LAMP3 and HSD11B1, preferably, the target protein is SEQ ID NO: 1 or SEQ ID NO: 2.

[0084] ROC curve

[0085] The term "ROC curve" refers to the Receiver Operating Characteristic Curve, a tool used to evaluate the performance of classification models. The ROC curve plots the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR), helping to understand the performance of a classifier at different thresholds. The True Positive Rate (TPR), also known as sensitivity, represents the proportion of samples correctly identified as positive to all actual positive samples. It is calculated as follows:

[0086] TPR=TP / (TP+FN)

[0087] Where TP (True Positive) is the number of samples that are actually positive and predicted as positive by the model, and FN (False Negative) is the number of samples that are actually positive but predicted as negative by the model. The false positive rate (FPR) represents the ratio of samples that are incorrectly identified as positive to all actual negative samples. Its calculation formula is:

[0088] FPR=FP / (TP+FN)

[0089] Among them, FP (False Positive) is the number of samples that are actually negative examples but predicted as positive examples by the model, and TN (True Negative) is the number of samples that are actually negative examples and predicted as negative examples by the model.

[0090] The ROC curve is plotted with the false positive rate (FPR) on the horizontal axis and the true positive rate (TPR) on the vertical axis. The closer the curve is to the upper left corner, the better the classifier's performance. Ideally, the ROC curve passes through the point (0,1), meaning the model is able to distinguish between positive and negative examples, with FPR = 0 (no false positives) and TPR = 1 (all positive examples are correctly identified). If the ROC curve is a diagonal line from the origin (0,0) to the point (1,1), this indicates that the model's predictions are random and have no real classification ability.

[0091] In ankylosing spondylitis biomarker screening, receiver operating characteristic (ROC) curves can be used to evaluate models that distinguish ankylosing spondylitis patients from healthy controls based on biomarker concentrations or signal intensities in blood (or other biological samples). If the ROC curve is close to the upper left corner, it means a high true positive rate is achieved at a low false positive rate, indicating that the model is able to effectively identify ankylosing spondylitis patients.

[0092] AUC (Area Under the Curve) is the area under the ROC curve. It is a quantitative metric used to more intuitively measure model performance. AUC values ​​range from 0.5 to 1. The closer the AUC is to 1, the stronger the model's discriminatory ability. AUC values ​​are now commonly calculated using built-in functions in statistical software or programming languages ​​(such as R and Python) in conjunction with computers.

[0093] In one or more embodiments, the expression level of the subject sample is increased or decreased when compared to the control sample. The expression of the measured protein is mathematically analyzed to obtain a probability. For the sample tested, if the probability value is greater than 0.5, the patient responds to treatment with an IL-17A inhibitor, and if the probability value is less than or equal to 0.5, the patient does not respond to treatment with an IL-17A inhibitor. Conventional mathematical analysis methods and processes for determining thresholds are known in the art, and exemplary methods are mathematical models, such as logistic regression models, support vector machines, and random forest models. Those skilled in the art are aware of conventional methods for constructing logistic regression models. For example, for differentially expressed markers, a logistic regression model, a support vector machine model, or a random forest model is constructed for two groups of samples, and the accuracy, sensitivity, and specificity of the test results and the area under the predictive characteristic curve (ROC) (AUC) of the model statistics are used to calculate the predicted probability value of the test set samples. An exemplary logistic regression model is as follows:

[0094] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0095] When using PEA to detect the plasma content of the marker, the threshold is 0.5. If the probability value is greater than 0.5, the patient responds to IL-17A inhibitor treatment. If the probability value is less than or equal to 0.5, the patient does not respond to IL-17A inhibitor treatment.

[0096] When other detection methods are used to measure the absolute or relative plasma levels of proteins, the judgment threshold may change due to the different presentation of the test results. However, the specific statistical relationship reflected by the model and the judgment results for the same patient will not change. When other methods are used to measure concentration or relative concentration, the threshold can be easily determined by those skilled in the art. For example, the threshold can be obtained by the following method: a) Collect samples from patients with known ankylosing spondylitis who have responded to and have not responded to interleukin-17A (IL-17A) inhibitors, and obtain the relative levels of LAMP3 and HSD11B1 proteins in these samples; b) Use logistic regression statistical analysis to construct a discriminant model for the immune efficacy of the protein combination. At this point, those skilled in the art will obtain a model with specific parameters and a judgment threshold. The more samples, the greater the sensitivity and specificity of the resulting model, and the more accurate the model's diagnostic results. Then, the patient's response to interleukin 17A (IL-17A) inhibitor treatment for ankylosing spondylitis can be determined by the following steps: c) obtaining the relative content of the protein combination of the sample to be tested, the method may be the same as or different from a); d) calculating the probability value of the sample based on the corresponding logistic regression model obtained in step b), and then determining the response to interleukin 17A (IL-17A) inhibitor treatment for ankylosing spondylitis.

[0097] For logistic regression statistical analysis, the model and parameters derived from the content measured by the same batch of experimental results are unique. Moreover, based on the statistical relationships and statistical principles obtained in this application, it is known that the parameters (including coefficients, sensitivity, specificity, and accuracy) of the model derived from the content measured by different batches of experimental results may vary slightly, but the specific statistical relationship reflected by the model will not change, that is, it has the ability to discriminate the therapeutic efficacy of interleukin-17A (IL-17A) inhibitors for ankylosing spondylitis.

[0098] After knowing the gene combination, technicians in this field can obtain a logistic regression model based on any sample treated with interleukin 17A (IL-17A) inhibitors for ankylosing spondylitis, and the model also has the function of identifying the therapeutic efficacy of interleukin 17A (IL-17A) inhibitors for ankylosing spondylitis.

[0099] According to statistical principles, for a fixed gene combination, the statistical relationship of the model constructed by a technician in this field based on the above method after testing any samples that respond to and do not respond to interleukin 17A (IL-17A) inhibitor treatment for ankylosing spondylitis is the same. Even if the parameters may change slightly, the model can still identify the efficacy of interleukin 17A (IL-17A) inhibitor treatment for ankylosing spondylitis.

[0100] Herein, the inventors discovered that the plasma levels of LAMP3 and HSD11B1 proteins are correlated with the therapeutic efficacy of interleukin-17A (IL-17A) inhibitors for ankylosing spondylitis. Further research has led to the construction of a mathematical model for determining the therapeutic efficacy of interleukin-17A (IL-17A) inhibitors for ankylosing spondylitis. In one or more embodiments, the logistic regression prediction model for the therapeutic efficacy of interleukin-17A (IL-17A) inhibitors for ankylosing spondylitis or the ankylosing spondylitis diagnostic model based on the plasma levels of the markers is:

[0101] logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

[0102] The validation cohort patient data were substituted into the above model for validation. The ROC analysis of the validation cohort showed that the AUC values ​​of LAMP3 and HSD11B1 were 0.813 and 0.688, respectively. Figure 5 ). The logistic regression model established by the discovery cohort was applied, and the AUC value of the validation cohort was 0.750 ( Figure 6 These results indicate that baseline LAMP3 and HSD11B1 levels have good predictive power for therapeutic efficacy.

[0103] The present invention also provides a device, characterized in that the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the following steps are implemented: (1) obtaining the content of LAMP3 and HSD11B1 proteins in a plasma sample, (2) obtaining probabilities using the content by constructing a model, and (3) predicting the efficacy of an interleukin-17A (IL-17A) inhibitor for ankylosing spondylitis or diagnosing ankylosing spondylitis based on the probabilities.

[0104] The present invention also provides a system for predicting the efficacy of interleukin 17A (IL-17A) inhibitors for ankylosing spondylitis, characterized in that it includes: a collection device for obtaining the content of LAMP3 and HSD11B1 proteins in a plasma sample, a data processing device for obtaining probability using the content by constructing a model, and a determination device for predicting the efficacy of interleukin 17A (IL-17A) inhibitors for ankylosing spondylitis or diagnosing ankylosing spondylitis based on the probability.

[0105] The present invention also provides a method for constructing a model for predicting the efficacy of interleukin-17A (IL-17A) inhibitors for ankylosing spondylitis, comprising the steps of: (1) obtaining the content of LAMP3 and HSD11B1 proteins in a plasma sample, and (2) drawing a receiver operating characteristic (ROC) curve of the protein combination and calculating the area under the curve, and establishing a model using a logistic regression statistical analysis method.

[0106] Example

[0107] Example 1. Sample collection

[0108] A total of 41 patients' blood samples and clinical data were collected before medication, 1 month after medication, and 4 months after medication. The patients' medication regimen was subcutaneous injection of Cosentyx (secukinumab injection), once a week in the first month and once a month starting from the second month. Peripheral blood samples of patients were obtained by venous blood collection, centrifuged at 2000rpm for 10 minutes, and plasma samples were obtained and stored in a -80 degree refrigerator for later use. A total of 123 plasma samples were obtained for Olink proteomics testing (immunoreactive protein group, a total of 92 proteins). The omics test was completed by Changzhou Tongshu Biotechnology Co., Ltd. The protein data and clinical data before medication, as well as the clinical data after 4 months of medication were used for efficacy prediction. Among them, the data of 27 patients were the discovery cohort, and the data of 14 patients were the validation cohort. The discovery cohort and validation cohort are two independent batches of data. The collection process, sample processing and data measurement of the two batches are completely consistent. The dosing regimen of the discovery cohort and the validation cohort are consistent.

[0109] Example 2. Data processing

[0110] First, based on the results of the discovery cohort, proteins with more than 30% of samples below the detection limit were excluded, and the remaining 80 proteins were used for subsequent analysis. By recursive feature elimination (a feature selection method based on machine learning models that selects the best feature subset by repeatedly training the model and eliminating the least important features), the baseline LAMP3 level and HSD11B1 level were identified as the best feature subset for efficacy prediction ( Figure 1 ).

[0111] The data were preprocessed, including normalization, removal of proteins with zero or near-zero variance, and removal of highly correlated or collinear proteins. The remaining 26 proteins were used for recursive feature elimination. The model was trained using a support vector machine, with 50 repetitions of ten-fold cross-validation. The importance of each protein feature was assessed by calculating the sum of the squares of its corresponding weights. Finally, the optimal feature subset was determined by calculating the average rank of each feature across all folds. Figure 1The horizontal axis is the number of protein features of the model, and the vertical axis is the accuracy of the model. In addition, the baseline LAMP3 and HSD11B1 levels of patients without improvement were significantly higher than those of patients with improvement ( Figure 2 ).

[0112] The clinical definition of patients with improvement and patients without improvement is that if the reduction value of the ASDAS-CRP score of the patient after 4 months of medication compared with the ASDAS-CRP score before medication is greater than 1.1, the patient is considered to have improved, otherwise it is considered to have no improvement. Among them, ASDAS-CRP is a key clinical parameter for measuring the disease activity of r-axSpA patients. The data obtained by Olink proteomics is NPX data (Normalization Protein eXpression). The source method of the data can be obtained by referring to the Olink official manual. The antibody for immune connection to the target protein carries a pre-designed oligonucleotide chain. After the two oligonucleotide sequences connected to the protein perform base complementary pairing, they are extended and amplified under the action of DNA polymerase, and then qPCR detection is performed. The data generated by the qPCR test is converted into a counts file and input into the NPX Manager software for data preprocessing to obtain the NPX data required for the subsequent analysis process.

[0113] Example 3. Model construction

[0114] Receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values ​​of LAMP3 and HSD11B1 were 0.857 and 0.813, respectively. Figure 3 ). Using the baseline levels of LAMP3 and HSD11B1 (Table 1), a logistic regression model was constructed, with an AUC value of 0.923 ( Figure 4 The specific formula of the model is: logit(p) = 30.010-4.027*HSD11B1-4.108*LAMP3. The parameter is the NPX protein expression data, which is a relative value, and p is the probability of improvement after drug treatment.

[0115] Table 1 LAMP3 and HSD11B1 protein levels and improvement in the discovery cohort

[0116]

[0117]

[0118] NR stands for no improvement, and R stands for improvement.

[0119] Example 4. Model Validation

[0120] These findings were further confirmed using validation cohort data (Table 2). ROC analysis of the validation cohort showed that the AUC values ​​of LAMP3 and HSD11B1 were 0.813 and 0.688, respectively ( Figure 5 ). The logistic regression model established by the discovery cohort was applied, and the AUC value of the validation cohort was 0.750 ( Figure 6 These results indicate that baseline LAMP3 and HSD11B1 levels have good predictive power for therapeutic efficacy.

[0121] Table 2 LAMP3 and HSD11B1 protein levels and improvement in the validation cohort

[0122] Patient number LAMP3 levels HSD11B1 levels Improvement 28 4.93297 2.84584 NR 29 3.58876 1.85445 R 30 6.42695 3.08059 NR 31 4.00056 2.98740 NR 32 4.55435 2.02409 NR 33 4.21101 1.72757 NR 34 4.46577 2.56482 R 35 3.48243 1.84436 R 36 3.66620 1.74741 R 37 3.95018 2.11941 R 38 4.95463 2.85664 R 39 4.11227 2.77519 R 40 5.11053 2.13635 NR 41 4.52169 1.58293 R

[0123] NR stands for no improvement, and R stands for improvement.

[0124] In general, the patient's therapeutic efficacy can be predicted based on the baseline LAMP3 and HSD11B1 levels. The LAMP3 and HSD11B1 levels can be substituted into the above formula to calculate the probability of improvement after medication. If the probability value is greater than 0.5, IL-17A inhibitors are recommended; otherwise, other drugs are recommended.

[0125] Part of this article

[0126] SEQ ID NO: 1 LAMP3 amino acid sequence

[0127] MPRQLSAAAALFASLAVILHDGSQMRAKAFPETRDYSQPTAAATVQDIKKPVQQPAKQAPHQTLAARFMDGHITFQTAATVKI

[0128] PTTTPATTKNTATTSPITYTLVTTQATPNNSHTAPPVTEVTVGPSLAPYSLPPTITPPAHTTGTSSSTVSHTTGNTTQPSNQTTLPATLSIALH

[0129] KSTTGQKPVQPTHAPGTTAAAHNTTRTAAPASTVPGPTLAPQPSSVKTGIYQVLNGSRLCIKAEMGIQLIVQDKESVFSPRRYFNIDPNAT

[0130] QASGNCGTRKSNLLLNFQGGFVNLTFTKDEESYYISEVGAYLTVSDPETIYQGIKHAVVMFQTAVGHSFKCVSEQSLQLSAHLQVKTTDVQ

[0131] LQAFDFEDDHFGNVDECSSDYTIVLPVIGAIVVGLCLMGMGVYKIRLRCQSSGYQRI

[0132] SEQ ID NO:2 HSD11B1 amino acid sequence

[0133] MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAA

[0134] SAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAG

[0135] KVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTL

[0136] LIRNPCRKILEFLYSTSYNMDRFINK。

Claims

1. An isolated protein combination, which is a predictive marker for the efficacy of an interleukin-17A inhibitor in ankylosing spondylitis, comprising LAMP3 and HSD11B1 proteins, Preferably, The amino acid sequence of LAMP3 protein is shown in SEQ ID NO: 1, and / or The amino acid sequence of the HSD11B1 protein is shown in SEQ ID NO:

2.

2. A reagent for detecting the levels of LAMP3 and HSD11B1 proteins in plasma, Preferably, the reagent is a reagent used to detect protein using any of the following methods: PEA, ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry, More preferably, the reagent is a reagent used for quantifying protein using the PEA method, More preferably, the reagent also includes a reagent for separating proteins from plasma.

3. A kit comprising the reagent according to claim 2, Preferably, the kit further comprises the protein combination according to claim 1.

4. Use of LAMP3 protein and HSD11B1 protein as biomarkers or detection reagents thereof in the preparation of reagents or kits for: (1) Predicting the efficacy of interleukin-17A inhibitors in ankylosing spondylitis, and / or (2) Diagnosis of ankylosing spondylitis, Preferably, the reagent detects the content of the protein in plasma in the sample, More preferably, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnosis model based on the plasma level of the marker is: logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

5. The use according to claim 4, characterized in that The reagents include specific antibodies against LAMP3 protein and / or specific antibodies against HSD11B1 protein. Preferably, the reagents include oligonucleotide probes used in the PEA method.

6. Use of the reagent according to claim 2 and the protein combination according to claim 1 in the preparation of a reagent or a kit for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis. Preferably, the reagent comprises a specific antibody against LAMP3 protein and / or a specific antibody against HSD11B1 protein. More preferably, the reagent comprises: (1) Reagents required for detecting the LAMP3 protein and HSD11B1 protein using PEA, or fluorescent dyes or fluorescently labeled probes used for qPCR detection (2) Optionally, a reagent for separating plasma proteins from plasma, preferably selected from alcohol reagents and reagents used for salting out.

7. The use according to claim 6, characterized in that The reagents include oligonucleotide probes used in the PEA method.

8. A device, characterized in that: The apparatus includes a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) Obtaining the content of LAMP3 and HSD11B1 proteins in plasma samples, (2) obtaining a probability using the content by constructing a model, and (3) To predict the efficacy of interleukin-17A inhibitors for ankylosing spondylitis or to diagnose ankylosing spondylitis based on probability, Preferably, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnosis model based on the plasma level of the marker is: logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

9. A system for predicting the efficacy of interleukin-17A inhibitors for ankylosing spondylitis, characterized in that: include: A collection device for obtaining the content of LAMP3 and HSD11B1 proteins in plasma samples, Data processing means for obtaining a probability by using the content by constructing a model, A determination device for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis or diagnosing ankylosing spondylitis based on probability, Preferably, the interleukin-17A inhibitor efficacy prediction model for ankylosing spondylitis or the ankylosing spondylitis diagnosis model based on the plasma level of the marker is: logit(p)=30.010-4.027*HSD11B1-4.108*LAMP3.

10. A method for constructing a model for predicting the efficacy of an interleukin-17A inhibitor for ankylosing spondylitis, comprising the steps of: (1) Obtaining the levels of LAMP3 and HSD11B1 proteins in plasma samples, and (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method.

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