Identifying antigen clusters for monitoring a global state of an immune system

• US 10,082,503 B2
• Filed: 03/13/2014
• Issued: 09/25/2018
• 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: Cohen, Irun R., Domany, Eytan, Quintana, Francisco Javier, Hed, Guy, Getz, Gad
• Primary Examiner(s): Gross, Christopher M
• Application Number:
  US14/210,409
• Publication Number:
  US 20140200159A1
• Time in Patent Office: 1,657 Days
• Field of Search: None
• 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 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

(d) 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 of (b) is classified as different from that exhibited with said plurality of antibodies being derived from said individuals free of said disease of (c).

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 an 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 an 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 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 of (i), wherein said immune reactivity is classified as different from that exhibited with said plurality of antibodies being derived from said individuals free of said disease of (ii); and

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