Patent Publication Number: US-2018045735-A1

Title: Methods and Compositions For The Diagnosis And Treatment Of Cancer and Autoimmune Disorders

Description:
This application is a continuation application of U.S. application Ser. No. 13/820,464 filed Mar. 1, 2013, which is a U.S. national phase filing of International Application No. PCT/US11/50210 filed Sep. 1, 2011, which claims the benefit of priority to U.S. provisional application having Ser. No. 61/380,063 filed on Sep. 3, 2010. This and all other extrinsic materials discussed herein are incorporated by reference in their entirety. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply. 
    
    
     FIELD OF THE INVENTION 
     The field of the invention is compositions and methods for diagnosis and treatment of various disorders and diseases. 
     BACKGROUND 
     Currently, no diagnostic test, therapeutic treatment, or vaccine has been developed as a result of high-throughput proteomic research methodologies. This seems surprising, as a solid foundation for such advances was seemingly laid during the recent genomics era. 
     Efforts spurred on by the Human Genome Project (HGP), whose goal was to sequence the human genome, resulted in detailed genome sequencing and annotation of infectious agents such as viruses, bacteria, and parasitic eukaryotes. The information that became available included the identification and sequences of all the open reading frames (ORFs), which are the instruction set for the assembly of proteins. The ORFs can be translated into an intermediary set of instructions called messenger RNA (mRNA), which can then be translated into proteins. Equipped with this information, researchers could determine which proteins were expressed in which tissue by measuring mRNA expression via oligos designed using the sequence information as probes using a Northern blot. 
     Unfortunately, traditional Northern blots became a bottleneck for researchers who desired to look at the expression of many genes. The need to multiplex northern blots lead to tools and methodologies that would allow for multiple parallel experiments, or multiplexing, to be performed. A significant advance was the creation of microarray printers, robotic devices that move in the X, Y, and Z directions and deposits tiny volumes of probes, or reporters, in an organized fashion onto a surface, generally a microscope slide. This methodology allowed for the creation of high density and highly multiplexed Northern blots. To gather data from these microscopic Northern blots, confocal microscope-based fluorescence scanners were used. These efforts were coupled with tools designed to analyze the significant amount of generated data. Such advances allowed researchers to collect massive amounts of data about the expression profiles of normal and diseased tissues. 
     Much effort has been exerted to connect the descriptive sequencing data and expression profiling data noted above to the prediction, or treatment, of disease. While there remains hope that such efforts will lead to valuable insights into human diseases, knowing identity and relative abundance does not seem to be sufficiently useful. 
     Surprisingly, the inventors have found that it is actually functional data that is required to accurately survey immune responses to human diseases. However, no algorithm is known to the inventors that can predict significantly antigenic epitopes from foreign proteins (e.g., bacteria, parasites, etc.), let alone endogenous, human proteins. Similarly the abundance or changes in the abundance of proteins in the body has not been a useful predictor of disease. Recently, it has been shown that circulating autoantibodies might be useful for predicting disease. Instead, circulating autoantibodies must be measured to determine which autoantibodies might serve as predictors of disease. However, the methodologies known to the inventors are unable to create a large enough expressible library of human proteins to cast a wide net, and to express and screen these proteins in a high-throughput manner. Current practice in the art teaches that for one to accurately detect autoantibodies, the protein(s) being used as bait for the antibodies should retain most, or all, of the post-translational modifications that would be present on the protein as it is naturally expressed in the body. Another concept common in the art is that researchers assume that there is a need to purify proteins before using them in functional assays, a process which may take months, to even years, for a single protein. 
     Consequently, there remains a large, unmet need to provide improved compositions and methods of antigen and autoantibody detection and monitoring for diagnostic and therapeutic applications. 
     SUMMARY OF THE INVENTION 
     Based on the above noted difficulties, a proteomics approach aiming to profile human autoantibodies reactivity that uses unpurified proteins expressed in an  E. coli  based cell-free system was not expected by those practicing the art to prove useful. Surprisingly, however, the inventors found that such approach worked very well, and could be used to identify both well-known and novel antigens and autoantibodies that could have not been identified using conventional methodologies. 
     The inventive subject matter discussed herein provides apparatus, systems and methods for identifying, analyzing, and monitoring autoantibody reactivity to specific antigens or sets of antigens, which can have diagnostic, prognostic, and therapeutic value, specifically with respect to various human diseases. This is especially important in the diagnosis and/or treatment of various human diseases, cancers, and autoimmune disorders. Exemplary diseases include breast cancer, lupus, lupus nepritis, systemic lupus erythematosus, polymyositis, rheumatoid arthritis, scleroderma, and Sjögren&#39;s syndrome, although the specific disease will depend upon the specific antigens or sets of antigens. 
     Thus, in some aspects, the disease is breast cancer, and the set of antigens has a sequence according to one or more of GENE ID BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, or UTP14a, or fragments thereof, or the disease is lupus nephritis (LN), and the set of antigens has a sequence according to one or more of GENE ID CD1D, IL6R, IRF8, ITGA2B, MYO1A, MYO7B, PSG1, PTBP1, or TPO, or fragments thereof. In further aspects, the disease is systemic lupus erythematosus (SLE), and the set of antigens has a sequence according to one or more of GENE ID CD1C, CD46, CENPQ, CFB, DPP4, HLA-DQB1, IL6R, ITGB2, KRTAP9-3, MLF1IP, MYT1L, POLR2H, SLC7A5, or TPO, or fragments thereof, or the disease is Lupus (SLE+LN), and the set of antigens has a sequence according to one or more of GENE ID DPP4, IL6R, ITGB2, MLF1IP, MYO1A, POLR2H, or TPO, or fragments thereof, or the disease is polymyositis (P), and the set of antigens has a sequence according to one or more of GENE ID CD14, CD1C, CD46, CD55, CFB, COL9A1, COLQ, DLAT, DPP4, FGF7, H3F3B, IL1RAPL2, IL6R, IL8, ITGB2, KRTAP9-3, MLF1IP, MYT1L, PADI4, PIP4K2C, PLAUR, POLR2H, POLR2I, PSG1, SLC7A5, or STK19, or fragments thereof. In yet further aspects, the disease is rheumatoid arthritis (RA), and the set of antigens has a sequence according to one or more of GENE ID APOH, BANK1, BLK, CD1C, CD14, CD3E, CD70, CD80, CD86, CEACAM6, CEACAM8, CENPT, CFB, COL1A2, DDC, DPP4, FCGR1A, H2AFX, H2AFY, H3F3B, HBA1, HBA2, HBD, HLA-DQB1, HSP90B1, HSPB7, IGHG2, IGHG4, IGHM, IGHV4-31, IL12A, IL6, IL6R, ITGB3BP, KRTAP13-1, KRTAP9-3, MBP, MLF1IP, MOBP, MS4A8B, MYH9, MYO1D, MYT1L, NMNAT2, NOL1, PDCD1, PIP4K2C, POLR2C, POLR2H, POLR2I, POLR2J2, POLR3D, PSIP1, SRP19, STAT4, or STK19, or fragments thereof, or the disease is Scleroderma (Sc), and the set of antigens has a sequence according to GENE ID IL6R, or a fragment thereof, or the disease is Sjögren&#39;s Syndrome (Sj), and the set of antigens has a sequence according to one or more of GENE ID APOH, CALR3, CD1C, CD14, CD34, CD3E, CD46, CD69, CD93, CEACAM8, CENPA, CENPQ, CFB, CHRNA1, COL20A1, COL4A6, DPP4, FCGR3A, H1F0, H2AFX, H3F3B, HBA1, HBA2, HBD, HBM, HLA-C, HLA-DQB1, HLA-F, HSPB7, IFNG, IGFL2, IGH2, IGHV7-81, IL1RAPL2, IL6R, ITGB2, KRT73, KRT19, KRTAP9-3, KRTAP9-8, MBP, MLF1IP, MYT1L, NOLA3, POLR2H, POLR2I, POLR3D, POLR3H, PTBP1, STK19, or UEVLD, or fragments thereof. 
     In one aspect, the inventive subject matter provides a new and useful tool that can accurately survey human diseases via the multiplexed combination of unpurified  E. coli  expressed proteomes, autoantibody detection, and characterized sera samples from human disease populations. 
     In another aspect, an antigen composition has a plurality of autoantibody reactive antigens associated with a carrier. At least two of the antigens can have (a) quantified and known relative autoantibody reactivities with respect to sera of a population affected by a disease, and (b) a known association with a disease parameter. Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints, and open-ended ranges should be interpreted to include commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary. 
     It is contemplated that the known reactivities may be characterized by a variety of factors, however, it is particularly preferred that the known reactivities are characterized by strength of immunogenicity and/or time course of the infection. It is generally preferred that the parameter is activity state of the disease, a previous exposure to the pathogen, the duration of exposure to the pathogen, a chronic infection, past disease, active infection, inactive infection, at least partial immunity to infection with the pathogen, and/or outcome upon treatment. 
     In yet another aspect, a method of predicting a likelihood of a patient having a disease or detecting a disease in a patient is contemplated, which includes the step of determining autoantibody reactivity against one or more antigens, or their variants, in a serum sample obtained from a patient. The presence of autoantibody reactivity against one or more of the antigens can advantageously indicate an increased likelihood of the patient having a disease. 
     In another embodiment, a method of predicting a likelihood of a patient having a disease can include determining autoantibody reactivity against one or more antigens, or their variants, in a sera sample obtained from a patient. A likelihood of a disease can then be predicted from reference samples derived from sera of patients diagnosed as having the disease, such that increased or decreased autoantibody reactivity against selected antigens can be positively correlated with increased likelihood of a disease in the patient. 
     Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         FIG. 1  shows representative images of a Tumor Associated Antigen (TAA) chip probed with serum from breast cancer patients and controls, showing several proteins that are recognized by antibodies in the serum in panels 1-2 and 5-8. 
         FIG. 2  is a histogram of the image data depicting the mean signal intensity for cancer patients (CA), population controls (P), and Bonferroni corrected p-value. 
         FIG. 3  shows representative images of a Tumor Associated Antigen (TAA) chip probed with serum from patients with cervical cancer (right panel) and a control group (left panel). 
         FIGS. 4-5  are representative images of a Human Autoimmunity (HA) chip probed with serum samples from patients with Sjögren&#39;s Syndrome, and serum samples from patients with Lupus, respectively. 
         FIG. 6  is a heat map of signal intensity data, and  FIGS. 7A-7B  is an enlarged view of a portion of the heat map of  FIG. 2C . 
         FIG. 8  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), Sjögren&#39;s Syndrome patient sera (Sj), lupus nephritis patient sera (LN) and systemic lupus erythematosus (SLE). 
         FIG. 9  shows representative images of a HA chip probed with anti-HA high affinity rat monoclonal to verify expression of proteins. 
         FIG. 10  is a heat map of signal intensity data, and  FIG. 11  is an enlarged view of a portion of the heat map of  FIG. 3B . 
         FIG. 12  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from lupus patients (L), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 13  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from lupus nephritis patients (LN), and Benj amini-Hochberg corrected p-values (BHp). 
         FIG. 14  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from systemic lupus erythematosus patients (SLE), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 15  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from polymyositis patients (P), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 16  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from rheumatoid arthritis patients (RA), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 17  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from scleroderma patients (Sc), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 18  is a chart comparing the mean signal intensities and standard errors for normal/healthy sera (N), sera from Sjögren&#39;s Syndrome patients (Sj), and Benjamini-Hochberg corrected p-values (BHp). 
         FIG. 19  is a heat map of signal intensity data of seven lupus nephritis patients. 
         FIGS. 20-26  are various charts of the serial bleeds from patient data. 
         FIGS. 27A-27H  show a heat map of signal intensity data. 
         FIGS. 28-29  are charts comparing the signal difference in population controls (PC) or relative control (RC) as the baseline, respectively, versus cases (CS). 
         FIG. 30  is a representative image of a sub-array representing approximately 207 different expression products and 18 control spots visualized using the C-terminal HA tag and the anti-HA antibody. 
         FIG. 31  is a chart showing a distribution of mean signal intensities for the QC probing. 
         FIGS. 32-33  are charts showing the percentage of expression products recognized. 
         FIG. 34  is a heat map with the individual normal donors (rows) and the proteins (columns), and  FIGS. 35A-35B  are a histogram of mean signal intensities of the proteins. 
         FIG. 36  is a heat map showing the reactivity pattern of the 143 serum samples, and  FIG. 37  is a histogram of all the reactive proteins. 
         FIG. 38  is a chart of the mean signal intensities, and  FIG. 39  is a receiver operator curve using the proteins listed in  FIG. 8C . 
         FIG. 40  is a heat map, and  FIG. 41  is a histogram of mean signal intensities of the proteins. 
         FIG. 42  is a bar chart that compares reactivity of a lupus group with disease controls. 
         FIGS. 43-44  are flowcharts of various embodiments of methods of predicting a likelihood of a patient having a disease. 
     
    
    
     DETAILED DESCRIPTION 
     One should appreciate that the disclosed techniques provide many advantageous technical effects including the ability to (a) identify biologically relevant antigens, sets of antigens, autoantibodies, and sets of autoantibodies, (b) enable the monitoring and analysis of treatment efficacy, via longitudinal monitoring of reactivity of an autoantibody, or a set of autoantibodies, against select human proteins, (c) identify, analyze, and monitor autoantibody reactivity to specific human protein antigens or antigen sets to facilitate diagnosis, prognosis, and treatment of cancers such as breast and pancreatic cancers or autoimmune disorders such as renal and non-renal lupus, polymyositis, rheumatoid arthritis, Scleroderma, and Sjögren&#39;s Syndrome, and (d) accurately survey human diseases via the combination of: unpurified proteomes, autoantibody detection and monitoring, and characterized sera samples, especially as they relate to use in diagnostic and therapeutic compositions and methods. 
     The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed. 
     In the following description, antigens are identified by either the gene descriptor for the gene that encodes the protein antigen or the name of the protein antigen. Thus, it should be understood that where the context indicates that a sequence or antigen is a protein sequence, a gene name for that sequence or antigen denotes the protein product for that gene. 
     The inventors have discovered numerous antigens that are capable of triggering autoantibody reactivity from a variety of human diseases and disorders, including breast cancer, pancreatic cancer, renal lupus, non-renal lupus, polymyositis, rheumatoid arthritis, Scleroderma, and Sjögren&#39;s Syndrome. It is contemplated that such antigens can be used by themselves, or more preferably, in combination with other antigens in the manufacture of a diagnostic devices, therapeutic compositions, and vaccines. 
     Contemplated compositions, devices, and methods comprise autoantibody reactive antigens from various human diseases including, for example, breast cancer, pancreatic cancer, renal lupus, non-renal lupus, polymyositis, rheumatoid arthritis, Scleroderma, and Sjögren&#39;s Syndrome, which could be used as a vaccine, as diagnostic markers, or as therapeutic agents. In particularly preferred embodiments, the antigens have quantified and known relative reactivities with respect to sera of a population infected with a disease, and have a known association with a parameter of the disease. Thus, the specific antigens can have a statistically high probability to elicit autoantibody responses in a relatively large group of patients. 
     In one embodiment, an antigen composition can include a plurality of autoantibody reactive antigens associated with a carrier. The antigens are preferably selected from the group consisting of BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, UTP14a, DPP4, IL6R, ITGB2, MLF1IP, MYO1A, POLR2H, CD1D, IRF8, ITGA2B, MYO7B, PSG1, PTBP1, CD1C, CD46, CENPQ, CFB, HLA-DQB1, KRTAP9-3, MYT1L, SLC7A5, TPO, CD14, CD55, COL9A1, COLQ, DLAT, FGF7, H3F3B, IL1RAPL2, IL8, PADI4, PIP4K2C, PLAUR, STK19, APOH, BANK1, BLK, CD3E, CD70, CD80, CD86, CEACAM6, CEACAM8, CENPT, COL1A2, DDC, FCGR1A, H2AFX, H2AFY, HBA1, HBA2, HBD, HSP90B1, HSPB7, IGHG2, IGHG4, IGHM, IGHV4-31, IL6, ITGB3BP, KRTAP13-1, MBP, MOBP, MS4A8B, MYH9, MYO1D, NMNAT2, NOL1, PDCD1, POLR2C, POLR2J2, POLR3D, PSIP1, SRP19, STAT4, CALR3, CD34, CD69, CD93, CENPA, CHRNA1, COL20A1, COL4A6, FCGR3A, H1F0, HBM, HLA-C, HLA-F, IFNG, IGFL2, IGH2, IGHV7-81, KRT73, KRT19, KRTAP9-8, NOLA3, POLR3H, and UEVLD, or fragments thereof. Additional information regarding each of the above-identified antigens is provided in Table 1 below. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Current 
                   
                   
                   
                   
               
               
                 Old 
                 Gene 
                   
                   
                   
                 UniGene 
               
               
                 Symbol 
                 Symbol 
                 Gene Title 
                 Class 
                 Function 
                 ID 
               
               
                   
               
             
            
               
                 B2G1 
                 APOH 
                 apolipoprotein H (beta-2- 
                 Serum protein 
                 Carrier 
                 Hs.445358 
               
               
                   
                   
                 glycoprotein I) 
                   
                 protein 
               
               
                 BANK1 
                 BANK1 
                 B-cell scaffold protein 
                 Signaling 
                 Immune 
                 Hs.480400 
               
               
                   
                   
                 with ankyrin repeats 1 
               
               
                 BLK 
                 BLK 
                 B lymphoid tyrosine 
                 Signaling 
                 Immune 
                 Hs.146591 
               
               
                   
                   
                 kinase 
               
               
                 CALR3 
                 CALR3 
                 calreticulin 3 
                 Chaperone 
                 Protein 
                 Hs.304020 
               
               
                   
                   
                   
                   
                 processing 
               
               
                 CD14 
                 CD14 
                 CD14 molecule 
                 Receptor 
                 Innate 
                 Hs.163867 
               
               
                   
                   
                   
                   
                 immunity 
               
               
                 CD18 
                 ITGB2 
                 integrin, beta 2 
                 Cell adhesion 
                 Cell-cell 
                 Hs.375957 
               
               
                   
                   
                 (complement component 3 
                   
                 interaction 
               
               
                   
                   
                 receptor 3 and 4 subunit) 
               
               
                 CD1C 
                 CD1C 
                 CD1c molecule 
                 Antigen 
                 Immune 
                 Hs.132448 
               
               
                   
                   
                   
                 presentation 
               
               
                 CD1D 
                 CD1D 
                 CD1d molecule 
                 Antigen 
                 Immune 
                 Hs.1799 
               
               
                   
                   
                   
                 presentation 
               
               
                 CD26 
                 DPP4 
                 dipeptidyl-peptidase 4 
                 Protease 
                 Protein 
                 Hs.368912 
               
               
                   
                   
                   
                   
                 processing 
               
               
                 CD34 
                 CD34 
                 CD34 molecule 
                 Cell adhesion 
                 Immune 
                 Hs.374990 
               
               
                 CD3E 
                 CD3E 
                 CD3e molecule, epsilon 
                 Signaling 
                 Immune 
                 Hs.3003 
               
               
                   
                   
                 (CD3-TCR complex) 
               
               
                 CD46 
                 CD46 
                 CD46 molecule, 
                 Complement 
                 Innate 
                 Hs.510402 
               
               
                   
                   
                 complement regulatory 
                   
                 immunity 
               
               
                   
                   
                 protein 
               
               
                 CD55 
                 CD55 
                 CD55 molecule, decay 
                 Complement 
                 Innate 
                 Hs.126517 
               
               
                   
                   
                 accelerating factor for 
                   
                 immunity 
               
               
                   
                   
                 complement (Cromer 
               
               
                   
                   
                 blood group) 
               
               
                 CD64A 
                 FCGR1A 
                 Fc fragment of IgG, high 
                 Fc receptor 
                 Immune 
                 Hs.77424 
               
               
                   
                   
                 affinity Ia, receptor 
               
               
                   
                   
                 (CD64) 
               
               
                 CD66b 
                 CEACAM8 
                 carcinoembryonic antigen- 
                 Cell adhesion 
                 Cell-cell 
                 Hs.41 
               
               
                   
                   
                 related cell adhesion 
                   
                 interaction 
               
               
                   
                   
                 molecule 8 
               
               
                 CD66c 
                 CEACAM6 
                 carcinoembryonic antigen- 
                 Multifunctional 
                 Innate 
                 Hs.466814 
               
               
                   
                   
                 related cell adhesion 
                   
                 immunity 
               
               
                   
                   
                 molecule 6 (non-specific 
               
               
                   
                   
                 cross reacting antigen) 
               
               
                 CD66f 
                 PSG1 
                 pregnancy specific beta-1- 
                 Growth 
                 Development, 
                 Hs.709192 
               
               
                   
                   
                 glycoprotein 1 
                 factor/hormone 
                 cell growth 
               
               
                 CD69 
                 CD69 
                 CD69 molecule 
                 Receptor 
                 Immune 
                 Hs.208854 
               
               
                 CD70 
                 CD70 
                 CD70 molecule 
                 Cytokine/chemokine 
                 Costimulatory 
                 Hs.501497 
               
               
                   
                   
                   
                 signaling 
               
               
                 CD80 
                 CD80 
                 CD80 molecule 
                 Co-stimulation 
                 Immune 
                 Hs.838 
               
               
                 CD86 
                 CD86 
                 CD86 molecule 
                 Co-stimulation 
                 Immune 
                 Hs.171182 
               
               
                 CD87 
                 PLAUR 
                 plasminogen activator, 
                 Receptor 
                 Protein 
                 Hs.466871 
               
               
                   
                   
                 urokinase receptor 
                   
                 processing 
               
               
                 CD93 
                 CD93 
                 CD93 molecule 
                 Cell adhesion 
                 Cell-cell 
                 Hs.97199 
               
               
                   
                   
                   
                   
                 interaction 
               
               
                 CD98 
                 SLC7A5 
                 solute carrier family 7 
                 Carrier 
                 Transport 
                 Hs.513797 
               
               
                   
                   
                 (cationic amino acid 
               
               
                   
                   
                 transporter, y+ system), 
               
               
                   
                   
                 member 5 
               
               
                 CENPA 
                 CENPA 
                 centromere protein A 
                 DNA binding 
                 Cell cycle 
                 Hs.1594 
               
               
                 CENPQ 
                 CENPQ 
                 centromere protein Q 
                 DNA binding 
                 Cell cycle 
                 Hs.88663 
               
               
                 CENPT 
                 CENPT 
                 centromere protein T 
                 DNA binding 
                 Cell cycle 
                 Hs.288382 
               
               
                 CFB 
                 CFB 
                 complement factor B 
                 Complement 
                 Innate 
                 Hs.69771 
               
               
                   
                   
                   
                   
                 immunity 
               
               
                 CHRNA1 
                 CHRNA1 
                 cholinergic receptor, 
                 Receptor 
                 Signaling 
                 Hs.434479 
               
               
                   
                   
                 nicotinic, alpha 1 (muscle) 
               
               
                 COL1A2 
                 COL1A2 
                 collagen, type I, alpha 2 
                 ECM 
                 Cell adhesion 
                 Hs.489142 
               
               
                 COL20A1 
                 COL20A1 
                 collagen, type XX, alpha 1 
                 ECM 
                 Cell adhesion 
                 Hs.271285 
               
               
                 COL4A6 
                 COL4A6 
                 collagen, type IV, alpha 6 
                 ECM 
                 Cell adhesion 
                 Hs.145586 
               
               
                 COL9A1 
                 COL9A1 
                 collagen, type IX, alpha 1 
                 ECM 
                 Cell adhesion 
                 Hs.590892 
               
               
                 COLQ 
                 COLQ 
                 collagen-like tail subunit 
                 ECM 
                 Neurotransmitter 
                 Hs.146735 
               
               
                   
                   
                 (single strand of 
                   
                 synthesis/clearance 
               
               
                   
                   
                 homotrimer) of 
               
               
                   
                   
                 asymmetric 
               
               
                   
                   
                 acetylcholinesterase 
               
               
                 DDC 
                 DDC 
                 dopa decarboxylase 
                 Enzyme 
                 Neurotransmitter 
                 Hs.359698 
               
               
                   
                   
                 (aromatic L-amino acid 
                   
                 synthesis/ 
               
               
                   
                   
                 decarboxylase) 
                   
                 clearance 
               
               
                 DLAT 
                 DLAT 
                 dihydrolipoamide S- 
                 Enzyme 
                 Metabolism 
                 Hs.335551 
               
               
                   
                   
                 acetyltransferase 
               
               
                 CD16a 
                 FCGR3A 
                 Fc fragment of IgG, low 
                 Fc receptor 
                 Immune 
                 Hs.694258 
               
               
                   
                   
                 affinity IIIa, receptor 
               
               
                   
                   
                 (CD16a) 
               
               
                 FGF7 
                 FGF7 
                 fibroblast growth factor 7 
                 Growth 
                 Development, 
                 Hs.567268 
               
               
                   
                   
                 (keratinocyte growth 
                 factor/hormone 
                 cell growth 
               
               
                   
                   
                 factor) 
               
               
                 H1F0 
                 H1F0 
                 H1 histone family, 
                 DNA binding 
                 Structural 
                 Hs.715673 
               
               
                   
                   
                 member 0 
               
               
                 H2AFX 
                 H2AFX 
                 H2A histone family, 
                 DNA binding 
                 Structural 
                 Hs.477879 
               
               
                   
                   
                 member X 
               
               
                 H2AFY 
                 H2AFY 
                 H2A histone family, 
                 DNA binding 
                 Structural 
                 Hs.599225 
               
               
                   
                   
                 member Y 
               
               
                 H3F3B 
                 H3F3B 
                 H3 histone, family 3B 
                 DNA binding 
                 Structural 
                 Hs.180877 
               
               
                   
                   
                 (H3.3B) 
               
               
                 HBA1 
                 HBA1 
                 hemoglobin, alpha 1 
                 Oxygen binding 
                 Metabolism 
                 Hs.449630 
               
               
                 HBA2 
                 HBA2 
                 hemoglobin, alpha 2 
                 Oxygen binding 
                 Metabolism 
                 Hs.449630 
               
               
                 HBD 
                 HBD 
                 hemoglobin, delta 
                 Oxygen binding 
                 Metabolism 
                 Hs.699280 
               
               
                 HBM 
                 HBM 
                 hemoglobin, mu 
                 Oxygen binding 
                 Metabolism 
                 Hs.647389 
               
               
                 HLA-C 
                 HLA-C 
                 major histocompatibility 
                 MHC 
                 Immune 
                 Hs.654404 
               
               
                   
                   
                 complex, class I, C 
               
               
                 HLA-DQB1 
                 HLA-DQB1 
                 major histocompatibility 
                 MHC 
                 Immune 
                 Hs.409934 
               
               
                   
                   
                 complex, class II, DQ beta 1 
               
               
                 HLA-F 
                 HLA-F 
                 major histocompatibility 
                 MHC 
                 Immune 
                 Hs.519972 
               
               
                   
                   
                 complex, class I, F 
               
               
                 HSP90B1 
                 HSP90B1 
                 heat shock protein 90 kDa 
                 Chaperone 
                 Protein 
                 Hs.192374 
               
               
                   
                   
                 beta (Grp94), member 1 
                   
                 processing 
               
               
                 HSPB7 
                 HSPB7 
                 heat shock 27 kDa protein 
                 Chaperone 
                 Protein 
                 Hs.502612 
               
               
                   
                   
                 family, member 7 
                   
                 processing 
               
               
                   
                   
                 (cardiovascular) 
               
               
                 IFNG 
                 IFNG 
                 interferon, gamma 
                 Cytokine/chemokine 
                 Immune 
                 Hs.856 
               
               
                   
                   
                   
                 signaling 
               
               
                 IGFL2 
                 IGFL2 
                 IGF-like family member 2 
                 Growth 
                 Development, 
                 Hs.99376 
               
               
                   
                   
                   
                 factor/hormone 
                 cell growth 
               
               
                 IGH2 
                 IGHE 
                 immunoglobulin heavy 
                 Immunoglobulin 
                 Immune 
                 Hs.700112 
               
               
                   
                   
                 locus /// ig heavy chain V- 
               
               
                   
                   
                 III region VH26-like 
               
               
                 IGHA1 /// 
                 IGHG2 or 
                 immunoglobulin heavy 
                 Immunoglobulin 
                 Immune 
                 Hs.460661 
               
               
                 IGHD /// 
                 IGHG4 
                 constant mu /// 
               
               
                 IGHG1 /// 
                   
                 hypothetical protein 
               
               
                 IGHG2 /// 
                   
                 LOC100132941 /// ig 
               
               
                 IGHG3 /// 
                   
                 heavy chain V-III region 
               
               
                 IGHM /// 
                   
                 VH26-like 
               
               
                 IGHV4-31 
               
               
                 /// 
               
               
                 LOC100132941 
               
               
                 /// 
               
               
                 LOC100289290 
               
               
                 /// 
               
               
                 LOC100290036 
               
               
                 /// 
               
               
                 LOC652494 
               
               
                 IGHM 
                 IGHM 
                 immunoglobulin heavy 
                 Immunoglobulin 
                 Immune 
                 — 
               
               
                   
                   
                 constant mu 
               
               
                 IGHV4-31 
                 Variable Ig 
               
               
                   
                 region 
               
               
                 IGHV7-81 
                 Variable Ig 
               
               
                   
                 region 
               
               
                 IL12A 
                 IL12A 
                 interleukin 12A (natural 
                 Cytokine/chemokine 
                 Immune 
                 Hs.673 
               
               
                   
                   
                 killer cell stimulatory 
                 signaling 
               
               
                   
                   
                 factor 1, cytotoxic 
               
               
                   
                   
                 lymphocyte maturation 
               
               
                   
                   
                 factor 1, p35) 
               
               
                 IL1RAPL2 
                 IL1RAPL2 
                 interleukin 1 receptor 
                 Signaling 
                 Immune 
                 Hs.675519 
               
               
                   
                   
                 accessory protein-like 2 
               
               
                 IL6 
                 IL6 
                 interleukin 6 (interferon, 
                 Cytokine/chemokine 
                 Immune 
                 Hs.654458 
               
               
                   
                   
                 beta 2) 
                 signaling 
               
               
                 IL6R 
                 IL6R 
                 interleukin 6 receptor 
                 Cytokine/chemokine 
                 Immune 
                 Hs.709210 
               
               
                   
                   
                   
                 signaling 
               
               
                 IL8 
                 IL8 
                 interleukin 8 
                 Cytokine/chemokine 
                 Immune 
                 Hs.624 
               
               
                   
                   
                   
                 signaling 
               
               
                 IRF8 
                 IRF8 
                 interferon regulatory 
                 Transcription 
                 Gene 
                 Hs.137427 
               
               
                   
                   
                 factor 8 
                 factor 
                 expression 
               
               
                 ITGA2B 
                 ITGA2B 
                 integrin, alpha 2b (platelet 
                 Cell adhesion 
                 Cell-cell 
                 Hs.411312 
               
               
                   
                   
                 glycoprotein IIb of IIb/IIIa 
                   
                 interaction 
               
               
                   
                   
                 complex, antigen CD41) 
               
               
                 ITGB3BP 
                 ITGB3BP 
                 integrin beta 3 binding 
                 Cell adhesion 
                 Cell-cell 
                 Hs.166539 
               
               
                   
                   
                 protein (beta3-endonexin) 
                   
                 interaction 
               
               
                 KRT73 
                 KRT73 
                 keratin 73 
                 Keratin 
                 Structural 
                 Hs.55410 
               
               
                 KRT19 
                 KRT19 
                 keratin 19 
                 Keratin 
                 Structural 
                 Hs.654568 
               
               
                 KRTAP13-1 
                 KRTAP13-1 
                 keratin associated protein 
                 Keratin 
                 Structural 
                 Hs.407653 
               
               
                   
                   
                 13-1 
               
               
                 KRTAP9-3 
                 KRTAP9-3 
                 keratin associated protein 
                 Keratin 
                 Structural 
                 Hs.307012 
               
               
                   
                   
                 9-3 
               
               
                 KRTAP9-8 
                 KRTAP9-8 
                 keratin associated protein 
                 Keratin 
                 Structural 
                 Hs.307011 
               
               
                   
                   
                 9-8 
               
               
                 MBP 
                 MBP 
                 myelin basic protein 
                 Cell adhesion 
                 Myelination 
                 Hs.551713 
               
               
                 MLF1IP 
                 MLF1IP 
                 MLF1 interacting protein 
                 DNA binding 
                 Cell cycle 
                 Hs.575032 
               
               
                 MOBP 
                 MOBP 
                 myelin-associated 
                 Cytoskeleton 
                 Trafficking 
                 Hs.121333 
               
               
                   
                   
                 oligodendrocyte basic 
               
               
                   
                   
                 protein 
               
               
                 MS4A8B 
                 MS4A8B 
                 membrane-spanning 4- 
                 Poorly 
                 Unknown 
                 Hs.150878 
               
               
                   
                   
                 domains, subfamily A, 
                 characterized 
               
               
                   
                   
                 member 8B 
               
               
                 MYH9 
                 MYH9 
                 myosin, heavy chain 9, 
                 Molecular motor 
                 Contractility 
                 Hs.474751 
               
               
                   
                   
                 non-muscle 
               
               
                 MYO1A 
                 MYO1A 
                 myosin IA 
                 Molecular motor 
                 Contractility 
                 Hs.5394 
               
               
                 MYO1D 
                 MYO1D 
                 myosin ID 
                 Molecular motor 
                 Contractility 
                 Hs.658000 
               
               
                 MYO7B 
                 MYO7B 
                 myosin VIIB 
                 Molecular motor 
                 Contractility 
                 Hs.154578 
               
               
                 MYT1L 
                 MYT1L 
                 myelin transcription factor 
                 Transcription 
                 Neuronal 
                 Hs.434418 
               
               
                   
                   
                 1-like 
                 factor 
                 development/ 
               
               
                   
                   
                   
                   
                 differentiation 
               
               
                 NMNAT2 
                 NMNAT2 
                 nicotinamide nucleotide 
                 Enzyme 
                 Metabolism 
                 Hs.497123 
               
               
                   
                   
                 adenylyltransferase 2 
               
               
                 NOL1 
                 NSUN5 
                 NOL1/NOP2/Sun domain 
                 Enzyme 
                 DNA 
                 Hs.647060 
               
               
                   
                   
                 family, member 5 
                   
                 methylation 
               
               
                 NOLA3 
                 NOP10 
                 NOP10 ribonucleoprotein 
                 RNA binding 
                 RNA 
                 Hs.14317 
               
               
                   
                   
                 homolog (yeast) 
                   
                 processing 
               
               
                 PADI4 
                 PADI4 
                 peptidyl arginine 
                 Enzyme 
                 Metabolism 
                 Hs.522969 
               
               
                   
                   
                 deiminase, type IV 
               
               
                 PDCD1 
                 PDCD1 
                 programmed cell death 1 
                 Receptor 
                 Immune 
                 Hs.158297 
               
               
                 PIP4K2C 
                 PIP4K2C 
                 phosphatidylinositol-5- 
                 Kinase/phosphatase 
                 Signaling 
                 Hs.144502 
               
               
                   
                   
                 phosphate 4-kinase, type 
               
               
                   
                   
                 II, gamma 
               
               
                 POLR2C 
                 POLR2C 
                 polymerase (RNA) II 
                 Enzyme 
                 Gene 
                 Hs.79402 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide C, 33 kDa 
               
               
                 POLR2H 
                 POLR2H 
                 polymerase (RNA) II 
                 Enzyme 
                 Gene 
                 Hs.432574 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide H 
               
               
                 POLR2I 
                 POLR2I 
                 polymerase (RNA) II 
                 Enzyme 
                 Gene 
                 Hs.47062 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide I, 14.5 kDa 
               
               
                 POLR2J2 
                 POLR2J2 
                 polymerase (RNA) II 
                 Enzyme 
                 Gene 
                 Hs.696339 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide J2 
               
               
                 POLR3D 
                 POLR3D 
                 polymerase (RNA) III 
                 Enzyme 
                 Gene 
                 Hs.148342 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide D, 44 kDa 
               
               
                 POLR3H 
                 POLR3H 
                 polymerase (RNA) III 
                 Enzyme 
                 Gene 
                 Hs.712617 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide H (22.9 kD) 
               
               
                 PSIP1 
                 PSIP1 
                 PC4 and SFRS1 
                 Transcription 
                 Gene 
                 Hs.658434 
               
               
                   
                   
                 interacting protein 1 
                 factor 
                 expression 
               
               
                 SRP19 
                 SRP19 
                 signal recognition particle 
                 RNA binding 
                 Gene 
                 Hs.637001 
               
               
                   
                   
                 19 kDa 
                   
                 expression 
               
               
                 STAT4 
                 STAT4 
                 signal transducer and 
                 Signaling 
                 Immune 
                 Hs.80642 
               
               
                   
                   
                 activator of transcription 4 
               
               
                 STK19 
                 STK19 
                 serine/threonine kinase 19 
                 Kinase 
                 Signaling 
                 Hs.654371 
               
               
                 TPO 
                 TPO 
                 thyroid peroxidase 
                 Enzyme 
                 Metabolism 
                 Hs.467554 
               
               
                 UEVLD 
                 UEVLD 
                 UEV and lactate/malate 
                 Enzyme 
                 Protein 
                 Hs.407991 
               
               
                   
                   
                 dehyrogenase domains 
                   
                 turnover 
               
               
                 BRCA1 
                 BRCA1 
                 breast cancer 1, early 
                 Protein binding 
                 DNA 
                 Hs.194143 
               
               
                   
                   
                 onset 
                   
                 replication/ 
               
               
                   
                   
                   
                   
                 repair 
               
               
                 CD88 
                 C5AR1 
                 complement component 
                 Complement 
                 Innate 
                 Hs.2161 
               
               
                   
                   
                 5a receptor 1 
                   
                 immunity 
               
               
                 CSF2RA 
                 CSF2RA 
                 colony stimulating factor 
                 Receptor 
                 Immune 
                 Hs.520937 
               
               
                   
                   
                 2 receptor, alpha, low- 
               
               
                   
                   
                 affinity (granulocyte- 
               
               
                   
                   
                 macrophage) 
               
               
                 HBZ 
                 HBZ 
                 hemoglobin, zeta 
                 Oxygen binding 
                 Metabolism 
                 Hs.585357 
               
               
                 HSPD1 
                 HSPD1 
                 heat shock 60 kDa protein 
                 Chaperone 
                 Immune, 
                 Hs.595053 
               
               
                   
                   
                 1 (chaperonin) 
                   
                 innate 
               
               
                   
                   
                   
                   
                 immunity 
               
               
                 IFNA7 
                 IFNA7 
                 interferon, alpha 7 
                 Cytokine/chemokine 
                 Immune 
                 Hs.282274 
               
               
                   
                   
                   
                 signaling 
               
               
                 IL17D 
                 IL17D 
                 interleukin 17D 
                 Cytokine/chemokine 
                 Immune 
                 Hs.655142 
               
               
                   
                   
                   
                 signaling 
               
               
                 KRT17 
                 KRT17 
                 keratin 17 
                 Keratin 
                 Structural 
                 Hs.2785 
               
               
                 KRT18 
                 KRT18 
                 keratin 18 
                 Keratin 
                 Structural 
                 Hs.406013 
               
               
                 KRT24 
                 KRT24 
                 keratin 24 
                 Keratin 
                 Structural 
                 Hs.87383 
               
               
                 KRT5 
                 KRT5 
                 keratin 5 
                 Keratin 
                 Structural 
                 Hs.433845 
               
               
                 MYL6 
                 MYL6 
                 myosin, light chain 6, 
                 Molecular motor 
                 Contractility 
                 Hs.632717 
               
               
                   
                   
                 alkali, smooth muscle and 
               
               
                   
                   
                 non-muscle 
               
               
                 MYO9B 
                 MYO9B 
                 myosin IXB 
                 Molecular motor 
                 Contractility 
                 Hs.123198 
               
               
                 PARP12 
                 PARP12 
                 poly (ADP-ribose) 
                 Enzyme 
                 DNA 
                 Hs.12646 
               
               
                   
                   
                 polymerase family, 
                   
                 replication/repair 
               
               
                   
                   
                 member 12 
               
               
                 CD31 
                 PECAM1 
                 platelet/endothelial cell 
                 Cell adhesion 
                 Cell-cell 
                 Hs.514412 
               
               
                   
                   
                 adhesion molecule 
                   
                 interaction 
               
               
                 POLR3GL 
                 POLR3GL 
                 polymerase (RNA) III 
                 Enzyme 
                 Gene 
                 Hs.591456 
               
               
                   
                   
                 (DNA directed) 
                   
                 expression 
               
               
                   
                   
                 polypeptide G (32 kD)-like 
               
               
                 SC65 
                 SC65 
                 synaptonemal complex 
                 Poorly 
                 Unknown 
                 Hs.446459 
               
               
                   
                   
                 protein SC65 
                 characterized 
               
               
                 SLC5A5 
                 SLC5A5 
                 solute carrier family 5 
                 Carrier 
                 Transport 
                 Hs.584804 
               
               
                   
                   
                 (sodium iodide 
               
               
                   
                   
                 symporter), member 5 
               
               
                 UTP14a 
                 UTP14a 
                 UTP14, U3 small 
                 RNA binding 
                 RNA 
                 Hs.458598 
               
               
                   
                   
                 nucleolar 
                   
                 processing 
               
               
                   
                   
                 ribonucleoprotein, 
               
               
                   
                   
                 homolog A (yeast) 
               
               
                 PTBP1 
                 PTBP1 
                 polypyrimidine tract 
                 RNA binding 
                 RNA 
                 Hs.172550 
               
               
                   
                   
                 binding protein 1 
                   
                 processing 
               
               
                   
               
            
           
         
       
     
     At least two of the selected antigens preferably have quantified and known relative autoantibody reactivities with respect to sera of a population affected by a disease, as well as a known association with a disease parameter. 
     In some contemplated embodiments, the carrier can be a pharmaceutically-acceptable carrier, and the composition can be formulated as a vaccine. In such embodiments, it is generally preferred that the vaccine comprises multiple (e.g., at least two, four, or six) antigens. Depending on the particular disease or disorder, it is contemplated that the antigens or fragments thereof can be at least partially purified and/or recombinant. 
     Alternatively, the carrier could be a solid carrier, and the plurality of antigens could be disposed on the carrier either as a mixture or as an array. In such arrays, it is contemplated that the antigens could have at least two distinct known reactivities and/or parameters. It is contemplated that the antigens or fragments thereof can be in crude expression extracts, in partially purified form (e.g., purity of less than 60%), or in highly purified form (e.g., purity of at least 95%). The antigens in such arrays may be recombinant or native. Alternatively, the solid phase need not be limited to planar arrays, but could also include, for example, beads, columns, dipstick-type formats, and other commercially suitable media. 
     In an alternative embodiment, two or more of the antigens can be immobilized on a surface, and the antigens can be associated with a single disease or more than one disease. 
     The surface can alternatively have antigen variants including, for example, truncated forms, non-glycosylated forms, recombinant forms, and chimeric forms. 
     In some contemplated embodiments, the disease is breast cancer, and the plurality of antigens are selected from the group consisting of BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, and UTP14a, or fragments thereof. 
     In other contemplated embodiments, the disease is lupus (L), and wherein the plurality of antigens are selected from the group consisting of DPP4, IL6R, ITGB2, MLF1IP, MYO1A, POLR2H, and TPO, or fragments thereof. In still other contemplated embodiments, the disease is lupus nepritis (LN), and wherein the plurality of antigens are selected from the group consisting of CD1D, IL6R, IRF8, ITGA2B, MYO1A, MYO7B, PSG1, PTBP1, and TPO, or fragments thereof. 
     In yet another contemplated embodiment, the disease can be systemic lupus erythematosus (SLE), and wherein the plurality of antigens are selected from the group consisting of CD1C, CD46, CENPQ, CFB, DPP4, HLA-DQB1, IL6R, ITGB2, KRTAP9-3, MLF1IP, MYT1L, POLR2H, SLC7A5, and TPO, or fragments thereof. In still another contemplated embodiment, the disease can be scleroderma (Sc) and the antigen can be IL6R, or a fragment thereof. 
     In other contemplated embodiments, the disease can be polymyositis (P), and wherein the plurality of antigens are selected from the group consisting of CD14, CD1C, CD46, CD55, CFB, COL9A1, COLQ, DLAT, DPP4, FGF7, H3F3B, IL1RAPL2, IL6R, IL8, ITGB2, KRTAP9-3, MLF1IP, MYT1L, PADI4, PIP4K2C, PLAUR, POLR2H, POLR2I, PSG1, SLC7A5, and STK19, or fragments thereof. 
     In an alternative embodiment, the disease can be rheumatoid arthritis (RA), and wherein the plurality of antigens are selected from the group consisting of APOH, BANK1, BLK, CD1C, CD14, CD3E, CD70, CD80, CD86, CEACAM6, CEACAM8, CENPT, CFB, COL1A2, DDC, DPP4, FCGR1A, H2AFX, H2AFY, H3F3B, HBA1, HBA2, HBD, HLA-DQB1, HSP90B1, HSPB7, IGHG2, IGHG4, IGHM, IGHV4-31, IL12A, IL6, IL6R, ITGB3BP, KRTAP13-1, KRTAP9-3, MBP, MLF1IP, MOBP, MS4A8B, MYH9, MYO1D, MYT1L, NMNAT2, NOL1, PDCD1, PIP4K2C, POLR2C, POLR2H, POLR2I, POLR2J2, POLR3D, PSIP1, SRP19, STAT4, and STK19, or fragments thereof. 
     In another embodiment, the disease can be Sjögren&#39;s syndrome (Sj), and wherein the plurality of antigens are selected from the group consisting of APOH, CALR3, CD1C, CD14, CD34, CD3E, CD46, CD69, CD93, CEACAM8, CENPA, CENPQ, CFB, CHRNA1, COL20A1, COL4A6, DPP4, FCGR3A, H1F0, H2AFX, H3F3B, HBA1, HBA2, HBD, HBM, HLA-C, HLA-DQB1, HLA-F, HSPB7, IFNG, IGFL2, IGH2, IGHV7-81, IL1RAPL2, IL6R, ITGB2, keratin 73, KRT19, KRTAP9-3, KRTAP9-8, MBP, MLF1IP, MYT1L, NOLA3, POLR2H, POLR2I, POLR3D, POLR3H, PTBP1, STK19, and UEVLD, or fragments thereof. 
     A list of diseases and antigen groups can be found in Table 2 below. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 L 
                 SLE 
                 LN 
                 P 
                 RA 
                 Sc 
                 Sj 
               
               
                   
               
             
            
               
                 CFB 
                 CD1C 
                 CD1D 
                 CD14 
                 APOH 
                 IL6R 
                 APOH 
               
               
                 DPP4 
                 CD46 
                 IL6R 
                 CD1C 
                 BANK1 
                   
                 CALR3 
               
               
                 IL6R 
                 CENPQ 
                 IRF8 
                 CD46 
                 BLK 
                   
                 CD1C 
               
               
                 ITGB2 
                 CFB 
                 ITGA2B 
                 CD55 
                 CD1C 
                   
                 CD14 
               
               
                 MLF1IP 
                 DPP4 
                 MYO1A 
                 CFB 
                 CD14 
                   
                 CD34 
               
               
                 MYO1A 
                 HLA-DQB1 
                 MYO7B 
                 COL9A1 
                 CD3E 
                   
                 CD3E 
               
               
                 POLR2H 
                 IL6R 
                 PSG1 
                 COLQ 
                 CD70 
                   
                 CD46 
               
               
                 TPO 
                 ITGB2 
                 PTBP1 
                 DLAT 
                 CD80 
                   
                 CD69 
               
               
                   
                 KRTAP9-3 
                 TPO 
                 DPP4 
                 CD86 
                   
                 CD93 
               
               
                   
                 MLF1IP 
                   
                 FGF7 
                 CEACAM6 
                   
                 CEACAM8 
               
               
                   
                 MYT1L 
                   
                 H3F3B 
                 CEACAM8 
                   
                 CENPA 
               
               
                   
                 POLR2H 
                   
                 IL1RAPL2  
                 CENPT 
                   
                 CENPQ 
               
               
                   
                 SLC7A5 
                   
                 IL6R 
                 CFB 
                   
                 CFB 
               
               
                   
                 TPO 
                   
                 IL8 
                 COL1A2 
                   
                 CHRNA1 
               
               
                   
                   
                   
                 ITGB2 
                 DDC 
                   
                 COL20A1 
               
               
                   
                   
                   
                 KRTAP9-3 
                 DPP4 
                   
                 COL4A6 
               
               
                   
                   
                   
                 MLF1IP 
                 FCGR1A 
                   
                 DPP4 
               
               
                   
                   
                   
                 MYT1L 
                 H2AFX 
                   
                 FCGR3A 
               
               
                   
                   
                   
                 PADI4 
                 H2AFY 
                   
                 H1F0 
               
               
                   
                   
                   
                 PIP4K2C 
                 H3F3B 
                   
                 H2AFX 
               
               
                   
                   
                   
                 PLAUR 
                 HBA1 
                   
                 H3F3B 
               
               
                   
                   
                   
                 POLR2H 
                 HBA2 
                   
                 HBA1 
               
               
                   
                   
                   
                 POLR2I 
                 HBD 
                   
                 HBA2 
               
               
                   
                   
                   
                 PSG1 
                 HLA-DQB1 
                   
                 HBD 
               
               
                   
                   
                   
                 SLC7A5 
                 HSP90B1 
                   
                 HBM 
               
               
                   
                   
                   
                 STK19 
                 HSPB7 
                   
                 HLA-C 
               
               
                   
                   
                   
                   
                 IGHG2 
                   
                 HLA-DQB1 
               
               
                   
                   
                   
                   
                 IGHG4 
                   
                 HLA-F 
               
               
                   
                   
                   
                   
                 IGHM 
                   
                 HSPB7 
               
               
                   
                   
                   
                   
                 IGHV4-31 
                   
                 IFNG 
               
               
                   
                   
                   
                   
                 IL12A 
                   
                 IGFL2 
               
               
                   
                   
                   
                   
                 IL6 
                   
                 IGH2 
               
               
                   
                   
                   
                   
                 IL6R 
                   
                 IGHV7-81 
               
               
                   
                   
                   
                   
                 ITGB3BP 
                   
                 IL1RAPL2 
               
               
                   
                   
                   
                   
                 KRTAP13-1 
                   
                 IL6R 
               
               
                   
                   
                   
                   
                 KRTAP9-3 
                   
                 ITGB2 
               
               
                   
                   
                   
                   
                 MBP 
                   
                 keratin 73 
               
               
                   
                   
                   
                   
                 MLF1IP 
                   
                 KRT19 
               
               
                   
                   
                   
                   
                 MOBP 
                   
                 KRTAP9-3 
               
               
                   
                   
                   
                   
                 MS4A8B 
                   
                 KRTAP9-8 
               
               
                   
                   
                   
                   
                 MYH9 
                   
                 MBP 
               
               
                   
                   
                   
                   
                 MYO1D 
                   
                 MLF1IP 
               
               
                   
                   
                   
                   
                 MYT1L 
                   
                 MYT1L 
               
               
                   
                   
                   
                   
                 NMNAT2 
                   
                 NOLA3 
               
               
                   
                   
                   
                   
                 NOL1 
                   
                 POLR2H 
               
               
                   
                   
                   
                   
                 PDCD1 
                   
                 POLR2I 
               
               
                   
                   
                   
                   
                 PIP4K2C 
                   
                 POLR3D 
               
               
                   
                   
                   
                   
                 POLR2C 
                   
                 POLR3H 
               
               
                   
                   
                   
                   
                 POLR2H 
                   
                 PTBP1 
               
               
                   
                   
                   
                   
                 POLR2I 
                   
                 STK19 
               
               
                   
                   
                   
                   
                 POLR2J2 
                   
                 UEVLD 
               
               
                   
                   
                   
                   
                 POLR3D 
                   
                   
               
               
                   
                   
                   
                   
                 PSIP1 
                   
                   
               
               
                   
                   
                   
                   
                 SRP19 
                   
                   
               
               
                   
                   
                   
                   
                 STAT4 
                   
                   
               
               
                   
                   
                   
                   
                 STK19 
               
               
                   
               
            
           
         
       
     
     In  FIG. 43 , one embodiment of a method  900  for predicting the likelihood of a patient having a disease or disorder can include step  910  of determining autoantibody reactivity against one or more antigens, or their variants, in a serum sample obtained from a patient. In step  920 , the one or more antigens are preferably selected from the group consisting of BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, UTP14a, DPP4, IL6R, ITGB2, MLF1IP, MYO1A, POLR2H, CD1D, IRF8, ITGA2B, MYO7B, PSG1, PTBP1, CD1C, CD46, CENPQ, CFB, HLA-DQB1, KRTAP9-3, MYT1L, SLC7A5, TPO, CD14, CD55, COL9A1, COLQ, DLAT, FGF7, H3F3B, IL1RAPL2, IL8, PADI4, PIP4K2C, PLAUR, STK19, APOH, BANK1, BLK, CD3E, CD70, CD80, CD86, CEACAM6, CEACAM8, CENPT, COL1A2, DDC, FCGR1A, H2AFX, H2AFY, HBA1, HBA2, HBD, HSP90B1, HSPB7, IGHG2, IGHG4, IGHM, IGHV4-31, IL6, ITGB3BP, KRTAP13-1, MBP, MOBP, MS4A8B, MYH9, MYO1D, NMNAT2, NOL1, PDCD1, POLR2C, POLR2J2, POLR3D, PSIP1, SRP19, STAT4, CALR3, CD34, CD69, CD93, CENPA, CHRNAL COL20A1, COL4A6, FCGR3A, H1F0, HBM, HLA-C, HLA-F, IFNG, IGFL2, IGH2, IGHV7-81, KRT73, KRT19, KRTAP9-8, NOLA3, POLR3H, and UEVLD, or fragments thereof. 
     Determining the autoantibody reactivity against the selected antigens or their variants in step 930 can advantageously indicate an increased likelihood of the patient having a disease, and can thereby provide a manner to detect one or more diseases in a patient. Depending upon the specific disease(s) to be identified, different antigens can be selected. 
     For example, to predict the likelihood of a patient having breast cancer, the step of determining autoantibody reactivity against one or more antigens or their variants can utilize one or more antigens selected from the group consisting of BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, and UTP14a, or fragments thereof. In this manner, antibody reactivity against one or more of BRCA1, CD88, CSF2RA, HBZ, HSPD1, IFNA7, IL12A, IL17D, KRT17, KRT18, KRT24, KRT5, MYL6, MYO9B, PARP12, PECAM1, POLR2I, POLR3GL, SC65, SLC5A5, and UTP14a, or fragments thereof, can indicate an increased likelihood of the patient having breast cancer. 
     As another example, to identify patients with lupus or the likelihood of a patient to have lupus, the one or more antigens are selected from the group consisting of DPP4, IL6R, ITGB2, MLF1IP, MYO1A, POLR2H, and TPO, or fragments thereof. Autoantibody reactivity can then be determined against the selected antigens or their variants, which can advantageously indicate an increased likelihood of the patient having lupus. 
     To identify patients with lupus nephritis or the likelihood of a patient to have lupus nephritis, the one or more antigens are preferably selected from the group consisting of CD1D, IL6R, IRF8, ITGA2B, MYO1A, MYO7B, PSG1, PTBP1, and TPO, or fragments thereof, and autoantibody reactivity can then be determined against the selected antigens or their variants to thereby indicate the likelihood of the patient having lupus nephritis. 
     To identify patients with systemic lupus erythematosus or predict the likelihood of a patient having systemic lupus erythematosus, the one or more antigens are preferably selected from the group consisting of CD1C, CD46, CENPQ, CFB, DPP4, HLA-DQB1, IL6R, ITGB2, KRTAP9-3, MLF1IP, MYT1L, POLR2H, SLC7A5, and TPO, or fragments thereof. Autoantibody reactivity can then be determined against the selected antigens or their variants, which can advantageously indicate an increased likelihood of the patient having systemic lupus erythematosus. 
     As yet another example, to identify patients with polymyositis, it is preferred that the antigens are selected from the group consisting of CD14, CD1C, CD46, CD55, CFB, COL9A1, COLQ, DLAT, DPP4, FGF7, H3F3B, IL1RAPL2, IL6R, IL8, ITGB2, KRTAP9-3, MLF1IP, MYT1L, PADI4, PIP4K2C, PLAUR, POLR2H, POLR2I, PSG1, SLC7A5, and STK19, or fragments thereof. Autoantibody reactivity can then be determined against the selected antigens or their variants, which can advantageously indicate an increased likelihood of the patient having polymyositis. 
     As a further example, to identify patients with rheumatoid arthritis, it is preferred that the antigens are selected from the group consisting of APOH, BANK1, BLK, CD1C, CD14, CD3E, CD70, CD80, CD86, CEACAM6, CEACAM8, CENPT, CFB, COL1A2, DDC, DPP4, FCGR1A, H2AFX, H2AFY, H3F3B, HBA1, HBA2, HBD, HLA-DQB1, HSP90B1, HSPB7, IGHG2, IGHG4, IGHM, IGHV4-31, IL12A, IL6, IL6R, ITGB3BP, KRTAP13-1, KRTAP9-3, MBP, MLFIIP, MOBP, MS4A8B, MYH9, MYO1D, MYT1L, NMNAT2, NOL1, PDCD1, PIP4K2C, POLR2C, POLR2H, POLR2I, POLR2J2, POLR3D, PSIP1, SRP19, STAT4, and STK19, or fragments thereof. Autoantibody reactivity can then be determined against the selected antigens or their variants, which can advantageously indicate an increased likelihood of the patient having rheumatoid arthritis. 
     To identify patients with scleroderma, it is preferred that the selected antigen is IL6R, or a fragment thereof. Autoantibody reactivity can then be determined against IL6R or its variants, which can advantageously indicate an increased likelihood of the patient having scleroderma. 
     To identify patients with Sjögren&#39;s syndrome, it is preferred that the antigens are selected from the group consisting of APOH, CALR3, CD1C, CD14, CD34, CD3E, CD46, CD69, CD93, CEACAM8, CENPA, CENPQ, CFB, CHRNA1, COL20A1, COL4A6, DPP4, FCGR3A, H1F0, H2AFX, H3F3B, HBA1, HBA2, HBD, HBM, HLA-C, HLA-DQB1, HLA-F, HSPB7, IFNG, IGFL2, IGH2, IGHV7-81, IL1RAPL2, IL6R, ITGB2, keratin 73, KRT19, KRTAP9-3, KRTAP9-8, MBP, MLF1IP, MYT1L, NOLA3, POLR2H, POLR2I, POLR3D, POLR3H, PTBP1, STK19, and UEVLD, or fragments thereof. Autoantibody reactivity can then be determined against the selected antigens or their variants, which can advantageously indicate an increased likelihood of the patient having Sjögren&#39;s syndrome. 
     In various embodiments, the reactivity level of at least 2, or at least 5, or at least 10, or at least 15, or at least 20, or at least 25 autoantibodies can be determined. Determining reactivity can be performed in numerous formats that are well known in the art. However, it is generally preferred that the determination is accomplished in a multiplex format, and especially in an array or “strip” format including, for example, arrays, or “strips” having at least one, more typically at least two, and even more typically at least 5, or at least 10, or at least 15, or at least 20, or at least 25 antigens. 
       FIG. 44  illustrates a flowchart of another embodiment of a method  1000  of detecting a disease in a patient includes step  1010  of determining autoantibody reactivity against one or more antigens, or their variants, in a sera sample obtained from a patient. 
     A likelihood of a disease can be predicted in step  1020  from reference samples derived from sera of patients diagnosed as having the disease, such that increased or decreased autoantibody reactivity against antigens selected from the group discussed above can be positively correlated in step  1030  with an increased likelihood of a disease in the patient. 
     The method can further include step  1022  of assaying the reactivity of autoantibodies in the sample, and step  1024  of normalizing the level of the reactivity against a level of at least one reference autoantibody reactivity in the sample to provide a normalized reactivity level. 
     The normalized reactivity level can then be compared in step  1026  with reactivity levels obtained from the reference samples derived from diseased patients. In this manner, increased normalized reactivity levels against antigens selected from the group of antigens listed in Table 1 positively correlates to an increased likelihood of a disease in the patients in step  1028 . 
     In other contemplated embodiments, a method of predicting the likelihood of a patient having a disease or disorder can include determining prognostic autoantibody reactivity against one or more specific antigens, or their variants, such as those described in Table 1, in a serum sample obtained from the patient, which can be normalized against the level of non-prognostic autoantibody reactivity in the serum sample, or of a reference set of autoantibody reactivity. Autoantibody reactivity against one or more of said specific antigens can be used to indicate an increased likelihood of the patient having a disease or disorder. 
     In an alternative embodiment, a method of predicting the likelihood of a patient having cancer can include determining the reactivity levels of autoantibodies against antigens, or their variants, presented hereinabove in a serum sample obtained from the patient, which is optionally normalized against the reactivity levels of other autoantibodies against antigens, or their variants, in said sera sample, or of a reference set of autoantibody reactivity levels. The data obtained in step (a) can be subjected to statistical analysis, and a likelihood of the patient having cancer can thus be determined. 
     In another embodiment, methods of preparing a personalized proteomics and autoantibody profile for a patient are contemplated, which include subjecting a sera sample from the patient to protein array chip analysis. The reactivity level of one or more autoantibodies can be determined against antigens or their variants (e.g., those listed in Table 1), and the reactivity level can optionally be normalized against control reactivity levels. A report can be created summarizing the data obtained by the analysis. Optionally, the report may include a prediction of the likelihood of severity of cancer in the patient and/or a recommendation for a treatment modality of the patient. 
     In a further aspect, methods for detecting one or more endogenous antibodies in a patient. In a still further aspect, methods are contemplated for detecting one or more autoantibodies in a patient. 
     In another aspect, antigens that triggered autoantibody reactivities are included in an antigen composition having two or more reactive antigens of a human disease or disorder and are associated with a carrier. The antigens can have quantified and known relative reactivities with respect to sera of a population infected with the organism, and can also have a known association with a disease parameter. Most preferably, the antigens are polypeptides or fragments thereof. 
     EXAMPLE 1 
     Human protein antigens in the following categories were selected for printing on the microarrays: (i) established autoantigens from autoimmune rheumatic diseases; (ii) established autoantigens from organ-specific autoimmune diseases; (iii) autoimmune disease associated molecules as described in recent literature (e.g. MHC molecules, complement components, signaling molecules); (iv) immunological targets with disease modifying potential (e.g. cytokines, chemokines, associated receptors, co-stimulatory molecules, etc.); and (v) proteins with no known immune reactivity (as controls). In total 797 proteins were selected for these experiments. 
     Human gene clones were obtained from the National Institutes of Health&#39;s (NIH) Mammalian Gene Collection (MGC) as cDNA clones. Amplicons of the human genes were obtained by PCR amplification of human genes from the cDNA clones. The primers (Sigma-Aldrich™ in St. Louis, Mo.) were made up of 20 base pairs (BPs) of gene-specific sequences and 20 BPs of adapter sequences. The adapter sequences were configured to be homologous to the cloning site of the linearized T7 expression vector pXT7 and allowed the PCR products to be cloned by homologous recombination in  Escherichia coli  DH5a cells. A polyhistidine (poly-His) fragment was incorporated at the 5′ end of the fusion protein. The amplicons with the flanking adapter sequences were used for in vivo recombination cloning into a T7 promoter based plasmid expression vector. 
     After the expressible library was verified to contain the correct inserts, the plasmids with human open reading frames (ORFs) were expressed using an in vitro transcription-translation system following the manufacturer&#39;s instructions (RTS 100 kit by Roche™ of Indianapolis, Ind.). Microarrays were printed onto nitrocellulose coated glass FAST slides (Whatman Inc.™ of Piscataway, N.J.) using an OmniGrid AccentTM microarray printer (DigiLab Inc.™ of Holliston, Mass.). Protein expression levels were monitored in the microarrays using anti-poly-His (clone His-1 by Sigma-Aldrich™ in St. Louis, Mo.) and anti-HA antibodies (clone 3F10 by Roche™ of Indianapolis, Ind.). The microarrays were blocked using 1×-blocking buffer (Whatman™, Sanford, Me.) for 30 minutes while the serum samples were pre-incubating. The blocking buffer was removed and the diluted antibodies were added to the microarrays and hybridized overnight in a humidified box. 
     The next day, the arrays were washed three times with Tris buffer-0.05% Tween-20, and the slides were incubated with biotin-conjugated goat anti-mouse, or biotin-conjugated goat anti-rat, immunoglobulin diluted 1/1,000 in blocking buffer. Secondary antibodies were added to the slides and incubated for one hour at room temperature. Following washing three times with Tris buffer-Tween 20, bound antibodies were detected by incubation with streptavidin-conjugated Sensilight P3 (Columbia Biosciences™ of Columbia, Md.). Following washing as before, additional three washes with Tris buffer saline, and a rinse with ultrapure water (18.2 Ohm), the slides were air dried under centrifugation and examined using a Perkin Elmer ScanArrray Express HT™ microarray scanner (Waltham, Mass.). Intensities were quantified using QuantArray™ software with measured values at each spot equaling the intensity at each spot minus the local background average. 
     While the study of human pathogens on microarray and related platforms has been successful, there was a lack of data or guidance in the art to support the use of the platforms detailed herein to study human diseases, cancers, or autoimmune disorders. To test whether autoantibodies would recognize antigens on the instant microarrays, the inventors chose to test tumor associated antigens (TAAs) for which there was current literature available. To create a TAA human protein microarray chip (TAA chip), the inventors chose 34 human proteins that had been shown to be autoantigens associated with cancer. The inventors surveyed breast cancer patients, population controls, and blood sister control sera on the TAA chip. 
     As shown in  FIG. 1 , the TAA chip was probed with serum from breast cancer patients and controls, and several proteins were recognized by antibodies in the serum (Panels  1 - 2  and  5 - 8 ). Specifically, the antibodies detected included, for example, breast 1 and 2 proteins (BRCA1 and BRCA2) and the epidermal growth factor receptor (EGFR) and EGFR-associated protein erythroblastic leukemia viral oncogene homolog 2 (ERBB2). The image data was quantified and analyzed.  FIG. 2  is a histogram comparing the mean signal intensity of the cancer patients (CA) with the population controls (P) and the Bonferroni corrected p-value (Bonferroni). As shown in the histogram, BRCA1 and EGFR were recognized differentially by breast cancer patient sera and the population control sera. 
     In a parallel study, the TAA chip was probed with serum from patients with cervical cancer and a healthy control. As shown in  FIG. 3 , there was a stark difference in the profile of antibodies against these TAAs in cervical cancer patients (right panel) compared with a healthy control (left panel). These data suggest that the instant platforms could not only detect autoantibodies, but could be used to determine antibody profiles in cancer patients, patients suffering from autoimmune disorders, and healthy individuals. 
     Armed with this unexpected success, the inventors created a comprehensive human protein array that would include even more proteins, and then interrogated the microarray with well-characterized, and clinically defined, human serum. Access to a well-characterized set of lupus serum was obtained, and a selection of human proteins to place on the microarray was completed. This Human Autoimmunity Chip (HA or HA1) consisted of 513 human proteins that included 442 unique proteins (the 34 TAAs discussed above and 409 proteins possibly associated with various autoimmune diseases). 
     Thirty-one lupus samples were probed including 15 systemic lupus erythematosus (SLE) samples, 16 lupus nephritis (LN) samples, 11 disease control samples (Sjögren&#39;s Syndrome (Sj)), and 16 normal controls.  FIG. 4  illustrates representative images of the HA chip probed with serum samples from patients with Sj as a disease control, and  FIG. 5  illustrates a representative image of the HA chip probed with serum samples from patients with Lupus. 
       FIG. 6  is a heat map of the signal intensity data for 59 serum samples (columns) and the proteins on the chip (rows) was created, which shows a difference in the reactivity pattern. The most reactive autoantigens in the serum samples from patients with lupus are shown in the enlarged portion of the heat map in  FIGS. 7A-7B . Unlike infectious diseases where a naïve (healthy) patient population shows little to no reactivity, there was significant reactivity to human proteins even in the normal/healthy populations. The outcome of the microarray testing showed a difference in the antigen recognition profile of LN and SLE samples when compared to control populations control sera. 
       FIG. 8  compares the mean signal intensities and standard errors of normal/healthy sera sourced from the US (N), the Sjögren&#39;s Syndrome patient sera (Sj), the lupus nephritis patient sera (LN) and the systemic lupus erythematosus (SLE). Further analysis revealed that there were circulating antibodies against small nuclear ribonucleoprotein polypeptides B, B1 and N (SNRPB and SNRPN), as well as to breast cancer antigen 1 (BRCA1), which are higher in both lupus groups than in the control groups. Having established that the platform could effectively detect circulating antibodies against human proteins, further experiments were conducted to expand the autoimmunity antigen sets and test the discovery platform with a much larger set of characterized samples from lupus patients. 
     EXAMPLE 2 
     Autoimmune Study 
     For the second version of the Human Autoimmunity Chip (HA2), an additional 218 proteins were targeted which had 109 splice variants in the MGC, totaling 327 additional proteins. HA2 was composed of 840 total human proteins, representing 660 unique proteins and their splice and/or cDNA variants. To interrogate this expanded set of proteins, serum samples were obtained from patients that had been diagnosed with LN (N=61), SLE (N=72), polymyositis (P) (N=26), rheumatoid arthritis (RA) (N=25), Scleroderma (Sc) (N=21) and Sj (N=23). Serum samples were also obtained from age- and sex-matched normal, healthy individuals (N) (N=10). 
     The second version of the HA chip (HA2) was probed with anti-HA high affinity rat monoclonal to verify expression of the proteins.  FIG. 9  illustrates sample images of HA2, in which the C-terminal HA tag (top panel) was detected and probed with normal sera (middle panel) and with sera from an autoimmune patient (bottom panel). 
     The chips were scanned and quantified using PerkinElmer ProscanArray Express™ v.4 software. The data from the mean-background columns was used to compile the raw data.  FIG. 10  is a heat map of the signal intensity data for the approximately 200,000 data points generated from the raw data to examine the data of reactivity patterns of the 238 serum samples (columns) and 840 proteins on the chip (rows). The heat map shows the autoantibody profile for patients with LN (N=61), patients with SLE (N=72), patients with P (N=26), patients with RA (N=25), patients with Sc (N=21), patients with Sj (N=23), and age and sex matched normal individuals (N=10). As better shown in  FIG. 11 , the heat map illustrates circulating antibodies to human proteins in normal individuals as well as in the disease groups, and shows the antigens that demonstrated the highest signals in SLE. 
     The data was dissected further to identify disease-specific biomarkers, and the raw data was normalized using variance stabilization normalization (VSN), which is an accepted method to deal with microarray data. Using the normalized data, mean signal intensities and the standard deviation and standard errors were calculated for each group of samples in the statistical environment known as R (www.r-project.org). To determine which antigens were potential biomarkers, the disease groups were compared with the normal group (N) using Benjamini-Hochberg corrected p-values (BHp) calculated from Bayesian regularized t-tests performed in R. To control for multiple testing conditions, p-values were adjusted using the Benjamini-Hochberg procedure for controlling the Family Wise Error Rate. All reported p-values are Benjamini-Hochberg corrected unless otherwise noted. Finally, the data was retransformed into an approximate raw scale by taking the base 2 anti-log of the values for bar plot visualizations. 
     The mean signal intensities and standard errors were plotted for antigens that are differentially reactive when compared to the normal group. Antibody profiles to human proteins associated to specific diseases were readily identified, as shown in  FIGS. 12-18 . Specifically,  FIG. 12  looks at sera from lupus patients (L),  FIG. 13  looks at sera from lupus nephritis patients (LN),  FIG. 14  looks at sera from systemic lupus erythematosus patients (SLE),  FIG. 15  looks at sera from polymyositis patients (P),  FIG. 16  looks at sera from rheumatoid arthritis patients (RA),  FIG. 17  looks at sera from scleroderma patients (Sc), and  FIG. 18  looks at sera from Sjögren&#39;s Syndrome patients (Sj). 
     When reviewing autoantibodies profiles, there are generally two potential outcomes of human disease: either an increase in circulating antibodies or a decrease in existing antibodies. As shown in  FIGS. 13 and 17  for LN and Sc, a significant decrease can be seen in circulating antibodies, whereas  FIGS. 12, 14-16, and 18  shows a net increase in the circulating antibodies for SLE, P, RA and Sj. 
     As can be seen in  FIGS. 13 and 14 , SLE patients have higher reactivity for CFB, (CD1C), POLSR2H, MLF1IP, keratin associated protein 9-3 (KRTAP9-3), ITGB, CD46 molecule (CD46), centromere protein Q (CENPQ), myelin transcription factor 1-like (MYT1L), major histocompatibility complex class II DQ beta 1 (HLA-DQB1), solute carrier family 7 (cationic amino acid transporter, y+ system) member 5 (SLC7A5) and DPP4. IL6R and TPO show lower reactivity in the SLE patients. Seven of the eight proteins show the same pattern of reactivity as seen for all lupus patients. Using the optimal linear combination of nine proteins, a receiver operator curve was created with an area under the curve of 0.990, sensitivity of 98% and specificity of 90%. LN patients showed lower reactivity for all nine differentially reactive proteins ( FIG. 3E ). Pregnancy specific beta-1-glycoprotein 1 (PSG1), interferon regulatory factor 8 (IRF8), IL6R, myosin 7B (MYO7B), TPO, ITGA2B, polypyrimidine tract binding protein 1 (PTBP1) MYO1A and CD1d molecule (CD1D) had higher reactivity to the normal population. Using all nine proteins, a receiver operator curve was created with an area under the curve of 0.908, sensitivity of 90% and specificity of 80%. 
     Serial bleeds for seven lupus nephritis (LN) patients that were undergoing treatment were probed on HA2. The serum samples were taken at different time points after treatment for LN had begun. The first time point in each of these serial bleeds, the “0.1” time points, were taken before treatment began. A heat map was created of the antigens that showed the most reactivity, and is shown in  FIG. 19 .  FIGS. 20-26  show line graphs of the serial bleeds that were created from the data for each patient (Ti, T2, T4, T7, T8, T10, and T14). The mean values form the normal groups were used as a reference and as first point on each of the graphs. 
     Much like the autoantibody profile at bleed 1, the changes seen in the subsequent bleeds appear very heterogeneous. The reactivity for patient Ti shows a downward trend from the first bleed. There is one antigen (actinin, alpha 2, ACTN2), however, that shows an substantial increase on bleeds 5 and 6 ( FIG. 20 ). Patient T4 on the other hand is shows almost no change over the time course, with high reactivity to one antigen small nuclear ribonucleoprotein 70 kDa (SNRP70) and low reactivity to the other reactive antigens ( FIG. 22 ). Patient T7 shows an increase of reactivity from the first three bleeds ( FIG. 23 ). The most reactive proteins being SNRPB, PSG1, sialophorin (SPN), and CD34 molecule (CD34). Beginning from bleed four we see that individual auto-antigens behave differently. Some reactivities increase while some decrease. Patient T8 shows a general decrease in reactivity over time with a few spikes at different bleeds for different proteins ( FIG. 24 ). Islet cell autoantigen 1, 69 kDa (ICA1) reactivity spiked at bleed 3, while CD36 reactivity spiked at bleed five and stayed elevated at bleed six. Patient T10, unlike the other four patients, started off with relatively low levels of reactivity. Reactivity to SNRPB and tumor necrosis factor receptor superfamily, member 4 (TNFRSF4) increased from bleed two to four, then settling back down to bleed one levels by bleed five ( FIG. 25 ). 
     When compared to the mean values of the normal population, the first time point shows elevated antibody levels for some antigens, and baseline or slightly lower antibody levels for others. Each patient also showed a distinct antibody profile and time course signature. Thus, the data suggests that the biomarkers discovered using the ADI platform described herein have the potential to allow for personalized tracking of the efficacy of a treatment via the change in antibody levels against certain human proteins. 
     EXAMPLE 3 
     Breast Cancer Study 
     The HA2 chip was interrogated with serum samples from 48 breast cancer cases (CS), 48 blood-relative (sister) controls (RC), and 48 population controls (PC). Data was collected for the 144 serum samples for 840 proteins on the array using an IgG-specific secondary antibody to detect antibodies bound to the proteins. The HA2 chips were scanned and quantified using PerkinElmer ProscanArray Express™ v.4 software. The data from the mean-background columns was used to compile the raw data. The raw data was visualized in a heat map of the signal intensity data shown in  FIGS. 27A-27H  for 144 serum samples (columns) and the most reactive proteins on the chip (rows), which illustrates the autoantibody profile for patients breast cancer, their sister controls and population controls. 
     The compiled data was normalized by the application of VSN in R. The mean signal intensities, standard deviations, standard errors and the Bayesian t-test were also calculated in R. Using the control population as a baseline, the percentage change in the signal intensity for proteins with a p-value of less than 0.05 was assessed. When comparing the relative changes in signal intensities for the 11 proteins in the CS group compared with the PC as shown in  FIG. 28 , the biggest changes were in the antibodies against BRCA1 followed by UTP14 homolog A (UTP14A) and complement component 5a receptor 1 (C5AR1 or CD88). The mean signal intensities for BRCA1 and CD88 were found to be lower than the baseline, while UTP14A showed an increase in the CS group.  FIG. 29  illustrates changes in signal intensities for the 11 proteins in the CS group compared with the RC. Similarly, the CS versus RC comparison showed increased levels of UTP14A. The largest increase, however, was for the known autoantigen synaptonemal complex protein SC65 (SC65). Nine other proteins showed lower signal intensities for the CS than for the RC. 
     EXAMPLE 4 
     Use of a High-Throughput Proteome Microarray to Identify Autoantibody Signatures in Patients with Systemic Lupus Erythematosus 
     Systemic lupus erythematosus (SLE/lupus) is an autoimmune disease with a complex etiopathology. Diagnosis is often difficult and management of the numerous clinical manifestations can be problematic, even for experienced clinicians. Serologically, it is characterized by autoantibodies to a diverse range of human proteins. Monitoring these antibodies, particularly specificity and titers, has been a mainstay of diagnosis and disease management for decades. However autoantibody measurement has never been entirely satisfactory for providing warnings of disease flares or organ involvement. 
     However, the use of serological methods remains attractive because they are relatively non-invasive and can be performed quickly. To that end, the inventors have developed a high-throughput proteomic microarray platform that allows thousands of protein gene products or antigens to be printed on a glass slide and used to interrogate sera from humans or animals (e.g., Molina D M, Morrow, W. J. W., Liang X L. Use of high-throughput proteomic microarrays for the discovery of disease-associated molecules. In Biomarkers in Drug Development: a handbook of practice, application and strategy. Eds. Bleavins M, Carini, C, Jurima-Romet, M, Rahbari, R. 2010. Wiley (New York) 2010). The arrays can advantageously be produced very quickly, and have been used with considerable success to identify diagnostic and vaccine candidates in a number of pathogen systems including, tuberculosis (e.g., Kunnath-Velayudhan S, Salamon H, Wang H Y, Davidow A L, Molina D M, Huynh V T, Cirillo D M, Michel G, Talbot E A, Perkins M D, Felgner P L, Liang X, Gennaro M L. 2010. Dynamic antibody responses to the  Mycobacterium tuberculosis  proteome. Proc Natl Acad Sci USA. 107(33):14703-8), brucellosis (e.g., Liang L, Leng D, Burk C, Nakajima-Sasaki R, Kayala M, Atluri V L, Pablo J, Unal B, Ficht T A, Gotuzzo E, Saito M, Morrow W J W, Liang X, Baldi P, Vinetz J, Felgner P L, Tsolis R M. 2010. Large scale immune profiling of infected humans and goats reveals differential recognition of  Brucella melitensis  antigens. PLoS Negl Trop Dis. 4(5):e673),  Chlamydia  (e.g., Molina D M, Pal S, Kayala M A, Teng A, Kim P J, Baldi P, Felgner P L, Liang X, de la Maza L M. 2009. Identification of immunodominant antigens of  Chlamydia trachomatis  using proteome microarrays. Vaccine 28 (17):3014-24), Lyme disease (e.g., Barbour A G, Jasinskas A, Kayala M A, Davies D H, Steere A C, Baldi P, Felgner P L. 2008. A genome-wide proteome array reveals a limited set of immunogens in natural infections of humans and white-footed mice with  Borrelia burgdorferi.  Infect Immun. 76(8):3374-89), as well as identify new targets of pemphigus auto-antibodies (e.g., Kalantari-Dehagi M, Molina D M, Farhadieh M, Morrow W J W, Liang X, Felgner P L, Grando S A. New targets of pemphigus vulgaris antibodies identified by protein array technology. Exp Dermatol. 20(2):154-6). 
     Sera were studied from patients attending the autoimmune rheumatic disease clinic at the University College Hospital over the past 25 years. All patients with lupus met the revised criteria of the American College of Rheumatology. Those considered to have kidney involvement had to have had a confirmatory biopsy. Patients with Sjögren&#39;s syndrome met the American European Consensus Criteria. Those with myositis had three out of the four of the criteria proposed by Bohan and Peter and those with rheumatoid arthritis all had four or more of the revised criteria of the American Rheumatism Association. 
     Human protein microarray chips were fabricated in the manner described above. The Human protein microarray chips were probed with human sera from systemic lupus erythomatosis, lupus nephritis, polymyositis, rheumatoid arthritis, scleroderma and Sjögrens&#39;s Syndrome patients, as well as age, sex, ethnicity matched normal healthy control sera. Prior to microarray probing, the sera were diluted to 1/100 in Protein Array Blocking Buffer (Whatman) containing  E. coli  lysate at a final concentration of 10%, approximately 1-2 mg/ml, and incubated for 30 minutes at room temperature while mixing. The microarrays were blocked using 1×-blocking buffer for 30 minutes while the serum samples were pre-incubating. The blocking buffer was removed and the diluted serum was added to the microarrays and hybridized overnight in a humidified box. Following washing, the slides were incubated with diluted biotinlyated goat anti-human IgG (H+L) (JacksonImmuno Research Laboratories Inc.™ of West Grove, Pa.) for one hour at room temperature with agitation. Following washing, bound antibodies were detected by incubation with streptavidin-conjugated Sensilight P3 (Columbia Biosciences™). Following washing and drying overnight, intensities were quantified using QuantArray™ software. Microarrays were scanned, quantified, and all signal intensities were corrected for background. 
     The statistical analysis was performed as previously described. Briefly, the data was calibrated and transformed using the VSN package in the R statistical environment. Differential reactivity analysis was then performed using Bayes-regularized t-tests. To address multiple comparisons, p-values were adjusted using the Benjamini-Hochberg procedure for controlling the Family Wise Error Rate (FWER). All reported p-values are Benjamini-Hochberg corrected unless otherwise noted. Finally, the data was retransformed into an approximate raw scale by taking the base 2 anti-log of the values for bar plot visualizations. 
     The antigens were ranked by their adjusted Benjamin-Hochberg p-values. Each antigen could serve as a single marker. A ROC curve analysis was performed to each of the antigens. From statistical literature, it is known that combining multiple markers increases the accuracy measured by the area under the ROC curve (AUROC). See, e.g., Su JQ and Liu J (1993). Linear combination of multiple diagnostic markers. Journal of American Statistical Association 88, 1350-1355 and Pepe M S and Thompson M L (2000). Combining diagnostic test results to increase accuracy.  Biostatistics  1(2): 123-140. Optimal linear combination (OLC) was used to progressively combine the top discriminating antigens, and the AUROC of each OLC was plotted with progressively increased number of antigens and the graph usually plateaued after certain number of antigens. That means it does not increase the accuracy of the combined marker by adding more antigens. Then the selected antigens are used for the final OLC. The ROC curve analysis was performed using the R packages ROCR and ROC which produces the empirical ROC curve, an estimate of the AUROC and a list of cut points and corresponding sensitivities and specificities. The optimal cut point was selected to be the closest to the point of (0,1), which is the accuracy for a gold standard. 
     A human autoimmune-associated protein (HAAP) chip was composed of 713 total human proteins, representing proteins identified as described above and their splice and/or cDNA variants. Only 48 clones were negative for cloning and sequencing. Once expressed and arrayed, the chips were probed with anti-polyHistidine and anti-HA antibodies to verify the expression of the proteins as a quality control (QC) method. The chips were scanned and quantified using PerkinElmer Proscan Array Express™ v.4 software. The data from the mean-background columns was used to compile the raw data.  FIG. 30  shows an image of one sub-array (out of 4) representing approximately 207 different expression products and 18 control spots visualized using the C-terminal HA tag and the anti-HA antibody.  FIG. 31  shows the distribution of the mean signal intensities for the QC probing, while  FIGS. 32 and 33  show that greater than 95% of the expression products were recognized via the detection of one or the other tag. 
     Normal Controls have Circulating Antibodies Against Human Proteins 
     Serum from 10 normal donors was probed on the HAAP chip to establish a baseline for the subsequent probing of lupus patient sera. Interestingly, the normal controls were found to have circulating auto-antibodies against proteins on the chip.  FIG. 34  is a heat map with the individual normal donors (rows) and the proteins (columns), which shows a cluster of reactivity towards the left side of the heat map as well as more heterogeneously distributed reactivity to proteins on the right side of the heat map. The mean signal intensities for the proteins were tabulated from the normalized data and the values plotted in a histogram shown in  FIGS. 35A-35B . 225 proteins or auto-antigens were recognized by the auto-antibodies in the serum of normal donors. 
     Profiling Lupus 
     Serum from 133 lupus patients was probed on the HAAP chip. The data collected was merged with the data for normal. The entire data set was normalized and the auto-antibody profile of the lupus patients was compared with that of the normal. A heat map shown in  FIG. 36  was created to examine the reactivity pattern of the 143 serum samples.  FIG. 37  illustrates a histogram of all the reactive proteins. To tease out the disease biomarkers, the raw data was normalized using VSN. Using the normalized data, the mean signal intensities, the standard deviation and standard errors were calculated for each group of samples in the statistical environment known as R. To determine which antigens were potential biomarkers, the disease groups were compared with the normal group using Benjamini-Hochberg corrected p-values (BHp) calculated from Bayesian regularized t-tests performed in R. As shown in  FIGS. 36-38 , there is a small subset of auto-antigens that show different reactivities profiles in the lupus patients from the normal donors, while there are a large number of proteins that are reactive in both groups. There are eight differentially reactive proteins for which the mean intensity, standard error and BHp were plotted. When looking at autoantibodies, there are two potential outcomes of human disease: an increase in circulating antibodies or a decrease in existing antibodies in response to disease pathology. The former is seen for five proteins: polymerase (RNA) II (DNA directed) polypeptide H (POLR2H), MLF1 interacting protein (MLF1IP), complement factor B (CFB), integrin beta 2 (complement component 3 receptor 3 and 4, ITGB2), and dipeptidyl-pepsidase 4 (DPP4). The latter is seen for Interleukin 6 receptor (IL6R), thrombopoietin (TPO) and myosin 1A (MYO1A). Using all eight proteins, a receiver operator curve is created shown in  FIG. 39  with and area under the curve of 0.986, sensitivity of 99% and specificity of 90%. 
     Serum from 95 patients with polymyositis, rheumatoid arthritis, scleroderma and Sjögren&#39;s syndrome were also probed to be used as autoimmune disease controls to determine whether or not we could identify lupus specific auto-antibodies. As shown in  FIGS. 40-42 , two versions of the same protein, protein small nuclear ribonucleoprotein polypeptides B and B1 (SNRPB), have higher reactivity in the lupus group than in the disease controls. 
     As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. 
     It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the scope of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.