US20040014069A1 - Identifying antigen clusters for monitoring a global state of an immune system - Google Patents

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

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US20040014069A1
US20040014069A1 US10/332,241 US33224103A US2004014069A1 US 20040014069 A1 US20040014069 A1 US 20040014069A1 US 33224103 A US33224103 A US 33224103A US 2004014069 A1 US2004014069 A1 US 2004014069A1
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binding moieties
antigens
disease
clustering
binding
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Irun Cohen
Eytan Domany
Francisco Quintana
Guy Hed
Gad Getz
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Yeda Research and Development Co Ltd
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Yeda Research and Development Co Ltd
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Assigned to YEDA RESEARCH AND DEVELOPMENT CO. LTD. reassignment YEDA RESEARCH AND DEVELOPMENT CO. LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HED, GUY, COHEN, IRUN R., DOMANY, EYTAN, GETZ, GAD, QUINTANA, FRANSISCO JAVIER
Publication of US20040014069A1 publication Critical patent/US20040014069A1/en
Priority to US12/357,449 priority Critical patent/US8010298B2/en
Priority to US13/207,486 priority patent/US8703654B2/en
Priority to US14/210,409 priority patent/US10082503B2/en
Priority to US16/123,385 priority patent/US20190004045A1/en
Priority to US16/748,001 priority patent/US11002735B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/564—Immunoassay; Biospecific binding assay; Materials therefor for pre-existing immune complex or autoimmune disease, i.e. systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, rheumatoid factors or complement components C1-C9
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present invention relates to a method, system and an article of manufacture for clustering and thereby identifying predefined binding moieties of one type which are reactive with undetermined binding moieties of a second type. More particularly, the present invention relates to a 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.
  • Autoimmune diseases include, for example, type 1 diabetes, Behcet's disease, multiple sclerosis, rheumatoid arthritis, idiopathic thrombocytopenic purpura and various diseases affecting every organ and almost every cell type in the body. These diseases tend to run a relapsing or chronic course, and in many cases affect young individuals in the prime of life.
  • the various autoimmune diseases are often difficult to diagnose early in their course because the clinical picture can, at times, be obscure at onset. It is even more difficult to identify incipient disease in persons at risk. Diagnosis and early diagnosis prior to accumulation of irreversible damage, is becoming more critical because specific immune therapies are now being implemented. To this end, see for example, U.S. Pat. Nos.
  • autoimmune diseases such as insulin, DNA, myelin basic protein, thyroglobulin and others.
  • Novel approaches are needed to support the diagnoses of specific autoimmune diseases in a way that would justify specific therapeutic interventions.
  • Chronic diseases that are not thought to be autoimmune are also in need of new diagnostic methods.
  • Many chronic conditions such as Alzheimer's disease of the brain, various dystrophies of the muscles, psoriasis of the skin, and others, involve inflammation, and one needs convenient tools to help categorize different types of inflammation. These conditions include degenerative and metabolic diseases. Inflamation is also a key factor in transplantation reactions, in healing and in tissue regeneration. The challenge is not only to diagnose the disease, but also to distinguish individuals who would benefit from a particular treatment from those individuals who would not.
  • Infectious diseases too, require better diagnostic discrimination between persons who will be susceptible to a particular treatment and persons who will not respond thereto. Certain infections can trigger autoimmune responses, and it is important to be able to diagnose persons who are destined to develop autoimmune diseases.
  • the immunotherapy of cancer is another situation in which it would be advantageous to classify persons with different types of immune reactivities to self-antigens; many, if not most tumor-associated antigens are self-antigens. Thus, it could be important in the design of therapeutic tumor vaccines to know what kind of autoimmune reactivity is found in the patient.
  • Various immunologic therapies are now being used. There is a critical need to develop markers that will enable the physician to monitor the response of the immune system to various treatments designed to arrest chronic inflammation and autoimmune diseases, vaccinate against infectious agents, or effect the immunotherapy of cancer.
  • Immune diagnosis and immune monitoring require ways to ascertain the state of an individual's immune system, and to record the dynamic evolution of changes induced by the various therapeutic interventions. Tools for diagnosis and monitoring are likely to require the integration of large amounts of information for the following reasons:
  • the human immune system is enormously complex and its long-term behavior is not easily explained by any particular genes or clones of cells in isolation.
  • many autoimmune diseases involve collectives of self-antigens and collective cross-regulation.
  • effective tumor immunotherapy may require controlled autoimmunity, and assays for the global state of autoimmunity are therefore essential.
  • the complexity of the immune system is such that one must develop bio-informatic methods that will allow a physician to monitor conveniently the global state of the patient's immune system in health, disease and therapeutic intervention.
  • bio-informatic methods that will allow a physician to monitor conveniently the global state of the patient's immune system in health, disease and therapeutic intervention.
  • the Immunoblotting and Densitometric Subtraction Method was developed as a technique for immunoblotting analysis of the reactions of natural autoantibodies in whole sera of patients (8).
  • densitometric subtraction natural autoantibodies present in healthy individuals were differentiated from disease-associated autoantibodies. This method is, however, limited to a few antigens and it does not solve the problem of variation among different experiments which is inherent to blot techniques.
  • Another method developed to detect antibody repertoires is the Multiple Spot Immunoassay, which assays the reactivities towards 42 different antigens coated onto nitrocellulose, in a western blot procedure (9).
  • the antibody staining in this system is analyzed, and the amount of antibody to the antigens can be semi-quantified, using IgG standards.
  • This method allows rapid screening of auto-antibodies but does not solve the problem of auto-antibodies found in healthy persons. Moreover, it does not solve the intrinsic variation associated with the western blot technique.
  • the principle technique in use for assaying antibody patterns is the Panama Blot System. This too is a western-blot system, and it is based on the blotting of undefined tissue extracts.
  • the Panama Blot employs double staining of nitrocellulose membranes to reveal both antibody reactivities and the migration position of the blotted proteins in the membrane (10, 11). This double staining allows the standardization of the results obtained for each patient.
  • the antigens used in the method are complex mixtures extracted from different tissues, they are not at all identified. Thus, this approach, even while facing the central problem of test variation, does not provide accurate information about the specific antigens recognized; it merely reveals patterns of reactivities, whose targets are totally unknown.
  • the blots tend to vary from test to test, according to the ill-defined tissue extraction and the varying separation of the proteins. Indeed, several different antigens are undoubtedly present in each band.
  • a method of classifying a specific object into a situation of at least two distinct situations comprising the steps of (a) classifying binding patterns of a plurality of undetermined first binding moieties, the plurality of undetermined first binding moieties being derived from a first group of objects being associated with a predefined first situation of the at least two distinct situations, and from at least one second group of objects being associated with a situation other than the first situation of the at least two distinct situations, to a predefined set of a plurality of potential second binding moieties by clustering at least some of the plurality of potential second binding moieties into a cluster of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects; and (b) using the cluster for determining whether the specific object is classifiable into the situation of the at least two distinct situations.
  • a system for classifying a specific object into a situation of at least two distinct situations comprising a data acquisition device and a computation device communicating therewith, the data acquisition device and the computation device being designed, constructed and configured for (a) classifying binding patterns of a plurality of undetermined first binding moieties, the plurality of undetermined first binding moieties being derived from a first group of objects being associated with a predefined first situation of the at least two distinct situations, and from at least one second group of objects being associated with a situation other than the first situation of the at least two distinct situations, to a predefined set of a plurality of potential second binding moieties by clustering at least some of the plurality of potential second binding moieties into a cluster of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects; and (b) using the cluster for determining whether the specific object is classifiable into the situation of the at least two distinct situations
  • the step of clustering at least some of the plurality of potential second binding moieties into clusters of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects is effected by a supervised classifier.
  • the supervised classifier is a neural network algorithm.
  • the step of clustering at least some of the plurality of potential second binding moieties into clusters of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects is effected by a unsupervised classifier.
  • the unsupervised classifier is a coupled two way clustering algorithm.
  • the method further comprising the step of scanning the second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects and selecting for a subset of the second binding moieties resulting in an optimal sensitivity.
  • the method further comprising the step of scanning the second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects and selecting for a subset of the second binding moieties resulting in an optimal specificity.
  • the method further comprising the step of scanning the second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects and selecting for a subset of the second binding moieties resulting in an optimal specificity and an optimal sensitivity.
  • the first binding moieties are immunoglobulins, whereas the second binding moieties are antigens.
  • the first situation is a human disease.
  • first binding moieties and the second binding moieties are each independently selected from the group consisting of nucleic acids, proteins, carbohydrates and fatty acids.
  • a method of clustering a subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having an impaired immune system and suffering from a disease comprising the steps of (a) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies being derived from the plurality of patients having the impaired immune system and suffering from the disease.
  • a system for clustering a subset of antigens of a plurality of antigens comprising a data acquisition device and a computation device communicating therewith, the data acquisition device and the computation device being designed, constructed and configured for (a) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies being derived from the plurality of patients having the impaired immune system and suffering from the disease.
  • a method of diagnosing a disease of a subject comprising the steps of (a) clustering a subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having an impaired immune system and suffering from the disease by (i) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (ii) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (iii) clustering the subset of antigens being reactive with the plurality of antibodies being derived from the plurality of patients having the impaired immune system and suffering from the disease; and (b) associating or deassociating serum of the subject with a cluster resulting from step (a)(iii).
  • a system for diagnosing a disease of a subject comprising a data acquisition device and a computation device communicating therewith, the data acquisition device and the computation device being designed, constructed and configured for (a) clustering a subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having an impaired immune system and suffering from the disease by (i) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (ii) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (iii) clustering the subset of antigens being reactive with the plurality of antibodies being derived from the plurality of patients having the impaired immune system and suffering from the disease; and (b) associating or deassociating serum of the subject with
  • an article of manufacture comprising a surface and antigens being arranged on the surface, each in an independent addressable location, the antigens including a plurality of subsets of antigens, each of the plurality of subsets of antigens being selected by a method of clustering a subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having an impaired immune system and suffering from a specific disease, the method being effected by (a) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies being derived from the plurality of patients having the impaired immune system and suffering from
  • the step of clustering is effected so as to include in the subset of antigens those antigens for which the patients and individuals best decompose into clusters according to a known clinical diagnosis of the patients and individuals.
  • the step of clustering is effected by a supervised classifier.
  • the supervised classifier is a neural network algorithm.
  • the step of clustering is effected by a unsupervised classifier.
  • the unsupervised classifier is a coupled two way clustering algorithm.
  • the step of clustering is effected so as to result in optimal sensitivity.
  • the step of clustering is effected so as to result in optimal specificity.
  • the step of clustering is effected so as to result in optimal specificity and optimal sensitivity.
  • the step of clustering is effected by (i) clustering the antibodies and the antigens and identifying all stable antibody and antigen clusters; (ii) scanning the antigen clusters, while using reactivity levels of antigens of each antigens cluster as a feature set representing first object sets containing either all of the antibodies or any of the stable antibody clusters; (iii) scanning the antibody clusters, while using reactivity levels of antibodies of each antibody cluster as a feature set representing second object sets containing either all of the antigens or any of the 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 the stable antibody and antigen clusters have been generated.
  • the 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 inflamation.
  • the autoimmune disease is selected from the group consisting of ankylosing spondylitis, uveitis, Goodpasture's syndrome, multiple sclerosis, Grave's disease, myasthenia gravis, systemic lupus erythematosus, systemic sclerosis, mixed connective tissue disease, dermatitis herpetiformis, celiac disease, ulcerative colitis, Crohn's disease, chronic active hepatitis, endometriosis, ulcerative colitis, insulin-dependent diabetes mellitus, psoriasis, pemphingus vulgaris, Hashimoto's thyroiditis, rheumatoid arthritis, idiopathic thrombocytopenic purpura, Sjogren's syndrome, uveroretinitis, autoimmune hemolytic anemia, vitiligo, primary biliary cirrhosis, inflammatory bowel disease, Bechet's disease,
  • the present invention successfully addresses the shortcomings of the presently known configurations by opening new horizons in the ability to monitor changes in the immune system in cases of pathologies such as autoimmune diseases and immune deficiencies and as a response to treatment, such as a radiotherapy and/or chemotherapy treatment or immune depressant therapy or specific immune modulation.
  • Implementation of the methods and systems of the present invention involves performing or completing selected tasks or steps manually, automatically, or a combination thereof.
  • several selected steps could be implemented by hardware or by software on any operating system of any firmware or a combination thereof.
  • selected steps of the invention could be implemented as an electronic chip or a circuit.
  • selected steps of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system.
  • selected steps of the methods of the invention could be described as being performed by a data processor, such as a computing platform (computation device) for executing a plurality of instructions.
  • FIG. 1 is a dendrogram of antigens, created by clustering all antigens using the full set of subjects. Each junction represents the breaking of a cluster into two smaller clusters. Two Clusters which are used for analysis of specific subject populations are labeled by the corresponding cluster number. The individual antigens of each of these clusters are colored as follows: cluster 66-white; cluster 47-gray.
  • FIG. 2 is a dendrogram created by clustering all subjects, using the full set of antigens. Each leaf represents a subject. Diabetes subjects are colored black, and healthy ones—white.
  • FIG. 3 is a subjects' dendrogram obtained by clustering the subjects, using the antigens of cluster 66. Leaf colors were assigned as in FIG. 2.
  • FIG. 4 is a subjects' dendrogram obtained by clustering the subjects, using the antigens of cluster 26. Leaf colors were assigned as in FIG. 2.
  • Antigen cluster 5 contains 2 antigen tests: GM Insulin and GM Aldolase.
  • FIG. 5 is a dendrogram created by clustering the subjects, using cluster 5.
  • the cluster that contains predominantly healthy subjects is marked by N.
  • FIG. 6 is a dendrogram created by clustering the subjects over antigen cluster 47. Leaf colors—as in FIG. 2.
  • FIG. 7 is a dendrogram created by clustering the subjects over antigen cluster 47. Leaf colors—as in FIG. 2.
  • FIG. 8 is a dendrogram created by clustering the subjects over antigen cluster 19, without including the 5 samples corresponding to YL.
  • FIG. 9 is a dendrogram created by clustering the subjects over antigen cluster 19, including the 5 samples corresponding to YL.
  • the cluster that contains the D1 and D2 groups is labelled.
  • FIG. 10 is a dendogram created by clustering the Bechet Disease samples and the healthy serum samples using antigen cluster 13.
  • FIG. 11 is a dendogram created by clustering the Bechet Disease samples and the healthy serum samples using antigen cluster 9.
  • the present invention is of a method, system and an article of manufacture which can be used for clustering and thereby identifying predefined binding moieties of a one type reactive with undetermined binding moieties of a second type.
  • the present invention can be used for clustering and thereby identifying predefined antigens reactive with undetermined immunoglobulins of antibodies derived from patient subjects with an impaired immune system.
  • the phrase “impaired immune system” refers to an immune system characterized by an abnormal activity, either over activity as a result of, for example, an autoimmune disease, infection or inflammation, or under activity as a result of, for example, an immune deficiency disease, chemotherapy, radiotherapy or the use of immune depressants.
  • the abnormal activity of the impaired immune system can also be the result of, or reflect, cancer, degenerative disease, metabolic disease, reaction to transplantation, trauma, mental disorder, intoxication, or genetic disease.
  • patterns of antibody reactivities are not limited to those present in serum antibodies, but antibodies may also be measured in whole blood or blood plasma, or in other body fluids such as saliva, intestinal secretions or urine, or in any other compartment in which antibodies are found.
  • antibody patterns for the present purpose may be detected using peptide libraries or organic synthetic compounds, as well as conventional antigens, as have been outlined here.
  • An antigen is defined as any molecule that can be bound by the antigen-combining site of an antibody or T-cell receptor molecule; therefore, various classes or types of molecular species can be used to detect antibody patterns, provided that the molecules can interact with specific antibodies.
  • a method of classifying into a predefined first situation such as a human disease, e.g., an autoimmune disease
  • a predefined first situation such as a human disease, e.g., an autoimmune disease
  • at least two distinct situations such as an autoimmune disease, healthy, and another autoimmune disease
  • a binding pattern of a plurality of undetermined first binding moieties such as serum immunoglobulins
  • a predefined set of a plurality of potential second binding moieties such as a predetermined set or sets of antigens.
  • the plurality of undetermined first binding moieties according to this aspect of the invention are derived from a first group of objects (e.g., patients) which are associated with the predefined first situation (e.g., the autoimmune disease) and from at least one second group of objects (e.g., healthy individuals) which are associated with a situation other than the first situation (e.g., are free of the autoimmune disease).
  • a first group of objects e.g., patients
  • the predefined first situation e.g., the autoimmune disease
  • second group of objects e.g., healthy individuals
  • the method according to this aspect of the present invention is effected by implementing the following method steps, in which, in a first step the plurality of undetermined first binding moieties of the first group of objects are assayed for binding to each of the plurality of potential second binding moieties. In a second step, which can precede, proceed or be simultaneous to the first step, the plurality of undetermined first binding moieties of the at least one second group of objects are assayed for binding to each of the plurality of potential second binding moieties. Finally, at least some of the plurality of potential second binding moieties are clustered into clusters of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects.
  • FIG. 10 provides a schematic representation of a system in accordance with the teachings of the present invention, which is referred to hereinbelow as system 20 .
  • System 20 includes a data acquisition device 22 and a computation device 24 communicating therewith.
  • device 22 may, for example, be an electro-optical device capable of collecting optical data from an analyzed sample 25 , such as, but not limited to a CCD, and may therefore be coupled to an optical magnification mechanism such as a microscope 26 or, in the alternative, device 22 may be a radioactive monitor having, either spatial resolution or matrix scanning capabilities.
  • computation device 24 preferably also communicates with a display device 28 which may serve for presentation of either raw data collected by data acquisition device 22 or the results of the analysis thereof.
  • the data acquisition device and the computation device are designed, constructed and configured to serve for (a) assaying the plurality of undetermined first binding moieties of the first group of objects for binding to each of the plurality of potential second binding moieties; (b) assaying the plurality of undetermined first binding moieties of the at least one second group of objects for binding to each of the plurality of potential second binding moieties; and (c) clustering at least some of the plurality of potential second binding moieties into clusters of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects, thereby classifying into the predefined first situation the binding pattern of the plurality of undetermined first binding moieties of the objects which are associated with the predefined first situation to the predefined set of the plurality of potential second binding moieties.
  • a method of classifying a specific object into a situation of at least two distinct situations is effected by implementing the following method steps, in which, in a first step, binding patterns of a plurality of undetermined first binding moieties, the plurality of undetermined first binding moieties are derived from a first group of objects which are associated with a predefined first situation of at least two distinct situations, and from at least one second group of objects which are associated with a situation other than the first situation of the at least two distinct situations, are classified into a predefined set of a plurality of potential second binding moieties by clustering at least some of the plurality of potential second binding moieties into a cluster of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects. Then, the cluster is used for determining whether the specific object is classifiable into the situation of the at least two distinct situations.
  • a system for classifying a specific object into a situation of at least two distinct situations includes a data acquisition device and a computation device communicating therewith essentially as described above.
  • the data acquisition device and the computation device are designed, constructed and configured for (a) classifying binding patterns of a plurality of undetermined first binding moieties, the plurality of undetermined first binding moieties are derived from a first group of objects which are associated with a predefined first situation of at least two distinct situations, and from at least one second group of objects which are associated with a situation other than the first situation of the at least two distinct situations, into a predefined set of a plurality of potential second binding moieties by clustering at least some of the plurality of potential second binding moieties into a cluster of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects; and (b) using the cluster for determining whether the specific object is classifiable into the situation of the
  • clustering at least some of the plurality of potential second binding moieties into clusters of second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects is effected by a supervised classifier, such as a neural network algorithm, or, preferably, by a unsupervised classifier, as is further exemplified in the Examples section that follows with respect to antibodies present in sera or other body fluids or secretions of autoimmune disease patients and known antigens.
  • the unsupervised classifier is, according to a presently preferred embodiment of the invention, a coupled two way clustering algorithm.
  • the described methods further include a step of scanning the second binding moieties which bind first binding moieties of the undetermined first binding moieties from the first group of objects and selecting for a subset of the second binding moieties resulting in an optimal sensitivity and/or optimal specificity.
  • the first and second binding moieties can be of any biological or chemical type which are capable of stable and monitorable interaction such moieties include, but are not limited to, nucleic acids (e.g., DNA or RNA), proteins (e.g., antigens and antibodies), carbohydrates, fatty acids, peptides, peptide libraries, organic compounds or tissue extracts. Mixed moieties may also find uses, e.g., glycoproteins or acylated proteins. Such binding moieties can be derived from commercial sources, biological sources or may be synthesized, produced in or purchased from specialized laboratories.
  • the situations according to the present invention may include, for example, medical situations of human beings or animals, different subtraction libraries, different display libraries, and the like.
  • a human disease can be for example an autoimmune disease, a cancer and an immune deficiency disease (which may, for example, be due to viral infection or treatment with immune depressants).
  • the autoimmune disease can be, for example, ankylosing spondylitis, uveitis, Goodpasture's syndrome, multiple sclerosis, Grave's disease, myasthenia gravis, systemic lupus erythematosus, systemic sclerosis, mixed connective tissue disease, dermatitis herpetiformis, celiac disease, ulcerative colitis, Crohn's disease, chronic active hepatitis, endometriosis, ulcerative colitis, insulin-dependent diabetes mellitus, psoriasis, pemphingus vulgaris, Hashimoto's thyroiditis, rheumatoid arthritis, idiopathic thrombocytopenic purpura, Sjogren's syndrome, uveroretinitis, autoimmune hemolytic anemia, vitiligo, primary biliary cirrhosis, inflammatory bowel disease, Bechet's disease, auricular chondritis, tympanosclerosis,
  • the invention described herein could also be applied to conditions like, 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 inflamation.
  • a method of clustering a subset of antigens of a plurality of antigens the subset of antigens are reactive with a plurality of antibodies derived from a plurality of patients having an impaired immune system and suffering from a disease.
  • the method according to this aspect of the present invention is effected by implementing the following method steps, in which, in a first step, binding of the plurality of antibodies derived from the plurality of patients with the plurality of antigens is assayed. In a second step of the method according to this aspect of the present invention, binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens is assayed. Finally, the subset of antigens which are reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease are clustered into a cluster.
  • a system for clustering a subset of antigens of a plurality of antigens includes a data acquisition device and a computation device communicating therewith.
  • the data acquisition device and the computation device are designed, constructed and configured for (a) assaying binding of the plurality of antibodies being derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies being derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease.
  • a method of diagnosing a disease of a subject is effected by implementing the following method steps, in which, in a first step, a subset of antigens of a plurality of antigens are clustered, the subset of antigens are reactive with a plurality of antibodies derived from a plurality of patients having an impaired immune system and suffering from the disease by (i) assaying binding of the plurality of antibodies derived from the plurality of patients with the plurality of antigens; (ii) assaying binding of a plurality of antibodies derived from a plurality of individuals free of the disease with the plurality of antigens; and (iii) clustering the subset of antigens which are reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease. Then, serum of the subject is associated or disassociated with a cluster resulting from step (a)(i
  • a system for diagnosing a disease of a subject includes a data acquisition device and a computation device communicating therewith.
  • the data acquisition device and the computation device are designed, constructed and configured for (a) clustering a subset of antigens of a plurality of antigens, the subset of antigens are reactive with a plurality of antibodies derived from a plurality of patients having an impaired immune system and suffering from the disease by (i) assaying binding of the plurality of antibodies derived from the plurality of patients with the plurality of antigens; (ii) assaying binding of a plurality of antibodies derived from a plurality of individuals free of the disease with the plurality of antigens; and (iii) clustering the subset of antigens which are reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease; and (b) associating or deassociating serum of the subject with a cluster resulting
  • an article of manufacture comprising a surface and antigens being arranged on the surface, each in an independent addressable location, the antigens including a subset of antigens being selected by a method of clustering the subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies being derived from a plurality of patients having an impaired immune system and suffering from a disease, the method being effected by (a) assaying binding of the plurality of antibodies derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease.
  • an article of manufacture comprising a surface and antigens being arranged on the surface, each in an independent addressable location, the antigens including a plurality of subsets of antigens, each of the plurality of subsets of antigens being selected by a method of clustering a subset of antigens of a plurality of antigens, the subset of antigens being reactive with a plurality of antibodies derived from a plurality of patients having an impaired immune system and suffering from a specific disease, the method being effected by (a) assaying binding of the plurality of antibodies derived from the plurality of patients with the plurality of antigens; (b) assaying binding of a plurality of antibodies derived from a plurality of individuals free of the disease with the plurality of antigens; and (c) clustering the subset of antigens being reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease.
  • the step of clustering the subset of antigens being reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease is effected so as to include in the subset of antigens those antigens for which the patients and individuals best decompose into clusters according to a known clinical diagnosis of the patients and individuals.
  • the step of clustering is effected by an unsupervised classifier, such as a coupled two way clustering algorithm.
  • the step of clustering is preferably effected so as to result in optimal sensitivity and/or optimal specificity.
  • the step of clustering the subset of antigens being reactive with the plurality of antibodies derived from the plurality of patients having the impaired immune system and suffering from the disease is effected by (i) clustering the antibodies and the antigens and identifying all stable antibody and antigen clusters; (ii) scanning the antigen clusters, while using reactivity levels of antigens of each antigens cluster as a feature set representing first object sets containing either all of the antibodies or any of the stable antibody clusters; (iii) scanning the antibody clusters, while using reactivity levels of antibodies of each antibody cluster as a feature set representing second object sets containing either all of the antigens or any of the 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 the stable antibody and antigen clusters have been generated.
  • a general approach to global antibody analysis is described herein aimed to quantitatively assay the binding of patients' antibodies to a large number (tens to hundreds or thousands) of different antigens of human origin (self-antigens) or from other sources, or to libraries of antigens (e.g. expression libraries, peptide libraries, etc), and to analyze, using bio-informatic technology, the global pattern of the reactivities to selected groups of antigens.
  • the bio-informatic process is done in two stages, as follows.
  • the first stage includes classification of the antibody patterns characteristic of particular diseases.
  • sera are collected from healthy persons and from persons known to suffer form certain diseases, and the sera are then assayed for the presence of antibodies binding to the various test antigens.
  • the assays described below were performed using standard ELISA techniques (see below), but the assay system can easily be miniaturized and made suitable for the types of automated technology now in use for genomic and proteinomic chips. To this end, see for example (15-18), which are incorporated herein by reference.
  • an informatics computer program designed to select sets of antigens is described, which program actually clusters together the antibody patterns of patients with particular diseases and separate these persons by their antibody patterns from the antibody patterns of healthy persons and from those of patients with other diseases.
  • the computer program described herein can scan the sets of antigens and antibodies and select those that provide the highest sensitivity (include the greatest number of persons with the disease) and the highest specificity (exclude the greatest numbers of persons with other diseases or no disease).
  • the biological term sensitivity is referred to as efficiency (E) and the term specificity is called purity (P).
  • the second stage involves the use of classifiers to test individual persons in need of diagnosis or theranosis. Once the recipe of antigens and reactivities have been established, the diagnosis of various diseases and the monitoring of the state of a subject's immune system are made according to the particular pattern classifiers. In other words, the fit of a patient's antibody pattern to a disease classifier reveals the disease or condition of interest.
  • the antigens that were used according to the present invention to reveal the global patterns reactivities can be divided into different sets according, for example, to their origin. Different members of each set can be used in the tests. Table 1 below provides a non-limiting example of antigens which can be used in accordance with the teachings of the present invention and which were used to reduce the present invention to practice, as is further detailed hereinunder.
  • Cytochrome P450-C Monooxigenases Catalase Peroxidase Tyrosinase Others Ribonuclease Nucleus Protein Histone II A DNA Double Stranded DNA Single Stranded DNA Synthetic Oligomers Poly C Poly A Poly T Poly G Poly ATA Poly TAT Plasma proteins Carriers Holo-Transferrin Albumin (human) Albumin (bovine) Albumin (chicken) Fetuin (bovine) Coagulation Factor II Factor VII Fibrinogen Fibrin Complement C 1 C 1 q Immune System Immunoglobulins Human IgG Human IgM Monoclonal P277 F(ab)2 Antibodies T Cell Receptors C9 N4 Cytokines and Interleukin 2 Chemokines Interleukin 10 Interleukin 4 Interferon- ⁇ Tissue Antigens Heat Shock Proteins Heat Shock Protein 60 and peptides p277 Ec27 p278 Islet Antigens GAD Insulin CNS Brain Extract Human MOG
  • ELISA plates 96 well, (Nalge Nunc International) are coated by incubation of the desired antigens dissolved in basic carbonate buffer pH 9.6, for 2 hours at 37° C., followed by an overnight incubation at 4° C. The plates are then washed with PBS Tween 0.05% (PBST), and blocked for 2 hours with BSA 3% at 37° C. The sera for analysis are then added at a dilution of 1:100 in BSA 0.3%. After 3 hours of incubation at 37° C., the sera are removed and the plates are washed with PBST. Bound antibodies are detected with an alkaline phosphatase conjugated antibody (Jackson ImmunoResearch Labs.
  • the “immune state” of subject i is represented by vector ⁇ right arrow over (A) ⁇ i (which is composed of 176 components).
  • the entire set of subjects is represented by 40 such vectors.
  • antigen j is represented by the (40 component) vector ⁇ right arrow over (A) ⁇ j .
  • the set of antigen tests were represented by 176 such vectors.
  • the aim was to classify the subjects, on the basis of the reaction patterns of the sera with the antigens, according to their known diagnosis, as healthy versus diseased.
  • the number of datapoints (the subjects) is about the same as the dimension of the space in which they are represented (the antigens). Therefore, it is most likely that a classifier that accomplishes this task can be found constructed in a supervised way according to preconceived categories. Such a classifier, however, would most likely run into problems of overtraining and have a low generalization ability.
  • c j,l is the correlation between two antigen vectors ( ⁇ 1 ⁇ c j,l ⁇ 1). This distance measure will be high if the antigens are correlated or anti-correlated, and low if they have no statistical dependence.
  • a cluster that is clearly separated from the rest of the datapoints can be identified, with high likelihood, by a supervised classifier (such as a trained neural network).
  • a supervised classifier such as a trained neural network.
  • D1 identifying type 1 diabetics
  • Clustering techniques are used according to the present invention to explore the structure of the data (15-17), that is, to reveal the natural classes present in it. This way one avoids introducing preconcieved notions and biases about the existence of various partitions, and uncover those partitions that are indeed present. Such methods of partitioning data are unsupervised.
  • a novel clustering algorithm known as SPC (18, 19), which is based on ideas adopted from statistical mechanics of disordered ferromagnets was used herein.
  • This algorithm uses the vectors mentioned above as its input.
  • N objects are clustered on the basis of the D dimensional vectors that represent them.
  • the D quantities that constitute the components of such a vector are called the set of features that were used to represent the objects.
  • the vertical position of the “stem” of each cluster indicates the value of T at which it appeared first (i.e., split off a larger “parent” cluster), as T was increased. Its horizontal position can be used as an indication of its proximity (in terms of similarity of expression patterns) to antigen clusters that have split from the same parent.
  • the anti-antigen (or antibody) reactivities data presented above are fairly special in that it makes good sense to perform clustering analysis in two ways.
  • Coupled Two Way Clustering is to narrow down both the features that one uses and the data points that are clustered. Possibly, only a small subset of the antigens participate in a particular process of interest, which takes place only in a subset of the sera; by focusing on small subsets, one lowers the noise induced by the other sera and antigens.
  • the different object sets O contain either all the sera or any cluster (which contains a subset of the sera). Similarly, one scans all stable clusters of sera and use them as the feature set F to identify stable clusters of antigens.
  • the CTWC algorithm was started by using the full set of 40 sera as the feature set to cluster the full set of 176 antigen reactivities.
  • the resulting dendrogram is shown in FIG. 1.
  • the vertical axis is the temperature parameter T that controls the resolution.
  • Stable antigen clusters were identified as those for which the interval between T c , the value of T at which the cluster appears, and T d , at which it disintegrates, is large. These clusters were recorded in the register V g .
  • those stable antigen clusters for which specific results are further shown below are numbered.
  • the antigens of each of these clusters, and of other antigen groups that were used, are presented in Table 3 and 4 below.
  • each registered antigen cluster vg (including the full group of antigens), the rows corresponding to the members of vg are taken out of the full matrix to form a submatrix. The columns of this submatrix are used to cluster the subjects, and stable clusters of subjects are registered in V s .
  • each registered cluster of subjects is clustered, using as the feature set the members of every registered cluster of antigens. Then, the antigen clusters are used as the objects and the different groups of sera as the features.
  • This algorithm has resulted in a list of registered clusters, of subjects and of antigens. From the list of subject clusters one chooses those with high purity and efficiency scores of the different classes of subjects (D1 and NH).
  • FIG. 2 presents the dendrogram obtained when all of the subjects
  • Coupled Two Way Clustering which identifies the features that can be found in the data, and separates them into classes (antigen clusters) in order to avoid redundancy. Then one can find which classifications of the data have some correspondence to the desired diagnosis, and combine them in order to obtain most efficient diagnoses.
  • FIG. 8 presents the dendrogram obtained using the antigens of cluster 19 (see FIG. 4) as the feature set, when the samples from the YL group were not included in the analysis. It can be seen that the samples corresponding to the D1 and D2 groups cluster togheter, separated from the group of healthy samples.
  • the antigens of group 19 seem to be extremely useful for diagnostic purposes because they identify D1 and D2 subjects.
  • the samples of the YL group were added in the study, they were grouped toghether with those of the healthy donors. Hence, even though initially these subjects were D1, after a year under treatment their reactivity profile (on the group 19 of antigens) has become close to that of healthy subjects.
  • the cluster of the diabetic subjects contains a very clear and stable subcluster, whose members are 4 out of the 5 D2 subjects (yellow) and a single D1 subject. This indicates that the algorithm described herein is capable of separating the D2 from the D1 subjects. See also Table 5, below.
  • FIG. D1 NH 66 1.00 0.69 3 26 0.65 0.81 4 5 0.75 0.83 5 47 0.55 1.00 6 14 0.95 0.70 7 Combination 0.95 0.90 D1
  • the method described herein by simultaneously evaluating several representative antibody autoreactivities, allows the efficient separation of different groups of patients undergoing several autoimmune pathologies (see Table 5 for summary of the results). It was shown that the immune profile of diabetes over 5 different groups of antigens differs from the immune profile of healthy subjects. The difference is reflected in the clustering results, which yield classifications with a relatively high purity and efficiency (see FIGS. 5 - 7 ). Most of the misclassified subjects in each classification were classified correctly by most of the other classifications. Thus, by combining the results of all the classifications, a very clear diagnosis was achieved.
  • a classifier could be constructed, able to identify an undetermined sample as corresponding to a healthy or diseased subject. This is extremely important for the management of, for example, Bechet's Disease, where no defined antigens have been isolated yet for diagnostic purposes.
  • An antigen chip in accordance with the teachings of the present invention is fabricated as follows: Optically flat, teflon-coated 96-well glass microscope plates (Erie Scientific, Protsmouth, N.H., USA) is activated for protein binding, by treatment with aminopropyltrimethoxysilane (APTS) (Aldrich Chemical, Milwaukee, Wis., USA) and Bis-sulfo-succinimidyl suberate (BS3) (Molecular BioSciences, Boulder, Colo., USA) as described (24).
  • APTS aminopropyltrimethoxysilane
  • BS3 Bis-sulfo-succinimidyl suberate
  • N-hydroxysuccinimide (NHS) activated plates are then coated with the antigens of interest, in quadruplicates, with the help of a microarray printer including, for example, a 36-capillary array print head mounted to a high resolution X-Y-Z positioning robot (7).
  • This device is able to precisely and simultaneously deliver up to 36 different solutions (200 pl per spot) to the flat surface of the pre-activated glass, generating spots of approximately 275 ⁇ m in diameter with a center to center spacing of 300 ⁇ m.
  • the development of the slides can be done according to the methods described by Conway de Macario et al. (26) for the Slide Immuno-Enzymatic Assay (SIA). Briefly, the 96-well microarray plate is washed 3 times with 1 ⁇ Tris buffered saline (TBS)+0.1% Tween 20 to remove excess unbound antigen. Nonspecific binding sites are blocked with 1% Bovine Serum Albumin (BSA) dissolved in Phosphate buffered saline (PBS). After blocking, the sera to be tested is added, diluted in 1% BSA, and is incubated at room temperature for one hour. Unbound antibodies are removed by washing the microplate 3 times with 1 ⁇ TBS+0.1% Tween 20.
  • BSA Bovine Serum Albumin

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CA2418217A1 (fr) 2002-01-31
US20140200159A1 (en) 2014-07-17
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US11002735B2 (en) 2021-05-11
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US20090258790A1 (en) 2009-10-15
US8010298B2 (en) 2011-08-30
US20190004045A1 (en) 2019-01-03
US20200225223A1 (en) 2020-07-16
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US10082503B2 (en) 2018-09-25
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US20110301065A1 (en) 2011-12-08

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