WO2008132176A2

WO2008132176A2 - Method for evaluating the response of an individual to tnf blocking therapy

Info

Publication number: WO2008132176A2
Application number: PCT/EP2008/055096
Authority: WO
Inventors: Bernard Lauwerys; Valérie BADOT; Frédéric HOUSSIAU; Benoît VAN DEN EYNDE
Original assignee: Universite Catholique de Louvain UCL; Cliniques Universitaires Saint Luc
Current assignee: Universite Catholique de Louvain UCL; Cliniques Universitaires Saint Luc
Priority date: 2007-04-27
Filing date: 2008-04-25
Publication date: 2008-11-06
Anticipated expiration: 2009-10-27
Also published as: WO2008132176A3

Abstract

The present invention is directed to methods to predict the responsiveness of a patient to a TNF blocking drug comprising the steps of: a) assessing in a synovial sample from said patient at least two genes or a fragment thereofs or proteins encoded by said at least two genes, wherein said genes or fragment thereof is as defined in claim 1, and b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). The invention is also directed to kits for predicting the response to a treatment with a TNF blocking agent comprising a low density microarray comprising probes suitable for hybridizing with at least two genes or fragments thereof as defined in the claims.

Description

Method for evaluating the response of an individual to TNF blocking therapy

Field of the invention

The present invention is directed to methods for determining the response of an individual to a treatment with TNF blocking agent, for instance individuals suffering from rheumatoid arthritis. In particular, the invention relates to methods to predict the responsiveness of a patient with rheumatoid arthritis to a TNF blocking drug.

Background of the invention

Rheumatoid arthritis (RA) is an autoimmune disease which causes chronic inflammation of the joints, particularly those of the hands and feet.

RA has a worldwide distribution and involves all ethnic groups. Although the disease can occur at any age, the prevalence increases with age and the peak incidence is between the fourth and sixth decade. The prevalence estimates for the North American population vary from 0.3% to 1.5%. Today, over 2,500,000 individuals are diagnosed with rheumatoid arthritis in the United States alone, with some statistics indicating from 6.5 to 8 million potentially afflicted with the disease. Women are affected 2-3 times more often than men.

The early symptoms of rheumatoid arthritis are mostly joint specific such as painful joints with joint swelling or tenderness, but may also include rather non-specific manifestations like stiffness, fever, subcutaneous nodules, and fatigue. Very characteristic is the symmetric involvement of joints. The joints of the hands, feet, knees and wrists are most commonly affected, with eventual involvement of the hips, elbows and shoulders. As the disease progresses, any type of motion becomes very painful and difficult leading eventually to a loss of function of the involved joints The more severe cases of rheumatoid arthritis can lead to intense pain and joint destruction. Some 300,000 bone and joint replacement surgical procedures are performed annually in an effort to alleviate the pain and mobility loss resultant from arthritis related joint destruction.

The effective treatment of rheumatoid arthritis has generally comprised a combination of medication, exercise, rest and proper joint protection therapy. The therapy for a particular patient depends on the severity of the disease and the joints that are involved. Non- steroidal anti-inflammatory drugs, corticosteroids, methotrexate and systemic immunosuppressants are widely used to reduce inflammation and joint destruction.

Over the last decade or more, it has become widely accepted that certain inflammatory cytokines are involved in the pathogenesis of RA. The most prominent of these is tumor necrosis factor (TNF). TNF levels are elevated in inflammatory joints. TNF-alpha blocking agents are thus routinely used in the care of rheumatoid arthritis patients with severe disease who resist conventional disease modifying drug therapies. Three TNF blocking agents (etanercept, infliximab and adalimumab) are currently available and others are in development; all of them display dramatic clinical effects on the course of the disease. Unfortunately, about 25% of the patients display poor clinical responses to these drugs, which results in disease progression, joint damage and unnecessary expenses related to the high cost of these therapies. There is currently no way to distinguish these "non- responder" patient, much less to predict the degree of response to TNF blockade therapy in any particular individual, and avoid unnecessary costly treatment. Therefore, there is a need for a means to determine whether a subject having RA is likely to respond to TNF blockade therapy.

Summary of the invention

The present inventors have identified a group of genes expressed in the synovium of RA patients that are predictive of individual responses to tumor necrosis factor ("TNF") inhibition.

One aspect of the invention relates to a method for predicting the response to a treatment with a TNF blocking agent in a patient comprising the steps of

(a) assessing in vivo or ex vivo, preferably in a synovial sample, at least one, preferably at least two genes or fragments thereof, or a protein encoded by said at least one, preferably at least two genes, wherein said genes or fragments thereof are selected from the group comprising ACP1 , ADAM8, ADAMDEC1 , ADH1 B, AF15Q14, ANGPTL1 , ANKRD22, ANLN, AP2A1 , AP2B1 , AP2S1 , APOBEC3B, APOBEC3C, APOL1 , AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1 , ASK, ASPM, ATAD2, AT0X1 , AURKB, BGN, BIRC5, BLVRA, BM039, BMP1 , BMP2, BRCA1 , BRIP1 , BRRN1 , BUB1 , BUB1 B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1 , CCNA2, CCNB1 , CCNB2, CCNE2, CCNF, CD163, CD1 D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1 , CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1 , CEB1 , CENPA, CENPE, CENPF, CENPF, CHEK1 , CHRDL1 , CHST1 , CKAP2, CKLF, CKS2, CNAP1 , COL13A1 , COP, CPSF5, CPT1 B, CR1 , CST1 , CTSL, CTSW, CXCL1 1 , CXCL3, CXCR4, DCLRE1 B, DDA3, DDX39, DEPDC1 , DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1 , DTR, DUFD1 , E2F1 , E2F2, E2F7, EBF, ECGF1 , ECT2, EFHD2, EGR2, ENPEP, EPSTI1 , ETS1 , EXO1 , EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1 , FEN1 , FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ1 1029, FLJ13052, FLJ20920, FLJ22573, FLJ23311 , FLJ40869, FMO1 , FOXM1 , FPR1 , G1 P2, GBP5, GGH, GGTLA1 , GMNN, GNLY, GPSM3, GSS, GTSE1 , GUCY1 B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1 , HELLS, HEYL, HIST2H2AA, HLA- C, HLA-F, HLA-G, HMGA1 , HMGB2, HMGN1 , HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1 , IL18, IL18RAP, IL21 R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1 , KIAA0101 , KIAA0186, KIF11 , KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1 , KLRC1 , KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1 , LOC1 13179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1 , MAN2A1 , MANBA, MAP4K1 , MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1 , MKI67, MNDA, MPHOSPH9, MYO1 F, MYO7A, NAGPA, NFKB2, NKTR, NMT1 , NPL, NUP210, NUP62, NUSAP1 , OIP5, ORC6L, PAFAH1 B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51 , PKM2, PKMYT1 , PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1 , POLQ, PPIF, PRC1 , PRG1 , PTTG1 , PVRL2, RAB27A, RACGAP1 , RAD51 , RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1 , S100A12, SCO2, SDF2L1 , SDS, SEMA4A, SERPINA1 , SERPINH1 , SF1 , SGOL2, SGPL1 , SHC1 , SHCBP1 , SIGLEC7, SIL, SIRPB1 , SLC20A1 , SLC39A8, SLCO4A1 , SMAD3, SMC4L1 , SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1 , SRD5A1 , SSR1 , STK10, STK6, STMN1 , SUSD1 , TACC3, TAP1 , TBC1 D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1 , TK1 , TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1 , TXNDC3, TYK2, TYMS, UBADC1 , UBE2C, UBE2S, UHRF1 , UMPK, UPP1 , URP2, VDR, WASL, WBSCR5, WDHD1 , ZAP70, ZCCHC6, and ZWINT, and

(b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In an embodiment, said method comprises (a) assessing ex vivo in a biological sample, preferably in a synovial sample, from said patient at least one gene or a fragment thereof, or the presence of the protein encoded by said at least one gene, preferably (a) assessing ex vivo in a biological sample, preferably in a synovial sample, from said patient at least two genes or fragments thereof, or the presence of the proteins encoded by said at least two genes, wherein said gene or fragment thereof is selected from the group listed herein and (b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In another embodiment, said method comprises (a) assessing in vivo at least one gene or a fragment thereof, or the presence of the protein encoded by said at least one gene, wherein said gene or fragment thereof is selected from the group listed herein and (b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In an embodiment, the present method comprises assessing the expression profile of at least one, preferably at least two genes or fragments thereof, as described herein, and predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In an embodiment, the present method comprises assessing the expression profile of at least 5 genes or fragments thereof, preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 69, 70, 80, 90, 100, 150, 200, 300, at least 352, or up to 352 genes or fragments thereof selected from the groups listed in Table 1.

In a particular embodiment, said synovial sample is a synovial tissue. In another particular embodiment, said synovial sample is a synovial fluid. In yet another embodiment said method is performed on cells from the synovial fluid.

The present inventors have designed methods based on the listed genes that are predictive of the response to TNF blocking agents in severe RA, and that are useful for determining whether an individual with rheumatoid arthritis will be a poor, moderate or good responder to TNF blocking therapy. The expression profiles of these genes are particularly useful for identifying the probability of a positive clinical response in a patient, with rheumatoid arthritis, to treatment with a TNF blocking drug.

Another aspect of the invention relates to a method to determine the probability of a positive clinical response in a patient, with rheumatoid arthritis, to treatment with a TNF blocking agent; comprising: (a) determining the levels of gene expression or protein synthesis of at least one, preferably at least two of the genes or fragments thereof listed in Table 1 in vivo or ex vivo in a synovial sample obtained from said patient, and (b) determining from the level of expression or the presence of the protein of said at least one , preferably at least two, gene or fragment thereof the probability that the patients will respond to a TNF inhibitor.

The method of the present invention is based on the comparison level of at least one gene as compared to standard values.

Another aspect of the invention relates to the use of a probe that hybridizes under stringent conditions to at least one gene or fragment thereof selected from the group of genes or fragments thereof as defined herein, or an antibody that binds to at least one protein or fragment thereof encoded by said at least one gene or fragment thereof, or a peptide that binds to at least one protein or fragment thereof encoded by said at least one gene or fragment thereof for predicting the response to a treatment with a TNF blocking agent in a patient. Preferably, the use of probes that hybridizes under stringent conditions to at least two genes or fragment thereof selected from the group of genes or fragments thereof as defined herein, or antibodies that binds to at least two proteins or fragments thereof encoded by said at least two genes or fragments thereof, or peptides that binds to at least two proteins or fragments thereof encoded by said at least two genes or fragments thereof for predicting the response to a treatment with a TNF blocking agent in a patient.

Another aspect of the invention relates to a kit for predicting the response to a treatment with a TNF blocking agent comprising a low density microarray comprising probes suitable for hybridizing with at least one gene or fragments thereof, preferably at least two, at least 5 genes or fragments thereof, preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 69, 70, 80, 90, 100, 150, 200, 300, at least 352, or up to 352 genes or fragments thereof selected from the group of genes listed herein.

Those skilled in the art will immediate recognize the many other effects and advantages of the present method and the numerous possibilities for end uses of the present invention from the detailed description and examples provided below.

Detailed description

The present invention provides several methods to predict or estimate the likelihood or probability that a patient with rheumatoid arthritis will respond with positive or favorable clinical results to treatment with a TNF blocking agent. These methods involve several forms of genomic or genetic analysis and proteomics.

In an embodiment, the invention relates to a method for predicting the response to a treatment with a TNF blocking agent in a patient comprising the steps of

(a) assessing in synovial tissue from said patient - in vivo or ex vivo - the expression of at least one gene or a fragment thereof, or the presence of the protein encoded by said at least one gene, preferably at least two genes or fragments thereof, or the presence of the proteins encoded by said at least two genes, wherein said gene or fragment thereof is selected from the group listed herein, and (b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a).

In an embodiment, the method for predicting the response to a treatment with a TNF blocking agent in a patient comprises the steps of:

(a) assessing in vivo or in a biological sample from said patient, preferably in a synovial sample from said patient, at least one gene or a fragment thereof or a protein encoded by said at least one gene, preferably at least two genes or fragments thereof, or the presence of the proteins encoded by said at least two genes, wherein said gene is selected from the group of genes as defined herein, and preferably from the group comprising MKI67, MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 , BUB1 , BUB1 B, ORC6L, CDC6, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4, AURKB, STMN1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF11 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5, CCNF, FANCD2, H2AFX, UBE2V1 , RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN, PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7, BIRC5, IL7R, IL18, IL18RAP, IL21 R, CXCL1 1 , CXCR4, IL13RA1 , TRAIL and gp130, and

(b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In an embodiment, said patient has rheumatoid arthritis, preferably severe rheumatoid arthritis.

In an embodiment, step (a) of assessing said at least one gene or a fragment thereof comprises the steps of

(i) assessing the level of expression of said at least one gene or a fragment thereof, and (ii) determining whether the level of expression assessed in step (i) is above or below a threshold value.

In an embodiment, step (a) of assessing said at least two genes or fragments thereof comprises the steps of

(i) assessing the level of expression of said at least two gene or fragments thereof, and (ii) determining whether the level of expression assessed in step (i) is above or below a threshold value.

In an embodiment, step (a) comprises assessing the expression profile of at least two genes or fragments thereof as described herein.

In an embodiment, the threshold value is determined before step (i) by: (M ) assessing the level of expression of said at least one gene or fragment thereof in a plurality of biological samples, preferably a plurality of synovial samples, from patients before treatment with said TNF blocking agent,

(12) assessing the level of expression of said at least one gene or fragment thereof in a plurality of biological samples, preferably a plurality of synovial samples, from patients after treatment with said TNF blocking agent, and

(13) correlating the response of the patients treated with said TNF blocking agent to the level of expression of said at least one gene determined in step (a) thereby determining the threshold value.

In an embodiment, the threshold value is determined before step (i) by: (M ) assessing the level of expression of said at least two genes or fragments thereof in a plurality of synovial samples from patients before treatment with said TNF blocking agent,

(12) assessing the level of expression of said at least two genes or fragments thereof in a plurality of synovial samples from patients after treatment with said TNF blocking agent, and

(13) correlating the response of the patients treated with said TNF blocking agent to the level of expression of said at least two genes determined in step (a) thereby determining the threshold value.

Preferably, the method comprises determining in vivo or in said biological sample preferably in said synovial sample, the expression level of at least one, preferably at least two to at least 352 genes or fragments thereof.

In an embodiment, said method comprises:

- providing in vivo or in said biological sample, preferably in said synovial sample, the gene expression level of at least one gene or fragment thereof as defined herein, - providing reference level by establishing gene expression level for reference samples, preferably reference synovial samples, from reference subjects which are poor, moderate and good responder to TNF inhibition,

- comparing the subject level together with reference level, and

- determining whether said biological sample, preferably said synovial sample, is from a poor, moderate or good responder to TNF inhibition.

More preferably, said method comprises determining in said biological sample, preferably said synovial sample, the expression level of at least 1 , 2, 30, 60, 100, 150, 200, 300, 352 genes or fragments thereof selected from the groups listed in Table 1.

In an embodiment, said method comprises: - providing in vivo or in said biological sample, preferably in said synovial sample, the gene expression level of at least two genes or fragments thereof as defined herein,

- providing reference level by establishing gene expression level for reference samples, preferably reference synovial samples, from reference subjects which are poor, moderate and good responder to TNF inhibition, - comparing the subject level together with reference level, and

As used herein the term "biological sample" refers to a sample that comprises a biomolecule that permits the expression level of a gene to be determined. Representative biomolecules include, but are not limited to total RNA, mRNA, and polypeptides, and derivatives of these molecules such as cDNAs and ESTs. As such, a biological sample can comprise a cell or a group of cells. Preferably, said biological sample is a synovial sample, more preferably a knee synovial sample. As used herein the term "subject" or "patient" or "individual" refers to any vertebrate species. Preferably, the term subject encompasses warm-blooded vertebrates, more preferably mammals. More particularly contemplated are mammals such as humans, as well as animals such as carnivores other than humans (such as cats and dogs), swine (pigs, hogs, and wild boars), poultry, ruminants (such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels), and horses.

When describing the invention, the terms used are to be construed in accordance with the following definitions, unless a context dictates otherwise:

As used in the specification and the appended claims, the singular forms "a", "an," and "the" include plural referents unless the context clearly dictates otherwise. By way of example, "a method" means one method or more than one method.

The term "and/or" as used in the present specification and in the claims implies that the phrases before and after this term are to be considered either as alternatives or in combination.

As used herein, the term "level" refers to the expression level data that can be used to compare the expression levels of different genes among various subjects.

As used herein the term "gene" encompasses sequences including, but not limited to a coding sequence, a promoter region, a transcriptional regulatory sequence, a non- expressed DNA segment that is a specific recognition sequence for regulatory proteins, a non-expressed DNA segment that contributes to gene expression, a DNA segment designed to have desired parameters, sense and anti-sense strands of genomic DNA (i.e. including any introns occurring therein), EST, RNA generated by transcription of genomic DNA (i.e. prior to splicing), RNA generated by splicing of RNA transcribed from genomic DNA, and proteins generated by translation of spliced RNA (e. g. including proteins both before and after cleavage of normally cleaved regions such as transmembrane signal sequences), cDNA made by reverse transcription of an RNA generated by transcription of genomic DNA (including spliced RNA) and fragments thereof, or combinations thereof.

As used herein the term "fragment" shall be understood to mean a nucleic acid that is the same as part of, but not all of a nucleic acid that forms a gene. The term "fragment" also encompasses a part, but not all of an intergenic region. The term "increased expression" and "decreased expression" refers to expression of the gene in a sample, at a greater or lesser level, respectively, than the level of expression of said gene (e. g. at least two-fold greater or lesser level) in a control (reference sample). The gene is said to be up-regulated or over-expressed or down-regulated or under- expressed if either the gene is present at a greater or lesser level, respectively, than the level in a control. Expression of a gene in a sample is "significantly" higher or lower than the level of expression of a gene in a control if the level of expression of the gene is greater or less, respectively, than the level by an amount greater than the standard error of the assay employed to assess expression, and preferably at least twice, and more preferably three, four, five or ten times that amount. Alternately, expression of the gene in the sample can be considered "significantly" higher or lower than the level of expression in a control if the level of expression is at least about two, and preferably at least about three, four, or five times, higher or lower, respectively, than the level of expression of the gene in said control. As used herein, the term "profile" refers to a repository of the expression level data that can be used to compare the expression levels of different genes among various subjects.

The degree of gene expression of said listed genes or fragment thereof is measured. The level of gene expression of these genes is able to distinguish between those patients who will respond well and those patients who will not respond well to a TNF blocking agent. In practice, the pattern of the expression of at least one of the listed genes or fragments thereof in a patient whose response status is unknown is compared to the pattern of the same genes in patients whose response statue is known. The mathematical similarity between the two patterns determines the probability that the unknown patient response will be similar to response of the known patient. The discovery and identification of these genes form part of the basis of this invention.

In various embodiments the gene expression pattern can be determined in a wide variety of ways including, but not limited to, measuring mRNA levels in a biological sample or measuring protein expression products in a biological sample. These can be performed either ex vivo for example in synovial tissue using low-density microarrays, or in vivo after injection of isotopic tracers allowing to identify and quantify the presence of specific markers in affected patients.

In an embodiment, the TNF blocking agents (also referred as TNF inhibitors) can be adalimumab (HUMIRA®, Abbott), infliximab (REMICADE®, Schering-Plough), etanercept (ENBREL®, Wyeth), certolizumab pegol (CIMZIA®, UCB) or Golimumab (Schering- Plough). In particular, the following genes are significantly up-regulated in a biological sample preferably synovial samples of poor-responders : genes involved in cytokine-cytokine receptor interactions : IL7R, IL18, IL18RAP, IL21 R, CXCL1 1 , CXCR4, IL13RA1 , TRAIL and genes involved in cell proliferation and control of the cell cycle: MKI67, MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 , BUB1 , BUB1 B, ORC6L, CDC6, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4, AURKB, STMN1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF11 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5, CCNF, FANCD2, H2AFX, UBE2V1 , RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN, PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7 and BIRC5, while gp130 is down-regulated in synovial samples from poor-responders.

In an embodiment, the level of expression of said at least one gene or fragment thereof in (vivo or in) said sample, preferably synovial sample, is assessed by detecting the level of expression of a protein or a fragment thereof encoded by said at least one gene or fragment thereof. In a preferred embodiment, the level of expression of said protein or fragment thereof is detected using a reagent which specifically binds with said protein or fragment thereof.

In an embodiment, the level of expression of at least two genes or fragments thereof in said synovial sample, is assessed by detecting the level of expression of proteins or fragments thereof encoded by said at least two genes or fragments thereof.

Preferably said, reagent is selected from the group consisting of an antibody, a fragment thereof or a derivative thereof. For some in vivo applications, said reagent is a peptide that binds specifically to the protein of interest.

In an embodiment, the level of expression is determined using a method selected from the group consisting of DNA microarray, reverse transcriptase polymerase chain reaction (RT PCR), immunohistochemistry, immunoblotting, and protein microarray. Preferably, the level of expression is determined using DNA-microarray, preferably low-density DNA- spotted microarray.

In an embodiment, the level of expression of said at least one gene or fragment thereof in said biological sample, preferably in said synovial sample, is assessed by detecting the level of expression of at least one transcribed polynucleotide or fragment thereof encoded by said at least one gene or fragment thereof. Preferably, said at least one transcribed polynucleotide or fragment thereof is a cDNA, or mRNA. In an embodiment, the step of detecting further comprises amplifying the transcribed polynucleotide. The step of detecting can be done using the method of quantitative RT PCR. n an embodiment, the level of expression of at least two genes or fragments thereof in said biological sample, preferably in said synovial sample, is assessed by detecting the level of expression of at least two transcribed polynucleotides or fragments thereof encoded by said at least two genes or fragments thereof. Preferably, said at least two transcribed polynucleotides or fragments thereof is a cDNA, or mRNA.

In an embodiment, the level of expression of said at least one gene or fragment thereof is assessed by detecting the presence of at least one transcribed polynucleotide or fragment thereof in a sample, preferably in a synovial sample, with a probe which anneals with the transcribed polynucleotide or fragment thereof under stringent hybridization conditions. In an embodiment, the level of expression of said at least two genes or fragments thereof is assessed by detecting the presence of at least two transcribed polynucleotides or fragments thereof in a sample, preferably in a synovial sample, with probes which anneals with the transcribed polynucleotides or fragments thereof under stringent hybridization conditions. The present invention therefore also provides arrays comprising probes for detection of polynucleotides (transcriptional state) or for detection of proteins (translational state) in order to detect differentially-expressed genes of the invention. By "array" is intended a solid support or substrate with peptide or nucleic acid probes attached to said support or substrate. Arrays typically comprise a plurality of different nucleic acid or peptide capture probes that are coupled to a surface of a substrate in different, known locations. These arrays, also described as "microarrays" or colloquially "chips" have been generally described in the art. These arrays may generally be produced using mechanical synthesis methods or light directed synthesis methods which incorporate a combination of photolithographic methods and solid phase synthesis methods. In one embodiment of the invention, microarrays are provided and used to measure the values to be included in the expression profiles. Microarrays are particularly well suited for this purpose because of the reproducibility between different experiments. In an embodiment, the step of determination of the level of expression is performed using DNA- microarray (also referred as gene chip array), preferably low-density DNA-spotted microarray. As used herein low-density DNA-spotted microarray comprises spotting probes suitable for hybridizing from at least 1 to 5000 genes or fragments thereof, preferably from at least 1 to 3000 genes or fragments thereof, more preferably from at least 1 to 2050 genes or fragment thereof, even more preferably from at least 1 to 500 genes, even more preferably from at least 1 to 352 genes. The skilled person is capable of designing oligonucleotide probes that can be used in methods of the present invention. Preferably, such probes are immobilized on a solid surface as to form an oligonucleotide microarray of the invention. The oligonucleotide probes useful in methods of the present invention are capable of hybridizing under stringent conditions to the at least one, at least two, at least three, at least five, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 50, at least 100, at least 120, at least 150, at least 180, at least 200, at least 220, at least 240, at least 250, at least 260, at least 264, at least 270, or at least 300 nucleic acids as described herein. In some embodiments, each probe in the array detects a nucleic acid molecule selected from the nucleic acid molecules listed in Table 1.

Although a planar array surface is preferred, the array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be peptides or nucleic acids on beads, gels, polymeric surfaces, and fibers such as fiber optics, glass or any other appropriate substrate. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation of an all-inclusive device.

The methods of the present invention are particularly useful for subjects with rheumatic arthritis, preferably severe rheumatic arthritis.

The sample can, of course, be subjected to a variety of well-known post-collection preparative and storage techniques (e. g. fixation, storage, freezing, lysis, homogenization, DNA or RNA extraction, ultrafiltration, concentration, evaporation, centrifugation, etc.) prior to determining the level of expression in the sample.

Expression of a gene according to the invention may be assessed by any of a wide variety of well known methods for detecting expression of a protein or transcribed molecule. Non- limiting examples suitable determination steps include immunological methods for detection of secreted, cell-surface, cytoplasmic, or nuclear proteins, protein purification methods, protein function or activity assays, nucleic acid hybridization methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods. Such methods may also include physical methods such as liquid and gas chromatography, mass spectroscopy, nuclear magnetic resonance and other imaging technologies.

In a preferred embodiment, the step of determination of the level of expression is performed using microarray, preferably DNA-microarray, more preferably low-density DNA-spotted microarray. Suitable probes for said microarray are identified hereunder.

In particular, a mixture of transcribed polynucleotides obtained from the sample, preferably from the synovial sample, is contacted with a substrate having fixed thereto a polynucleotide complementary to or homologous with at least a portion (e. g. at least 7, 10, 15, 20, 25, 30, 40, 50, 100, 250, 296, or more nucleotide residue) of a RNA transcript encoded by a gene for use in the invention. If polynucleotides complementary to or homologous with a RNA transcript encoded by the gene for use in the invention are differentially detectable on the substrate (e. g. detectable using radioactivity, different chromophores or fluorophores), are fixed to different selected positions, then the levels of expression of a plurality of genes can be assessed simultaneously using a single substrate.

When the assay has an internal control, which can be, for example, a known quantity of a nucleic acid derived from a gene for which the expression level is either known or can be accurately determined, unknown expression levels of other genes can be compared to the known internal control. More specifically, when the assay involves hybridizing labeled total RNA to a solid support comprising a known amount of nucleic acid derived from reference genes, an appropriate internal control could be a housekeeping gene (e. g. glucose-6- phosphate dehydrogenase or elongation factor-1 ), a housekeeping gene being defined as a gene for which the expression level in all cell types and under all conditions is substantially the same. Use of such an internal control allows a discrete expression level for a gene to be determined (e. g. relative to the expression of the housekeeping gene) both for the nucleic acids present on the solid support and also between different experiments using the same solid support. This discrete expression level can then be normalized to a value relative to the expression level of the control gene (for example, a housekeeping gene). As used herein, the term "normalized", and grammatical derivatives thereof, refers to a manipulation of discrete expression level data wherein the expression level of a reference gene is expressed relative to the expression level of a control gene. For example, the expression level of the control gene can be set at 1 , and the expression levels of all reference genes can be expressed in units relative to the expression of the control gene.

In one embodiment, nucleic acids isolated from a biological sample, preferably from a synovial sample, are hybridized to a microarray, wherein the microarray comprises nucleic acids corresponding to those genes to be tested as well as internal control genes. The genes are immobilized on a solid support, such that each position on the support identifies a particular gene. Solid supports include, but are not limited to nitrocellulose and nylon membranes. Solid supports can also be glass or silicon-based (i.e. gene "chips"). Any solid support can be used in the methods of the presently claimed subject matter, so long as the support provides a substrate for the localization of a known amount of a nucleic acid in a specific position that can be identified subsequent to the hybridization and detection steps.

A microarray can be assembled using any suitable method known to one of skill in the art, and any one microarray configuration or method of construction is not considered to be a limitation of the disclosure.

The present invention also encompasses a method for predicting the response to a treatment with a TNF blocking agent in a patient, said method comprising:

- first obtaining a polynucleotide sample from a biological sample, preferably from a synovial sample, and - then reacting the sample polynucleotide obtained in the first step with probes immobilized on a solid support having polynucleotide sequences corresponding to all or part of at least one gene or fragment thereof, preferably to all or part of at least two genes or fragments thereof, selected from the group listed in Table 1 , and

- detecting the reaction product and comparing with a reference reaction product. The present invention also encompasses the use of a probe that hybridizes under stringent conditions to at least one gene or fragment thereof, preferably the use of probes that hybridizes under stringent conditions to at least two genes or fragments thereof, from a biological sample, preferably from a synovial sample, said gene or fragment thereof being selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, APOBEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1, ASK, ASPM, ATAD2, ATOX1, AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1, BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1, CHRDL1, CHST1, CKAP2, CKLF, CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1, CTSL, CTSW, CXCL11, CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1, FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, LOC113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA, MPHOSPH9, MYO1F, MYO7A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12, SCO2, SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SGOL2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1, TK1, TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT, or an antibody that binds to at least one protein or fragment thereof encoded by said at least one gene or fragment thereof, or antibodies that bind to at least two proteins or fragments thereof encoded by said at least two genes or fragments thereof, for predicting the response to a treatment with a TNF blocking agent in a patient. Also provided are kits for use in practicing the subject methods. The term "kit" as used herein refers to any combination of reagents or apparatus that can be used to perform a method of the invention.

The present invention also provides kits useful for predicting the response to a treatment with a TNF blocking agent in a patient. In one embodiment, the invention provides a kit predicting the response to a treatment with a TNF blocking agent in a patient, the kit comprising a low density microarray comprising probes suitable for hybridizing with at least one gene or fragment thereof, preferably at least two genes or fragments thereof, selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, APOBEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1, ASK, ASPM, ATAD2, AT0X1, AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1, BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1, CHRDL1, CHST1, CKAP2, CKLF, CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1, CTSL, CTSW, CXCL11, CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBX05, FCN1, FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, L0C113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA, MPHOSPH9, MYO1F, MYO7A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12, SCO2, SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SG0L2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1 , TK1 , TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT. In one embodiment, said probes selectively hybridize to a sequence at least 95% identical to a sequence of a gene or fragment thereof listed Table 1. Preferably said probes are selected from the group of probes listed in Table 1. In an embodiment, said microarray comprises probes suitable for hybridizing with at least 352 genes or fragments thereof selected from the group of genes or fragment thereof listed herein.

The kit may comprise a plurality of reagents, each of which is capable of binding specifically with a nucleic acid or polypeptide corresponding to a gene for use in the invention. Suitable probe for binding with a nucleic acid (e. g. a genomic DNA, an mRNA, a spliced mRNA, a cDNA, or the like) include complementary nucleic acids. For example, the nucleic acid reagents may include oligonucleotides (labeled or non-labeled) fixed to a substrate, labeled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.

In an embodiment, the kit comprises a nucleic acid probe that binds specifically with a gene nucleic acid or a fragment of the nucleic acid.

The kit may further comprise means for performing PCR reactions. The kit may further comprise media and solution suitable for taking a sample, preferably a synovial sample, and for extracting RNA from said blood sample.

The kit can further comprise additional components for carrying out the method of the invention, such as RNA extraction solutions, purification column and buffers and the like. The kit of the invention can further include any additional reagents, reporter molecules, buffers, excipients, containers and/or devices as required described herein or known in the art, to practice a method of the invention.

The various components of the kit may be present in separate containers or certain compatible components may be pre-combined into a single container, as desired. In addition to the above components, the kits may further include instructions for practicing the present invention. These instructions may be present in the kits in a variety of forms, one or more of which may be present in the kit.

One form in which these instructions may be present is as printed information on a suitable medium or substrate, e. g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e. g., diskette, CD, etc., on which the information has been recorded. In an embodiment, said kit further comprises a computer-readable medium comprising one or more digitally encoded expression profiles, where each profile has one or more values representing the expression of said at least one gene as defined herein. Said digitally encoded expression profiles are preferably profiles of poor, moderate and good responder to TNF blocking therapy. The invention also provides a computer-readable medium comprising one or more digitally encoded expression profiles, where each profile has one or more values representing the expression of said at least one gene or fragment thereof, as listed herein that are differentially-expressed in a poor, moderate or good responder to TNF blockade therapy. In some embodiments, the digitally-encoded expression profiles are comprised in a database.

The kits according to the invention may comprise a microarray as defined above and a computer readable medium as described above. The array comprises a substrate having addresses, where each address has a probe that can specifically bind a nucleic acid molecule (by using an oligonucleotide array) or a peptide (by using a peptide array) that is differentially-expressed in at least one poor, moderate or good responder, preferably in the joints of a poor, moderate or good responder, preferably in a synovial sample from a poor, moderate or good responder. The results are converted into a computer-readable medium that has digitally-encoded expression profiles containing values representing the expression level of a nucleic acid molecule detected by the array. Any other convenient means may be present in the kits.

The invention also provides for the storage and retrieval of a collection of data relating to poor, moderate or good responder to TNF blockade therapy specific gene expression data of the present invention, including sequences and expression levels in a computer data storage apparatus.

The method of the invention can also be performed in vivo on a patient after injection of isotopic tracers allowing to identify and quantify the presence of the genes or of the encoded protein thereof in affected patients.

The present invention therefore also provides a method for predicting the response to a treatment with a TNF blocking agent in a patient comprising the steps of:

(a) assessing in vivo at least one gene or a fragment thereof selected from the group comprising ACP1 , ADAM8, ADAMDEC1 , ADH1 B, AF15Q14, ANGPTL1 , ANKRD22, ANLN, AP2A1 , AP2B1 , AP2S1 , APOBEC3B, APOBEC3C, APOL1 , AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1 , ASK, ASPM, ATAD2, ATOX1 , AURKB, BGN, BIRC5, BLVRA, BM039, BMP1 , BMP2, BRCA1 , BRIP1 , BRRN1 , BUB1 , BUB1 B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1 , CCNA2, CCNB1 , CCNB2, CCNE2, CCNF, CD163, CD1 D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1 , CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1 , CEB1 , CENPA, CENPE, CENPF, CENPF, CHEK1 , CHRDL1 , CHST1 , CKAP2, CKLF, CKS2, CNAP1 , COL13A1 , COP, CPSF5, CPT1 B, CR1 , CST1 , CTSL, CTSW, CXCL1 1 , CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1, FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA- C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, LOC113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA, MPHOSPH9, MYO1F, MYO7A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12, SCO2, SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SGOL2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1, TK1, TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT, and (b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a). In an embodiment, said patient has rheumatoid arthritis.

In an embodiment the method comprises the steps of (i) assessing in said patient the level of expression of at least one gene or a fragment thereof selected from the group as defined herein,

(ii) determining whether the level of expression assessed in step (i) is above or below a threshold value, and (iii) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (ii).

In an embodiment, the level of expression of said at least one gene or fragment thereof in said patient is assessed by detecting the level of expression of a protein or a fragment thereof encoded by said at least one gene or fragment thereof.

Preferably, the level of expression of said protein or fragment thereof is detected using a reagent which specifically binds with said protein or fragment thereof. Said reagent can be selected from the group consisting of a peptide, an antibody, or a fragment thereof. Preferably, the level of expression of said protein or fragment thereof is detected by measuring or detecting joint uptake of the reagent.

Preferably, said reagent is labeled with a radioactive isotope, which can be detected by radio-imaging. Suitable radioactive isotope can be selected from the group comprising Technetium⁹⁹"¹, Carbon¹¹, Oxygen¹⁵, Nitrogen¹³, Rubidium⁸², Gallium⁶⁷, Gallium⁶⁸, Yttrium⁹⁰, Molybdenum⁹⁹, Iodine¹²³'¹²⁴'¹³¹ Fluorine¹⁸, Phosphorus³², Copper⁶², Thallium²⁰¹ , Copper⁶⁴, Copper⁶², Indium¹¹¹, and Xenon¹³³.

Suitable radio-imaging method can be selected from the group consisting of single photon emission computed tomography (SPECT), positron emission tomography (PET) and gamma cameras.

The present invention discloses at least one, at least two, at least 10, at least 50, at least 100, at least 120, at least 150, at least 180, at least 200, at least 220, at least 240, at least 250, at least 260, or at least 352 genes described herein that are differentially-expressed in poor, moderate or good responder to TNF blockade. Accordingly, these genes and their gene products are potential therapeutic targets that are useful in methods of screening test compounds to identify therapeutic compounds for the treatment of rheumatic arthritis. The differentially-expressed genes of the invention may be used in cell-based screening assays involving recombinant host cells expressing the differentially-expressed gene product. The recombinant host cells are then screened to identify compounds that can activate the product of the differentially-expressed gene (i.e. agonists) or inactivate the product of the differentially-expressed gene (i.e. antagonists). The following Table and examples are intended to illustrate and to substantiate the present invention.

Table 1 list about 439 genes or fragments thereof used as synovial markers that are useful for predicting the response to TNF blocking agents in severe RA. Table 1

Previous studies cited by the reviewer were conducted in PBMC of patients and not in their synovial tissue. It is a very significant difference in the course of rheumatoid arthritis. The pathophysiological mechanisms at work in rheumatoid arthritis are active in the synovium. The drugs administered are active on synoviocytes. The present invention provides the advantages of providing relevant information for predicting the response to a treatment with a TNF blocking agent in an patient with RA. Predicting such response using blood cells is not as effective because the gene expression profiles in these cell populations are not related to the mechanisms studied. The present inventors have shown there was no correlation between the expression profiles obtained from synovial biopsies and the expression profiles in PBMC.

Examples

Identification of penes associated with response to TNF blockade in patients with severe rheumatoid arthritis

Patients and synovial biopsies: All patients met the American College of Rheumatology (ACR) criteria for the diagnosis of rheumatoid arthritis (Felson et al. American College of Rheumatology. Preliminary definition of improvement in rheumatoid arthritis. Arthritis Rheum. 1995; 38: 727-35). They were 18 females and 7 males. They were average 55.2 year-old (range 18-83). Average disease duration was 10 years (range 1-36 years). They all had active disease at the time of tissue sampling that was resistant to conventional therapy. All patients were treated with disease modifying anti-rheumatic drugs (DMARD's) (23 with methotrexate and 2 with leflunomide) and 18 of them with low-dose steroids. All of them had a swollen knee at the time of the baseline needle-arthroscopic procedure. The study was approved by the ethical committee of the Universite catholique de Louvain, and informed consent was obtained from all patients.

Synovial biopsies were obtained by needle-arthroscopy from the knee of all patients before (TO) and 12 weeks (T12) after initiation of adalimumab therapy. For each procedure, 4 to 8 synovial samples were snap frozen in liquid nitrogen and stored at -80° for later RNA extraction. The same amount of tissue was also kept at -80° for immunostaining experiments on frozen sections. The remaining material was stored in formaldehyde and paraffin embedded for conventional optical evaluation and immunostaining of selected cell markers. Disease activity at TO and T12 was evaluated using standard disease activity scores (DAS) such as DAS-28 CRP (Prevoo ML, et al. Modified disease activity scores that include twenty-eight-joint counts: development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum 1995; 38: 44-8), and response to therapy (good responders versus moderate responders versus poor responders) was assessed based on changes in DAS according to the EULAR (European League Against Rheumatism) response criteria (van Gestel et al., Arthritis Rheum; 39: 34-40). Table 2 shows the EULAR response criteria using the DAS and DAS-28.

Table 2

DAS28 at Endpoint Improvement in DAS or DAS28 from Baseline > 1.2 > 0.6 and < 1.2 < 0.6

< 3 2 Good

> 3 2 and < 5.1 Moderate

> 5 1 None

Microarray hybridization and statistical interpretation

Total RNA was extracted from the synovial biopsies using the Nucleospin® RNA Il extraction kit (Macherey-Nagel GmbH & Co, Dϋren, Germany), including DNase treatment of the samples. 1 μg or more total RNA could be extracted from 12 samples at TO and 12 samples at T12 for further processing. Labeling of RNA (cRNA synthesis) was performed according to a standard Affymetrix® procedure (One-Cycle Target Labeling kit, Affymetrix

UK Ltd, High Wycombe, United Kingdom); briefly total RNA was first reverse transcribed into single-stranded cDNA using a T7-Oligo(dT) Promoter Primer and Superscript Il reverse transcriptase. Next, RNase H was added together with E. CoIi DNA polymerase I and E. CoIi DNA ligase, followed by a short incubation with T4 DNA polymerase in order to achieve synthesis of the second-strand cDNA. The double-stranded cDNA was purified and served as a template for the overnight in vitro transcription reaction, carried out in the presence of T7 RNA polymerase and a biotinylated nucleotide analog/ribonucleotide mix.

At the end of this procedure, the biotinylated complementary RNA (cRNA) was cleaned, and fragmented by a 35 minute incubation at 95°c.

GeneChip® Human genome U133 Plus 2.0 Arrays (Affymetrix UK Ltd, High Wycombe, UK) were hybridized overnight at 45°c in monoplicates with 10 μg cRNA. The slides were then washed and stained using the EukGE-WS2v5 Fluidics protocol on the Genechip® Fluidics Station (Affymetrix) before being scanned on a Genechip® Scanner 3000. Statistical and pathway analyses were performed using TMEV, Genespring, FatiGO and GOStat. Data were retrieved on GCOS software for the initial normalization and analysis steps. The number of positive genes was between 49 and 55% on each slide. After scaling on all probe set (to a value of 100), the amplification scale was reported between 1.1 and 2.5 for all the slides. The signals given by the poly-A RNA controls, hybridization controls and housekeeping/control genes (GAPDH 375' ratio < 2) were indicative of the good quality of the amplification and hybridization procedures. Further statistical analyses were performed using the Genespring® software (Agilent Technologies Inc). For each slide, scaled data were normalized to the 50th percentile per chip and to the median per gene. The data were analyzed by ANOVA for identification of differential gene expression at TO and T12 between good-, moderate- and poor-responders, with further restriction of the number of genes based on a minimal fold change between good- and moderate- versus poor-responders set at 1.5. Pathway analyses were performed using Gostat (http://gostat.wehi.edu.au), an application that finds statistically overrepresented Gene Ontology (GO) terms within a group of genes. Around 439 genes displayed differences in expression patterns (Table 1 ) and can be used as synovial markers that are useful for predicting the response to TNF blocking agents in severe RA.

Results

According to EULAR response criteria (Table 2), 13 patients were good-, 7 moderate- and 5 poor-responders at week 12 of TNF-blocking therapy with adalimumab. RNA was extracted from 12 biopsies at baseline (5 good-, 4 moderate- and 3 poor-responders), labeled and hybridized in monoplicates on Genechip® Human Genome U 133 Plus 2.0 slides. The genes differentially expressed among the three groups based on ANOVA analyses are listed in Table 1. 411 out of the 54,675 transcripts present on the slides were up-regulated and 28 down-regulated in the synovial biopsies from the poor-responders to TNF blocking therapy as compared to the two other groups. Pathway analyses indicated that these genes are enriched in genes belonging to two different GO (Gene Ontology) families: cell cycle and cytokine - cytokine receptor interactions. In particular, the following genes, involved in the regulation of cell cycle and cell proliferation, are up-regulated in synovial tissue from poor-responders : MKI67, MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 , BUB1 , BUB1 B, ORC6L, CDC6, E2F2, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4, AURKB, STMN1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF1 1 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5, CCNF, FANCD2, H2AFX, UBE2V1 , RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN, PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7 and BIRC5. Similarly, IL7R, IL18, IL18RAP, IL21 R, CXCL11 , CXCR4, IL13RA1 , TRAIL were found to be up-regulated while gp130 is down-regulated in synovial biopsies from poor-responders. lmmunohistochemistry on frozen sections

After initial blocking of endogenous peroxidases (Peroxydase blocking reagent, DakoCytomation), frozen sections of the synovial biopsy samples were stained with the following antibodies: IL-7R (Sigma-Aldrich) and CXCL1 1 (Abeam). After incubation with the primary antibody, slides were sequentially incubated with HRP Envision rabbit or mouse secondary antibody conjugated to a HRP labeled polymer (DAKO EnVision™ + System, DakoCytomation) and DAB+ chromagen (DakoCytomation). The slides were finally counterstained with hematoxylin and eosin for further analyses. Semi-quantitative analyses were performed using a semi-quantitative scale from 0 to 3. Quantitative analyses were performed using ImageJ. Six digitalized pictures (400X magnification) were obtained for each slide. The surface of the staining (S) and the surface of the nuclei (N) were calculated for each picture and the normalized staining surface was calculated as the S/N ratio.

Quantitative evaluation of IL-7R, IL18rap and CXCL11 immunostaining showed that these molecules are significantly up-regulated in the synovium of poor-responders as compared to good- and moderate-responders.

Example

A 47-year-old woman suffers from severe rheumatoid arthritis. Despite adequate therapy with several disease-modifying anti-rheumatic drugs (DMARD's) including salazopyrine, leflunomide and methotrexate, she still has active disease with involvement of many joints at clinical examination and X-ray evidence of progression of bone erosions and joint damage. General physical evaluation is indicative of a severe deterioration of her general health. Therefore, her physician considers the possibility of treating her with TNF blocking agents. Because the patient needs efficient therapy, and also because the cost of the therapy is very high, her physician wants to evaluate the probability that she will respond to therapy using one of the following method according to embodiments of the present invention:

Microarray evaluation of gene expression in the synovial biopsies of the patient.

Synovial biopsies and/or synovial fluid is obtained by needle aspiration or by needle arthroscopy from one joint (preferentially the knee) of the patient. RNA is extracted from the synovial biopsies, labeled and hybridized on a low-density microarray spotted with oligonucleotides or cDNA fragments selected from Table 1 , or preferably encoding at least one gene selected from : MKI67, MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 ,

BUB1 , BUB1 B, ORC6L, CDC6, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4, AURKB, STMN1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF11 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5, CCNF, FANCD2, H2AFX, UBE2V1 , RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN, PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7, BIRC5, IL7R, IL18, IL18RAP, IL21 R, CXCL11 , CXCR4, IL13RA1 , TRAIL and gp130. The levels of gene expression are compared to standard values associated with response to therapy. Based on these results, the patient is categorized as poor responder, moderate or good responder to TNF blocking therapy. The physician decides to start therapy in the patient; alternatively the physician considers the prescription of another kind of therapy.

Evaluation of gene expression in the synovial biopsies of the patient by real-time PCR. Synovial biopsies and/or synovial fluid is obtained by needle aspiration or by needle arthroscopy from one joint (preferentially the knee) of the patient. RNA is extracted from the synovial biopsies and cDNA is synthesized using a standard procedure. Evaluation of the expression of one, or two or more of the genes as listed in table 1 is performed by real-time PCR. The levels of gene expression are compared to standard values associated with response to therapy. Based on these results, the patient is categorized as poor responder, moderate or good responder to TNF blocking therapy.

Evaluation of the concentration of specific proteins in the biopsies or synovial fluid of the patient.

Crude synovial fluid, or crude cell lysate from the synovial fluid, or crude cell lysate from the synovial biopsies are obtained from the patient. Determination of the concentration of one or more of the following molecules selected from the group comprising MKI67,

MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 , BUB1 , BUB1 B, ORC6L, CDC6,

E2F2, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4,

AURKB, STM N 1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF1 1 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5, CCNF, FANCD2, H2AFX, UBE2V1 ,

RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN,

PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7, BIRC5, IL7R, IL18, IL18RAP, IL21 R,

CXCL11 , CXCR4, IL13RA1 , TRAIL and gp130, is performed by sandwich ELISA, Western

Blot or by protein arrays using specific antibodies directed against them. Based on the results, the patient is categorized as poor responder, moderate or good responder to TNF blocking therapy.

Isotope-scan or SPECT (Single Photon Emission Computed Tomography)

The patient is injected with a Technetium-99m labeled antibody directed against one of the following markers (cell surface markers) : IL7R, IL18, IL18RAP, IL21 R, CXCL1 1 , CXCR4, IL13RA1 , TRAIL and gp130. (Non-limiting examples of suitable cell surface markers can be selected from Table 1 ). After the injection, joint uptake of the tracer is evaluated by planar scintigraphy or by SPECT and the total intensity of radiation is quantified. The results of the quantification are compared to standard values associated with response to therapy. Based on these results, the patient is categorized as poor responder, moderate or good responder to TNF blocking therapy. The physician decides to start therapy in the patient; alternatively the physician considers the prescription of another kind of therapy.

PET-Scan (Positron-Emission Tomography)

The patient is injected with peptides that bind specifically to one of the following markers (cell surface markers): IL7R, IL18, IL18RAP, IL21 R, CXCL1 1 , CXCR4, IL13RA1 , TRAIL and gp130. (Non-limiting examples of suitable cell surface markers can be selected from Table 1 ). For example, a peptide can be designed based on the protein sequence of IL-7 that will bind specifically to the IL-7R. Conversely, a peptide can be designed based on the sequence of the IL-18 receptor that will bind specifically to IL-18. These peptides are stably chelated to positron emitting metals (Copper-64 or other metals). The patient can be injected with peptides recognizing one single target but can also be injected in one session with peptides recognizing different targets that are labeled with different tracers. After the injection, joint uptake of the tracer(s) is evaluated by positron-emission tomography and the total intensity of radiation is quantified. The results of the quantification are compared to standard values associated with response to therapy. Based on these results, the patient is categorized as poor responder, moderate or good responder to TNF blocking therapy. The physician decides to start therapy in the patient; alternatively the physician considers the prescription of another kind of therapy.

Gene expression profiles in the peripheral blood and in synovial tissue of patients with rheumatoid arthritis

This example shows the lack of concordance between the gene expression profiles in the peripheral blood and in synovial tissue of patients with rheumatoid arthritis. These data indicate that the synovial tissue provides a distinct and unique profile of gene expression, for which blood cell circulating are not informative, hence the need to investigate directly, in synovial samples.

Global analyses of gene expression were performed in sorted CD4 T and B cells from RA patients with active disease before initiation of therapy and compared them with systemic lupus (SLE) or osteoarthritis (OA) patients. None of the genes differentially expressed in RA cells matched those found to be differentially expressed in RA synovial tissue compared to SLE and OA synovial biopsies (see Table 3), neither did they match the genes differentially expressed in RA synovial tissue of poor-responders to adalimumab therapy. This difference in gene expression between circulating elements of the immune system and synovial tissue from RA patients can be explained by the fact that in synovial tissue, the cell population that is actively involved in the pathophysiological process and is responsible for a great part of the differential patterns of gene expression, is made of synovial fibroblasts. These cells are also the main target of adalimumab therapy in RA. These cells are not present in the circulation and, therefore, cannot influence gene expression profiles in PBMC or sorted CD4 T and B cells from RA patients.

Table 3

Claims

1. Method for predicting the response to a treatment with a TNF blocking agent in a patient comprising the steps of (a) assessing in a synovial sample from said patient at least two genes or fragments thereof or proteins encoded by said at least two genes, wherein said genes or fragments thereof are selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, APOBEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1, ASK, ASPM, ATAD2, AT0X1,

AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1, BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1, CHRDL1, CHST1, CKAP2, CKLF,

CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1, CTSL, CTSW, CXCL11, CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1, FEN1, FHL3, FKSG14,

FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA,

IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, LOC113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA,

MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA, MPHOSPH9, MYO1F, MY07A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF,

PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1 , S100A12, SCO2, SDF2L1 , SDS, SEMA4A, SERPINA1 , SERPINH1 , SF1 , SGOL2, SGPL1 , SHC1 , SHCBP1 , SIGLEC7, SIL, SIRPB1 , SLC20A1 , SLC39A8, SLCO4A1 , SMAD3, SMC4L1 , SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1 , SRD5A1 , SSR1 , STK10, STK6, STMN1 , SUSD1 , TACC3, TAP1 , TBC1 D7, TCEB3, TCF19, TCF3, TEAD4,

TFEC, TFG, TGIF2, TIMELESS, TIMP1 , TK1 , TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1 , TXNDC3, TYK2, TYMS, UBADC1 , UBE2C, UBE2S, UHRF1 , UMPK, UPP1 , URP2, VDR, WASL, WBSCR5, WDHD1 , ZAP70, ZCCHC6, and ZWINT, and (b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a).

2. Method according to claim 1 , wherein step (a) of assessing said at least two genes or fragments thereof comprises the steps of

(i) assessing the level of expression of said at least two genes or fragments thereof, and

(11) determining whether the level of expression assessed in step (i) is above or below a threshold value.

3. Method according to claim 2, wherein the threshold value is determined before step (i) by (i1 ) assessing the level of expression of said at least two genes or fragments thereof in a plurality of synovial samples from patients before treatment with said TNF blocking agent,

4. Method according to any of claims 1 to 3 wherein said TNF blocking agent is selected from the group comprising adalimumab, infliximab, etanercept, certolizumab pegol, and Golimumab.

5. Method according to any of claims 1 to 4, wherein said patient has rheumatoid arthritis, preferably severe rheumatoid arthritis.

6. Method according to any of the claims 1 to 5, wherein said at least two genes are selected from MKI67, MCM7, MAD2L1 , CCNB1 , CCNB2, CDC25C, PKMYT1 , BUB1 , BUB1 B, ORC6L, CDC6, CDC2, CCNA2, CCNE2, E2F1 , CDKN3, SHC1 , E2F7, ZWINT, CDCA5, DBF4, AURKB, STMN1 , AURKA, PBK, E2F8, TCF19, EXO1 , NUSAP1 , UBE2C, CKS2, CENPE, KIF1 1 , BRRN1 , CKAP2, MCM2, CEP55, SPAG5,

CCNF, FANCD2, H2AFX, UBE2V1 , RACGAP1 , TTK, CDC20, TPX2, KIF23, GTSE1 , PTTG1 , KNTC2, CNAP1 , E2F2, ANLN, PRC1 KIF15, RPS6KB2, KIF2C, HCAP-G, DLG7, BIRC5, IL7R, IL18, IL18RAP, IL21 R, CXCL1 1 , CXCR4, IL13RA1 , TRAIL and gp130.

7. Method according to any of the claims 1 to 6, wherein the level of expression of said at least two genes or fragments thereof in said sample is assessed by detecting the level of expression of proteins or fragments thereof encoded by said at least two genes or fragment thereof.

8. Method according to claim 7, wherein the level of expression of said proteins or fragments thereof is detected using a reagent which specifically binds with said proteins or fragments thereof.

9. Method according to claim 8, wherein the reagent is selected from the group consisting of a peptide, an antibody, a fragment thereof or a derivative thereof.

10. Method according to any of the claims 2 to 9 wherein the level of expression is determined using a method selected from the group consisting of DNA microarray, RT

PCR, immunohistochemistry , immunoblotting, and protein microarray.

1 1. Method according to any of claims 2 to 10, wherein the level of expression is determined using DNA-microarray, preferably low-density DNA-spotted microarray.

12. Method according to any of the claims 2 to 10, wherein the level of expression of said at least two genes or fragments thereof in said synovial sample is assessed by detecting the level of expression of at least two transcribed polynucleotides or fragments thereof encoded by said at least two genes or fragments thereof.

13. Method according to claim 12 wherein said at least two transcribed polynucleotides or fragments thereof is a cDNA, or mRNA.

14. Method according to any of the claims 12 to 13, wherein the step of detecting further comprises amplifying the transcribed polynucleotide.

15. Method according to claim 14, wherein the step of detecting is using the method of quantitative reverse transcriptase polymerase chain reaction.

16. Method according to any of the claims 2 to 15, wherein the level of expression of said at least two genes or fragment thereof is assessed by detecting the presence of at least two transcribed polynucleotides or fragment thereof in a synovial sample with probes which anneals with the transcribed polynucleotides or fragments thereof under stringent hybridization conditions.

17. Use of probes that hybridizes under stringent conditions to at least two genes or fragments thereof, from a synovial sample, said genes or fragment thereofs being selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, APOBEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET,

ARRDC1, ASK, ASPM, ATAD2, AT0X1, AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1, BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1,

CHRDL1, CHST1, CKAP2, CKLF, CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1, CTSL, CTSW, CXCL11, CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1,

FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18,

IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, LOC113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ,

MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA, MPHOSPH9, MYO1F, MY07A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4,

PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12, SCO2, SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SGOL2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3,

TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1, TK1, TLR2, TMEM8, TMPO, TNFSF10, TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT, or an antibody that binds to at least one protein or fragment thereof encoded by said at least one gene or fragment thereof, or a peptide that binds to at least one protein or fragment thereof encoded by said at least one gene or fragment thereof, for predicting the response to a treatment with a TNF blocking agent in a patient .

18. Kit for predicting the response to a treatment with a TNF blocking agent comprising a low density microarray comprising probes suitable for hybridizing with at least two genes or fragments thereof selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, AP0BEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1, ASK, ASPM, ATAD2, AT0X1, AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1,

BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1, CHRDL1, CHST1, CKAP2, CKLF, CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1, CTSL, CTSW,

CXCL11, CXCL3, CXCR4, DCLRE1B, DDA3, DDX39, DEPDC1, DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBXO5, FCN1, FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573,

FLJ23311, FLJ40869, FMO1, F0XM1, FPR1, G1P2, GBP5, GGH, GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1,

KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C, KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, LOC113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1, MKI67, MNDA,

MPHOSPH9, MYO1F, MYO7A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12, SCO2,

SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SGOL2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1, TK1, TLR2, TMEM8, TMPO, TNFSF10,

TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT.

19. Kit according to claim 18, wherein said microarray comprises probes suitable for hybridizing with at least 352 genes or fragments thereof selected from the group as listed in claim 18.

20. Kit according to any of claims 18 to 19, further comprising a computer-readable medium comprising one or more digitally encoded expression profiles, where each profile has one or more values representing the expression of said at least two genes as defined in claim 18.

21. Kit according to claim 20, wherein said digitally encoded expression profiles are profiles of poor, moderate and good responder to TNF blocking therapy.

22. Method for predicting the response to a treatment with a TNF blocking agent in a patient comprising the steps of (a) assessing in vivo at least one gene or a fragment thereof selected from the group comprising ACP1, ADAM8, ADAMDEC1, ADH1B, AF15Q14, ANGPTL1, ANKRD22, ANLN, AP2A1, AP2B1, AP2S1, APOBEC3B, APOBEC3C, APOL1, AQP9, ARHGAP22, ARHGAP4, ARHGAP9, ARHGDIB, ARL7, ARMET, ARRDC1, ASK, ASPM, ATAD2, AT0X1, AURKB, BGN, BIRC5, BLVRA, BM039, BMP1, BMP2, BRCA1, BRIP1, BRRN1, BUB1, BUB1B, C10orf3, C13orf3, C14orf94, C20orf129, C22orf18, C9orf76, CARHSP1, CCNA2, CCNB1, CCNB2, CCNE2, CCNF, CD163, CD1D, CD3Z, CDC2, CDC20, CDC25C, CDC6, CDCA1, CDCA2, CDCA3, CDCA5, CDCA7, CDKN3, CDT1, CEB1, CENPA, CENPE, CENPF, CENPF, CHEK1, CHRDL1, CHST1, CKAP2, CKLF, CKS2, CNAP1, COL13A1, COP, CPSF5, CPT1B, CR1, CST1 , CTSL, CTSW, CXCL11 , CXCL3, CXCR4, DCLRE1 B, DDA3, DDX39, DEPDC1 ,

DKFZP434G2226, DKFZP434L0117, DKFZp762E1312, DLG7, DNAJC9, DNMT1, DTR, DUFD1, E2F1, E2F2, E2F7, EBF, ECGF1, ECT2, EFHD2, EGR2, ENPEP, EPSTI1, ETS1, EXO1, EZH2, FAM20A, FANCD2, FBXO23, FBX05, FCN1, FEN1, FHL3, FKSG14, FLJ10156, FLJ10199, FLJ10719, FLJ11029, FLJ13052, FLJ20920, FLJ22573, FLJ23311, FLJ40869, FMO1, FOXM1, FPR1, G1P2, GBP5, GGH,

GGTLA1, GMNN, GNLY, GPSM3, GSS, GTSE1, GUCY1B3, H2AFV, H2AFX, H2AFZ, HCAP-G, HCLS1, HELLS, HEYL, HIST2H2AA, HLA-C, HLA-F, HLA-G, HMGA1, HMGB2, HMGN1, HMMR, HSPC242, IDH2, IFI30, IFI44, IL13RA1, IL18, IL18RAP, IL21R, IL27RA, IL2RG, IL6, IL6ST, IL7R, INDO, IQGAP3, IRF7, ITGA4, ITIH5, KCNK6, KCNMB1, KIAA0101, KIAA0186, KIF11, KIF14, KIF20A, KIF23, KIF2C,

KIF4A, KLIP1, KLRC1, KNSL7, KNTC2, KPNA2, Kua, LAK, LAMP3, LAT, LBP, LENG4, LGALS8, LGALS9, LILRB3, LMNB1, L0C113179, LOC144997, LOC146909, LOC201292, LOC56926, LOC90522, LOC92799, LTBP4, LY6E, LYZ, MAC30, MAD2L1, MAN2A1, MANBA, MAP4K1, MAPK13, MCFP, MCM2, MCM4, MCM7, MELK, MFAP4, MFAP5, MGC10986, MGC24665, MGC29814, MGC3248, MGST1,

MKI67, MNDA, MPHOSPH9, MYO1F, MYO7A, NAGPA, NFKB2, NKTR, NMT1, NPL, NUP210, NUP62, NUSAP1, OIP5, ORC6L, PAFAH1B3, PASK, PCDH17, Pfs2, PGDS, PHF19, PIR51, PKM2, PKMYT1, PLA2G2D, PLA2G7, PLIN, PLK4, PMSCL1, POLQ, PPIF, PRC1, PRG1, PTTG1, PVRL2, RAB27A, RACGAP1, RAD51, RAMP, RASSF4, RBMS3, RELB, RFC4, RGS18, RHOF, RPS6KB2, RRM2, RTN1, S100A12,

SCO2, SDF2L1, SDS, SEMA4A, SERPINA1, SERPINH1, SF1, SG0L2, SGPL1, SHC1, SHCBP1, SIGLEC7, SIL, SIRPB1, SLC20A1, SLC39A8, SLCO4A1, SMAD3, SMC4L1, SOD2, SOX5, SPAG5, Spc24, Spc25, SPEC1, SRD5A1, SSR1, STK10, STK6, STMN1, SUSD1, TACC3, TAP1, TBC1D7, TCEB3, TCF19, TCF3, TEAD4, TFEC, TFG, TGIF2, TIMELESS, TIMP1, TK1, TLR2, TMEM8, TMPO, TNFSF10,

TNRC5, TOMM22, TOP2A, TOPK, TPP2, TPX2, TRIP13, TROAP, TTK, TUBA1, TXNDC3, TYK2, TYMS, UBADC1, UBE2C, UBE2S, UHRF1, UMPK, UPP1, URP2, VDR, WASL, WBSCR5, WDHD1, ZAP70, ZCCHC6, and ZWINT, and

(b) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (a).

23. Method according to claim 22, wherein said patient has rheumatoid arthritis.

24. Method according to claim 22 or 23, comprising the steps of

(i) assessing in said patient the level of expression of at least one gene or a fragment thereof selected from the group as defined in claim 22,

(ii) determining whether the level of expression assessed in step (i) is above or below a threshold value, and

(iii) predicting the response to the treatment with the TNF blocking agent in said patient by evaluating the results of step (ii).

25. Method according to any of claims 22 to 24, wherein said TNF blocking agent is selected from the group comprising adalimumab, infliximab, etanercept, certolizumab pegol, and Golimumab.

26. Method according to claim 24 or 25, wherein the level of expression of said at least one gene or fragment thereof in said patient is assessed by detecting the level of expression of a protein or a fragment thereof encoded by said at least one gene or fragment thereof.

27. Method according to claim 26, wherein the level of expression of said protein or fragment thereof is detected using a reagent which specifically binds with said protein or fragment thereof.

28. Method according to claim 27, wherein the reagent is selected from the group consisting of a peptide, an antibody, or a fragment thereof.

29. Method according to claim 27 or 28, wherein said reagent is labeled with a radioactive isotope.

30. Method according to claim 29, wherein said radioactive isotope can be detected by radio-imaging.

31. Method according to claim 29 or 30, wherein said radioactive isotope is selected from the group comprising Technetium⁹⁹"¹, Carbon¹¹, Oxygen¹⁵, Nitrogen¹³, Rubidium⁸²,

Gallium⁶⁷, Gallium⁶⁸, Yttrium⁹⁰, Molybdenum⁹⁹, Iodine¹²³'¹²⁴'¹³¹ Fluorine¹⁸, Phosphorus³², Copper⁶², Thallium²⁰¹, Copper⁶⁴, Copper⁶², Indium¹¹¹, and Xenon¹³³.

32. Method according to claim 30 or 31 , wherein said radio-imaging is selected from the group consisting of single photon emission computed tomography (SPECT), positron emission tomography (PET) and gamma cameras.