Biomarkers and methods for assessing psoriatic arthritis disease activity

Abstract

To achieve maximum therapeutic effects for individual patients by specifically quantifying and assessing a subject's disease activity at any particular time, determining the therapeutic effects on disease activity, and predicting future outcomes.SOLUTION: Provided herein are biomarkers and methods for generating scores useful for assessing psoriatic arthritis (PsA) disease activity in subjects previously diagnosed with PsA. The invention also provides predictive model methods based on the biomarkers, as well as computer systems, software embodiments of the models for scoring and optionally classifying samples, and methods of recommending optimal therapeutic regimens.SELECTED DRAWING: None

Description

[Technical field]

[0001] Related Applications This application claims priority to U.S. Provisional Application No. 62 / 234,526, filed Sep. 29, 2015, the entire contents of which are incorporated herein by reference. [Background technology]

[0002] background This application relates to the field of bioinformatics and inflammatory and autoimmune diseases, and to methods for assessing response to therapy for inflammatory diseases, in particular disease activity in psoriatic arthritis (PsA). PsA is an example of an inflammatory disease, a chronic systemic autoimmune disease that occurs in approximately 30% of patients with psoriasis. Psoriasis is a disease that manifests as scaly red and white patches on the skin.

[0003] PsA causes pain, stiffness, and swelling in and around every joint in the body. PsA typically affects the large joints, especially those of the lower legs, the distal joints of the fingers and toes, and even the sacroiliac joints of the back and pelvis. There are five common patterns of PsA: arthritis of the distal interphalangeal joints, destructive arthritis, symmetric polyarthritis indistinguishable from rheumatoid arthritis, asymmetric oligoarthritis, and spondyloarthropathy. Persistent inflammation from PsA can lead to joint damage.

[0004] The impact of PsA varies based on the affected joints and severity of symptoms. Common secondary effects of PsA are fatigue and anemia. Similar to psoriasis, PsA symptoms may flare and subside, even changing locations in the same subject. PsA is associated with significant morbidity and disability, thus resulting in a large socio-economic burden. To achieve maximum therapeutic benefit for individual patients, it is important to be able to clearly quantify and assess the subject's disease activity at a specific time, determine the effect of treatment on disease activity, and predict future outcomes. Summary of the Invention

[0005] overview The present teachings relate to biomarkers associated with inflammatory diseases, including autoimmune diseases such as psoriatic arthritis, and methods of using the biomarkers to measure disease activity in a subject, as well as response to treatment.

[0006] In one aspect, a method for generating protein level data for a first subject is provided, the method comprising performing at least one immunoassay on a first blood sample from the first subject to generate a first dataset comprising protein level data for at least four protein markers, wherein the at least four protein markers are chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (β-urogastron) (EGF); interleukin 6 (interferon, β2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A. (VEGFA), wherein the first subject has previously been diagnosed with psoriatic arthritis (PsA). In another embodiment, performing at least one immunoassay comprises obtaining a first blood sample, the first blood sample comprising the protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between the reagents and the markers; and detecting the complexes to generate the data. In one embodiment, the at least one immunoassay comprises a multiplex assay. In one embodiment, the at least four protein markers comprise at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.In one embodiment, the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers comprise at least 10 markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers comprise at least 11 markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the plurality of protein markers consists of at most 500 protein markers. In one embodiment, the plurality of protein markers consists of at most 250 protein markers. In one embodiment, the plurality of protein markers consists of at most 100 protein markers. In one embodiment, the plurality of protein markers consists of at most 50 protein markers. In one embodiment, the plurality of protein markers consists of at most 25 protein markers. In one embodiment, the plurality of protein markers consists of at most 12 protein markers.In one embodiment, CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1), CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2), EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3), IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4), LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5), MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6), and MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 7). RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12).

[0007] In another aspect, a method for generating a protein level score is provided, comprising: performing at least one immunoassay on a first blood sample from a subject previously diagnosed with psoriatic arthritis (PsA) to generate protein level data for each protein marker of a plurality of protein markers, the plurality of protein markers being selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A. (VEGFA); and combining the protein level data of each test marker to generate a protein level score. In one embodiment, performing at least one immunoassay comprises obtaining a first blood sample, the first blood sample comprising said protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between the reagents and the markers; and detecting the complexes to generate said data. In one embodiment, the at least one immunoassay comprises a multiplex assay. In one embodiment, the at least four protein markers comprise at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.In one embodiment, the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the at least four protein markers include at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. In one embodiment, the plurality of test markers includes CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA.In one embodiment, CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1), CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2), EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3), IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4), LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5), MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6), and MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 7). RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12).

[0008] In another aspect, a method is provided for providing a quantitative measure of psoriatic arthritis (PsA) disease activity in a subject previously diagnosed with PsA. The method includes providing a quantitative measure of psoriatic arthritis (PsA) disease activity in a subject previously diagnosed with PsA, and includes the steps of: performing at least one immunoassay on a first sample from the subject to detect an immunological finding of chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A. (VEGFA); and applying an algorithm to the first dataset to determine a first disease activity score, the first disease activity score providing a quantitative measure of PsA disease activity in the subject. In one embodiment, the method further comprises receiving a second dataset related to a second sample obtained from the subject, the first sample and the second sample being obtained from the subject at different times; applying an algorithm to the second dataset to determine a second disease activity score; and comparing the first disease activity score to the second disease activity score to determine a change in the disease activity score, the change indicating a change in psoriatic arthritis disease activity in the subject. In one embodiment, the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.In one embodiment, CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1), CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2), EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3), IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4), LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5), MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6), and MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 7). RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12).

[0009] In another aspect, a system for scoring a sample is provided, the system comprising: a memory for storing a first data set associated with a first sample obtained from a subject previously diagnosed with psoriatic arthritis (PsA), the first data set comprising: chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (β-urogastron) (EGF); interleukin 6 (interferon, β2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and a processor communicatively coupled to the memory, the processor configured to apply an algorithm to a first dataset to determine a first disease activity score, the first disease activity score providing a quantitative measure of PsA disease activity in the subject, the algorithm being trained with data representing the at least four markers in samples from subjects with known PsA disease activity. In one embodiment, the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.In one embodiment, CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1), CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2), EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3), IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4), LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5), MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6), and MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 7). RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12).

[0010] In another aspect, a method is provided for recommending a treatment regimen in a subject previously diagnosed with psoriatic arthritis (PsA). The method includes the steps of: a) performing a first immunoassay on a first sample from the subject at a first time point to generate a first score based on a first set of quantitative data, the first set of quantitative data including expression data for at least four biomarkers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (β-urogastron) (EGF); interleukin 6 (interferon, β2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); b). At a second time point after the first time point, a second immunoassay is performed on a second sample from the subject to generate a second score based on a second set of quantitative data, the second set of quantitative data comprising expression data of the at least four biomarkers; c) determining whether there is a difference between the first score and the second score; and d) recommending a treatment regimen or modifying an existing treatment regimen based on the difference between the first score and the second score. In one embodiment, if the difference between the first score and the second score indicates a decrease or no change in PsA disease activity, a non-biological treatment regimen is recommended. In one embodiment, if the difference between the first score and the second score indicates an increase in PsA disease activity, a biological treatment regimen is recommended.In one embodiment, performing at least one immunoassay comprises obtaining a first sample, the first sample comprising said protein marker; contacting the first sample with a plurality of different reagents; forming a plurality of different complexes between said reagents and said markers; and detecting said complexes to generate said data. In one embodiment, the first and second immunoassays comprise multiplex assays. In one embodiment, the at least four protein markers comprise at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.In one embodiment, CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1), CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2), EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3), IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4), LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5), MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6), and MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 7). RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12). [The present invention 1001] performing at least one immunoassay on a first blood sample from a first subject to generate a first data set including protein level data for at least four protein markers; 1. A method for generating protein level data for a first subject, comprising: the at least four protein markers include at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); The first subject has previously been diagnosed with psoriatic arthritis (PsA), The method. [The present invention 1002] Conducting at least one immunoassay obtaining a first blood sample, the first blood sample comprising said protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data. The method of the present invention 1001, comprising: [The present invention 1003] The method of any one of claims 1 to 10, wherein at least one of the immunoassays comprises a multiplex assay. [The present invention 1004] 1001. The method of claim 1001, wherein the at least four protein markers include at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1005] 1001. The method of claim 1001, wherein the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1006] 1001. The method of claim 1001, wherein the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1007] 1001. The method of the present invention, wherein the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1008] 1001. The method of the present invention, wherein the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1009] 1001. The method of claim 1001, wherein the at least four protein markers include at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1010] 1001. The method of the present invention, wherein the at least four protein markers include at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1011] 1001. The method of the present invention, wherein said plurality of protein markers consists of at most 500 types of protein markers. [The present invention 1012] 1001. The method of claim 1001, wherein said plurality of protein markers consists of at most 250 types of protein markers. [The present invention 1013] 1001. The method of the present invention, wherein said plurality of protein markers consists of at most 100 types of protein markers. [The present invention 1014] 1001. The method of claim 1001, wherein said plurality of protein markers consists of at most 50 protein markers. [The present invention 1015] 1001. The method of claim 1001, wherein said plurality of protein markers consists of at most 25 types of protein markers. [The present invention 1016] 1001. The method of claim 1001, wherein said plurality of protein markers consists of at most 12 types of protein markers. [The present invention 1017] CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The method of the present invention 1001, wherein the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO: 7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12). [The present invention 1018] 1. A method for generating a protein level score, comprising the steps of: performing at least one immunoassay on a first blood sample from a subject previously diagnosed with psoriatic arthritis (PsA) to generate protein level data for each protein marker of the plurality of protein markers; the plurality of protein markers comprises a plurality of test markers including at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and Combining the protein level data for each test marker to generate a protein level score. [The present invention 1019] Conducting at least one immunoassay obtaining a first blood sample, the first blood sample comprising said protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data. The method of the present invention 1018, comprising: [The present invention 1020] The method of any one of claims 10 to 18, wherein at least one immunoassay comprises a multiplex assay. [The present invention 1021] The method of the present invention, wherein the at least four protein markers include at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1022] The method of the present invention, wherein the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1023] The method of the present invention, wherein the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1024] The method of the present invention, wherein the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1025] The method of the present invention, wherein the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1026] The method of the present invention, wherein the at least four protein markers include at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1027] The method of the present invention, wherein the at least four protein markers include at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1028] The method of any one of claims 1 to 10, wherein the plurality of test markers comprises CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA. [The present invention 1029] CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The method of the present invention 1018, wherein the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO: 7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12). [The present invention 1030] 1. A method for providing a quantitative measure of psoriatic arthritis (PsA) disease activity in a subject previously diagnosed with PsA, the method comprising the steps of: performing at least one immunoassay on a first sample from the subject to generate a first dataset comprising quantitative data including at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and Applying an algorithm to the first data set to determine a first disease activity score, wherein the first disease activity score provides a quantitative measure of PsA disease activity in the subject. [The present invention 1031] receiving a second dataset relating to a second sample obtained from the subject, the first sample and the second sample being obtained from the subject at different times; applying an algorithm to the second data set to determine a second disease activity score; and comparing the first disease activity score to the second disease activity score to determine a change in disease activity score, which change indicates a change in psoriatic arthritis disease activity in the subject. The method of the present invention 1030 further comprising: [The present invention 1032] The method of the present invention 1030, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1033] CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The method of the present invention 1030, wherein the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO: 7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12). [The present invention 1034] 1. A system for scoring a sample, comprising: A storage memory for storing a first data set relating to a first sample obtained from a subject previously diagnosed with psoriatic arthritis (PsA), a memory for storage, the first dataset comprising quantitative data for at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and a processor communicatively connected to the storage memory, The processor is configured to apply an algorithm to a first dataset to determine a first disease activity score, where the first disease activity score provides a quantitative measure of PsA disease activity in the subject, and the algorithm is trained with data representing at least four markers in samples from subjects with known PsA disease activity. [The present invention 1035] The system of the present invention 1034, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1036] CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The system of the present invention 1034, wherein the amino acid sequence of NCBI RefSeq NP_002413.1 (sequence number 7) is at least 90% identical, RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (sequence number 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (sequence number 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (sequence number 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (sequence number 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (sequence number 12). [The present invention 1037] 1. A method for recommending a treatment regimen in a subject previously diagnosed with psoriatic arthritis (PsA), comprising the steps of: a) performing a first immunoassay on a first sample from the subject at a first time point and generating a first score based on a first set of quantitative data; the first set of quantitative data includes expression data for at least four biomarkers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); b) performing a second immunoassay on a second sample from the subject at a second time point after the first time point and generating a second score based on a second set of quantitative data; a second set of quantitative data comprising expression data for said at least four biomarkers; c) determining whether there is a difference between the first score and the second score; and d) recommending a treatment regimen or modifying an existing treatment regimen based on the difference between the first score and the second score. [The present invention 1038] The method of the present invention, wherein a non-biological treatment regimen is recommended if the difference between the first score and the second score indicates a decrease or no change in PsA disease activity. [The present invention 1039] The method of the present invention, wherein if the difference between the first score and the second score indicates increased PsA disease activity, a biological treatment regimen is recommended. [The present invention 1040] performing said at least one immunoassay obtaining a first sample, the first sample comprising said protein marker; contacting the first sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data. The method of the present invention 1037, comprising: [The present invention 1041] The method of claim 1037, wherein the first and second immunoassays comprise a multiplex assay. [The present invention 1042] The method of the present invention 1037, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA. [The present invention 1043] CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The method of the present invention 1037, wherein the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO: 7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO: 8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO: 9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO: 10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO: 11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO: 12). [Brief description of the drawings]

[0011] Those skilled in the art will understand that the drawings, described below, are for illustration purposes only and are not intended to limit the scope of the present teachings in any way. [Figure 1] FIG. 1 shows the correlation of MBDA algorithm predictions and CRP with clinical assessment of disease activity, as described in Example 1. [Diagram 2]2 shows the MBDA scores for subjects at baseline and 6-month visits according to the description of Example 1. MBDA scores are shown by treatment group and time point. Only subjects with MBDA scores available at both baseline and 6 months are shown. [Diagram 3] A high-level block diagram of a computer (1600) is shown. At least one processor (1602) is shown connected to a chipset (1604). Additionally, a memory (1606), a recording device (1608), a keyboard (1610), a graphics adapter (1612), a pointing device (1614), and a network adapter (1616) are also connected to the chipset (1604). A display (1618) is connected to the graphics adapter (1612). In one embodiment, the functionality of the chipset (1604) is provided by a memory controller hub (1620) and an I / O controller hub (1622). In another embodiment, the memory (1606) is directly connected to the processor (1602) instead of the chipset (1604). The recording device (1608) is any device capable of retaining data, such as a hard drive, a compact disc read-only memory (CD-ROM), a DVD, or a solid-state memory device. The memory (1606) holds instructions and data used by the processor (1602). The pointing device (1614), which may be a mouse, trackball, or other type of pointing device, is used in combination with the keyboard (1610) to input data into the computer system (1600). The graphics adapter (1612) displays images and other information on the display (1618). The network adapter (1616) connects the computer system (1600) to a local or wide area network. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Description of various aspects These and other features of the present teachings will become more apparent from the description herein. While the present teachings are described in conjunction with various embodiments, they are not limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those skilled in the art.

[0013] The present teachings generally relate to the identification of biomarkers associated with subjects having inflammatory and / or autoimmune diseases, such as psoriatic arthritis (PsA), that are useful in determining or assessing disease activity.

[0014] Most of the terms used herein have the meanings that those skilled in the art would ascribe to them. Terms specifically defined herein have the meanings provided in the context of the present teachings as a whole and as generally understood by those skilled in the art. In the event of a conflict between the definition of a term or phrase as understood in the art and the definition of a term or phrase specifically taught herein, the present specification shall control. It should be noted that the singular forms "a", "an" and "the" used in this specification and the appended claims include the plural references unless the context clearly indicates otherwise.

[0015] definition "Accuracy" refers to the degree to which a measured or calculated value corresponds to its actual value. "Accuracy" in clinical trials relates to the ratio of actual results (true positive or true negative, where the subject is correctly classified as having a disease or as healthy / normal, respectively) to incorrectly classified results (false positive or false negative, where the subject is incorrectly classified as having a disease or as healthy / normal, respectively). Other and / or equivalent terms for "accuracy" include, for example, "sensitivity", "specificity", "positive predictive value (PPV)", "AUC", "negative predictive value (NPV)", "likelihood" and "odds ratio". "Analytical accuracy", in the context of the present teachings, refers to the reproducibility and predictability of the measurement process. Analytical accuracy can be summarized, for example, in the following measurements: measurements of the coefficient of variation (CV), and tests of matching and calibration of the same samples or controls at different time points or with different assessors, users, instruments and / or reagents. For a review of considerations in evaluating new biomarkers, see, e.g., R. Vasan, Circulation 2006, 113(19):2335-2362.

[0016] The term "administer" as used herein refers to placing a composition in a subject in a manner or route that results in at least partial localization of the composition at a desired site so that a desired effect occurs.Routes of administration include both local administration and systemic administration.Generally, local administration results in a greater amount of the composition being delivered to a specific site compared to the entire body of the subject, while systemic administration results in delivery to essentially the entire body of the subject.

[0017] The term "algorithm" encompasses any formula, model, mathematical expression, algorithmic, analytical or programmed process, or statistical method or classification analysis, that receives one or more inputs or parameters, whether continuous or categorical, and calculates an output value, index, index value or score. Examples of algorithms include, but are not limited to, regression operators such as ratios, sums, exponents or coefficients, transformations and normalization of biomarker values ​​(including, but not limited to, normalization schemes based on clinical parameters such as age, sex, ethnicity, etc.), rules and guidelines, statistical classification models, and population-directed neural networks. Additionally, used in the context of biomarkers are linear and nonlinear equations and statistical classification analyses to determine the relationship between (a) the level of the biomarker detected in a subject sample and (b) the level of disease activity for each subject.

[0018] The term "analyte" in the context of the present teachings means any substance that is measured, and can include biomarkers, markers, nucleic acids, electrolytes, metabolites, proteins, sugars, carbohydrates, fats, lipids, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, metabolites, mutants, variants, polymorphisms, variants, fragments, subunits, degradation products, and other elements. For simplicity, rather than using standard protein symbols to refer to genes as well as gene products / proteins, standard gene symbols are used throughout; for example, APOA1 as used herein can refer to the gene APOA1 and the protein ApoAI. Generally, hyphens are omitted from analyte names and symbols herein (IL-6=IL6).

[0019] "Analyzing" includes determining a value or set of values ​​associated with a sample by measuring the level of an analyte in the sample. "Analyzing" may further include comparing the level to the component level in a sample or set of samples from the same subject or from another subject(s). The biomarkers of the present teachings can be analyzed by a variety of conventional methods known in the art. Some such methods include, but are not limited to, measuring serum protein, sugar, metabolite or other analyte levels, measuring enzyme activity, and measuring gene expression.

[0020] The term "antibody" refers to any immunoglobulin-like molecule that reversibly binds to another with the required selectivity. Thus, the term includes any such molecule that can selectively bind to a biomarker of the present teachings. The term includes immunoglobulin molecules that can bind to an epitope present on an antigen. The term is intended to include not only complete immunoglobulin molecules such as monoclonal and polyclonal antibodies, but also antibody isotypes, recombinant antibodies, bispecific antibodies, humanized antibodies, chimeric antibodies, anti-idiotypic (anti-ID) antibodies, single chain antibodies, Fab fragments, F(ab') fragments, fusion protein antibody fragments, immunoglobulin fragments, Fv fragments, single chain Fv fragments, and chimeras that contain immunoglobulin sequences, and variants of any of the above that contain the required selective antigen recognition site.

[0021] "Autoimmune disease" includes any disease, as defined herein, that results from an immune response against substances and tissues normally present in the body. Examples of diseases suspected or known to be autoimmune diseases include: rheumatoid arthritis, early rheumatoid arthritis (RA), axial spondyloarthritis, juvenile idiopathic arthritis, seronegative spondyloarthritis, ankylosing spondylitis, psoriatic arthritis (PsA), antiphospholipid syndrome, autoimmune hepatitis, Behcet's disease, bullous pemphigoid, celiac disease, Crohn's disease, dermatomyositis, Goodpasture's syndrome, Graves' disease, Hashimoto's disease, idiopathic spondyloarthritis, and idiopathic spondyloarthritis. Thrombocytopenic purpura, IgA nephropathy, Kawasaki disease, systemic lupus erythematosus, mixed connective tissue disease, multiple sclerosis, myasthenia gravis, polymyositis, primary biliary cirrhosis, psoriasis, scleroderma, Sjögren's syndrome, ulcerative colitis, vasculitis, Wegener's granulomatosis, temporal arteritis, Takayasu's arteritis, Henoch-Schönlein purpura, leukocytoclastic vasculitis, arteritis nodosa, Churg-Strauss syndrome, and mixed cryoglobulinemic vasculitis.

[0022] In the context of the present teachings, "biomarker" or "marker" includes, but is not limited to, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, and metabolites, as well as their associated metabolites, mutants, isoforms, variants, polymorphisms, variants, fragments, subunits, degradation products, elements, and other analyte- or sample-derived indicators. Biomarkers can also include mutant proteins, mutant nucleic acids, copy number changes, and / or transcriptional variants. Biomarkers further include non-blood-derived and non-analyte physiological markers of health status, and / or other factors or markers that are not measured from a sample (e.g., a biological sample such as a body fluid), such as clinical parameters and traditional factors for clinical evaluation. Biomarkers can also include any indicator that is calculated and / or mathematically generated. Biomarkers can also include combinations of any one or more of the above measurements, including time trends and time lags.Biomarkers include, but are not limited to, apolipoprotein AI (APOA1); apolipoprotein C-III (APOC3); calprotectin; chemokine (CC motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1 or YKL-40); C-reactive protein, pentraxin related (CRP); epidermal growth factor (β-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1); ICTP; interleukin 18 (interferon gamma-inducing factor) (IL18); interleukin 1, beta (IL1B); interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan sulfate or KS; leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); pyridinoline (a cross-link formed in collagen derived from three lysine residues), sometimes referred to herein as PYD; resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA).

[0023] In the context of the present teachings, "clinical assessment" or "clinical data point" or "clinical endpoint" can refer to a measure of disease activity or severity. Clinical assessment can include a score, value, or set of values ​​that can be obtained from the evaluation of a sample (or a population of samples) from a subject(s) under a given condition. Clinical assessment can also be a questionnaire that the subject fills out. Clinical assessment can also be predicted by biomarkers and / or other parameters. One of skill in the art will recognize that clinical assessments of RA may include, by way of example and without limitation, one or more of the following: DAS, DAS28, DAS28-ESR, DAS28-CRP, HAQ, mHAQ, MDHAQ, physician global assessment VAS, patient global assessment VAS, pain VAS, fatigue VAS, overall VAS, sleep VAS, SDAI, CDAI, RAPID3, RAPID4, RAPID5, ACR20, ACR50, ACR70, SF-36 (a well-validated measure of general health status), RA MRI score (RAMRIS; or RA MRI Scoring System), total Sharp score (TSS), van der Heijde modified TSS, van der Heijde modified Sharp score (or Sharp-van der Heijde score (SHS)), Larsen score, TJC, swollen joint count (SJC), CRP titer (or level), and ESR. Those skilled in the art will recognize that clinical assessment of PsA may also include, by way of example and not limitation, one or more of the following: physician's global assessment, HAQ score, skin BSA, and CDAI.

[0024] In the context of the present teachings, the term "clinical parameters" encompasses any measure of the subject's health status. Clinical parameters can be used to obtain a clinical assessment of the subject's disease activity. Clinical parameters can include, but are not limited to, the following: treatment regimen (including but not limited to DMARDs, whether conventional or biological, steroids, etc.), TJC, SJC, morning stiffness, arthritis in three or more joint areas, arthritis of the wrist, psoriasis, symmetric arthritis, rheumatoid nodules, radiographic changes and other imaging, sex / gender, age, race / ethnicity, disease duration, diastolic and systolic blood pressure, resting heart rate, height, weight, body mass index, family history, CCP status (i.e., whether the subject is anti-CCP antibody positive or negative), CCP titer, RF status, RF titer, ESR, CRP titer, menopausal status, and smoker / non-smoker.

[0025] "Clinical assessment" and "clinical parameter" are not mutually exclusive terms. There may be overlap between the members of these two categories. For example, CRP titer is used as a clinical assessment of disease activity; alternatively, it is used as an indicator of the subject's health status and thus serves as a clinical parameter.

[0026] The term "computer" has its meaning as generally known in the art; i.e., a machine for manipulating data according to a sequence of instructions. For illustrative purposes only, FIG. 3 is a high-level block diagram of a computer (1600). As is known in the art, a "computer" may have different and / or other components than those shown in FIG. 3. Furthermore, the computer 1600 may not have the specific depicted components. Moreover, the recording device (1608) may be local and / or remote from the computer (1600) (e.g., embodied in a recording area network (SAN)). As is known in the art, the computer (1600) is adapted to execute computer program modules for providing the functionality described herein. The term "module" as used herein refers to the logic of a computer program utilized to provide a specified function. Thus, the modules may be implemented in hardware, firmware and / or software. In one embodiment, the program modules are stored in the recording device (1608), loaded into the memory (1606), and executed by the processor (1602). Aspects of the entities described herein may include other and / or different modules than those described herein. Further, functionality attributed to a module may be performed by other or different modules in other aspects. Also, in this description, the term "module" may be omitted for purposes of clarity and convenience.

[0027] The term "cytokine" in the present teachings refers to a substance secreted by certain cells of the immune system that transmits signals locally between cells and thus affects other cells. The term "cytokine" encompasses "growth factors." "Chemokines" are also cytokines. They are a subset of cytokines that can induce chemotaxis in cells; therefore, they are also known as "chemotactic cytokines."

[0028] "DAS" refers to the disease activity score, well known to those skilled in the art, which is a measure of RA activity in a subject. See D. van der Heijde et al., Ann. Rheum. Dis. 1990, 49(11):916-920. As used herein, "DAS" refers to this particular disease activity score. "DAS28" includes the assessment of 28 specific joints. This is a current standard that is well recognized in research and clinical practice. Because DAS28 is a well-recognized standard, it is often simply referred to as "DAS". Unless otherwise specified, "DAS" herein encompasses DAS28. DAS28 can be calculated for RA subjects according to the criteria outlined in the das-score.nl website maintained by the Department of Rheumatology at the University Medical Centre in Nijmegen, The Netherlands. The number of swollen joints, i.e., swollen joints out of a total of 28 (SJC28), and the number of tender joints, i.e., tender joints out of a total of 28 (TJC28), are assessed in each subject. In some DAS28 calculations, the subject's general health (GH) is also a component and can be measured on a 100 mm visual analogue scale (VAS). With respect to "Patient Global Health Assessment" (or simply "Patient Global Assessment"), GH can also be referred to herein as PG or PGA. Thus, "Patient Global Health Assessment VAS" refers to GH measured on a visual analogue scale.

[0029] "DAS28-CRP" (or "DAS28CRP") is a DAS28 assessment calculated using CRP instead of ESR (see below). CRP is produced in the liver. Normally, there is little or no CRP circulating in an individual's serum; CRP is generally present in the body during episodes of acute inflammation or infection. Thus, high or elevated amounts of CRP in serum may be associated with acute infection or inflammation. Serum levels of CRP above 1 mg / dL are usually considered high. Most inflammation and infections result in CRP levels above 10 mg / dL. The amount of CRP in a subject's serum can be quantified, for example, using the DSL-10-42100 ACTIVE® US C-reactive protein enzyme-linked immunosorbent assay (ELISA) developed by Diagnostics Systems Laboratories, Inc. (Webster, TX). CRP production is associated with the radiological progression of RA. See M. Van Leeuwen et al., Br. J. Rheum. 1993, 32(suppl.):9-13. Therefore, CRP is considered a suitable alternative to ESR in measuring RA disease activity. See R. Mallya et al., J. Rheum. 1982, 9(2):224-228 and F. Wolfe, J. Rheum. 1997, 24:1477-1485.

[0030] DAS28-CRP can be calculated according to any of the following formulas, with or without the GH component, where "CRP" represents the amount of this protein present in the subject's serum (mg / L), "sqrt" represents square root, and "ln" represents natural logarithm: TIFF2025013479000001.tif30160

[0031] "DAS28-ESR" is the DAS28 assessment that also measures each subject's ESR (in mm / hr). The DAS28-ESR can be calculated according to the following formula: TIFF2025013479000002.tif30166

[0032] Unless otherwise specified herein, the term "DAS28" as used in the present teachings may refer to DAS28-ESR or DAS28-CRP obtained by any of the above four formulas; alternatively, DAS28 may refer to another reliable DAS28 formula as known in the art.

[0033] A "dataset" or "data" is a set of numerical values ​​resulting from the evaluation of a sample (or a population of samples) under desired conditions. The values ​​of a dataset can be obtained, for example, by experimentally obtaining measurements from the samples and constructing a dataset from these measurements; or by obtaining the dataset from a service provider, such as a laboratory, or from a server or database on which the dataset is stored.

[0034] As used herein, "difference" refers to an increase or decrease in the measurable expression of a biomarker or panel of biomarkers as compared to the measurable expression of the same biomarker or panel of biomarkers in a second sample.

[0035] In the context of the present teachings, the term "disease" includes any disorder, condition, illness, disorder, etc. that manifests, for example, in a disordered or improperly functioning organ, part, structure, or system of the body, resulting, for example, from a genetic or developmental error, infection, toxin, nutritional deficiency or imbalance, toxicity, or adverse environmental factors.

[0036] DMARDs can be conventional or biologic. Examples of DMARDs that are generally considered conventional include, but are not limited to, MTX, azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), cyclosporine (CSA, or cyclosporine), doxycycline (DOXY), hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide (LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Other examples of conventional DMARDs include, but are not limited to, folinic acid, D-penicillamine, auranofin gold, aurothioglucose gold, thiomalate gold, cyclophosphamide, and chlorambucil. Examples of biological DMARDs (or biologics) include, but are not limited to, tumor necrosis factor (TNF) alpha molecule-targeting biologics and TNF inhibitors, such as infliximab, adalimumab, etanercept, and golimumab. Other classes of biological DMARDs include IL-1 inhibitors, such as anakinra, T cell modulators, such as abatacept, B cell modulators, such as rituximab, and IL-6 inhibitors, such as tocilizumab.

[0037] As used herein, "immunoassay" refers to a biochemical assay that uses one or more antibodies to measure the presence or concentration of an analyte or biomarker in a biological sample. As used herein, "multiplex immunoassay" refers to a biochemical assay that uses two or more antibodies to simultaneously measure multiple analytes in one run / cycle of the assay.

[0038] In the context of the present teachings, "inflammatory disease" includes, without limitation, any disease as defined herein that results from a biological response of vascular tissue to harmful stimuli, including, but not limited to, stimuli such as pathogens, damaged cells, irritants, antigens, and in the case of autoimmune diseases, to substances or tissues normally present in the body. Non-limiting examples of inflammatory diseases include RA, ankylosing spondylitis, psoriatic arthritis, psoriasis, atherosclerosis, asthma, autoimmune diseases, chronic inflammation, chronic prostatitis, glomerulonephritis, hypersensitivity, inflammatory bowel disease, pelvic inflammatory disease, reperfusion injury, transplant rejection, and vasculitis.

[0039] As used herein, "interpretation function" means translating a set of observed data into a meaningful determination of particular interest; for example, an interpretation function can be a predictive model produced by utilizing one or more statistical algorithms to translate a dataset of observed biomarker data into a meaningful determination of disease activity or condition of a subject.

[0040] In the context of the present teachings, "measuring" or "measurement" means determining the presence, absence, quantity, amount, or effective amount of a substance in a clinical sample or a sample derived from a subject, e.g., the concentration level of such a substance, or assessing the value or classification of a clinical parameter of the subject.

[0041] "Performance" in the context of the present teachings relates, for example, to the quality and overall usefulness of a model, algorithm, or prognostic test. Considerations in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, usage characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and the relative cost of the various reagents and components of the test. Performing can mean the act of carrying out a function.

[0042] A "population" is a grouping of subjects with similar specified characteristics. Grouping may be, for example, but not limited to, by clinical parameters, clinical assessments, treatment regimens, disease states (e.g., diseased or healthy), levels of disease activity, etc. When using MBDA scores to compare disease activity between populations, an aggregate value may be determined based on the observed MBDA scores of subjects in the population at a particular time point, for example, in a longitudinal study. The aggregate value may be based on any mathematical or statistical formula known in the art to arrive at a meaningful aggregate value from a collection of individual data points; for example, the mean, median, median of the mean, etc.

[0043] A "predictive model", which term is used herein synonymously with a "multivariate model" or simply a "model", is a mathematical construct developed using one or more statistical algorithms to classify a set of data. The term "predict" refers to generating a value for a data point without actually performing a clinical diagnostic procedure that would normally or otherwise be required to generate the data point; "predict" as used in connection with this model should not be understood to refer solely to the model's ability to predict a particular outcome. A predictive model can provide an interpretation function; for example, a predictive model can be created by utilizing one or more statistical algorithms or methods that convert a data set of observed data into a meaningful value of disease activity or disease state of a subject. For some examples of statistical tools useful in model development, see "Calculating the MBDA Score".

[0044] "Prognosis" is a prediction of the likely outcome of a disease. Estimation of prognosis is useful, for example, in determining an appropriate treatment regimen for a subject.

[0045] As used in the present teachings, "quantitative dataset" or "quantitative data" refers to data derived from, for example, the detection and combined measurement of expression of multiple (i.e., two or more) biomarkers in a subject sample. Quantitative datasets are used to generate scores for identifying, monitoring, and treating disease states, and are used in characterizing the biological state of a subject. Different biomarkers can be detected depending on the disease state or physiological state of interest.

[0046] As used herein, "recommend" refers to making a recommendation for a treatment regimen or precluding (i.e., not recommending) a particular treatment regimen for a subject. Such a recommendation, optionally in conjunction with other information, may serve as a basis for a clinician to apply a particular treatment regimen to an individual subject.

[0047] In the context of the present teachings, a "sample" refers to any biological sample isolated from a subject. Samples include, but are not limited to, single or multiple cells, cell fragments, aliquots of bodily fluids, whole blood, platelets, serum, plasma, red blood cells, white blood cells, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, peritoneal fluid, and interstitial or extracellular fluid. The term "sample" also encompasses fluids in the spaces between cells, such as gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucus, sputum, semen, sweat, urine, or any other bodily fluid. A "blood sample" can refer to whole blood or fractions thereof, such as blood cells, red blood cells, white blood cells, platelets, serum, and plasma. Samples can be obtained from a subject by means including, but not limited to, venipuncture, voiding, ejaculation, massage, biopsy, needle aspiration, lavage, scraping, surgical incision, or intervention or other means known in the art.

[0048] A "score" is a value or set of values ​​selected to provide a quantitative measure of a variation or characteristic of a subject's condition and / or to identify, differentiate, or otherwise characterize the subject's condition. The values ​​that make up a score can be based on quantitative data resulting in a measurement of one or more sample components, e.g., obtained from a subject, or from clinical parameters, or from clinical evaluation, or any combination thereof. In certain embodiments, a score can be derived from a single component, parameter, or assessment, while in other embodiments, a score is derived from multiple components, parameters, and / or assessments. A score can be based on or derived from an interpretation function; for example, an interpretation function is derived from a particular predictive model using any of a variety of statistical algorithms known in the art. A "change in score" can refer to, for example, the absolute change in score from one time point to the next, or the percent change in score, or the change in score per unit time (i.e., the rate of score change). As used herein, "score" is used interchangeably with MBDA score, as defined below.

[0049] In the context of the present teachings, a "multi-biomarker disease activity index score", "MBDA score", or simply "MBDA" is a score that uses quantitative data to provide a quantitative measure of inflammatory disease activity or inflammatory disease state in a subject. A set of data from specifically selected biomarkers, for example from a set of disclosed biomarkers, is input into an interpretation function according to the present teachings to derive an MBDA score. The interpretation function, in some embodiments, can be generated from predictions or multivariate modeling based on statistical algorithms. The input to the interpretation function can include the results of testing two or more of the disclosed set of biomarkers, alone or in combination with clinical parameters and / or clinical evaluations, also described herein. In some embodiments of the present teachings, the MBDA score is a quantitative measure of autoimmune disease activity. In some embodiments, the MBDA score is a quantitative measure of RA disease activity. In some embodiments, the MBDA score is a quantitative measure of PsA disease activity. As used herein, MBDA can refer to the VECTRA® DA score.

[0050] In the context of the present teachings, "statistically significant" means that the observed change is greater than would be expected to occur by chance alone (e.g., a "false positive"). Statistical significance can be determined by any of a variety of methods known in the art. An example of a commonly used measure of statistical significance is the p-value. The p-value represents the probability of obtaining a given result equal to a particular data point, where that data point is the result of chance alone. Results are often considered highly significant (not by chance) at a p-value of 0.05 or less.

[0051] In the context of the present teachings, a "subject" is generally a mammal. The subject may be a patient. The term "mammal" as used herein includes, but is not limited to, humans, non-human primates, dogs, cats, mice, rats, cows, horses, and pigs. Non-human mammals are advantageously used as subjects that represent animal models of inflammation. The subject may be male or female. The subject may be a person who has previously been diagnosed or confirmed to have an inflammatory disease, such as PsA. The subject may have already undergone or is undergoing therapeutic intervention for an inflammatory disease. The subject may also be a person who has not previously been diagnosed with an inflammatory disease; for example, the subject may be a person who shows one or more symptoms or risk factors of an inflammatory condition, or a subject who does not show symptoms or risk factors of an inflammatory condition, or a subject who is asymptomatic for an inflammatory disease.

[0052] As described herein, a "therapeutic regimen," "treatment," or "treatment" encompasses any clinical management and intervention of a subject, whether biological, chemical, physical, or a combination thereof, aimed at maintaining, improving, enhancing, or otherwise altering the subject's condition. These terms are used interchangeably herein. Treatment includes, but is not limited to, the administration of prophylactic or therapeutic compounds (including conventional DMARDs, biological DMARDs, nonsteroidal anti-inflammatory drugs (NSAIDs), such as COX-2 selective inhibitors, and corticosteroids), exercise therapy, physical therapy, dietary modification and / or supplementation, bariatric surgical intervention, administration of pharmaceuticals and / or anti-inflammatory drugs (prescription or over-the-counter), and any other treatment known in the art to be effective in preventing, delaying the onset of, or ameliorating a disease. A "response to treatment" includes the response of a subject to any of the treatments described above, whether biological, chemical, physical, or a combination thereof. A "course of treatment" refers to the dosage, duration, extent, etc., of a particular treatment or treatment regimen.

[0053] As used herein, a "time point" refers to a description of time that can be effectively described using a single point. A time point can also be described as the smallest detectable time range. A time point can refer to a state of an aspect of time, or a description of a period of time. Such a time point or time range can include, for example, on the order of seconds, minutes, to hours, or days.

[0054] Use of the Present Teachings in Assessing Disease Activity In some embodiments of the present teachings, biomarkers can be used to derive MBDA scores, as described herein.The MBDA scores can be used to assess the disease state and / or disease activity of inflammatory and autoimmune diseases, particularly RA or PsA.In certain embodiments, MBDA scores can be used to assess the disease state and / or disease activity of PsA in response to therapy.

[0055] Identifying the subject's inflammatory disease status allows for the prognosis of the disease, which in turn allows for informed selection, initiation, adjustment, or increase or decrease of various treatment regimens to slow, inhibit or prevent the subject's progression to a more advanced disease state. In some embodiments, subjects can therefore be identified as having a certain level of inflammatory disease activity and / or being in a certain disease state based on the determination of their MBDA score, and selected to initiate or accelerate treatment as defined herein to prevent or delay further progression of inflammatory disease. In other embodiments, subjects identified as having a certain level of inflammatory disease activity and / or being in a certain state of inflammatory disease by MBDA score can be selected to reduce or discontinue their treatment if the subject shows improvement or remission.

[0056] Disease activity rating In some embodiments of the present teachings, the MBDA score derived as described herein can be used to grade inflammatory disease activity, for example, as high, moderate, or low. The score can be varied based on a set of values ​​selected by a physician. For example, the score can be set such that values ​​are given a range of 0-100, and the difference between two scores is at least one point in value. The physician can then assign disease activity based on the values. For example, in some embodiments, a score of 1-29 represents low disease activity, a score of 30-44 represents a moderate level of disease activity, and a score of 45-100 represents high disease activity. The disease activity score can be varied based on the range of the score. For example, if a range of 0-200 is utilized, a score of 1-58 can represent a low level of disease activity. The difference can be determined based on the likelihood of the score range. For example, when using a score range of 0 to 100, a small difference in score can be about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 points difference; a medium difference in score can be about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, or 50 points difference. Thus, by way of example, a physician may define a small difference in score as about ≦6 points, a medium difference in score as about 7 to 20 points, and a large difference in score as about >20 points. The difference may be expressed in any unit, such as percentage points. For example, a physician may define a small difference as about 6≦ percentage points, a medium difference as about 7-20 percentage points, and a large difference as about >20 percentage points.

[0057] In some embodiments of the present teachings, autoimmune disease activity can be graded in this way.In other embodiments, RA disease activity can be graded in this way.In other embodiments, PsA disease activity can be graded in this way.Because MBDA score correlates well with the traditional clinical assessment of inflammatory disease activity, for example PsA, in other embodiments of the present teachings, bone damage itself in a subject or population, and therefore disease progression, can be tracked by using and applying MBDA score.

[0058] MBDA scores can be used for several purposes. On a subject-specific basis, they provide a context for understanding the relative level of disease activity. MBDA ratings of disease activity can be used, for example, to guide clinicians in determining treatment and setting a course of treatment, and / or to inform clinicians that a subject is in remission. Furthermore, it provides a means to more accurately assess and document the qualitative level of disease activity in a subject. It is also useful in terms of assessing clinical differences between populations of subjects in a practice. For example, the tool can be used to assess the relative effectiveness of various treatments. Furthermore, it is useful in terms of assessing clinical differences between different practices. This would allow physicians to determine what global level of disease control their colleagues are achieving, and / or health care groups to compare their results between different practices for both cost and relative effectiveness. Because MBDA scores show strong associations with established disease activity assessments, such as the DAS28, MBDA scores can provide a quantitative measure for monitoring a subject's degree of disease activity and response to treatment.

[0059] Subject Screening Certain embodiments of the present teachings can also be used to screen subject populations in various settings. For example, health maintenance organizations, public health organizations, or school health plans can screen subject groups to identify those in need of therapeutic intervention, as described above. Other embodiments of these teachings can be used to collect disease activity data on one or more subject populations to identify subject disease states as a whole, for example, to determine the effectiveness of clinical management of a population or to determine gaps in clinical management. Insurance companies (e.g., health, life, or disability) may require screening of applicants in the process of determining insurance coverage for possible interventions. Data collected in such population screenings would be valuable, for example, in the operations of health maintenance organizations, public health plans, and insurance companies, especially when linked to clinical progression to conditions such as RA or PsA.

[0060] Such arrays or collections of data can be stored in machine-readable media and used in various health-related data management systems to provide, among other things, improved medical services, cost-effective medical care, and improved insurance operations.See, for example, US Patent Application No. 2002 / 0038227; US Patent Application No. 2004 / 0122296; US Patent Application No. 2004 / 0122297; and US Patent No. 5,018,067. Such systems can access data directly from internal data records or remotely from one or more data storage locations as further detailed herein. Thus, in health-related data management systems where it is important to manage the progression of inflammatory diseases in a population in order to reduce disease-related employment productivity loss, disability and surgery, and thus reduce medical costs, various embodiments of the present teachings provide improvements including the use of data arrays that include measurements of biomarkers as defined herein and / or assessments of disease state and activity resulting from those biomarker measurements.

[0061] Calculating the score In some embodiments of the present teachings, inflammatory disease activity in a subject is measured by measuring the levels of two or more biomarkers in the serum of an inflammatory disease subject, and then applying an interpretation function to convert the levels of the biomarkers into a single MBDA score.This MBDA score provides a quantitative measure of inflammatory disease activity in a subject, and correlates well with conventional clinical assessment of inflammatory disease activity (e.g., DAS28 or CDAI score in RA or PsA), as shown in the following examples.In some embodiments, the disease activity measured in this way is related to autoimmune disease.In some embodiments, the disease activity measured in this way is related to RA.In some embodiments, the disease activity measured in this way is related to PsA. Biomarkers include: apolipoprotein AI (APOA1); apolipoprotein C-III (APOC3); calprotectin; chemokine (CC motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1 or YKL-40); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (β-urogastron) (EGF); intercellular adhesion molecule 1 (ICAM1); ICTP; interleukin 18 (interferon gamma-inducing factor) (IL18); interleukin 1, beta (IL1B); interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan sulfate or KS; leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); pyridinoline (a cross-link formed in collagen derived from three lysine residues), sometimes referred to herein as PYD; resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA). A selection of biomarkers of the present invention is described in detail in US 2011 / 0137851. Calprotectin is a heteropolymer comprising two protein subunits, gene symbols S100A8 and S100A9.ICTP is the carboxy-terminal telopeptide region of type I collagen that is released during the degradation of mature type I collagen. Type I collagen exists as fibers in tissues; in bone, type I collagen molecules are cross-linked. The ICTP peptide is immunochemically intact in blood. (For the type I collagen gene, see official symbol COL1A1, HUGO Gene Nomenclature Committee; also known as OI4; α1 type I collagen; collagen α1 chain type I; collagen of skin, tendon, and bone, α1 chain; and proα1 collagen type 1). Keratan sulfate (KS, or keratosulfate) is not the product of a distinct gene but refers to any of several sulfated glycosaminoglycans. They are synthesized in the central nervous system and are found especially in cartilage and bone. Keratan sulfate is a large, highly hydrated molecule that can act as a cushion in joints to absorb mechanical shock. In some embodiments, the biomarkers can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 of IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1. In some embodiments, the biomarkers can include at least four biomarkers, including IL6, EGF, SAA1, and CRP.

[0062] In some embodiments, the interpretation function is based on a predictive model. Established statistical algorithms and methods well known in the art that are useful as models or in designing predictive models include, but are not limited to, analysis of variance (ANOVA); Bayesian networks; boosting and Ada boosting; bootstrap aggregating (or bagging) algorithms; decision tree classification methods such as classification and regression trees (CART), boosted CART, random forests (RF), recursive partitioning trees (RPART), and others; Curds and Whey: CW);Curd-and-Whey-Lasso;Dimensionality reduction methods, such as Principal Component Analysis (PCA) and factor rotation or factor analysis;Discriminant analysis, such as Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), and Quadratic Discriminant Analysis;Discriminant Function Analysis (DFA);Factor rotation or factor analysis;Genetic algorithms;Hidden Markov Models;Kernel-based machine algorithms, such as Kernel Density Estimation, Kernel Partial Least Squares algorithms, Kernel Matching Pursuit algorithms, Kernel Fisher Discriminant Analysis algorithms, and Kernel Principal Component Analysis algorithms;Linear regression and generalized linear models, including or utilizing Forward Linear Stepwise Regression, Lasso (or LASSO) shrinkage selection method, and Elastic Net regularization and selection method;glmnet (Lasso and Elastic Net Regularized Generalized Linear Models);Logistic Regression (LogReg);Meta-learning algorithms;Nearest neighbor methods for classification or regression, such as K-Nearest Neighbors (KNN);Nonlinear regression or classification algorithms;Neural networks;Partial Least Squares;Rule-based classification;shrunken centroid (SC); stratified inverse regression; product model data exchange specification, application adaptation construct (StepAIC); super principal component (SPC) regression; and support vector machine (SVM) and recursive support vector machine (RSVM). Additionally, clustering algorithms known in the art may be useful for determining subject subgroups.

[0063] Logistic regression is a classical predictive modeling method for the selection of binary response variables, e.g., treatment 1 versus treatment 2. It is used to model both linear and non-linear aspects of the data variables and provides easily interpretable odds ratios.

[0064] Discriminant function analysis (DFA) uses a set of analytes as variables (roots) to discriminate between two or more naturally occurring groups. DFA is used to test for analytes that are significantly different between groups. Using forward stepwise DFA, it is possible to select the set of analytes that best discriminates between the tested groups. Specifically, at each step, all variables are reviewed to determine which variables best discriminate between the groups. This information is then included in a discriminant function, represented by the root, which is an equation consisting of a linear combination of analyte concentrations to predict group membership. The discriminant power of the final equation can be observed as a line plot of the root values ​​obtained for each group. This approach identifies groups of analytes whose profiles can be revealed using changes in analyte concentration levels to assess treatment efficacy. DFA models can also create arbitrary scores that can classify new subjects as either "healthy" or "diseased." To facilitate use of this score in the medical community, the score can be rescaled so that a value of 0 indicates healthy subjects and a score greater than 0 indicates increasing disease activity.

[0065] Classification and Regression Trees (CART) perform a logical split (if / then) of data to create a decision tree. All observations that fall into a given node are classified according to the most common outcome within that node. The results of CART are easily interpretable - just follow a series of if / then tree branches until a classification occurs.

[0066] Support Vector Machines (SVMs) classify objects into two or more classes. Examples of classes include a set of treatment options or a set of prognostic options. Each object is assigned to a class based on its similarity to (or its distance from) objects in a training dataset where the exact class assignment of each object is known. A measure of similarity of a new object to known objects is determined using support vectors that define a region in a potentially high-dimensional space (>R6).

[0067] The process of bootstrap aggregating, or "bagging", is computationally simple. In the first stage, a given dataset is randomly resampled a specified number of times (e.g., thousands of times), effectively providing that many new datasets. The new datasets are called "bootstrapped resamples" of the data, and each is then used to build a model. Next, in the classification model example, the class of every new observation is predicted by the number of classification models created in the first stage. The final class decision is based on a "majority vote" of the classification models; that is, the final classification call is determined by counting the number of times a new observation falls into a given group and taking the majority classification (33%+ for a 3-class system). In the logistic regression model example, if the logistic regression is bagged 1000 times, there will be 1000 logistic models, each providing the probability of a sample belonging to class 1 or 2.

[0068] Curds and whey (CW) using ordinary least squares (OLS) is another predictive modeling method. See L. Breiman and JH Friedman, J. Royal. Stat. Soc. B 1997, 59(1):3-54. This method exploits the correlation between response variables to improve prediction accuracy compared to the usual approach of performing individual regressions of each response variable against a common set of predictor variables X. In CW, Y=XB * S, where Y=(y kj ), where k is the kth patient and j is for the jth response (j=1 for TJC, j=2 for SJC, etc.), B is obtained using OLS, and S is the reduced matrix calculated from the canonical coordinate system. Another method is the combination of Curds and Whey with Lasso (CW-Lasso). Instead of using OLS to obtain B as in the CW case, here Lasso is used and the parameters are adjusted according to the Lasso approach.

[0069] Many of these techniques are useful in combination with biomarker selection methods (e.g., forward, backward, or stepwise selection), or for the complete enumeration of all potential panels of a given size or genetic algorithms, or they can themselves include biomarker selection methodologies in their own techniques. Such techniques can be coupled with information criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), or cross-validation to quantify the trade-off between the inclusion of additional biomarkers and model improvement, and to minimize overfitting. The resulting predictive models can be validated in other studies or cross-validated in the study in which they were originally trained, for example using techniques such as Leave-One-Out (LOO) and 10-fold cross-validation (10-Fold CV).

[0070] An example of an interpretation function that gives an MBDA score, derived from the statistical modeling approach described above, is shown by the following function: TIFF2025013479000003.tif4153 where MBDA is the MBDA score, b 0-n is a constant, DAIMRK 1-n x is the xth power of the serum concentration of n different biomarkers selected from the biomarkers disclosed herein.The MBDA score obtained in this way for RA subjects with known clinical assessment (e.g., DAS28 score) can then be compared with such known assessment to determine the level of correlation between the two assessments, and thus determine the accuracy of the MBDA score and its underlying predictive model.See examples below for specific formulas and constants.

[0071] More generally, the function can be written as follows: TIFF2025013479000004.tif5128 where MBDA is the MBDA score, F is the function, DAIMRK 1-n x is the x power of the serum concentrations of n different biomarkers selected from the biomarkers disclosed herein. This function is described in the next paragraph.

[0072] An interpretation function for providing the MBDA score can also be derived based on a model constructed to predict a component of a disease activity assessment, such as DAS28-CRP, rather than predicting disease activity overall. See Example 1. An example of such a function is given by the following, where biomarkers are used to provide an improved predictive component of the DAS score: TIFF2025013479000005.tif101170 where serum levels of all biomarkers except CRP are x 1 / 10 All biomarkers are in pg / mL, and ln is the natural logarithm or log e It is.

[0073] If CRP units are obtained in mg / L and other markers in pg / mL, The file is TIFF2025013479000006.tif19154.

[0074] The MBDA score can be further rounded and capped to provide an integer, scaled MBDA score between 1 and 100. To achieve this, the previous function can be rewritten as follows: TIFF2025013479000007.tif19128The gene names of the biomarkers in the above formula represent the concentrations of those markers and depend on the type of assay used.

[0075] In some embodiments of the present teachings, it is not necessary to compare the MBDA score to a predetermined "norm," "normal," "control," "standard," "healthy," "pre-disease," or other similar indices for the MBDA score to provide a quantitative measure of a subject's inflammatory disease activity.

[0076] In other embodiments of the present teachings, the amount of one or more biomarkers in a sample can be measured and used to derive an MBDA score. The MBDA score is then compared to a "normal" or "control" level or value, using techniques such as, for example, a reference or discrimination limit or a threshold that defines risk, to define cutoff points and / or outliers for inflammatory disease. The normal level is then an indication of the level of one or more biomarkers or a combination of biomarkers that is typically found in subjects that do not suffer from the inflammatory disease being evaluated. Other terms for "normal" or "control" include, for example, "reference," "indicative," "baseline," "standard," "healthy," "pre-disease," and the like. Such normal levels may vary based on whether a biomarker is used alone or in a formula combined with other biomarkers to output a score. Alternatively, the normal level may be a database of biomarker patterns from previously tested subjects that have not changed to the inflammatory disease being evaluated over a clinically relevant period of time. Reference (normal, control) values ​​can also be derived from control subjects or populations with known levels or states of inflammatory disease activity, for example. In some embodiments of the present teachings, the reference value can be derived from one or more subjects who have been treated for an inflammatory disease, or from one or more subjects who have a low risk of developing an inflammatory disease, or from subjects who have shown improvement in inflammatory disease activity factors (e.g., clinical parameters as defined herein) as a result of receiving treatment. In some embodiments, the reference value can be derived from one or more subjects who have not received treatment; for example, samples can be taken from (a) subjects who have received initial treatment for an inflammatory disease, and (b) subjects who have received subsequent treatment for an inflammatory disease, to monitor the progress of treatment. The reference value can also be derived from a disease activity algorithm or a calculated index from a population study.

[0077] Biomarker measurements The amount of one or more biomarkers of the present teachings can be represented as a value. The value can be one or more numerical values ​​resulting from the evaluation of a sample, for example, by measuring the level of the biomarker in the sample with an assay performed in a laboratory, or can be derived from a data set obtained from a provider such as a laboratory, or from a data set stored on a server. The biomarker level can be measured using any of several techniques known in the art. The present teachings encompass such techniques, and further include all subject fasting and / or time-based sampling techniques for measuring biomarkers.

[0078] The actual measurement of the level of the biomarker can be measured at the protein or nucleic acid level using any method known in the art. Detection of "protein" includes detection of full-length protein, mature protein, preprotein, polypeptide, isoform, mutant, variant, post-translationally modified protein and variants thereof and can be detected by any suitable method. The level of the biomarker can be measured at the protein level, for example, by measuring the serum level of peptides encoded by the gene products described herein, or by measuring the enzymatic activity of these protein biomarkers. Such methods are well known in the art and include, for example, immunoassays based on antibodies, aptamers or molecular imprints against the proteins encoded by the genes. Any biological material can be used to detect / quantify the protein or its activity. Alternatively, a suitable method can be selected to measure the activity of the protein encoded by the biomarker gene depending on the activity of each protein analyzed. For biomarker proteins, polypeptides, isoforms, mutants and variants thereof known to have enzymatic activity, the activity can be measured in vitro using enzymatic assays known in the art. Such assays include, but are not limited to, protease assays, kinase assays, phosphatase assays, reductase assays, among others. Modulation of the kinetics of enzyme activity can be ascertained by measuring the rate constant, KM, using known algorithms, e.g., Hill plots, Michaelis-Menten equations, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plots.

[0079] Using sequence information provided by public database entries for biomarkers, expression of the biomarkers can be detected and measured by techniques well known to those of skill in the art. For example, nucleic acid sequences in the sequence databases that correspond to the nucleic acids of the biomarkers can be used to construct primers and probes for detecting and / or measuring the biomarker nucleic acids. These probes can be used, for example, in Northern or Southern blot hybridization analysis, ribonuclease protection assays, and / or methods for quantitatively amplifying specific nucleic acid sequences. As another example, sequences in the sequence databases can be used to construct primers for specifically amplifying the biomarker sequences in amplification-based detection and quantification methods, such as reverse transcription-based polymerase chain reaction (RT-PCR) and PCR. When changes in gene expression are associated with gene amplification, nucleotide deletions, gene polymorphisms, post-translational modifications, and / or mutations, sequence comparisons in the test and reference populations can be made by comparing the relative amounts of DNA sequences examined in the test and reference populations.

[0080] As an example, Northern hybridization analysis using a probe that specifically recognizes one or more of these sequences can be used to measure gene expression. Expression can also be measured using RT-PCR; for example, polynucleotide primers specific for differentially expressed biomarker mRNA sequences reverse transcribe the mRNA into DNA, which can then be amplified by PCR and visualized and quantified. Biomarker RNA can also be quantified using other target amplification methods, such as TMA, SDA, and NASBA, or signal amplification methods (e.g., bDNA). Ribonuclease protection assays can also be used to measure gene expression by using a probe that specifically recognizes one or more biomarker mRNA sequences.

[0081] Alternatively, it is possible to measure the metabolites of biomarker proteins and nucleic acids. The term "metabolite" includes any chemical or biochemical product of a metabolic process, such as any compound produced by processing, cleavage or consumption of a biomolecule (e.g., protein, nucleic acid, carbohydrate, or lipid). Metabolites can be detected by a variety of methods known to those skilled in the art, including refractive index spectroscopy (RI), ultraviolet spectroscopy (UV), fluorescence spectroscopy, radiochemical analysis, near infrared spectroscopy (near IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry, ion spray spectrometry combined with mass spectrometry, capillary electrophoresis, NMR and IR detection. See WO 04 / 056456 and WO 04 / 088309; each of which is incorporated herein by reference in its entirety. In this regard, other biomarker analytes can be measured using the detection methods described above or other methods known to those of skill in the art. For example, circulating calcium ions (Ca 2+ ) can be detected in samples using fluorescent dyes such as the Fluo series, Fura-2A, Rhod-2, among others. Other biomarker metabolites can be similarly detected using reagents specifically designed or tailored to detect such metabolites.

[0082] In some embodiments, detection of the biomarker is performed by contacting the subject sample with a reagent, forming a complex between the reagent and the analyte, and detecting the complex. Examples of "reagents" include, but are not limited to, nucleic acid primers and antibodies.

[0083] In some embodiments of the present teachings, an antibody binding assay is used to detect the biomarker; for example, a sample from a subject is contacted with an antibody reagent that binds to the biomarker analyte to produce a reaction product (or complex) comprising the antibody reagent and the analyte, and the presence (or absence) or amount of the complex is measured. Antibody reagents useful for detecting biomarker analytes can be monoclonal, polyclonal, chimeric, recombinant, or fragments of the foregoing, as detailed above, and the step of detecting the reaction product can be performed using any suitable immunoassay. The sample from the subject is generally a biological fluid as described above, and can be a sample of the same biological fluid used to perform the methods described above.

[0084] Immunoassays performed according to the present teachings can be homogeneous or heterogeneous. Immunoassays performed according to the present teachings can be multiplexed. In homogeneous assays, the immune reaction can include a specific antibody (e.g., an anti-biomarker protein antibody), a labeled analyte, and a sample of interest. The label generates a signal, and when the labeled analyte binds to the antibody, the signal resulting from the label is changed, either directly or indirectly. Both the immunological binding reaction and the detection of the degree of binding can be performed in a homogeneous solution. Immunochemical labels that can be used include, but are not limited to, free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, and coenzymes. Immunoassays include competitive assays.

[0085] In the heterogeneous assay approach, the reagents can be the sample of interest, the antibody, and the reagent that produces the detectable signal. The above samples can be used. The antibody can be immobilized on a support such as beads (such as protein A and protein G agarose beads), a plate or a slide, and can be contacted in liquid phase with a sample expected to contain the biomarker. The support is separated from the liquid phase, and either the support phase or the liquid phase is analyzed using methods known in the art for detecting a signal. The signal is related to the presence of the analyte in the sample. Methods for producing a detectable signal include, but are not limited to, the use of radioactive, fluorescent, or enzymatic labels. For example, if the antigen to be detected contains a second binding site, the antibody that binds to that site is conjugated to a detectable (signal generating) group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the biomarker in the test sample. Examples of suitable immunoassays include, but are not limited to, immunoblotting, immunoprecipitation, immunofluorescence, chemiluminescence, electrochemiluminescence (ECL), and / or enzyme-linked immunoassays (ELISA).

[0086] Those of skill in the art will be familiar with the many specific immunoassay formats and variations thereof that may be useful in practicing the methods disclosed herein. See, e.g., E. Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, FL. See also: U.S. Patent No. 4,727,022 to C. Skold et al., entitled "Novel Methods for Modulating Ligand-Receptor Interactions and their Application"; U.S. Patent No. 4,659,678 to GC Forrest et al., entitled "Immunoassay of Antigens"; U.S. Patent No. 4,376,110 to GS David et al., entitled "Immunometric Assays Using Monoclonal Antibodies"; U.S. Patent No. 4,275,149 to D. Litman et al., entitled "Macromolecular Environment Control in Specific Receptor Assays"; U.S. Patent No. 4,233,402 to E. Maggio et al., entitled "Reagents and Method Employing Channeling"; and U.S. Patent No. 4,230,797 to R. Boguslaski et al., entitled "Heterogenous Specific Binding Assay Employing a Coenzyme as Label".

[0087] The antibodies can be conjugated to a solid support suitable for the assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells (formed from materials such as latex or polystyrene)) according to known techniques, such as passive binding. The antibodies described herein can also be conjugated to detectable labels or groups according to known techniques, such as radioactive labels (e.g., 35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine).

[0088] Antibodies may also be useful for detecting post-translational modifications of biomarkers. Examples of post-translational modifications include, but are not limited to, tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, citrullination, and glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect phosphorylated amino acids in one or more proteins of interest and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well known to those skilled in the art and are commercially available. Post-translational modifications can also be measured using metastable ions in reflector matrix-assisted laser desorption / ionization-time of flight mass spectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002, 2(10):1445-1451.

[0089] Treatment regimen The present invention provides a method for recommending treatment regimens after determining the difference in expression of biomarkers disclosed herein.The measurement score derived from the expression level of biomarkers disclosed herein over a period of time can provide clinicians with a dynamic picture of the biological state of a subject.Thus, these aspects of the present teachings provide subject-specific biological information that is beneficial for treatment decisions and facilitates monitoring of treatment response, leading to faster and more optimized treatment, better control of disease activity, and an increase in the proportion of subjects who achieve remission.

[0090] The treatment strategy for autoimmune diseases is confounded by the fact that some autoimmune diseases, such as PsA, are classifications given to a group of subjects with a wide variety of associated symptoms. This suggests that certain subtypes of PsA are caused by specific cell types or cytokines. The likely result is that no single treatment will prove optimal for treatment. With the increasing number of treatment options available for PsA, the need for individualized treatment directed by immunological prognostic factors of treatment outcome is essential. In various aspects of the present teachings, algorithms derived from biomarkers can be used to quantify treatment response in PsA subjects. Identifying patients who are likely to respond to a given treatment will lead to more personalized medicine and improved treatment efficacy, which is the purpose of the present invention.

[0091] Reference standard for treatment In many embodiments, the levels of one or more analyte biomarkers or the levels of a particular panel of analyte biomarkers in a sample are compared to a reference standard ("reference standard" or "reference level") to direct treatment decisions. The expression levels of one or more biomarkers can be combined into a score, which can represent disease activity. The reference standard used in the embodiments disclosed herein can include the average, mean, or median levels of one or more analyte biomarkers, or the levels of a particular panel of analyte biomarkers in a control population. The reference standard can further include an earlier time point for the same subject. For example, the reference standard can include a first time point, and the levels of one or more analyte biomarkers can be re-examined at a second, third, fourth, fifth, sixth time point, etc. Any time point earlier than a particular time point can be considered as a reference standard. The reference standard can further include a cut-off value or other statistical attribute of a control population or of the same subject at an earlier time point, such as the mean level of one or more analyte biomarkers or the standard deviation from that level of a particular panel of analyte biomarkers, etc. In some embodiments, the control population can include healthy individuals or the same subject prior to administration of a treatment.

[0092] In some embodiments, a score may be obtained from a baseline time point and a different score may be obtained from a later time point. The first time point may be when the first treatment regimen is initiated. The first time point may also be when the first immunoassay is performed. The time point may be hours, days, months, years, etc. In some embodiments, the time point is 1 month. In some embodiments, the time point is 2 months. In some embodiments, the time point is 3 months. In some embodiments, the time point is 4 months. In some embodiments, the time point is 5 months. In some embodiments, the time point is 6 months. In some embodiments, the time point is 7 months. In some embodiments, the time point is 8 months. In some embodiments, the time point is 9 months. In some embodiments, the time point is 10 months. In some embodiments, the time point is 11 months. In some embodiments, the time point is 12 months. In some embodiments, the time point is 2 years. In some embodiments, the time point is 3 years. In some embodiments, the time point is 4 years. In some embodiments, the time point is 5 years. In some embodiments, the time point is 10 years.

[0093] The difference in score can be interpreted as a decrease in disease activity. For example, a lower score can indicate a lower level of disease activity. In these circumstances, a second score that is lower than the reference score or the first score means that the subject's disease activity is reduced (improved) during the first and second periods. Alternatively, a higher score can indicate a lower level of disease activity. In these circumstances, a second score that is higher than the reference score or the first score also means that the subject's disease activity is improved during the first and second periods.

[0094] The difference in score can also be interpreted as an increase in disease activity. For example, a lower score can indicate a higher level of disease activity. In these circumstances, a second score that is lower than the reference score or the first score means that the disease activity of the subject increases (worsens) between the first and second periods. Alternatively, a higher score can indicate a higher level of disease activity. In these circumstances, a second score that is higher than the reference score or the first score also means that the disease activity of the subject worsens between the first and second periods.

[0095] These differences can vary. For example, if the difference in score is interpreted as a decrease in disease activity, a large difference can mean a larger decrease in disease activity than a smaller or moderate difference. Alternatively, if the difference in score is interpreted as an increase in disease activity, a large difference can mean a larger increase in disease activity than a smaller or moderate difference.

[0096] Standard therapy for treatment In some embodiments, the patient is treated with a standard therapy, or less aggressively, based on the difference in scores.Standard therapy is any therapy that is the standard of care for autoimmune disorders.Standards of care may vary over time and geography, but those skilled in the art can easily determine the appropriate standard of care with reference to relevant medical literature.

[0097] In some embodiments, more aggressive therapy than standard therapy includes initiating therapy earlier than standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering additional therapy than standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering treatment on an accelerated schedule compared to standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering additional therapy not required by standard therapy.

[0098] In some embodiments, a less aggressive treatment than standard treatment includes slowing treatment compared to standard treatment. In some embodiments, a less aggressive treatment than standard treatment includes administering less treatment than standard treatment. In some embodiments, a less aggressive treatment than standard treatment includes administering treatment on a slowed down schedule compared to standard treatment. In some embodiments, a less aggressive treatment than standard treatment includes not administering treatment at all.

[0099] Treating autoimmune disorders In one aspect, the physician adjusts the treatment based on a comparison between the different scores. In one aspect, the physician adjusts the treatment by selecting and administering a different drug. In one aspect, the physician adjusts the treatment by selecting and administering a different combination of drugs. In one aspect, the physician adjusts the treatment by adjusting the dosage of the drugs. In one aspect, the physician adjusts the treatment by adjusting the dosing schedule. In one aspect, the physician adjusts the treatment by adjusting the duration of treatment. In one aspect, the physician adjusts the treatment by selecting and administering a different combination of drugs and adjusting the dosage of the drugs. In one aspect, the physician adjusts the treatment by selecting and administering a different combination of drugs and adjusting the dosing schedule. In one aspect, the physician adjusts the treatment by selecting and administering a different combination of drugs and adjusting the duration of treatment. In one aspect, the physician adjusts the treatment by adjusting the dosage and dosing schedule of the drugs. In one aspect, the physician adjusts the treatment by adjusting the dosage of the drugs and adjusting the duration of treatment. In one aspect, the physician adjusts the treatment by adjusting the dosing schedule and adjusting the duration of treatment. In one aspect, a physician adjusts treatment by selecting and administering a different drug, adjusting the dosage of the drug, and adjusting the dosing regimen. In one aspect, a physician adjusts treatment by selecting and administering a different drug, adjusting the dosage of the drug, and adjusting the duration of treatment. In one aspect, a physician adjusts treatment by selecting and administering a different drug, adjusting the dosage regimen, and adjusting the duration of treatment. In one aspect, a physician adjusts treatment by selecting and administering a different drug, adjusting the dosage of the drug, adjusting the dosing regimen, and adjusting the duration of treatment. In one aspect, a physician adjusts treatment by selecting and administering a different drug, adjusting the dosage of the drug, adjusting the dosing regimen, and adjusting the duration of treatment.

[0100] In one embodiment, the less aggressive treatment includes slowing down the treatment. In one embodiment, the less aggressive treatment includes selecting and administering a less effective drug. In one embodiment, the less aggressive treatment includes reducing the frequency of treatment. In one embodiment, the less aggressive treatment includes shortening the treatment period. In one embodiment, the less aggressive treatment includes selecting and administering a less effective drug and reducing the dosage of the drug. In one embodiment, the less aggressive treatment includes selecting and administering a less effective drug and slowing down the dosing regimen. In one embodiment, the less aggressive treatment includes selecting and administering a less effective drug and shortening the treatment period. In one embodiment, the less aggressive treatment includes reducing the dosage of the drug and slowing down the dosing regimen. In one embodiment, the less aggressive treatment includes reducing the dosage of the drug and shortening the treatment period. In one embodiment, the less aggressive treatment includes slowing down the dosing regimen and shortening the treatment period. In one embodiment, the less aggressive treatment includes selecting and administering a less potent drug, reducing the dosage of the drug, and slowing down the administration schedule. In one embodiment, the less aggressive treatment includes selecting and administering a less potent drug, reducing the dosage of the drug, and shortening the duration of treatment. In one embodiment, the less aggressive treatment includes selecting and administering a less potent drug, slowing down the administration schedule, and shortening the duration of treatment. In one embodiment, the less aggressive treatment includes selecting and administering a less potent drug, reducing the dosage of the drug, slowing down the administration schedule, and shortening the duration of treatment. In one embodiment, the less aggressive treatment includes selecting and administering a less potent drug, reducing the dosage of the drug, slowing down the administration schedule, and shortening the duration of treatment. In some embodiments, the less aggressive treatment includes administering only non-drug based treatments.

[0101] In another aspect of the application, the treatment comprises more aggressive treatment than standard therapy. In one embodiment, the more aggressive treatment comprises an extended treatment period. In one embodiment, the more aggressive treatment comprises an increased frequency of the dosing regimen. In one embodiment, the more aggressive treatment comprises selecting and administering a more potent drug and increasing the dosage of the drug. In one embodiment, the more aggressive treatment comprises selecting and administering a more potent drug and accelerating the dosing regimen. In one embodiment, the more aggressive treatment comprises selecting and administering a more potent drug and extending the treatment period. In one embodiment, the more aggressive treatment comprises increasing the dosage of the drug and accelerating the dosing regimen. In one embodiment, the more aggressive treatment comprises increasing the dosage of the drug and extending the treatment period. In one embodiment, the more aggressive treatment comprises accelerating the dosing regimen and extending the treatment period. In one embodiment, the more aggressive treatment comprises selecting and administering a more potent drug, increasing the dosage of the drug and accelerating the dosing regimen. In one aspect, the more aggressive treatment involves selecting and administering a more potent drug, increasing the dosage of the drug, and extending the treatment period. In one aspect, the more aggressive treatment involves selecting and administering a more potent drug, accelerating the dosing regimen, and extending the treatment period. In one aspect, the more aggressive treatment involves increasing the dosage of the drug, accelerating the dosing regimen, and extending the treatment period. In one aspect, the more aggressive treatment involves selecting and administering a more potent drug, increasing the dosage of the drug, accelerating the dosing regimen, and extending the treatment period. In some aspects, the more aggressive treatment involves administering a combination of drug-based and non-drug-based treatments.

[0102] Therapeutic agents can be conventional or biologic. Examples of therapeutic agents, such as disease modifying anti-rheumatic drugs (DMARDs), generally considered conventional, include, but are not limited to, MTX, azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), cyclosporine (CSA, or cyclosporine), doxycycline (DOXY), hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide (LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Conventional therapeutic agents also include nonsteroidal anti-inflammatory drugs (NDAIDS), such as aspirin, ibuprofen, oxaprozin, piroxicam, indomethacin, etodolac, meclofenamate, meloxicam, naproxen, ketoprofen, nabumetone, tolmetin sodium, and diclofenac.Other examples of conventional therapeutic agents include, but are not limited to, folinic acid, D-penicillamine, gold auranofin, gold aurothioglucose, gold thiomalate, cyclophosphamide, and chlorambucil.Examples of biologics include, but are not limited to, biological agents that target tumor necrosis factor (TNF) alpha molecules and TNF inhibitors, such as infliximab, adalimumab, etanercept, and golimumab. Other classes of biologics include IL1 inhibitors, such as anakinra, T cell modulators, such as abatacept, B cell modulators, such as rituximab, and IL6 inhibitors, such as tocilizumab.

[0103] To identify additional therapeutic agents or drugs suitable for a particular subject, a test sample from the subject can be exposed to the therapeutic agent or drug and the level of one or more biomarkers can be measured. The level of one or more biomarkers can be compared to samples from the subject before and after treatment, or before and after exposure to the therapeutic agent or drug, or to samples from one or more subjects who have shown improvement in inflammatory disease status or activity (e.g., clinical parameters or traditional laboratory risk factors) as a result of such treatment or exposure.

[0104] Clinical Evaluation of the Present Teachings In some embodiments of the present teachings, the MBDA score is adjusted to the population, the endpoint or clinical evaluation, and / or the intended use. For example, the MBDA score can be used to evaluate subjects for primary prevention and for secondary prevention and management. In primary evaluation, the MBDA score can be used to predict and risk stratify future symptoms or disease sequelae, to predict prognosis of disease activity and rate of change, and to indicate future treatment regimens. In secondary prevention and clinical management, the MBDA score can be used for prognosis and risk stratification. The MBDA score can be used to support clinical decision-making, for example, to determine whether to postpone intervention or treatment, whether to recommend preventive screening for at-risk patients, whether to recommend increased visits, whether to recommend increased testing, and whether to recommend therapeutic intervention. The MBDA score can also be useful for selecting treatment, confirming treatment response, adjusting and dosing treatment, monitoring ongoing treatment effects, and indicating changes in treatment regimens.

[0105] System for conducting disease activity tests Tests for measuring disease activity according to various embodiments of the present teachings can be performed in various systems that are typically used to obtain test results, such as results from immunological or nucleic acid detection assays. Such systems can include modules that automate sample preparation, modules that automate tests, such as measuring biomarker levels, modules that facilitate testing of multiple samples, and / or modules that are programmed to assay the same or different tests on each sample. In some embodiments, the testing system includes one or more sample preparation modules, clinical chemistry modules, and immunoassay modules on one platform. Testing systems are typically designed such that they also include modules that collect, store, and track results, for example, by connecting to hardware and utilizing databases therein. Examples of such modules include physical and electronic data recording devices, such as hard drives, flash memory, and magnetic tapes, as are well known in the art. Testing systems also generally include modules for reporting and / or visualizing results. Some examples of reporting modules include visual displays or graphical user interfaces, links to databases, printers, and the like. See the "Machine-Readable Recording Media" section below.

[0106] One embodiment of the present invention includes a system for determining inflammatory disease activity of a subject. The system, in some embodiments, uses a module for applying a formula to inputs including measured levels of biomarkers in a panel as described herein to output a score. In some embodiments, the measured levels of biomarkers are test results, which are used as inputs to a computer programmed to apply the formula. The system can include other inputs, such as one or more clinical parameters in addition to or in combination with the results of biomarkers to derive an output score, such as, for example, treatment regimen, TJC, SJC, morning stiffness, arthritis in three or more joint areas, arthritis of the wrist, symmetric arthritis, rheumatoid nodules, radiographic changes and other imaging, sex / gender, age, race / ethnicity, disease duration, height, weight, body mass index, family history, CCP status, RF status, ESR, smoker / non-smoker, etc. The system, in some embodiments, can apply a formula to inputs of biomarker levels and then output a disease activity score, which can then be analyzed in conjunction with other inputs, such as other clinical parameters. In other embodiments, the system is designed to apply biomarker and non-biomarker inputs (eg, clinical parameters, etc.) together into a formula and then report a composite output disease activity index.

[0107] Several testing systems are currently available that can be used to practice various aspects of the present teachings, see, for example, the ARCHITECT series of integrated immunochemistry systems, which are high-throughput automated clinical chemistry analyzers (ARCHITECT is a registered trademark of Abbott Laboratories, Inc., Abbott Park, Ill. 60064). C. Wilson et al., "Clinical Chemistry Analyzer Sub-System Level Performance," American Association for Clinical Chemistry Annual Meeting, Chicago, Ill, Jul. 23-27, 2006; and H. J. Kisner, "Product development: the making of the Abbott ARCHITECT," Clin. Lab. Manage. Rev. 1997 Nov.-Dec., 11(6):419-21; A. Ognibene et al., "A new modular chemiluminescence immunoassay analyzer evaluation," Clin. Chem. Lab. Med. 2000 March, 38(3):251-60; JW Park et al., "Three-year experience in using total laboratory automation system," Southeast Asian J. Trop. Med. Public Health 2002, 33 Suppl 2:68-73; D. Pauli et al., "The Abbott Architect c8000: analytical performance and See, "productivity characteristics of a new analyzer applied to general chemistry testing," Clin. Lab. 2005, 51(1-2):31-41.

[0108] Another testing system useful with embodiments of the present teachings is the VITROS system (VITROS is a registered trademark of Johnson & Johnson, New Brunswick, NJ). This system is a chemistry analyzer for laboratories and clinics used to obtain test results from blood and other body fluids. Another testing system is the DIMENSION system (DIMENSION is a registered trademark of Dade Behring, Deerfield Ill.), a system for analyzing body fluids, which includes computer software and hardware to operate the analyzer and to analyze data generated by the analyzer.

[0109] Tests required for various embodiments of the present teachings, such as measuring biomarker levels, can be performed in laboratories certified under the Clinical Laboratory Improvement Amendments (42 USC Section 263(a)) or other Federal or State law, or other country, state, or provincial law governing the operation of laboratories that analyze samples for clinical purposes. Such laboratories include, for example, Laboratory Corporation of America, 358 South Main Street, Burlington, NC 27215 (headquarters); Quest Diagnostics, 3 Giralda Farms, Madison, NJ 07940 (headquarters); and other reference and clinical chemistry laboratories.

[0110] kit Other embodiments of the present teachings include biomarker detection reagents packaged together in the form of a kit for performing any of the assays of the present teachings. In certain embodiments, the kit includes oligonucleotides that specifically identify one or more biomarker nucleic acids based on homology and / or complementarity with the biomarker nucleic acids. The sequence of the oligonucleotides can correspond to fragments of the biomarker nucleic acids. For example, the oligonucleotides can be more than 200 nucleotides, 200, 150, 100, 50, 25, 10, or less than 10 nucleotides in length. In other embodiments, the kit includes an antibody against a protein encoded by the biomarker nucleic acid. The kits of the present teachings can also include an aptamer. The kits can include, in separate containers, inter alia, nucleic acids or antibodies (antibodies bound to a solid phase matrix or antibodies packaged separately from reagents for binding to the matrix), control formulations (positive and / or negative), and / or detectable labels, such as, but not limited to, fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, and radioactive labels. Instructions for carrying out the assay, optionally along with instructions for generating an MBDA score, can be included in the kit, e.g., written, tape, VCR, or CD-ROM. The assay can be in the form of, e.g., a Northern hybridization or a sandwich ELISA, as known in the art.

[0111] In some embodiments of the present teachings, the biomarker detection reagents can be immobilized on a solid phase matrix, such as a porous strip, to form at least one biomarker detection site. In some embodiments, the measurement or detection area of ​​the porous strip can include multiple sites containing nucleic acid. In some embodiments, the test strip can also include negative and / or positive control sites. Also, the control sites can be located on a strip separate from the test strip. If desired, the different detection sites can include different amounts of immobilized nucleic acid, for example, a higher amount in the first detection site and a lower amount in subsequent sites. Upon addition of the test sample, the number of sites that exhibit a detectable signal provides a quantitative indication of the amount of biomarker present in the sample. The detection sites can be configured in any shape that allows for suitable detection, for example, in the shape of a bar or dot spanning the width of the test strip.

[0112] In other embodiments of the present teachings, the kit can include a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by the MBDA markers. In various embodiments, the expression of one or more sequences represented by the MBDA markers can be confirmed based on binding to the array. In some embodiments, the substrate array can be on a solid substrate, known as a "chip." See, for example, U.S. Pat. No. 5,744,305. In some embodiments, the substrate array can be a solution array; for example, xMAP (Luminex, Austin, TX), Cyvera (Illumina, San Diego, CA), RayBio antibody array (RayBiotech, Norcross, GA), CellCard (Vitra Bioscience, Mountain View, CA) and Quantum Dots' Mosaic (Invitrogen, Carlsbad, CA).

[0113] Machine-readable recording medium The machine-readable recording medium can include, for example, a data recording material encoded with machine-readable data or data arrays. The data and the machine-readable recording medium can be used for various purposes when used with a machine programmed with instructions for using said data. Such purposes include, but are not limited to, storing, accessing, and manipulating information regarding inflammatory disease activity of a subject or population over time, or disease activity in response to treatment for an inflammatory disease, or drug discovery for an inflammatory disease, and the like. The data, including the measurement of biomarkers of the present teachings and / or the assessment of disease activity or disease state from these biomarkers, can be implemented into a computer program that runs on a programmable computer (including a processor, a data recording system, one or more input devices, one or more output devices, and the like). The program code can be applied to the input data to perform the functions described herein and generate output information. This output information can then be applied to one or more output devices according to methods well known in the art. The computer can be, for example, a personal computer, a microcomputer, or a workstation of conventional design.

[0114] The computer programs can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system, such as the computer system shown in FIG. 3. The programs can also be implemented in machine or assembly language. The programming language can be a compiled or interpreted language. Each computer program can be stored in a recording medium or device, such as a ROM, a magnetic disk, and can be read by a programmable computer to configure and operate the computer when the recording medium or device is read by the computer to perform the described procedures. Any health-related data management system of the present teachings can be implemented as a computer-readable recording medium configured with a computer program, where the recording medium operates the computer in a specific manner to perform various functions as described herein.

[0115] The biomarkers disclosed herein can be used to generate a "subject biomarker profile" that is taken from a subject with an inflammatory disease. The subject biomarker profile can then be compared to a reference biomarker profile to monitor the progression or rate of progression of the inflammatory disease or to monitor the effectiveness of a treatment for the inflammatory disease. The biomarker profiles of the embodiments of the present teachings (reference and subject) can be included in a machine readable medium, particularly an analog tape such as a CD-ROM or USB flash media readable medium. Such machine readable media can also include additional test results, such as measurements of clinical parameters and clinical evaluations. The machine readable medium can also include subject information; for example, the subject's medical history or family history. The machine readable medium can also include information regarding other disease activity algorithms and calculated scores or indices, as described herein. EXAMPLES

[0116] Aspects of the present disclosure can be further understood with reference to the following examples, which should not be construed as limiting the scope of the disclosure in any way.

[0117] The practice of the present disclosure employs conventional methods of protein chemistry, biochemistry, recombinant DNA technology and pharmacology within the skill of the art, unless otherwise indicated. Such techniques are fully described in the literature. See, for example, T. Creighton, Proteins: Structures and Molecular Properties, 1993, W. Freeman and Co.; A. Lehninger, Biochemistry, Worth Publishers, Inc. (current addition); J. Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd Edition, 1989; Methods In Enzymology, S. Colowick and N. Kaplan, eds., Academic Press, Inc.; Remington's Pharmaceutical Sciences, 18th Edition, 1990, Mack Publishing Company, Easton, PA; Carey and Sundberg, Advanced Organic Chemistry, Vols. A and B, 3rd Edition, 1992, Plenum Press.

[0118] The practice of the present disclosure also employs conventional methods of statistical analysis within the art, unless otherwise indicated, and such techniques are fully explained in the literature. For example, J. Little and D. Rubin, Statistical Analysis with Missing Data, 2nd Edition 2002, John Wiley and Sons, Inc., NJ; M. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series) 2003, Oxford University Press, Oxford, UK; X. Zhoue et al., Statistical Methods in Diagnostic Medicine 2002, John Wiley and Sons, Inc., NJ; See T. Hastie et al, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition 2009, Springer, NY; W. Cooley and P. Lohnes, Multivariate procedures for the behavioral science 1962, John Wiley and Sons, Inc. NY; E. Jackson, A User's Guide to Principal Components 2003, John Wiley and Sons, Inc., NY.

[0119] Example 1: Derivation of MBDA score This example shows how to derive a multi-biomarker disease activity (MBDA) score based on a dataset of quantitative biomarker data. In this example, an MBDA score is determined from the biomarker data using an interpretation function based on a set of predictive models, each predicting a component of DAS28-CRP, in this example TJC, SJC and patient global health assessment (GHA). The derivation of the MBDA score as described in this example is described in detail in USSN 12 / 905,984, which is incorporated herein by reference in its entirety.

[0120] Development and evaluation of MBDA algorithms Training Data The MBDA algorithm was trained using clinical and biomarker data from patients in the InFoRM and BRASS studies. The InFoRM (Index For Rheumatoid Arthritis Measurement) study is a multicenter, observational study of a North American RA population. Patients recruited to train the algorithm were recruited from 25 sites in the United States and Canada between April and September 2009. Inclusion criteria were patients >18 years of age with a diagnosis of RA by a board-certified rheumatologist. Patients participating in concurrent therapeutic drug trials with biologic and placebo arms were excluded. The study included three visits for each patient, approximately 3 months apart, during which clinical data and biological samples were collected.

[0121] BRASS is an observational study of approximately 1,000 RA patients treated at the RB Brigham Arthritis and Musculoskeletal Diseases Clinical Research Center at Brigham and Women's Hospital in Boston, Massachusetts. Inclusion criteria were patients >18 years of age with a diagnosis of RA by a board-certified rheumatologist. The study included annual visits with collection of clinical data and biological samples, as well as patient questionnaires at each visit.

[0122] The initial dataset used for training consisted of visit 1 data for 512 InFoRM patients. The 512 patient visits were selected to have clinical characteristics representative of the entire study population at the time of selection and, furthermore, to have been assessed by a limited number of joint assessors. The number of joint assessors was limited to 12; so that assessor-specific biases could be evaluated and taken into account in the development of the algorithm. The mean age of these patients was 58.9 years (range 20-91 years) and 76% were women. The mean SJC and TJC were 4.28 and 5.49, respectively.

[0123] Serum from 512 InFoRM visitors was assayed for 25 candidate biomarkers. These biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD, MMP1, ICAM1, calprotectin, CHI3L1 (YKL40), MMP3, EGF, IL1RA, VCAM1, LEP, RETN, CRP, IL8, APOA1, APOC3, CCL22, IL1B, IL6R, IL18, keratan sulfate, and ICTP. All biomarker assays were performed on the Meso Scale Discovery (MSD®) platform. For details on the development and validation of biomarker assays, see Example 1 of US 2011 / 0137851.

[0124] Biomarkers were prioritized based on (1) univariate association with disease activity, (2) contribution to multivariate models of disease activity, and (3) assay performance.

[0125] Assays for the 20 candidate biomarkers were performed on a second set of patient samples, including 167 samples from BRASS and 29 samples from InFoRM. These 20 candidate biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD, MMP1, ICAM1, calprotectin, YKL40, MMP3, EGF, IL1RA, VCAM1, leptin, resistin, CRP, IL8, CCL22, IL1B, and IL6R. Samples were selected to enrich the overall training data for extreme disease activity, as well as to provide a good representation of patients with moderate disease activity. Enriching the extreme phenotypes may result in improved training of the algorithm; however, the resulting training population should still be well representative of the type of patients in which the algorithm will be used in independent validation, and the intended use population. The 167 BRASS samples were intended to represent an equal number of patients with low, moderate, and high disease activity. The 29 InFoRM samples were selected to represent patients with high disease activity, since patients with low and moderate activity were already well represented by the original 512 training samples.

[0126] 4. Data Analysis Prior to statistical analysis, all assay data were reviewed for pass / fail criteria for parameters including inter-assay CV, intra-assay CV, percent of samples within the measurable range of the calibration curve, and four serum process controls within the range of the calibration curve. Biomarker values ​​that were not within the measurable range of the calibration curve were marked as missing data and imputed with the lowest / highest detection value across all samples in a given biomarker assay during data export. If the intra-assay CV of a biomarker concentration calculated from two replicates exceeded 30%, it was also considered missing and excluded from univariate analysis. In multivariate analysis, individual biomarkers were completely excluded if more than 20% of their data values ​​were missing, and other missing data were imputed by the KNN algorithm (nearest neighbor with k=5). In the data used to train the algorithm, no biomarkers were excluded from multivariate analysis because they all had less than 20% missing values. To make the distribution of each biomarker's values ​​more normal, concentration values ​​were transformed by ×0.1 before further analysis. This transformation has a similar effect as the log transformation, but avoids the occurrence of negative values. The imputed and transformed biomarker dataset is denoted as X_(n×m), where X is the protein data from n markers and m samples.

[0127] In univariate analyses, Pearson correlations were performed between the levels of each biomarker and disease activity measures, including DAS28-CRP4, DAS28-ESR4, SJC, TJC, GHA, SDAI, and CDAI.

[0128] For the multivariate analysis, statistical models were developed by five different regression methods. In the first regression method (1), the forward stepwise least squares regression equation Y = Xβ + ε is applied, where Y is a column vector containing the observations, β is the coefficient vector, and ε is the residual. In this model, forward selection starts with no variables. Then, given a set of predictors X, we perform a simple linear regression of Y on X1, selecting the one with the maximum absolute correlation with the response Y. The residual vector is now orthogonal to X1 and is considered to be the new response variable. Then, we project the other predictors to be orthogonal to X1, and repeat the forward selection process.

[0129] The second method (2) uses the Lasso to 2 The biomarkers are prioritized (based on the value of the α-value) and a Lasso model is obtained. The "lasso" in this model minimizes the sum of squared residuals, provided that the sum of the absolute values ​​of the coefficients is less than a constant. This method results in an interpretable model, demonstrating the stability of ridge regression. See R. Tibshirani, J. Royal Stat. Soc. B 1996, 58(1):267-288.

[0130] The third method (3) applies Elastic Net, a combination of Lasso and ridge penalty, which promotes grouping effects when strongly correlated predictors tend to separate and cluster together, either in or out of the model. See T. Zou, J. Royal Stat. Soc. B 2005, 67(2):301-320. In each of the above three methods, the markers selected at each step are recorded.

[0131] The fourth method (4) is the multivariate response by Curds and Whey (CW) using ordinary least squares (OLS). See L. Breiman and JH Friedman, J. Royal. Stat. Soc. B 1997, 59(1):3-54. This method exploits the correlation between response variables (e.g., components of DAS) to improve prediction accuracy compared to the usual approach of performing individual regressions of each response variable on a common set of predictor variables X. In CW, Y=XB*S, where Y=(y kj ), where k is for the kth patient and j is for the jth response (j=1 for TJC, j=2 for SJC, etc.), B is obtained using OLS, and S is the reduced matrix calculated from the canonical coordinate system. This approach thus creates sub-models corresponding to each component of the DAS.

[0132] The fifth method (5) is a combination of Curds and Whey and Lasso (CW-Lasso). Instead of using OLS to obtain B as in CW, Lasso was used and parameters were adjusted to suit the Lasso approach.

[0133] The performance of the five regression methods was compared using 70 / 30 cross-validation (repeated training on 70% of the data randomly selected and testing on the remaining 30%). The number of markers in each regression model was selected using nested 10-fold cross-validation, and once the number of markers was selected for a given analysis method, the best-fit model of that size was used to represent that method. For the CW approach (methods 4 and 5), nested 10-fold cross-validation was used for each submodel corresponding to each component of the DAS. Models developed using the CW-Lasso method performed best overall. The following sections consist primarily of results using the CW-Lasso approach.

[0134] The 20 candidate biomarkers examined in all training samples were prioritized according to a number of criteria, including: strength of association with disease activity and contribution to the multivariate model; consistency of correlation with disease activity across performance and training data sets; CRP was excluded from all TJC, SJC and PGA submodels both because it was included in DAS28-CRP4 and because it did not improve the predictive accuracy of the submodels in independent test samples (but is used in the final MBDA score calculation as part of the MBDA formula); robust assay performance (IL1B was excluded from the final modeling because its concentrations were too frequently below the detection limit of the immunoassay); known drug effects (IL6R was excluded from the final modeling because it is known to be strongly affected by tocilizumab, independent of the drug's effect on disease activity); and stability (IL8 was excluded from the final modeling because its measurable levels are known to increase dramatically if serum samples are not kept refrigerated). These criteria led to 15 candidate biomarkers that were considered for inclusion in the final algorithm, see Table 1.

[0135] [Table 1]

[0136] Algorithm training While all data were used for biomarker prioritization, a subset was used to train the final algorithm. This subset was selected to have a wide range of disease activity levels, so that patients with all disease activity levels were well represented. Comparisons were made of the performance of models trained using the following samples: BRASS samples only (167 in total); BRASS samples + InFoRM samples (selected to have a uniform disease activity distribution) (167 + ~100); or BRASS samples + InFoRM samples (with a disease activity distribution similar to BRASS samples) (167 + ~100).

[0137] Model performance was evaluated on an independent set of BRASS and InFoRM samples (70 in total) set aside for this purpose. The DAS28-CRP distribution in this independent test set was similar to that of previous studies (approximately normal distribution). As shown below, the correlation (r) with DAS28-CRP and area under the ROC curve (AUROC) for predicting high and low DAS using a median cutoff was higher when BRASS samples + "BRASS-like" InFoRM samples were used for training, but the difference was not statistically significant. In Table 2 below, the Lasso regression method is used.

[0138] [Table 2]

[0139] For the final training, the combination of BRASS samples + "BRASS-like" InFoRM samples was selected. The CW-Lasso regression method was selected for the development of the final algorithm because it showed good performance in cross-validation within the training set and in testing with 512 patients from InFoRM and CAMERA (see "MBDA Algorithm Performance" below for a description of the algorithm testing on samples from different cohorts). Using this method, a reduced matrix was applied to predict TJC and SJC. 10-fold cross-validation showed that the following 13 markers were optimal for performance. See Table 3.

[0140] [Table 3]

[0141] From this set, PYD and calprotectin were excluded due to high assay failure rates. The remaining 11 biomarkers showed very similar algorithm performance to the full set of 13. The algorithm developed by CW-Lasso regression was selected for validation, and these 11 markers were used to estimate DAS28-CRP with data from BRASS samples + BRASS-like InFoRM samples. Estimates of TJC, SJC, and PGHA were combined with CRP test results in a formula similar to that used to calculate DAS28-CRP. TIFF2025013479000011.tif22132

[0142] where IPTJC = predicted improved TJC, IPSJC = predicted improved SJC, PPGHA = predicted PGHA, and PDAS is predicted DAS28-CRP. (Details are defined below; see "Selected Algorithms"). The MBDA score is the result from this formula.

[0143] Table 4 shows the correlation between the actual values ​​of TJC, SJC, PGHA and DAS28-CRP and those predicted by the PDAS algorithm in the two cohorts studied, CAMERA and InFoRM.

[0144] [Table 4]

[0145] Selected Algorithm The 11 markers + CRP Lasso models selected from the training process are as follows: TIFF2025013479000013.tif88170

[0146] For the final DA algorithm, results from the 11-marker + CRP CW-Lasso model were scaled and rounded to whole numbers on a scale of 1 to 100 such that an MBDA score of 1 was equivalent to a DAS28-CRP value of 0 and an MBDA score of 100 was equivalent to a DAS28-CRP value of 9.4.

[0147] The gene names in the above formula correspond to serum protein concentrations as obtained on the MSD® platform. Biomarker concentrations were obtained in the ranges (95% confidence intervals) shown in Table 5.

[0148] [Table 5]

[0149] Performance of the MBDA algorithm To independently test the performance of the algorithm previously developed in this example, a total of 120 serum samples from the CAMERA study were analyzed. The samples were obtained from the Computer Assisted Management in Early Rheumatoid Arthritis Study (CAMERA). Between 1999 and 2003, all patients with early rheumatoid arthritis who fulfilled the 1987 revised American College of Rheumatology (ACR) criteria for rheumatoid arthritis (i.e., disease duration ≤1 year) were recruited to participate in this 2-year randomized, open-label, prospective, multicenter, strategic study. As a result, 299 patients were studied. The patients attended the outpatient clinic of one of six rheumatology departments in the province of Utrecht, The Netherlands, and collaborated with the Utrecht Rheumatoid Arthritis Cohort Study Group. Patient inclusion criteria were that patients must be over 16 years old and have been symptomatic for less than 1 year. Exclusion criteria were previous use of glucocorticoids or any DMARDs, use of cytotoxic or immunosuppressive drugs in the 3 months prior to enrollment, alcohol abuse defined as more than 2 units per day, and psychiatric problems precluding compliance with the study protocol. At baseline, all patients were monitored for medical conditions that would preclude the use of MTX. This screening included chest x-ray, liver enzymes, albumin, hepatitis serology, serum creatinine, and complete blood count. An independent person performed the randomization into blocks of nine per hospital. The medical ethics committees of all participating hospitals approved the study, and all patients provided written informed consent before entering the study.

[0150] The cohort in this study had the following characteristics: 69% female, 68% CCP positive, 74% RF positive, 100% taking MTX, 100% taking non-biologic DMARDs, and 0% taking biologic DMARDs. Furthermore, the mean age of the cohort was 52 years (standard deviation (SD) + / - 14.7 years), with a minimum age of 17 years and a maximum age of 78 years. The mean DAS28-CRP of this cohort was 5.0 (SD + / - 1.9), with a minimum of 0.9 and a maximum of 8.4.

[0151] A subpopulation of 72 subjects was selected from the CAMERA cohort for this example. All 72 patients were represented by baseline (time 0) visit and sample, and 48 were also represented by 6-month visit and sample. Within the selected visits, a wide distribution of DAS28-CRP scores was shown, ranging from a minimum of 0.96 to a maximum of 8.4.

[0152] Of these, 72 samples were taken from the subjects' baseline visit and 48 from the 6-month post-baseline visit. The concentrations of 23 serum protein biomarkers were measured in each sample: APOA1, APOC3, calprotectin, CCL22, CHI3L1 (YKL40), CRP, EGF, ICAM1, IL18, IL1B, IL1RA, IL6, IL6R, IL8, LEP, MMP1, MMP3, PYD, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA. Marker concentrations were measured by customized immunoassays using either a Meso Scale Discovery SECTOR® Imager 6000 or individual ELISAs.

[0153] The association between each biomarker and the clinical evaluation measurements of DAS28-CRP, SJC28 and TJC28 was evaluated by Pearson correlation (r) for log-transformed concentrations. The correlation p-value was adjusted for multiple hypothesis testing by estimating false positive rate (FDR) using the Benjamini-Hochberg method. See J. Royal Stat. Soc. B 1995 57(1):289-300.

[0154] Of the 23 proteins tested, 14 showed statistically significant correlations with DAS28-CRP, 11 with SJC28, and 9 with TJC28 (FDR<0.05). See Table 6, which shows the Pearson correlations (r) between the individual biomarkers and each clinical disease activity measure. The q values ​​reflect the FDR and were calculated by adjusting the p-values ​​for multiple hypothesis testing. Statistically significant associations (q<0.05) are shown in bold. As shown in Table 6, the individual biomarkers associated with disease activity represented various pathways (functional categories) related to RA disease pathophysiology.

[0155] [Table 6]

[0156] Two pre-specified algorithms using a subset of these 23 biomarkers, the prototype and final algorithms, were applied to calculate the total MBDA score for each subject at each visit (baseline and 6 months). These algorithms were trained in a prior study with independent samples from other clinical cohorts. The performance of the algorithms was assessed by Pearson correlation (r) and area under the receiver operating characteristic curve (AUROC) for discriminating between high and low disease activity at the baseline and 6 months visits. The reference classification for the ROC analysis was based on a DAS28-CRP of 2.67, which was the threshold separating remission / low disease activity from moderate and high disease activity.

[0157] Prototype Algorithms for Multivariate Models The first algorithm, i.e. the “prototype algorithm”, which uses a linear combination of protein biomarkers, was trained on subject samples to directly estimate DAS28 and is illustrated by the following formula described elsewhere herein: TIFF2025013479000016.tif4153 where MBDA is the MBDA score, b 0-n is a constant, DAIMRK 1-n x are the x-power transformed serum concentrations of n different biomarkers selected from a panel of biomarkers.

[0158] The prototype algorithm used in this example is given by the following formula: TIFF2025013479000017.tif20156

[0159] This prototype algorithm achieved a Pearson correlation (r) of 0.65 and an AUROC of 0.84 for DAS28-CRP.

[0160] Biomarker selection for the final algorithm A second algorithm was derived using serum biomarker concentrations to estimate the three clinical assessments separately: TJC28, SJC28 and PGHA, all of which are based on the formula used to calculate DAS28-CRP: Note that this is a component of TIFF2025013479000018.tif11165.

[0161] Biomarkers were then selected to predict and estimate clinical assessment of disease activity, specifically PGHA, TJC28 and SJC28. The obtained estimates were combined with serum CRP concentration measurements to calculate the total MBDA score. See Figure 2; three panels are shown: biomarkers predicting clinical disease activity measurements, their union, and CRP. CW-Lasso method was used to predict individual components of DAS28, namely TJC28, SJC28 and PGHA. Note that biomarker terms are included in CW-Lasso if they help improve the performance of the cross-validated model; this criterion does not mean that each term is statistically significant by univariate analysis. A biomarker can contribute significantly to a multivariate model even if it does not have a significant univariate correlation, and it may not contribute significantly to a multivariate model even if it has a significant univariate correlation. Indeed, a comparison of the algorithms predicting the clinical outcomes (a)-(c) above, including the biomarkers in Table 3, shows that not all biomarkers in each algorithm were individually statistically correlated with the clinical outcome. For example, serum concentration values ​​of EGF, LEP, VEGFA, and VCAM1 are all included in the algorithm to predict TJC28, but each of these markers individually showed a q value of ≥0.28 for correlation with TJC. However, the inclusion of these markers improves the performance of the multivariate model in an independent cross-validation test set.

[0162] The total MBDA score derived according to the method of this example was expressed as an integer between 1 and 100. The formula used to derive this score is provided below: TIFF2025013479000019.tif13159, where PTJC = predicted TJC28, PSJC = predicted SJC28, and PPGHA = predicted PGA. This example includes data from the following sets of biomarkers: SAA1, IL6, CHI3L1, EGF, TNFRSF1A, LEP, VEGFA, and VCAM1 for PTJC; SAA1, IL6, EGF, CHI3L1, and TNFRSF1A for PSJC; SAA1, MMP1, LEP, TNFRSF1A, VEGFA, EGF, MMP3, VCAM1, and RETN for PPGHA; plus (+) CRP. Thus, in total, data from the following set of 12 markers were used to derive the MBDA score: CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1 and VEGFA. A clinical assessment of predicted disease activity was developed according to the following formula: TIFF2025013479000020.tif65166

[0163] The performance of the above algorithm in deriving the MBDA score was evaluated by Pearson correlation (r) and area under the receiver operating characteristic curve (AUROC) for discriminating between high and low disease activity at baseline and 6-month visits. The Pearson correlation was 0.73 and the AUROC was 0.87; the criterion classification for the ROC analysis was based on a DAS28-CRP threshold of 2.67, which distinguishes remission / low disease activity from moderate and high disease activity. The change in biomarker-based MBDA score between the baseline and 6-month visits was evaluated by paired Wilcoxon rank-sum test.

[0164] To ensure that the performance of the second algorithm was not overestimated due to the inclusion of two samples for some patients, we also analyzed a randomly selected subset of samples that included only one visit for each subject. The algorithm performed equally well on these subsets. Potential bias in the AUROC due to an imbalance in numbers between the low and high disease activity groups was also analyzed using a DAS28-CRP cutoff of 2.67. When the cutoff was set at the median DAS28-CRP of 4.6, the AUROC was 0.83.

[0165] It was found that when predictions of the individual components of DAS28 produced by the MBDA algorithm were correlated with actual TJC28, SJC28 and PGHA, the correlation coefficients tended to be higher (and therefore provide a better correlation with clinical disease activity measures) than those of CRP, a marker commonly used alone as an indicator of RA disease activity. See Figure 1.

[0166] Next, an analysis was performed to examine whether MBDA scores changed in response to the treatment protocol used in the CAMERA study. For all subjects for whom MBDA scores were available at both visits (baseline and 6 months), the median score decreased from 52 to 37 (p=2.2E-6; n=46). See Figure 2. The intensive and conventional treatment groups were considered separately. There was a significant decrease in median MBDA scores from 52 to 36 in the intensive treatment group (p=2.5E-5; n=31). In the conventional treatment group, the median MBDA score decreased from 59 to 45 (p=0.06; n=15).

[0167] In conclusion, this example demonstrates that serum protein biomarkers representing various biological pathways were consistently associated with RA disease activity. A pre-specified MBDA algorithm combining information from several of these biomarkers performed well in predicting RA disease activity when evaluated in an independent test set. The estimates of the TJC, SJC and PGHA algorithms correlated with actual clinical measurements of disease activity. Furthermore, the subsequent MBDA scores of the analyzed subjects were reduced after and in response to treatment compared to the initial MBDA scores.

[0168] Example 2: Use of the MBDA score to assess disease activity in psoriatic arthritis This example demonstrates that the MBDA score described in Example 1 can be used to assess disease activity in patients with psoriatic arthritis (PsA).

[0169] The considerable heterogeneity in clinical domains of PsA lesions can make the assessment of disease activity difficult. As a result, there is interest in developing biomarkers to assess disease activity. The MBDA assay described in Example 1 is a weighted composite of 12 serum protein biomarkers and has been shown to correlate with rheumatoid arthritis disease activity (Curtis JR, et al. Arthritis Care Res 64: 1794-1803 (2012)). The purpose of this example was to investigate whether the biomarker panel of the MBDA test correlates with disease activity between different domains of PsA.

[0170] method A cross-sectional sample of 30 adult patients with PsA fulfilling CASPAR criteria were recruited from the UCSD Arthritis Clinic (Taylor W, et al. Arthritis Rheum 54:2665-73 (2006)). Clinical data and serum samples were collected, and serum was analyzed for MBDA score and its individual biomarkers (Hambardzumyan K, et al. Ann Rheum Dis. 74:1102-9 (2015)), as well as ICAM-1, IL-6R, and MDC.

[0171] Table 7 describes the characteristics of the 30 patients in this example.

[0172] [Table 7]

[0173] result The mean MBDA score was 39.6, which would be consistent with a moderate disease activity score in RA (Curtis et al., (2012)). In the PsA cohort, the MBDA score correlated most strongly with the physician global assessment (Pearson correlation coefficient, r=0.57), DAS28 (r=0.53), and skin BSA (r=0.50). Correlations were lower with the CDAI (r=0.35), SJC28 (r=0.35), patient global assessment (r=0.08), HAQ score (r=0.15), and pain score (r=0.12). Analysis of individual biomarkers showed strong correlations, often higher than the composite MBDA score: SAA, INFRI, MDC and leptin with skin BSA (r=0.85, 0.84, 0.75 and 0.72, respectively); leptin and ICAM-1 with DAS29 score (r=0.62 and 0.52, respectively); and SAA and VCAM-1 with PhGA (r=0.67 and 0.61, respectively). These data suggest that the MBDA score obtained in Example 1 may correlate with disease activity of PsA.

[0174] All publications and patent applications cited in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

[0175] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of the invention that certain changes and modifications can be made without departing from the spirit or scope of the invention as set forth in the appended claims.

[0176] Sequence information SEQUENCE LISTING <110> CRESCENDO BIOSCIENCE, INC. 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Trp 340 345 350 Ala Leu Asp Leu Asp Asp Phe Gln Gly Ser Phe Cys Gly Gln Asp Leu 355 360 365 Arg Phe Pro Leu Thr Asn Ala Ile Lys Asp Ala Leu Ala Ala Thr 370 375 380 <210> 2 <211> 224 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_000558.2 <400> 2 Met Glu Lys Leu Leu Cys Phe Leu Val Leu Thr Ser Leu Ser His Ala 1 5 10 15 Phe Gly Gln Thr Asp Met Ser Arg Lys Ala Phe Val Phe Pro Lys Glu 20 25 30 Ser Asp Thr Ser Tyr Val Ser Leu Lys Ala Pro Leu Thr Lys Pro Leu 35 40 45 Lys Ala Phe Thr Val Cys Leu His Phe Tyr Thr Glu Leu Ser Ser Thr 50 55 60 Arg Gly Tyr Ser Ile Phe Ser Tyr Ala Thr Lys Arg Gln Asp Asn Glu 65 70 75 80 Ile Leu Ile Phe Trp Ser Lys Asp Ile Gly Tyr Ser Phe Thr Val Gly 85 90 95 Gly Ser Glu Ile Leu Phe Glu Val Pro Glu Val Thr Val Ala Pro Val 100 105 110 His Ile Cys Thr Ser Trp Glu Ser Ala Ser Gly Ile Val Glu Phe Trp 115 120 125 Val Asp Gly Lys Pro Arg Val Arg Lys Ser Leu Lys Lys Gly Tyr Thr 130 135 140 Val Gly Ala Glu Ala Ser Ile Ile Leu Gly Gln Glu Gln Asp Ser Phe 145 150 155 160 Gly Gly Asn Phe Glu Gly Ser Gln Ser Leu Val Gly Asp Ile Gly Asn 165 170 175 Val Asn Met Trp Asp Phe Val Leu Ser Pro Asp Glu Ile Asn Thr Ile 180 185 190 Tyr Leu Gly Gly Pro Phe Ser Pro Asn Val Leu Asn Trp Arg Ala Leu 195 200 205 Lys Tyr Glu Val Gln Gly Glu Val Phe Thr Lys Pro Gln Leu Trp Pro 210 215 220 <210> 3 <211> 1207 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_001954.2 <400> 3 Met Leu Leu Thr Leu Ile Ile Leu Leu Pro Val Val Ser Lys Phe Ser 1 5 10 15 Phe Val Ser Leu Ser Ala Pro Gln His Trp Ser Cys Pro Glu Gly Thr 20 25 30 Leu Ala Gly Asn Gly Asn Ser Thr Cys Val Gly Pro Ala Pro Phe Leu 35 40 45 Ile Phe Ser His Gly Asn Ser Ile Phe Arg Ile Asp Thr Glu Gly Thr 50 55 60 Asn Tyr Glu Gln Leu Val Val Asp Ala Gly Val Ser Val Ile Met Asp 65 70 75 80 Phe His Tyr Asn Glu Lys Arg Ile Tyr Trp Val Asp Leu Glu Arg Gln 85 90 95 Leu Leu Gln Arg Val Phe Leu Asn Gly Ser Arg Gln Glu Arg Val Cys 100 105 110 Asn Ile Glu Lys Asn Val Ser Gly Met Ala Ile Asn Trp Ile Asn Glu 115 120 125 Glu Val Ile Trp Ser Asn Gln Gln Glu Gly Ile Ile Thr Val Thr Asp 130 135 140 Met Lys Gly Asn Asn Ser His Ile Leu Leu Ser Ala Leu Lys Tyr Pro 145 150 155 160 Ala Asn Val Ala Val Asp Pro Val Glu Arg Phe Ile Phe Trp Ser Ser 165 170 175 Glu Val Ala Gly Ser Leu Tyr Arg Ala Asp Leu Asp Gly Val Gly Val 180 185 190 Lys Ala Leu Leu Glu Thr Ser Glu Lys Ile Thr Ala Val Ser Leu Asp 195 200 205 Val Leu Asp Lys Arg Leu Phe Trp Ile Gln Tyr Asn Arg Glu Gly Ser 210 215 220 Asn Ser Leu Ile Cys Ser Cys Asp Tyr Asp Gly Gly Ser Val His Ile 225 230 235 240 Ser Lys His Pro Thr Gln His Asn Leu Phe Ala Met Ser Leu Phe Gly 245 250 255 Asp Arg Ile Phe Tyr Ser Thr Trp Lys Met Lys Thr Ile Trp Ile Ala 260 265 270 Asn Lys His Thr Gly Lys Asp Met Val Arg Ile Asn Leu His Ser Ser 275 280 285 Phe Val Pro Leu Gly Glu Leu Lys Val Val His Pro Leu Ala Gln Pro 290 295 300 Lys Ala Glu Asp Asp Thr Trp Glu Pro Glu Gln Lys Leu Cys Lys Leu 305 310 315 320 Arg Lys Gly Asn Cys Ser Ser Thr Val Cys Gly Gln Asp Leu Gln Ser 325 330 335 His Leu Cys Met Cys Ala Glu Gly Tyr Ala Leu Ser Arg Asp Arg Lys 340 345 350 Tyr Cys Glu Asp Val Asn Glu Cys Ala Phe Trp Asn His Gly Cys Thr 355 360 365 Leu Gly Cys Lys Asn Thr Pro Gly Ser Tyr Tyr Cys Thr Cys Pro Val 370 375 380 Gly Phe Val Leu Leu Pro Asp Gly Lys Arg Cys His Gln Leu Val Ser 385 390 395 400 Cys Pro Arg Asn Val Ser Glu Cys Ser His Asp Cys Val Leu Thr Ser 405 410 415 Glu Gly Pro Leu Cys Phe Cys Pro Glu Gly Ser Val Leu Glu Arg Asp 420 425 430 Gly Lys Thr Cys Ser Gly Cys Ser Ser Pro Asp Asn Gly Gly Cys Ser 435 440 445 Gln Leu Cys Val Pro Leu Ser Pro Val Ser Trp Glu Cys Asp Cys Phe 450 455 460 Pro Gly Tyr Asp Leu Gln Leu Asp Glu Lys Ser Cys Ala Ala Ser Gly 465 470 475 480 Pro Gln Pro Phe Leu Leu Phe Ala Asn Ser Gln Asp Ile Arg His Met 485 490 495 His Phe Asp Gly Thr Asp Tyr Gly Thr Leu Leu Ser Gln Gln Met Gly 500 505 510 Met Val Tyr Ala Leu Asp His Asp Pro Val Glu Asn Lys Ile Tyr Phe 515 520 525 Ala His Thr Ala Leu Lys Trp Ile Glu Arg Ala Asn Met Asp Gly Ser 530 535 540 Gln Arg Glu Arg Leu Ile Glu Glu Gly Val Asp Val Pro Glu Gly Leu 545 550 555 560 Ala Val Asp Trp Ile Gly Arg Arg Phe Tyr Trp Thr Asp Arg Gly Lys 565 570 575 Ser Leu Ile Gly Arg Ser Asp Leu Asn Gly Lys Arg Ser Lys Ile Ile 580 585 590 Thr Lys Glu Asn Ile Ser Gln Pro Arg Gly Ile Ala Val His Pro Met 595 600 605 Ala Lys Arg Leu Phe Trp Thr Asp Thr Gly Ile Asn Pro Arg Ile Glu 610 615 620 Ser Ser Ser Leu Gln Gly Leu Gly Arg Leu Val Ile Ala Ser Ser Asp 625 630 635 640 Leu Ile Trp Pro Ser Gly Ile Thr Ile Asp Phe Leu Thr Asp Lys Leu 645 650 655 Tyr Trp Cys Asp Ala Lys Gln Ser Val Ile Glu Met Ala Asn Leu Asp 660 665 670 Gly Ser Lys Arg Arg Arg Leu Thr Gln Asn Asp Val Gly His Pro Phe 675 680 685 Ala Val Ala Val Phe Glu Asp Tyr Val Trp Phe Ser Asp Trp Ala Met 690 695 700 Pro Ser Val Met Arg Val Asn Lys Arg Thr Gly Lys Asp Arg Val Arg 705 710 715 720 Leu Gln Gly Ser Met Leu Lys Pro Ser Ser Leu Val Val Val His Pro 725 730 735 Leu Ala Lys Pro Gly Ala Asp Pro Cys Leu Tyr Gln Asn Gly Gly Cys 740 745 750 Glu His Ile Cys Lys Lys Arg Leu Gly Thr Ala Trp Cys Ser Cys Arg 755 760 765 Glu Gly Phe Met Lys Ala Ser Asp Gly Lys Thr Cys Leu Ala Leu Asp 770 775 780 Gly His Gln Leu Leu Ala Gly Gly Glu Val Asp Leu Lys Asn Gln Val 785 790 795 800 Thr Pro Leu Asp Ile Leu Ser Lys Thr Arg Val Ser Glu Asp Asn Ile 805 810 815 Thr Glu Ser Gln His Met Leu Val Ala Glu Ile Met Val Ser Asp Gln 820 825 830 Asp Asp Cys Ala Pro Val Gly Cys Ser Met Tyr Ala Arg Cys Ile Ser 835 840 845 Glu Gly Glu Asp Ala Thr Cys Gln Cys Leu Lys Gly Phe Ala Gly Asp 850 855 860 Gly Lys Leu Cys Ser Asp Ile Asp Glu Cys Glu Met Gly Val Pro Val 865 870 875 880 Cys Pro Pro Ala Ser Ser Lys Cys Ile Asn Thr Glu Gly Gly Tyr Val 885 890 895 Cys Arg Cys Ser Glu Gly Tyr Gln Gly Asp Gly Ile His Cys Leu Asp 900 905 910 Ile Asp Glu Cys Gln Leu Gly Glu His Ser Cys Gly Glu Asn Ala Ser 915 920 925 Cys Thr Asn Thr Glu Gly Gly Tyr Thr Cys Met Cys Ala Gly Arg Leu 930 935 940 Ser Glu Pro Gly Leu Ile Cys Pro Asp Ser Thr Pro Pro Pro His Leu 945 950 955 960 Arg Glu Asp Asp His His Tyr Ser Val Arg Asn Ser Asp Ser Glu Cys 965 970 975 Pro Leu Ser His Asp Gly Tyr Cys Leu His Asp Gly Val Cys Met Tyr 980 985 990 Ile Glu Ala Leu Asp Lys Tyr Ala Cys Asn Cys Val Val Gly Tyr Ile 995 1000 1005 Gly Glu Arg Cys Gln Tyr Arg Asp Leu Lys Trp Trp Glu Leu Arg 1010 1015 1020 His Ala Gly His Gly Gln Gln Gln Lys Val Ile Val Val Ala Val 1025 1030 1035 Cys Val Val Val Leu Val Met Leu Leu Leu Leu Ser Leu Trp Gly 1040 1045 1050 Ala His Tyr Tyr Arg Thr Gln Lys Leu Leu Ser Lys Asn Pro Lys 1055 1060 1065 Asn Pro Tyr Glu Glu Ser Ser Arg Asp Val Arg Ser Arg Arg Pro 1070 1075 1080 Ala Asp Thr Glu Asp Gly Met Ser Ser Cys Pro Gln Pro Trp Phe 1085 1090 1095 Val Val Ile Lys Glu His Gln Asp Leu Lys Asn Gly Gly Gln Pro 1100 1105 1110 Val Ala Gly Glu Asp Gly Gln Ala Ala Asp Gly Ser Met Gln Pro 1115 1120 1125 Thr Ser Trp Arg Gln Glu Pro Gln Leu Cys Gly Met Gly Thr Glu 1130 1135 1140 Gln Gly Cys Trp Ile Pro Val Ser Ser Asp Lys Gly Ser Cys Pro 1145 1150 1155 Gln Val Met Glu Arg Ser Phe His Met Pro Ser Tyr Gly Thr Gln 1160 1165 1170 Thr Leu Glu Gly Gly Val Glu Lys Pro His Ser Leu Leu Ser Ala 1175 1180 1185 Asn Pro Leu Trp Gln Gln Arg Ala Leu Asp Pro Pro His Gln Met 1190 1195 1200 Glu Leu Thr Gln 1205 <210> 4 <211> 212 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_000591.1 <400> 4 Met Asn Ser Phe Ser Thr Ser Ala Phe Gly Pro Val Ala Phe Ser Leu 1 5 10 15 Gly Leu Leu Leu Val Leu Pro Ala Ala Phe Pro Ala Pro Val Pro Pro 20 25 30 Gly Glu Asp Ser Lys Asp Val Ala Ala Pro His Arg Gln Pro Leu Thr 35 40 45 Ser Ser Glu Arg Ile Asp Lys Gln Ile Arg Tyr Ile Leu Asp Gly Ile 50 55 60 Ser Ala Leu Arg Lys Glu Thr Cys Asn Lys Ser Asn Met Cys Glu Ser 65 70 75 80 Ser Lys Glu Ala Leu Ala Glu Asn Asn Leu Asn Leu Pro Lys Met Ala 85 90 95 Glu Lys Asp Gly Cys Phe Gln Ser Gly Phe Asn Glu Glu Thr Cys Leu 100 105 110 Val Lys Ile Ile Thr Gly Leu Leu Glu Phe Glu Val Tyr Leu Glu Tyr 115 120 125 Leu Gln Asn Arg Phe Glu Ser Ser Glu Glu Gln Ala Arg Ala Val Gln 130 135 140 Met Ser Thr Lys Val Leu Ile Gln Phe Leu Gln Lys Lys Ala Lys Asn 145 150 155 160 Leu Asp Ala Ile Thr Thr Pro Asp Pro Thr Thr Asn Ala Ser Leu Leu 165 170 175 Thr Lys Leu Gln Ala Gln Asn Gln Trp Leu Gln Asp Met Thr Thr His 180 185 190 Leu Ile Leu Arg Ser Phe Lys Glu Phe Leu Gln Ser Ser Leu Arg Ala 195 200 205 Leu Arg Gln Met 210 <210> 5 <211> 167 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_000221.1 <400> 5 Met His Trp Gly Thr Leu Cys Gly Phe Leu Trp Leu Trp Pro Tyr Leu 1 5 10 15 Phe Tyr Val Gln Ala Val Pro Ile Gln Lys Val Gln Asp Asp Thr Lys 20 25 30 Thr Leu Ile Lys Thr Ile Val Thr Arg Ile Asn Asp Ile Ser His Thr 35 40 45 Gln Ser Val Ser Ser Lys Gln Lys Val Thr Gly Leu Asp Phe Ile Pro 50 55 60 Gly Leu His Pro Ile Leu Thr Leu Ser Lys Met Asp Gln Thr Leu Ala 65 70 75 80 Val Tyr Gln Gln Ile Leu Thr Ser Met Pro Ser Arg Asn Val Ile Gln 85 90 95 Ile Ser Asn Asp Leu Glu Asn Leu Arg Asp Leu Leu His Val Leu Ala 100 105 110 Phe Ser Lys Ser Cys His Leu Pro Trp Ala Ser Gly Leu Glu Thr Leu 115 120 125 Asp Ser Leu Gly Gly Val Leu Glu Ala Ser Gly Tyr Ser Thr Glu Val 130 135 140 Val Ala Leu Ser Arg Leu Gln Gly Ser Leu Gln Asp Met Leu Trp Gln 145 150 155 160 Leu Asp Leu Ser Pro Gly Cys 165 <210> 6 <211> 469 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_002412.1 <400> 6 Met His Ser Phe Pro Pro Leu Leu Leu Leu Leu Phe Trp Gly Val Val 1 5 10 15 Ser His Ser Phe Pro Ala Thr Leu Glu Thr Gln Glu Gln Asp Val Asp 20 25 30 Leu Val Gln Lys Tyr Leu Glu Lys Tyr Tyr Asn Leu Lys Asn Asp Gly 35 40 45 Arg Gln Val Glu Lys Arg Arg Asn Ser Gly Pro Val Val Glu Lys Leu 50 55 60 Lys Gln Met Gln Glu Phe Phe Gly Leu Lys Val Thr Gly Lys Pro Asp 65 70 75 80 Ala Glu Thr Leu Lys Val Met Lys Gln Pro Arg Cys Gly Val Pro Asp 85 90 95 Val Ala Gln Phe Val Leu Thr Glu Gly Asn Pro Arg Trp Glu Gln Thr 100 105 110 His Leu Thr Tyr Arg Ile Glu Asn Tyr Thr Pro Asp Leu Pro Arg Ala 115 120 125 Asp Val Asp His Ala Ile Glu Lys Ala Phe Gln Leu Trp Ser Asn Val 130 135 140 Thr Pro Leu Thr Phe Thr Lys Val Ser Glu Gly Gln Ala Asp Ile Met 145 150 155 160 Ile Ser Phe Val Arg Gly Asp His Arg Asp Asn Ser Pro Phe Asp Gly 165 170 175 Pro Gly Gly Asn Leu Ala His Ala Phe Gln Pro Gly Pro Gly Ile Gly 180 185 190 Gly Asp Ala His Phe Asp Glu Asp Glu Arg Trp Thr Asn Asn Phe Arg 195 200 205 Glu Tyr Asn Leu His Arg Val Ala Ala His Glu Leu Gly His Ser Leu 210 215 220 Gly Leu Ser His Ser Thr Asp Ile Gly Ala Leu Met Tyr Pro Ser Tyr 225 230 235 240 Thr Phe Ser Gly Asp Val Gln Leu Ala Gln Asp Asp Ile Asp Gly Ile 245 250 255 Gln Ala Ile Tyr Gly Arg Ser Gln Asn Pro Val Gln Pro Ile Gly Pro 260 265 270 Gln Thr Pro Lys Ala Cys Asp Ser Lys Leu Thr Phe Asp Ala Ile Thr 275 280 285 Thr Ile Arg Gly Glu Val Met Phe Phe Lys Asp Arg Phe Tyr Met Arg 290 295 300 Thr Asn Pro Phe Tyr Pro Glu Val Glu Leu Asn Phe Ile Ser Val Phe 305 310 315 320 Trp Pro Gln Leu Pro Asn Gly Leu Glu Ala Ala Tyr Glu Phe Ala Asp 325 330 335 Arg Asp Glu Val Arg Phe Phe Lys Gly Asn Lys Tyr Trp Ala Val Gln 340 345 350 Gly Gln Asn Val Leu His Gly Tyr Pro Lys Asp Ile Tyr Ser Ser Phe 355 360 365 Gly Phe Pro Arg Thr Val Lys His Ile Asp Ala Ala Leu Ser Glu Glu 370 375 380 Asn Thr Gly Lys Thr Tyr Phe Phe Val Ala Asn Lys Tyr Trp Arg Tyr 385 390 395 400 Asp Glu Tyr Lys Arg Ser Met Asp Pro Gly Tyr Pro Lys Met Ile Ala 405 410 415 His Asp Phe Pro Gly Ile Gly His Lys Val Asp Ala Val Phe Met Lys 420 425 430 Asp Gly Phe Phe Tyr Phe Phe His Gly Thr Arg Gln Tyr Lys Phe Asp 435 440 445 Pro Lys Thr Lys Arg Ile Leu Thr Leu Gln Lys Ala Asn Ser Trp Phe 450 455 460 Asn Cys Arg Lys Asn 465 <210> 7 <211> 477 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_002413.1 <400> 7 Met Lys Ser Leu Pro Ile Leu Leu Leu Leu Cys Val Ala Val Cys Ser 1 5 10 15 Ala Tyr Pro Leu Asp Gly Ala Ala Arg Gly Glu Asp Thr Ser Met Asn 20 25 30 Leu Val Gln Lys Tyr Leu Glu Asn Tyr Tyr Asp Leu Lys Lys Asp Val 35 40 45 Lys Gln Phe Val Arg Arg Lys Asp Ser Gly Pro Val Val Lys Lys Ile 50 55 60 Arg Glu Met Gln Lys Phe Leu Gly Leu Glu Val Thr Gly Lys Leu Asp 65 70 75 80 Ser Asp Thr Leu Glu Val Met Arg Lys Pro Arg Cys Gly Val Pro Asp 85 90 95 Val Gly His Phe Arg Thr Phe Pro Gly Ile Pro Lys Trp Arg Lys Thr 100 105 110 His Leu Thr Tyr Arg Ile Val Asn Tyr Thr Pro Asp Leu Pro Lys Asp 115 120 125 Ala Val Asp Ser Ala Val Glu Lys Ala Leu Lys Val Trp Glu Glu Val 130 135 140 Thr Pro Leu Thr Phe Ser Arg Leu Tyr Glu Gly Glu Ala Asp Ile Met 145 150 155 160 Ile Ser Phe Ala Val Arg Glu His Gly Asp Phe Tyr Pro Phe Asp Gly 165 170 175 Pro Gly Asn Val Leu Ala His Ala Tyr Ala Pro Gly Pro Gly Ile Asn 180 185 190 Gly Asp Ala His Phe Asp Asp Asp Glu Gln Trp Thr Lys Asp Thr Thr 195 200 205 Gly Thr Asn Leu Phe Leu Val Ala Ala His Glu Ile Gly His Ser Leu 210 215 220 Gly Leu Phe His Ser Ala Asn Thr Glu Ala Leu Met Tyr Pro Leu Tyr 225 230 235 240 His Ser Leu Thr Asp Leu Thr Arg Phe Arg Leu Ser Gln Asp Asp Ile 245 250 255 Asn Gly Ile Gln Ser Leu Tyr Gly Pro Pro Pro Asp Ser Pro Glu Thr 260 265 270 Pro Leu Val Pro Thr Glu Pro Val Pro Pro Glu Pro Gly Thr Pro Ala 275 280 285 Asn Cys Asp Pro Ala Leu Ser Phe Asp Ala Val Ser Thr Leu Arg Gly 290 295 300 Glu Ile Leu Ile Phe Lys Asp Arg His Phe Trp Arg Lys Ser Leu Arg 305 310 315 320 Lys Leu Glu Pro Glu Leu His Leu Ile Ser Ser Phe Trp Pro Ser Leu 325 330 335 Pro Ser Gly Val Asp Ala Ala Tyr Glu Val Thr Ser Lys Asp Leu Val 340 345 350 Phe Ile Phe Lys Gly Asn Gln Phe Trp Ala Ile Arg Gly Asn Glu Val 355 360 365 Arg Ala Gly Tyr Pro Arg Gly Ile His Thr Leu Gly Phe Pro Pro Thr 370 375 380 Val Arg Lys Ile Asp Ala Ala Ile Ser Asp Lys Glu Lys Asn Lys Thr 385 390 395 400 Tyr Phe Phe Val Glu Asp Lys Tyr Trp Arg Phe Asp Glu Lys Arg Asn 405 410 415 Ser Met Glu Pro Gly Phe Pro Lys Gln Ile Ala Glu Asp Phe Pro Gly 420 425 430 Ile Asp Ser Lys Ile Asp Ala Val Phe Glu Glu Phe Gly Phe Phe Tyr 435 440 445 Phe Phe Thr Gly Ser Ser Gln Leu Glu Phe Asp Pro Asn Ala Lys Lys 450 455 460 Val Thr His Thr Leu Lys Ser Asn Ser Trp Leu Asn Cys 465 470 475 <210> 8 <211> 108 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_065148.1 <400> 8 Met Lys Ala Leu Cys Leu Leu Leu Leu Pro Val Leu Gly Leu Leu Val 1 5 10 15 Ser Ser Lys Thr Leu Cys Ser Met Glu Glu Ala Ile Asn Glu Arg Ile 20 25 30 Gln Glu Val Ala Gly Ser Leu Ile Phe Arg Ala Ile Ser Ser Ile Gly 35 40 45 Leu Glu Cys Gln Ser Val Thr Ser Arg Gly Asp Leu Ala Thr Cys Pro 50 55 60 Arg Gly Phe Ala Val Thr Gly Cys Thr Cys Gly Ser Ala Cys Gly Ser 65 70 75 80 Trp Asp Val Arg Ala Glu Thr Thr Cys His Cys Gln Cys Ala Gly Met 85 90 95 Asp Trp Thr Gly Ala Arg Cys Cys Arg Val Gln Pro 100 105 <210> 9 <211> 122 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_000322.2 <400> 9 Met Lys Leu Leu Thr Gly Leu Val Phe Cys Ser Leu Val Leu Gly Val 1 5 10 15 Ser Ser Arg Ser Phe Phe Ser Phe Leu Gly Glu Ala Phe Asp Gly Ala 20 25 30 Arg Asp Met Trp Arg Ala Tyr Ser Asp Met Arg Glu Ala Asn Tyr Ile 35 40 45 Gly Ser Asp Lys Tyr Phe His Ala Arg Gly Asn Tyr Asp Ala Ala Lys 50 55 60 Arg Gly Pro Gly Gly Ala Trp Ala Ala Glu Val Ile Ser Asp Ala Arg 65 70 75 80 Glu Asn Ile Gln Arg Phe Phe Gly His Gly Ala Glu Asp Ser Leu Ala 85 90 95 Asp Gln Ala Ala Asn Glu Trp Gly Arg Ser Gly Lys Asp Pro Asn His 100 105 110 Phe Arg Pro Ala Gly Leu Pro Glu Lys Tyr 115 120 <210> 10 <211> 455 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_001056.1 <400> 10 Met Gly Leu Ser Thr Val Pro Asp Leu Leu Leu Pro Leu Val Leu Leu 1 5 10 15 Glu Leu Leu Val Gly Ile Tyr Pro Ser Gly Val Ile Gly Leu Val Pro 20 25 30 His Leu Gly Asp Arg Glu Lys Arg Asp Ser Val Cys Pro Gln Gly Lys 35 40 45 Tyr Ile His Pro Gln Asn Asn Ser Ile Cys Cys Thr Lys Cys His Lys 50 55 60 Gly Thr Tyr Leu Tyr Asn Asp Cys Pro Gly Pro Gly Gln Asp Thr Asp 65 70 75 80 Cys Arg Glu Cys Glu Ser Gly Ser Phe Thr Ala Ser Glu Asn His Leu 85 90 95 Arg His Cys Leu Ser Cys Ser Lys Cys Arg Lys Glu Met Gly Gln Val 100 105 110 Glu Ile Ser Ser Cys Thr Val Asp Arg Asp Thr Val Cys Gly Cys Arg 115 120 125 Lys Asn Gln Tyr Arg His Tyr Trp Ser Glu Asn Leu Phe Gln Cys Phe 130 135 140 Asn Cys Ser Leu Cys Leu Asn Gly Thr Val His Leu Ser Cys Gln Glu 145 150 155 160 Lys Gln Asn Thr Val Cys Thr Cys His Ala Gly Phe Phe Leu Arg Glu 165 170 175 Asn Glu Cys Val Ser Cys Ser Asn Cys Lys Lys Ser Leu Glu Cys Thr 180 185 190 Lys Leu Cys Leu Pro Gln Ile Glu Asn Val Lys Gly Thr Glu Asp Ser 195 200 205 Gly Thr Thr Val Leu Leu Pro Leu Val Ile Phe Phe Gly Leu Cys Leu 210 215 220 Leu Ser Leu Leu Phe Ile Gly Leu Met Tyr Arg Tyr Gln Arg Trp Lys 225 230 235 240 Ser Lys Leu Tyr Ser Ile Val Cys Gly Lys Ser Thr Pro Glu Lys Glu 245 250 255 Gly Glu Leu Glu Gly Thr Thr Thr Lys Pro Leu Ala Pro Asn Pro Ser 260 265 270 Phe Ser Pro Thr Pro Gly Phe Thr Pro Thr Leu Gly Phe Ser Pro Val 275 280 285 Pro Ser Ser Thr Phe Thr Ser Ser Ser Thr Tyr Thr Pro Gly Asp Cys 290 295 300 Pro Asn Phe Ala Ala Pro Arg Arg Glu Val Ala Pro Pro Tyr Gln Gly 305 310 315 320 Ala Asp Pro Ile Leu Ala Thr Ala Leu Ala Ser Asp Pro Ile Pro Asn 325 330 335 Pro Leu Gln Lys Trp Glu Asp Ser Ala His Lys Pro Gln Ser Leu Asp 340 345 350 Thr Asp Asp Pro Ala Thr Leu Tyr Ala Val Val Glu Asn Val Pro Pro 355 360 365 Leu Arg Trp Lys Glu Phe Val Arg Arg Leu Gly Leu Ser Asp His Glu 370 375 380 Ile Asp Arg Leu Glu Leu Gln Asn Gly Arg Cys Leu Arg Glu Ala Gln 385 390 395 400 Tyr Ser Met Leu Ala Thr Trp Arg Arg Arg Thr Pro Arg Arg Glu Ala 405 410 415 Thr Leu Glu Leu Leu Gly Arg Val Leu Arg Asp Met Asp Leu Leu Gly 420 425 430 Cys Leu Glu Asp Ile Glu Glu Ala Leu Cys Gly Pro Ala Ala Leu Pro 435 440 445 Pro Ala Pro Ser Leo Leo Arg 450 455 <210> 11 <211> 739 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_001069.1 <400> 11 Met Pro Gly Lys Met Val Val Ile Leu Gly Ala Ser Asn Ile Leu Trp 1 5 10 15 Ile Met Phe Ala Ala Ser Gln Ala Phe Lys Ile Glu Thr Thr Pro Glu 20 25 30 Ser Arg Tyr Leu Ala Gln Ile Gly Asp Ser Val Ser Leu Thr Cys Ser 35 40 45 Thr Thr Gly Cys Glu Ser Pro Phe Phe Ser Trp Arg Thr Gln Ile Asp 50 55 60 Ser Pro Leu Asn Gly Lys Val Thr Asn Glu Gly Thr Thr Ser Thr Leu 65 70 75 80 Thr Met Asn Pro Val Ser Phe Gly Asn Glu His Ser Tyr Leu Cys Thr 85 90 95 Ala Thr Cys Glu Ser Arg Lys Leu Glu Lys Gly Ile Gln Val Glu Ile 100 105 110 Tyr Ser Phe Pro Lys Asp Pro Glu Ile His Leu Ser Gly Pro Leu Glu 115 120 125 Ala Gly Lys Pro Ile Thr Val Lys Cys Ser Val Ala Asp Val Tyr Pro 130 135 140 Phe Asp Arg Leu Glu Ile Asp Leu Leu Lys Gly Asp His Leu Met Lys 145 150 155 160 Ser Gln Glu Phe Leu Glu Asp Ala Asp Arg Lys Ser Leu Glu Thr Lys 165 170 175 Ser Leu Glu Val Thr Phe Thr Pro Val Ile Glu Asp Ile Gly Lys Val 180 185 190 Leu Val Cys Arg Ala Lys Leu His Ile Asp Glu Met Asp Ser Val Pro 195 200 205 Thr Val Arg Gln Ala Val Lys Glu Leu Gln Val Tyr Ile Ser Pro Lys 210 215 220 Asn Thr Val Ile Ser Val Asn Pro Ser Thr Lys Leu Gln Glu Gly Gly 225 230 235 240 Ser Val Thr Met Thr Cys Ser Ser Glu Gly Leu Pro Ala Pro Glu Ile 245 250 255 Phe Trp Ser Lys Lys Leu Asp Asn Gly Asn Leu Gln His Leu Ser Gly 260 265 270 Asn Ala Thr Leu Thr Leu Ile Ala Met Arg Met Glu Asp Ser Gly Ile 275 280 285 Tyr Val Cys Glu Gly Val Asn Leu Ile Gly Lys Asn Arg Lys Glu Val 290 295 300 Glu Leu Ile Val Gln Glu Lys Pro Phe Thr Val Glu Ile Ser Pro Gly 305 310 315 320 Pro Arg Ile Ala Ala Gln Ile Gly Asp Ser Val Met Leu Thr Cys Ser 325 330 335 Val Met Gly Cys Glu Ser Pro Ser Phe Ser Trp Arg Thr Gln Ile Asp 340 345 350 Ser Pro Leu Ser Gly Lys Val Arg Ser Glu Gly Thr Asn Ser Thr Leu 355 360 365 Thr Leu Ser Pro Val Ser Phe Glu Asn Glu His Ser Tyr Leu Cys Thr 370 375 380 Val Thr Cys Gly His Lys Lys Leu Glu Lys Gly Ile Gln Val Glu Leu 385 390 395 400 Tyr Ser Phe Pro Arg Asp Pro Glu Ile Glu Met Ser Gly Gly Leu Val 405 410 415 Asn Gly Ser Ser Val Thr Val Ser Cys Lys Val Pro Ser Val Tyr Pro 420 425 430 Leu Asp Arg Leu Glu Ile Glu Leu Leu Lys Gly Glu Thr Ile Leu Glu 435 440 445 Asn Ile Glu Phe Leu Glu Asp Thr Asp Met Lys Ser Leu Glu Asn Lys 450 455 460 Ser Leu Glu Met Thr Phe Ile Pro Thr Ile Glu Asp Thr Gly Lys Ala 465 470 475 480 Leu Val Cys Gln Ala Lys Leu His Ile Asp Asp Met Glu Phe Glu Pro 485 490 495 Lys Gln Arg Gln Ser Thr Gln Thr Leu Tyr Val Asn Val Ala Pro Arg 500 505 510 Asp Thr Thr Val Leu Val Ser Pro Ser Ser Ile Leu Glu Glu Gly Ser 515 520 525 Ser Val Asn Met Thr Cys Leu Ser Gln Gly Phe Pro Ala Pro Lys Ile 530 535 540 Leu Trp Ser Arg Gln Leu Pro Asn Gly Glu Leu Gln Pro Leu Ser Glu 545 550 555 560 Asn Ala Thr Leu Thr Leu Ile Ser Thr Lys Met Glu Asp Ser Gly Val 565 570 575 Tyr Leu Cys Glu Gly Ile Asn Gln Ala Gly Arg Ser Arg Lys Glu Val 580 585 590 Glu Leu Ile Ile Gln Val Thr Pro Lys Asp Ile Lys Leu Thr Ala Phe 595 600 605 Pro Ser Glu Ser Val Lys Glu Gly Asp Thr Val Ile Ile Ser Cys Thr 610 615 620 Cys Gly Asn Val Pro Glu Thr Trp Ile Ile Leu Light Light Light Ala Glu 625 630 635 640 Thr Gly Asp Thr Val Leu Lys Ser Ile Asp Gly Ala Tyr Thr Ile Arg 645 650 655 Lys Ala Gln Leu Lys Asp Ala Gly Val Tyr Glu Cys Glu Ser Lys Asn 660 665 670 Lys Val Gly Ser Gln Leu Arg Ser Leu Thr Leu Asp Val Gln Gly Arg 675 680 685 Glu Asn Asn Lys Asp Tyr Phe Ser Pro Glu Leu Leu Val Leu Tyr Phe 690 695 700 Ala Ser Ser Leu Ile Ile Pro Ala Ile Gly Met Ile Ile Tyr Phe Ala 705 710 715 720 Arg Lys Ala Asn Met Lys Gly Ser Tyr Ser Leu Val Glu Ala Gln Lys 725 730 735 Looking for Light <210> 12 <211> 371 <212> PRT <213> Homo sapiens <308> NCBI RefSeq NP_001020539.2 <400> 12 Met Thr Asp Arg Gln Thr Asp Thr Ala Pro Ser Pro Ser Tyr His Leu 1 5 10 15 Leu Pro Gly Arg Arg Arg Thr Val Asp Ala Ala Ala Ser Arg Gly Gln 20 25 30 Gly Pro Glu Pro Ala Pro Gly Gly Gly Val Glu Gly Val Gly Ala Arg 35 40 45 Gly Val Ala Leu Lys Leu Phe Val Gln Leu Leu Gly Cys Ser Arg Phe 50 55 60 Gly Gly Ala Val Val Arg Ala Gly Glu Ala Glu Pro Ser Gly Ala Ala 65 70 75 80 Arg Ser Ala Ser Ser Gly Arg Glu Glu Pro Gln Pro Glu Glu Gly Glu 85 90 95 Glu Glu Glu Glu Lys Glu Glu Glu Arg Gly Pro Gln Trp Arg Leu Gly 100 105 110 Ala Arg Lys Pro Gly Ser Trp Thr Gly Glu Ala Ala Val Cys Ala Asp 115 120 125 Ser Ala Pro Ala Ala Arg Ala Pro Gln Ala Leu Ala Arg Ala Ser Gly 130 135 140 Arg Gly Gly Arg Val Ala Arg Arg Gly Ala Glu Glu Ser Gly Pro Pro 145 150 155 160 His Ser Pro Ser Arg Arg Gly Ser Ala Ser Arg Ala Gly Pro Gly Arg 165 170 175 Ala Ser Glu Thr Met Asn Phe Leu Leu Ser Trp Val His Trp Ser Leu 180 185 190 Ala Leu Leu Leu Tyr Leu His His Ala Lys Trp Ser Gln Ala Ala Pro 195 200 205 Met Ala Glu Gly Gly Gly Gln Asn His His Glu Val Val Lys Phe Met 210 215 220 Asp Val Tyr Gln Arg Ser Tyr Cys His Pro Ile Glu Thr Leu Val Asp 225 230 235 240 Ile Phe Gln Glu Tyr Pro Asp Glu Ile Glu Tyr Ile Phe Lys Pro Ser 245 250 255 Cys Val Pro Leu Met Arg Cys Gly Gly Cys Cys Asn Asp Glu Gly Leu 260 265 270 Glu Cys Val Pro Thr Glu Glu Ser Asn Ile Thr Met Gln Ile Met Arg 275 280 285 Ile Lys Pro His Gln Gly Gln His Ile Gly Glu Met Ser Phe Leu Gln 290 295 300 His Asn Lys Cys Glu Cys Arg Pro Lys Lys Asp Arg Ala Arg Gln Glu 305 310 315 320 Asn Pro Cys Gly Pro Cys Ser Glu Arg Arg Lys His Leu Phe Val Gln 325 330 335 Asp Pro Gln Thr Cys Lys Cys Ser Cys Lys Asn Thr Asp Ser Arg Cys 340 345 350 Lys Ala Arg Gln Leu Glu Leu Asn Glu Arg Thr Cys Arg Cys Asp Lys 355 360 365 Pro Arg Arg 370

Claims

1. performing at least one immunoassay on a first blood sample from a first subject to generate a first data set including protein level data for at least four protein markers; 1. A method for generating protein level data for a first subject, comprising: the at least four protein markers include at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); The first subject has previously been diagnosed with psoriatic arthritis (PsA), The method.

2. Conducting at least one immunoassay obtaining a first blood sample, the first blood sample comprising said protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data.

2. The method of claim 1, comprising:

3. 10. The method of claim 1, wherein at least one immunoassay comprises a multiplex assay.

4. The method of claim 1, wherein the at least four protein markers include at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

5. The method of claim 1, wherein the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

6. The method of claim 1, wherein the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

7. The method of claim 1, wherein the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

8. The method of claim 1, wherein the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

9. The method of claim 1, wherein the at least four protein markers include at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

10. The method of claim 1, wherein the at least four protein markers include at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

11. The method of claim 1, wherein the plurality of protein markers consists of at most 500 protein markers.

12. The method of claim 1, wherein the plurality of protein markers consists of at most 250 protein markers.

13. The method of claim 1, wherein the plurality of protein markers consists of at most 100 protein markers.

14. The method of claim 1, wherein the plurality of protein markers consists of at most 50 protein markers.

15. The method of claim 1, wherein the plurality of protein markers consists of at most 25 protein markers.

16. The method of claim 1, wherein the plurality of protein markers consists of at most 12 protein markers.

17. CHI3L1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); 2. The method of claim 1, wherein said amino acid sequence is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO:7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO:8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO:9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO:10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO:11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO:12).

18. 1. A method for generating a protein level score, comprising the steps of: performing at least one immunoassay on a first blood sample from a subject previously diagnosed with psoriatic arthritis (PsA) to generate protein level data for each protein marker of the plurality of protein markers; the plurality of protein markers comprises a plurality of test markers including at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and Combining the protein level data for each test marker to generate a protein level score.

19. Conducting at least one immunoassay obtaining a first blood sample, the first blood sample comprising said protein marker; contacting the first blood sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data.

20. The method of claim 18, comprising:

20. 20. The method of claim 18, wherein at least one immunoassay comprises a multiplex assay.

21. 20. The method of claim 18, wherein the at least four protein markers include at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

22. 20. The method of claim 18, wherein the at least four protein markers include at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

23. 19. The method of claim 18, wherein the at least four protein markers include at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

24. 20. The method of claim 18, wherein the at least four protein markers include at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

25. 20. The method of claim 18, wherein the at least four protein markers include at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

26. 20. The method of claim 18, wherein the at least four protein markers include at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

27. 19. The method of claim 18, wherein the at least four protein markers include at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

28. 20. The method of claim 18, wherein the plurality of test markers comprises CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA.

29. CHI3L1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); 19. The method of claim 18, wherein said amino acid sequence is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO:7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO:8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO:9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO:10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO:11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO:12).

30. 1. A method for providing a quantitative measure of psoriatic arthritis (PsA) disease activity in a subject previously diagnosed with PsA, the method comprising the steps of: performing at least one immunoassay on a first sample from the subject to generate a first dataset comprising quantitative data including at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and Applying an algorithm to the first data set to determine a first disease activity score, wherein the first disease activity score provides a quantitative measure of PsA disease activity in the subject.

31. receiving a second dataset relating to a second sample obtained from the subject, the first sample and the second sample being obtained from the subject at different times; applying an algorithm to the second data set to determine a second disease activity score; and comparing the first disease activity score to the second disease activity score to determine a change in disease activity score, which change indicates a change in psoriatic arthritis disease activity in the subject.

31. The method of claim 30, further comprising:

32. The method of claim 30, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

33. CHI3L1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); 31. The method of claim 30, wherein said amino acid sequence is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO:7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO:8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO:9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO:10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO:11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO:12).

34. 1. A system for scoring a sample, comprising: A storage memory for storing a first data set relating to a first sample obtained from a subject previously diagnosed with psoriatic arthritis (PsA), a memory for storage, the first data set comprising quantitative data for at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); and a processor communicatively connected to the storage memory, The processor is configured to apply an algorithm to a first dataset to determine a first disease activity score, where the first disease activity score provides a quantitative measure of PsA disease activity in the subject, and the algorithm is trained with data representing at least four markers in samples from subjects with known PsA disease activity.

35. The system of claim 34, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

36. CHI3L1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); The system of claim 34, wherein said amino acid sequence is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO:7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO:8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO:9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO:10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO:11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO:12).

37. 1. A method for recommending a treatment regimen in a subject previously diagnosed with psoriatic arthritis (PsA), comprising the steps of: a) performing a first immunoassay on a first sample from the subject at a first time point and generating a first score based on a first set of quantitative data; the first set of quantitative data includes expression data for at least four biomarkers selected from chitinase 3-like 1 (cartilage glycoprotein 39) (CHI3L1); C-reactive protein, pentraxin related (CRP); epidermal growth factor (beta-urogastron) (EGF); interleukin 6 (interferon, beta2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or vascular endothelial growth factor A (VEGFA); b) performing a second immunoassay on a second sample from the subject at a second time point after the first time point and generating a second score based on a second set of quantitative data; a second set of quantitative data comprising expression data for said at least four biomarkers; c) determining whether there is a difference between the first score and the second score; and d) recommending a treatment regimen or modifying an existing treatment regimen based on the difference between the first score and the second score.

38. 38. The method of claim 37, wherein if the difference between the first score and the second score indicates a decrease or no change in PsA disease activity, then a non-biologic treatment regimen is recommended.

39. 38. The method of claim 37, wherein if the difference between the first score and the second score indicates increased PsA disease activity, then a biological therapy regimen is recommended.

40. performing said at least one immunoassay obtaining a first sample, the first sample comprising said protein marker; contacting the first sample with a plurality of different reagents; forming a plurality of different complexes between said reagent and said marker; and detecting said complexes and generating said data.

38. The method of claim 37, comprising:

41. 38. The method of claim 37, wherein the first and second immunoassays comprise a multiplex assay.

42. 38. The method of claim 37, wherein the at least four protein markers include at least five, six, seven, eight, nine, ten, or eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

43. CHI3L1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001267.2 (SEQ ID NO: 1); CRP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000558.2 (SEQ ID NO: 2); EGF is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_001954.2 (SEQ ID NO: 3); IL6 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000591.1 (SEQ ID NO: 4); LEP is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_000221.1 (SEQ ID NO: 5); MMP1 is at least 90% identical to the amino acid sequence in NCBI RefSeq NP_002412.1 (SEQ ID NO: 6); 38. The method of claim 37, wherein said amino acid sequence is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1 (SEQ ID NO:7), RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1 (SEQ ID NO:8), SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2 (SEQ ID NO:9), TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1 (SEQ ID NO:10), VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1 (SEQ ID NO:11), and VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2 (SEQ ID NO:12).

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