TWI889135B - Methods for assessing therapeutic response to janus kinase inhibitor treatment in patients with rheumatoid arthritis - Google Patents

Abstract

The present invention provides a method for assessing the therapeutic response of rheumatoid arthritis (RA) patients to Janus kinase (JAK) inhibitor treatment. The method includes: (a) obtaining a biological sample from patients diagnosed with RA; (b) conducting baseline biomarker expression level tests, where the biomarkers are selected from a group of antibodies, including anti-SNRK antibody, anti-MAGEE1 antibody, anti-DGKK antibody, anti-HUWE1 antibody, anti-GTSF1L antibody, anti-RIMBP2 antibody, anti-TACC2 antibody, anti-PHF14 antibody, anti-ZNF827 antibody, anti-FILIP1 antibody, anti-FRMPD4 antibody, and anti-ZNF329 antibody; (c) comparing the baseline levels with the reference levels; and (d) based on the assessment, deciding whether to administer a therapeutically effective dose of a JAK inhibitor to the patient or not.

Description

Methods for detecting the therapeutic response of patients with rheumatoid arthritis to treatment with a Janus kinase inhibitor

The present invention relates to a method for evaluating the therapeutic response of a patient with rheumatic arthritis to treatment with a Janus kinase inhibitor.

Rheumatoid arthritis (RA) is a chronic arthritis caused by multiple causes, characterized by persistent synovitis, joint/bone destruction and decreased quality of life. Treatment drugs include conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs), biologic disease-modifying anti-rheumatic drugs (bDMARDs) and targeted synthetic disease-modifying anti-rheumatic drugs (tsDMARDs). JAK (Janus kinase) inhibitors achieve therapeutic effects by blocking the signal transduction of JAK/STAT activation in the etiology of rheumatoid arthritis. Therefore, they have shown effectiveness in the treatment of rheumatoid arthritis. Despite the efficacy of biologic disease-modifying antirheumatic drugs (bDMARDs) and Janus kinase inhibitors in treatment, approximately 15-20% of patients still show adverse reactions to drug treatment. In addition, the heterogeneity of the pathogenesis of rheumatic arthritis and the pathology associated with additional extra-articular manifestations show the complexity of its clinical management. In addition to affecting the joints, rheumatic arthritis may also cause different pathological changes in multiple organs and systems, which makes the management of the disease more challenging. Taken together, these unmet needs pose a major challenge to the precision medicine approach in rheumatic arthritis. In order to treat rheumatoid arthritis more effectively and reduce the cost burden of using biologics and JAK inhibitors, it is necessary to find biomarkers that can predict the effectiveness of these drugs in patients. This personalized and precise treatment approach will help to manage the disease more accurately while reducing related medical expenses.

Proteomic studies are being widely applied to develop new biomarkers for the identification of biomarkers of response to treatment with disease-modifying antirheumatic drugs (DMARDs). Currently, an unbiased proteomic technique, called phage immunoprecipitation sequencing (PhIP-Seq), employs a synthetic version of the human complete peptisome (T7-Pep). The method uses oligonucleotide library synthesis to encode protein-scale peptide libraries (peptidomes, also known as peptisomes) and is displayed on phage. Subsequently, immunoprecipitation is performed using single antibodies, followed by high-throughput DNA sequencing analysis. These immune-related peptidomes can be simultaneously screened for peptide-related autoantibodies, providing a powerful method to identify new biomarkers.

The present invention provides a method for evaluating the response of patients with rheumatoid arthritis to Janus kinase inhibitor treatment. The present invention discloses a method for determining the patient's response to treatment by measuring the baseline expression of a specific biomarker and comparing it with the reference expression. Based on the determination result, it can be decided whether to give the patient Janus kinase inhibitor treatment. The method can help doctors better individualize treatment plans and provide more effective and accurate treatment for patients, thereby improving treatment effects and the quality of life of patients.

According to one purpose of the present invention, a method for evaluating the response of a rheumatoid arthritis patient to a Janus kinase inhibitor treatment is provided, comprising: (a) providing a biological sample, wherein the biological sample is from a rheumatoid arthritis patient; (b) performing a detection method on the biological sample, wherein the detection method is used to determine a basal expression level of at least one biomarker; wherein the biomarker is freely selected from a group of antibodies, wherein the group of antibodies includes anti-SNRK antibodies, anti-MAGE E1 antibody, anti-DGKK antibody, anti-HUWE1 antibody, anti-GTSF1L antibody, anti-RIMBP2 antibody, anti-TACC2 antibody, anti-PHF14 antibody, anti-ZNF827 antibody, anti-FILIP1 antibody, anti-FRMPD4 antibody and anti-ZNF329 antibody; (c) comparing the basal expression level of the biomarker with a reference expression level of the biomarker, wherein when the biomarker is selected from anti-HUWE1 antibody, anti-DGKK antibody and SNRK antibody, the When the baseline expression amount of the biomarker is higher than the reference expression amount of the biomarker, it is determined that the rheumatoid arthritis patient has a good response to Janus kinase inhibitor treatment; wherein, when the biomarker is selected from anti-MAGEE1 antibody, anti-ZNF827 antibody, anti-PHF14 antibody, anti-FRMPD4 antibody, anti-RIMBP2 antibody, anti-FILP1 antibody, anti-ZNF329 antibody, anti-GTSF1L antibody and anti-TACC2 antibody, the biomarker If the baseline expression level of the biomarker is higher than the reference expression level of the biomarker, the patient with rheumatoid arthritis is judged to have an adverse response to Janus kinase inhibitor treatment; and (d) when the patient with rheumatoid arthritis is judged to have a good response to Janus kinase inhibitor treatment, a therapeutically effective amount of a JAK inhibitor is given to the patient; when the patient with rheumatoid arthritis is judged to have an adverse response to Janus kinase inhibitor treatment, the patient is not given Janus kinase inhibitor treatment.

According to the method provided by the present invention, when the biomarker is selected from anti-HUWE1 antibody, anti-DGKK antibody and SNRK antibody, the baseline expression of the biomarker is higher than the reference expression of the biomarker, then the rheumatoid arthritis patient is judged to have a good response to Janus kinase inhibitor treatment, and thus anti-HUWE1 antibody, anti-DGKK antibody and SNRK antibody are defined as good biomarkers for treatment.

According to the method provided by the present invention, when the biomarker is selected from anti-HUWE1 antibody, anti-DGKK antibody and SNRK antibody, the baseline expression of the biomarker is higher than the reference expression of the biomarker, then the rheumatoid arthritis patient is determined to have a good response to Janus kinase inhibitor treatment, and thus anti-HUWE1 antibody, anti-DGKK antibody and SNRK antibody are defined as biomarkers of good response.

According to the method provided by the present invention, when the biomarker is selected from anti-MAGEE1 antibody, anti-ZNF827 antibody, anti-PHF14 antibody, anti-FRMPD4 antibody, anti-RIMBP2 antibody, anti-FILP1 antibody, anti-ZNF329 antibody, anti-GTSF1L antibody and anti-TACC2 antibody, the baseline expression of the biomarker is higher than the reference expression of the biomarker, then the rheumatoid arthritis patient is determined to have an adverse reaction to Janus kinase inhibitor treatment, and thus anti-MAGEE1 antibody, anti-ZNF827 antibody, anti-PHF14 antibody, anti-FRMPD4 antibody, anti-RIMBP2 antibody, anti-FILP1 antibody, anti-ZNF329 antibody, anti-GTSF1L antibody and anti-TACC2 antibody are defined as adverse reaction biomarkers of treatment.

According to the present invention, a method for evaluating the response of a rheumatoid arthritis patient to a Janus kinase inhibitor treatment is provided, wherein the baseline expression level is the expression level of the rheumatoid arthritis patient before receiving JAK inhibitor treatment; wherein the reference expression level is determined based on the cutoff point of the biomarker expression level of patients with good or poor treatment.

The cutoff-value was obtained through ROC curve analysis, and its purpose was to ensure an acceptable range of sensitivity and specificity, so as to more accurately evaluate the response of rheumatoid arthritis patients to Janus kinase inhibitor treatment. This cutoff point was determined to find a balance in treatment evaluation, so that patients with positive responses to treatment would not be missed, nor would patients without positive responses be wrongly judged.

In one embodiment of the present invention, the detection method may be an immunoassay method. In another embodiment, the immunoassay method is selected from the group consisting of enzyme-linked immunosorbent assay, Western blot analysis, immunoprecipitation analysis, radioimmunoassay, and immunochromatography analysis.

In one embodiment of the present invention, in a method for evaluating a rheumatoid arthritis patient's response to treatment with a Janus kinase inhibitor, the evaluated Janus kinase inhibitor is selected from Tofacitinib, Baricitinib, Upadacitinib and Ruxolitinib.

In another embodiment of the present invention, the Janus kinase inhibitor is Tofacitinib.

According to the present invention, a method for evaluating the response of a rheumatoid arthritis patient to a Janus kinase inhibitor treatment is provided, wherein the anti-SNRK antibody can recognize a sequence of continuous amino acids in SEQ NO: 1, the anti-MAGEE1 antibody can recognize a sequence of continuous amino acids in SEQ NO: 2, the anti-DGKK antibody can recognize a sequence of continuous amino acids in SEQ NO: 3, the anti-HUWE1 antibody can recognize a sequence of continuous 20-50 amino acids in SEQ NO: 4, the anti-GTSF1L antibody can recognize a sequence of continuous 20-50 amino acids in SEQ NO: 5, the anti-RIMBP2 antibody can recognize a sequence of continuous amino acids in SEQ NO: 6, the anti-TACC2 antibody can recognize a sequence of continuous amino acids in SEQ NO: 7, NO:7, the anti-PHF14 antibody can recognize the sequence of SEQ NO:8, the anti-ZNF827 antibody can recognize the sequence of SEQ NO:9, the anti-FILIP1 antibody can recognize the sequence of SEQ NO:10, the anti-FRMPD4 antibody can recognize the sequence of SEQ NO:11 and the anti-ZNF329 antibody can recognize the sequence of SEQ NO:12.

According to the above method, the sequence of the consecutive amino acids can be a sequence of 20-50 consecutive amino acids; further, the sequence of the consecutive amino acids can be a sequence of 20-30 consecutive amino acids. In other words, the amino acid sequence can be used as an antigen-antibody binding with the antibody, and the amount of the binding can be used to determine the expression amount of the antibody.

According to one embodiment, the method described in the present invention, wherein the biomarker is selected from anti-SNRK antibody or anti-HUWE1 antibody.

According to one embodiment, the anti-SNRK antibody can recognize a sequence of 20-50 consecutive amino acids in SEQ NO: 1.

According to another embodiment, the anti-HUWE1 antibody can recognize a sequence of 20-50 consecutive amino acids in SEQ NO: 4.

According to another embodiment, the anti-SNRK antibody can recognize 20-30 consecutive amino acids located in SEQ NO: 13.

According to another embodiment, the anti-HUWE1 antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 14.

According to another embodiment, the biomarker is selected from anti-ZNF827 antibody or anti-RIMBP2 antibody.

According to one embodiment, the anti-ZNF827 antibody can recognize a sequence of 20-50 consecutive amino acids in SEQ NO: 6.

According to another embodiment, the anti-RIMBP2 antibody can recognize a sequence of 20-50 consecutive amino acids in SEQ NO: 9.

According to another embodiment, the anti-ZNF827 antibody can recognize 20-30 consecutive amino acids located in SEQ NO: 16.

According to another embodiment, the anti-RIMBP2 antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 15.

According to another object of the present invention, a method for evaluating a rheumatoid arthritis patient's response to a Janus kinase inhibitor treatment comprises: (a) providing a biological sample, wherein the biological sample is from a rheumatoid arthritis patient; (b) performing a detection method on the biological sample, wherein the detection method is used to determine a baseline expression level of at least one biomarker; wherein the biomarker is freely selected from a group consisting of antibodies, wherein the group consisting of antibodies includes anti-HUWE1 antibodies, anti-RIMBP2 antibodies and anti-ZNF827 antibodies; (c) comparing the baseline expression level of the biomarker with a reference expression level of the biomarker; wherein, when the biomarker is selected from anti-HUWE1 antibodies and SNRK antibodies, the baseline expression level of the biomarker is higher than the reference expression level of the biomarker. wherein, when the biomarker is selected from anti-ZNF827 antibody and anti-RIMBP2 antibody, the baseline expression amount of the biomarker is higher than the reference expression amount of the biomarker, then it is determined that the rheumatoid arthritis patient has a relieved response to Janus kinase inhibitor treatment; wherein, when the biomarker is selected from anti-ZNF827 antibody and anti-RIMBP2 antibody, the baseline expression amount of the biomarker is higher than the reference expression amount of the biomarker, then it is determined that the rheumatoid arthritis patient has a relieved response to Janus kinase inhibitor treatment. and (d) when it is determined that the rheumatoid arthritis patient has a relieving response to the Janus kinase inhibitor treatment, a therapeutically effective amount of a JAK inhibitor is given to the patient; when it is determined that the rheumatoid arthritis patient has no relieving response to the Janus kinase inhibitor treatment, it is not recommended to give the patient Janus kinase inhibitor treatment.

Disease remission usually refers to a temporary or complete reduction or improvement of disease symptoms or disease status, especially in the treatment of certain diseases. In the present invention, it is pointed out that the definition of remission of Janus kinase inhibitor treatment is determined according to the EULAR response criteria.

According to the method of the present invention, the basal expression level is the expression level of the rheumatoid arthritis patient before receiving JAK inhibitor treatment.

According to the method of the present invention, the reference expression level is determined based on the cutoff point of the biomarker expression level of patients with or without remission. This cutoff point is based on ROC curve analysis, and its purpose is to ensure an acceptable range of sensitivity and specificity to more accurately evaluate the response of rheumatoid arthritis patients to Janus kinase inhibitor treatment.

According to one embodiment, the biomarker is selected from anti-SNRK antibodies.

According to another embodiment, the anti-SNRK antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 13.

According to one embodiment, the biomarker is selected from anti-HUWE1 antibodies.

According to another embodiment, the anti-HUWE1 antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 14.

According to one embodiment, the biomarker is selected from anti-RIMBP2 antibodies.

According to another embodiment, the anti-RIMBP2 antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 15.

Figure 1 shows a heat map of 20 antigens that distinguish RA patients who respond well or poorly to JAK inhibitor treatment using the PhIP-Seq platform.

Figure 2 shows the receiver operating characteristic (ROC) curves for distinguishing the therapeutic effects of JAK inhibitor treatment using individual 20 antigens. The X-axis represents the false positive rate (FPR) and the Y-axis represents the true positive rate (TPR).

Figure 3 shows the ROC curves of anti-HUWE1 antibody (A) against peptide SEQ NO: 14, anti-SNRK antibody (B) against peptide SEQ NO: 13, anti-ZNF827 antibody (C) against peptide SEQ NO: 16, and anti-RIMBP2 antibody (D) against peptide SEQ NO: 15 for distinguishing good and bad responses to JAK inhibitor treatment in patients with rheumatoid arthritis.

Figure 4 shows the ROC curves of anti-HUWE1 antibody (A) against peptide SEQ NO: 14, anti-SNRK antibody (B) against peptide SEQ NO: 13, anti-ZNF827 antibody (C) against peptide SEQ NO: 16, and anti-RIMBP2 antibody (D) against peptide SEQ NO: 15 for distinguishing between patients with rheumatic arthritis who have remission and those who have no remission during JAK inhibitor treatment.

Various embodiments of the present invention are discussed in more detail below. However, the embodiments may be specific to various applications contemplated by the present invention and may be practiced in various specific environments. These embodiments are for illustrative purposes only and do not limit the scope of the present disclosure. Specific test cases for evaluating the therapeutic response to Janus kinase inhibitor therapy are further illustrated below.

Example 1

This study was divided into two phases and included two groups. Twelve biologic-naïve patients who met the 2010 revised diagnostic criteria for rheumatic arthritis of the American College of Rheumatology and received 24 weeks of JAK inhibitor (tofacitinib) treatment were consecutively recruited from the China Medical University Hospital. This study was approved by the hospital's institutional review board (CMUH111-REC3-108), and written consent was obtained from each participant in accordance with the Declaration of Helsinki.

Evaluation of disease activity during treatment response: Disease activity score by 28 joints (DAS28) was used to evaluate disease activity in rheumatoid arthritis. Treatment response was evaluated at week 24 in two ways: (1) DAS28 < 2.6 after treatment was considered disease remission, and (2) patients were classified as good responders, moderate responders, or poor responders according to the European Alliance of Associations for Rheumatology (EULAR) response criteria. According to the definition, a good responder is one whose DAS28 after treatment decreases by more than (△DAS28>1.2) compared to before treatment, and DAS28≦3.2 at the time of assessment; a moderate responder is one whose △DAS28>1.2 and DAS28>3.2 at the time of assessment, or △DAS28 is 0.6-1.2 and DAS28≦5.1 at the time of assessment; a poor responder is one whose △DAS28<0.6 and DAS28>5.1 at the time of assessment. Patients with good and moderate responses are also classified as responders to the treatment.

Clinical phenotype:

Table 1 shows the clinical characteristics of patients with rheumatic arthritis who received 24 weeks of tofacitinib treatment in terms of EULAR treatment response.

Data in Table 1 are presented as mean ± SD or median (interquartile range); Mann-Whitney U test was used to compare group differences between numerical variables. *p<0.05, compared with poor responders, determined by Mann-Whitney test. Binary variables were compared using Yates's continuous correction or Fisher's exact test. #p<0.05, ##p<0.01, compared with poor responders, determined by chi-square test.

Example 2

Identify differentially expressed peptides to differentiate between patients with rheumatoid arthritis who respond well and those who do not respond well to JAK inhibitor therapy.

Materials and methods:

(1) Phage immunoprecipitation sequence (PhIP-Seq) analysis: PhIP-Seq is an innovative technology that combines peptide library-based antibody detection with next-generation sequencing. With its sequencing and nucleic acid synthesis capabilities, this technology allows for efficient identification and characterization of antibody responses. It offers high throughput, excellent sensitivity, and the ability to analyze large numbers of peptide fragments simultaneously. PhIP-Seq has important potential in seroepidemiology studies and can provide valuable insights into antibody responses in different disease contexts.

(2) Study group: Twelve biologic-naïve patients who met the 2010 revised diagnostic criteria for rheumatic arthritis of the American College of Rheumatology were consecutively recruited and received 24 weeks of tofacitinib treatment. The institutional review board of the hospital approved this study (CMUH111-REC3-108), and written consent was obtained from each participant in accordance with the Declaration of Helsinki.

(3) Experimental protocol: Serum samples were tested using the PhIP-Seq platform (CDI Laboratories, Inc., Mayaguez, PR, USA). Sample screening was performed in 1 mL PBS (pH 7.4) containing a phage library (approximately 10 ¹¹ PFU) and 0.2 μL serum (approximately 2 μg IgG). In addition, as a negative control, a set of 8 independent buffer-only samples without added serum (beads only) was included. The mixture was rotated at 4°C overnight to allow the antibodies to bind to the phage-displayed peptide target. The next day, 40 μL of a 1:1 protein A/G-coated magnetic bead suspension was added and rotated at 4°C for 4 hours to capture all immunoglobulins (IgG). The beads were then washed three times with TBS (pH 7.4) containing 0.1% NP-40 using a BRAVO liquid handler (Agilent, Santa Clara, CA, USA). The beads were then resuspended in 20 μL of Herculase II Fusion Polymerase PCR1 master mix by BRAVO to amplify the book insert. The Illumina P5/P7 adapter was incorporated after 20 cycles of polymerase chain reaction, sample-specific barcoding, and 2 μL of PCR1 product in the subsequent PCR2 reaction. After another 20 PCR cycles, PCR2 products were pooled and sequenced using Illumina NextSeq to obtain single-end 50-nucleotide reads.

(4) Library cloning: An E. coli-optimized library containing approximately 250,000 oligonucleotides encoding 90-amino acid peptides with 45 amino acid overlaps was created using the Python software package pepsyn ( https://github.com/lasersonlab/pepsyn ). The resulting oligonucleotides were first synthesized on a releasable microarray, then amplified by PCR using an attached pepsyn adapter sequence, and finally cloned into a modified T7Select 10-3b medium copy vector (Millipore) at one time. This vector displays the library peptide in the form of a C-terminal fusion with the T7 bacteriophage outer sheath protein 10B and carries a C-terminal FLAG tag. Database peptides were packaged in vitro using the T7Select packaging kit (Millipore) and plate-amplified to yield an average of 100 plaques/peptide. The quality of database peptides was assessed by Sanger sequencing of the entire insert of a single plaque, and clonal distribution was quantified by Illumina sequencing.

(5) Data evaluation of PhIP-Seq analysis: Differentially expressed peptide signatures were evaluated using the PhIP-Seq platform containing 259,345 peptides. Reads were multiplexed and aligned to the human peptide library using exact matching. Next, the reads in each sample were compared to mock immunoprecipitation (mock-IP) containing only buffer using the R software package edgeR, using a negative binomial model. Selective gene-level enrichment values were obtained by calculating Z-scores (greater than 0) and then hierarchically clustered using Spearman correlation to create heat maps. The software returned test statistics and fold change values for each peptide. Each individual sample was considered “positive” if the fold change for identified enriched peptides (hits) was greater than 5. In peptide microarrays, autoantibody responses to antigens on the array were classified as biomarkers if they met all of the following criteria: (i) a high fold change in penetrance in the test group (pFC ³ 5.0-fold), (ii) a high frequency of penetrance in the test group (pFreq ³ 50%), and (iii) a low frequency % of penetrance in a pooled negative control group (pFreq < 20%).

Results: Based on the fold difference (Z-score) in the preliminary study data, two different expression clusters (top 20 potential biomarkers) that distinguished good and bad EULAR responses to JAK inhibitors in patients with rheumatic arthritis were listed (Figure 1).

Individual ROC analysis of the top 20 biomarkers clearly showed that some biomarkers (e.g., HUWE1, ZNF827, FRMPD4, RIMBP2, and FILIP1) could distinguish good responders from poor responders with high sensitivity and specificity.

All genes were clustered hierarchically by hierarchical analysis (Pearson correlation) and heat maps of gene expression with mock-IP plots with more than 5-fold changes were generated. The color code at the top of the heat map represents the EULAR response to JAK inhibitors. The color bar represents the calculated Z-score of all individual gene expression (Figure 2).

Table 2 shows the good response biomarkers to JAK inhibitor treatment. Good response biomarkers include antibodies to DGKK, SNRK, and HUWE1, with AUCs of 0.743, 0.786, and 0.8, respectively.

Table 3 shows the adverse effect biomarkers of JAK inhibitor treatment. The adverse effect biomarkers include antibodies against MAGEE1, ZNF827, PHF14, FRMPD4, RIMBP2, FILIP1, ZNF329, GTSF1L, and TACC2, with AUCs of 0.771, 0.9, 0.771, 0.8, 0.8, 0.8, 0.8, 0.8, 0.771, and 0.8, respectively.

Table 4 shows the gene names and peptide sequences corresponding to antibodies against SNRK, MAGEE1, DGKK, HUWE1, GTSF1L, RIMBP2, TACC2, PHF14, ZNF827, FILIP1, FRMPD4 and ZNF329.

Implementation Example 3

Validation of candidate peptide fragment biomarkers using indirect enzyme-linked immunosorbent assay (ELISA): Based on the AUC results in Figure 2, anti-ZNF827, anti-RIMBP-2, anti-HUWE1, and anti-SNRK biomarkers were further selected to validate the performance of distinguishing good and poor responses in patients with rheumatoid arthritis treated with tofacitinib.

(1) Materials and methods

Target peptide sequence:

Peptide SEQ NO: 13, 14, 15 and 16 correspond to the target peptide sequences for anti-SNRK antibody, anti-HUWE1 antibody, anti-RIMBP2 antibody and anti-ZNF827 antibody, respectively, and these peptide sequences were synthesized with 95% purity (provided by ThermoFisher Scientific, Massachusetts, USA). Table 5 shows these peptide sequences and the corresponding biomarkers.

Indirect enzyme-linked immunosorbent assay (ELISA): The target peptide was diluted to 100 nM in carbonate buffer and plated overnight at 4°C on a 96-well microplate. After washing with wash buffer (PBS containing 0.02% Triton X100), the plated microplate was blocked with blocking buffer (1 M ethanolamine) for 1 hour at room temperature. All participating plasma or serum was diluted (1:500, using 1% BSA) and then placed on the blocked microplate for 1 hour at room temperature. After treatment, the microplate was washed 4 times and then reacted with HRP (peroxidase label) labeled anti-human IgG (1:5000, using PBS containing 1% BSA) for 1 hour. The HRP enzyme reaction was detected using 3,3',5,5'-tetramethylaniline, the reaction was carried out for 20 minutes, and then 2N sulfuric acid was used to terminate the reaction. The absorbance value changed from blue to yellow, and the results were measured at 405nm.

(2) Statistical analysis

Statistical analysis was performed by comparing allele frequencies between cases and controls, i.e., good responders and non-responders. The association of SNPs was tested by Fisher's exact test and ranked according to the lowest P value. All P values were two-tailed. Odds ratios (ORs) were calculated using Haldane's modified method. Fisher's exact test was used to examine the differences in the frequencies of significant alleles between rheumatic arthritis patients with different treatment responses. Correlation coefficients were obtained by nonparametric Spearman's rank correlation test. Multivariate logistic regression models were constructed to evaluate factors predicting EULAR treatment response. ROC curve analysis was performed using MedCalc v.14 to determine the area under the ROC curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC).

(3) Results: ROC curves were used to evaluate the performance of anti-ZNF827, anti-RIMBP2, anti-HUWE1, and anti-SNRK in distinguishing good responses from bad responses. As shown in Figure 3, anti-HUWE1 and anti-SNRK antibodies performed better in predicting EULAR responses to JAK inhibitors (AUCs of 0.768 and 0.814, respectively). At cut-off values of 0.324 and 0.31, anti-HUWE1 and anti-SNRK antibodies showed the highest predictive ability, with sensitivities of 75.0% and 80.0%, specificities of 71.4% and 71.4%, and accuracies of 73.5% and 76.5%, respectively (p<0.001) (Table 6).

ROC curves were used to evaluate the performance of anti-ZNF827 antibody, anti-RIMBP2 antibody, anti-HUWE1 antibody and anti-SNRK antibody in distinguishing whether or not remission was achieved. As shown in Figure 4, the optimal cutoff values of anti-HUWE1 antibody, anti-SNRK antibody and anti-RIMBP2 antibody were 0.355, 0.399 and 0.355, respectively, the AUCs were 0.869, 0.838 and 0.728, respectively, the sensitivity (SEN) was 83.3%, 83.3% and 75%, respectively, the specificity (SPE) was 84.8%, 78.8% and 57.6%, respectively, and the accuracy (ACC) was 84.4%, 80.0% and 62.2%, respectively (Table 7).

Therefore, the present disclosure provides a method for evaluating whether the biomarkers disclosed in the present invention can be used as therapeutic response indicators for JAK inhibitor therapy. This method significantly improves their accuracy, and the content of the present disclosure has potential application value in the relevant market.

Although embodiments of the present disclosure have been broadly described, it is important to note that viable alternative embodiments are possible. Therefore, the intent and scope of the application attached to this document should not be limited to the description of the embodiments provided.

Professionals familiar with this technical field will understand that the structure of this disclosure can be modified and changed in various ways while maintaining the scope and essence of the content. Therefore, the intention of this disclosure is to include adaptations and changes to this document as long as they fall within the scope defined by the subsequent application.

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Claims (7)

A method for evaluating a rheumatoid arthritis patient's response to a Janus kinase inhibitor treatment, comprising: (a) providing a biological sample, wherein the biological sample is from a rheumatoid arthritis patient; (b) performing a detection method on the biological sample, wherein the detection method is used to determine a baseline expression level of at least one biomarker, wherein the baseline expression level is the expression level of the rheumatoid arthritis patient before receiving the JAK inhibitor treatment; wherein the biomarker is a SNRK antibody; and (c) comparing the baseline expression level of the biomarker with the expression level of the biomarker. A reference expression level of a biomarker, wherein the reference expression level is determined based on the cutoff point of the biomarker expression level of a well-treated patient; wherein, when the biomarker is an SNRK antibody, if the basal expression level of the biomarker is higher than the reference expression level of the biomarker, it is determined that the rheumatoid arthritis patient has a good response to the Janus kinase inhibitor treatment; wherein the Janus kinase inhibitor is selected from Tofacitinib, Baricitinib, Upadacitinib and Ruxolitinib. The method as described in claim 1, wherein the Janus kinase inhibitor is selected from Tofacitinib. The method as described in claim 1, wherein the anti-SNRK antibody can recognize a sequence of 20-50 consecutive amino acids in SEQ NO: 1. The method as described in claim 1, wherein the anti-SNRK antibody can recognize 20-30 consecutive amino acids located in SEQ NO: 13. A method for evaluating the response of a rheumatoid arthritis patient to a Janus kinase inhibitor treatment, comprising: (a) providing a biological sample, wherein the biological sample is from a rheumatoid arthritis patient; (b) performing a detection method on the biological sample, wherein the detection method is used to determine a baseline expression level of at least one biomarker, wherein the baseline expression level is the expression level of the rheumatoid arthritis patient before receiving the JAK inhibitor treatment; wherein the biomarker is an anti-SNRK antibody; and (c) comparing the baseline expression level of the biomarker The present expression level of the biomarker and a reference expression level of the biomarker, wherein the reference expression level is determined based on the cut-off point of the biomarker expression level of the patient who has achieved remission after treatment; wherein, when the basal expression level of the biomarker is higher than the reference expression level of the biomarker, it is determined that the rheumatoid arthritis patient has a remission response to the Janus kinase inhibitor treatment; wherein the Janus kinase inhibitor is selected from Tofacitinib, Baricitinib, Upadacitinib and Ruxolitinib. The method as described in claim 5, wherein the Janus kinase inhibitor is selected from Tofacitinib. The method as described in claim 5, wherein the anti-SNRK antibody can recognize a sequence of 20-30 consecutive amino acids in SEQ NO: 13.

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