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US 2020/0300853 A1Published application
Biomarkers and methods for measuring and monitoring juvenile idiopathic arthritis activity
AssigneeCrescendo Bioscience, Inc.
InventorsScott Eastman, Eric Sasso
Life of this patent
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Abstract
Biomarkers useful for assessing inflammatory disease or flare activity, in particular in juvenile idiopathic arthritis, 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.
Claims32
A method for monitoring the presence or absence of juvenile idiopathic arthritis (JIA) disease activity in a subject, or for predicting flare activity in a subject having JIA, the method comprising:
- providing a test sample comprising a sample of bodily fluid taken from the mammal;
- determining sample concentrations for three or more biomarkers selected from the group consisting of alpha-2-macroglobulin (A2M);
- amyloid P component, serum (SAP);
- angiopoietin 1 (AGP1) antithrombin III (ATIII);
- ataxia telangiectasia mutated (ATM);
- B-cell activating factor (BAFF);
- chemokine (C—C motif) ligand 2 (CCL2);
- chemokine (C—C motif) ligand 3 (CCL3);
- chemokine (C—C motif) ligand 11 (CCL11);
- chemokine (C—C motif) ligand 22 (CCL22);
- chemokine (C—X—C motif) ligand 9 (CXCL9);
- chemokine (C—X—C motif) ligand 10 (CXCL10);
- CD40 ligand (CD40LG);
- C-reactive protein (CRP);
- complement C3;
- complement C4;
- complement factor H (CFH);
- epidermal growth factor (EGF);
- gelsolin (GSN);
- granzyme (GZM);
- haptoglobin (HP);
- heat shock protein 60 (HSP60);
- interleukin 6 (IL-6);
- leptin (LEP);
- MF;
- matrix metalloproteinase-1 (MMP1);
- matrix metalloproteinase-3 (MMP3);
- matrix metalloproteinase-9 (MMP9);
- resistin (RETN);
- serum amyloid (SAA);
- tumor necrosis factor receptor, type 1 (TNF-R1);
- vascular cell adhesion molecule-1 (VCAM1);
- vascular endothelial growth factor A (VEGF-A);
- Calprotectin;
- intercellular adhesion molecule 1 (ICAM-1);
- interleukin-1 beta (IL-1B);
- interleukin-6 receptor (IL-6R);
- interleukin-8 (IL-8);
- interleukin-8 (IL-10);
- interleukin-8 (IL-17);
- interleukin-8 (IL-18);
- interleukin-8 (IL-21);
- L-selectin;
- MDC;
- P-selectin;
- pyridinoline (PYD);
- S100 A12;
- S100A14;
- TIMP metallopeptidase inhibitor 1 (TIMP1);
- TNF receptor-associated protein 1 (TRAP-1);
- transthyretin (TTR);
- tumor protein 53 (TP53);
- and YKL-40;
- determining whether the sample concentration for each said biomarker is statistically significantly greater than minimum diagnostic concentrations of corresponding control biomarkers that are indicative of JIA;
- and
- classifying disease activity of JIA in the subject or predicting flare activity in the subject, based at least in part on the determination of whether the sample concentrations for the biomarkers from the subject are statistically significantly greater than minimum diagnostic concentrations indicative of JIA.
53 more elements
A method for monitoring the presence or absence of juvenile idiopathic arthritis (JIA) disease activity in a subject, or for predicting flare activity in a subject having JIA, the method comprising:
- determining a first dataset associated with samples from a population of individuals wherein said population is negative for JIA, wherein said first dataset comprises quantitative data for three or more biomarkers selected from the group consisting of alpha-2-macroglobulin (A2M);
- amyloid P component, serum (SAP);
- angiopoietin 1 (AGP1) antithrombin III (ATIII);
- ataxia telangiectasia mutated (ATM);
- B-cell activating factor (BAFF);
- chemokine (C—C motif) ligand 2 (CCL2);
- chemokine (C—C motif) ligand 3 (CCL3);
- chemokine (C—C motif) ligand 11 (CCL11);
- chemokine (C—C motif) ligand 22 (CCL22);
- chemokine (C—X—C motif) ligand 9 (CXCL9);
- chemokine (C—X—C motif) ligand 10 (CXCL10);
- CD40 ligand (CD40LG);
- C-reactive protein (CRP);
- complement C3;
- complement C4;
- complement factor H (CFH);
- epidermal growth factor (EGF);
- gelsolin (GSN);
- granzyme (GZM);
- haptoglobin (HP);
- heat shock protein 60 (HSP60);
- interleukin 6 (IL-6);
- leptin (LEP);
- MF;
- matrix metalloproteinase-1 (MMP1);
- matrix metalloproteinase-3 (MMP3);
- matrix metalloproteinase-9 (MMP9);
- resistin (RETN);
- serum amyloid (SAA);
- tumor necrosis factor receptor, type 1 (TNF-R1);
- vascular cell adhesion molecule-1 (VCAM1);
- vascular endothelial growth factor A (VEGF-A);
- YKL-40;
- Calprotectin;
- intercellular adhesion molecule 1 (ICAM-1);
- interleukin-1 beta (IL-1B);
- interleukin-6 receptor (IL-6R);
- interleukin-8 (IL-8);
- interleukin-8 (IL-10);
- interleukin-8 (IL-17);
- interleukin-8 (IL-18);
- interleukin-8 (IL-21);
- L-selectin;
- MDC;
- P-selectin;
- pyridinoline (PYD);
- S100 A12;
- S100A14;
- TIMP metallopeptidase inhibitor 1 (TIMP1);
- TNF receptor-associated protein 1 (TRAP-1);
- transthyretin (TTR);
- tumor protein 53 (TP53);
- and YKL-40;
- determining a plurality of DAI scores for the individuals in said population based on the first dataset;
- deriving an aggregate DAI 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 DAI score for said subject;
- comparing the aggregate DAI value to the DAI score for the subject;
- and
- determining disease activity of JIA in the subject, or predicting flare activity in the subject, based at least in part on said comparison.
57 more elements
A computer-implemented method for generating quantitative data for a subject, said method comprising:
- performing at least one immunoassay on a first sample from the first subject to generate a first dataset comprising the quantitative data, wherein the quantitative data comprises at least three or more biomarkers selected from the group consisting of alpha-2-macroglobulin (A2M);
- amyloid P component, serum (SAP);
- angiopoietin 1 (AGP1) antithrombin III (ATIII);
- ataxia telangiectasia mutated (ATM);
- B-cell activating factor (BAFF);
- chemokine (C—C motif) ligand 2 (CCL2);
- chemokine (C—C motif) ligand 3 (CCL3);
- chemokine (C—C motif) ligand 11 (CCL11);
- chemokine (C—C motif) ligand 22 (CCL22);
- chemokine (C—X—C motif) ligand 9 (CXCL9);
- chemokine (C—X—C motif) ligand 10 (CXCL10);
- CD40 ligand (CD40LG);
- C-reactive protein (CRP);
- complement C3;
- complement C4;
- complement factor H (CFH);
- epidermal growth factor (EGF);
- gelsolin (GSN);
- granzyme (GZM);
- haptoglobin (HP);
- heat shock protein 60 (HSP60);
- interleukin 6 (IL-6);
- leptin (LEP);
- MF;
- matrix metalloproteinase-1 (MMP1);
- matrix metalloproteinase-3 (MMP3);
- matrix metalloproteinase-9 (MMP9);
- resistin (RETN);
- serum amyloid (SAA);
- tumor necrosis factor receptor, type 1 (TNF-R1);
- vascular cell adhesion molecule-1 (VCAM1);
- vascular endothelial growth factor A (VEGF-A);
- YKL-40;
- Calprotectin;
- intercellular adhesion molecule 1 (ICAM-1);
- interleukin-1 beta (IL-1B);
- interleukin-6 receptor (IL-6R);
- interleukin-8 (IL-8);
- interleukin-8 (IL-10);
- interleukin-8 (IL-17);
- interleukin-8 (IL-18);
- interleukin-8 (IL-21);
- L-selectin;
- MDC;
- P-selectin;
- pyridinoline (PYD);
- S100 A12;
- S100A14;
- TIMP metallopeptidase inhibitor 1 (TIMP1);
- TNF receptor-associated protein 1 (TRAP-1);
- transthyretin (TTR);
- tumor protein 53 (TP53);
- and YKL-40;
- comparing the first dataset to a trained dataset representing the at least three or more biomarkers;
- and
- generating quantitative data that is derived from the difference between the first and trained datasets, wherein the first subject has JIA or is suspected of having JIA.
53 more elements
Dependencies are inferred from the claim text.