METHODS OF DIAGNOSING INFLAMMATORY BOWEL DISEASE
The present invention provides methods, systems, and code for accurately classifying whether a sample from an individual is associated with inflammatory bowel disease (IBD) or a clinical subtype thereof. In particular, the present invention is useful for classifying a sample from an individual as an IBD sample using a statistical algorithm and/or empirical data. The present invention is also useful for differentiating between a clinical subtype of IBD such as Crohn's disease (CD) and ulcerative colitis (UC) using a statistical algorithm and/or empirical data. Thus, the present invention provides an accurate diagnostic prediction of IBD or a clinical subtype thereof and prognostic information useful for guiding treatment decisions.
1 - 27 . (canceled)
28 . A method for classifying whether a sample from an individual is associated with a clinical subtype of IBD, said method comprising:
(a) determining the presence or level of at least one marker selected from the group consisting of an anti-neutrophil antibody, anti- Saccharomyces cerevisiae antibody, antimicrobial antibody, and combinations thereof in said sample; and
(b) classifying said sample as a Crohn's disease (CD) sample, ulcerative colitis (UC) sample, or non-IBD sample using a statistical algorithm based upon the presence or level of said at least one marker.
29 . The method of claim 28 , wherein said anti-neutrophil antibody is selected from the group consisting of an anti-neutrophil cytoplasmic antibody (ANCA), perinuclear anti-neutrophil cytoplasmic antibody (pANCA), and combinations thereof.
30 . The method of claim 28 , wherein said anti- Saccharomyces cerevisiae antibody is selected from the group consisting of anti- Saccharomyces cerevisiae immunoglobulin A (ASCA-IgA), anti- Saccharomyces cerevisiae immunoglobulin G (ASCA-IgG), and combinations thereof.
31 . The method of claim 28 , wherein said antimicrobial antibody is selected from the group consisting of an anti-outer membrane protein C (anti-OmpC) antibody, anti-flagellin antibody, anti-I2 antibody, and combinations thereof.
32 . The method of claim 28 , wherein said method comprises determining the presence or level of at least two markers.
33 . The method of claim 28 , wherein said method comprises determining the presence or level of at least three markers.
34 . The method of claim 28 , wherein said method comprises determining the presence or level of at least four markers.
35 . The method of claim 28 , wherein said method comprises determining the presence or level of at least five markers.
36 . The method of claim 28 , wherein said method comprises determining the presence or level of at least six markers.
37 . The method of claim 28 , wherein said method comprises determining the presence or level of ANCA, ASCA-IgA, ASCA-IgG, anti-OmpC antibody, anti-flagellin antibody, and pANCA.
38 . The method of claim 28 , wherein the presence or level of said at least one marker is determined using an immunoassay.
39 . The method of claim 38 , wherein said immunoassay is an enzyme-linked immunosorbent assay (ELISA).
40 . The method of claim 28 , wherein the presence or level of said at least one marker is determined using an immunohistochemical assay.
41 . The method of claim 40 , wherein said immunohistochemical assay is an immunoflourescence assay.
42 . The method of claim 28 , wherein said sample is selected from the group consisting of serum, plasma, whole blood, and stool.
43 . The method of claim 28 , wherein said statistical algorithm is a learning statistical classifier system.
44 . The method of claim 43 , wherein said learning statistical classifier system is selected from the group consisting of a classification and regression tree, boosted tree, neural network, random forest, support vector machine, general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof.
45 . The method of claim 44 , wherein said learning statistical classifier system is a combination of at least two learning statistical classifier systems.
46 . The method of claim 45 , wherein said at least two learning statistical classifier systems comprise a classification and regression tree or random forest and a neural network.
47 . The method of claim 46 , wherein said at least two learning statistical classifier systems are used in tandem.
48 . The method of claim 47 , wherein said classification and regression tree or random forest is first used to generate a prediction or probability value based upon the presence or level of said at least one marker.
49 . The method of claim 48 , wherein said neural network is then used to classify said sample as a CD sample, UC sample, or non-IBD sample based upon said prediction or probability value and the presence or level of said at least one marker.
50 . The method of claim 49 , wherein said neural network classifies said sample as a CD sample or UC sample with an overall accuracy of at least about 90%.
51 . The method of claim 28 , wherein said method further comprises sending the results from said classification to a clinician.
52 . The method of claim 28 , wherein said method further provides a diagnosis in the form of a probability that said individual has CD or UC.
53 . The method of claim 28 , wherein said method further comprises administering to said individual a therapeutically effective amount of a drug useful for treating one or more symptoms associated with CD or UC.
54 . The method of claim 53 , wherein said drug is selected from the group consisting of aminosalicylates, corticosteroids, thiopurines, methotrexate, monoclonal antibodies, free bases thereof, pharmaceutically acceptable salts thereof, derivatives thereof, analogs thereof, and combinations thereof.
55 . The method of claim 28 , wherein said individual has been previously diagnosed with IBD.
56 . The method of claim 28 , wherein said individual has not been previously diagnosed with IBD.
57 - 93 . (canceled)