IDENTIFYING ANTIGEN CLUSTERS FOR MONITORING A GLOBAL STATE OF AN IMMUNE SYSTEM

• US 20090258790A1
• Filed: 01/22/2009
• Published: 10/15/2009
• Est. Priority Date: 07/24/2000
• Status: Active Grant

• Current Assignee: Yeda Research and Development Co. Ltd. (Weizmann Institute of Science)
• Original Assignee: Yeda Research and Development Co. Ltd. (Weizmann Institute of Science)
• Inventors: Hed, Guy, COHEN, Irun R., Getz, Gad, Domany, Eytan, Quintana, Francisco Javier
• Granted Patent: US 8,010,298 B2
• Time in Patent Office: Days
• US Class Current: 506/9
• CPC Class Codes: G01N 33/564 for pre-existing immune complex or autoimmune disease , i.e. systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, rheumatoid factors or complement components C1-C9
• CPC Class Codes: G01N 33/6803 General methods of protein analysis not limited to specific proteins or families of proteins
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

Method, system and an article of manufacture for clustering and thereby identifying predefined antigens reactive with undetermined immunoglobulins of sera derived from patient subjects in need of diagnosis of disease or monitoring of treatment.

Claims

1. A method of clustering a subset of antigens of a plurality of antigens, said subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having a disease, whereby the disease is associated with autoantibodies, the method comprising the steps of:

(a) providing a plurality of test antigens, said test antigens being self-antigens or antigens derived from a library

(b) assaying binding of said plurality of antibodies being derived from said plurality of patients with said plurality of antigens;

(c) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of said disease with said plurality of antigens; and

(c) bioinformatically clustering a subset of antigens exhibiting an immune reactivity with said plurality of antibodies being derived from said plurality of patients having said disease, wherein said immune reactivity is different from that exhibited with said plurality of antibodies being derived from said individuals free of said disease.

2. The method of claim 1, wherein said step of clustering is effected so as to include in said subset of antigens those antigens for which said patients and individuals best decompose into clusters according to a known clinical diagnosis of said patients and individuals.

3. The method of claim 1, wherein said step of clustering is effected by a supervised classifier.

4. The method of claim 3, wherein said supervised classifier is a neural network algorithm.

5. The method of claim 1, wherein said step of clustering is effected by a unsupervised classifier.

6. The method of claim 5, wherein said unsupervised classifier is a coupled two way clustering algorithm or super-paramagnetic clustering.

7. The method of claim 1, wherein said step of clustering is effected so as to result in optimal sensitivity.

8. The method of claim 1, wherein said step of clustering is effected so as to result in optimal specificity.

9. The method of claim 1, wherein said step of clustering is effected so as to result in optimal specificity and optimal sensitivity.

10. The method of claim 1, wherein said step of clustering is effected by:

(i) clustering said antibodies and said antigens and identifying all stable antibody and antigen clusters;

(ii) scanning said antigen clusters, while using reactivity levels of antigens of each antigen cluster as a feature set representing first object sets containing either all of said antibodies or any of said stable antibody clusters;

(iii) scanning said antibody clusters, while using reactivity levels of antibody of each antibody cluster as a feature set representing second object sets containing either all of said antigens or any of said stable antigen clusters;

(iv) tracking all antibody and antigen stable clusters thus generated;

(v) repeating steps (i)-(iv) until no new antibody and antigen stable clusters being generated, thereby obtaining final stable antigens and antibody clusters and pointers identifying how all of said stable antibody and antigen clusters have been generated.

11. The method of claim 1, wherein said disease is selected from the group consisting of a autoimmune disease, a cancer, an immune deficiency disease, a degenerative disease, a metabolic disease, an infectious disease, a genetic disease, a mental disorder, an organ transplantation, an injury or an intoxication, or any condition involving cytokines or inflammation.

12. A method of diagnosing a disease of a subject, the method comprising the steps of:

(a) providing a plurality of test antigens;

(b) clustering a subset of antigens of said plurality of test antigens, said test antigens being self-antigens or auto-antigens derived from a library, said subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients suffering from said disease by;

(i) assaying binding of said plurality of antibodies being derived from said plurality of patients with said plurality of test antigens;

(ii) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of said disease with said plurality of test antigens; and

(iii) bioinformatically clustering said subset of antigens exhibiting an immune reactivity with said plurality of antibodies being derived from said plurality of patients having said disease, wherein said immune reactivity is different from that exhibited with said plurality of antibodies being derived from said individuals free of said disease; and

(c) associating or deassociating the antibodies of said subject with a cluster resulting from step (a)(iii).

13. The method of claim 12, wherein said step of clustering is effected so as to include in said subset of antigens those antigens for which said patients and individuals best decompose into clusters according to a known clinical diagnosis of said patients and individuals.

14. The method of claim 12, wherein said step of clustering is effected by a supervised classifier.

15. The method of claim 14, wherein said supervised classifier is a neural network algorithm.

16. The method of claim 12, wherein said step of clustering is effected by a unsupervised classifier.

17. The method of claim 16, wherein said unsupervised classifier is a coupled two way clustering algorithm or super-paramagnetic clustering.

18. The method of claim 12, wherein said step of clustering is effected so as to result in optimal sensitivity.

19. The method of claim 12, wherein said step of clustering is effected so as to result in optimal specificity.

20. The method of claim 12, wherein said step of clustering is effected so as to result in optimal specificity and optimal sensitivity.

21. A system for monitoring the state of the immune system of a subject suffering from a disease, the system comprising a data acquisition device and a computation device communicating therewith, said data acquisition device and said computation device being designed, constructed and configured for:

(a) providing a plurality of test antigens;

(b) clustering a subset of antigens of said plurality of test antigens, said test antigens being self-antigens or auto-antigens derived from a library, said subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients suffering from said disease by;

(i) assaying binding of said plurality of antibodies being derived from said plurality of patients with said plurality of test antigens;

(ii) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of said disease with said plurality of test antigens; and

(iii) bioinformatically clustering said subset of antigens exhibiting an immune reactivity with said plurality of antibodies being derived from said plurality of patients having said disease, wherein said immune reactivity is different from that exhibited with said plurality of antibodies being derived from said individuals free of said disease; and

(c) associating or deassociating serum of said subject with a cluster resulting from step (a)(iii).

22. The system of claim 21, wherein said step of clustering is effected by:

(i) clustering said antibodies and said antigens and identifying all stable antibody and antigen clusters;

(ii) scanning said antigen clusters, while using reactivity levels of antigens of each antigen cluster as a feature set representing first object sets containing either all of said antibodies or any of said stable antibody clusters;

(iii) scanning said antibody clusters, while using reactivity levels of antibodies of each antibody cluster as a feature set representing second object sets containing either all of said antigens or any of said stable antigen clusters;

(iv) tracking all antibody and antigen stable clusters thus generated;

(v) repeating steps (i)-(iv) until no new antibody and antigen stable clusters being generated, thereby obtaining final stable antigen and antibody clusters and pointers identifying how all of said stable antibody and antigen clusters have been generated.

23. The system of claim 21, wherein said disease is selected from the group consisting of a autoimmune disease, a cancer, an immune deficiency disease, a degenerative disease, a metabolic disease, an infectious disease, a genetic disease, a mental disorder, an organ transplantation, an injury or an intoxication, or any condition involving cytokines or inflammation.

24. A method of diagnosing Type I or Type II diabetes in a subject, the method comprising, assaying binding of antibodies being derived from the subject with antigens of cluster 19 including Actin, Spectrin, Vimentin, Fibronectin, Collagen, Collagen I, Collagen IX, Peroxidase, Tyrosinase, Enolase, Ribonuclease, Human Serum Albumin, Recombinant Albumin, Fibrin, H IgM, HSP60, p277, Brain Extract, Ec27, P278, KLH and Glucocerebroside, wherein antibody binding with the antigens is indicative of Type I or Type II diabetes in the subject, wherein said binding to the antigens is greater than that of a binding of a serum from a healthy subject free of Type I diabetes to said antigens.

25. A method of diagnosing Type I diabetes in a subject, the method comprising, assaying binding of antibodies being derived from the subject with antigens of cluster 5 comprising Insulin and Aldolase, cluster 14 comprising Cartilage extract, cluster 26 comprising Histone II A, Lysine, Arginine, Laminin, Collagen X, Fibrinogen and Collagen VII, cluster 47 comprising Collagen X and Insulin and cluster 66 comprising Cardiolipin and Cartilage extract, wherein an antibody binding with the antigens is indicative of Type I diabetes in the subject, wherein said binding to the antigens is greater than that of a binding of a serum from a healthy subject free of Type I diabetes to said antigens.