Biomarkers and methods for measuring and monitoring axial spondyloarthritis activity

Abstract

Biomarkers useful for diagnosing and assessing inflammatory disease activity, for prediction of risk for progressive spinal and joint damage, in particular for axial spondyloarthritis (axSpA) and ankylosing spondylitis (AS), and for generating a dataset are provided, along with kits for measuring expression of the biomarkers. The invention also provides predictive models, based on the biomarkers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples.

1 .- 8 . (canceled) 
     
     
         9 . A method for monitoring the presence or absence of axial spondyloarthritis (axSpA) disease activity in a subject, for diagnosing axSpA in a subject, or for predicting risk of progressive damage, the method comprising:
 determining a first dataset associated with samples from a population of individuals wherein said population is negative for axSpA, wherein said first dataset comprises quantitative data for at least three biomarkers, wherein the at least three biomarkers selected from a group comprising calprotectin (dimer of S100A8 and S100A9 protein subunits; MRP-8/14); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1, or YKL-40); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 6 (IL6); interleukin 8 (IL8); interleukin 1, beta (IL1B); interleukin 6 receptor (IL6R); leptin (LEP); Macrophage-derived chemokine (MDC); 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 or TNF-R1); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA); determining a plurality of MBDA scores for the individuals in said population based on the first dataset;   deriving an aggregate MBDA value for said population;   determining a second dataset associated with a sample from said subject wherein said second dataset comprises the selected biomarkers;   determining a MBDA score for said subject;   comparing the aggregate MBDA value to the MBDA score for the subject; and   determining disease activity of axSpA in the subject, or diagnosing axSpA in the subject, based at least in part on said comparison.   
     
     
         10 . The method of  claim 9  wherein the biomarkers comprise VCAM-1, EGF, VEGF-A, IL-6, TNF-R1, MMP-1, MMP-3, YKL-40, Leptin, Resistin, SAA, and CRP. 
     
     
         11 . The method of  claim 9  wherein said datasets are obtained by a method comprising:
 obtaining said samples from said population and said sample from said subject, wherein said samples comprise a plurality of analytes; 
 contacting said samples with reagents; 
 generating a plurality of complexes between said reagents with said plurality of analytes; and 
 detecting said plurality of complexes to obtain said datasets wherein said datasets comprise quantitative data for said biomarkers. 
 
     
     
         12 . The method of  claim 9  wherein said MBDA score for the subject is predictive of a clinical assessment. 
     
     
         13 . The method of  claim 12  wherein said clinical assessment is selected from the group consisting of Ankylosing Spondylitis Disease Activity Score (ASDAS), the Stoke Ankylosing Spondylitis Spinal Score (SASSS); the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS); Ankylosing Spondylitis Quality of Life Scale (ASQOL); Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Global Score (BAS-G), Bath Ankylosing Spondylitis Metrology Index (BASMI), Dougados Functional Index (DFI), Health Assessment Questionnaire for the Spondyloarthropathies (HAQ-S), Revised Leeds Disability Questionnaire (RLDQ), and MRI. 
     
     
         14 . The method of  claim 9 , further comprising:
 receiving a third dataset associated with a second sample obtained from said subject, wherein said sample obtained from said subject and said second sample are obtained from said subject at different times;   determining a second MBDA score for said subject from said third dataset; and   comparing said MBDA score and said second MBDA score for said subject to determine a change in said MBDA scores, wherein said change indicates a change in axSpA activity in said subject, or the presence of axSpA in the subject.   
     
     
         15 . The method of  claim 9  wherein a report is prepared in a format that is capable of being disseminated to the subject or a caregiver of the subject that provides information allowing the subject or caregiver to make decisions based on the diagnosis. 
     
     
         16 . The method of  claim 9  wherein said subject has received a treatment for axSpA, and further comprising the steps of:
 determining a second MBDA score for a second subject wherein said second subject is of the same species as said first subject and wherein said second subject has received treatment for axSpA; 
 comparing said MBDA score of said subject to said second MBDA score; and 
 determining a treatment efficacy for said first subject based on said score comparison. 
 
     
     
         17 .- 18 . (canceled) 
     
     
         19 . A computer-implemented method for scoring a sample, said method comprising:
 receiving a first dataset associated with a first sample obtained from a first subject, wherein said first dataset comprises quantitative data for three or more biomarkers, wherein the three or more biomarkers are selected from a group comprising calprotectin (dimer of S100A8 and S100A9 protein subunits; MRP-8/14); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1, or YKL-40); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 6 (IL6); interleukin 8 (IL8); interleukin 1, beta (IL1B); interleukin 6 receptor (IL6R); leptin (LEP); Macrophage-derived chemokine (MDC); 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 or TNF-R1); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA); and   determining, by a computer processor, a first MBDA score from said first dataset using an interpretation function, wherein said first MBDA score provides a classification of disease activity of axSpA in the subject, a diagnosis of axSpA in the subject, or a prediction of risk of progressive damage.   
     
     
         20 . The method of  claim 19  wherein the biomarkers comprise VCAM-1, EGF, VEGF-A, IL-6, TNF-R1, MMP-1, MMP-3, YKL-40, Leptin, Resistin, SAA, and CRP. 
     
     
         21 . The method of  claim 19 , further comprising:
 receiving a second dataset associated with a second sample obtained from said first subject, wherein said first sample and said second sample are obtained from said first subject at different times;   determining a second MBDA score from said second dataset using said interpretation function; and   comparing said first MBDA score and said second MBDA score to determine a change in said MBDA scores, wherein said change indicates a change in said inflammatory disease activity in said first subject, or a diagnosis of axSpA in the subject.   
     
     
         22 . The method of  claim 19  wherein said datasets are obtained by a method comprising:
 obtaining said samples from said population and said sample from said subject, wherein said samples comprise a plurality of analytes; 
 contacting said samples with reagents; 
 generating a plurality of complexes between said reagents with said plurality of analytes; and 
 detecting said plurality of complexes to obtain said datasets wherein said datasets comprise quantitative data for said biomarkers. 
 
     
     
         23 . The method of  claim 19  wherein said MBDA score for the subject is predictive of a clinical assessment. 
     
     
         24 . The method of  claim 23  wherein said clinical assessment is selected from the group consisting of Ankylosing Spondylitis Disease Activity Score (ASDAS), the Stoke Ankylosing Spondylitis Spinal Score (SASSS); the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS); Ankylosing Spondylitis Quality of Life Scale (ASQOL); Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Global Score (BAS-G), Bath Ankylosing Spondylitis Metrology Index (BASMI), Dougados Functional Index (DFI), Health Assessment Questionnaire for the Spondyloarthropathies (HAQ-S), Revised Leeds Disability Questionnaire (RLDQ), and MRI. 
     
     
         25 . The method of  claim 19  wherein a report is prepared in a format that is capable of being disseminated to the subject or a caregiver of the subject that provides information allowing the subject or caregiver to make decisions based on the diagnosis. 
     
     
         26 .- 27 . (canceled) 
     
     
         28 . A method of treating a subject, the method comprising
 classifying disease activity of axSpA in the subject, diagnosing axSpA in the subject, or predicting risk of progressive damage, according to claim  1 ; and   selecting an axSpA therapeutic regimen based on said MBDA score.   
     
     
         29 . The method of  claim 28  comprising providing said axSpA therapeutic regimen. 
     
     
         30 . The method of  claim 28 , further comprising determining a response to the treatment based on said MBDA score. 
     
     
         31 . The method of  claim 28 , further comprising determining an axSpA treatment course based on said MBDA score. 
     
     
         32 .- 40 . (canceled)