Gene expression profiling for identification, monitoring and treatment of multiple sclerosis

• US 20080183395A1
• Filed: 07/13/2007
• Published: 07/31/2008
• Est. Priority Date: 06/28/1999
• Status: Abandoned Application

• Current Assignee: Life Technologies Corporation (Thermo Fisher Scientific Incorporated)
• Original Assignee: Life Technologies Corporation (Thermo Fisher Scientific Incorporated)
• Inventors: Wassmann, Karl, Siconolfi, Lisa, Bankaitis-Davis, Danute, Trollinger, David B., Tryon, Victor, Bevilacqua, Michael
• Application Number:
  US11/827,892
• Publication Number:
  US 20080183395A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 1/689 for bacteria
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G01N 2333/525 Tumor necrosis factor [TNF]
• CPC Class Codes: G01N 2800/285 Demyelinating diseases; Multipel sclerosis
• CPC Class Codes: G01N 2800/52 Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
• CPC Class Codes: G01N 33/6863 Cytokines, i.e. immune system proteins modifying a biological response such as cell growth proliferation or differentiation, e.g. TNF, CNF, GM-CSF, lymphotoxin, MIF or their receptors
• CPC Class Codes: G01N 33/6896 Neurological disorders, e.g. Alzheimer's disease
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

The present invention provides methods of characterizing multiple sclerosis or inflammatory conditions associated with multiple sclerosis using gene expression profiling.

Claims

1. A method for predicting an increased risk to an adverse effect from anti-TNF therapy in a subject, based on a sample from the subject, the sample providing a source of RNAs, said method comprising:

a) assessing a profile data set of a plurality of members, each member being a quantitative measure of the amount of a distinct RNA constituent in a panel of constituents selected so that measurement of the constituents enables characterization of the presumptive signs of multiple sclerosis or an inflammatory condition related to multiple sclerosis, wherein such measure for each constituent is obtained under measurement conditions that are substantially repeatable to produce a patient data set; and

b) comparing the patient data set to a baseline profile data set, wherein the baseline profile data set is related to said multiple sclerosis or inflammatory condition related to multiple sclerosis;

wherein a similarity between the patient data set and the baseline profile data set indicates a risk of an adverse effect from anti-TNF therapy in the subject.

2. The method of claim 1, wherein said subject has an inflammatory condition selected from the group consisting of rheumatoid arthritis, psoriasis, ankylosing spondylitis, psoriatic arthritis and Crohn'"'"'s diseases.

3. The method of claim 2, wherein said sample is obtained prior to administering an anti-TNF therapeutic to the subject.

4. The method of claim 2, wherein said sample is obtained during the course of anti-TNF therapy.

5. The method of claim 2, wherein is obtained after administration of an anti-TNF therapeutic to the subject.

6. The method of claim 1, wherein the panel comprises 10 or fewer constituents.

7. The method of claim 1, wherein the panel comprises 5 or fewer constituents.

8. The method of claim 1, wherein the panel comprises 2 constituents,

9. The method of claim 1, wherein the panel of constituents distinguishes from a normal and a MS-diagnosed subject with at least 75% accuracy.

10. The method of claim 1, wherein the panel includes ITGAM.

11. The method of claim 10, wherein the panel further includes CD4 and MMP9.

12. The method of claim 10, wherein the panel further includes ITGA4 and MMP9.

13. A method according to claim 12, wherein the panel further includes CALCA.

14. A method according to claim 13, wherein the panel further includes CXCR3.

15. A method according to claim 12, wherein the panel further includes NFKB1B.

16. A method according to claim 15, wherein the panel further includes CXCR3.

17. The method of claim 1, wherein the panel includes HLADRA.

18. The method of claim 1, wherein the panel includes two or more constituents from Table 4 or 10.

30. A method for determining a profile data set according to claim 1 or 19, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than five percent.

31. A method of claim 1, or 19, wherein the measurement conditions that are substantially repeatable are within a degree of repeatability of better than three percent.

32. A method of claim 1, or 19, wherein efficiencies of amplification for all constituents are substantially similar.

33. A method of claim 1 or 19, wherein the efficiency of amplification for all constituents is within two percent.

34. A method of claim 1, or 19, wherein the efficiency of amplification for all constituents is less than one percent.

35. A method of claim 1 or 19 wherein the sample is selected from the group consisting of blood, a blood fraction, body fluid, a population of cells and tissue from the subject.

19. A method for predicting an increased risk of an adverse effect from anti-TNF therapy in a subject, based on a sample from the subject, the sample providing a source of RNAs, said method comprising:

a) determining a quantitative measure of the amount of at least one constituent of Table 4 or 10 as a distinct RNA constituent, wherein such measure is obtained under measurement conditions that are substantially repeatable to produce a patient data set;

b) comparing the patient data set to a baseline profile data set, wherein the baseline profile data set is related to said multiple sclerosis or inflammatory condition related to multiple sclerosis;

wherein a similarity between the patient data set and the baseline profile data set indicates a risk of an adverse effect from anti-TNF therapy in the subject.

20. The method of claim 19, wherein said constituent is HLDRA.

21. The method of claim 20, further comprising determining a quantitative measure of at least one constituent selected from the group consisting of ITGAL, CASP9, NFKBIB, STAT2, NFKB1, ITGAM, ITGAL, CD4, IL1B, HSPA1A, ICAM1, IFI16, or TGFBR2.

22. The method of claim 19, wherein said constituent is CASP9.

23. The method of claim 22, further comprising determining a quantitative measure of at least one constituent selected from the group consisting of VEGFB, CD14, or JUN.

24. The method of claim 19, wherein said constituent is ITGAL

25. The method of claim 24, further comprising determining a quantitative measure of at least one constituent selected from the group consisting of P13, ITGAM, TGFBR2

26. The method of claim 19, wherein said constituent is STAT3

27. The method of claim 26, further comprising determining a qualitative measure of CD14.

28. The method of claim 19, wherein the constituents distinguish from a normal and a MS-diagnosed subject with at least 75% accuracy.

29. The method of claim 19, comprising determining a qualitative measure of three constituents in any combination shown on Table 7.