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METHODS, ARRAYS AND USES THEREOF FOR DIAGNOSING OR DETECTING AN AUTOIMMUNE DISEASE

20220163524 · 2022-05-26

    Inventors

    Cpc classification

    International classification

    Abstract

    The present invention provides a method for diagnosing or detecting an autoimmune disease in an individual, the method comprising or consisting of the steps of (a) providing a sample obtained from an individual to be tested; and (b) measuring the presence and/or amount in the test sample of one or more biomarkers selected from the group defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D) and/or Table 1(E) wherein the presence and/or amount in the sample of the one or more biomarker(s) is indicative of an autoimmune disease in the individual. The invention also provides an array and a kit suitable for use in the methods of the invention.

    Claims

    1. A method for diagnosing or detecting an autoimmune disease in an individual, the method comprising or consisting of the steps of: a) providing a sample obtained from an individual to be tested; and b) measuring the presence and/or amount in the test sample of one or more biomarkers selected from the group defined in Table 1(A); wherein the presence and/or amount in the sample of the one or more biomarker(s) selected from the group defined in Table 1(A) is indicative of an autoimmune disease in the individual.

    2. The method according to claim 1 further comprising or consisting of the steps of: c) providing one or more control samples; and d) measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the individual is identified as having an autoimmune disease by comparing the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) with the presence and/or amount in the control samples.

    3. The method according to claim 2 wherein the control samples of step (c) are provided from a healthy individual (negative control) and/or from an individual with an autoimmune disease (positive control).

    4. The method according to claim 2 or 3 wherein the control samples of step (c) are provided from an individual with systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren's syndrome (SS) or systemic vasculitis (SV).

    5. The method according to claim 4 wherein the control samples of step (c) are provided from an individual with systemic lupus erythematosus subtype 1 (SLE-1), systemic lupus erythematosus subtype 2 (SLE-2) or systemic lupus erythematosus subtype 3 (SLE-3).

    6. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of two or more of the biomarkers defined in Table 1(A), for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 of the biomarkers defined in Table 1(A).

    7. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(A)i.

    8. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(A)ii.

    9. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(A)iii.

    10. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of biomarkers defined in Table 1(A)i, Table 1(A)ii and/or Table 1(A)iii.

    11. The method according to any one of the preceding claims wherein the autoimmune disease is selected from: systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren's syndrome (SS) or systemic vasculitis (SV).

    12. The method according to any one of the preceding claims wherein the one or more biomarker(s) selected from the group defined in Table 1(A) are biomarkers which are also present in Table 2(A).

    13. The method according to any one of the preceding claims wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 2(A).

    14. The method according to any one of the preceding claims, wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 1(B).

    15. The method according to any one of the preceding claims, wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 1(C).

    16. The method according to any one of the preceding claims, wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 1(D).

    17. The method according to any one of the preceding claims, wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 1(E).

    18. A method for diagnosing or detecting systemic lupus erythematosus in an individual comprising or consisting of the steps of: a) providing one or more sample obtained from an individual with, or suspected of having, an autoimmune disease; and b) measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1(B); wherein the presence and/or amount in the one or more test sample of the one or more biomarker(s) selected from the group defined in Table 1(B) is indicative of systemic lupus erythematosus.

    19. The method according to claim 18 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(B)i.

    20. The method according to claim 18 or 19 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(B)ii.

    21. The method according to any one of claims 18 to 20 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(B)iii.

    22. The method according to any one of claims 18 to 21 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of biomarkers defined in Table 1(B)i, Table 1(B)ii and/or Table 1(B)iii.

    23. The method according to any one of claims 18 to 22 wherein the one or more biomarker(s) selected from the group defined in Table 1(B) are biomarkers which are also present in Table 2(B).

    24. The method according to any one of claims 18 to 23 wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 2(B).

    25. The method according to any one of claims 18 to 24 further comprising or consisting of the steps of: c) providing one or more control sample; and d) measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the patient is identified as having systemic lupus erythematosus by comparing the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) with the presence and/or amount in the control samples.

    26. A method for diagnosing or detecting rheumatoid arthritis in an individual comprising or consisting of the steps of: a) providing one or more sample obtained from an individual with, or suspected of having, rheumatoid arthritis; and b) measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1(C); wherein the presence and/or amount in the one or more test sample of the one or more biomarker(s) selected from the group defined in Table 1(C) is indicative of rheumatoid arthritis.

    27. The method according to claim 26 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(C)i.

    28. The method according to claim 26 or 27 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(C)ii.

    29. The method according to any one of claims 26 to 28 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(C)iii.

    30. The method according to any one of claims 26 to 29 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of biomarkers defined in Table 1(C)i, Table 1(C)ii and/or Table 1(C)iii.

    31. The method according to any one of claims 26 to 30 wherein the one or more biomarker(s) selected from the group defined in Table 1(C) are biomarkers which are also present in Table 2(C).

    32. The method according to any one of claims 26 to 31 wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 2(C).

    33. The method according to any one of claims 26 to 32 further comprising or consisting of the steps of: c) providing one or more control sample; and d) measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the patient is identified as having rheumatoid arthritis by comparing the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) with the presence and/or amount in the control samples.

    34. A method for diagnosing or detecting Sjögren's syndrome in an individual comprising or consisting of the steps of: a) providing one or more sample obtained from an individual with, or suspected of having, an autoimmune disease; and b) measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1(D); wherein the presence and/or amount in the one or more test sample of the one or more biomarker(s) selected from the group defined in Table 1(D) is indicative of Sjögren's syndrome.

    35. The method according to claim 34 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(D)i.

    36. The method according to claim 34 or 35 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(D)ii.

    37. The method according to any one of claims 34 to 36 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(D)iii.

    38. The method according to any one of claims 34 to 37 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of biomarkers defined in Table 1(D)i, Table 1(D)ii and/or Table 1(D)iii.

    39. The method according to any one of claims 34 to 38 wherein the one or more biomarker(s) selected from the group defined in Table 1(D) are biomarkers which are also present in Table 2(D).

    40. The method according to any one of claims 34 to 39 wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 2(D).

    41. The method according to any one of claims 34 to 40 further comprising or consisting of the steps of: c) providing one or more control sample; and d) measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the patient is identified as having Sjögren's syndrome by comparing the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) with the presence and/or amount in the control samples.

    42. A method for diagnosing or detecting systemic vasculitis in an individual comprising or consisting of the steps of: a) providing one or more sample obtained from an individual with, or suspected of having, an autoimmune disease; and b) measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1(E); wherein the presence and/or amount in the one or more test sample of the one or more biomarker(s) selected from the group defined in Table 1(E) is indicative of systemic vasculitis.

    43. The method according to claim 42 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(E)i.

    44. The method according to claim 42 or 43 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(E)ii.

    45. The method according to any one of claims 42 to 44 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of one or more of the biomarkers defined in Table 1(E)iii

    46. The method according to any one of claims 42 to 45 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of biomarkers defined in Table 1(E)i, Table 1(E)ii and/or Table 1(E)iii.

    47. The method according to any one of claims 42 to 46 wherein the one or more biomarker(s) selected from the group defined in Table 1(E) are biomarkers which are also present in Table 2(E).

    48. The method according to any one of claims 42 to 47 wherein the method further comprises measuring the presence and/or amount of one or more of the biomarkers defined in Table 2(E).

    49. The method according to any one of claims 42 to 48 further comprising or consisting of the steps of: c) providing one or more control sample; and d) measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the patient is identified as having systemic vasculitis by comparing the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) with the presence and/or amount in the control samples.

    50. The method according to any one of the preceding claims wherein the method is repeated using a test sample taken from the same individual at a different time period to the previous test sample(s) used.

    51. The method according to claim 50 wherein the method is repeated using a test sample taken between 1 day to 104 weeks to the previous test sample(s) used, for example, between 1 week to 100 weeks, 1 week to 90 weeks, 1 week to 80 weeks, 1 week to 70 weeks, 1 week to 60 weeks, 1 week to 50 weeks, 1 week to 40 weeks, 1 week to 30 weeks, 1 week to 20 weeks, 1 week to 10 weeks, 1 week to 9 weeks, 1 week to 8 weeks, 1 week to 7 weeks, 1 week to 6 weeks, 1 week to 5 weeks, 1 week to 4 weeks, 1 week to 3 weeks, or 1 week to 2 weeks.

    52. The method according to claim 50 or 51 wherein the method is repeated using a test sample taken every period from the group consisting of: 1 day, 2 days, 3 day, 4 days, 5 days, 6 days, 7 days, 10 days, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 15 weeks, 20 weeks, 25 weeks, 30 weeks, 35 weeks, 40 weeks, 45 weeks, 50 weeks, 55 weeks, 60 weeks, 65 weeks, 70 weeks, 75 weeks, 80 weeks, 85 weeks, 90 weeks, 95 weeks, 100 weeks, 104, weeks, 105 weeks, 110 weeks, 115 weeks, 120 weeks, 125 weeks and 130 weeks.

    53. The method according to any one of claims 50 to 52 wherein the method is repeated at least once, for example, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, 20 times, 21 times, 22 times, 23, 24 times or 25 times.

    54. The method according to any one of the preceding claims wherein a diagnosis in a patient of an autoimmune disease is subsequently confirmed using one or more additional diagnostic tests for the autoimmune disease.

    55. The method according to any one of the preceding claims wherein step (b) comprises measuring the expression of the protein or polypeptide of the one or more biomarker(s).

    56. The method according to claim 55 wherein step (b) and/or step (d) is performed using one or more first binding agents each capable of binding specifically to a biomarker protein or polypeptide to be measured.

    57. The method according to claim 56 wherein the first binding agent is an antibody or a fragment thereof.

    58. The method according to claim 57 wherein the antibody or fragment thereof is a recombinant antibody or fragment thereof.

    59. The method according to claim 56 or 57 wherein the antibody or fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.

    60. The method according to any one of claims 56 to 59 wherein the first binding agent is immobilised on a surface.

    61. The method according to any one of the preceding claims wherein the one or more biomarker(s) in the test sample is labelled with a directly or indirectly detectable moiety.

    62. The method according to claim 61 wherein the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.

    63. The method according to claim 61 or 62 wherein the detectable moiety is biotin.

    64. The method according to claim 63 wherein in step (b) and/or step (d) the biotinylated biomarkers are detected using streptavidin labelled with a detectable moiety selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.

    65. The method according to claim 64 wherein the detectable moiety is fluorescent moiety (for example an Alexa Fluor dye, e.g. Alexa647).

    66. The method according to any one of the preceding claims wherein step (b) and/or step (d) is performed using an array.

    67. The method according to claim 66 wherein the array is selected from the group consisting of: macroarray; microarray; nanoarray.

    68. The method according to any one of the preceding claims wherein the method comprises: (i) labelling biomarkers present in the sample with biotin; (ii) contacting the biotin-labelled proteins with an array comprising a plurality of scFv immobilised at discrete locations on its surface, the scFv having specificity for one or more of the proteins in Table 1(A), Table 1(B), Table 1(C), Table 1(D) and/or Table 1(E); (iii) contacting the biotin-labelled proteins (immobilised on the scFv) with a streptavidin conjugate comprising a fluorescent dye; and (iv) detecting the presence of the dye at discrete locations on the array surface wherein the expression of the dye on the array surface is indicative of the expression of a biomarker from Table 1(A), Table 1(B), Table 1(C), Table 1(D) and/or Table 1(E) in the sample.

    69. The method according to any one of the preceding claims wherein step (b) and/or step (d) comprises measuring the expression of a nucleic acid molecule encoding the one or more biomarkers.

    70. The method according to claim 69 wherein the nucleic acid molecule is an mRNA molecule.

    71. The method according to claim 69 wherein the nucleic acid molecule is a DNA molecule, such as a cDNA or ctDNA molecule.

    72. The method according claim 70 or 71, wherein measuring the expression of the one or more biomarker(s) in step (b) and/or step (d) is performed using a method selected from the group consisting of Southern hybridisation, Northern hybridisation, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative real-time PCR (qRT-PCR), nanoarray, microarray, macroarray, autoradiography and in situ hybridisation.

    73. The method according to any one of claims 70 to 72, wherein measuring the expression of the one or more biomarker(s) in step (b) is determined using a DNA microarray.

    74. The method according to any one of claims 70 to 73, wherein measuring the expression of the one or more biomarker(s) in step (b) and/or step (d) is performed using one or more binding moieties, each individually capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in Table 1(A), Table 1(B), Table 1(C), Table 1(D) and/or Table 1(E).

    75. The method according to claim 74, wherein the one or more binding moieties each comprise or consist of a nucleic acid molecule.

    76. The method according to claim 74 or 75, wherein the one or more binding moieties each comprise or consist of DNA.

    77. The method according to any one of claims 74 to 76 wherein the one or more binding moieties are 5 to 100 nucleotides in length, for example 15 to 35 nucleotides in length.

    78. The method according to any one of claims 74 to 77 wherein the binding moiety comprises a detectable moiety.

    79. The method according to claim 78 wherein the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety (for example, a radioactive atom); or an enzymatic moiety.

    80. The method according to claim 79 wherein the detectable moiety comprises or consists of a radioactive atom.

    81. The method according to claim 80 wherein the radioactive atom is selected from the group consisting of technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186, rhenium-188 and yttrium-90.

    82. The method according to claim 79 wherein the detectable moiety of the binding moiety is a fluorescent moiety.

    83. The method according to any one of the preceding claims wherein the sample provided in step (a) and/or step (c) is selected from the group consisting of unfractionated blood, plasma, serum, tissue fluid, milk, bile, synovial fluid, and urine.

    84. The method according to claim 83, wherein the sample provided in step (a) and/or step (c) is selected from the group consisting of unfractionated blood, plasma and serum.

    85. The method according to claim 83 or 84, wherein the sample provided in step (a) and/or step (c) is serum.

    86. The method according to any one of the preceding claims wherein the predictive accuracy of the method, as determined by an ROC AUC value, is at least 0.50, for example at least 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 0.96, 0.97, 0.98 or at least 0.99.

    87. The method according to claim 86 wherein the predictive accuracy of the method, as determined by an ROC AUC value, is at least 0.70.

    88. The method according to any one of the preceding claims wherein, in the event that the individual is diagnosed with an autoimmune disease, the method comprises an additional step of administering to the individual a therapy for said autoimmune disease.

    89. The method according to claim 88 wherein the autoimmune disease therapy is selected from the group consisting of: Nonsteroidal anti-inflammatory drugs (NSAID) such as Ibuprofen and Naproxen; Immune-suppressing drugs such as Corticosteroids; synthetic DMARDs (such as Methotrexate, cyclophosphoamide); and Biologicals (such as TNF-inhibitors, IL-inhibitors); and combinations thereof.

    90. An array for diagnosing or detecting an autoimmune disease in an individual comprising one or agents suitable for measuring the presence and/or amount of one or more biomarkers selected from the group defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D), and/or Table 1(E).

    91. An array according to claim 78 comprising one or more binding agents as defined in any one of claims 56 to 65 or 74 to 82.

    92. An array according to claim 78 or 79 wherein the one or more binding agents are collectively capable of binding to all of the proteins defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D), and/or Table 1(E).

    93. Use of one or more biomarkers selected from the group defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D), or Table 1(E) as a biomarker for diagnosing or detecting an autoimmune disease in an individual, optionally wherein the autoimmune disease is selected from systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren's syndrome (SS) or systemic vasculitis (SV).

    94. The use according to claim 81 wherein all of the biomarkers defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D), and/or Table 1(E) are used as a biomarker for diagnosing or detecting an autoimmune disease in an individual, optionally wherein the autoimmune disease is selected from systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren's syndrome (SS) or systemic vasculitis (SV).

    95. A kit for diagnosing or detecting an autoimmune disease in an individual comprising: i) one or more first binding agents as defined in any one of claims 56 to 65 or 74 to 82, ii) (optionally) instructions for performing the method as defined in any one of claims 1 to 89.

    96. A method of treating an autoimmune disease in an individual comprising the steps of: (a) diagnosing an individual with an autoimmune disease using a method according to any one of claims 1 to 89; and (b) providing the individual with a therapy to treating said autoimmune disease.

    97. A method or use substantially as described herein.

    98. An array or kit substantially as described herein.

    Description

    [0237] Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following figures:

    [0238] FIG. 1 shows a schematic outline of the antibody microarray process, applied on serum samples from Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), Sjögren syndrome (SS), Systemic Vasculitis (SV) and healthy controls (H). For each analysis (Wilcoxon, leave-one-out cross validation and signature development) each group was set against the remaining samples, i.e. (A) H versus SLE+RA+SS+SV (B) SLE versus RA+SS+SV (C) RA versus SLE+SS+SV (D) SS versus SLE+RA+SV and (E) SV versus SLE+RA+SS.

    [0239] FIG. 2: (A) ROC-curve including AUC-value generated from leave-one-out cross validation analysis on healthy versus autoimmune diseases (SLE, RA, SS and SV). (B) Heatmap from supervised analysis including the top 25 differentially expressed analytes (Wilcoxon analysis q<0.05) between healthy (light bars) and autoimmune diseases (dark bars) which include SLE, RA, SS and SV. Individual clone suffixes are shown in brackets.

    [0240] FIG. 3: ROC-curves with AUC-values generated from LOO CV analysis, representing (A) SLE vs. RA+SS+SV (B) SV vs. SLE+RA+SS (C) RA vs. SLE+SS+SV (D) and SS vs. SLE+RA+SV.

    [0241] FIG. 4: PCA plots of supervised analysis based on 40-plex biomarker panels representing SLE (A) SV (B) SS (C) and RA (D).

    [0242] FIG. 5: Venn diagrams representing the overlap of variables generated from differential analysis (Wilcoxon signed rank test, q<0.05) for SLE, RA, SS and SV. Since an analyte may be targeted by more than one antibody, diagram (A) represents the overlap of antibodies whereas (B) represents the overlap on an analyte level. Disease specific analytes are outlined in diagram (B). Diagram created at http://bioinformatics.psb.ugent.be/webtools/Venn/

    EXAMPLE

    Introduction

    Objective

    [0243] Early and correct diagnosis of autoimmune diseases (AID) pose a clinical challenge due to the multifaceted nature of symptoms which also may change over time. The aim of this study was to perform protein expression profiling of the four systemic AIDs: Systemic Lupus Erythematosus (SLE); Systemic Vasculitis (SV), e.g. ANCA associated Vasculitis; Rheumatoid Arthritis (RA); and Sjögrens Syndrome (SS), and healthy controls and to identify candidate biomarker signatures for differential classification.

    Method

    [0244] A total of 316 serum samples collected from patients diagnosed with SLE, RA, SS, SV and healthy controls, were analysed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was performed using Wilcoxon rank sum test and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting disease.

    Results

    [0245] In this study we were able to classify individual AIDs with high accuracy, as demonstrated by ROC Area Under the Curve (ROC AUC) values ranging between 0.955 to 0.803. In addition, the group of AIDs could be separated from healthy controls at a ROC AUC-value of 0.938. Disease specific candidate biomarker signature as well as a general autoimmune signature were identified, including several deregulated analytes.

    Conclusion

    [0246] This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial, complex diseases such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ and tissue related damage.

    Materials and Methods

    Clinical Samples

    [0247] This retrospective study included a total of 316 serum samples collected from healthy controls (n=77) and patients diagnosed with a systemic autoimmune disease (n=239). All samples were collected from Department of Rheumatology Skåne University Hospital (Malmo or Lund). Patients were diagnosed with either SLE (n=39), RA (n=45), SS (n=73) or SV (n=82) and considered, according to their specific clinical criteria's, to be in an active disease when samples were collected. For SLE patients, disease activity was defined using the SLEDAI-2000-score (19) (mean score 7, range 1-19), and all RA patients demonstrated elevated CRP levels (mean 31 (13-55) mg/L). ANCA-specificity in SV patients were defined according to MPO+/− or PR3+/− status and all Sjögren samples were collected from patients that fulfilled the 2002 American-European Consensus Group Criteria (20) for primary SS. As controls, serum samples from healthy individuals with no previous history of autoimmune disease were used. Within the AID cohort mean age was 59 years and the female to male ratio 168:82 whereas the mean age in healthy controls was 60 years and a female to male ratio of 66:11 (Table 3). Ethical approval for the study was granted by the regional ethics review board in Lund, Sweden.

    TABLE-US-00026 TABLE 3 Clinical data of patients included in the study Healthy Autoimmune diseases controls Parameter SLE RA SS SV H Total No. of samples n = 39 n = 45 n = 73 n = 82 n = 77 n = 316 Female:male ratio 33:6 32:13 71:2 32:50 66:11 234:83 Mean age years 51 (29- 65 (38- 61 (24- 60 (11- 50 60 (11- (range) 77) 85) 85) 83) (18-81) 85) Disease specific variables SLE SLEDAI mean 7 (range) (1-19) RA CRP (mg/L) 31 (13-55) SV ANCA phenotype MPO 40 (active/inactive) (20/20) PR3 42 (active/inactive) (20/22)

    Antibody Microarray Production and Analysis

    [0248] A total of 394 recombinant scFv antibodies were selected from in-house designed large phage display libraries (21, 22) (Supplementary Table S1). Of these, 379 of the scFv antibodies were directed against 161 (mainly immunoregulatory) antigens. The remaining 15 scFv antibodies were directed against 15 short amino acid motifs (4-6 amino acids long), denoted CIMS antibodies. For some analytes more than one scFv antibody clone (2-9) targeting different epitopes, were chosen to minimize the risk of impaired antibody activity followed by epitope masking during sample labelling process. All scFv antibodies were produced, according to standardized protocols, in 15 mL E. coli cultures and purified to from the cell periplasmic space using the MagneHis™ Protein Purification system (Promega, Madison, Wis., USA) and a KingFisher96 robot (Thermo Fisher Scientific. Waltham, Mass., USA). Buffer exchange to PBS was performed using a Zeba™ 96-well desalt spin plate (Pierce) and concentration and purity of the scFvs was determined using Nanodrop at 280 nm (NanoDrop Technologies, Wilmington, USA) and 10% SDS-PAGE (InVitrogen, Carlsbad, Ca, USA). Production of 26×28 subarrays were generated by a noncontact printer (SciFlexarrayer S11, Scenion, Berlin, Germany). Briefly described, single droplets (300 pL) of scFv antibody solutions, PBS (blank) or biotinylated BSA (position marker), were printed on Blank Polymer Maxisorp slides (NUNC A/S, Roskilde, Denmark) and allowed to absorb to the surface. Antibody microarrays were analysed as previously described (23). In brief, biotinylated samples were added to individual subarrays, and bound proteins were detected using Alexa-647 labelled streptavidin. Slides were scanned at 635 nm using the LS Reloaded™ laser scanner (Tecan) at a fixed laser scanning setting of 150% PMT gain.

    Data Pre-Processing

    [0249] Data pre-processing were performed as described. In brief, spot signal intensities were quantified using the Immunovia Quant™ software, v1.0 (Immunovia AB, Lund, Sweden). Signal intensities with local background subtraction were used for data analysis. Each data point represented the mean value of three technical replicate spots, unless any replicate CV exceeded 15%, in which case the worst performing replicate was eliminated and the average value of the two remaining replicates were used instead. The data was normalised using a two-step strategy. First, the data was normalised according to day-to-day variation using the “subtract by group mean” approach as previously described (24, 25). In the second step, a modified semi-global normalisation was used to minimize any array-to-array variations. In this approach 15% of the antibodies displaying the lowest CV-values over all samples were identified and used to calculate a scaling factor as previously described (26, 27). Quality control and visualization of potential outliers were performed using the Qlucore Omice Explorer 3.1 software (Qlucore AB, Lund, Sweden).

    Data Analysis

    [0250] A schematic outline of the analysis process is demonstrated in FIG. 1. For differential protein expression analysis, leave-one-out cross-validation and subsequent signature development, one group (H, SLE, RA, SS or SV) was set against the remaining groups. Analysis 1A in FIG. 1 refer to identification of a general autoimmune signature where healthy controls (H) was set against the autoimmune diseases, meaning H versus SLE+RA+SS+SV. When performing analysis within the AID group (FIG. 1B-E) each individual disease was set against the group of the remaining three diseases as described (B) SLE versus RA+SS+SV (C) RA versus SLE+SS+SV (D) SS versus SLE+RA+SV and (E) SV vs. SLE+RA+SS.

    [0251] Significantly up- or down-regulated proteins were identified using Wilcoxon signed-rank test (q<0.05) and p-values were adjusted with the Benjamini and Hochberg method (28). Venn diagram including differentially expressed analytes was created at http://bioinformatics.psb.ugent.be/webtools/Venn/. For supervised classification analysis a linear support vector machine (SVM) combined with a leave-one-out classification algorithm was used to evaluate the predictive performance of a model. In the LOO CV procedure one sample was removed, and the remaining samples were used to train the model. The left-out sample was then used to test the model and the process was repeated until every sample had been used as a test sample. A decision values for each excluded sample was thus generated, corresponding to the distance to the hyper plane and a receiver operating characteristic (ROC) curve was constructed. The area under the curve (AUC) was then calculated and used as a measure of the prediction performance of the classifier.

    [0252] To define a condensed biomarker signature for the differential profiling analysis a ranking procedure combined with two levels of K-fold cross validation loops were used. In short, the outer K-fold cross validation loop was used to test a condensed biomarker signature of a given length and the inner loop was used to define a ranking of the antibodies. The final condensed biomarker signature, of a given size, was then assembled using all ranking lists analyzed in the outer loop. For more details see supplemental information below.

    Results

    [0253] The aim of this study was to perform differential protein expression profiling of autoimmune diseases and healthy controls and to identify condensed biomarker signatures for disease classification. To this end, a total of 316 serum samples, collected from healthy controls (n=77) and patients diagnosed with SLE (n=39), RA (n=45), SV (n=82) or SS (n=73) were analysed on 394-plex antibody microarrays. One sample collected from a patient with Sjögren syndrome was removed from analysis due to technical reasons. One antibody, targeting Keratin-19, was failed during printing process and removed from further analysis, though two clones targeting the same antigen remained. Altogether, a total of 315 samples and 393 antibodies were used for final data analysis, differential profiling, and signature development. Visualization of the data set in Qlucore™ revealed no differences in relation to array block, sample labelling day, assay day or scanning positions, suggesting that eventual technical differences had successfully been removed during normalisation.

    Differential Protein Expression Profiling of Healthy and Autoimmune Serum Samples

    [0254] In a first step of analysis we wanted to investigate if a signature reflecting AID (including SLE, RA, SS and SV) could be identified. Altogether, we were able to demonstrate that AID could be separated from healthy controls and that a biomarker signature, indicative for AID could indeed be identified. This was done by using SVM analysis combined with a leave-one-out cross validation, including all antibodies (n=393), which demonstrated that AIDs could be separated from healthy controls with a ROC AUC-value of 0.938 (FIG. 2A). Since LOO CV analysis utilizes all antibodies for classification, we wanted to investigate if healthy and autoimmune samples still could be classified using a smaller set of antibodies. Using a ranking procedure (see method section), the 40 best performing antibodies were selected (Supplementary table S2), able to classify AID and healthy controls by a predictive AUC-value of 0.928. These results clearly show that AIDs can be differentiated from healthy controls using a protein signature which paves the way for a diagnostic test of AIDs.

    [0255] Next, we were interested in which analytes were deregulated among the AIDs. By using Wilcoxon signed rank test, a total of 77 analytes, targeted by 114 antibodies were found to be differentially expressed (q<0.05) between AIDs and healthy controls. Among the upregulated some of the most interesting included antibodies targeting Apolipoprotein A1, IL-6, IL-12, TNF-α, IL-16, Osteopontin, PRKCZ and DLG4, whereas antibodies targeting C3, IL-4, VEGF, KKCC1-1 and SPDLY-1 were found among the downregulated. A heatmap including the top 25 antibodies and their corresponding analytes revealed some separation of the two groups (FIG. 2B, Supplementary data table S3). Supported by the fact that separation of AID from healthy controls could be achieved using two different approaches, though Wilcoxon signed rank test is a nonparametric test and rely on multiple testing, whereas the K-fold cross validation is an algorithm within machine learning to estimate the prediction error, we compared the lists including the top 25 antibodies with the 40-plex panel. Some overlap could be observed including antibodies targeting the analytes C3, C4, RPS6KA2, KCC2B-3C5 and UBC9. Altogether, these results indicated that a general AID signature is present, involving upregulation of several analytes with immunoregulatory functions.

    Differential Protein Expression Profiling of SLE, RA, SS and SV

    [0256] Considering that many autoimmune diseases display similar symptoms, making clinical diagnosis challenging, we turned our focus towards the AID group for protein expression profiling analysis (FIG. 1 analysis B-E). Herein, a total of four setups was performed as followed (B) SLE vs. RA+SS+SV (C) RA vs. SLE+SS+SV (D) SS vs. SLE+RA+SV and (E) SV vs. SLE+RA+SS. Leave-one-out cross validation analysis, showed that classification of respectively AID-type could be achieved at high accuracies as presented by ROC AUC values ranging from 0.955 at the highest to 0.803 as the lowest (FIG. 3). The best separation was achieved for SLE with a ROC AUC-value of 0.955 (FIG. 3A) followed by SV and RA which were classified at ROC AUC-values of 0.937 (FIG. 3B) and 0.858 (FIG. 3C), respectively whereas SS demonstrated a ROC AUC-value of 0.803 (FIG. 3D).

    [0257] Again, we were interested in if the different groups could be separated using shorter biomarker signatures. Condensed biomarker signatures for SLE, RA, SS and SV respectively were identified using the same procedure as previous (Supplementary data table S2, B-E). Herein, using the disease-specific signatures, SLE was again found to be classified with highest accuracy (AUC=0.964) followed by SV (AUC=0.939) SS (AUC=0.795) and RA (AUC=0.793), as presented by PCA-plots in FIG. 4. A closer look at these four diseases specific signatures, revealed that antibodies targeting analytes such as C3, C4, Apolipoprotein A1 and Factor B were present on more than one list. However, analytes unique for each signature was also identified, such as Lewis x and TNF-α in SLE, PRKCZ and PTK6 in RA, IL-8 and RANTES in SS and C1q and IL-18 in SV, which could indicate the presence of disease specific markers. Altogether, by applying 394-plex antibody microarrays interfaced with stringent data analysis, 40-plex antibody signatures capable of classifying the autoimmune diseases SLE, RA, SS and SV at high predictive powers, were pinpointed.

    [0258] To further explore the serum proteomes of SLE, RA, SS and SV, differentially expressed analytes for respective disease type were identified (Wilcoxon. q<0.05) (Supplemental table S3 B-E). In total, the highest number of differentially expressed analytes was found for SV (n=326 antibodies targeting 160 analytes) followed in decreasing order by SS (n=207 antibodies targeting 127 analytes), SLE (n=127 antibodies targeting 85 analytes) and RA (n=114 antibodies targeting 81 analytes). Considering the complexity of underlying molecular alterations in AID and that both common as disease specific alteration would be of interest, we investigated the amount of overlap of analytes. Firstly, we investigated the overlap based on an antibody level, i.e. relating to the specific clones, irrespective of which analytes they targeted. This revealed a major overlap (FIG. 5A) which was not surprising considering the high number of antibodies generated from differential analysis. Secondly, we focused on an analyte level, which from a biological perspective would be more interesting. As seen in the Venn diagram (FIG. 5B) a few disease specific analytes were observed, of which OSBPL3, PRKCZ, SPDLY-1 and one CIMS antibody were found only in SLE whereas PTK6, UCHL5 and CIMS (10) were found in RA. Only one disease specific analyte, UBE2C was found in SS. However, a higher number of disease specific analytes (n=10) was found in SV, and included BKT, CIMS (11), CIMS (23), CIMS (16) INADL-1, Sialyl Lewis x, LUM, DPOLM, β-galactosidase and TOP1B. A summary including the top 25 antibodies and their specific targets for each disease, are presented in Supplementary table S3. Out of those top 25 lists, most analytes within SLE, RA and SS were found to be upregulated (15, 21 and 25 respectively), whereas the opposite, e.g. downregulation of most analytes (n=23) was observed in SV. Accordingly, the overlap with the condensed biomarker signatures for respective diseases was also investigated and revealed some overlap. Altogether, these results indicated that biologic events, including deregulation of specific analytes for each disease type, could be identified which may indicate different pathogenetic routes, and which can be used to further understand the complexity behind disease progression and for further diagnostic tools.

    Discussion

    [0259] Autoimmune diseases today pose a global health issue, affecting millions of people around the globe (29). Diffuse, general symptoms, such as fatigue, inflammation and joint pain, that change in severity over time, shared among several diseases, make clinical diagnosis challenging and there is an urgent need for refined clinical tools for early and differential diagnosis. In this study, candidate biomarker signatures for the autoimmune diseases RA, SLE, SS and SV, were pinpointed. Altogether the results showed that leave-one-out cross validation analysis including all antibodies (n=393) could accurately classify individual AIDs at AUC-values ranging between 0.955-0.803 (FIG. 3). In addition, panels including 40 antibodies could still classify the autoimmune diseases at high accuracy, with AUC values ranging between 0.964-0.793 (FIG. 4). These results show that using a multiplexed approach to reflect the pathogenetic complexity in autoimmune disorders looks very promising and is a venue to continue and explore in order to identify new targets for early and differential diagnosis of autoimmune diseases. The need for better biomarkers in autoimmune diseases is huge. Blood-based biomarkers constitute a simple non-invasive approach, suitable for both discovery biomarker analysis as for the clinical setting and constitute a major ground within the autoimmune community research. Although a few biomarkers have been found as earlier manifestations for disease, such as the presence of antinuclear antibodies (ANAs) in SLE (30, 31) and a-CCP in RA (32, 33), many biomarkers either display too low a specificity and/or sensitivity, are used one-by-one or too few to reflect the complexity in disease (16, 34). Biomarkers for differential diagnosis are thus difficult to identify, and refined tools for correct and early diagnosis are urgently needed to prevent severe organ and tissue related damage. This study utilized an antibody microarray platform targeting mainly immunoregulatory proteins and has an advantage when it comes to identifying levels of proteomic changes within systemic autoimmune disorders, as previously demonstrated by the delivery of candidate biomarkers signatures for classification of SLE and systemic sclerosis, and SLE disease activity (17, 18, 27, 35, 36).

    [0260] Based from the classification analysis, SLE and SV were found to be easiest to separate from the others (AUC values of 0.955 and 0.937 respectively) while RA and SS were a bit more difficult to separate (AUC=0.858 and AUC=0.803 respectively) (FIGS. 3 and 4). This may partly be explained by the fact that Sjögren syndrome often overlaps in patients with SLE and RA and similar pathogenetic mechanisms have been suggested (3, 4). To our knowledge, samples in this study were collected from patients diagnosed with primary Sjögren syndrome. However, it cannot be ruled out that some RA and SLE patients may have developed SS later, which could contribute to the lower AUC-values. In addition, RA patients are difficult to diagnose since the symptoms of disease often mimic the ones of other inflammatory diseases, especially in early stages of disease. Analysing of the serum proteome in patients with primary but also secondary Sjögren syndrome, RA and SLE would indeed be of great value for decoding underlying molecular pathways, which would be important from a diagnostic and therapeutic perspective.

    [0261] Important to address, is the low number of samples used in this study which confer a limiting factor since an independent data set for validation was lacking. The use of supervised learning algorithms may pose a problem when its applied in small data sets due to the risk of overfitting, which may lead to poor performance in new sample sets (38, 39). Considering this, the approach used for feature extraction and subsequent generation of condensed signatures in this study was carefully selected to avoid the risk of overtraining. Ultimately, a short signature with high predictive power may always from a logistical and cost-effective view be preferred. However, there is always a trade between the length of the signature and performance, which is why we in this first study, compromised to include 40 antibodies in the final consensus lists. Also, the high number of antibodies most likely reflect that pinpointed diseases do share similar pathogenetic pathways, and thus a higher number of antibodies for differential diagnosis, may from this perspective be necessary. This may also be supported by the major overlap of analytes observed from the differential analysis (Wilcoxon) (FIG. 3 and Supplemental table S2), which further stress the significance of larger data sets to contribute even more stringent analysis.

    [0262] Based from the differential protein expression analysis, only a small number of disease specific analytes were found (Supplemental table S3B-E, FIG. 5B). The complement system is highly involved in the pathogenesis of autoimmune diseases (37) and the major overlap of deregulated analytes may suggest similar molecular mechanisms underlying disease progression in autoimmunity. Only one analyte, UBEC2, was found uniquely in SS. UBEC2, is a member of the a ubiquitin-conjugating enzyme family which is involved in the process of destruction of mitotic cyclins and for cell cycle progression (38, 39). Interestingly, Ro52 has previously been identified as an E3 ubiquitin ligase of which increased expression may lead to increased apoptosis and for promoting auto reactivity as in the generation of Ro52 autoantibodies (40). Compared to the other AIDs, the majority of analytes were found to be downregulated among SV samples, which could explain the high number of differentially expressed analytes within this group. The reason for this difference however, can only be speculated, but may indicate that the underlying molecular events taking place in systemic vasculitis, is different from the other three diseases. Considering that vasculitis is more common in SLE patients this is unexpected and further studies aiming at these two groups would be of particular interest. Further studies, with bigger sample sets stratified by disease phenotype may help to clarify the underlying role of disease specific analytes and to aid in the search for novel candidate biomarkers for therapeutic strategies.

    [0263] In this study several analytes, involved in immunoregulatory response, were found to be deregulated among the AIDs compared to the healthy controls (Supplemental table S3A). As expected, one of the upregulated analytes was TNF-α which already has been demonstrated as a promising therapeutic target for treatment with biological TNF inhibitors, especially in RA (41, 42). Other analytes included the pro-inflammatory cytokine IL-6, which also is highly interesting from a therapeutic perspective when it comes to treatment of blockade strategy treatment in autoimmune diseases (43). The level of Osteopontin has previously been demonstrated to be elevated in SLE patients which we could confirm in this study. Osteopontin has been suggested to be associated with SLE development and a potential marker for SLE activity and organ damage (44). Altogether, these data suggest that a more general autoimmune signature may be present, including several already known and novel markers that may play significant roles within autoimmunity. In addition, the finding of a candidate biomarker signature for classification of AIDs from healthy controls, which is supported also from other studies (14) further strengthen the potential of using our antibody microarray platform for biomarker discovery in autoimmune diseases. A tool, able to function as sensor for autoimmune diseases, resulting in the transferal of patients to the right instance, would be of high significance for early and correct diagnosis.

    [0264] The four systemic autoimmune diseases (SLE, RA, SS and SV) analysed in this study were chosen based on the fact that they share many clinical symptoms and in addition, three of them e.g. SLE, RA and SS, are among the most common AIDs. SV is not that common, though associated with a very poor prognosis.

    Conclusion

    [0265] We here demonstrate that a general AID biomarker signature could be delineated and that individual AIDs (SLE, RA, SS and SV) could be classified at high accuracies using a multiplexed microarray. These results together with previous studies (15, 16, 27, 34), suggest the fact that the use of a multiplexed approach is more suitable for decoding multifactorial diseases such as autoimmune diseases and will play a significant role for future diagnostic purposes, essential to prevent severe organ and tissue related damage.

    Supplementary Data

    Defining a Condensed Biomarker Signature

    [0266] A linear support vector machine (SVM) was used as the classification method when defining the condensed biomarker signature. See the scripts detailed in Supplementary table S4.

    [0267] To rank a given signature a 5-fold cross validation scheme, repeated 15 times, was used as follows: (i) For each training dataset an SVM model was trained. (ii) The corresponding validation dataset was used to estimate the importance of each individual protein in the signature. This was accomplished by removing a given protein (i.e. replacing its expression value by the mean value over all samples) and measure the change of the validation performance. An important protein will result in a large decrease of the validation performance. This procedure was repeated for each validation dataset in the repeated K-fold cross validation procedure. The average change of validation performance for each protein was then computed, giving a final ranking list of all proteins in the signature.

    [0268] To obtain an unbiased estimate of the performance of a condensed signature, according to the computed ranking list, it is not possible to again use the dataset used to obtain the ranking of the proteins. An additional test set is needed. To this end an outer 5-fold cross validation loop, repeated two times, was introduced with the purpose of evaluating condensed signatures of different lengths. The average test AUC value was used as the estimate of the performance of a condensed signature with a given length.

    [0269] The different ranking lists generated by the outer loop are slightly different from each other in terms of the rank of a specific protein. The final condensed signature of a given length was assembled by log-rank averaging of all ten lists.

    Supplementary Tables

    [0270]

    TABLE-US-00027 SUPPLEMENTARY TABLE S1 The number of antibodies targeting the specific proteins. The molecular design of the antibodies display high on-chip functionality and has been validated in terms of specificity, affinity and performance using MesoScale Discovery, ELISA, MS and SPR-analysis. No. of No. of No. of anti- anti- anti- body body body Protein Full name clones Protein Full name clones Protein Full name clones AKT3 RAC-gamma 2 IgM IgM 5 OSTP Osteopontin 3 serine/threoninE−protein kinase Angiomotin Angiomotin 2 IL-10 Interleukin-10 3 OTU6B OTU domain-containing 2 protein 6B ANM5 Protein arginine N- 2 IL-11 Interleukin-11 3 OTUB1-1 Ubiquitin thioesterase 2 methyltransferase 5 OTUB1 APLF Aprataxin and PNK-like 2 IL-12 Interleukin-12 4 OTUB2-1 Ubiquitin thioesterase 2 factor OTUB2 APOA4 Apolipoprotein A4 3 IL-13 Interleukin-13 3 P85A Phosphatidylinositol 3- 3 kinase regulatory subunit alpha Apolipoprotein Apolipoprotein A1 3 IL-16 Interleukin-16 3 PAK4-1 Serine/threoninE−protein 2 A1 kinase PAK 4 ARHGC-1 Rho guanine nucleotide 1 IL-18 Interleukin-18 3 PAK5 Serine/threoninE−protein 2 exchange factor 12 kinase PAK 7 ATP5B ATP synthase subunit 3 IL-b-ra Interleukin-1 receptor 3 PARP1 Poly [ADP-ribose] 1 beta, mitochondria! antagonist protein polymerase 1 BIRC2 Baculoviral IAP repeat- 2 IL-1α Interleukin-1 alpha 3 PARP6B Partitioning defective 6 2 containing protein 2 homolog beta BKT TyrosinE−protein kinase 4 IL-1β Interleukin-1 beta 3 PGAM5 Serine/threoninE−protein 2 BTK phosphatase PGAM5, mitochondrial C1 Plasma protease C1 4 IL-2 Interleukin-2 3 PRD14 PR domain zinc finger 2 esterase inhibitor protein 14 inhibitor C1q Complement C1q 1 IL-3 Interleukin-3 3 PRDM8-1 PR domain zinc finger 2 protein 8 C1s Complement C1s 1 IL-4 Interleukin-4 4 PRKCZ Protein kinase C zeta type 2 C3 Complement C3 6 IL-5 Interleukin-5 3 PRKG2 cGMP-dependent protein 2 kinase 2 C4 Complement C4 4 IL-6 Interleukin-6 8 Procathepsin Cathepsin W 1 W C5 Complement C5 3 IL-7 Interleukin-7 2 Properdin Properdin 1 (Factor P) CBPP22 Calcineurin B 2 IL-8 Interleukin-8 3 PSA ProstatE−specific antigen 1 homologous protein 1 CD40 CD40 protein 4 IL-9 Interleukin-9 3 PTK6 Protein-tyrosine kinase 6 1 CD40L CD40 ligand 1 INADL- InaD-like protein 2 PTN13-1 TyrosinE−protein 2 1 phosphatase non-receptor type 13 CDK2 Cyclin-dependent kinase 2 Integrin Integrin alpha-10 1 PTPN1 TyrosinE−protein 3 2 α-10 phosphatase non-receptor type 1 CENTG1 Arf-GAP with GTPase, 2 Integrin Integrin alpha-11 1 PTPRD Receptor-type tyrosinE− 2 ANK repeat and PH α-11 protein phosphatase delta domain-containing protein 2 CHEK2 Serine/threoninE−protein 2 ITCH-2 E3 ubiquitin-protein 2 PTPRJ Receptor-type tyrosinE− 2 kinase Chk2 ligase Itchy homolog protein phosphatase eta CHX10 Visual system homeobox 3 JAK3 TyrosinE−protein 1 PTPRK Receptor-type tyrosinE− 2 2 kinase JAK3 protein phosphatase kappa CSNK1E Casein kinase 1 isoform 2 KCC2B-3 Calcium/calmodulin- 2 PTPRN2 Receptor-type tyrosinE− 2 epsilon dependent protein protein phosphatase N2 kinase type 11 subunit beta CT17 Choleratoxin subunit B 1 KCC4 Calcium/calmodulin- 2 PTPRO Receptor-type tyrosinE− 2 dependent protein protein phosphatase O kinase type IV Cystatin C Cystatin-C 4 Keratin Keratin, type I 3 PTPRT Receptor-type tyrosinE− 2 19 cytoskeletal 19 protein phosphatase T DCNL1 DCN1-like protein 1 2 KIAA0882 TBC1 domain family 3 RANTES C-C motif chemokine 5 3 member 9 Calcium/calmodulin- 2 RPS6KA2 Ribosomal protein S6 3 Digoxin Digoxin 1 KKCC1-1 dependent protein kinase alpha-2 kinase kinase 1 DLG1-1 Disks large homolog 1 2 KRASE GTPase KRas 1 SHC1 SHC-transforming protein 1 2 DLG2-1 Disks large homolog 2 2 KSYK TyrosinE−protein 2 Sialyl Lewis x Sialyl Lewis x 1 kinase SYK DLG4-2 Disks large homolog 4 2 LDL Apolipoprotein B-100 2 SNTA1 Alpha-1-syntrophin 2 DPOLM DNA-directed DNA/RNA 2 Leptin Leptin 1 Sox11a Transcription factor SOX- 1 polymerase mu 11 DUSP7 Dual specificity protein 2 Lewis x Lewis x 2 SPDLY-1 Protein Spindly 2 phosphatase 7 DUSP9 Dual specificity protein 1 Lewis y Lewis y 1 STAP1 Signal-transducing adaptor 2 phosphatase 9 protein 1 EGFR Epidermal growth factor 1 LIN7A Protein lin-7 homolog 2 STAP2 Signal-transducing adaptor 4 receptor A protein 2 Eotaxin Eotaxin 3 LUM Lumican 1 STAT1 Signal transducer and 2 activator of transcription 1- alpha/beta Factor B Complement factor B 4 MAGI1- MembranE−associated 2 TENS4 Tensin-4 1 1 guanylate kinase, WW and PDZ domain- containing protein 1 FASN FASN protein 4 MAP2K Dual specificity 2 TGF-β Transforming growth factor 3 2 mitogen-activated beta-1 protein kinase kinase 2 FER TyrosinE−protein kinase 2 MAP2K Dual specificity 2 TM peptide TM peptide 1 Fer 6 mitogen-activated protein kinase kinase 6 GAK GAK protein 3 MAPK9 Mitogen-activated 2 TNFRSF14 Tumor necrosis factor 2 protein kinase 9 receptor superfamily member 14 GEM GTP-binding protein 2 MARK1-1 Serine/threoninE− 2 TNFRSF3 Tumor necrosis factor 3 GEM protein kinase MARK1 receptor superfamily member GLP-1 Glucagon-like peptidE−1 1 MARK2-1 Serine/threoninE− 2 TNF-α Tumor necrosis factor 3 protein kinase MARK2 GLP-1R Glucagon-like peptide 1 1 MATK MegakaryocytE− 3 TNF-β Lymphotoxin-alpha 4 receptor associated tyrosinE− protein kinase GM-CSF GranulocytE−macrophage 6 MCP-1 C-C motif chemokine 2 9 TOPB1 DNA topoisomerase 2- 2 colony-stimulating factor binding protein 1 GNAI3 Guanine nucleotidE− 2 MCP-3 C-C motif chemokine 7 3 UBC9 SUMO-conjugating enzyme 3 binding protein G(k) UBC9 subunit alpha GORS2-1 Golgi reassembly- 2 MCP-4 C-C motif chemokine 3 UBE2C Ubiquitin-conjugating 2 stacking protein 2 13 enzyme E2 C GPRK5 G protein-coupled 1 MD2L1 Mitotic spindle 2 UBP7 Ubiquitin carboxyl-terminal 2 receptor kinase 5 assembly checkpoint hydrolase 7 protein MAD2A GRIP2-1 Glutamate receptor- 2 MK01 Mitogen-activated 4 UCHL5 Ubiquitin carboxyl-terminal 1 interacting protein 2 protein kinase 1 hydrolase isozyme L5 HADH2 HADH2 protein 4 MK08 Mitogen-activated 3 UPF3B Regulator of nonsense 2 protein kinase 8 transcripts 3B Her2/ErbB2 Receptor tyrosinE−protein 4 Mucin-1 Mucin-1 6 VEGF Vascular endothelial growth 4 kinase erbB-2 factor HLA- HLA-DR/DP 1 MYOM Myomesin-2 2 β- DR/DP 2 galactosidase Beta-galactosidase 1 ICAM-1 Intercellular adhesion 1 NDC80 Kinetochore protein 2 molecule 1 NDC80 homolog IFN-γ Interferon gamma 3 NOS1-1 Nitric oxide synthase, 2 brain OSBPL Oxysterol-binding 2 3 protein-related protein 3

    TABLE-US-00028 SUPPLEMENTARY TABLE S2 Condensed panels of antibodies, based on a ranking procedure combined with a K-fold cross validation. The individual scFv antibody clone number is shown in brackets 0. (A) H vs. (B) SLE vs. SLE + RA + SS + SV RA + SS + SV (C) RA vs. SLE + SS + SV (D) SS vs. SLE + RA + SV (E) SV vs. SLE + RA + SS Antibody Rank Antibody Rank Antibody Rank Antibody Rank Antibody Rank C3 (4)  1.0 C3 (4)  1.2 C3 (4)  1.7 C1 inh. (2)  1.5 Factor B (3)  1.2 C3 (6)  2.5 C3 (6)  1.7 VEGF (3)  4.3 C1 inh. (4)  2.1 Apo-A1 (3)  2.5 IL-4 (3)  3.4 C3 (5)  3.5 KKCC1-1 (1)  4.7 OTUB1-1 (2)  2.8 C3 (6)  3.0 C3 (3)  4.2 C4 (2)  3.5 CIMS (10)  5.5 C1 inh. (3)  5.5 C3 (3)  5.8 C5 (3)  5.3 C3 (3)  5.8 CIMS (4)  5.8 MCP-1 (3)  6.4 CIMS (27)  6.8 C3 (5)  6.7 MAGI1-1 (1)  6.1 Factor B (2)  6.9 HADH2 (3)/GSN  8.1 IgM (1)  9.2 C1 inh. (1)  8.9 VEGF (3)  6.8 CIMS (13)  7.8 Angiomotin (2) 11.5 Apo-A1 (2) 10.0 C4 (4) 11.7 C4 (3) 10.0 C3 (5)  7.9 IL-13 (2) 12.8 C4 (4) 10.9 UBC9 (3) 14.3 Apo-A1 (3) 10.7 PRKG2 (2)  9.8 PRKG2 (2) 13.2 KKCC1-1 (1) 11.1 IL-1ra (3) 16.4 C3 (2) 12.1 C1 inh. (1) 12.7 Factor B (3) 14.7 IL-4 (3) 11.3 CIMS (10) 17.5 C1 inh. (1) 12.6 GM-CSF (1) 19.7 CIMS (25) 17.8 C1 inh. (2) 14.3 CIMS (27) 17.7 Apo-A1 (2) 14.5 PKB gamma (1) 19.7 CSNK1E (2) 18.2 C1 inh. (4) 14.4 GM-CSF (1) 18.6 OTUB1-1 (2) 14.6 C5 (2) 20.9 Apo-A1 (2) 20.4 C1q 15.3 C1 inh. (4) 18.9 C5 (3) 16.7 MCP-4 (2) 22.6 IL-6 (8) 22.2 C1 inh. (3) 17.5 IL-1a (1) 21.3 IL-6 (6) 17.1 LUM 22.9 IL-3 (3) 24.2 C4 (3) 17.9 TNF-a (2) 24.8 IL-4 (3) 21.0 PTN13-1 (1) 24.2 CIMS (8) 26.4 CIMS (10) 19.0 HADH (3)/GSN 26.3 PAK7 (1) 24.3 Factor B (3) 24.4 PTPRD (1) 28.5 IgM (2) 20.5 MAGI1-1 (1) 26.6 TNF-a (1) 28.5 C3 (6) 26.2 IL-6 (5) 28.9 Angiomotin (2) 23.8 KCC2B-3 (1) 28.8 Lewis x (1) 29.5 IgM (1) 26.7 ARHGC-1 (1) 36.4 PTPRD (1) 24.1 LUM 31.0 GM-CSF (6) 30.1 EGFR 27.6 Properdin 37.1 MCP-1 (9) 25.9 UPF3B (2) 31.6 C4 (4) 32.4 NOS1-1 (2) 29.5 CIMS (28) 37.7 C4 (2) 27.9 GLP-1 R 33.7 NOS1-1 (2) 33.2 Procathepsin W 30.8 DCNL1 (2) 39.1 IL-3 (3) 30.4 FASN (4) 34.9 DPOLM (2) 33.5 IgM (5) 31.0 PRKG2 (1) 39.7 CIMS (14) 32.1 TNF-a (1) 36.1 TNFRSF3 (2) 34.9 Apo-A4 (2) 32.1 GAK (3) 40.1 GM-CSF (1) 32.4 TopBP1 (1) 37.6 VEGF (2) 34.9 IL-9 (1) 32.7 TBC1D9 (2) 41.6 IL-12 (3) 32.8 IL-8 (1) 37.9 IL-1ra (3) 35.3 PTK6 33.1 TNFRSF14 (2) 42.1 C5 (3) 33.2 Factor B (3) 38.3 OTUB2-1 (2) 41.8 hSpindly (2) 33.6 CIMS (9) 42.1 Cystatin C (2) 34.8 FER (1) 39.0 IL-13 (2) 42.3 GRIP-2 (1) 36.5 Factor B (2) 43.1 IL-18 (2) 35.2 CDK-2 (1) 39.4 MCP-1 (5) 46.8 CHEK2 (1) 36.6 MARK2-1 (2) 43.3 MUC1 (1) 38.4 CIMS (16) 39.5 MATK (3) 47.6 MAPKK 2 (2) 39.2 Sox11a 43.9 DLG4-2 (2) 41.6 RPS6KA2 (3) 39.6 MUC1 (4) 47.9 ITCH-2 (1) 39.5 KKCC1-1 (2) 44.3 IL-3 (2) 41.7 GRK5 (1) 41.2 IgM (3) 48.3 CIMS (8) 39.5 MUC1 (5) 44.7 CIMS (13) 42.2 MAPKK 6 (1) 41.5 PTPPRN2 (1) 53.6 C3 (2) 40.6 GEM (2) 49.1 UPF3B (1) 42.7 C4(3) 42.6 OTU6B (1) 54.0 Cystatin C (1) 41.4 MARK1-1 (1) 49.1 CT17 43.2 UBP7 (1) 43.3 MARK2-1 (2) 55.0 IgM (2) 42.2 C5 (3) 49.3 INFRSF14 (2) 43.7 C4 (1) 43.4 BIRC2 (1) 55.4 CIMS (9) 42.8 RANTES (2) 49.7 MCP-1 (8) 47.3 AGAP-2 (1) 43.5 hSpindly (1) 56.0 PRKCZ (1) 48.9 IL-12 (3) 51.7 MAPKK 6 (2) 48.7 VEGF (3) 45.0 IL-4 (4) 57.1 IL-1ra (3) 49.9 MAPKK 6 (1) 51.8 CIMS (25) 51.8 HLA-DR/DP 46.9 HADH2 (4) 59.9 IL-4 (1) 50.5 IL-8 (2) 51.8 TNFRSF3 (3) 51.9 CIMS (4) 47.4 MAPKK 6 (1) 61.5 Cystatin C (3) 51.9 UPF3B (1) 52.9 Angiomotin (1) 52.4

    TABLE-US-00029 SUPPLEMENTARY TABLE S3 Top 25 differentially expressed analytes from Wilcoxon signed-rank test (q < 0.05), including AUC and 95% Cl. For analytes targeted by multiple clones, individual clone suffix is shown in brackets. (C) RA vs. SLE + SS + SV (A) H vs. SLE + RA + SS + SV (B) SLE vs. Ra + SS + SV 95% Antigen q-value AUC 95% Cl Antigen q-value AUC 95% Cl Antigen q-value AUC Cl C3 (4) 6.11312E−23 0.896 0.869-0.923 C4 (2) 1.92829E−13 0.911 0.876-0.945 Cl inh. 3.74741E−05 0.755 0.691- (1) 0.816 UBC9 (3) 2.46399E−14 0.815 0.772-0.857 C3 (6) 2.26317E−13 0.906 0.867-0.941 PRKG2 0.000270946 0.733 0.662- (2) 0.800 C3 (6) 4.78988E−13 0.798 0.756-0.838 C3 (4) 1.3471E−11 0.878 0.823-0.926 KKCC1-1 0.000415853 0.721 0.651- (1) 0.791 C3 (5) 7.46857E−13 0.794 0.750-0.838 C3 (3) 1.72755E−11 0.873 0.821-0.921 PTN13-1 0.000415853 0.722 0.649- (1) 0.789 C3 (2) 3.67974E−11 0.774 0.727-0.820 C4 (3) 5.26683E−11 0.864 0.815-0.910 IgM (1) 0.00204718 0.698 0.629- 0.760 RPS6KA2 5.19795E−11 0.773 0.722-0.821 C4 (4) 7.43386E−09 0.826 0.770-0.881 IgM (5) 0.00204718 0.700 0.632- (1) 0.770 C3 (3) 1.19753E−10 0.766 0.721-0.812 Apo-A1 2.89798E−06 0.778 0.714-0.834 PKB 0.00204718 0.694 0.615- (2) gamma 0.764 (1) CIMS (10) 9.17488E−10 0.755 0.707-0.801 VEGF 1.73961E−05 0.758 0.695-0.821 CDK-2 0.00204718 0.697 0.629- (3) (2) 0.763 STAP1 (1) 1.82011E−09 0.749 0.696-0.799 Apo-A1 1.91513E−05 0.754 0.691-0.811 MAPKK 0.00204718 0.695 0.622- (3) 2 (2) 0.765 P85A (3) 2.55717E−08 0.735 0.686-0.783 MAGI1-1 1.96854E−05 0.755 0.685-0.820 GLP-1 0.00262381 0.691 0.613- (1) 0.757 KCC2B-3 3.02553E−08 0.732 0.684-0.778 C3 (5) 0.000042344 0.747 0.677-0.816 IgM (2) 0.00262381 0.691 0.621- (2) 0.760 INADL-1 4.1601E−08 0.73 0.678-0.782 C1 inh. 9.27677E−05 0.738 0.683-0.789 OTUB2-1 0.00271334 0.689 0.618 - (2) (3) (I) 0.759 DLG4-2 (I) 4.75923E−08 0.728 0.680-0.773 IgM (3) 0.000367382 0.722 0.656-0.791 C5 (3) 0.00343315 0.682 0.616- 0.749 INADL-1 9.039E−08 0.726 0.670-0.781 NOS1-1 0.000392197 0.721 0.651-0.794 IL-6 (7) 0.00343315 0.687 0.613- (1) (1) 0.757 Osteopontin 1.71762E−07 0.72 0.660-0.772 C1 inh. 0.000592225 0.715 0.657-0.774 SNTA1 0.00343315 0.683 0.611- (2) (4) (1) 0.763 UCHL5 2.22831E−07 0.719 0.664-0.769 BIRC2 0.000898398 0.711 0.644-0.779 CHEK2 0.00343315 0.684 0.613- (2) (1) 0.749 C4 (2) 2.65945E−07 0.717 0.669-0.761 C1 inh. 0.000944125 0.709 0.650-0.763 MATK 0.00392043 0.680 0.606- (2) (1) 0.751 MARK1-1 3.98633E−07 0.712 0.657-0.763 IgM (2)* 0.00137751 0.703 0.641-0.763 IL-1b 0.00473801 0.677 0.606- (2) (2) 0.750 C5 (3) 4.38712E−07 0.712 0.661-0.761 GM-CSF 0.00148405 0.700 0.627-0.773 TNFRSF3 0.00473801 0.677 0.605- (1) (2) 0.743 PRKCZ (1) 1.8135E−06 0.702 0.643-0.758 GAK (2) 0.00156823 0.701 0.630-0.772 DLG4-2 0.00473801 0.677 0.605- (2) 0.746 C3 (1) 2.27779E−06 0.7 0.646-0.752 INFRSF14 0.00178729 0.697 0.626-0.766 IL-8 (2) 0.00549152 0.673 0.598- (1) 0.745 STAP1 (2) 2.29734E−06 0.702 0.650-0.752 Lewis y 0.00205928 0.695 0.620-0.767 TNF-b 0.00559346 0.672 0.599- (3) 0.742 KKCC1-1 2.55033E−06 0.698 0.649-0.747 IL-4 (3) 0.0021822 0.691 0.621-0.761 Lewis y 0.00578037 0.669 0.592- (2) 0.746 PTPN1 (3) 3.68037E−06 0.697 0.642-0.750 MCP-1 (9) 0.0021822 0.696 0.619-0.767 NOS1-1 0.00578037 0.672 0.598- (2) 0.740 hSpindly 3.68037E−06 0.695 0.642-0.751 Cystatin 0.00251973 0.690 0.620-0.758 IL-lb 0.00619337 0.670 0.599- (2) C (2) (1) 0.743 (D) SS vs. SLE + RA + SV (E) SV vs. SLE + RA + SS Antigen q-value AUC 95% Cl Antigen q-value AUC 95% Cl MATK (2) 1.95164E−05 0.718 0.658-0.775 IgM (2) 1.17114E−15 0.844 0.797-0.884 GEM (2) 1.95164E−05 0.723 0.665-0.781 IgM (1) 1.42329E−13 0.817 0.772-0.863 Her2/ErbB2 2.39878E−05 0.705 0.644-0.767 IL-4 (3) 2.45402E−13 0.812 0.760-0.860 (2) MATK (3) 2.39878E−05 0.705 0.644-0.762 Angiomotin 4.55055E−13 0.810 0.760-0.856 (2) GEM (1) 2.39878E−05 0.710 0.653-0.764 GM-CSF 1.05443E−12 0.804 0.755-0.851 (1) PAR-6B (2) 2.39878E−05 0.707 0.644-0.766 MUC1 (1) 1.53178E−12 0.802 0.753-0.848 DCNL1 (2) 2.39878E−05 0.705 0.646-0.758 IL-11 (2) 1.70405E−12 0.801 0.750-0.848 ITCH-2 (1) 2.39878E−05 0.709 0.648-0.771 PSA 1.71093E−12 0.799 0.747-0.849 FER (1) 2.39878E−05 0.710 0.649-0.772 RANTES 3.34687E−12 0.794 0.742-0.845 (1) KSYK (1) 2.84351E−05 0.703 0.646-0.760 IL-12 (3) 3.34687E−12 0.795 0.742-0.842 HADH2 5.49172E−05 0.694 0.635-0.752 IL-4 (2) 6.81591E−12 0.792 0.742-0.841 (3)/GSN CSNK1E 5.49172E−05 0.695 0.634-0.759 UPF3B (1) 7.46307E−12 0.790 0.739-0.837 (2) GORS2-1 6.66891E−05 0.695 0.630-0.755 IL-3 (2) 7.63932E−12 0.788 0.736-0.837 (2) IL-3 (3) 7.03667E−05 0.692 0.634-0.749 CT17 1.04423E−11 0.787 0.731-0.838 Her2/ErbB2 7.03667E−05 0.690 0.627-0.750 PTPRD (1) 1.04423E−11 0.787 0.740-0.834 (1) TNFRSF14 7.03667E−05 0.692 0.629-0.759 BTK (1) 1.31095E−11 0.784 0.730-0.837 (2) UBP7 (1) 9.32392E−05 0.688 0.620-0.750 TNF-b (1) 1.67381E−11 0.782 0.732-0.830 R-PTP-eta 9.32392E−05 0.687 0.625-0.749 IgM (3) 2.02089E−11 0.782 0.731-0.833 (2) DCNL1 (1) 9.32392E−05 0.687 0.627-0.747 IL-10 (3) 2.02813E−11 0.782 0.725-0.835 LIN7A (2) 9.32392E−05 0.687 0.621-0.750 MCP-4 (1) 3.10126E−11 0.778 0.723-0.830 RANTES 0.000106877 0.684 0.622-0.744 CD40 3.10126E−11 0.780 0.726-0.829 (2) ligand KCC2B-3 0.000113498 0.684 0.620-0.746 MCP-4 (3) 3.10126E−11 0.778 0.722-0.832 (1) Her2/ErbB2 0.000132889 0.683 0.615-0.740 TBC1D9 3.10126E−11 0.779 0.723-0.834 (3) (2) CHP1 (1) 0.000142836 0.683 0.620-0.745 PAR-6B 3.10126E−11 0.777 0.721-0.829 (1) IL-lb (3) 0.000145011 0.681 0.620-0.743 C4 (3) 3.38326E−11 0.775 0.726-0.822

    TABLE-US-00030 SUPPLEMENTARY TABLE S5 Amino acid sequences for CIMS antibodies used in the Examples Clone index motif Peptide sequence Amino acid sequence CIMS (4) GIVKYLYEDEG EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYAMHWVRQ APGKGLEWVSSISNRGSRTFYADSVKGRFTISRDNSKNTLYL QMNSLRAEDTAVYYCARDHRWDPGAFDIWGQGTLVTVSS GGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGS SSNIGADYDVHWYQQLPGTAPKLLIYGNSNRPSGVPDRFS GSKSGTSASLAISGLRSEDEADYYCAAWDDGLSGVVFGGGT KLTVLGEQKLISEEDLSGSAAAHHHHHH CIMS (8) DFAEDK EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQA PGKGLEWVSSISSSSSYIYYADSVKGRFTISRDNSKNTLYLQM NSLRAEDTAVYYCARLFFSGGATRAAFDIWGQGTLVTVSSG GGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGGS SNIGSNTVNWYQQLPGTAPKLLIYGNSNRPSGVPDRFSGSK SGTSASLAISGLRSEDEADYYCAAWDDSLNGRVFGGGTKLT VLGEQKLISEEDLSGSAAAHHHHHH CIMS (9) LTEFAK EVQLLESGGGLVQPGGSLRLSCAASGFTFSDYYMSWIRQAP GKGLEWVSSISSRSSYIYYADSVKGRFTISRDNSKNTLYLQM NSLRAEDTAVYYCAKDREYYDILTGYPSMDVWGQGTLVTV SSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCT GSSSNIGAGYDVHWYQQLPGTAPKLLIYDNNKRPSGVPDR FSGSKSGTSASLAISGLRSEDEADYYCSAWDESLSGVVFGGG TKLTVLGEQKLISEEDLSGSAAAHHHHHH CIMS (10) TEEQLK EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYGMHWVRQA PGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQ MNSLRAEDTAVYYCARSRYGSGMDVWGQGTLVTVSSGG GGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSS NVGVNYVYWYQQLPGTAPKLLIYSHNQRPSGVPDRFSGSK SGTSASLAISGLRSEDEADYYCAAWDDSLNGVVFGGGTKLT VLGEQKLISEEDLSGSAAAHHHHHH CIMS (13) SSAYSR EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQA PGKGLEWVSAISGSGGRTYYTDSVRDRFTISRDNSKNTLYLQ MNSLRAEDTAVYYCARDLMPVCQYCYGMDVWGQGTLVT VSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISC TGSSSNIGAGYDVHWYQQLPGTAPKLLIYSNNQRPSGVPD RFSGSKSGTSASLAISGLRSEDEADYYCQSYDSSLNKDVVFG GGTKLTVLGEQKLISEEDLSGSAAAHHHHHH CIMS (14) SSAYSR EVQLLESGGGLVQPGGSLRLSCAASGFTFSDYYMSWVRQA PGKGLEWVADIKRDGSTRYYGDSVKGRFTISRDNSKNTLYL QMNSLRAEDTAVYYCARDRLVAGLFDYWGQGTLVTVSSG GGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSS SNIGAGYDVHWYQQLPGTAPKLLIYGNSNRPSGVPDRFSG SKSGTSASLAISGLRSEDEADYYCAAWDDSLSVLFGGGTKLT VLGEQKLISEEDLSGSAAAHHHHHH CIMS (16) EDFR EVQLLESGGGLVQPGGSLRLSCAASGFTFDDYGMSWVRQ APGKGLEWVSAISGSGGSTYYADPVKGRFTISRDNSKNTLYL QMNSLRAEDTAVYYCARSRYGSGMDVWGQGTLVTVSSG GGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSS SNIGSNYVYWYQQLPGTAPKLLIYKSNQRPSGVPDRFSGSK SGTSASLAISGLRSEDEADYYCAAWDDRLNAVVFGGGTKLT VLGEQKLISEEDLSGSAAAHHHHHH CIMS (25) LSADHR EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYAMSWVRQA PGKGLEWVAFIRYDGSNKYYADSVKGRFTISRDNSKNTLYL QMNSLRAEDTAVYYCARDAVGGDSYVLDYWGQGTLVTVS SGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSG SSSNIGSNAVNWYQQLPGTAPKLLIYGNSNRPSGVPDRFSG SKSGTSASLAISGLRSEDEADYYCAAWDDSLNGWVFGGGT KLTVLGEQKLISEEDLSGSAAAHHHHHH CIMS (27) SEAHLR EVQLLESGGGLVQPGGSLRLSCAASGFTFTSYSMSWVRQA PGKGLEWVSAIGTGGGTYYADSVKGRFTISRDNSKNTLYLQ MNSLRAEDTAVYYCARVNWNDAFDYWGQGTLVTVSSGG GGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSS NIGNNAVNWYQQLPGTAPKLLIYRNDQRPSGVPDRFSGSK SGTSASLAISGLRSEDEADYYCSTWDDSLSGVFFGGGTKLTV LGEQKLISEEDLSGSAAAHHHHHH CIMS (28) SEAHLR EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQA PGKGLEWVAAIWSDGSNKYYADSVKGRFTISRDNSKNTLYL QMNSLRAEDTAVYYCAKVGATDDAFDIWGQGTLVTVSSG GGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSS SNIGSNTVNWYQQLPGTAPKLLIYGNSNRPSGVPDRFSGSK SGISASLAISGLRSEDEADYYCAAWDDSLNGPVFGGGTKLT VLGEQKLISEEDLSGSAAAHHHHHH

    TABLE-US-00031 SUPPLEMENTARY TABLE S6 Amino acid sequences for scFvs directed against core biomarkers Antibody Full protein Sequence (VH-linker-VL-tag) MCP-1 (3) EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYGMHWVRQAPGKGLEWVAVISYDG SNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKSHYYDTTSFDYWG QGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGTN PVNWYQQLPGTAPKLLIYDNNKRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYC AAWDDSLSGVVFGGGTKLTVLGEQKLISEEDLSGSAAAHHHHHH [SEQ ID NO: 1] Procathepsin W EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSSMSASG GSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGSYGMDVWGQ GTLVIVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSTSNIGSYA VNWYQQLPGTAPKLLIYGNNNRPSGVPDRFSGSKSGTSASLAISGPRSEDEADYYCA AWDDSLNGGVFGGGTKLTVLGDYKDDDDKAAAHHHHHH [SEQ ID NO: 2] IL-4 (3) EVQLLESGGGLVQPGGSLRLSCAASGFTFSNAWMSWVRQAPGKGLEWVSAISGS GGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARAIAARPFDYWGQ GTLVIVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGATSNIGAGY DIHWYQQLPGTAPKLLIYSTNNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCA AWDDSLNGPVFGGGTKLTVLGEQKLISEEDLSGSAAAHHHHHH [SEQ ID NO: 3] Factor B (2) EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVAVISYDG RFIYYSDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSYGGNLAMDVWGQ GTLVIVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGY DVHWYQQLPGTAPKLLIYDNNKRPSGVPDRFSGSNSGTSASLAISGLRSEDEADYYC AAWDDRLNGRVVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHH H [SEQ ID NO: 4] Factor B (3) EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVAVISYDG SNQYYADSVRGRFTISKDNSKNTLYLQMNSLRAEDTAVYYCAREWHYSLDVWGQG TLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGSNTV NWYQQLPGTAPKLLIYRNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAA WDDSLSVRVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 5] UPF3B (1) EVQLLESGGGLVQPGGSLRLSCAASGFTFSDYYMTWIRQAPGKGLEWVSDISWNG SRTHYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCSSHLVYWGQGTLVTV SSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWY QQLPGTAPKLLIYDNNKRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCQTYDSS LSGSVVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 6] HADH2 (3) EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSYMSWVRQAPGKGLEWVSSISSYGY and/or GSN YTGYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSYGSWYFDYWGQG TLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLN WYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGF VGPSTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 7] PAK-7 (1) EVQLLESGGGLVQPGGSLRLSCAASGFTFSGYSMSWVRQAPGKGLEWVSSISSSYSS TYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGSFGFDYWGQGTLVT VSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQ QKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYYGVL PTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 8] KKCC1-1 (3) EVQLLESGGGLVQPGGSLRLSCAASGFTFYYSYMYWVRQAPGKGLEWVSAISGSG GSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSYTPASYRFDYWG QGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYL NWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQS YSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 9] MAGI1-1 (1) EVQLLESGGGLVQPGGSLRLSCAASGFTFSYSSMSWVRQAPGKGLEWVSGISGSGY STYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGYSGFDYWGQGTLV TVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQ QKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYGYATL PTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 10] OTUB1-1 (2) EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSG GSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARYHYYAGFDYWGQG TLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLN WYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSG SF LPTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH [SEQ ID NO: 11] FER (1) EVQLLESGGGLVQPGGSLRLSCAASGFTFGYSYMHWVRQAPGKGLEWVSYISSYG GYTGYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSSVDSVVWYGGYI DYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRAS QSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFAT YYCQQSGFNSPHTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHH HH [SEQ ID NO: 12]

    REFERENCES

    [0271] 1. Thomas, S. L., Griffiths, C., Smeeth, L., Rooney, C. & Hall, A. J. Burden of mortality associated with autoimmune diseases among females in the United Kingdom. Am J Public Health 100, 2279-2287 (2010). [0272] 2. Walsh, S. J. & Rau, L. M. Autoimmune diseases: a leading cause of death among young and middle-aged women in the United States. Am J Public Health 90, 1463-1466 (2000). [0273] 3. Manoussakis, M. N. et al. Sjögren's syndrome associated with systemic lupus erythematosus: clinical and laboratory profiles and comparison with primary Sjogren's syndrome. Arthritis and rheumatism 50, 882-891 (2004). [0274] 4. Toro-Dominguez, D., Carmona-Saez, P. & Alarcon-Riquelme, M. E. Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjogren's syndrome uncovered through gene expression meta-analysis. Arthritis research & therapy 16, 489 (2014). [0275] 5. Alexander, E. L., Hirsch, T. J., Arnett, F. C., Provost, T. T. & Stevens, M. B. Ro(SSA) and La(SSB) antibodies in the clinical spectrum of Sjögren's syndrome. J Rheumatol 9, 239-246 (1982). [0276] 6. Falk, R. J. & Jennette, J. C. Anti-neutrophil cytoplasmic autoantibodies with specificity for myeloperoxidase in patients with systemic vasculitis and idiopathic necrotizing and crescentic glomerulonephritis. N Engl J Med 318, 1651-1657 (1988). [0277] 7. Rekvig, O. P. Anti-dsDNA antibodies as a classification criterion and a diagnostic marker for systemic lupus erythematosus: critical remarks. Clin Exp Immunol 179, 5-10 (2015). [0278] 8. Tervaert, J. W. et al. Autoantibodies against myeloid lysosomal enzymes in crescentic glomerulonephritis. Kidney Int 37, 799-806 (1990). [0279] 9. Rasmussen, A. et al. Previous diagnosis of Sjögren's Syndrome as rheumatoid arthritis or systemic lupus erythematosus. Rheumatology (Oxford) 55, 1195-1201 (2016). [0280] 10. Haller-Kikkatalo, K. et al. Demographic associations for autoantibodies in disease-free individuals of a European population. Sci Rep 7, 44846 (2017). [0281] 11. Tan, E. M. et al. Range of antinuclear antibodies in “healthy” individuals. Arthritis and rheumatism 40, 1601-1611 (1997). [0282] 12. Wandstrat, A. E. et al. Autoantibody profiling to identify individuals at risk for systemic lupus erythematosus. Journal of autoimmunity 27, 153-160 (2006). [0283] 13. Borrebaeck, C. A. & Wingren, C. Transferring proteomic discoveries into clinical practice. Expert review of proteomics 6, 11-13 (2009). [0284] 14. Carlsson, A. et al. Serum Protein Profiling of Systemic Lupus Erythematosus and Systemic Sclerosis Using Recombinant Antibody Microarrays. Mol Cell Proteomics 10 (2011). [0285] 15. Eisenberg, R. Why can't we find a new treatment for SLE? Journal of autoimmunity 32, 223-230 (2009). [0286] 16. Gibson, D. S. et al. Diagnostic and prognostic biomarker discovery strategies for autoimmune disorders. Journal of proteomics 73, 1045-1060 (2010). [0287] 17. Borrebaeck, C. A., Sturfelt, G. & Wingren, C. Recombinant antibody microarray for profiling the serum proteome of SLE. Methods Mol Biol 1134, 67-78 (2014). [0288] 18. Petersson, L. et al. Multiplexing of miniaturized planar antibody arrays for serum protein profiling—a biomarker discovery in SLE nephritis. Lab on a chip 14, 1931-1942 (2014). [0289] 19. Gladman, D. D., Ibanez, D. & Urowitz, M. B. Systemic lupus erythematosus disease activity index 2000. J Rheumatol 29, 288-291 (2002). [0290] 20. Vitali, C. et al. Classification criteria for Sjögren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Annals of the rheumatic diseases 61, 554-558 (2002). [0291] 21. Soderlind, E. et al. Recombining germline-derived CDR sequences for creating diverse single-framework antibody libraries. Nature biotechnology 18, 852-856 (2000). [0292] 22. Sall, A. et al. Generation and analyses of human synthetic antibody libraries and their application for protein microarrays. Protein engineering, design & selection PEDS 29, 427-437 (2016). [0293] 23. Skoog, P. et al. Tumor tissue protein signatures reflect histological grade of breast cancer. PloS one 12, e0179775 (2017). [0294] 24. Wu, Y. W. & Wooldridge, P. J. The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis. Nursing research 54, 212-216 (2005). [0295] 25. Delfani, P. et al. Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics. PloS one 11, e0159138 (2016). [0296] 26. Carlsson, A. et al. Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases. Proceedings of the National Academy of Sciences of the United States of America 108, 14252-14257 (2011). [0297] 27. Carlsson, A. et al. Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays. Molecular & cellular proteomics: MCP 10, M110 005033 (2011). [0298] 28. Y, B. Y.a.H. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 12 (1995). [0299] 29. Cooper, G. S., Bynum, M. L. & Somers, E. C. Recent insights in the epidemiology of autoimmune diseases: improved prevalence estimates and understanding of clustering of diseases. Journal of autoimmunity 33, 197-207 (2009). [0300] 30. Arbuckle, M. R. et al. Development of autoantibodies before the clinical onset of systemic lupus erythematosus. N Engl J Med 349, 1526-1533 (2003). [0301] 31. Eriksson, C. et al. Autoantibodies predate the onset of systemic lupus erythematosus in northern Sweden. Arthritis research & therapy 13, R30 (2011). [0302] 32. van Gaalen, F. A. et al. Autoantibodies to cyclic citrullinated peptides predict progression to rheumatoid arthritis in patients with undifferentiated arthritis: a prospective cohort study. Arthritis and rheumatism 50, 709-715 (2004). [0303] 33. Visser, H., le Cessie, S., Vos, K., Breedveld, F. C. & Hazes, J. M. How to diagnose rheumatoid arthritis early: a prediction model for persistent (erosive) arthritis. Arthritis and rheumatism 46, 357-365 (2002). [0304] 34. Mohan, C. & Assassi, S. Biomarkers in rheumatic diseases: how can they facilitate diagnosis and assessment of disease activity? BMJ 351, h5079 (2015). [0305] 35. Ingvarsson, J. et al. Design of recombinant antibody microarrays for serum protein profiling: targeting of complement proteins. Journal of proteome research 6, 3527-3536 (2007). [0306] 36. Petersson, L. et al. Miniaturization of multiplexed planar recombinant antibody arrays for serum protein profiling. Bioanalysis 6, 1175-1185 (2014). [0307] 37. Chen, M., Daha, M. R. & Kallenberg, C. G. The complement system in systemic autoimmune disease. Journal of autoimmunity 34, J276-286 (2010). [0308] 38. Pickart, C. M. Mechanisms underlying ubiquitination. Annu Rev Biochem 70, 503-533 (2001). [0309] 39. Weissman, A. M. Themes and variations on ubiquitylation. Nat Rev Mol Cell Biol 2, 169-178 (2001). [0310] 40. Espinosa, A. et al. The Sjögren's syndrome-associated autoantigen Ro52 is an E3 ligase that regulates proliferation and cell death. J Immunol 176, 6277-6285 (2006). [0311] 41. Feldmann, M. & Maini, R. N. Anti-TNF alpha therapy of rheumatoid arthritis: what have we learned? Annu Rev Immunol 19, 163-196 (2001). [0312] 42. Lipsky, P. E. et al. Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. N Engl J Med 343, 1594-1602 (2000). [0313] 43. Choy, E. H. et al. Therapeutic benefit of blocking interleukin-6 activity with an anti-interleukin-6 receptor monoclonal antibody in rheumatoid arthritis: a randomized, double-blind, placebo-controlled, dose-escalation trial. Arthritis and rheumatism 46, 3143-3150 (2002). [0314] 44. Kaleta, B. Role of osteopontin in systemic lupus erythematosus. Arch Immunol Ther Exp (Warsz) 62, 475-482 (2014). [0315] 45. Delfani P, Dexlin Mellby L, Nordstrom M, et al. Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics. PLoS One (2016); 11: e0159138. [0316] 46. Steinhauer C, Wingren C, Hager A C, Borrebaeck C A. Single framework recombinant antibody fragments designed for protein chip applications. Biotechniques (2002); Suppl: 38-45. [0317] 47. Wingren C, Borrebaeck C A. Antibody microarray analysis of directly labelled complex proteomes. Curr Opin Biotechnol (2008); 19:55-61. [0318] 48. Wingren C, Steinhauer C, Ingvarsson J, Persson E, Larsson K, Borrebaeck C A. Microarrays based on affinity-tagged single-chain Fv antibodies: sensitive detection of analyte in complex proteomes. Proteomics (2005); 5:1281-91. [0319] 49. Johnson W E, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. (2007); 8(1):118-27.