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WO2025101929A1 - Biomarqueurs pour prédire l'apparition d'une polyarthrite rhumatoïde clinique - Google Patents

Biomarqueurs pour prédire l'apparition d'une polyarthrite rhumatoïde clinique Download PDF

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WO2025101929A1
WO2025101929A1 PCT/US2024/055169 US2024055169W WO2025101929A1 WO 2025101929 A1 WO2025101929 A1 WO 2025101929A1 US 2024055169 W US2024055169 W US 2024055169W WO 2025101929 A1 WO2025101929 A1 WO 2025101929A1
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subset
acpa
clinical
kit
subjects
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Inventor
Mark Andrew GILLESPIE
Xiaojun Li
Adam Kenneth SAVAGE
Samir Rachid ZAIM
Kevin Dale DEANE
Vernon Michael Holers
Ted Richard MIKULS
Jess David EDISON
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University of Nebraska Lincoln
Uniformed Services University of Health Sciences
University of Colorado System
University of Colorado Colorado Springs
Allen Institute
University of Nebraska System
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University of Nebraska Lincoln
Uniformed Services University of Health Sciences
University of Colorado System
University of Colorado Colorado Springs
Allen Institute
University of Nebraska System
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/564—Immunoassay; Biospecific binding assay; Materials therefor for pre-existing immune complex or autoimmune disease, i.e. systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, rheumatoid factors or complement components C1-C9
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/10—Musculoskeletal or connective tissue disorders
    • G01N2800/101—Diffuse connective tissue disease, e.g. Sjögren, Wegener's granulomatosis
    • G01N2800/102—Arthritis; Rheumatoid arthritis, i.e. inflammation of peripheral joints
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation

Definitions

  • the current disclosure provides systems and methods to predict the progression from at- risk of developing rheumatoid arthritis (pre-RA) to physician-confirmed rheumatoid arthritis (i.e., Clinical RA) within defined time periods as well as to identify stage-specific biologic pathways of disease development.
  • methods include measuring the expression of select biomarkers to predict progression of pre-RA to Clinical RA within three years.
  • Rheumatoid arthritis is one of the most common forms of chronic inflammatory arthritis in the world, affecting between 0.5-1 % of the population.
  • RA includes a stage of development that can be termed ‘pre-RA’ which indicates that the subject is at-risk of developing RA.
  • Pre-RA can be defined as the presence of abnormal biomarkers and/or other features (e.g., articular symptoms) prior to the development of physician-confirmed RA.
  • the current disclosure describes biomarkers to predict the progression from at-risk of developing rheumatoid arthritis (pre-RA) to physician-confirmed rheumatoid arthritis (i.e., Clinical RA) within defined time periods as well as to identify stage-specific biologic pathways of disease development.
  • pre-RA rheumatoid arthritis
  • Clinical RA physician-confirmed rheumatoid arthritis
  • the described biomarkers can be used to identify interventions to prevent, delay, or modulate future RA.
  • the described biomarkers can be used to predict progression to Clinical RA within three years.
  • the described biomarkers can be used to identify potential stage-specific targets for preventive interventions.
  • FIG. 1 Study overview. Samples from at-risk rheumatoid arthritis (pre-RA) and healthy controls (HC) from the Department of Defense Serum Repository (DoDSR) were evaluated to test the hypothesis that proteomic testing can identify evolving biologic features in pre-RA. 197 proteins on the Olink platform and 4 additional circulating proteins including 3 auto-antibody markers were analyzed in serum obtained from the DoDSR. Samples were available up to 21 years prior to RA diagnosis, and HC were age, race, body mass index (BMI), and time-matched samples from healthy donors.
  • pre-RA pre-risk rheumatoid arthritis
  • HC healthy controls
  • DoDSR Department of Defense Serum Repository
  • ACPA anti-citrullinated protein antibodies
  • FIG. 3 Autoantibody levels for cyclic citrullinated peptide 3 (CCP3), rheumatoid factor (RF) immunoglobulin (lg)A, and RFIgM for each sample from -15 to 0 years prior to clinical RA diagnosis.
  • LOESS locally estimated scatterplot smoothing
  • FIGs. 4A, 4B Rule-in Biomarker results.
  • the violin plots show the interpolated protein expression for subjects in the RA at time of diagnosis (triangle), and the protein expression for age, sex, BMI, and time-matched samples for HC subjects (star).
  • FIGs. 5A, 5B CCP3 and RFIgA provide limited ability to predict conversion (progression from pre-disease to disease).
  • 5A The left, middle, and right panels show the corresponding receiver operating characteristic (ROC) curve for predicting 3-year, 2-year, and 1-year conversion in this dataset.
  • ROC receiver operating characteristic
  • 5B Including protein biomarkers in the prediction model provides a statistically- significant signal for predicting RA conversion.
  • the barplots show the median p-value for each analyte’s contribution to predicting 1 , 2, and 3-year conversion after adjusting for CCP3 and RFIgA. This process was repeated iteratively until additional biomarkers did not add signal into the analysis.
  • the list of analytes from top to bottom are: TNFRSF10A, SIT1, LAG3, FGF21 , CLEC4D, ZBTB17, LRRN1 , OPTC, HS6ST1 , SEZ6L2, DGKZ, GFRA2, GCNT1 , MASP1 , SEMA4C, ITGA11 , RFIgM, SKAP1, CXCL11 , IGSF3, GALNT2, IL10, IRAK4, DNAJB1 , PIK3AP1 , and MRC2.
  • FIGs. 6A, 6B (6A) Final list of protein biomarkers and auto-antibody counts and their respective coefficients for each of the yearly predictive models. (6B) ROC curves corresponding to predicting RA conversion in 3-years, 2-years and 1 -years pre-disease onset within ACPA+ samples. The area under the curve (AUC)s and the 95% confidence intervals are shown above each ROC curve.
  • FIGs. 7A, 7B Evaluating diagnostic model utility.
  • (7A) The predictions between year models were sampled and assessed for correlation. The average pairwise correlation between 2 models is shown in each cell of the heatmap.
  • (7B) Probability predictions for each ACPA+ sample over time, for the 3-year, 2-year, and 1-year model. A regression line was overlaid to show model predictions trends over time.
  • FIGs. 8A-8C Model Predictions Discriminate between HC and RA-Converters.
  • (9A) ROC curves corresponding to predicting RA conversion in 3-years, 2-years and 1 -years pre-disease onset across all RA samples (ACPA+ and ACPA-).
  • (8B) Probability predictions for each RA sample overtime, for the 3-year, 2-year, and 1-year model. A regression line was overlaid to show model predictions trends over time.
  • (8C Boxplots show distribution of the Conversion time for samples the model predicted to convert (true) vs those it did not (false) for cases and controls.
  • FIGs. 9A-9C Model predictions across all (ACPA+ and ACPA-) RA subjects.
  • FIGs. 10A-10C External validation in Cross-Sectional Altra Validation.
  • (10B) Model probability predictions split by group. Healthy Colorado HC.
  • BRI_HC Benaroya Research Institute Healthy Control.
  • Non-converter ACPA+ individuals who did not get RA.
  • Converter ACPA+ individuals who got RA.
  • Early RA subjects who enrolled with RA.
  • (10C) ACPA+ subset of ALTRA with 3 year, 2 year, and 1 year models. ROC Curves performance when only including any Converters and only ACPA+ non-converter subjects with at least 3 years of data (N 23).
  • FIGs. 11 A, 11 B External validation in Longitudinal Altra Validation.
  • (11A) Altra Validation Demonstrates Model Predictions Discriminate between ACPA+ Conversion and Non-Conversion samples in external datasets.
  • FIG. 12 DoD proteomics modeling overview. Linear mixed effect models were fit for each cohort to enable protein expression interpolation at given time points. The interpolated protein expressions for each individual were used to capture yearly differences in expression between RA and HC, 0 to 15 years prior to diagnosis, to identify the evolution of RA. Finally, these longitudinal biomarkers were aggregated into pathways to identify pathway enrichment over time. [0019] FIGs. 13A, 13B. Heatmap showing longitudinal signature of 104 proteins that were differentially expressed between samples from individuals who developed RA, and controls, pre- and post-RA diagnosis. (13A) Each cell denotes the Log2 fold change differences between individuals with RA and HC.
  • the legend shows which cells denote higher normalized protein expression in RA, and which cells represent lower expression in RA.
  • Each cell denotes significance (false discovery rate (FDR) ⁇ 0.05) for each protein at each time point.
  • FDR false discovery rate
  • FIGs. 14A, 14B (14A) Enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways in specific years prior to RA Diagnosis. The list of conditions from top to bottom are: Hypertrophic cardiomyopathy (HCM), Toxoplasmosis, Dilated cardiomyopathy (DCM), Intestinal immune network for IgA production, Tuberculosis, Human cytomegalovirus infection, Toll-like receptor signaling pathway, Th17 cell differentiation, T cell receptor signaling pathway, Measles, Malaria, MARK signaling pathway, Inflammatory bowel disease (IBD), Epstein-Barr virus infection, Chagas disease (American trypanosomiasis), C-type lectin receptor signaling pathway, Cellular senescence, Axon guidance, Leishmaniasis, Pertussis, PI3K-Akt signaling pathway, Hepatitis B, B cell receptor signaling pathway, Chronic myeloid leukemia, Kaposi sarcoma-associated
  • FIGs. 15A, 15B (15A) Heatmap visualizations, and (15B) K-Means clustering with gapstatistic analysis suggests potentially two clusters of ACPA+ RA subjects.
  • FIGs.16A-16D (16A) Log2 Transformed CCP3 Levels for the three distinct RA subgroups (2 CCP3+ RA subgroups from FIG. ig15, and the CCP3- RA subgroups), and HC. (16B) Log2 Transformed RFIgM Levels for the three distinct RA subgroups, and the HC. (16C) K-Means clustering on the four distinct clusters.
  • (17A) First heatmap demonstrates the Log2(RA/HC) fold change for each differential protein for the ACPA+ vs HC contrast, while the second heatmap demonstrates whether that difference was statistically significant.
  • Y-axis of heatmap reads: LAG3, ITGA11, LAMP3, CLSTN3, SEMA4C, TGFB1, MASP1, ITGA6, TREM1, GCNT1, PRDX1, PRDX6, CCL7, VEGFA, APBB1IP, IL10, EIF5A, CXCL11, CLEC6A, SLAMF8, IL6, KRT19, CLEC4G, ITGB6, GALNT2, EGLN1, SIT1, SRPK2, CBL, TANK, FLI1, IGSF3, DCBLD2, TDRKH, CXCL12, HS3ST3B1, AMIGO2, HEXIM1, ZBTB16, BCR, LRRN1, BGN, GKN1, IL17C, GLB1,
  • FIG. 18 Venn Diagram showing differential protein overlap for differences at baseline (year of RA Diagnosis) between biomarkers for ACPA+ and biomarkers for ACPA-. [0025] FIG. 19. Scatterplot showing the directional significance of protein hits.
  • X-axis Log10(Adj p-value) x sign(Log2FC) for ACPA+ contrast.
  • FIGs. 20A, 20B Raincloud plots contrasting protein expression for seropositive RA subjects (RA+), seronegative RA subjects (RA-), and HC for IRAK4 (20A) and IL10 (20B).
  • FIGs. 21A, 21 B Raincloud plots contrasting protein expression for seropositive RA subjects (RA+), seronegative RA subjects (RA-), and HC for SEZ6L2 and CNTNAP2 (21A) and DPPIO and NF2 (21 B).
  • FIG. 22 Panel Selection from Olink to Meso-scale Discovery. Starting with the original 197 proteomic features measured in Olink, a subset of proteins were identified that were predictive of disease onset and for which assays were available in MSD.
  • FIG. 23 Proposed Assay Protocol by MSD.
  • FIG. 24 Standard Curves with limits of detection per analyte. Standard curves show proteins are mostly well captured within dynamic range of spike controls. The 14 analyses demonstrated that the biological commercial samples were well captured within the dynamic range of the ‘spiked’ controls, indicating viable calibration.
  • FIG. 25 Intra Plate correlation per Analyte Across the 3 plate replicates. To assess technical replication within a plate, the spearman correlation was calculated between duplicate runs for each subject within a plate for each analyte, and then repeated the correlation analyses across Plate Replicate 1 , Plate Replicate 2, Plate Replicate 3. The median across the three plates was then used to assess technical replication across the three plates. The figure shows that the median intra plate correlation per analyte across plates is > 0.85 for all proteins, indicating the assays are highly reproducible.
  • FIG. 26 The distribution of Coefficient of Variation (CV) for each protein.
  • the median CV is expressed as a % next to the analyte name.
  • the analytical validation shows a CV ⁇ 25% for all proteins.
  • FIG. 27 Graphical overview of Olink-to-MSD Signature Validation.
  • the MSD expression of proteins were mapped onto their Olink equivalents by constructing linear regression models that translate MSD expression into the equivalent Olink dynamic range.
  • FIG. 28 Correlation between protein measurements in the Olink technology (Y-axis) and the MSD technology (X-axis). It was observed that 10 out of 13 proteins showed statistically significant and linear correlations between their MSD and Olink assay measurements.
  • FIGs. 29A, 29B Retrained model coefficient on the panel of highly concordant proteins.
  • 29A The 3-year disease onset classification model was retrained on the subset of proteins that showed high concordance between MSD and Olink, and the modeling results are shown in a forest plot.
  • FIG. 30 Comparison of the DoD retrospective RA Cohort and the Prospective At-risk Altra and TipRA cohorts.
  • FIGs. 31 A, 31 B Proteomic signature model applied onto the TipRA external validation cohort.
  • (31 A) The ROC curve line with a 95% confidence interval shaded area.
  • FIGs. 32A, 32B (32A) CCP3+VEGFD predictive performance across all 3 cohorts. Left: ROC curves for all 3 cohorts with their 95% confidence intervals. (32B) Boxplots of model scores across all 3 cohorts, separated by outcome status.
  • FIG. 33 The utility of CCP3-only can be compared to CCP3, VEGFD Model predictions that combine CCP3 + Proteomic signals (see FIG. 32B).
  • RA Rheumatoid arthritis
  • Pre-RA or at- risk of developing RA
  • Pre-RA can be defined as the presence of abnormal biomarkers and/or other features (e.g., articular symptoms) prior to the development of physician-confirmed RA (i.e., Clinical RA).
  • rheumatoid factor or antibodies directed against citrullinated proteins (anti-citrullinated protein antibodies (ACPA)
  • RF rheumatoid factor
  • ACPA anti-citrullinated protein antibodies
  • the present disclosure describes evaluation of samples from pre-RA and controls from the Department of Defense Serum Repository (DoDSR) to determine whether proteomic testing can identify evolving biologic features in pre-RA that are useful to improve prediction for future Clinical RA and identify potential stage-specific targets for preventive interventions.
  • DoDSR Department of Defense Serum Repository
  • This disclosure also describes follow-up experiments using the MSD platform to validate findings from the DoDSR cohort in two external clinical cohorts, TipRA and ALTRA.
  • the present disclosure describes evaluation of 197 proteins on the Clink platform in serum obtained from the DoDSR from the pre-RA period from 213 validated RA cases and 215 controls (Table 1). Samples were available up to 21 years prior to RA diagnosis. Samples were also tested for ACPA and RF using anti-cyclic citrullinated peptide 3 (anti-CCP3) and RF-lgA and RF-IgM ELISA assays (Werfen, Spain). Linear mixed effect models and Mann-Whitney testing were applied to evaluate participant-specific trajectories for pre-RA and controls in discrete time periods prior to RA diagnosis (i.e. , in the pre-RA period), and to identify proteins and KEGG Pathways. All p-values were adjusted for multi-testing with false discovery rate (FDR).
  • FDR false discovery rate
  • Table 1 Demographics of subjects in DoDSR study.
  • CCP3, RFIgA, and RFIgM showed significant elevation in pre-RA, with the highest levels observed ⁇ 5 years prior to diagnosis (FIG. 3).
  • 104 proteins that were differentially expressed in pre-RA samples compared to controls were identified (FIGs. 13A, 13B).
  • 60/104 proteins were differentially expressed and 21 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were enriched ⁇ 5 years of RA diagnosis (FIG.
  • FIG. 18 there are 101 biomarkers (protein and serology) in the Venn Diagram comparing ACPA+ vs HC at the time of RA diagnosis and ACPA- vs. HC at the time of RA diagnosis.
  • 56 are common to ACPA+ and ACPA- (APBB1 IP, BCL2L11 , BGN, BOC, CBL, CCP3lgG, CFC1 , Circulating Calprotectin (CirCalpro), CXCL11, CXCL12, DCBLD2, DCTN1 , DFFA, DGKZ, DNAJB1 , EGLN1 , ENTPD6, FAM19A5, FGF21 , FLI1, FXYD5, GCNT1, GFRA2, GKN1 , HCLS1 , HS6ST1 , ICA1 , IGSF3, IL10, IL6, ITGA6, ITGB6, LAG3, TGFB1 , LGALS7, LYAR,
  • the biomarkers include TRIM21 , TNFRSF10A, SIT1 , SEZ6L2, RFIgA, RFIgM, LAG3, GCNT1 , FGF21 , Circulating Calprotectin, CCP3, APBB1IP, GCG, SLAMF8, SLITRK2, SORCS2, FAM19A5, BGN, AMIGO2, CBL, OMG, CFC1 , SKAP1 , MRC2, LYAR, SLITRK6, GALNT2, GFRA2, BCL2L11 , MOG, GKN1 , PRDX6, METAP1 D, NFATC1 , TACSTD2, TDRKH, ARHGEF12, SEMA4C, CDNF, BOC, TP53, ARHGAP1 , ATG4A, SULT2A1 , LGALS7, BCR, PCDH17
  • detected levels of ACPA/anti-CCP3, RFIgA, and/or RFIgM are used.
  • time to onset of Clinical RA can be predicted.
  • methods and kits disclosed herein predict a time to onset of Clinical RA in more than 5 years and a time to onset of Clinical RA in less than 5 years.
  • methods and kits predict a time to onset of Clinical RA in more than 3 years and a time to onset of Clinical RA in less than 3 years.
  • increased or decreased levels of the biomarkers indicates the presence of pre-RA or RA.
  • Increased or decreased levels can be represented or indicated by a “detection value” (e.g., representing a concentration, ratio, percent, etc.).
  • the disclosure provides that increased or decreased levels are indicative of time to onset of Clinical RA
  • different reference points can lead to different directional changes indicative of timing relative to Clinical RA. For example, one may collect a data set of detected expression levels of a set of biomarkers from patients who developed Clinical RA. If this data set were used as the reference range, then progression to Clinical RA would be indicated if there was no significant difference from the reference range. An increase or decrease in detected expression level is indicative of progression to Clinical RA when the reference range is in relation to normal samples (e.g., healthy controls).
  • the biomarkers identified in the present disclosure can be used to determine biological pathways that are relevant to certain time points in pre-RA evolution.
  • biomarkers can be used to identify specific processes and pathways that can be targeted with preventive interventions.
  • biomarker refers to a biological indicator found at different levels in a sample from a subject.
  • Biomarkers can be derived from various biological sources such as tissues, cells, or body fluids.
  • the expression of a biomarker or set of biomarkers compared to a reference level, wherein the biomarker or set of biomarkers are derived from a subject can be used to stratify the subject as a healthy individual, a pre-RA individual or a Clinical RA individual.
  • biomarker or set of biomarkers compared to a reference level can be used to predict a timeframe for onset of Clinical RA. All descriptions of predicted onset are in comparison to the time of measuring the biomarker(s) unless described otherwise.
  • Particular embodiments of the present disclosure use detected expression levels of the following biomarkers: APBB1 IP, BCL2L11 , BGN, BOC, CBL, CCP3lgG, CFC1 , Calprotectin (e.g., Circulating Calprotectin), CXCL11 , CXCL12, DCBLD2, DCTN1 , DFFA, DGKZ, DNAJB1 , EGLN1 , ENTPD6, FAM19A5, FGF21, FLI1 , FXYD5, GCNT1 , GFRA2, GKN1 , HCLS1 , HS6ST1 , ICA1 , IGSF3, IL10, IL6, ITGA6, ITGB6, LAG3, TGFB1 , LGALS7, LYAR, MAP2K6, MASP1 , MCP3, MRC2, PCDH17, PPP1 R9B, PRDX1, PRDX6, RFIgA, RFIgM, SEMA4C, SH
  • Particular embodiments of the present disclosure use detected expression levels of the following biomarkers: ARHGEF12, BCL2L11 , BOC, CCL7, CXCL11 , CXCL12, DCTN1 , DCTN2, DFFA, DGKZ, DNAJB1 , EGLN1 , FAM19A5, FGF21, GCNT1 , GDNF, GFRA2, HCLS1, IL6, IRAK4, ITGA11 , ITGA6, LAG3, TGFB1 LGALS7, LYAR, MAP2K6, MASP1 , MCP3, MOG, NF2, OMG, PPP1 R9B, PRDX1, PRDX6, SEMA4C, SH2D1A, SIT1 , SKAP1, SLITRK6, STC1 , TAFA5, TDRKH, and/or TP53 to distinguish pre-RA subjects 0-15 years prior to disease onset from healthy subjects.
  • Particular embodiments of the present disclosure use detected expression levels of AMIGO2, APBB1 IP, ARHGAP1 , ATG4A, BCR, BGN, CBL, CD8A, CDNF, CDSN, CFC1 , CKAP4, CLEC4G, CLEC6A, DCBLD2, DPP10, EIF5A, ENTPD6, FAM3B, FLI1, FXYD5, GALNT2, GCG, GKN1, GLB1, HEXIM1, HNMT, HS3ST3B1, HS6ST1, ICA1, IGSF3, IL10, IL17C, IL7, IRAKI, ITGB6, KRT19, LAMP3, LY75, MRC2, NFATC1, NFATC3, NFKBIE, OPG, PCDH17, PIK3AP1, PLXNA4, PTH1R, SEZ6L2, SLAMF8, SLITRK2, SORCS2, SRPK2, SULT2A1 , TACSTD2, TANK,
  • Particular embodiments of the present disclosure use detected expression levels of CLSTN3 and/or META P1 D to distinguish pre-RA subjects greater than 5 years from disease onset from healthy subjects.
  • Particular embodiments of the present disclosure can predict onset of Clinical RA in ACPA+ subjects.
  • Particular embodiments of the present disclosure use detected expression levels of APBB1IP, BCL2L11, BGN, BOC, CBL, CCL7, CCP3lgG, CFC1, Calprotectin (e.g., circulating calprotectin), CXCL11, CXCL12, DCBLD2, DCTN1, DFFA, DGKZ, DNAJB1, EGLN1, ENTPD6, FAM19A5, FGF21, FLI1, FXYD5, GCNT1, GFRA2, GKN1, HCLS1, HS6ST1, ICA1, IGSF3, IL10, IL6, ITGA6, ITGB6, LAG3, TGFB1, LGALS7, LYAR, MAP2K6, MASP1, MCP3, MRC2, PCDH17, PPP1R9B, PRDX1, PRDX6, RFIgA, RFIgM, SEMA4C, SH2D1A, SIT
  • Particular embodiments of the present disclosure use detected expression levels of GCG, SORCS2, AMIGO2, GALNT2, MOG, NFATC1, TACSTD2, ARHGEF12, CDNF, ARHGAP1, ATG4A, BCR, HS3ST3B1, NFKBIE, DCTN2, CLSTN3, GLB1, IRAK4, CLEC4G, IRAKI, CDSN, KRT19, HNMT, NFATC3, EIF5A, PLXNA4, CKAP4, TREM1, SRPK2, PIK3AP1 , FAM3B, LAMP3, CLEC6A, TANK, VEGFA, CD8A, GDNF, OPG, IL17C, ZBTB16, SLAMF8, SLITRK2, FAM19A5, BGN, CBL, CFC1, SKAP1, MRC2, LYAR, SLITRK6, GFRA2, BCL2L11, GKN1, PRDX6, TDRKH, SEMA4C, BOC, TP
  • Particular embodiments of the present disclosure use detected expression levels of GCG, S0RCS2, AMIG02, GALNT2, NFATC1 , TACSTD2, CDNF, ARHGAP1, ATG4A, BCR, HS3ST3B1, NFKBIE, GLB1 , CLEC4G, IRAKI, CDSN, KRT19, HNMT, NFATC3, EIF5A, PLXNA4, CKAP4, TREM1, SRPK2, PIK3AP1, FAM3B, LAMP3, CLEC6A, TANK, VEGFA, CD8A, OPG, IL17C, ZBTB16, SLAMF8, SLITRK2, BGN, CBL, CFC1 , MRC2, GKN1 , PCDH17, APBB1 IP, HS6ST1 , IGSF3, FLI1 , ENTPD6, FXYD5, IL10, ICA1 , ITGB6, DCBLD2, SULT2A1 , TPSAB1
  • Particular embodiments of the present disclosure use detected expression levels of CLSTN3 and/or M ETAP1 D to predict onset of Clinical RA in more than 5 years in ACPA+ subjects.
  • methods and systems disclosed herein can be used to predict the onset of Clinical RA within 3 years.
  • Particular embodiments use detected expression levels of VEGFA, CXCL11 , IL17RB, OPG, LAG3, VEGFD, PRDX6, IL-7, IL-17A, CXCL12, FGF21, and/or Calprotectin or a subset thereof to predict the onset of clinical RA within 3 years in ACPA+ subjects.
  • Particular embodiments use detected expression levels of APBB1 IP, CCP3, Circulating Calprotectin, FGF21 , GCNT1, LAG3, RFIgA, SEZ6L2, SIT1 , TNFRSF10A, and/or TRIM21 or a subset thereof to predict the onset of clinical RA within 3 years in ACPA+ subjects.
  • Particular embodiments use detected expression levels of Circulating Calprotectin, CCP3, CXCL11 , CXCL12, FGF21 , IL7, IL17RB, OPG, PRDX6, RFIgA, VEGFA, and/or VEGFD or a subset thereof to predict the onset of clinical RA within 3 years in ACPA+ subjects.
  • Particular embodiments use detected expression levels of CCP3 and/or VEGFD to predict the onset of clinical RA within 3 years in ACPA+ subjects.
  • sex of the subject can be considered in predicting the onset of clinical RA in ACPA+ subjects.
  • Particular embodiments of the present disclosure can predict onset of Clinical RA in ACPA- subjects.
  • Particular embodiments of the present disclosure use the detected expression levels of APBB1 IP, BCL2L11 , BGN, BOC, CBL, CCP3lgG, CFC1 , Circulating Calprotectin, CXCL11 , CXCL12, DCBLD2, DCTN1, DFFA, DGKZ, DNAJB1 , EGLN1 , ENTPD6, FAM19A5, FGF21 , FLI1 , FXYD5, GCNT1 , GFRA2, GKN1 , HCLS1 , HS6ST1 , ICA1 , IGSF3, IL10, IL6, ITGA6, ITGB6, LAG3, TGFB1, LGALS7, LYAR, MAP2K6, MASP1 , MCP3, MRC2, PCDH17, PPP1 R9B, PRDX1 , PRDX6, RFIgA, RFIgM,
  • the present disclosure describes use of the detected expression level of CNTNAP2, SEZ6L2, DPP10, NF2, SLAMF8, SLITRK2, FAM19A5, BGN, CBL, CFC1, SKAP1 , MRC2, LYAR, SLITRK6, GFRA2, BCL2L11 , GKN1 , PRDX6, TDRKH, SEMA4C, BOC, TP53, FGF21 , LGALS7, PCDH17, APBB1IP, HS6ST1, IGSF3, DNAJB1, FLI1, MAP2K6, ENTPD6, GCNT1, PPP1R9B, HCLS1, DGKZ, PRDX1, DCTN1, ITGA6, FXYD5, EGLN1, IL10, STC1, SH2D1A, ICA1, DFFA, CL12, SIT1, MASP1, LAG3, ITGB6, MCP3, TGFB1, CXCL11, DCBLD2, IL6, OMG, METAP1
  • Particular embodiments of the present disclosure use the detected expression levels of SEZ6L2, DPP10, SLAMF8, SLITRK2, BGN, CBL, CFC1, MRC2, GKN1, PCDH17, APBB1IP, HS6ST1, IGSF3, FLI1, ENTPD6, FXYD5, IL10, ICA1, ITGB6, DCBLD2, SULT2A1, TPSAB1, LY75, PTH1R, HEXIM1, and/or IL7 to predict onset of Clinical RA within 5 years in ACPA- subjects.
  • Particular embodiments of the present disclosure use the detected expression levels of METAP1D to predict onset of Clinical RA in more than 5 years in ACPA- subjects.
  • biomarkers OMG, METAP1D, SULT2A1, TPSAB1, LY75, PTH1R, HEXIM1, ITGA11, and/or IL7 do not have enough signal to be significant in either the ACPA+ alone or ACPA- alone analyses, however, they can be treated as “common” to both since they are differential when these groups are aggregated. Alternatively, some biomarkers may not be differential when aggregating all RA subjects together as the signal can be masked but may be differential when ACPA+ and ACPA- subjects are split. These biomarkers are important for differentiating RA subtypes.
  • biomarker may also actively exclude use of a biomarker.
  • certain embodiments exclude use of any biomarker other than GCG, SORCS2, AMIGO2, GALNT2, MOG, NFATC1, TACSTD2, ARHGEF12, CDNF, ARHGAP1, ATG4A, BCR, HS3ST3B1, NFKBIE, DCTN2, CLSTN3, GLB1, IRAK4, CLEC4G, IRAKI, CDSN, KRT19, HNMT, NFATC3, EIF5A, PLXNA4, CKAP4, TREM1, SRPK2, PIK3AP1, FAM3B, LAMP3, CLEC6A, TANK, VEGFA, CD8A, GDNF, OPG, IL17C, ZBTB16, SLAMF8, SLITRK2, FAM19A5, BGN, CBL, CFC1, SKAP1, MRC2, LYAR, SLITRK6, GFRA2, BCL2L11, GKN1, PRDX
  • Certain embodiments exclude use of any biomarker other than SEZ6L2, DPP10, NF2, SLAMF8, SLITRK2, FAM19A5, BGN, CBL, CFC1, SKAP1, MRC2, LYAR, SLITRK6, GFRA2, BCL2L11, GKN1, PRDX6, TDRKH, SEMA4C, BOC, TP53, FGF21, LGALS7, PCDH17, APBB1IP, HS6ST1 , IGSF3, DNAJB1, FLI1 , MAP2K6, ENTPD6, GCNT1 , PPP1 R9B, HCLS1 , DGKZ, PRDX1 , DCTN1 , ITGA6, FXYD5, EGLN1 , IL10, STC1 , SH2D1A, ICA1, DFFA, CL12, SIT1 , MASP1, LAG3, ITGB6, MCP3, TGFB1 , CXCL11 , DCBLD
  • values of the detected biomarkers can be calculated into a score.
  • Each value can be weighted evenly within an algorithm generating a score, or the values for particular biomarkers can be weighted more heavily in reaching the score.
  • biomarkers with higher area under the curve (AUG) and/or level difference scores could be weighted more heavily than biomarkers with lower AUC and/or level difference scores.
  • Biomarkers may also be grouped into classes, and each class given a weighted score. For example, biomarker values for distinguishing normal samples from individuals who developed RA may be grouped into classes (e.g., Class 1, Class 2, Class 3, etc.), and each class may be weighted differently.
  • Any biomarker or class of biomarkers can be included in a particular value calculation.
  • Class 1 is included.
  • Class 2 is included.
  • Class 3 is included.
  • groups of classes can be included, for example, Class 1 and Class 2; Class 1 and Class3; and/or Class 2 and Class 3.
  • Particular classes can also be excluded.
  • Class 1 is excluded.
  • Class 2 is excluded.
  • Class 3 is excluded.
  • a biomarker includes a protein or an indication of protein expression.
  • methods can include detection of full-length proteins, mature proteins, pre-proteins, polypeptides, isoforms, mutations, post-translationally modified proteins, messenger RNA (mRNA) and variants thereof, and can be detected in any suitable manner.
  • mRNA messenger RNA
  • a biomarker is detected by contacting a sample with reagents (e.g., antibodies), generating complexes of reagent and biomarker(s), and detecting the complexes.
  • reagents e.g., antibodies
  • Biomarkers can be detected using numerous detection methods known to one of ordinary skill in the art.
  • Particular embodiments for detecting and measuring biomarker levels can use methods including agglutination, chemiluminescence, electro-chemiluminescence (ECL), enzyme-linked immunoassays (ELISA), quantitative PCR (qPCR), surface plasmon resonance (SPR), immunoblotting (e.g., western blotting), immunodiffusion, immunoelectrophoresis, immunofluorescence, immunohistochemistry, immunoprecipitation, mass-spectrometry, chromatography, radioimmunoassay, or other quantitative or qualitative proteomic assays. See also, e.g., E. Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, Fla; and U.S. Pat. Nos. 4,727,022; 4,659,678; 4,376,110; 4,275,149; 4,233,402; and 4,230,797.
  • the level of expression of target proteins can be measured using immunoassays which may use an antibody as a probe to identify a biomarker.
  • An antibody can be a whole antibody or a binding fragment of an antibody.
  • Antibodies can be conjugated to a solid support suitable for a diagnostic 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) in accordance with known techniques, such as passive binding.
  • Antibodies can be conjugated to detectable labels or groups such as radiolabels (e.g., 35S, 1251, 1311), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
  • radiolabels e.g., 35S, 1251, 1311
  • enzyme labels e.g., horseradish peroxidase, alkaline phosphatase
  • fluorescent labels e.g., fluorescein, Alexa, green fluorescent protein, rhodamine
  • biomarker levels can be assessed by a protein activity assay.
  • protein activity assays include protease assays, kinase assays, phosphatase assays, and reductase assays, among many others.
  • levels or amounts of biomarkers of the present disclosure can be measured by chromatography, a process in which a chemical mixture carried by a liquid or gas is separated into components as a result of differential distribution of the chemical entities as they flow around or over a stationary liquid or solid phase.
  • the chromatography is liquid chromatography (LC), a process of selective retardation of one or more components of a fluid solution as the fluid uniformly percolates through a column of a finely divided substance, or through capillary passageways. The retardation results from the distribution of the components of the mixture between one or more stationary phases and the bulk fluid, (i.e., mobile phase), as this fluid moves relative to the stationary phase(s).
  • LC liquid chromatography
  • Liquid chromatography includes reverse phase liquid chromatography (RPLC), high performance liquid chromatography (HPLC) and high turbulence liquid chromatography (HTLC).
  • RPLC reverse phase liquid chromatography
  • HPLC high performance liquid chromatography
  • HTLC high turbulence liquid chromatography
  • the degree of separation is increased by forcing the mobile phase under pressure through a stationary phase, typically a densely packed column.
  • the chromatography is gas chromatography (GC), a process in which a sample mixture is vaporized and injected into a stream of carrier gas (such as nitrogen or helium) moving through a column containing a stationary phase composed of a liquid or a particulate solid and is separated into its component compounds according to the affinity of the compounds for the stationary phase.
  • GC gas chromatography
  • the chromatography is thin layer chromatography.
  • Thin layer chromatography separates compounds on a thin layer of adsorbent material typically including a coating of silica gel on a glass plate or plastic sheet.
  • levels or amounts of biomarkers of the present disclosure can be measured by ultraviolet (UV) spectroscopy, which measures the attenuation of a beam of light after it passes through a sample or after the light is reflected from the sample surface.
  • UV ultraviolet
  • the absorption measurements can be at a single wavelength of light or can be over a spectral range.
  • levels or amounts of biomarkers of the present disclosure can be measured by capillary electrophoresis, which separates molecules in submillimeter diameter capillaries, microfluidic, or nanofluidic channels containing electrolyte solutions under the influence of an electric field.
  • Capillary electrophoresis can include gel electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, and micellar electrokinetic chromatography. Separated molecules appear as peaks with different retention times in an electropherogram, which reports detector response as a function of time.
  • MS mass spectrometry
  • MS technology generally includes (1) ionizing the compounds to form charged compounds; and (2) detecting the molecular weight of the charged compound and calculating a mass-to-charge ratio (m/z).
  • the compound may be ionized and detected by any suitable means.
  • a “mass spectrometer” generally includes an ionizer and an ion detector. See, e.g., US 6,204,500; 6,107,623; 6,268,144; 6,124,137; Wright et al., Prostate Cancer and Prostatic Diseases. 1999, 2:264-76; and Merchant and Weinberger, Electrophoresis. 2000, 21 :1164-67.
  • Samples may be processed or purified to obtain preparations that are particularly suitable for analysis by mass spectrometry.
  • Such purification will usually include chromatography, such as liquid chromatography, and may also often involve an additional purification procedure that is performed prior to chromatography.
  • chromatography such as liquid chromatography
  • Various procedures may be used for this purpose depending on the type of sample or the type of chromatography. Examples include filtration, extraction, precipitation, centrifugation, delipidization, dilution, combinations thereof and the like.
  • Probes for nucleic acids or proteins against a biomarker can be linked to chips, such as microarray chips. See, for example, US 5,143,854; 6,087,112; 5,215,882; 5,707,807; 5,807,522; 5,958,342; 5,994,076; 6,004,755; 6,048,695; 6,060,240; 6,090,556; and 6,040,138.
  • Microarray refers to a solid carrier or support that has a plurality of molecules bound to its surface at defined locations.
  • the solid carrier or support can be made of any material.
  • the material can be hard, such as metal, glass, plastic, silicon, ceramics, and textured and porous materials; or soft materials, such as gels, rubbers, polymers, and other non-rigid materials.
  • the material can also be nylon membranes, epoxy-glass and borofluorate-glass.
  • the solid carrier or support can be flat, but need not be and can include any type of shape such as spherical shapes (e.g., beads or microspheres).
  • the solid carrier or support can have a flat surface as in slides and micro-titer plates having one or more wells.
  • the probes can be attached to one of a variety of solid substrates capable of withstanding the reagents and conditions necessary for use of the array.
  • Examples include polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene and polystyrene; ceramic; silicon; silicon dioxide; modified silicon; (fused) silica, quartz or glass; functionalized glass; paper, such as filter paper; diazotized cellulose; nitrocellulose filter; nylon membrane; and polyacrylamide gel pad.
  • Substrates that are transparent to light are useful for arrays that may be used in an assay that involves optical detection
  • Examples of array formats include membrane or filter arrays (for example, nitrocellulose, nylon arrays), plate arrays (for example, multiwell, such as a 24-, 96-, 256-, 384-, 864- or 1536- well, microtitre plate arrays), pin arrays, and bead arrays (for example, in a liquid “slurry”).
  • Arrays on substrates such as glass or ceramic slides are often referred to as chip arrays or “chips.” Such arrays are well known in the art.
  • a probe can be labeled with a detectable label.
  • a “detectable label” refers to a molecule capable of detection, including fluorescers, chemiluminescers, dyes, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, enzyme subunits, metal ions, semiconductor nanoparticles, chromophores, ligands (e.g., biotin, streptavidin, or haptens), and radioactive isotopes.
  • fluorescer refers to a substance or a portion thereof which is capable of exhibiting fluorescence in a detectable range.
  • Particular examples of labels which may be used include horseradish peroxidase (HRP), SYBR® (Molecular Probes, Inc.
  • binding of probes and biomarkers on microarrays can be detected by scanning the microarray with a variety of laser or CCD-based scanners, and extracting features with software packages, for example, Imagene (Biodiscovery, Hawthorne, CA), Feature Extraction Software (Agilent), Scanalyze (Eisen, M. 1999. SCANALYZE User Manual; Stanford Univ., Stanford, CA Ver 2.32.), or GenePix (Axon Instruments).
  • software packages for example, Imagene (Biodiscovery, Hawthorne, CA), Feature Extraction Software (Agilent), Scanalyze (Eisen, M. 1999. SCANALYZE User Manual; Stanford Univ., Stanford, CA Ver 2.32.), or GenePix (Axon Instruments).
  • Embodiments disclosed herein can be used with high throughput screening (HTS).
  • HTS refers to a format that performs at least 100 assays, at least 500 assays, at least 1000 assays, at least 5000 assays, at least 10,000 assays, or more per day.
  • HTS refers to a format that performs at least 100 assays, at least 500 assays, at least 1000 assays, at least 5000 assays, at least 10,000 assays, or more per day.
  • HTS methods involve a logical or physical array of either samples, or the nucleic acid or protein biomarkers, or both.
  • Appropriate array formats include both liquid and solid phase arrays.
  • assays employing liquid phase arrays e.g. , for hybridization of nucleic acids, binding of antibodies or other receptors to ligand, etc., can be performed in multiwell or microtiter plates.
  • Microtiter plates with 96, 384, or 1536 wells are widely available, and even higher numbers of wells, e.g., 3456 and 9600 can be used.
  • the choice of microtiter plates is determined by the methods and equipment, e.g., robotic handling and loading systems, used for sample preparation and analysis.
  • HTS assays and screening systems are commercially available from, for example, Zymark Corp. (Hopkinton, MA); Air Technical Industries (Mentor, OH); Beckman Instruments, Inc. (Fullerton, CA); Precision Systems, Inc. (Natick, MA), etc. These systems typically automate entire procedures including all sample and reagent pipetting, liquid dispensing, timed incubations, and final readings of the microplate in detector(s) appropriate for the assay. These configurable systems provide HTS as well as a high degree of flexibility and customization. The manufacturers of such systems provide detailed protocols for the various methods of HTS.
  • these systems and methods provide a quantitative detection of the biomarker in the sample being assayed, i.e., an evaluation or assessment of the actual amount or relative abundance of the biomarker in the sample being assayed.
  • the quantitative detection may be absolute or, when comparing two or more different biomarkers in a sample, relative.
  • the term “quantifying” when used in the context of quantifying a biomarker in a sample can refer to absolute or to relative quantification. Absolute quantification can be accomplished by inclusion of known concentration(s) of one or more control biomarkers and referencing, e.g., normalizing, the detected level of the biomarker with the known control biomarkers (e.g., through generation of a standard curve).
  • relative quantification can be accomplished by comparison of detected levels or amounts between two or more different biomarkers to provide a relative quantification of each of the two or more biomarkers, e.g., relative to each other.
  • the actual measurement of values of the biomarkers can be determined using any method known in the art.
  • a biomarker is detected by measuring the expression level of a protein or proteins. Still, a biomarker can also be detected by measuring the expression levels of gene transcripts. Methods of measuring the expression of gene transcripts are known in the art.
  • Measurement of mRNA levels can also be assessed. Any technique for determining expression levels of mRNA can be used including Northern blot analysis, fluorescent in situ hybridization (FISH), RNase protection assays (RPA), microarrays, PCR-based, or other technologies for measuring RNA levels can be used.
  • FISH fluorescent in situ hybridization
  • RPA RNase protection assays
  • microarrays PCR-based, or other technologies for measuring RNA levels can be used.
  • Up- or down-regulation of genes can be detected indirectly using, for example, cDNA arrays, cDNA fragment fingerprinting, cDNA sequencing, clone hybridization, differential display, differential screening, FRET detection, liquid microarrays, PCR, RT-PCR, quantitative RT-PCR analysis with TaqMan assays, molecular beacons, microelectric arrays, oligonucleotide arrays, polynucleotide arrays, serial analysis of gene expression (SAGE), and/or subtractive hybridization. Further hybridization technologies that may be used are described in, for example, U.S. Pat. Nos.
  • the changes (e.g., increases or decreases) in biomarker expression can be assessed.
  • the detected amount of one or more biomarkers can be indicated as a value.
  • the value can be one or more numerical values resulting from the assaying of a sample, and can be derived, e.g., by measuring the level of the biomarker(s) in the sample by an assay, or from a dataset obtained from a provider such as a laboratory, or from a dataset stored on a server.
  • the value may be qualitative or quantitative.
  • the methods and kits provide a reading or evaluation, e.g., assessment, of whether or not the biomarker is present in the sample being assayed.
  • the methods and kits provide a quantitative value, i.e., an evaluation or assessment of the actual amount or relative abundance of the biomarker in the sample being assayed.
  • the quantitative value may be absolute or relative.
  • the term “quantifying” when used in the context of quantifying a biomarker in a sample can refer to absolute or to relative quantification.
  • Absolute quantification can be accomplished by inclusion of samples with known parameters as one or more control biomarkers and referencing, e.g., normalizing the detected level of the experimental biomarker with the known control biomarkers (e.g., through generation of a standard curve).
  • relative quantification can be accomplished by comparison of generated detection values between two or more different biomarkers to provide a relative quantification of each of the two or more biomarkers, e.g., relative to each other.
  • the actual measurement of values for the biomarkers can be determined using any method known in the art.
  • Detected biomarker levels can be compared to one or more reference levels.
  • Reference levels can be obtained from one or more relevant datasets.
  • a "dataset” as used herein is a set of numerical values resulting from evaluation of a sample (or population of samples) under a desired condition. The values of the dataset can be obtained, for example, by experimentally obtaining measures from sample(s) and constructing a dataset from these measurements.
  • the reference level can be based on e.g., any mathematical or statistical formula useful and known in the art for arriving at a meaningful aggregate reference level from a collection of individual datapoints; e.g., mean, median, median of the mean, etc.
  • a reference level or dataset to create a reference level can be obtained from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored.
  • Reference levels can be obtained from one or more relevant datasets.
  • a reference level from a dataset can be derived from previous measures derived from a population.
  • a “population” is any grouping of subjects or samples of like specified characteristics. The grouping could be according to, for example, clinical parameters, clinical assessments, therapeutic regimens, disease status, severity of symptoms, etc.
  • a population is a group of subjects with normal or healthy samples.
  • a population is a group of subjects with pre-RA or Clinical RA.
  • conclusions are drawn based on whether a detection value is statistically significantly different or not statistically significantly different from a reference level.
  • a measure is not statistically significantly different if the difference is within a level that would be expected to occur based on chance alone. In contrast, a statistically significant difference is one that is greater than what would be expected to occur by chance alone.
  • Statistical significance or lack thereof can be determined by any of various methods well-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 equivalent to a particular datapoint, where the datapoint is the result of random chance alone. A result is often considered significant (not random chance) at a p-value less than 0.05.
  • detection values obtained based on the biomarkers and/or other dataset components can be subjected to an analytic process with chosen parameters.
  • the parameters of the analytic process may be those disclosed herein or those derived using the guidelines described herein.
  • the analytic process used to generate a result may be any type of process capable of distinguishing normal samples from pre-RA samples or samples from individuals who will develop Clinical RA based on level detection, for example, a linear algorithm, a quadratic algorithm, a decision tree algorithm, or a voting algorithm.
  • the analytic process may set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 60%, at least 70%, at least 80%, at least 90%, at least 95% or higher.
  • detection relies on performing a detection and/or quantification assay on the sample.
  • the receiver operating characteristics (ROC) curve is a graph plotting sensitivity (true positive rate), which is defined in this setting as the percentage of pre-RA cases with a positive test on the Y axis and false positive rate (1-specificity), i.e. , the number of normal cases with a positive test on the X-axis. False positive rate refers to the percentage of normal subjects falsely found to have a positive test.
  • the area under the ROC curves indicates the accuracy of the test in identifying normal from abnormal cases (Hanley & McNeil, Radiology 1982; 143:29-36).
  • the AUC is the area under the ROC plot from the curve to the diagonal line from the point of intersection of the X- and Y- axes and with an angle of incline of 45° (a test with no discrimination between two groups, e.g., normal samples vs. pre-RA), has a 45° diagonal line from the lower left to the upper right corner).
  • the higher the area under the ROC curve the greater the accuracy of the test in predicting the condition of interest.
  • An area ROC 1.0 indicates a perfect test, which is positive in all cases with the disorder (e.g., pre-RA) and negative in all normal individuals without the disorder (e.g., normal samples). Thus, the closer the plot is to the upper left corner, the higher the overall accuracy of the test.
  • the method includes utilizing a logistic regression model (e.g., as described in the
  • a logistic regression model can be trained on sample sets (training sets) as where p and p stand for the probability score of the specified sample being positive and the intercept or coefficient of each biomarker.
  • Kits disclosed herein include materials to assay a sample for the level of one or more biomarkers disclosed herein.
  • Materials include reagents to contact the one or more biomarkers disclosed herein (e.g., antibodies, aptamers, antibody fragments, nanobodies, affinity peptides (e.g., peptide ligands)), resulting in the generation of complexes of the reagent(s) and biomarker(s). Additional embodiments include detection reagents (e.g., reagents to detect the complexes).
  • biomarkers e.g., antibodies, aptamers, antibody fragments, nanobodies, affinity peptides (e.g., peptide ligands)
  • Exemplary detection reagents can include nanoparticles (e.g., gold nanoparticles, silver nanoparticles, silica nanoparticles, carbon nanotubes, polymer nanoparticles, semiconductor nanoparticles, quantum dots), beads (e.g., cellulose beads, magnetic beads, polystyrene beads, streptavidin-coated beads), fluorophores, enzymes and substrates (e.g., horseradish peroxidase, alkaline phosphatase, p-Galactosidase), luminescent proteins, or chemiluminescent substrates), radioactive isotopes or radiolabels (e.g., 32P and 13C), enzymes (e.g., luciferase, horseradish peroxidase, alkaline phosphatase), dyes (e.g., rhodamine and cyanine), fluorescent tags or dyes (e.g., GFP, YFP, FITC), or biotin
  • Particular embodiments can include reference levels and/or control conditions (positive and/or negative).
  • Instructions for carrying out and interpreting biomarker detection assays can also be included in a kit. Instructions can be provided in written, taped, videoed, VCR, CD-ROM, flashdrive, USB formats or can be provided on a website or other remote location.
  • kits exclude equipment (e.g., plate readers). In particular embodiments, kits exclude materials commonly found in laboratory settings (pipettes; test tubes; distilled H2O).
  • a kit includes materials needed for detecting and/or quantifying the biomarkers disclosed herein.
  • the kit can be specific to a particular use such as a kit used to i) distinguish pre-RA subject from healthy subjects, ii) predict onset of Clinical RA, iii) predict onset of Clinical RA to be within 5 years, iv) predict onset of Clinical RA to be in more than 5 years, v) distinguish ACPA+ pre-RA subjects from healthy subject, vi) predict onset of Clinical RA in ACPA+ subjects, vii) predict onset of Clinical RA to be within 5 years in ACPA+ subjects, viii) predict onset of Clinical RA to be in more than 5 years in ACPA+ subjects, ix) predict onset of Clinical RA to be within 3 years in ACPA+ subjects, x) distinguish ACPA- pre-RA subjects from healthy subjects, xi) predict onset of Clinical RA in ACPA- subjects, xii) predict onset of Clinical RA to be within 5 years in
  • the systems and methods disclosed herein utilize materials, methods, and reagents necessary to predict the progression from pre-RA to Clinical RA. Furthermore the systems and methods can stratify subjects as i) healthy, having pre-RA, or having Clinical RA; and/or ii) ACPA+ or ACPA-. Particular embodiments include assaying a sample derived from a subject for the presence or level of disclosed biomarkers or biomarker combinations.
  • the assayed sample can be any appropriate biological sample.
  • Particular embodiments utilize blood samples (e.g., whole blood samples, serum samples, plasma samples).
  • whole blood samples are centrifuged, and serum or plasma samples are isolated and refrigerated or frozen at -80°C for later analysis.
  • the sample is obtained from a healthy subject, a subject having pre-RA, or a subject having clinical RA.
  • a method includes obtaining a sample from a subject; performing a biomarker detection and/or quantification assay on the sample; and determining one or more biomarker levels based on the assaying.
  • the method further includes stratify subjects as healthy, having pre-RA, or having Clinical RA; and/or ACPA+ or ACPA-.
  • the method further includes predicting time of onset of Clinical RA. The stratification of subjects and/or the time of onset of Clinical RA can be determined by comparing the one or more biomarker levels to a reference level.
  • a prediction according to the systems and methods disclosed herein can direct a treatment regimen. For example, a prediction of a subject having Clinical RA or developing Clinical RA within the year may direct a more aggressive treatment course. A prediction of greater than 5 years until Clinical RA may direct a less aggressive treatment course.
  • Those of ordinary skill in the art classify treatments as aggressive, moderate, minimal or “no” treatment based on a subject’s prognosis, diagnosis, or predicted time of onset.
  • a treatment undergoing a clinical trial is considered to be an experimental treatment. Once the treatment is approved by a relevant regulatory agency within a jurisdiction, the treatment is no longer experimental in that jurisdiction.
  • RA classification pre-RA or Clinical RA
  • Treatments for pre-RA and/or Clinical RA include lifestyle changes or physical therapy, anti-inflammatory medications, disease-modifying antirheumatic drugs (DMARDs), cytokine antagonists, cytokine agonists, corticosteroids, surgery, or other pharmacological agents. Treatments can be used to delay, prevent, or treat rheumatoid arthritis.
  • Pre-RA patients and subjects that do not need an aggressive treatment might be recommended to make lifestyle changes. For example, changing exercise regimens, diet, or quitting habits (e.g., smoking) might be lifestyle changes recommended to avoid the progression to Clinical RA. In some cases, Clinical RA can be prevented by healthy lifestyle choices.
  • Anti-inflammatory medications can include non-steroidal anti-inflammatory drug(s) also referred to as NSAIDS.
  • NSAIDs can relieve pain and reduce inflammation.
  • Some over the counter NSAIDs include ibuprofen and naproxen sodium.
  • DMARDs are a class of drugs indicated for the treatment of inflammatory arthritides, including rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS).
  • DMARDs can include conventional synthetic DMARDs, biologic DMARDs, targeted synthetic DMARDs, or biosimilar DMARDs. They work by modulating biological pathways that promote disease pathology. Examples of a DMARD are hydroxychloroquine, leflunomide, methotrexate, parenteral gold, oral gold and sulfasalazine.
  • Cytokine antagonists can include any antibody or molecule that inhibits ore reduces activity of cytokines or growth factors such as TNF, IL-1 , IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL- 15, IL-16, IL-18, IL-21 , IL-23, interferons, EMAP-II, GM-CSF, FGF, or PDGF.
  • This can also include cytokine suppressive anti-inflammatory drugs (CSAIDs).
  • a cytokine antagonist includes a janus kinase (JAK) inhibitor.
  • Corticosteroids suppress the immune system and can be used to treat RA, inflammatory bowel disease (IBD), asthma, allergies, and several other conditions.
  • IBD inflammatory bowel disease
  • a commonly used corticosteroid for the treatment of RA includes prednisone.
  • a treatment can include a pain reliever and/or an immunosuppressant.
  • the biomarker levels are measured on a regular basis in order to surveil the progression of RA.
  • the biomarker levels are measured every 1 month to every 3 years.
  • the biomarker levels are measured every 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, or more.
  • the biomarker levels are measured 1 year, every 2 years, every 3 years, or more.
  • a method including: obtaining a sample derived from a subject; detecting a level of biomarkers selected from TRIM21 , TNFRSF10A, SIT1 , SEZ6L2, RFIgA, LAG3, GCNT1 , FGF21 , Circulating Calprotectin (CirCalpro), CCP3, CCL7, APBB1 IP, GCG, RFIgM, SLAMF8, SLITRK2, SORCS2, FAM19A5, BGN, AMIGO2, CBL, OMG, CFC1 , SKAP1 , MRC2, LYAR, SLITRK6, GALNT2, GFRA2, BCL2L11, MOG, GKN1 , PRDX6, METAP1 D, NFATC1 , TACSTD2, TDRKH, ARHGEF12, SEMA4C, CDNF, BOC, TP53, ARHGAP1, ATG4A, SULT2A1 , LGALS7, BCR, PCDH17,
  • RA status includes an indication that the subject is healthy, is at risk for RA (pre-RA), or has physician-confirmed RA (Clinical RA).
  • the detecting includes detecting levels ofTRIM21, TNFRSF10A, SIT1, SEZ6L2, RFIgA, LAG3, GCNT1 , FGF21 , Circulating Calprotectin, CCP3, APBB1IP, GCG, RFIgM, SLAMF8, SLITRK2, SORCS2, FAM19A5, BGN, AMIGO2, CBL, OMG, CFC1, SKAP1, MRC2, LYAR, SLITRK6, GALNT2, GFRA2, BCL2L11, MOG, GKN1, PRDX6, METAP1D, NFATC1, TACSTD2, TDRKH, ARHGEF12, SEMA4C, CDNF, BOC, TP53, ARHGAP1, FGF21, SEZ6L2, ATG4A, SULT2A1, LGALS7, BCR, PCDH17, APBB1IP, HS6ST1, IGSF3, HS3ST3B1,
  • detecting the level includes performing an agglutination test, a chemiluminescence test, an electro-chemiluminescence (ECL) test, an enzyme-linked immunoassay (ELISA), quantitative PCR (qPCR), surface plasmon resonance (SPR), immunoblotting, immunodiffusion, immunoelectrophoresis, immunofluorescence, immunohistochemistry, immunoprecipitation, mass-spectrometry, chromatography, or radioimmunoassay, or other quantitative or qualitative proteomic assays.
  • ECL electro-chemiluminescence
  • ELISA enzyme-linked immunoassay
  • qPCR quantitative PCR
  • SPR surface plasmon resonance
  • the treatment includes a lifestyle change or physical therapy, an anti-inflammatory medication, a disease-modifying antirheumatic drug (DMARD), a cytokine antagonist, cytokine agonist, a corticosteroid, or surgery.
  • DMARD disease-modifying antirheumatic drug
  • the anti-inflammatory medication includes a nonsteroidal anti-inflammatory drug (NSAID).
  • NSAID nonsteroidal anti-inflammatory drug
  • DMARD includes a conventional synthetic DMARD, a biologic DMARD, a targeted synthetic DMARD, or a biosimilar DMARD.
  • DMARD includes hydroxychloroquine, leflunomide, methotrexate, parenteral gold, oral gold, or sulfasalazine.
  • cytokine antagonist includes an inhibitor of TNF, IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-15, IL-16, IL-18, IL-21, IL-23, interferons, EMAP-II, GM-CSF, FGF, or PDGF.
  • a kit including reagents to detect a level of biomarkers selected from TRIM21 , TNFRSF10A, SIT1 , SEZ6L2, RFIgA, LAG3, GCNT1 , FGF21 , Circulating Calprotectin (CirCalpro), CCP3, APBB1 IP, GCG, RFIgM, SLAMF8, SLITRK2, SORCS2, FAM19A5, BGN, AMIGO2, CBL, OMG, CFC1 , SKAP1, MRC2, LYAR, SLITRK6, GALNT2, GFRA2, BCL2L11 , MOG, GKN1 , PRDX6, METAP1 D, NFATC1 , TACSTD2, TDRKH, ARHGEF12, SEMA4C, CDNF, BOC, TP53, ARHGAP1 , ATG4A, SULT2A1 , LGALS7, BCR, PCDH17, HS6ST1 , IGSF3,
  • kit of embodiment 54, wherein the subset has 1-20 biomarkers, 21-50 biomarkers, or 51-100 biomarkers.
  • kits of embodiments 54 or 55 wherein the subset thereof includes APBB11 P, BCL2L11 , BGN, BOC, CBL, CCP3lgG, CFC1 , Calprotectin (e.g., Circulating Calprotectin), CXCL11 , CXCL12, DCBLD2, DCTN1, DFFA, DGKZ, DNAJB1 , EGLN1 , ENTPD6, FAM19A5, FGF21 , FLI1 , FXYD5, GCNT1 , GFRA2, GKN1 , HCLS1 , HS6ST1 , ICA1, IGSF3, IL10, IL6, ITGA6, ITGB6, LAG3, TGFB1 , LGALS7, LYAR, MAP2K6, MASP1 , MCP3, MRC2, PCDH17, PPP1 R9B, PRDX1 , PRDX6, RFIgA, RFIgM, SEMA4C, SH2D1A,
  • kit of embodiment 65 wherein the subset thereof includes of CCP3 and VEGFD.
  • kit of any of embodiments 54-70 further including reagents to detect circulating anti- citrullinated protein antibodies (ACPA).
  • ACPA circulating anti- citrullinated protein antibodies
  • kit of any of embodiments 54-71 further including a reference level.
  • kit of any of embodiments 54-75 further including a treatment for rheumatoid arthritis.
  • kits of embodiment 76, where in the treatment includes an anti-inflammatory medication, a disease-modifying antirheumatic drug (DMARD), a cytokine antagonist, or a corticosteroid.
  • DMARD disease-modifying antirheumatic drug
  • cytokine antagonist a cytokine antagonist
  • corticosteroid a corticosteroid
  • kits of embodiment 77, wherein the anti-inflammatory medication includes a nonsteroidal anti-inflammatory drug (NSAID).
  • NSAID nonsteroidal anti-inflammatory drug
  • kits of embodiments 77 or 78, wherein the DMARD includes a conventional synthetic DMARD, a biologic DMARD, a targeted synthetic DMARD, or a biosimilar DMARD.
  • cytokine antagonist includes an inhibitor of TNF, IL-1 , IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-15, IL-16, IL-18, IL-21 , IL-23, interferons, EMAP-II, GM-CSF, FGF, or PDGF.
  • kit of any of embodiments 76-81 wherein the treatment includes a janus kinase (JAK) inhibitor.
  • JK janus kinase
  • Example 1 Serum samples obtained from the Department of Defense Serum Repository (DoDSR) from the at-risk rheumatoid arthritis (pre-RA) period from 213 validated cases who develop clinical RA and 215 controls without RA were evaluated (see Table 1) for 197 proteins on the Olink platform.
  • samples were tested for antibodies directed against citrullinated proteins (ACPA) and rheumatoid factor (RF) using the anti-CCP3 and RF-lgA and IgM assays (quantiflash) as well as serum calprotectin (Werfen, Spain).
  • a set of diagnostic protein markers and auto-antibody measurements was identified to develop a classification model that predicts whether a subject will convert to RA within 3-years.
  • AUC a separate clinical cohort studying rheumatoid arthritis
  • longitudinal proteins and pathways distinguishing pre-RA and HC in 0-15 years prior to RA diagnosis (PRAD) were identified.
  • Anti-CCP3, RFIgA, and RFIgM showed significant elevation in pre-RA, with the highest levels observed ⁇ 5 years prior to diagnosis suggesting two phases of pre-RA.
  • the proteins and pathways illustrate distinct patterns of pre-RA disease development, highlighting potential biological pathways involved with disease development that may be
  • This example describes a longitudinal nested case-control study examining the proteomic and clinical markers of RA cases and time, age, sex, and BMI-matched HC samples (FIG. 1). 197 proteins on the Olink platform in serum obtained from the DoDSR from the pre-RA period from 213 validated RA cases and 215 controls were evaluated (Table 1). Pre-RA serum samples were utilized to identify differences in protein and auto-antibody markers between RA samples and HC. In this example, a subset of proteins and auto-antibody features show differential expression between RA subjects and HCs, particularly within 5-years prior to RA disease onset. This information was used to develop a diagnostic panel to predict whether at-risk individuals are likely to develop rheumatoid arthritis within 3 years. The disclosed diagnostic classifier was validated in an external clinical cohort, to demonstrate general utility. Additionally, the differential longitudinal proteins described in this disclosure highlight potential biological pathways involved with disease development that can be targeted for preventive interventions in a stage-specific manner.
  • Diagnostic Panel Auto-antibody Markers for RA. The progression of these markers was modeled as a function of time-to-diagnosis to identify any association between auto-antibodies and time to disease diagnosis (FIG. 3). The LOESS regression curves show that CCP3, RFIgA and RFIgM all show sustained differences between preRA (circle) and HC (triangle) samples as far back as 10-15 years, with the highest levels observed within 5 years of diagnosis.
  • CCP3 and RFIgA/M exhibit strong increases within 3-5 years of diagnosis, a large number of samples demonstrate elevated values as far back as 10-15 years prior to diagnosis, demonstrating both 1) their ability to differentiate preRA from HC samples, and 2) the limited precision auto-antibody tests have with predicting time-to-event given the potential for elevated values 10-15 years prior to actual disease onset.
  • biomarkers include the 4 auto-antibody ELISA markers and 34 Clink proteins. While the majority of biomarkers demonstrated elevated values in RA relative to control, certain biomarkers (e.g., SKAP1 , GFRA2) resulted in lower expression in RA subjects relative to HC. Additionally, screening for analytes that were associated with conversion (time to diagnosis) was completed in the subset of ACPA+ subjects in the training set, and 12 analytes associated with time (p ⁇ 0.1) were identified. The combination of the rule-in and linear regression markers were used to construct a panel of biomarkers to predict conversion to rheumatoid arthritis.
  • biomarkers e.g., SKAP1 , GFRA2
  • the final list of biomarkers is in FIG. 6A, and the respective ROC curves of their corresponding diagnostic classifier is in FIG. 6B.
  • the results demonstrate that a combined panel of biomarkers and auto-antibody counts can significantly improve prediction accuracy and help diagnose time to RA.
  • FIG. 3 The auto-antibody biomarkers (FIG. 3) suggest that approximately 5-years within diagnosis, converters develop a signature that separates them from HCs, so a protein signature for 5-years was developed (FIG. 6A). However, for clinical utility, a model to focus on 3-year, 2- year, and 1-year conversion was developed (FIG. 6B) since (1) most ACPA+ were within a 5-year window, and (2) several current clinical trials and studies focus on preventing and studying RA onset within a 3-year time window.
  • DoD Test Set stratified RA subjects independent of ACPA Positivity. When excluding the HC from the analysis, this diagnostic classifier was found to discriminate between samples from RA subjects close to conversion vs samples further away from conversion (FIGs. 8A-8C).
  • DoD Test Set Performance on ACPA- and HCs illustrates discriminative power. While the classification model is designed to identify ACPA+ conversion, the performance on all samples in the hold-out set was also evaluated, which included HC and ACPA- RA samples. The results (FIGs. 9A-9C) demonstrate that this diagnostic tool discriminates between individuals converting to RA from everyone else.
  • ACPA+ Early RA 15 are treated as individuals diagnosed with clinical RA during the course of study (ACPA+ Converter), and 33 are ACPA+ individuals who have not yet been diagnosed with RA (ACPA+ Non-Converter).
  • An independent cohort of 52 ACPA- HC individuals (Table 2) recruited from Benaroya, and a set of 11 site HC from Colorado.
  • the Benaroya Research Institute (BRI) participants were recruited through the Sound Life Project, recruiting individuals either ranged from 25-35 or 55-65 participants with anti-CCP3 levels ⁇ 40 units, and without history of diagnosed chronic disease, autoimmune disease, severe allergy, or chronic infection.
  • Table 2 Altra Demographics. All measurements reflect values at first visit. ACPA, anti- citrullinated protein antibodies; IQR, interquartile range; RF, rheumatoid factor; SD, standard deviation)
  • the first validation using all cross-sectional samples demonstrated that the disclosed diagnostic classifier was powered to discriminate between samples from subjects who converted to RA and everyone else.
  • the second validation included any converter and only ACPA+ nonconverter individuals enrolled for at least 3 years (N 23), see FIG. 10C. The performance shows potential power to discriminate, but the low sample size limits statistical power.
  • FIGs. 13A and 13B illustrating the rich longitudinal patterns of differential expression (FIG. 13A) and the binary significance patterns (FIG. 13B).
  • ACPA+ Heterogeneity To identify ACPA heterogeneity in the RA converters, heterogeneity was assessed at time of diagnosis. To do this, RA patients were separated based on ACPA positivity, and then ACPA+ subjects with CCP3 and the top 3 rule-in biomarkers were clustered to see if there were differences among them.
  • ACPA+ Heterogeneity identifies some differences in protein biomarkers.
  • the groups that showed differences in the CCP3 baseline (diagnosis) heterogeneity analysis were selected, and those results were evaluated longitudinally to identify whether these differences remained longitudinally. While the levels clearly varied between the RA subgroups at diagnosis, a longitudinal analysis (FIG. 16A) demonstrated that these differences also translated into distinct trajectories in auto-antibody production over time.
  • the CCP3+2 showed elevated levels of CCP3 throughout the course of the study, while CCP3- individuals demonstrated nearly identical levels of CCP3 to the HC.
  • RFIgM (FIG. 16B), another auto-antibody marker, displayed the same behaviors with distinct trajectories longitudinally.
  • a look at heterogeneity within the proteomic biomarker space indicates that a subset of proteins show differences between the RA subclusters (FIG. 16C), while the large majority of proteins show shared signatures across RA subjects.
  • IL10 (FIG. 16D) was one of the select proteins that demonstrated differences across the distinct categories.
  • ACPA+ vs ACPA- RA Subjects To identify differences between ACPA+ and ACPA- subjects, longitudinal biomarkers were identified comparing (1) ACPA+ RA vs. HCs (FIG. 17A), and (2) ACPA- RA vs HCs (FIG. 17B), and then differential proteins were compared and contrasted across both groups of RA subjects.
  • results from ACPA+ RA vs HCs showed 97 proteins that were differentially expressed, while results from ACPA- RA vs HCs showed 60 proteins differentially expressed over time.
  • the longitudinal biomarker panels illustrate a change in protein signature starting approximately 3-5 years prior to RA onset, indicating a change in phenotype that can be leveraged for RA prediction.
  • the longitudinal biomarkers indicate that most proteins change expression approximately 2 years prior to disease onset, which is a much more sudden change in protein expression changes and indicates a different set of biomarkers for predicting disease onset.
  • FIG. 19 A look comparing the directional significance of the protein hits (FIG. 19) demonstrates that largely, most protein signatures are shared independent of seropositivity. However, a subset of proteins shows distinct behavior in ACPA+ individuals, with I L10 and IRAK4 being the most notable differences.
  • the four unique proteins in the seronegative (RA-) RA converters show interesting patterns that can provide visibility into specific predictive biomarkers and treatment opportunities in ACPA- subjects that differentiate them both from HCs and ACPA+ subjects.
  • Table 3 Diagnostic Classifier Signature.
  • CirCalpro Circulating calprotectin
  • TNFRSF10A TNF Receptor Superfamily Member 10a
  • TRIM21 Tripartite motif-containing protein 21.
  • Example 2 Evidence Supporting Viable Assay. Identifying Compatible Proteins to Developing a Scalable MSD Assay. Using protein signatures that are able to identify whether ACPA+ individuals will develop rheumatoid arthritis (RA) within 3-years using the Olink technology for capturing relative protein abundance is described elsewhere herein. The reduction to practice component of this includes developing a scalable assay on the specific proteins that comprise the identified signature using the Meso-scale Discovery (MSD) quantitative platform, which is widely used in clinical testing. Starting with the original 197 proteomic features measured in Olink, a subset of proteins were identified that were predictive of disease onset and for which assays were available in MSD (FIG. 22).
  • MSD Meso-scale Discovery
  • MSD Assay Protocol Given the constraint of 50 uL of volume per subject, the following protocol was proposed including the following four distinct Plates:
  • IL-10 IL-10, VEGFA, CXCL11 , TNF-alpha, IL17RB, OPG, LAG3, VEGFD, PRDX6
  • the protocol is a 3-day process, as outlined below (FIG. 23):
  • this protocol ensures that all proteins are detectable and quantified provided the restriction of 50 uL of sample volume.
  • FIG. 25 shows that the median intra plate correlation per analyte across plates is > 0.85 for all proteins and > 0.9 for 12/14 proteins (FIG. 25), indicating the assays are highly reproducible.
  • the pooled variance can be calculated as follows:
  • Table 4 The number of samples available to validate proteomic findings using the MSD assay.
  • Correlation between Protein measurements in Clink and MSD In FIG. 28, it was observed that 10 out of 13 proteins showed statistically significant and linear correlations between their MSD and Clink assay measurements. Given these findings, LAG3 and IL-10 were removed from subsequent analyses, while II17-RB, the anti-correlation was addressed by reversing the sign of the final model coefficient.
  • TipRA is a prospective cohort of 77 ACPA+ subjects (CCP3 IgG) without inflammatory arthritis (IA). Similar to the DoD MSD validation cohort, all subjects are ACPA, and additional demographic details are presented in FIG. 30 (notably age, gender, absolute CCP3 values, and the number of individuals who were diagnosed with RA within 3- years).
  • FIGs. 31 A and 31 B show the ROC curve and a boxplot of the model predictions by outcome.
  • the boxplot of model probabilities (FIG. 31 B) by diagnosis outcome provides additional evidence of how this model’s probability ‘score’ separates the two outcomes.
  • VEGFD + CCP3 Provide Performance across all 3-cohorts.
  • a smaller protein panel composed of VEGFD and CCP3, using log(CCP3/VEGFD) as the test score.
  • the 2-panel score was able to distinguish RA patients converted within 3 years from those not converted within 3 years across all 3 clinical cohorts.
  • the results (FIG. 32A) show the following AUCS (95% confidence intervals) for the 3 cohorts: DoD AUG is 0.6307 (0.5183-0.7431); Tipra AUC is 0.6926 (0.5328-0.8524); and Altra AUG is 0.6531 (0.486-0.8203).
  • FIG. 32B The boxplots (FIG. 32B) show that the model scores provided by CCP3 and VEGFD provide statistically significant results for p ⁇ 0.1 for all 3 cohorts, and p ⁇ 0.05 for DoD and TipRA, indicating it is a robust panel that generalizes across cohorts and sample types.
  • CCP3 is one of the strongest predictors of future RA (Rantapaa-Dahlqvist, et al. Arthritis & Rheumatism 48.10 (2003): 2741- 2749).
  • CCP3 alone is not powerful enough to determine when an individual may get RA as individuals may test positive for ACPA as far back as 10-15 years before disease onset ( Figure 1 D of Kelmenson et al., 2020, Arthritis Rheumatol. 72(2):251 -261).
  • FIG. 32A the performance of CCP3-alone
  • Model-based predictions FIG.
  • each embodiment disclosed herein can comprise, consist essentially of or consist of its particular stated element, step, ingredient or component.
  • the terms “include” or “including” should be interpreted to recite: “comprise, consist of, or consist essentially of.”
  • the transition term “comprise” or “comprises” means has, but is not limited to, and allows for the inclusion of unspecified elements, steps, ingredients, or components, even in major amounts.
  • the transitional phrase “consisting of” excludes any element, step, ingredient or component not specified.
  • the transition phrase “consisting essentially of” limits the scope of the embodiment to the specified elements, steps, ingredients or components and to those that do not materially affect the embodiment.
  • the term “about” has the meaning reasonably ascribed to it by a person skilled in the art when used in conjunction with a stated numerical value or range, i.e., denoting somewhat more or somewhat less than the stated value or range, to within a range of ⁇ 20% of the stated value; ⁇ 19% of the stated value; ⁇ 18% of the stated value; ⁇ 17% of the stated value; ⁇ 16% of the stated value; ⁇ 15% of the stated value; ⁇ 14% of the stated value; ⁇ 13% of the stated value; ⁇ 12% of the stated value; ⁇ 11 % of the stated value; ⁇ 10% of the stated value; ⁇ 9% of the stated value; ⁇ 8% of the stated value; ⁇ 7% of the stated value; ⁇ 6% of the stated value; ⁇ 5% of the stated value; ⁇ 4% of the stated value; ⁇ 3% of the stated value; ⁇ 2% of the stated value; or ⁇ 1% of the stated value.

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Abstract

Sont décrits des biomarqueurs pour prédire la progression d'un risque de développement d'une polyarthrite rhumatoïde (pré-PR) à une polyarthrite rhumatoïde confirmée par un médecin (PR clinique) dans des périodes définies. Les biomarqueurs décrits peuvent être utilisés pour identifier des interventions afin de prévenir, retarder ou moduler une PR clinique future et pour identifier des cibles spécifiques à un stade potentiel pour des interventions préventives.
PCT/US2024/055169 2023-11-09 2024-11-08 Biomarqueurs pour prédire l'apparition d'une polyarthrite rhumatoïde clinique Pending WO2025101929A1 (fr)

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Citations (3)

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Publication number Priority date Publication date Assignee Title
WO2008027256A2 (fr) * 2006-08-25 2008-03-06 Wyeth Expression génétique des lymphocytes b associées à l'arthrite
WO2015091524A1 (fr) * 2013-12-16 2015-06-25 INSERM (Institut National de la Santé et de la Recherche Médicale) Polymorphismes pour le diagnostic de la polyarthrite rhumatoïde
US20200399703A1 (en) * 2018-01-24 2020-12-24 Genentech, Inc. Diagnostic and therapeutic methods for the treatment of rheumatoid arthritis (ra)

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Publication number Priority date Publication date Assignee Title
WO2008027256A2 (fr) * 2006-08-25 2008-03-06 Wyeth Expression génétique des lymphocytes b associées à l'arthrite
WO2015091524A1 (fr) * 2013-12-16 2015-06-25 INSERM (Institut National de la Santé et de la Recherche Médicale) Polymorphismes pour le diagnostic de la polyarthrite rhumatoïde
US20200399703A1 (en) * 2018-01-24 2020-12-24 Genentech, Inc. Diagnostic and therapeutic methods for the treatment of rheumatoid arthritis (ra)

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Title
DE BRITO ROCHA, BALDO DANIELLE CRISTIANE, ANDRADE LUIS EDUARDO COELHO: "Clinical and pathophysiologic relevance of autoantibodies in rheumatoid arthritis", ADVANCES IN RHEUMATOLOGY, vol. 59, no. 1, 1 December 2019 (2019-12-01), XP093316666, ISSN: 2523-3106, DOI: 10.1186/s42358-018-0042-8 *

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