METHODS FOR DIAGNOSING AND MONITORING THE STATUS OF SYSTEMIC LUPUS ERYTHEMATOSUS

• US 20090298060A1
• Filed: 11/09/2007
• Published: 12/03/2009
• Est. Priority Date: 11/09/2006
• Status: Active Grant

• Current Assignee: CareDx, Inc.
• Original Assignee: XDx Incorporated
• Inventors: LAL, Preeti G., Williams, Gavin E., Sun, Jingtao, Dedrick, Russell L., Fry, Kirk E.
• Granted Patent: US 8,148,067 B2
• Time in Patent Office: Days
• US Class Current: 435/6.11
• CPC Class Codes: C12Q 1/6809 Methods for determination or identification of nucleic acids involving differential detection
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/112 Disease subtyping, staging or classification

Abstract

The invention presents a method of diagnosing or monitoring the status of systemic lupus erythematosus (SLE) in a subject or patient comprising detecting the expression of all genes of a diagnostic set in the subject or patient wherein the diagnostic set comprises two or more genes having expression correlated with the classification or status of SLE; and diagnosing or monitoring the status of SLE in the subject or patient by applying at least one statistical method to the expression of the genes of the diagnostic set.

Claims

1. A method of diagnosing or monitoring the status of systemic lupus erythematosus (SLE) in a subject or patient comprising:

detecting the expression of all genes of a diagnostic set in the subject or patient wherein the diagnostic set comprises two or more genes having expression correlated with the classification or status of SLE; and

diagnosing or monitoring the status of SLE in the subject or patient by applying at least one statistical method to the expression of the genes of the diagnostic set.

2. The method of claim 1 wherein the statistical method is a prediction algorithm.

3. The method of claim 2 wherein the prediction algorithm produces a number or single value indicative of the status of SLE in the subject or patient.

4. The method of claim 1 wherein the statistical method further comprises classification of the subject or patient into one of at least two classes of SLE.

5. The method of claim 4 wherein the statistical method is optimized to maximize the separation among longitudinally stable classes of SLE.

6. The method of claim 1 wherein the diagnostic set further comprises at least one gene selected from each of at least two gene clusters selected from cluster 1, cluster 2, cluster 3, cluster 4, cluster 5, cluster 6, cluster 7, cluster 8, cluster 9, cluster 10, cluster 11;

cluster 12, cluster 13, cluster 14, and cluster 15 of Table 1.

7. The method of claim 4 wherein classification of the subject or patient into one of at least two classes of SLE further comprises:

detecting the expression of two or more gene whose expression correlates with the expression of the IFI27 from about 0.5 to about 1.0 and from about −

0.5 to about −

1.0 calculated using a Pearson correlation; and

classifying a subject or patient as having type 1 or type 2 SLE based on the expression of the two or more genes.

8. The method of claim 7 wherein one of the two or more genes is selected from Table 2.

9. The method of claim 7 wherein the classifying step uses a linear algorithm to produce an interferon response (INFr) score.

10. The method of claim 9 wherein a high INFr score is correlated with type I SLE and a low INFr score is correlated with type II SLE.

11. The method of claim 9 wherein at least one of the linear algorithm that produces an INFr score comprises IFI27+IFI144*(1.1296)+OAS3*(1.8136).

12. The method of claim 7 wherein the Pearson correlation is selected from a range of 0.5, 0.4, 0.3, and 0.2 of these genes.

14. The method of claim 1 wherein the expression of all genes in the diagnostic set is detected using a nucleic acid technology.

15. The method of claim 14 wherein the nucleic acid technology further comprises hybridization or amplification in a quantitative real-time polymerase chain reaction.

16. The method of claim 15 wherein hybridization occurs in solution or on a substrate selected from magnetic or nonmagnetic beads, chips, fibers, filters, gels, membranes, microparticles, plates, polymers, slides, capillary tubing, and wafers with surface features selected from channels, columns, pins, pores, trenches, and wells.

17. The method of claim 1 wherein detecting expression of all genes further comprises isolating RNA from a subject or patient sample.

18. The method of claim 19 wherein expression is proportional to the amount of RNA isolated from the sample.

19. The method of claim 17 wherein the sample further comprises a body fluid or tissue obtained by any sampling means.

20. The method of claim 19 wherein the body fluid is selected from ascites, bile, whole blood or a blood fraction, cerebrospinal fluid, lymph, sputum, and urine.

21. The method of claim 19 wherein the tissue sample is selected from central nervous system, joints, kidneys, liver, lungs, oral cavity, sinuses, skin, and vasculature.

22. The method of claim 19 wherein the sampling means is selected from aspiration of a body fluid, a biopsy of a tissue or an organ, drawing of peripheral blood, endoscopy, and lavage followed by aspiration.

23. The method of claim 1 wherein detecting expression comprises using at least one primer or probe set to detect the expression of each of the genes in the diagnostic set.

24. The method of claim 23 wherein the primers or probe sets are oligonucleotides selected from natural or synthetic cDNA, genomic DNA, locked nucleic acids, peptide nucleic acids, and RNA.

25. The method of claim 23, wherein the primers or probe sets comprise a diagnostic kit.

26. The method of claim 7 wherein classifying a subject or patient as type 1 SLE or type 2 SLE comprises assigning a subject or patient to a clinical trial.

28. The method of claim 1 wherein the statistical method is selected from analysis of variance, classification algorithms, classification and regression trees, Fisher'"'"'s Exact Test, linear algorithm, linear discriminatory analysis, linear regression, logistic algorithm, multiple regression, nearest shrunken centroids classifier, Pearson correlation, prediction algorithm, significance analysis of microarrays, one-tailed T-tests, two-tailed T-tests, voting algorithm, and Wilcoxon'"'"'s signed ranks test.

29. The method of claim 1 wherein status of SLE in a subject or patient is incipient flare or disease activity.

30. The method of claim 1 wherein status of SLE in a subject or patient comprises a response to a therapeutic agent administered to the patient.

31. The method of claim 30 wherein the therapeutic agent is selected from ACE inhibitors, aspirin, azathioprine, B7RP-1-fc, β

-blockers, brequinar sodium, campath-1H, celecoxib, chloroquine, corticosteroids, coumadin, cyclophosphamide, cyclosporin A, dehydroepiandrosterone, deoxyspergualin, dexamethasone, diclofenac, dolobid, etodolac, everolimus, FK778, feldene, fenoprofen, flurbiprofen, heparin, hydralazine, hydroxychloroquine, CTLA-4 or LFA3 immunoglobulin, ibuprofen, indomethacin, ISAtx-247, ketoprofen, ketorolac, leflunomide, meclophenamate, mefenamic acid, mepacrine, 6-mercaptopurine, meloxicam, methotrexate, mizoribine, mycophenolate mofetil, naproxen, oxaprozin, Plaquenil, NOX-100, prednisone, methyprenisone, rapamycin (sirolimus), sulindac, tacrolimus (FK506), thymoglobulin, tolmetin, tresperimus, U0126, and antibodies including but not limited to alpha lymphocyte antibodies, adalimumab, anti-CD3, anti-CD25, anti-CD52 anti-IL2R, and anti-TAC antibodies, basiliximab, daclizumab, etanercept, hu5C8, infliximab, OKT4, and natalizumab.

32. The method of claim 1 wherein status of SLE in a subject or patient comprises response to an immunosuppressant administered to a patient.

33. The method of claim 32 wherein the immunosuppressant is selected from aspirin, azathioprine, chloroquine, corticosteroids, cyclophosphamide, cyclosporin A, dehydroepiandrosterone, deoxyspergualin, dexamethasone, everolimus, fenoprofen, hydralazine, hydroxychloroquine, immunoglobulin, ibuprofen, indomethacin, leflunomide, ketoprofen, meclophenamate, mepacrine, 6-mercaptopurine, methotrexate, mizoribine, mycophenolate mofetil, naproxen, prednisone, methyprenisone, rapamycin (sirolimus), solumedrol, tacrolimus (FK506), thymoglobulin, tolmetin, tresperimus, and triamcinoline.

34. The method of claim 1 wherein diagnosing and monitoring the status of SLE further comprises screening a subject exhibiting symptoms of a rheumatic disease for SLE.

35. The method of claim 34 wherein the rheumatic disease is selected from ankylosing spondylitis, dermatomyositis, autoimmune hepatitis, hepatitis-C (hep-C), polymyalgia rheumatica, polymyositis, rheumatoid arthritis (RA), scleroderma, systemic sclerosis, Sjogren'"'"'s disease, systemic vasculitis, and Whipple'"'"'s disease.

13. A method of diagnosing or monitoring the status of systemic lupus erythematosus (SLE) in a subject or patient comprising:

detecting the expression of all genes of a diagnostic set in a subject or patient wherein the diagnostic set comprises at least one gene from each of at least two gene clusters selected from cluster 1, cluster 2, cluster 3, cluster 4, cluster 5, cluster 6, cluster 7, cluster 8, cluster 9, cluster 10, cluster 11;

cluster 12, cluster 13, cluster 14, and cluster 15 of Table 1; and

diagnosing or monitoring the status of SLE in the subject or patient based on expression of the genes in the diagnostic set.

27. A method of diagnosing a patient as having a longitudinally stable classification of SLE comprising:

detecting the expression of two or more genes whose expression correlates with the expression of the IFI27 from about 0.5 to about 1.0 and from about −

0.5 to about −

1.0 calculated using Pearson correlation; and

diagnosing the patient as having type I or type II SLE based analyzing the expression of the two or more genes using a statistical method.

36. A method of producing a probe set for diagnosing or monitoring SLE in a subject or patient comprising:

selecting at least one gene from each of at least two of the gene clusters of Table 1 and at least two genes from Table 2; and

producing a probe set consisting of at least one oligonucleotide that detects the expression of each of the selected genes.

37. The method of claim 36 wherein the probe set is used in a diagnostic kit.

38. A method for predicting flare in a patient diagnosed with SLE comprising:

analyzing expression in a sample from the patient to produce a gene expression profile wherein

a first portion of the analysis comprises using the expression of at least one gene selected from each of at least two of the clusters 1 through 15 of Table 1 and at least one statistical method to produce a patient gene expression profile, and

a second portion of the analysis comprises using expression of at least two genes selected from Table 2 and a linear algorithm to classify the patient as having type 1 SLE or type 2 SLE; and

predicting flare by comparing the patient gene expression profile at least one reference profile.

39. The method of claim 38 wherein reference profile is selected from at least one normal subject, at least one patient classified as having type 1 SLE with quiescent status, at least one patient classified as having type 1 SLE in flare, at least one patient classified as having type 2 SLE with quiescent status, at least one patient classified as having type 2 SLE in flare.