BIOMARKERS PREDICTIVE OF THE RESPONSIVENESS TO TNFALPHA INHIBITORS IN AUTOIMMUNE DISORDERS
The invention provides methods for predicting responsiveness to TNFα inhibitors in a subject suffering from an autoimmune disorder, such as rheumatoid arthritis. The methods involve assaying for expression of one or more biomarkers in the subject that are predictive of responsiveness to TNFα inhibitors. A preferred biomarker of the invention is CD11c. The methods can further comprise selecting a treatment regimen with a TNFα inhibitor in an autoimmune disorder subject based upon expression of the biomarker(s) in the subject. The methods can further comprise administering a TNFα inhibitor to the subject according to the selected treatment regimen. Kits that include means for measuring expression of one or more biomarkers that are predictive of responsiveness to TNFα inhibitors for an autoimmune disorder are also provided. Methods of preparing and using databases, and computer program products therefore, for selecting an autoimmune disorder subject for treatment with a TNFα inhibitor are also provided.
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This application is a continuation application of U.S. patent application Ser. No. 12/130,373, filed on May 30, 2008, which claims the benefit of priority to U.S. provisional patent application No. 60/932,888 filed on May 31, 2007. The contents of the above-mentioned priority application is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTIONAutoimmune disorders are a significant and widespread medical problem. For example, rheumatoid arthritis (RA) is an autoimmune disease affecting more than two million people in the United States. RA causes chronic inflammation of the joints and typically is a progressive illness that has the potential to cause joint destruction and functional disability. The cause of rheumatoid arthritis is unknown, although genetic predisposition, infectious agents and environmental factors have all been implicated in the etiology of the disease. In active RA, symptoms can include fatigue, lack of appetite, low grade fever, muscle and joint aches and stiffness. Also during disease flare ups, joints frequently become red, swollen, painful and tender, due to inflammation of the synovium. Furthermore, since RA is a systemic disease, inflammation can affect organs and areas of the body other than the joints, including glands of the eyes and mouth, the lung lining, the pericardium, and blood vessels.
Traditional treatments for the management of RA and other autoimmune disorders include fast acting “first line drugs” and slower acting “second line drugs.” The first line drugs reduce pain and inflammation. Example of such first line drugs include aspirin, naproxen, ibuprofen etodolac and other nonsteroidal anti-inflammatory drugs (NSAIDs), as well as corticosteroids, given orally or injected directly into tissues and joints. The second line drugs promote disease remission and prevent progressive joint destruction and are also referred to as disease-modifying anti-rheumatic drugs or DMARDs. Examples of second line drugs include gold, hydrochloroquine, azulfidine and immunosuppressive agents, such as methotrexate, azathioprine, cyclophosphamide, chlorambucil and cyclosporine. Many of these drugs, however, can have detrimental side-effects. Thus, additional therapies for rheumatoid arthritis and other autoimmune disorders have been sought.
More recently, biological therapies have been applied to the treatment of autoimmune disorders such as rheumatoid arthritis. For example, three TNFα inhibitors, REMICADE™ (infliximab), a chimeric anti-TNFα mAb, ENBREL™ (etanercept), a TNFR-Ig Fc fusion protein, and HUMIRA™ (adalimumab), a human anti-TNFα mAb, have been approved by the FDA for treatment of rheumatoid arthritis. While such biologic therapies have demonstrated success in the treatment of rheumatoid arthritis and other autoimmune disorders, not all subjects treated respond, or respond well, to a TNFα inhibitor. The use of TNFα inhibitors such as TNFα inhibitors typically is more expensive than traditional treatments and usually requires administration by injection, which, at least for certain agents, may require that the patient visit a medical office on a frequent basis. Thus, it would be very helpful to predict in advance of treatment whether a rheumatoid arthritis patient is likely to be responsive to treatment with a TNFα inhibitor. Accordingly, ways for predicting responsiveness to a TNFα inhibitor in patients having autoimmune disorders, such as rheumatoid arthritis patients, are of particular interest.
SUMMARY OF THE INVENTIONThis invention provides methods and compositions for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, such as rheumatoid arthritis, based on the discovery that the expression patterns of particular biomarkers in the subject correlate with responsiveness to a TNFα inhibitor. Using microarray analysis of monocytes from representative rheumatoid arthritis (RA) patients treated with an anti-TNFα monoclonal antibody, 82 differentially expressed genes predictive of responsiveness to TNFα inhibitor treatment were identified by pairwise comparisons between future RA responders and future RA non-responders to anti-TNFα therapy. Furthermore, hierarchical clustering and TaqMan®-PCR of RA responders/non-responders pre-treatment identified one gene of particular interest, CD11c, which was fully predictive of future response to anti-TNFα treatment.
Accordingly, in one aspect, the invention pertains to a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder (e.g., rheumatoid arthritis). The method comprises: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (corresponding to the sequences set forth in Table 9)
Within SEQ ID NOs: 1-82, certain genes were found to be upregulated in RA responders to TNFα inhibitors. Accordingly, in one embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82 (corresponding to sequences from Table 9 that are increased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 31, 37, 44, 47, 62 and 70 (corresponding to sequences from Table 9 that are increased in ≧90% of responders vs. non-responders). Even more preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 44. (corresponding to CD11c, from Table 9, which is increased in 100% of responders vs. non-responders). In each of these embodiments, increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
Within SEQ ID NOs: 1-82, certain genes were found to be downregulated in RA responders to TNFα inhibitors. Accordingly, in one embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81. (corresponding to sequences from Table 9 that are decreased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 11, 29, 65, 73 and 74 (corresponding to sequences from Table 9 that are decreased in ≧90% of responders vs. non-responders). Even more preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 11 or SEQ ID NO: 74 (corresponding to sequences from Table 9 that are decreased in 100% of responders vs. non-responders). In each of these embodiments, decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another aspect, the invention provides a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject, wherein the one or more biomarkers is selected from the group consisting of Aquaporin 3 (Genbank Accession No. NM—004925); Similar to ribosomal protein S24, clone MGC:8595 (Genbank Accession No. NM—033022); Transmembrane emp24 domain trafficking protein 2 (Genbank Accession No. NM—006815; Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (Genbank Accession No. NM—000454); Calmodulin 1 (phosphorylase kinase, delta) (Genbank Accession No. NM—006888); Guanine nucleotide binding protein (G protein), beta polypeptide 1 (Genbank Accession No. NM—002074); Prothymosin, alpha (gene sequence 28) (Genbank Accession No. NM—002823); Homo sapiens isocitrate dehydrogenase 1 (NADP+) soluble (IDH1) (Genbank Accession No. NM—005896); Tumor protein D52 (Genbank Accession Nos. NM—001025252, NM—001025253, NM—005079); Early growth response 1 (Genbank Accession No. NM—001964); Homo sapiens predicted osteoblast protein (GS3786) (Genbank Accession Nos. NM—014888, NM—001040020); Cytochrome c oxidase subunit VIIb (Genbank Accession No. NM—001866); CUG triplet repeat, RNA binding protein 2 (Genbank Accession No. NM—001025077, NM—001025076, NM—006561); Ubiquinol-cytochrome c reductase hinge protein (Genbank Accession No. NM—006004); Homo sapiens leptin receptor gene-related protein (HS0BRGRP) (Genbank Accession No. NM—017526); Wiskott-Aldrich syndrome protein interacting protein (Genbank Accession Nos. NM—001077269, NM—003387); CD97 antigen (Genbank Accession Nos. NM—001025160, NM—001784, NM—078481); Glutamate-cysteine ligase, catalytic subunit (Genbank Accession No. NM—001498); Crystallin, zeta (quinone reductase) (Genbank Accession No. NM—001889); Rap guanine nucleotide exchange factor (GEF) 2 (Genbank Accession No. NM—014247); Ataxin 1 (Genbank Accession No. NM—000332); Adaptor-related protein complex 1, sigma 2 subunit (Genbank Accession No. NM—003916); Ectonucleotide pyrophosphatase/phosphodiesterase 4 (Genbank Accession No. NM—014936); Desmocollin 2 (Genbank Accession Nos. NM—024422, NM—004949); MAL, T-cell differentiation protein (Genbank Accession Nos. NM—002371, NM—022438, NM—022439, NM—022440); Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (Genbank Accession No. NM—005476); Chemokine (C—C motif) ligand 3 (Genbank Accession Nos. NM—001001437, NM—021006); Carboxypeptidase A3 (Genbank Accession No. NM—001870); Charcot-Leyden crystal protein (Genbank Accession No. NM—001828); NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 1, 7 kDa (Genbank Accession No. NM—004545); Interleukin 8 receptor, beta (Genbank Accession No. NM—001557); Platelet factor 4 variant 1 (Genbank Accession No. NM—002620); Poly(A) binding protein interacting protein 1 (Genbank Accession No. NM—006451); ATP-binding cassette, sub-family C (CFTR/MRP), member 3 (Genbank Accession No. NM—003786); Actinin, alpha 1 (Genbank Accession No. NM—001102); NAD kinase (Genbank Accession No. NM—023018); Platelet/endothelial cell adhesion molecule (CD31 antigen) (Genbank Accession No. NM—000442); Esterase D/formylglutathione hydrolase (Genbank Accession No. NM—001984); Chromosome 20 open reading frame 111 (Genbank Accession No. NM—016470); Sterol-C4-methyl oxidase-like (Genbank Accession Nos. NM—001017369, NM—006745); PIM-1 oncogene (Genbank Accession No. NM—002648); GATA binding protein 2 (Genbank Accession No. NM—032638); Cathepsin Z (Genbank Accession No. NM—001336); Integrin alpha-X (antigen CD11c) (Genbank Accession No. NM—000887); Lectin, galactoside-binding, soluble, 8 (galectin 8) (Genbank Accession Nos. NM—006499, NM—201545); CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) (Genbank Accession Nos. NM—006889, NM—175862); Interleukin 8 (Genbank Accession No. NM—000584); Fc fragment of IgE, high affinity I, receptor for alpha polypeptide (Genbank Accession No. NM—002001); Actin, gamma 1 (Genbank Accession No. NM—001614); KIAA0746 protein (Genbank Accession No. NM—015187); Glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID) (Genbank Accession No. NM—002076); Transcription factor 4 (Genbank Accession Nos. BF592782, CR612521); Major histocompatibility complex, class II, DQ alpha 1 (Genbank Accession Nos. NM—002122, NM—020056); Cell division cycle 2-like 6 (CDK8-like) (Genbank Accession No. NM—015076); Major histocompatibility complex, class II, DQ beta 1 (Genbank Accession No. XM—942240); Phospholipase C-like 2 (Genbank Accession No. NM—015184); Coagulation factor II (thrombin) receptor-like 1 (Genbank Accession No. NM—005242); TM2 domain containing 1 (Genbank Accession No. NM—032027); Splicing factor 3b, subunit 1, 155 kDa (Genbank Accession No. NM—012433); SUB1 homolog (S. cerevisiae) (Genbank Accession No. NM—006713); MRNA; cDNA DKFZp564O0862 (Genbank Accession No. AI278204); Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease) (Genbank Accession Nos. NM—201413, NM—000484, NM—201414); Cytochrome b-5 (Genbank Accession Nos. NM—001914, NM—148923); Cold autoinflammatory syndrome 1 (Genbank Accession No. NM—183395); Neugrin, neurite outgrowth associated (Genbank Accession Nos. NM—016645, NM—001033088); Ribosomal protein S26, 40S ribosomal protein (Genbank Accession No. XM—941927); CCR4-NOT transcription complex, subunit 6 (Genbank Accession No. NM—015455); Ubiquinol-cytochrome c reductase complex (7.2 kD) (Genbank Accession Nos. NM—013387, NM—001003684); Hepatocellular carcinoma-associated antigen 112 (Genbank Accession No. NM—018487); Kruppel-like factor 11 (Genbank Accession No. XM—001129527); GGA binding partner (Genbank Accession No. NM—018318); Cornichon homolog 4 (Drosophila) (Genbank Accession No. NM—014184); Hypothetical protein FLJ21616 (Genbank Accession No. NM—024567); Homo sapiens hypothetical protein FLJ10134 (Genbank Accession No. NM—018004); Nuclear prelamin A recognition factor (Genbank Accession Nos. NM—012336, NM—001038618); Erythroblast membrane-associated protein (Genbank Accession Nos. NM—018538, NM—001017922); LR8 protein (Genbank Accession No. NM—014020); Likely ortholog of mouse limb-bud and heart gene (LBH) (Genbank Accession No. NM—030915); Calmin (calponin-like, transmembrane) (Genbank Accession No. NM—024734); Chromosome 14 open reading frame 156 (Genbank Accession No. NM—031210); Guanine nucleotide binding protein (G protein) alpha 12 (Genbank Accession No. NM—007353); and SRY (sex determining region Y)-box 18 (Genbank Accession No. NR—003287) (corresponding to biomarkers listed in Table 9).
Within the above-listed biomarkers, certain genes were found to be upregulated in RA responders to TNFα inhibitors. Accordingly, in one embodiment, the one or more biomarkers is selected from the group consisting of Guanine nucleotide binding protein (G protein), beta polypeptide 1; Prothymosin, alpha (gene sequence 28); Early growth response 1; Homo spaiens leptin receptor gene-related protein (HS0BRGRP); Wiskott-Aldrich syndrome protein interacting protein; CD97 antigen; Crystallin, zeta (quinone reductase); Adaptor-related protein complex 1, sigma 2 subunit; Desmocollin 2; Chemokine (C—C motif) ligand 3; Interleukin 8 receptor, beta; ATP-binding cassette, sub-family C (CFTR/MRP), member 3; Actinin, alpha 1; NAD kinase; Platelet/endothelial cell adhesion molecule (CD31 antigen); Esterase D/formylglutathione hydrolase; Chromosome 20 open reading frame 111; Cathepsin Z; Integrin alpha-X (antigen CD11c); Lectin, galactoside-binding, soluble, 8 (galectin 8); CD86 antigen (CD28 antigen ligand 2, B7-2 antigen); Interleukin 8; Actin, gamma 1; Glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID); Cell division cycle 2-like 6 (CDK8-like); Major histocompatibility complex, class II, DQ beta 1; Coagulation factor II (thrombin) receptor-like 1; Splicing factor 3b, subunit 1, 155 kDa; mRNA; cDNA DKEZp564O0862; Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease); Cold autoinflammatory syndrome 1; Kruppel-like factor 11; Nuclear prelamin A recognition factor; Calmin (calponin-like, transmembrane); and SRY (sex determining region Y)-box 18 (corresponding to biomarkers from Table 9 that are increased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is selected from the group consisting of Interleukin 8 receptor, beta; Platelet/endothelial cell adhesion molecule (CD31 antigen); Integrin alpha-X (antigen CD11c); Interleukin 8; Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease); and Kruppel-like factor 11 (corresponding to biomarkers from Table 9 that are decreased in ≧80% of responders vs. non-responders). Even more preferably, the one or more biomarkers is Integrin alpha-X (antigen CD11c) (corresponding to a biomarker from Table 9 that is increased in 100% of responders vs. non-responders). In each of these embodiments, increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
Within the above-listed biomarkers, certain genes were found to be downregulated in RA responders to TNFα inhibitors. Accordingly, in one embodiment, the one or more biomarkers is selected from the group consisting of Aquaporin 3; Similar to ribosomal protein S24, clone MGC:8595; Transmembrane emp24 domain trafficking protein 2; Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1; Calmodulin 1 (phosphorylase kinase, delta); Homo sapiens isocitrate dehydrogenase 1 (NADP+) soluble (IDH1); Tumor protein D52; Homo sapiens predicted osteoblast protein (GS3786); Cytochrome c oxidase subunit VIIb; CUG triplet repeat, RNA binding protein 2; Ubiquinol-cytochrome c reductase hinge protein; Glutamate-cysteine ligase, catalytic subunit; Rap guanine nucleotide exchange factor (GEF) 2; Ataxin 1; Ectonucleotide pyrophosphatase/phosphodiesterase 4; MAL, T-cell differentiation protein; Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase; Carboxypeptidase A3; Charcot-Leyden crystal protein; NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 1, 7 kDa; Platelet factor 4 variant 1; Poly(A) binding protein interacting protein 1; Sterol-C4-methyl oxidase-like; PIM-1 oncogene; GATA binding protein 2; Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide; KIAA0746 protein; Transcription factor 4; Major histocompatibility complex, class II, DQ alpha 1; Phospholipase C-like 2; TM2 domain containing 1; SUB1 homolog (S. cerevisiae); Cytochrome b-5; Neugrin, neurite outgrowth associated; Ribosomal protein S26, 40S ribosomal protein; CCR4-NOT transcription complex, subunit 6; Ubiquinol-cytochrome c reductase complex (7.2 kD); Hepatocellular carcinoma-associated antigen 112; GGA binding partner; Cornichon homolog 4 (Drosophila); Hypothetical protein FLJ21616; Homo sapiens hypothetical protein FLJ10134; Erythroblast membrane-associated protein; LR8 protein; Likely ortholog of mouse limb-bud and heart gene (LBH); Chromosome 14 open reading frame 156; and Guanine nucleotide binding protein (G protein) alpha 12 (corresponding to biomarkers from Table 9 that are decreased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is selected from the group consisting of Homo sapiens predicted osteoblast protein (GS3786); Charcot-Leyden crystal protein; Neugrin, neurite outgrowth associated; Hypothetical protein FLJ21616; and Homo sapiens hypothetical protein FLJ10134 (corresponding to biomarkers from Table 9 that are decreased in ≧90% of responders vs. non-responders). Even more preferably, the one or more biomarkers is Homo sapiens predicted osteoblast protein (GS3786) or Homo sapiens hypothetical protein FLJ10134 (corresponding to biomarkers from Table 9 that are decreased in 100% of responders vs. non-responders). In each of these embodiments, decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In yet another aspect, the invention provides a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising: (i) assaying the subject for increased expression of a biomarker, which biomarker is CD11c, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on increased expression of CD11c in the subject.
In yet another aspect, the invention provides a method for predicting responsiveness to a TNFα inhibitor, which TNFα inhibitor is adalimumab, in a subject having an autoimmune disorder, the method comprising: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to adalimumab in an autoimmune disorder, and (ii) predicting responsiveness of the subject to adalimumab based on expression of the one or more biomarkers in the subject. Preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (corresponding to biomarkers set forth in Table 9).
In one embodiment of the methods of the invention, a sample from the subject is assayed for expression of mRNA encoding the one or more biomarkers. In another embodiment of the methods of the invention, a sample from the subject is assayed for protein expression of the one or more biomarkers.
In one embodiment, the methods of the invention further comprise selecting a treatment regimen with the TNFα inhibitor based upon expression of the one or more biomarkers in the subject. In another embodiment, the methods of the invention further comprise administering the TNFα inhibitor to the subject according to the treatment regimen such that autoimmune disorder is inhibited in the subject.
A preferred TNFα inhibitor of the invention is an anti-tumor necrosis factor-alpha (TNFα) antibody, or antigen-binding portion thereof. The anti-TNFα antibody, or antigen-binding portion thereof, can be, for example, a humanized antibody, a chimeric antibody or a multivalent antibody. For example, the anti-TNFα antibody, or antigen-binding portion thereof, can be infliximab or golimumab. In another embodiment, the anti-TNFα antibody, or antigen-binding portion thereof, is a human antibody. For example, the anti-TNFα antibody, or antigen-binding portion thereof, can be an isolated human antibody that dissociates from human TNFα with a Kd of 1×10−8 M or less and a Koff rate constant of 1×10−3 s−1 or less, both determined by surface plasmon resonance, and neutralizes human TNFα cytotoxicity in a standard in vitro L929 assay with an IC50 of 1×10−7 M or less. In another embodiment, the anti-TNFα antibody, or antigen-binding portion thereof, is an isolated human antibody with the following characteristics:
a) dissociates from human TNFα with a Koff rate constant of 1×10−3 s−1 or less, as determined by surface plasmon resonance;
b) has a light chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 305, or modified from SEQ ID NO: 305 by a single alanine substitution at position 1, 4, 5, 7 or 8 or by one to five conservative amino acid substitutions at positions 1, 3, 4, 6, 7, 8 and/or 9;
c) has a heavy chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 306, or modified from SEQ ID NO: 306 by a single alanine substitution at position 2, 3, 4, 5, 6, 8, 9, 10 or 11 or by one to five conservative amino acid substitutions at positions 2, 3, 4, 5, 6, 8, 9, 10, 11 and/or 12.
In another embodiment, the anti-TNFα antibody, or antigen-binding portion thereof, is an isolated human antibody with a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 303 and a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 304. In yet another embodiment, the anti-TNFα antibody, or antigen-binding portion thereof, is adalimumab. Yet another example of a TNFα inhibitor is etanercept.
Preferably, in the methods of the invention, the subject is a human.
In another aspect, the invention pertains to a kit for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder (e.g., rheumatoid arthritis). The kit comprises:
a) means for isolating monocytes;
b) means for measuring expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder;
c) means for measuring expression of at least one housekeeping gene; and
d) instructions for use of the kit to predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82.
In one embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82, more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 31, 37, 44, 47, 62 and 70, even more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 44 (CD11c). In each of these embodiments, the instructions for use of the kit instruct that increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81, more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 11, 29, 65, 73 and 74, even more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 11 or SEQ ID NO: 74. In each of these embodiments, the instructions for use of the kit instruct that decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In one embodiment of the kit, the means for measuring expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder comprises a nucleic acid preparation sufficient to detect expression of mRNA encoding the biomarker in a sample from the subject. In another embodiment of the kit, the means for measuring expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder comprises an antibody preparation sufficient to detect protein expression of the biomarker in a sample from the subject. The kit can further comprise a TNFα inhibitor for treating the autoimmune disorder in the subject.
In another aspect, the invention pertains to methods of monitoring an autoimmune disorder (e.g., RA) in a subject having the autoimmune disorder (e.g., RA). These methods are based, at least in part, on microarray analysis of monocytes from representative rheumatoid arthritis (RA) patients treated with an anti-TNFα monoclonal antibody, including (i) hierarchical clustering with the genes resulting from a simultaneous comparison between RA versus ND and RA responders pre- versus post-anti-TNFα therapy, (ii) prediction analysis of microarrays (PAM); and (iii) hierarchical clustering based on the comparison between RA responders and non-responders post-treatment.
Accordingly, in another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 83-133 (corresponding to biomarkers set forth in Table 3), thereby monitoring the autoimmune disorder in the subject.
In another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-177, 110, 112, 118, 123 and 131 (corresponding to biomarkers set forth in Table 4), thereby monitoring the autoimmune disorder in the subject. In one embodiment, preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-150, 112, 118 and 131 (upregulated biomarkers from Table 4), wherein expression of the one or more biomarkers is increased in the subject. In another embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 151-177, 110 and 123 (downregulated biomarkers from Table 4), wherein expression of the one or more biomarkers is decreased in the subject.
In another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-292, 91 and 97 (corresponding to biomarkers set forth in Table 5), thereby monitoring the autoimmune disorder in the subject. In one embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-185; 187-200, 202, 203, 205-207; 211, 213, 214, 216, 220, 221, 226, 228, 229, 231, 232, 234, 235, 238-247, 249, 250, 253, 262-265, 268-282 and 285-288 (upregulated biomarkers from Table 5), wherein expression of the one or more biomarkers is increased in the subject. In another embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 186, 201, 204, 208, 209, 91, 210, 212, 97, 215, 217-219, 222-225, 227, 230, 233, 236, 237, 248, 251, 252, 254-261, 266, 267, 283, 284 and 289-292 (downregulated biomarkers from Table 5), wherein expression of the one or more biomarkers is decreased in the subject.
In another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 87, 97, 101, 293, 92, 272, 93, 107, 108, 121 and 123 (corresponding to pre-treatment biomarkers set forth in Table 6), and wherein the subject is monitored prior to treatment with a TNFα inhibitor, thereby monitoring the autoimmune disorder in the subject.
In another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 290, 209, 98, 112, 116, 121, 130, 155, 92, 289, 216 and 131 (corresponding to post-treatment biomarkers set forth in Table 6), and wherein the subject is monitored after treatment with a TNFα inhibitor, thereby monitoring the autoimmune disorder in the subject.
In yet another aspect, the invention provides a method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 97, 102, 29, 294, 295, 91, 131, 290, 100, 134, 296, 297, 298, 299, 136, 174 and 300 (corresponding to biomarkers set forth in Table 7), thereby monitoring the autoimmune disorder in the subject.
In yet another aspect, the invention pertains to a method of building a database for use in selecting a subject having an autoimmune disorder (e.g., RA) for treatment with a TNFα inhibitor. The method comprises: receiving, in a computer system, biomarker expression patterns from a plurality of subjects having an autoimmune disorder; and storing the biomarker expression pattern from each subject such that the biomarker expression pattern is associated with an identifier of the subject, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82. The identifier of the subject can be, for example, a numerical identifier coded to an identity of the subject. In a preferred embodiment, the method further comprises receiving, in the computer system, one or more treatment regimens for treatment of the autoimmune disorder in a subject such that the treatment regimen is associated with the biomarker expression pattern of the subject and the identifier of the subject.
The invention also pertains to a computer program product containing executable instructions that when executed cause a processor to perform operations comprising: receiving, in a computer system, a biomarker expression pattern of a subject at one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder; and storing the biomarker expression pattern such that the biomarker expression pattern is associated with an identifier of the subject, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82. The computer program can further cause the processor to perform an operation comprising: receiving, in the computer system, a treatment regimen for treatment of the autoimmune disorder in the subject such that the treatment regimen is associated with the biomarker expression pattern of the subject and the identifier of the subject.
In yet another aspect, the invention pertains to a method of selecting an autoimmune disorder subject for treatment with a TNFα inhibitor, the method comprising: (i) identifying, in a database comprising a plurality of autoimmune disorder subjects, a subject whose database entry is associated with a biomarker expression pattern that is predictive of responsiveness to treatment with a TNFα inhibitor, and (ii) selecting the subject for treatment with a TNFα inhibitor, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82. The method can further comprise selecting a treatment regimen by identifying, in the database, a treatment regimen that has been associated with the biomarker expression pattern of the subject and with an identifier of the subject.
The invention also pertains to a computer program product containing executable instructions that when executed cause a processor to perform operations comprising: (i) identifying, in a database including a plurality of autoimmune disorder subjects associated with biomarker expression patterns, a subject that is associated with a biomarker expression pattern that is predictive of responsiveness to treatment with a TNFα inhibitor; and (ii) outputting the identified subject as a subject to be treated with a TNFα inhibitor; wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82. In one embodiment, the computer program further causes the processor to perform an operation comprising outputting a treatment regimen that is associated with the subject to be treated with the TNFα inhibitor.
This invention provides methods for predicting responsiveness to a TNFα inhibitor in a subject suffering from an autoimmune disorder, and methods for selecting a treatment regimen with a TNFα inhibitor, based on expression of particular biomarkers in the subject to be treated. The invention is based, at least in part, on the observation that altered expression of particular biomarkers in a subject suffering from rheumatoid arthritis is associated with increased or decreased responsiveness to therapy with a TNFα inhibitor. Microarray analysis, hierarchical clustering and TaqMan®-PCR analysis were used to examine normal donors (NA) and rheumatoid arthritis (RA) patients, who were categorized as being responsive to treatment with an anti-TNFα antibody (RA responders) or nonresponsive to treatment with an anti-TNFα antibody (RA nonresponders). A panel of 82 genes were identified whose expression was altered (upregulated or downregulated) in patients identified as either future RA responders or future RA nonresponders, demonstrating the ability of these genes to act as biomarkers for predicting responsiveness to TNFα inhibitor treatment. In particular, one gene, encoding the antigen CD11c, was identified as fully predicting the future response to anti-TNFα treatment. Accordingly, the expression pattern of one or more biomarkers can be assessed in RA subjects for which TNFα inhibitor therapy is being considered, or subjects suffering from other autoimmune disorders amenable to TNFα inhibitor therapy, to thereby predict responsiveness of the subject to such therapy and/or to aid in the selection of an appropriate treatment regimen.
Furthermore, additional patterns of biomarker expression were identified by (i) hierarchical clustering with the genes resulting from a simultaneous comparison between RA versus ND and RA responders pre- versus post-anti-TNFα therapy; (ii) prediction analysis of microarrays; and (iii) hierarchical clustering based on the comparison between RA responders and non-responders post-treatment. Accordingly, the biomarker expression patterns described herein also can be using in monitoring an autoimmune disorder in a subject, e.g., monitoring the responsiveness of the subject to a particular therapy or assisting in the diagnosis or prognosis of the autoimmune disorder (e.g., RA) in the subject.
In order that the present invention may be more readily understood, certain terms are first defined. Additional definitions are set forth throughout the detailed description.
The term “predicting responsiveness to a TNFα inhibitor”, as used herein, is intended to refer to an ability to assess the likelihood that treatment of a subject with a TNFα inhibitor will or will not be effective in (e.g., provide a measurable benefit to) the subject. In particular, such an ability to assess the likelihood that treatment will or will not be effective typically is exercised before treatment with the TNFα inhibitor is begun in the subject. However, it is also possible that such an ability to assess the likelihood that treatment will or will not be effective can be exercised after treatment has begun but before an indicator of effectiveness (e.g., an indicator of measurable benefit) has been observed in the subject.
The term “TNFα inhibitor” as used herein is intended to encompass agents including proteins, antibodies, antibody fragments, fusion proteins (e.g., Ig fusion proteins or Fc fusion proteins), multivalent binding proteins (e.g., DVD Ig), small molecule TNFα antagonists and similar naturally- or nonnaturally-occurring molecules, and/or recombinant and/or engineered forms thereof, that, directly or indirectly, inhibits TNFα activity, such as by inhibiting interaction of TNFα with a cell surface receptor for TNFα, inhibiting TNFα protein production, inhibiting TNFα gene expression, inhibiting TNFα secretion from cells, inhibiting TNFα receptor signaling or any other means resulting in decreased TNFα activity in a subject. The term “TNFα inhibitor” also includes agents which interfere with TNFα activity. Examples of TNFα inhibitors include etanercept (ENBREL™, Amgen), infliximab (REMICADE™, Johnson and Johnson), human anti-TNF monoclonal antibody adalimumab (D2E7/HUMIRA™, Abbott Laboratories), CDP 571 (Celltech), and CDP 870 (Celltech), as well as other compounds which inhibit TNFα activity, such that when administered to a subject suffering from or at risk of suffering from a disorder in which TNFα activity is detrimental (e.g., RA), the disorder is treated. The term also includes each of the anti-TNFα human antibodies and antibody portions described herein as well as those described in U.S. Pat. Nos. 6,090,382; 6,258,562; 6,509,015, and in U.S. patent application Ser. Nos. 09/801,185 (U.S. Publication No. 20030092059) and 10/302,356 (U.S. Publication No. 20030219438), each incorporated by reference herein.
The term “antibody” as referred to herein includes whole antibodies and any antigen binding fragment (i.e., “antigen-binding portion”) or single chains thereof. An “antibody” refers to a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds, or an antigen binding portion thereof. Each heavy chain is comprised of a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region. The heavy chain constant region is comprised of three domains, CH1, CH2 and CH3. Each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region. The light chain constant region is comprised of one domain, CL. The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDR), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and VL is composed of three CDRs and four FRs, arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The variable regions of the heavy and light chains contain a binding domain that interacts with an antigen. The constant regions of the antibodies may mediate the binding of the immunoglobulin to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (Clq) of the classical complement system.
The term “antibody” is also intended to encompass dual-specific antibodies and bispecific antibodies. The term “dual-specific antibody”, as used herein, refers to full-length antibodies that can bind two different antigens (or epitopes) in each of its two binding arms (a pair of HC/LC) (see e.g., PCT publication WO 02/02773). Accordingly a dual-specific binding protein has two identical antigen binding arms, with identical specificity and identical CDR sequences, and is bi-valent for each antigen it binds to. The term “bispecific antibody”, as used herein, refers to full-length antibodies that are generated by quadroma technology (see Milstein, C. and A. C. Cuello (1983) Nature, 305:537-40), by chemical conjugation of two different mAbs (see Staerz, U. D., et al. (1985) Nature 314:628-31), or by knob-into-hole or similar approaches which introduces mutations in the Fc region (see Holliger, P., T. Prospero, and G. Winter (1993) Proc. Natl. Acad. Sci. USA 90:6444-8), resulting in multiple different immunogloblin species of which only one is the functional bispecific antibody. By molecular function, a bispecific antibody binds one antigen (or epitope) on one of its two binding arms (one pair of HC/LC), and binds a different antigen (or epitope) on its second arm (a different pair of HC/LC). By this definition, a bispecific antibody has two distinct antigen binding arms (in both specificity and CDR sequences), and is mono-valent for each antigen it binds to. Thus, when used herein, a “bispecific” antibody of the invention has one binding arm that is specific for an epitope of TNFα and a second binding arm that is specific for a different antigen or epitope.
The term “antigen-binding portion” of an antibody (or simply “antibody portion”), as used herein, refers to one or more fragments of an antibody that retain the ability to specifically bind to an antigen. It has been shown that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody. Examples of binding fragments encompassed within the term “antigen-binding portion” of an antibody include (i) a Fab fragment, a monovalent fragment consisting of the VL, VH, CL and CH1 domains; (ii) a F(ab′)2 fragment, a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region; (iii) a Fd fragment consisting of the VH and CH1 domains; (iv) a Fv fragment consisting of the VL and VH domains of a single arm of an antibody, (v) a dAb fragment (Ward et al., (1989) Nature 341:544-546), which consists of a VH domain; and (vi) an isolated complementarity determining region (CDR). Furthermore, although the two domains of the Fv fragment, VL and VH, are coded for by separate genes, they can be joined, using recombinant methods, by a synthetic linker that enables them to be made as a single protein chain in which the VL and VH regions pair to form monovalent molecules (known as single chain Fv (scFv); see e.g., Bird et al. (1988) Science 242:423-426; and Huston et al. (1988) Proc. Natl. Acad. Sci. USA 85:5879-5883). Such single chain antibodies are also intended to be encompassed within the term “antigen-binding portion” of an antibody. These antibody fragments are obtained using conventional techniques known to those with skill in the art, and the fragments are screened for utility in the same manner as are intact antibodies.
The terms “monoclonal antibody” or “monoclonal antibody composition” as used herein refer to a preparation of antibody molecules of single molecular composition. A monoclonal antibody composition displays a single binding specificity and affinity for a particular epitope.
The terms “chimeric antibody” or “chimeric monoclonal antibody” are intended to refer to antibodies in which the variable region sequences are derived from one species and the constant region sequences are derived from another species, such as an antibody in which the variable region sequences are derived from a mouse antibody and the constant region sequences are derived from a human antibody. Such “chimeric antibodies” can be prepared by standard recombinant technology well established in the art. For example, a nucleic acid encoding a VH region from a mouse antibody can be operatively linked to a nucleic acid encoding the heavy chain constant regions from a human antibody and, likewise, a nucleic acid encoding a VL region from a mouse antibody can be operatively linked to a nucleic acid encoding the light chain constant region from a human antibody.
The terms “humanized antibody” or “humanized monoclonal antibody” are intended to refer to antibodies in which CDR sequences derived from the germline of a non-human mammalian species, such as a mouse, have been grafted onto human framework sequences. Additional framework region modifications may be made within the human framework sequences. Such “humanized antibodies” can be prepared by standard recombinant technology well established in the art. For example, nucleic acids encoding the CDR1, CD2 and CDR3 regions from a VH region of a mouse antibody can be operatively linked to nucleic acids encoding the FR1, FR2, FR3 and FR4 regions of a human VH region, and the entire “CDR-grafted” VH region can be operatively linked to nucleic acid encoding the heavy chain constant regions from a human antibody. Likewise, nucleic acids encoding the CDR1, CD2 and CDR3 regions from a VL region of a mouse antibody can be operatively linked to nucleic acids encoding the FR1, FR2, FR3 and FR4 regions of a human VL region, and the entire “CDR-grafted” VL region can be operatively linked to nucleic acid encoding the light chain constant region from a human antibody.
The term “human antibody”, as used herein, is intended to refer to antibodies having variable regions in which both the framework and CDR regions are derived from human germline immunoglobulin sequences. Furthermore, if the antibody contains a constant region, the constant region also is derived from human germline immunoglobulin sequences. Human antibodies may include amino acid residues not encoded by human germline immunoglobulin sequences (e.g., mutations introduced by random or site-specific mutagenesis in vitro or by somatic mutation in vivo).
The term “human monoclonal antibody” refers to antibodies displaying a single binding specificity which have variable regions in which both the framework and CDR regions are derived from human germline immunoglobulin sequences. Human monoclonal antibodies can be produced by a hybridoma which includes a B cell obtained from a transgenic nonhuman animal, e.g., a transgenic mouse, having a genome comprising a human heavy chain transgene and a light chain transgene fused to an immortalized cell. The term “human monoclonal antibody”, as used herein, also includes all human antibodies that are prepared, expressed, created or isolated by recombinant means, such as (a) antibodies isolated from an animal (e.g., a mouse) that is transgenic or transchromosomal for human immunoglobulin genes or a hybridoma prepared therefrom, (b) antibodies isolated from a host cell transformed to express the human antibody, e.g., from a transfectoma, (c) antibodies isolated from a recombinant, combinatorial human antibody library, and (d) antibodies prepared, expressed, created or isolated by any other means that involve splicing of human immunoglobulin gene sequences to other DNA sequences. Such recombinant human antibodies have variable regions in which the framework and CDR regions are derived from human germline immunoglobulin sequences. Such recombinant human antibodies, however, can be subjected to in vitro mutagenesis (or, when an animal transgenic for human Ig sequences is used, in vivo somatic mutagenesis) and thus the amino acid sequences of the VH and VL regions of the recombinant antibodies are sequences that, while derived from and related to human germline VH and VL sequences, may not naturally exist within the human antibody germline repertoire in vivo.
The terms “Ig fusion protein” and “Fc fusion protein” are intended to refer to a recombinant, composite protein comprising a polypeptide of interest operatively linked to a constant region portion of immunoglobulin, typically the hinge, CH2 and CH3 domains of heavy chain constant region, more typically the human IgG1 hinge, CH2 and CH3 domains. The polypeptide of interest operatively linked to the Fc portion can be, for example, a full-length protein or only a portion of a full-length protein, such as one or more extracellular domains of a protein, e.g., one or more extracellular domains of a cell-surface protein. Such “Ig fusion proteins” can be prepared by standard recombinant technology well established in the art. For example, a nucleic acid encoding the polypeptide of interest can be operatively linked to a nucleic acid encoding the hinge, CH2 and CH3 domains of a heavy chain constant region.
The term “multivalent binding protein”, as a form of TNFα inhibitor, is used in this specification to denote a binding protein comprising two or more antigen binding sites. Examples of multivalent binding proteins include dual variable domain (DVD) binding proteins. The multivalent binding protein is preferably engineered to have three or more antigen binding sites, and is generally not a naturally occurring antibody. A multivalent binding protein also can be a “multispecific binding protein.” The term “multispecific binding protein” refers to a binding protein capable of binding two or more related or unrelated targets (wherein, with respect to this specification at least one of the targets is TNFα). Dual variable domain (DVD) binding proteins, as used herein, are binding proteins that comprise two or more antigen binding sites and are tetravalent or multivalent binding proteins. Such DVDs may be monospecific, i.e., capable of binding one antigen (e.g., TNFα) or multispecific, i.e., capable of binding two or more antigens (e.g., TNFα and one or more other antigens). DVD binding proteins comprising two heavy chain DVD polypeptides and two light chain DVD polypeptides are referred to as “DVD Ig.” Each half of a DVD Ig comprises a heavy chain DVD polypeptide, and a light chain DVD polypeptide, and two antigen binding sites. Each binding site comprises a heavy chain variable domain and a light chain variable domain with a total of 6 CDRs involved in antigen binding per antigen binding site. DVD binding proteins and methods of making DVD binding proteins are disclosed in US. Publication No. 20070071675, the entire contents of which are specifically incorporated herein by reference.
As used herein, the term “biomarker” is intended to encompass a substance that is used as an indicator of a biologic state and includes genes (and nucleotide sequences of such genes), mRNAs (and nucleotide sequences of such mRNAs) and proteins (and amino acid sequences of such proteins). A “biomarker expression pattern” is intended to refer to a quantitative or qualitative summary of the expression of one or more biomarkers in a subject, such as in comparison to a standard or a control.
The terms “increased” or “increased expression” and “decreased” or “decreased expression”, with respect to the expression pattern of a biomarker(s), are used herein as meaning that the level of expression is increased or decreased relative to a constant basal level of expression of a household, or housekeeping, gene, whose expression level does not significantly vary under different conditions. A nonlimiting example of such a household, or housekeeping, gene is GAPDH. Other suitable household, or housekeeping, gene are well-established in the art.
As used herein, the term “CD11c” refers to a protein having a full-length amino acid sequence as set forth at Genbank Accession No. NP—000878 (also shown as SEQ ID NO: 302) and encoded by a full-length nucleotide sequence as set forth at Genbank Accession No. NM—000887 (also shown as SEQ ID NO: 301). CD11c is also known in the art as CD11C, CD11c antigen, Integrin alpha X, complement component 3 receptor 4 subunit, ITGAX, LeuM5, Integrin alpha X precursor, Leukocyte adhesion glycoprotein p150,p95 alpha chain, and Leukocyte adhesion receptor p150 subunit, which terms may be used interchangeably herein to refer to CD11c.
As used herein, the term “Affymetrix ID” refers to a numerical identifier that corresponds to a sequence entry in an Affymetrix database, which entry includes the sequence as well as additional information relating to the sequence and corresponding protein. The sequence entries, and additional information in the entries, for each Affymetrix ID are publicly available (e.g., by entering the Affymetrix ID number into the Affymetrix database search engine, e.g., at https://www.affymetrix.com/analysis/netaffx/index.affx). All sequence entries (such as Genbank Accession numbers), and additional information provided for each entry, corresponding to each of the Affymetrix ID numbers disclosed herein are hereby specifically incorporated by reference in their entirety.
As used herein, the term “subject” includes humans, and non-human animals amenable to TNFα inhibitor therapy, e.g., preferably mammals, such as non-human primates, sheep, dogs, cats, horses and cows.
As used herein, the term “autoimmune disorder subject” or “AD subject” is intended to refer to a subject (e.g., human patient) suffering from an autoimmune disorder.
As used herein, the term “rheumatoid arthritis subject” or “RA subject” is intended to refer to a subject (e.g., human patient) suffering from rheumatoid arthritis.
As used herein, the term “treatment regimen” is intended to refer to one or more parameters selected for the treatment of a subject, e.g., with a TNFα inhibitor, which parameters can include, but are not necessarily limited to, the type of agent chosen for administration, the dosage, the formulation, the route of administration and the frequency of administration.
Various aspects of the invention are described in further detail in the following subsections.
Prediction of Responsiveness to a TNFα Inhibitor for Autoimmune DisordersIn one aspect, the invention pertains to a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, such as rheumatoid arthritis. Typically, the method comprises (i) assaying the subject for the expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject. As used herein, the term “one or more biomarkers” is intended to mean that at least one biomarker in a disclosed list of biomarkers is assayed and, in various embodiments, more than one biomarker set forth in the list may be assayed, such as two, three, four, five, ten, twenty, thirty, forty, fifty, more than fifty, or all the biomarkers in the list may be assayed.
Predicting responsiveness of the subject to the TNFα inhibitor “based on expression of the one or more biomarkers in the subject” typically involves comparing the level, or pattern, of expression of the one or more biomarkers in the subject to a known standard or control (which known standard or control may be derived from, for example, a normal subject, a pre-established TNFα inhibitor responder or a pre-established TNFα inhibitor non-responder). In a preferred embodiment, the level of expression of the biomarker(s) is measured in parallel with measurement of the level of expression of one or more “housekeeping” genes, such as GAPDH, whose expression level is not altered by the autoimmune disorder. The level of expression of the biomarker(s) is determined to be “increased” or “decreased” relative to a constant basal level of expression of the housekeeping gene. Examples of suitable housekeeping genes, such as GAPDH, that can be used for comparison purposes are well known in the art.
Preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (corresponding to sequences of the biomarkers set forth in Table 9). Thus, at least one of the biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 is assayed and, in various embodiments, for example, two, three, four, five, ten, twenty, thirty, forty, fifty, more than fifty, or all the biomarkers in the list may be assayed.
In a preferred embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82 (corresponding to sequences from Table 9 that are increased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 31, 37, 44, 47, 62 and 70 (corresponding to sequences from Table 9 that are increased in ≧90% of responders vs. non-responders). Even more preferably, at least one of the biomarkers to be assayed is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 44 (corresponding to the biomarker CD11c, which, as set forth in Table 9, is increased in 100% of responders vs. non-responders). In each of these embodiments, increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor (e.g., increased expression relative to a standard or control level of expression, which standard or control level of expression can be based, for example, on the level of expression in previously established TNFα inhibitor non-responder RA subjects).
In another preferred embodiment, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81 (corresponding to sequences from Table 9 that are decreased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 11, 29, 65, 73 and 74 (corresponding to sequences from Table 9 that are decreased in ≧90% of responders vs. non-responders). Even more preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 11 or SEQ ID NO: 74 (corresponding to sequences from Table 9 that are decreased in 100% of responders vs. non-responders). In each of these embodiments, decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor (e.g., decreased expression relative to a standard or control level of expression, which standard or control level of expression can be based, for example, on the level of expression in previously established TNFα inhibitor non-responder RA subjects).
In another aspect, the invention provides a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in the autoimmune disorder, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject, wherein the one or more biomarkers is selected from the group consisting of Aquaporin 3 (Genbank Accession No. NM—004925); Similar to ribosomal protein S24, clone MGC:8595 (Genbank Accession No. NM—033022); Transmembrane emp24 domain trafficking protein 2 (Genbank Accession No. NM—006815; Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (Genbank Accession No. NM—000454); Calmodulin 1 (phosphorylase kinase, delta) (Genbank Accession No. NM—006888); Guanine nucleotide binding protein (G protein), beta polypeptide 1 (Genbank Accession No. NM—002074); Prothymosin, alpha (gene sequence 28) (Genbank Accession No. NM—002823); Homo sapiens isocitrate dehydrogenase 1 (NADP+) soluble (IDH1) (Genbank Accession No. NM—005896); Tumor protein D52 (Genbank Accession Nos. NM—001025252, NM—001025253, NM—005079); Early growth response 1 (Genbank Accession No. NM—001964); Homo sapiens predicted osteoblast protein (GS3786) (Genbank Accession Nos. NM—014888, NM—001040020); Cytochrome c oxidase subunit VIIb (Genbank Accession No. NM—001866); CUG triplet repeat, RNA binding protein 2 (Genbank Accession No. NM—001025077, NM—001025076, NM—006561); Ubiquinol-cytochrome c reductase hinge protein (Genbank Accession No. NM—006004); Homo sapiens leptin receptor gene-related protein (HS0BRGRP) (Genbank Accession No. NM—017526); Wiskott-Aldrich syndrome protein interacting protein (Genbank Accession Nos. NM—001077269, NM—003387); CD97 antigen (Genbank Accession Nos. NM—001025160, NM—001784, NM—078481); Glutamate-cysteine ligase, catalytic subunit (Genbank Accession No. NM—001498); Crystallin, zeta (quinone reductase) (Genbank Accession No. NM—001889); Rap guanine nucleotide exchange factor (GEF) 2 (Genbank Accession No. NM—014247); Ataxin 1 (Genbank Accession No. NM—000332); Adaptor-related protein complex 1, sigma 2 subunit (Genbank Accession No. NM—003916); Ectonucleotide pyrophosphatase/phosphodiesterase 4 (Genbank Accession No. NM—014936); Desmocollin 2 (Genbank Accession Nos. NM—024422, NM—004949); MAL, T-cell differentiation protein (Genbank Accession Nos. NM—002371, NM—022438, NM—022439, NM—022440); Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (Genbank Accession No. NM—005476); Chemokine (C—C motif) ligand 3 (Genbank Accession Nos. NM—001001437, NM—021006); Carboxypeptidase A3 (Genbank Accession No. NM—001870); Charcot-Leyden crystal protein (Genbank Accession No. NM—001828); NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 1, 7 kDa (Genbank Accession No. NM—004545); Interleukin 8 receptor, beta (Genbank Accession No. NM—001557); Platelet factor 4 variant 1 (Genbank Accession No. NM—002620); Poly(A) binding protein interacting protein 1 (Genbank Accession No. NM—006451); ATP-binding cassette, sub-family C (CFTR/MRP), member 3 (Genbank Accession No. NM—003786); Actinin, alpha 1 (Genbank Accession No. NM—001102); NAD kinase (Genbank Accession No. NM—023018); Platelet/endothelial cell adhesion molecule (CD31 antigen) (Genbank Accession No. NM—000442); Esterase D/formylglutathione hydrolase (Genbank Accession No. NM—001984); Chromosome 20 open reading frame 111 (Genbank Accession No. NM—016470); Sterol-C4-methyl oxidase-like (Genbank Accession Nos. NM—001017369, NM—006745); PIM-1 oncogene (Genbank Accession No. NM—002648); GATA binding protein 2 (Genbank Accession No. NM—032638); Cathepsin Z (Genbank Accession No. NM—001336); Integrin alpha-X (antigen CD11c) (Genbank Accession No. NM—000887); Lectin, galactoside-binding, soluble, 8 (galectin 8) (Genbank Accession Nos. NM—006499, NM—201545); CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) (Genbank Accession Nos. NM—006889, NM—175862); Interleukin 8 (Genbank Accession No. NM—000584); Fc fragment of IgE, high affinity I, receptor for alpha polypeptide (Genbank Accession No. NM—002001); Actin, gamma 1 (Genbank Accession No. NM—001614); KIAA0746 protein (Genbank Accession No. NM—015187); Glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID) (Genbank Accession No. NM—002076); Transcription factor 4 (Genbank Accession Nos. BF592782, CR612521); Major histocompatibility complex, class II, DQ alpha 1 (Genbank Accession Nos. NM—002122, NM—020056); Cell division cycle 2-like 6 (CDK8-like) (Genbank Accession No. NM—015076); Major histocompatibility complex, class II, DQ beta 1 (Genbank Accession No. XM—942240); Phospholipase C-like 2 (Genbank Accession No. NM—015184); Coagulation factor II (thrombin) receptor-like 1 (Genbank Accession No. NM—005242); TM2 domain containing 1 (Genbank Accession No. NM—032027); Splicing factor 3b, subunit 1, 155 kDa (Genbank Accession No. NM—012433); SUB1 homolog (S. cerevisiae) (Genbank Accession No. NM—006713); mRNA; cDNA DKFZp564O0862 (Genbank Accession No. AI278204); Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease) (Genbank Accession Nos. NM—201413, NM—000484, NM—201414); Cytochrome b-5 (Genbank Accession Nos. NM—001914, NM—148923); Cold autoinflammatory syndrome 1 (Genbank Accession No. NM—183395); Neugrin, neurite outgrowth associated (Genbank Accession Nos. NM—016645, NM—001033088); Ribosomal protein S26, 40S ribosomal protein (Genbank Accession No. XM—941927); CCR4-NOT transcription complex, subunit 6 (Genbank Accession No. NM—015455); Ubiquinol-cytochrome c reductase complex (7.2 kD) (Genbank Accession Nos. NM—013387, NM—001003684); Hepatocellular carcinoma-associated antigen 112 (Genbank Accession No. NM—018487); Kruppel-like factor 11 (Genbank Accession No. XM—001129527); GGA binding partner (Genbank Accession No. NM—018318); Cornichon homolog 4 (Drosophila) (Genbank Accession No. NM—014184); Hypothetical protein FLJ21616 (Genbank Accession No. NM—024567); Homo sapiens hypothetical protein FLJ10134 (Genbank Accession No. NM—018004); Nuclear prelamin A recognition factor (Genbank Accession Nos. NM—012336, NM—001038618); Erythroblast membrane-associated protein (Genbank Accession Nos. NM—018538, NM—001017922); LR8 protein (Genbank Accession No. NM—014020); Likely ortholog of mouse limb-bud and heart gene (LBH) (Genbank Accession No. NM—030915); Calmin (calponin-like, transmembrane) (Genbank Accession No. NM—024734); Chromosome 14 open reading frame 156 (Genbank Accession No. NM—031210); Guanine nucleotide binding protein (G protein) alpha 12 (Genbank Accession No. NM—007353); and SRY (sex determining region Y)-box 18 (Genbank Accession No. NR—003287) (corresponding to biomarkers listed in Table 9).
In a preferred embodiment, the one or more biomarkers is selected from the group consisting of Guanine nucleotide binding protein (G protein), beta polypeptide 1; Prothymosin, alpha (gene sequence 28); Early growth response 1; Homo spaiens leptin receptor gene-related protein (HS0BRGRP); Wiskott-Aldrich syndrome protein interacting protein; CD97 antigen; Crystallin, zeta (quinone reductase); Adaptor-related protein complex 1, sigma 2 subunit; Desmocollin 2; Chemokine (C—C motif) ligand 3; Interleukin 8 receptor, beta; ATP-binding cassette, sub-family C (CFTR/MRP), member 3; Actinin, alpha 1; NAD kinase; Platelet/endothelial cell adhesion molecule (CD31 antigen); Esterase D/formylglutathione hydrolase; Chromosome 20 open reading frame 111; Cathepsin Z; Integrin alpha-X (antigen CD11c); Lectin, galactoside-binding, soluble, 8 (galectin 8); CD86 antigen (CD28 antigen ligand 2, B7-2 antigen); Interleukin 8; Actin, gamma 1; Glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID); Cell division cycle 2-like 6 (CDK8-like); Major histocompatibility complex, class II, DQ beta 1; Coagulation factor II (thrombin) receptor-like 1; Splicing factor 3b, subunit 1, 155 kDa; mRNA; cDNA DKEZp564O0862; Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease); Cold autoinflammatory syndrome 1; Kruppel-like factor 11; Nuclear prelamin A recognition factor; Calmin (calponin-like, transmembrane); and SRY (sex determining region Y)-box 18 (corresponding to biomarkers listed in Table 9 that are increased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is selected from the group consisting of Interleukin 8 receptor, beta; Platelet/endothelial cell adhesion molecule (CD31 antigen); Integrin alpha-X (antigen CD11c); Interleukin 8; Amyloid beta (A4) precursor protein (peptidase nexin-II, Alzheimer disease); and Kruppel-like factor 11 (corresponding to biomarkers listed in Table 9 that are increased in ≧90% of responders vs. non-responders). Even more preferably, at least one of the biomarkers is Integrin alpha-X (antigen CD11c) (corresponding to a biomarker listed in Table 9 that is increased in 100% of responders vs. non-responders). In each of the embodiments, increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another preferred embodiment, the one or more biomarkers is selected from the group consisting of Aquaporin 3; Similar to ribosomal protein S24, clone MGC:8595; Transmembrane emp24 domain trafficking protein 2; Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1; Calmodulin 1 (phosphorylase kinase, delta); Homo sapiens isocitrate dehydrogenase 1 (NADP+) soluble (IDH1); Tumor protein D52; Homo sapiens predicted osteoblast protein (GS3786); Cytochrome c oxidase subunit VIIb; CUG triplet repeat, RNA binding protein 2; Ubiquinol-cytochrome c reductase hinge protein; Glutamate-cysteine ligase, catalytic subunit; Rap guanine nucleotide exchange factor (GEF) 2; Ataxin 1; Ectonucleotide pyrophosphatase/phosphodiesterase 4; MAL, T-cell differentiation protein; Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase; Carboxypeptidase A3; Charcot-Leyden crystal protein; NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 1, 7 kDa; Platelet factor 4 variant 1; Poly(A) binding protein interacting protein 1; Sterol-C4-methyl oxidase-like; PIM-1 oncogene; GATA binding protein 2; Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide; KIAA0746 protein; Transcription factor 4; Major histocompatibility complex, class II, DQ alpha 1; Phospholipase C-like 2; TM2 domain containing 1; SUB1 homolog (S. cerevisiae); Cytochrome b-5; Neugrin, neurite outgrowth associated; Ribosomal protein S26, 40S ribosomal protein; CCR4-NOT transcription complex, subunit 6; Ubiquinol-cytochrome c reductase complex (7.2 kD); Hepatocellular carcinoma-associated antigen 112; GGA binding partner; Cornichon homolog 4 (Drosophila); Hypothetical protein FLJ21616; Homo sapiens hypothetical protein FLJ10134; Erythroblast membrane-associated protein; LR8 protein; Likely ortholog of mouse limb-bud and heart gene (LBH); Chromosome 14 open reading frame 156; and Guanine nucleotide binding protein (G protein) alpha 12 (corresponding to biomarkers listed in Table 9 that are decreased in ≧80% of responders vs. non-responders). More preferably, the one or more biomarkers is selected from the group consisting of Homo sapiens predicted osteoblast protein (GS3786); Charcot-Leyden crystal protein; Neugrin, neurite outgrowth associated; Hypothetical protein FLJ21616; and Homo sapiens hypothetical protein FLJ10134 (corresponding to biomarkers listed in Table 9 that are decreased in ≧90% of responders vs. non-responders). Even more preferably, the one or more biomarkers is Homo sapiens predicted osteoblast protein (GS3786) or Homo sapiens hypothetical protein FLJ10134 (corresponding to biomarkers listed in Table 9 that are decreased in 100% of responders vs. non-responders). In each of these embodiments, decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In a particularly preferred aspect, the invention provides a method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder the method comprising: (i) assaying the subject for increased expression of a biomarker, which biomarker is CD11c, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on increased expression of CD11c in the subject.
In yet another particularly preferred embodiment, the invention provides a method for predicting responsiveness to a TNFα inhibitor, which TNFα inhibitor is adalimumab, in a subject having an autoimmune disorder the method comprising: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to adalimumab in the autoimmune disorder, and (ii) predicting responsiveness of the subject to adalimumab based on expression of the one or more biomarkers in the subject. Preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or selected from the group consisting of the biomarkers set forth in Table 9). In more preferred embodiments, the subsets of sequences within SEQ ID NO: 1-82 that are either increased or decreased, as set forth in detail above, can be assayed.
In the methods of the invention for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in the autoimmune disorder can be assayed in the subject using techniques well-established in the art. In a preferred embodiment, the expression of the one or more biomarkers in the subject is assayed by obtaining an mRNA sample from the subject (e.g., isolated from peripheral blood mononuclear cells, by standard methods) and detecting the expression of mRNA(s) encoding the one or more biomarkers in the mRNA sample using standard molecular biology techniques, such as PCR analysis. A preferred method of PCR analysis is revers transcriptase-polymerase chain reaction (RT-PCR). Other suitable systems for mRNA sample analysis include microarray analysis (e.g., using Affymetrix's microarray system or Illumina's BeadArray Technology).
Additionally or alternatively, in certain situations it may be possible to assay for the expression of one or more biomarkers at the protein level, using a detection reagent that detects the protein product encoded by the mRNA of the biomarker(s). For example, if an antibody reagent is available that binds specifically to the biomarker protein product to be detected, and not to other proteins, then such an antibody reagent can be used to detect the expression of the biomarker of interest in a cellular sample from the subject, or a preparation derived from the cellular sample, using standard antibody-based techniques known in the art, such as FACS analysis, ELISA and the like.
It will be readily understood by the ordinarily skilled artisan that essentially any technical means established in the art for detecting biomarkers, at either the nucleic acid or protein level, can be adapted to detection of the biomarkers discussed herein and applied in the methods of the current invention for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder.
The biomarkers described herein were originally identified in patients having rheumatoid arthritis (see the Examples) and thus a particularly preferred autoimmune disorder in which to apply the methods of the invention is rheumatoid arthritis. The mechanism of action of the TNFα pathway, however, is thought to be common to a large number of autoimmune disorders and TNFα inhibitors have been shown to be effective therapy in a variety of different autoimmune disorders. Accordingly, the method of the invention for predicting responsiveness to a TNFα inhibitor can be applied to essentially any autoimmune disorder in which TNFα inhibitor therapy is applied. Other preferred autoimmune disorders include Crohn's disease, ulcerative colitis, psoriasis, psoriatic arthritis, juvenile arthritis and ankylosing spondilitis, Other non-limiting examples of autoimmune disorders include autoimmune diabetes, multiple sclerosis, systemic lupus erythematosus, rheumatoid spondylitis, gouty arthritis, allergy, autoimmune uveitis, nephrotic syndrome, multisystem autoimmune diseases, autoimmune hearing loss, adult respiratory distress syndrome, shock lung, chronic pulmonary inflammatory disease, pulmonary sarcoidosis, pulmonary fibrosis, silicosis, idiopathic interstitial lung disease, chronic obstructive pulmonary disease, asthma, restenosis, spondyloarthropathies, Reiter's syndrome, autoimmune hepatitis, inflammatory skin disorders, vasculitis of large vessels, medium vessels or small vessels, endometriosis, prostatitis and Sjogren's syndrome.
Biomarkers for Monitoring an Autoimmune Disorder in a SubjectIn another aspect, the invention provides methods of monitoring an autoimmune disorder in a subject having the autoimmune disorder based on biomarker expression patterns established using microarray analysis of, for example, RA subjects vs. normal donors, RA subjects vs. RA subjects treated with a TNFα inhibitor and/or RA subjects treated with a TNFα inhibitor vs. RA responders to TNFα inhibitors. In these monitoring methods, the subject is assayed for expression of one or more biomarkers (using techniques, for example, as described in the previous section), thereby monitoring the autoimmune disorder in the subject.
In one embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 83-133 (or the biomarkers set forth by name in Table 3, namely v-maf musculoaponeurotic fibrosarcoma oncogene homolog F; Diphtheria toxin receptor (DTR); DEAH (Asp-Glu-Ala-His) box polypeptide 15; Ribonucleotide reductase M2 polypeptide; Solute carrier family 6, member 8; 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; Ferrochelatase (protoporphyria); Nuclear factor, interleukin 3 regulated; Thrombomodulin; Major histocompatibility complex, class II, DM beta; Forkhead box O3A; Hemoglobin, gamma A, gamma G; Synuclein, alpha (non A4 component of amyloid precursor); Hemoglobin, gamma A; Amphiregulin (schwannoma-derived growth factor); Lipoyltransferase 1; Solute carrier family 4, anion exchanger, member 1; S100 calcium binding protein A12 (calgranulin C); Keratin 1 (epidermolytic hyperkeratosis); Carbonic anhydrase I; CD1C antigen, c polypeptide; Tumor necrosis factor, alpha-induced protein 6; Ribonuclease, RNase A family, 2; Hemoglobin, delta; Transferrin receptor (p90, CD71); Ring finger protein 10; Chromosome 1 open reading frame 63; Hemoglobin, alpha 1, alpha 2; CD69 antigen (p60, early T-cell activation antigen; APEX nuclease (multifunctional DNA repair enzyme) 1; Membrane-spanning 4-domains, subfamily A, member 3; Heat shock 70 kDa protein 8; Hypothetical protein MGC12760; GABA(A) receptor-associated protein like 1, like 3; Aminolevulinate, delta-, synthase 2; Major histocompatibility complex, class II, DP alpha 1; Morf4 family associated protein 1-like 1; Aldehyde dehydrogenase 1 family, member A1; Formin binding protein 4; Zinc finger protein 24 (KOX 17); Hypothetical protein L0054103; Selenium binding protein 1; Hematopoietically expressed homeobox; Major histocompatibility complex, class II, DM alpha; Eukaryotic translation initiation factor 2-alpha kinase 1; Ankyrin repeat domain 49; Hypothetical protein FLJ20701; Zinc finger protein 331; Membrane-spanning 4-domains, subfamily A, member 4; Tensin 1; and Family with sequence similarity 46, member A, respectively).
In another embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-177, 110, 112, 118, 123 and 131 (or the biomarkers set forth by name in Table 4, namely Uncharacterized hypothalamus protein HT007; Dual specificity phosphatase 22; Proteasome (prosome, macropain) subunit, beta type, 7; Membrane-spanning 4-domains, subfamily A, member 4; DKFZP434C171 protein; Protein phosphatase 1, catalytic subunit, beta isoform; Splicing factor, arginine/serine-rich 5; Sorting nexin 11; Farnesyltransferase, CAAX box, alpha; Peroxisomal D3,D2-enoyl-CoA isomerise; Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide; UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1; Hypothetical protein FLJ11259; APEX nuclease (multifunctional DNA repair enzyme) 1; Geranylgeranyl diphosphate synthase 1; Down syndrome critical region gene 5; Calpain 7; Major histocompatibility complex, class II, DP alpha 1; Brix domain containing 5; Chromosome 21 open reading frame 59; Tumor necrosis factor (ligand) superfamily, member 10; Chromosome 10 open reading frame 86; CD1D antigen, d polypeptide; Ewing sarcoma breakpoint region 1; Ribonuclease P 40 kDa subunit; PHD finger protein 20; Thioredoxin interacting protein; Ubiquinol-cytochrome c reductase core protein II; Hypothetical protein FLJ22662; Preimplantation protein 3; DKFZP564G2022 protein; Dipeptidase 2; Hemoglobin, alpha 1, alpha 2; Frequently rearranged in advanced T-cell lymphomas; DEAD (Asp-Glu-Ala-Asp) box polypeptide 48; Tumor necrosis factor, alpha-induced protein 2; Nucleophosmin (nucleolar phosphoprotein B23, numatrin); Interleukin 13 receptor, alpha 1; Leukocyte specific transcript 1, LST1; CGI-121 protein; RAS p21 protein activator 4/hypothetical protein FLJ21767; Cathepsin S; CD63 antigen (melanoma 1 antigen); JTV1 gene; KIAA0174; Thrombospondin 1; Hypothetical protein L0054103; Interferon regulatory factor 1; and SEC11-like 1 (S. cerevisiae)).
More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-150, 112, 118 (or the biomarkers set forth by name in Table 4 as being upregulated, namely Uncharacterized hypothalamus protein HT007; Dual specificity phosphatase 22; Proteasome (prosome, macropain) subunit, beta type, 7; Membrane-spanning 4-domains, subfamily A, member 4; DKEZP434C171 protein; Protein phosphatase 1, catalytic subunit, beta isoform; Splicing factor, arginine/serine-rich 5; Sorting nexin 11; Farnesyltransferase, CAAX box, alpha; Peroxisomal D3,D2-enoyl-CoA isomerise; Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide; UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1; Hypothetical protein FLJ11259; APEX nuclease (multifunctional DNA repair enzyme) 1; Geranylgeranyl diphosphate synthase 1; Down syndrome critical region gene 5; Calpain 7; Major histocompatibility complex, class II, DP alpha 1; Brix domain containing 5; and Chromosome 21 open reading frame 59), wherein expression of the one or more biomarkers is increased in the subject.
Additionally or alternatively, more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 151-177, 110 and 123 (or the biomarkers set forth by name in Table 4 as being downregulated, namely Tumor necrosis factor (ligand) superfamily, member 10; Chromosome 10 open reading frame 86; CD1D antigen, d polypeptide; Ewing sarcoma breakpoint region 1; Ribonuclease P 40 kDa subunit; PHD finger protein 20; Thioredoxin interacting protein; Ubiquinol-cytochrome c reductase core protein II; Hypothetical protein FLJ22662; Preimplantation protein 3; DKFZP564G2022 protein; Dipeptidase 2; Hemoglobin, alpha 1, alpha 2; Frequently rearranged in advanced T-cell lymphomas; DEAD (Asp-Glu-Ala-Asp) box polypeptide 48; Tumor necrosis factor, alpha-induced protein 2; Nucleophosmin (nucleolar phosphoprotein B23, numatrin); Interleukin 13 receptor, alpha 1; Leukocyte specific transcript 1, LST1; CGI-121 protein; RAS p21 protein activator 4/hypothetical protein FLJ21767; Cathepsin S; CD63 antigen (melanoma 1 antigen); JTV1 gene; KIAA0174; Thrombospondin 1; Hypothetical protein L0054103; Interferon regulatory factor 1; and SEC11-like 1 (S. cerevisiae), wherein expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-292, 91 and 97 (or the biomarkers set forth by name in Table 5, namely HLA-B associated transcript-1 (D6S81E); Interleukin enhancer binding factor 2, 45 kD (ILF2); Isolate Liv chaperone protein HSP90 beta (HSP90BETA) mRNA; Lysyl-tRNA synthetase mRNA, complete cds; nuclear gene for mitochondrial product; alternatively spliced; Tryptophanyl-tRNA synthetase (WARS); Profilin 1 (PFN1), mRNA; Seryl-tRNA synthetase (SARS); Similar to KIAA1007 protein, clone MGC:692, mRNA, complete cds; CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344); Homo sapiens KIAA0064 gene product (KIAA0064), mRNA; Chromosome segregation 1 (yeast homolog)-like (CSE1L), mRNA; Protein phosphatase 1, regulatory subunit 7 (PPP1R7), mRNA; DEADH (Asp-Glu-Ala-AspHis) box polypeptide 1 (DDX1), mRNA; Threonyl-tRNA synthetase (TARS), mRNA; Dead box protein 15 mRNA, complete cds; SRY (sex determining region Y)-box 4/DEF=Human DNA sequence from clone RP3-322L4 on chromosome 6; Galactosidase, beta 1 (GLB1), mRNA; ATP-binding cassette, sub-family E (OABP), member 1; NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 6 (14 kD, B14) (NDUFA6), mRNA; Pericentriolar material 1 (PCM1), mRNA; Excision repair cross-complementing rodent repair deficiency, complementation group 3 (ERCC3), mRNA; KIAA0907 protein (KIAA0907), mRNA; v-crk avian sarcoma virus CT10 oncogene homolog; 6-phosphofructo-2-kinasefructose-2,6-biphosphatase 3 (PFKFB3), mRNA./PROD=6-phosphofructo-2-kinase-fructose-2,6-biphosphatase 3; KIAA0766 gene product (KIAA0766), mRNA; N-acetylgalactosaminidase, alpha- (NAGA), mRNA; Protein tyrosine phosphatase, non-receptor type substrate 1 (PTPNS1), mRNA; Crystallin, zeta (quinone reductase) (CRYZ), mRNA; KIAA0429 gene product (KIAA0429), mRNA; SET domain, bifurcated 1 (SETDB1), mRNA; NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 3 (12 kD, B12) (NDUFB3), mRNA; Diphtheria toxin receptor (DTR); Thrombomodulin (THBD), mRNA; Protein phosphatase 1A (formerly 2C), magnesium-dependent, alpha isoform (PPM1A), mRNA; CD37 antigen (CD37), mRNA; Hemoglobin, gamma G (HBG2), mRNA; Protein phosphatase 1D magnesium-dependent, delta isoform (PPM1D), mRNA; Hematopoietically expressed homeobox (HHEX), mRNA; Amphiregulin (schwannoma-derived growth factor) (AREG), mRNA; Early growth response 2 (Krox-20 (Drosophila) homolog) (EGR2), mRNA; 2,5-oligoadenylate synthetase 1 (40-46 kD) (OAS1), transcript variant E16, mRNA; Early growth response 3 (EGR3), mRNA./PROD=early growth response 3; Homo sapiens MD-2 protein (MD-2), mRNA; Homo sapiens MAX dimerization protein (MAD), mRNA; DKEZP586A011 protein (DKEZP586A011), mRNA; 78 kDa gastrin-binding protein mRNA, complete cds; Transferrin receptor (p90, CD71), clone MGC:3151, mRNA, complete cds; Homo sapiens mRNA; cDNA DKFZp564N1272 (from clone DKFZp564N1272); complete cds; GABA-A receptor-associated protein like 1 (GABARAPL1) mRNA, complete cds; Translocation protein 1; Homo sapiens clone 016b03 My027 protein mRNA, complete cds; Protein kinase-related oncogene (PIM1) mRNA, complete cds; MADS box transcription enhancer factor 2, polypeptide C (myocyte enhancer factor 2C); eIF-2-associated p67 homolog mRNA, complete cds; Human mRNA for hCREM (cyclic AMP-responsive element modulator) type 2 protein, complete cds; Apurinic endonuclease (APE) mRNA, complete cds./PROD=apurinic endonuclease; TATA box binding protein (TBP)-associated factor, RNA polymerase II, D, 100 kD; Human mRNA for ZFM1 protein alternatively spliced product, complete cds./PROD=ZFM1 protein, alternatively spliced product; Trachea cellular apoptosis susceptibility protein (CSE1) mRNA, complete cds; Similar to eukaryotic translation initiation factor 3, subunit 8 (110 kD), clone MGC:8693, mRNA, complete cds; GABA-A receptor-associated protein mRNA, complete cds./PROD=GABA-A receptor-associated protein; Human (clone 2-5) synuclein (NACP) mRNA, complete cds; Human putative ribosomal protein 51 mRNA; Aldehyde dehydrogenase 1, soluble (ALDH1), mRNA./PROD=aldehyde dehydrogenase 1, soluble; Homo sapiens mRNA for KIAA1057 protein, partial cds; RBP1-like protein; Ring finger protein 4; KIAA0197 protein; Homo sapiens mRNA; cDNA DKFZp586F1323 (from clone DKFZp586F1323); Homo sapiens mRNA; cDNA DKFZp5641052 (from clone DKFZp5641052); Homo sapiens mRNA for Hmob33 protein, 3 untranslated region; Homo sapiens mRNA; cDNA DKFZp586F1019 (from clone DKFZp586F1019); partial cds; Myosin, light polypeptide 4, alkali; atrial, embryonic; H. sapiens novel gene from PAC 117P20, chromosome 1; Homo sapiens clone 24582 mRNA sequence; Heterogeneous nuclear ribonucleoprotein H1 (H); Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide; Cyclin T2; cDNA: FLJ23227 fis, clone CAE00645, highly similar to AF052138; Hypothetical protein FLJ12619; C-terminal binding protein 1; SEC22, vesicle trafficking protein (S. cerevisiae)-like 1; Hemoglobin, alpha 1; Homo sapiens clone 24659 mRNA sequence/DEF=Homo sapiens clone 24659 mRNA sequence; Calcium channel, voltage-dependent, PQ type, alpha 1A subunit; H. sapiens SMAS mRNA; H. sapiens HEX gene encoding homeobox related protein; Mitogen-activated protein kinase kinase kinase 4; Serine palmitoyltransferase (LCB2) mRNA, partial cds; KIAA0971 protein/DEF=Homo sapiens cDNA FLJ11495 fis, clone HEMBA1001950, highly similar to Homo sapiens mRNA for KIAA0971 protein; ESTs, Hs.97109; ESTs, Weakly similar to ALU7_HUMAN ALU; DEAD-box protein abstrakt (ABS), mRNA; KIAA1513 protein (KIAA1513), mRNA; Cell division protein FtsJ (FJH1), mRNA; F-box only protein 3 (FBXO3), mRNA; Purinergic receptor (family A group 5) (P2Y5), mRNA; Integral inner nuclear membrane protein (MAN1), mRNA; Fanconi anemia, complementation group F (FANCF), mRNA; Hypothetical protein FLJ12820 (FLJ12820), mRNA; Hypothetical protein FLJ13119 (FLJ13119), mRNA; Hypothetical protein FLJ20189 (FLJ20189), mRNA; Hypothetical protein FLJ20701 (FLJ20701), mRNA; Homo sapiens chromosome 12 open reading frame 5 (C12ORF5), mRNA; Hypothetical protein FLJ22555 (FLJ22555), mRNA; Hypothetical protein FLJ11110 (FLJ11110), mRNA; CGI-12 protein (LOC51001), mRNA; Triggering receptor expressed on myeloid cells 1 (TREM1), mRNA; betaGlcNAc beta 1,4-galactosyltransferase. polypeptide 5; PNAS-25 mRNA, complete cds.; Homo sapiens mRNA for FLJ00043 protein, partial cds; Homo sapiens cDNA: FLJ21737 fis, clone COLF3396; Homo sapiens cDNA FLJ13399 fis, clone PLACE1001395/DEF=Homo sapiens cDNA FLJ13399 fis, clone PLACE1001395; Novel MAFF (v-maf musculoaponeurotic fibrosarcoma (avian) oncogene family, protein F) LIKE protein; Heparin-binding EGF-like growth factor mRNA, complete cds; E. coli 7,8-diamino-pelargonic acid (bioA), biotin synthetase (bioB), 7-keto-8-amino-pelargonic acid synthetase (bioF), bioC protein, and dethiobiotin synthetase (bioD), complete cds; Escherichia coli/REF=J04423/DEF=E. coli bioC protein corresponding to nucleotides 4609-4883 of J04423/LEN=777 (−5 and −3 represent transcript regions 5 prime and 3 prime respectively)).
More preferably, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-185; 187-200, 202, 203, 205-207; 211, 213, 214, 216, 220, 221, 226, 228, 229, 231, 232, 234, 235, 238-247, 249, 250, 253, 262-265, 268-282 and 285-288 (or the biomarkers set forth by name in Table 5 as being upregulated, namely HLA-B associated transcript-1 (D6S81E); Interleukin enhancer binding factor 2, 45 kD (ILF2); Isolate Liv chaperone protein HSP90 beta (HSP90BETA) mRNA; Lysyl-tRNA synthetase mRNA, complete cds; nuclear gene for mitochondrial product; alternatively spliced; Tryptophanyl-tRNA synthetase (WARS); Profilin 1 (PFN1), mRNA; Seryl-tRNA synthetase (SARS); Similar to KIAA1007 protein, clone MGC:692, mRNA, complete cds; Homo sapiens KIAA0064 gene product (KIAA0064), mRNA; Chromosome segregation 1 (yeast homolog)-like (CSE1L), mRNA; Protein phosphatase 1, regulatory subunit 7 (PPP1R7), mRNA; DEADH (Asp-Glu-Ala-AspHis) box polypeptide 1 (DDX1), mRNA; Threonyl-tRNA synthetase (TARS), mRNA; Dead box protein 15 mRNA, complete cds; SRY (sex determining region Y)-box 4/DEF=Human DNA sequence from clone RP3-322L4 on chromosome 6; Galactosidase, beta 1 (GLB1), mRNA; ATP-binding cassette, sub-family E (OABP), member 1; NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 6 (14 kD, B14) (NDUFA6), mRNA; Pericentriolar material 1 (PCM1), mRNA; Excision repair cross-complementing rodent repair deficiency, complementation group 3 (ERCC3), mRNA; KIAA0907 protein (KIAA0907), mRNA; v-crk avian sarcoma virus CT10 oncogene homolog; KIAA0766 gene product (KIAA0766), mRNA; N-acetylgalactosaminidase, alpha- (NAGA), mRNA; Crystallin, zeta (quinone reductase) (CRYZ), mRNA; KIAA0429 gene product (KIAA0429), mRNA; SET domain, bifurcated 1 (SETDB1), mRNA; CD37 antigen (CD37), mRNA; Protein phosphatase 1D magnesium-dependent, delta isoform (PPM1D), mRNA; Hematopoietically expressed homeobox (HHEX), mRNA; 2,5-oligoadenylate synthetase 1 (40-46 kD) (OAS1), transcript variant E16, mRNA; DKFZP586A011 protein (DKFZP586A011), mRNA; 78 kDa gastrin-binding protein mRNA, complete cds; Homo sapiens clone 016b03 My027 protein mRNA, complete cds; MADS box transcription enhancer factor 2, polypeptide C (myocyte enhancer factor 2C); eIF-2-associated p67 homolog mRNA, complete cds; Apurinic endonuclease (APE) mRNA, complete cds./PROD=apurinic endonuclease; TATA box binding protein (TBP)-associated factor, RNA polymerase II, D, 100 kD; Trachea cellular apoptosis susceptibility protein (CSE1) mRNA, complete cds; Similar to eukaryotic translation initiation factor 3, subunit 8 (110 kD), clone MGC:8693, mRNA, complete cds; Human putative ribosomal protein 51 mRNA; Aldehyde dehydrogenase 1, soluble (ALDH1), mRNA./PROD=aldehyde dehydrogenase 1, soluble; Homo sapiens mRNA for KIAA1057 protein, partial cds; RBP1-like protein; Ring finger protein 4; KIAA0197 protein; Homo sapiens mRNA; cDNA DKFZp586F1323 (from clone DKFZp586F1323); Homo sapiens mRNA; cDNA DKFZp5641052 (from clone DKFZp5641052); Homo sapiens mRNA for Hmob33 protein, 3 untranslated region; Homo sapiens mRNA; cDNA DKFZp586F1019 (from clone DKFZp586F1019); partial cds; H. sapiens novel gene from PAC 117P20, chromosome 1; Homo sapiens clone 24582 mRNA sequence; Cyclin T2; H. sapiens HEX gene encoding homeobox related protein; Mitogen-activated protein kinase kinase kinase 4; Serine palmitoyltransferase (LCB2) mRNA, partial cds; KIAA0971 protein/DEF=Homo sapiens cDNA FLJ11495 fis, clone HEMBA1001950, highly similar to Homo sapiens mRNA for KIAA0971 protein; DEAD-box protein abstrakt (ABS), mRNA; KIAA1513 protein (KIAA1513), mRNA; Cell division protein FtsJ (FJH1), mRNA; F-box only protein 3 (FBXO3), mRNA; Purinergic receptor (family A group 5) (P2Y5), mRNA; Integral inner nuclear membrane protein (MAN1), mRNA; Fanconi anemia, complementation group F (FANCF), mRNA; Hypothetical protein FLJ12820 (FLJ12820), mRNA; Hypothetical protein FLJ13119 (FLJ13119), mRNA; Hypothetical protein FLJ20189 (FLJ20189), mRNA; Hypothetical protein FLJ20701 (FLJ20701), mRNA; Homo sapiens chromosome 12 open reading frame 5 (C12ORF5), mRNA; Hypothetical protein FLJ22555 (FLJ22555), mRNA; Hypothetical protein FLJ11110 (FLJ11110), mRNA; CGI-12 protein (LOC51001), mRNA; PNAS-25 mRNA, complete cds.; Homo sapiens mRNA for FLJ00043 protein, partial cds; Homo sapiens cDNA: FLJ21737 fis, clone COLF3396; Homo sapiens cDNA FLJ13399 fis, clone PLACE1001395/DEF=Homo sapiens cDNA FLJ13399 fis, clone PLACE1001395), wherein expression of the one or more biomarkers is increased in the subject.
Additionally or alternatively, more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 186, 201, 204, 208, 209, 91, 210, 212, 97, 215, 217-219, 222-225, 227, 230, 233, 236, 237, 248, 251, 252, 254-261, 266, 267, 283, 284 and 289-292 (or the biomarkers set forth by name in Table 5 as being down-regulated, namely CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344); 6-phosphofructo-2-kinasefructose-2,6-biphosphatase 3 (PFKFB3), mRNA./PROD=6-phosphofructo-2-kinase-fructose-2,6-biphosphatase 3; Protein tyrosine phosphatase, non-receptor type substrate 1 (PTPNS1), mRNA; NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 3 (12 kD, B12) (NDUFB3), mRNA; Diphtheria toxin receptor (DTR); Thrombomodulin (THBD), mRNA; Protein phosphatase 1A (formerly 2C), magnesium-dependent, alpha isoform (PPM1A), mRNA; Hemoglobin, gamma G (HBG2), mRNA; Amphiregulin (schwannoma-derived growth factor) (AREG), mRNA; Early growth response 2 (Krox-20 (Drosophila) homolog) (EGR2), mRNA; Early growth response 3 (EGR3), mRNA./PROD=early growth response 3; Homo sapiens MD-2 protein (MD-2), mRNA; Homo sapiens MAX dimerization protein (MAD), mRNA; Transferrin receptor (p90, CD71), clone MGC:3151, mRNA, complete cds; Homo sapiens mRNA; cDNA DKFZp564N1272 (from clone DKFZp564N1272); complete cds; GABA-A receptor-associated protein like 1 (GABARAPL1) mRNA, complete cds; Translocation protein 1; Protein kinase-related oncogene (PIM1) mRNA, complete cds; Human mRNA for hCREM (cyclic AMP-responsive element modulator) type 2 protein, complete cds; Human mRNA for ZFM1 protein alternatively spliced product, complete cds./PROD=ZFM1 protein, alternatively spliced product; GABA-A receptor-associated protein mRNA, complete cds./PROD=GABA-A receptor-associated protein; Human (clone 2-5) synuclein (NACP) mRNA, complete cds; Myosin, light polypeptide 4, alkali; atrial, embryonic; Heterogeneous nuclear ribonucleoprotein H1 (H); Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide; cDNA: FLJ23227 fis, clone CAE00645, highly similar to AF052138; Hypothetical protein FLJ12619; C-terminal binding protein 1; SEC22, vesicle trafficking protein (S. cerevisiae)-like 1; Hemoglobin, alpha 1; Homo sapiens clone 24659 mRNA sequence/DEF=Homo sapiens clone 24659 mRNA sequence; Calcium channel, voltage-dependent, PQ type, alpha 1A subunit; H. sapiens SMA5 mRNA; ESTs, Hs.97109; ESTs, Weakly similar to ALU7_HUMAN ALU; Triggering receptor expressed on myeloid cells 1 (TREM1), mRNA; betaGlcNAc beta 1,4-galactosyltransferase. polypeptide 5; Novel MAFF (v-maf musculoaponeurotic fibrosarcoma (avian) oncogene family, protein F) LIKE protein; Heparin-binding EGF-like growth factor mRNA, complete cds; E. coli 7,8-diamino-pelargonic acid (bioA), biotin synthetase (bioB), 7-keto-8-amino-pelargonic acid synthetase (bioF), bioC protein, and dethiobiotin synthetase (bioD), complete cds; Escherichia coli/REF=J04423/DEF=E. coli bioC protein corresponding to nucleotides 4609-4883 of J04423/LEN=777 (−5 and −3 represent transcript regions 5 prime and 3 prime respectively)), wherein expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 87, 97, 101, 293, 92, 272, 93, 107, 108, 121 and 123 (or the biomarkers set forth by name in Table 6 as pre-treatment biomarkers, namely Solute carrier family 6; Amphiregulin; Keratin 1; Hemoglobin, alpha 1; MHC-II, DM beta; Purinergic receptor P2Y, G-protein coupled 5; Forkhead box O3A; Transferrin receptor (p90, CD71); Ring finger protein 10; Formin binding protein 4; and Hypothetical protein LOC54103), wherein the subject is monitored prior to treatment with a TNFα inhibitor.
In yet another embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 290, 209, 98, 112, 116, 121, 130, 155, 92, 289, 216 and 131 (or the biomarkers set forth by name in Table 6 as post-treatment biomarkers, namely Diphteria toxin receptor; Heparin-binding EGF-like growth factor; Lipoyltransferase 1; APEX nuclease (multifunct. DNA repair enzyme) 1; GABA(A) receptor-associated protein like 1/3; Formin binding protein 4; Zinc finger protein 331; Ribonuclease P 40 kDa subunit; MHC class II, DM beta; v-maf fibrosarc. oncogene homolog F (avian); 2′,5′-oligoadenylate synthetase 1, 40/46 kDa; and Membrane-spanning 4-domains, subfam. A, memb. 4), wherein the subject is monitored after treatment with a TNFα inhibitor.
In yet another embodiment of the monitoring method, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 97, 102, 29, 294, 295, 91, 131, 290, 100, 134, 296, 297, 298, 299, 136, 174 and 300 (or the biomarkers set forth by name in Table 7, namely Amphiregulin; Carbonic anhydrase 1; Charcot-Leyden crystal protein; Clusterin C; Tumor necrosis factor alpha induced protein 6; Thrombomodulin; Membrane-spanning 4-domains, subfamily A, member 4; Diptheria toxin receptor; S100 calcium binding protein A1; Uncharacterized hypothalamus protein HT007; MHC-class-II; HLA-DR alpha; Hypothetical protein L0054103; Tumor necrosis factor alpha; Interleukin 1 beta; Proteasome subunit beta type 7 precursor; and Protein KIAA0174; Microsomal signal peptidase 18 kDa subunit).
A preferred autoimmune disorder in which to apply the methods of the invention for monitoring an autoimmune disorder is rheumatoid arthritis. However, the monitoring methods can be applied to essentially any autoimmune disorder in which TNFα inhibitor therapy is applied, including the autoimmune disorders listed in the previous section.
Selection and Use of Treatment Regimens with TNFα Inhibitors
Given the observation that the expression pattern of particular biomarkers in an autoimmune disorder subject influences the responsiveness of the subject to a TNFα inhibitor, one can select an appropriate treatment regimen for the subject based on the expression of one or more biomarkers in the subject. Accordingly, in one embodiment, the above-described method for predicting the responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder further comprises selecting a treatment regimen with the TNFα inhibitor based upon expression of the one or more biomarkers in the subject. In another aspect, the method still further comprises administering the TNFα inhibitor to the subject according to the treatment regimen such that the autoimmune disorder is inhibited in the subject.
In another embodiment, the invention provides a method for selecting a treatment regimen for therapy with a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising:
assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor for treatment of the autoimmune disorder; and
selecting a treatment regimen with a TNFα inhibitor based upon expression of the one or more biomarkers in the subject.
In yet another embodiment, the invention provides a method of treating a subject having an autoimmune disorder with a TNFα inhibitor, the method comprising:
assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor for treatment of the autoimmune disorder;
selecting a treatment regimen with a TNFα inhibitor based upon expression of the one or more biomarkers in the subject; and
administering the TNFα inhibitor according to the treatment regimen such that the subject is treated for the autoimmune disorder.
The treatment regimen that is selected typically includes at least one of the following parameters and more typically includes many or all of the following parameters: the type of agent chosen for administration, the dosage, the formulation, the route of administration and/or the frequency of administration.
Particularly preferred TNFα inhibitors are biologic agents that have been approved by the FDA for use in humans in the treatment of rheumatoid arthritis, which agents include adalimumab (HUMIRA™), infliximab (REMICADE™) and etanercept (ENBREL™), most preferably adalimumab (HUMIRAT™).
In one embodiment, the TNFα inhibitor is an anti-tumor necrosis factor-alpha (TNFα) antibody, or antigen-binding portion thereof. For example, the anti-TNFα antibody, or antigen-binding portion thereof, can be a humanized antibody, a chimeric antibody or a multivalent antibody.
In another embodiment, the anti-TNFα antibody, or antigen-binding portion thereof, is a human antibody, preferably a human antibody, or antigen-binding portion thereof, that binds to human TNFα with high affinity and a low off rate, and has a high neutralizing capacity. Preferably, the human antibodies are recombinant, neutralizing human anti-hTNFα antibodies. The most preferred recombinant, neutralizing antibody used in the method of the invention is referred to herein as adalimumab, also referred to as HUMIRA® or D2E7 (the amino acid sequence of the adalimumab VL region is shown in SEQ ID NO: 303; the amino acid sequence of the adalimumab VH region is shown in SEQ ID NO: 304). The properties of D2E7 (adalimumab; Humira®) have been described in Salfeld et al., U.S. Pat. Nos. 6,090,382, 6,258,562, and 6,509,015, which are each incorporated by reference herein.
Other examples of TNFα antibodies include chimeric and humanized murine anti-hTNFα antibodies which have undergone clinical testing for treatment of rheumatoid arthritis (see e.g., Elliott et al. (1994) Lancet 344:1125-1127; Elliot et al. (1994) Lancet 344:1105-1110; Rankin et al. (1995) Br. J. Rheumatol. 34:334-342). In another embodiment, the TNFα antibody used in the invention is infliximab (Remicade®, Johnson and Johnson; described in U.S. Pat. No. 5,656,272, incorporated by reference herein), CDP571 (a humanized monoclonal anti-TNF-alpha IgG4 antibody), CDP 870 (a humanized monoclonal anti-TNF-alpha antibody fragment), an anti-TNF dAb (Peptech), and CNTO 148 (golimumab; Medarex and Centocor, see also WO 02/12502).
In one embodiment, the TNFα inhibitors used in the methods of the invention include adalimumab antibodies and antibody portions, adalimumab-related antibodies and antibody portions, adalimumab-related DVD-Ig or dual specific antibodies, and other human antibodies and antibody portions with equivalent properties to adalimumab, such as high affinity binding to hTNFα with low dissociation kinetics and high neutralizing capacity. In one embodiment, a treatment regimen of the invention provides treatment with an isolated human antibody, or an antigen-binding portion thereof, that dissociates from human TNFα with a Kd of 1×10−8 M or less and a Koff rate constant of 1×10−3 s−1 or less, both determined by surface plasmon resonance, and neutralizes human TNFα cytotoxicity in a standard in vitro L929 assay with an IC50 of 1×10−7 M or less. More preferably, the isolated human antibody, or antigen-binding portion thereof, dissociates from human TNFα with a Koff of 5×10−4 s−1 or less, or even more preferably, with a Koff of 1×10−4 s−1 or less. More preferably, the isolated human antibody, or antigen-binding portion thereof, neutralizes human TNFα cytotoxicity in a standard in vitro L929 assay with an IC50 of 1×10−8 M or less, even more preferably with an IC50 of 1×10−9 M or less and still more preferably with an IC50 of 1×10−10 M or less. In a preferred embodiment, the antibody is an isolated human recombinant antibody, or an antigen-binding portion thereof.
It is well known in the art that antibody heavy and light chain CDR3 domains play an important role in the binding specificity/affinity of an antibody for an antigen. Accordingly, in another aspect, the TNFα inhibitor used in the treatment method of the invention is a human anti-TNFα antibody that has slow dissociation kinetics for association with hTNFα and that has light and heavy chain CDR3 domains that structurally are identical to or related to those of adalimumab. Position 9 of the adalimumab VL CDR3 can be occupied by Ala or Thr without substantially affecting the Koff. Accordingly, a consensus motif for the adalimumab VL CDR3 comprises the amino acid sequence: Q-R-Y-N-R-A-P-Y-(T/A) (SEQ ID NO: 305). Additionally, position 12 of the adalimumab VH CDR3 can be occupied by Tyr or Asn, without substantially affecting the Koff. Accordingly, a consensus motif for the adalimumab VH CDR3 comprises the amino acid sequence: V-S-Y-L-S-T-A-S-S-L-D-(Y/N) (SEQ ID NO: 306). Moreover, as demonstrated in Example 2 of U.S. Pat. No. 6,090,382, the CDR3 domain of the adalimumab heavy and light chains is amenable to substitution with a single alanine residue (at position 1, 4, 5, 7 or 8 within the VL CDR3 or at position 2, 3, 4, 5, 6, 8, 9, 10 or 11 within the VH CDR3) without substantially affecting the Koff. Still further, the skilled artisan will appreciate that, given the amenability of the adalimumab VL and VH CDR3 domains to substitutions by alanine, substitution of other amino acids within the CDR3 domains may be possible while still retaining the low off rate constant of the antibody, in particular substitutions with conservative amino acids. Preferably, no more than one to five conservative amino acid substitutions are made within the adalimumab VL and/or VH CDR3 domains. More preferably, no more than one to three conservative amino acid substitutions are made within the adalimumab VL and/or VH CDR3 domains. Additionally, conservative amino acid substitutions should not be made at amino acid positions critical for binding to hTNFα. Positions 2 and 5 of the adalimumab VL CDR3 and positions 1 and 7 of the adalimumab VH CDR3 appear to be critical for interaction with hTNFα and thus, conservative amino acid substitutions preferably are not made at these positions (although an alanine substitution at position 5 of the adalimumab VL CDR3 is acceptable, as described above) (see U.S. Pat. No. 6,090,382).
Accordingly, in another embodiment, the antibody or antigen-binding portion thereof preferably contains the following characteristics:
a) dissociates from human TNFα with a Koff rate constant of 1×10−3 s−1 or less, as determined by surface plasmon resonance;
b) has a light chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 305, or modified from SEQ ID NO: 305 by a single alanine substitution at position 1, 4, 5, 7 or 8 or by one to five conservative amino acid substitutions at positions 1, 3, 4, 6, 7, 8 and/or 9;
c) has a heavy chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 306, or modified from SEQ ID NO: 306 by a single alanine substitution at position 2, 3, 4, 5, 6, 8, 9, 10 or 11 or by one to five conservative amino acid substitutions at positions 2, 3, 4, 5, 6, 8, 9, 10, 11 and/or 12.
More preferably, the antibody, or antigen-binding portion thereof, dissociates from human TNFα with a Koff of 5×10−4 s−1 or less. Even more preferably, the antibody, or antigen-binding portion thereof, dissociates from human TNFα with a Koff of 1×10−4 s−1 or less.
In yet another embodiment, the antibody or antigen-binding portion thereof preferably contains a light chain variable region (LCVR) having a CDR3 domain comprising the amino acid sequence of SEQ ID NO: 305, or modified from SEQ ID NO: 305 by a single alanine substitution at position 1, 4, 5, 7 or 8, and with a heavy chain variable region (HCVR) having a CDR3 domain comprising the amino acid sequence of SEQ ID NO: 306, or modified from SEQ ID NO: 306 by a single alanine substitution at position 2, 3, 4, 5, 6, 8, 9, 10 or 11. Preferably, the LCVR further has a CDR2 domain comprising the amino acid sequence of SEQ ID NO: 307 (i.e., the adalimumab VL CDR2) and the HCVR further has a CDR2 domain comprising the amino acid sequence of SEQ ID NO: 308 (i.e., the adalimumab VH CDR2). Even more preferably, the LCVR further has CDR1 domain comprising the amino acid sequence of SEQ ID NO: 309 (i.e., the adalimumab VL CDR1) and the HCVR has a CDR1 domain comprising the amino acid sequence of SEQ ID NO: 310 (i.e., the adalimumab VH CDR1). The framework regions for VL preferably are from the VκI human germline family, more preferably from the A20 human germline Vk gene and most preferably from the adalimumab VL framework sequences shown in FIGS. 1A and 1B of U.S. Pat. No. 6,090,382. The framework regions for VH preferably are from the VH3 human germline family, more preferably from the DP-31 human germline VH gene and most preferably from the adalimumab VH framework sequences shown in FIGS. 2A and 2B of U.S. Pat. No. 6,090,382.
Accordingly, in another embodiment, the antibody or antigen-binding portion thereof preferably contains a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 303 (i.e., the adalimumab VL) and a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 304 (i.e., the adalimumab VH). In certain embodiments, the antibody comprises a heavy chain constant region, such as an IgG1, IgG2, IgG3, IgG4, IgA, IgE, IgM or IgD constant region. Preferably, the heavy chain constant region is an IgG1 heavy chain constant region or an IgG4 heavy chain constant region. Furthermore, the antibody can comprise a light chain constant region, either a kappa light chain constant region or a lambda light chain constant region. Preferably, the antibody comprises a kappa light chain constant region. Alternatively, the antibody portion can be, for example, a Fab fragment or a single chain Fv fragment.
In other embodiments, the TNFα inhibitor of the invention is etanercept (described in WO 91/03553 and WO 09/406,476), infliximab (described in U.S. Pat. No. 5,656,272), CDP571 (a humanized monoclonal anti-TNF-alpha IgG4 antibody), CDP 870 (a humanized monoclonal anti-TNF-alpha antibody fragment), D2E7 (a human anti-TNF mAb), soluble TNF receptor Type I, or a pegylated soluble TNF receptor Type I (PEGs TNF-R1).
The TNFα antibody of the invention can be modified. In some embodiments, the TNFα antibody or antigen binding fragments thereof, is chemically modified to provide a desired effect. For example, pegylation of antibodies and antibody fragments of the invention may be carried out by any of the pegylation reactions known in the art, as described, for example, in the following references: Focus on Growth Factors 3:4-10 (1992); EP 0 154 316; and EP 0 401 384 (each of which is incorporated by reference herein in its entirety). Preferably, the pegylation is carried out via an acylation reaction or an alkylation reaction with a reactive polyethylene glycol molecule (or an analogous reactive water-soluble polymer). A preferred water-soluble polymer for pegylation of the antibodies and antibody fragments of the invention is polyethylene glycol (PEG). As used herein, “polyethylene glycol” is meant to encompass any of the forms of PEG that have been used to derivatize other proteins, such as mono (C1-C10) alkoxy- or aryloxy-polyethylene glycol.
Methods for preparing pegylated antibodies and antibody fragments of the invention will generally comprise the steps of (a) reacting the antibody or antibody fragment with polyethylene glycol, such as a reactive ester or aldehyde derivative of PEG, under conditions whereby the antibody or antibody fragment becomes attached to one or more PEG groups, and (b) obtaining the reaction products. It will be apparent to one of ordinary skill in the art to select the optimal reaction conditions or the acylation reactions based on known parameters and the desired result.
In yet another embodiment of the invention, TNFα antibodies or fragments thereof can be altered wherein the constant region of the antibody is modified to reduce at least one constant region-mediated biological effector function relative to an unmodified antibody. To modify an antibody of the invention such that it exhibits reduced binding to the Fc receptor, the immunoglobulin constant region segment of the antibody can be mutated at particular regions necessary for Fc receptor (FcR) interactions (see e.g., Canfield, S. M. and S. L. Morrison (1991) J. Exp. Med. 173:1483-1491; and Lund, J. et al. (1991) J. Immunol. 147:2657-2662). Reduction in FcR binding ability of the antibody may also reduce other effector functions which rely on FcR interactions, such as opsonization and phagocytosis and antigen-dependent cellular cytotoxicity.
An antibody or antibody portion of the invention can be derivatized or linked to another functional molecule (e.g., another peptide or protein). Accordingly, the antibodies and antibody portions of the invention are intended to include derivatized and otherwise modified forms of the anti-TNFα antibodies described herein, including immunoadhesion molecules. For example, an antibody or antibody portion of the invention can be functionally linked (by chemical coupling, genetic fusion, noncovalent association or otherwise) to one or more other molecular entities, such as another antibody (e.g., a bispecific antibody or a diabody), a detectable agent, a cytotoxic agent, a pharmaceutical agent, and/or a protein or peptide that can mediate associate of the antibody or antibody portion with another molecule (such as a streptavidin core region or a polyhistidine tag).
One type of derivatized antibody is produced by crosslinking two or more antibodies (of the same type or of different types, e.g., to create bispecific antibodies). Suitable crosslinkers include those that are heterobifunctional, having two distinctly reactive groups separated by an appropriate spacer (e.g., m-maleimidobenzoyl-N-hydroxysuccinimide ester) or homobifunctional (e.g., disuccinimidyl suberate). Such linkers are available from Pierce Chemical Company, Rockford, Ill.
Useful detectable agents with which an antibody or antibody portion of the invention may be derivatized include fluorescent compounds. Exemplary fluorescent detectable agents include fluorescein, fluorescein isothiocyanate, rhodamine, 5-dimethylamine-1-napthalenesulfonyl chloride, phycoerythrin and the like. An antibody may also be derivatized with detectable enzymes, such as alkaline phosphatase, horseradish peroxidase, glucose oxidase and the like. When an antibody is derivatized with a detectable enzyme, it is detected by adding additional reagents that the enzyme uses to produce a detectable reaction product. For example, when the detectable agent horseradish peroxidase is present, the addition of hydrogen peroxide and diaminobenzidine leads to a colored reaction product, which is detectable. An antibody may also be derivatized with biotin, and detected through indirect measurement of avidin or streptavidin binding.
Selection of the particular parameters of the treatment regimen can be based on known treatment parameters for the TNFα inhibitor previously established in the art. For example, a non-limiting example of a treatment regimen for adalimumab (HUMIRA™) is 40 mg every other week by subcutaneous injection. A non-limiting example of a treatment regimen for etanercept (ENBREL™) is 50 mg/week by subcutaneous injection. A non-limiting example of a treatment regimen for infliximab (REMICADE™) is 3 mg/kg by intravenous infusion at weeks 0, 2 and 6, then every 8 weeks. A treatment regimen can include administration of the TNFα inhibitor alone or can include combination of the TNFα inhibitor with other therapeutic agents, such as methotrexate (e.g., 10-20 mg/week) or prednisolone (e.g., 10 mg/week). Other suitable treatment regimens for the TNFα inhibitors discussed herein will be readily apparent to the ordinarily skilled artisan based on prior studies of preferred administration parameters for the TNFα inhibitor.
For administration to a subject, a TNFα inhibitor typically is formulated into a pharmaceutical composition containing the TNFα inhibitor and a pharmaceutically acceptable carrier. Therapeutic compositions typically must be sterile and stable under the conditions of manufacture and storage. Pharmaceutical compositions also can be administered in combination therapy, i.e., combined with other agents, such as other TNFα inhibitors and/or other therapeutic agents, such as traditional therapeutic agents for the treatment of autoimmune disorders, such as rheumatoid arthritis.
As used herein, “pharmaceutically acceptable carrier” includes any and all solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible. Preferably, the carrier is suitable for intravenous, intramuscular, subcutaneous, parenteral, spinal or epidermal administration (e.g., by injection or infusion). Depending on the route of administration, the active compound may be coated in a material to protect the compound from the action of acids and other natural conditions that may inactivate the compound.
The pharmaceutical compositions may include one or more pharmaceutically acceptable salts. A “pharmaceutically acceptable salt” refers to a salt that retains the desired biological activity of the parent compound and does not impart any undesired toxicological effects (see e.g., Berge, S. M., et al. (1977) J. Pharm. Sci. 66:1-19). Examples of such salts include acid addition salts and base addition salts. Acid addition salts include those derived from nontoxic inorganic acids, such as hydrochloric, nitric, phosphoric, sulfuric, hydrobromic, hydroiodic, phosphorous and the like, as well as from nontoxic organic acids such as aliphatic mono- and dicarboxylic acids, phenyl-substituted alkanoic acids, hydroxy alkanoic acids, aromatic acids, aliphatic and aromatic sulfonic acids and the like. Base addition salts include those derived from alkaline earth metals, such as sodium, potassium, magnesium, calcium and the like, as well as from nontoxic organic amines, such as N,N′-dibenzylethylenediamine, N-methylglucamine, chloroprocaine, choline, diethanolamine, ethylenediamine, procaine and the like.
A pharmaceutical composition also may include a pharmaceutically acceptable anti-oxidant. Examples of pharmaceutically acceptable antioxidants include: (1) water soluble antioxidants, such as ascorbic acid, cysteine hydrochloride, sodium bisulfate, sodium metabisulfite, sodium sulfite and the like; (2) oil-soluble antioxidants, such as ascorbyl palmitate, butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), lecithin, propyl gallate, alpha-tocopherol, and the like; and (3) metal chelating agents, such as citric acid, ethylenediamine tetraacetic acid (EDTA), sorbitol, tartaric acid, phosphoric acid, and the like.
Examples of suitable aqueous and nonaqueous carriers that may be employed in the pharmaceutical compositions include water, ethanol, polyols (such as glycerol, propylene glycol, polyethylene glycol, and the like), and suitable mixtures thereof, vegetable oils, such as olive oil, and injectable organic esters, such as ethyl oleate. Proper fluidity can be maintained, for example, by the use of coating materials, such as lecithin, by the maintenance of the required particle size in the case of dispersions, and by the use of surfactants.
These compositions may also contain adjuvants such as preservatives, wetting agents, emulsifying agents and dispersing agents. Prevention of presence of microorganisms may be ensured both by sterilization procedures and by the inclusion of various antibacterial and antifungal agents, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents, such as sugars, sodium chloride, and the like into the compositions. In addition, prolonged absorption of the injectable pharmaceutical form may be brought about by the inclusion of agents which delay absorption such as aluminum monostearate and gelatin.
Pharmaceutically acceptable carriers include sterile aqueous solutions or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersion. The use of such media and agents for pharmaceutically active substances is known in the art. Except insofar as any conventional media or agent is incompatible with the active compound, use thereof in the pharmaceutical compositions of the invention is contemplated. Supplementary active compounds can also be incorporated into the compositions.
A TNFα inhibitor of the present invention can be administered via one or more routes of administration using one or more of a variety of methods known in the art. As will be appreciated by the skilled artisan, the route and/or mode of administration will vary depending upon the desired results. A preferred route of administration, particularly for antibody agents, is by intravenous injection or infusion. Other preferred routes of administration include intramuscular, intradermal, intraperitoneal, subcutaneous, spinal or other parenteral routes of administration, for example by injection or infusion. The phrase “parenteral administration” as used herein means modes of administration other than enteral and topical administration, usually by injection, and includes, without limitation, intravenous, intramuscular, intraarterial, intrathecal, intracapsular, intraorbital, intracardiac, intradermal, intraperitoneal, transtracheal, subcutaneous, subcuticular, intraarticular, subcapsular, subarachnoid, intraspinal, epidural and intrasternal injection and infusion. Alternatively, a TNFα inhibitor of the invention can be administered via a non-parenteral route, such as a topical, epidermal or mucosal route of administration, for example, intranasally, orally, vaginally, rectally, sublingually or topically.
In a preferred embodiment, the subject to be treated with the TNFα inhibitor is a human subject.
A preferred autoimmune disorder in which to apply the methods of the invention for selecting and using a treatment regimen is rheumatoid arthritis. However, these methods can be applied to essentially any autoimmune disorder in which TNFα inhibitor therapy is applied, including the autoimmune disorders listed in the previous sections
Kits of the InventionIn another aspect, the invention pertains to kits for carrying out the methods of the invention. For example, in one embodiment, the invention provides a kit for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder. In one embodiment, the kit comprises:
a) means for isolating monocytes;
b) means for measuring expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder;
c) means for measuring expression of at least one housekeeping gene; and
d) instructions for use of the kit to predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or the biomarkers set forth by name in Table 9, as described above)
In one embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82 (or the biomarkers set forth by name in Table 9 as being increased in ≧80% of responders vs. non-responders, as described above), more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 31, 37, 44, 47, 62 and 70 (or the biomarkers set forth by name in Table 9 as being increased in ≧90% of responders vs. non-responders, as described above), and even more preferably at least one of the biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 44 (or the CD11c biomarkers set forth by name in Table 9 as being increased in 100% of responders vs. non-responders). In each of these embodiments, preferably the instructions for use of the kit instruct that increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81 (or the biomarkers set forth by name in Table 9 as being decreased in ≧80% of responders vs. non-responders, as described above), more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 11, 29, 65, 73 and 74 (or the biomarkers set forth by name in Table 9 as being decreased in ≧90% of responders vs. non-responders, as described above), and even more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 11 or SEQ ID NO: 74 (or the biomarkers set forth by name in Table 9 as being decreased in 100% of responders vs. non-responders, as described above). In each of these embodiments, preferably the instructions for use of the kit instruct that decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 83-133 (or the biomarkers set forth by name in Table 3, as described above).
In yet another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-177, 110, 112, 118, 123 and 131 (or the biomarkers set forth by name in Table 4, as described above), more preferably SEQ ID NO: 134-150, 112, 118 (or the biomarkers set forth by name in Table 4 as being upregulated, as described above), wherein the instructions instruct that expression of the one or more biomarkers is increased in the subject, and/or more preferably SEQ ID NO: 151-177, 110 and 123 (or the biomarkers set forth by name in Table 4 as being downregulated, as described above), wherein the instructions instruct that expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-292, 91 and 97 (or the biomarkers set forth by name in Table 5, as described above), more preferably SEQ ID NO: 178-185; 187-200, 202, 203, 205-207; 211, 213, 214, 216, 220, 221, 226, 228, 229, 231, 232, 234, 235, 238-247, 249, 250, 253, 262-265, 268-282 and 285-288 (or the biomarkers set forth by name in Table 5 as being upregulated, as described above), wherein the instructions instruct that expression of the one or more biomarkers is increased in the subject, and/or more preferably SEQ ID NO: 186, 201, 204, 208, 209, 91, 210, 212, 97, 215, 217-219, 222-225, 227, 230, 233, 236, 237, 248, 251, 252, 254-261, 266, 267, 283, 284 and 289-292 (or the biomarkers set forth by name in Table 5 as being downregulated, as described above), wherein the instructions instruct that expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 87, 97, 101, 293, 92, 272, 93, 107, 108, 121 and 123 (or the biomarkers set forth by name in Table 6 as pre-treatment biomarkers, as described above), wherein the instructions instruct that the subject is monitored prior to treatment with a TNFα inhibitor. In yet another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 290, 209, 98, 112, 116, 121, 130, 155, 92, 289, 216 and 131 (or the biomarkers set forth by name in Table 6 as post-treatment biomarkers, as described above), wherein the instructions instruct that the subject is monitored after treatment with a TNFα inhibitor.
In yet another embodiment of the kit, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 97, 102, 29, 294, 295, 91, 131, 290, 100, 134, 296, 297, 298, 299, 136, 174 and 300 (or the biomarkers set forth by name in Table 7, as described above).
In a preferred embodiment, the means for measuring expression in the subject of the one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder comprises a nucleic acid preparation sufficient to detect expression of mRNA encoding the one or more biomarkers in a sample from the subject, such as a peripheral blood mononuclear cell sample from which mRNA is obtained by standard methods. This nucleic acid preparation includes at least one, and may include more than one, nucleic acid probe or primer, the sequence(s) of which is designed such that the nucleic acid preparation can detect the expression of mRNA encoding the biomarker(s) of interest in the sample from the subject. A preferred nucleic acid preparation includes two or more PCR primers that allow for PCR amplification of a segment of the mRNA encoding the biomarker(s) of interest. In a particularly preferred embodiment, the kit comprises a nucleic acid preparation sufficient to detect expression of CD11c mRNA in a sample from the subject.
Alternatively, the means for detecting expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder can comprise a reagent that detects the gene product of the mRNA encoding the biomarker(s) of interest sufficient to distinguish it from other gene products in a sample from the subject. A non-limiting example of such a reagent is a monoclonal antibody preparation (comprising one or more monoclonal antibodies) sufficient to detect protein expression of the biomarker(s) of interest in a sample from the subject, such as a peripheral blood mononuclear cell sample. In a particularly preferred embodiment, the kit comprises a monoclonal antibody preparation sufficient to detect expression of CD11c protein in a sample from the subject.
The means for measuring expression of the one or more biomarkers can also include, for example, buffers or other reagents for use in an assay for evaluating biomarker expression (e.g., at either the mRNA or protein level). The instructions can be, for example, printed instructions for performing the assay for evaluating the expression of the one or more biomarkers.
In a preferred embodiment, the means for measuring expression of at least one housekeeping gene comprises a nucleic acid preparation sufficient to detect expression of mRNA of the housekeeping gene (e.g., GAPDH) in a sample from the subject, such as a peripheral blood mononuclear cell sample from which mRNA is obtained by standard methods. This nucleic acid preparation includes at least one, and may include more than one, nucleic acid probe or primer, the sequence(s) of which is designed such that the nucleic acid preparation can detect the expression of mRNA of the housekeeping gene(s) in the sample from the subject. A preferred nucleic acid preparation includes two or more PCR primers that allow for PCR amplification of a segment of the mRNA of the housekeeping gene(s). Alternatively, the means for detecting expression in the subject of at least one housekeeping gene can comprise a reagent that detects the gene product of housekeeping gene sufficient to distinguish it from other gene products in a sample from the subject. A non-limiting example of such a reagent is a monoclonal antibody preparation (comprising one or more monoclonal antibodies) sufficient to detect protein expression of housekeeping gene product in a sample from the subject, such as a peripheral blood mononuclear cell sample.
The means for isolating monocytes can comprise one or more reagents that can be used to separate monocytes from other cell types in a sample of peripheral blood mononuclear cells, for example by positive selection of the monocytes or by negative selection in which all other cell types other than monocytes are removed. In one embodiment, a reagent that binds CD14 on monocytes (e.g., an anti-CD14 antibody) is included in the kit as means to isolate monocytes via positive selection. Alternatively, in another embodiment, reagents such as those commercially available in the Monocyte Isolation Kit II (Miltenyi Biotec, Auburn, Calif.) can be used for negative selection, in which non-monocytes (T cells, B cells, NK cells, dendritic cells, basophils) are indirectly magnetically labeled using a cocktail of biotin-conjugated antibodies against CD3, CD7, CD16, CD19, CD56, CD123 and CD235a (Glycophorin A), as well as anti-biotin MicroBeads, and then highly pure unlabeled monocytes are obtained by depletion of the magnetically labeled cells.
In another embodiment, the kit can further comprise a TNFα inhibitor for treating an autoimmune disorder in the subject. Preferred TNFα inhibitors for use in the kit include the TNFα inhibitors described in detail above with respect to treatment regimens, in particular anti-TNFα antibodies such as adalimumab, infliximab and/or golimumab, and/or Ig fusion proteins such as etanercept.
Preferably, the kit is designed for use with a human subject.
Databases and Computer ProgramsIn another aspect, the invention pertains to methods of building a database for use in selecting an autoimmune disorder subject for treatment with a TNFα inhibitor, or for use in selecting or monitoring a treatment regimen in an autoimmune disorder subject. The method can comprise receiving, in a computer system, biomarker expression patterns from a plurality of subjects having an autoimmune disorder; and storing the biomarker expression pattern from each subject such that the biomarker expression pattern is associated with an identifier of the subject, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or the biomarkers set forth by name in Table 9, as described above). The identifier of the subject can be, for example, the name of the subject or a numerical or symbolic identifier coded to the identity of the subject. The method can further comprise receiving, in the computer system, one or more treatment regimens for treatment of an autoimmune disorder in a subject such that the treatment regimen is associated with the biomarker expression pattern of the subject and the identifier of the subject. A user can enter the subject's biomarker expression pattern, and optionally the subject's treatment regimen(s), into the computer system. Alternatively, the subject's biomarker expression pattern can be received directly from equipment used in determining the expression of one or more biomarkers in a sample from the subject.
In another aspect, the invention provides a computer program product useful for building a database for use in selecting or monitoring an autoimmune disorder subject for treatment with a TNFα inhibitor. The computer program can contain executable instructions that when executed cause a processor to perform operations comprising: receiving, in a computer system, a biomarker expression pattern of a subject at one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder; and storing the biomarker expression pattern such that the biomarker expression pattern is associated with an identifier of the subject, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or the biomarkers set forth by name in Table 9, as described above). Optionally, the computer program can further cause the processor to perform an operation comprising: receiving, in the computer system, a treatment regimen for treatment of an autoimmune disorder in the subject such that the treatment regimen is associated with the biomarker expression pattern of the subject and the identifier of the subject.
In another aspect, the invention provides a method of selecting an autoimmune disorder subject for a treatment with a TNFα inhibitor. The method can comprise: (i) identifying, in a database comprising a plurality of autoimmune disorder subjects, a subject whose database entry is associated with a biomarker expression pattern that is predictive of responsiveness to treatment with a TNFα inhibitor, and (ii) selecting the subject for treatment with a TNFα inhibitor, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or the biomarkers set forth by name in Table 9, as described above). The method can further comprise selecting a treatment regimen by identifying, in the database, a treatment regimen that has been associated with the biomarker expression pattern of the subject and with an identifier of the subject.
In yet another aspect, the invention provides a computer program product useful for identifying and/or selecting a subject for treatment with a TNFα inhibitor. The computer program can contain executable instructions that when executed cause a processor to perform operations comprising: (i) identifying, in a database including a plurality of autoimmune disorder subjects associated with biomarker expression patterns, a subject that is associated with a biomarker expression pattern that is predictive of responsiveness to treatment with a TNFα inhibitor; and (ii) outputting the identified subject as a subject to be treated with a TNFα inhibitor; wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82 (or the biomarkers set forth by name in Table 9, as described above). The computer program can further cause the processor to perform an operation comprising outputting a treatment regimen that is associated with the subject to be treated with the TNFα inhibitor.
In one embodiment of the above-described methods and computer program, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82 (or the biomarkers set forth by name in Table 9 as being increased in ≧80% of responders vs. non-responders, as described above), more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 31, 37, 44, 47, 62 and 70 (or the biomarkers set forth by name in Table 9 as being increased in ≧90% of responders vs. non-responders, as described above), and even more preferably at least one of the biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 44 (or the biomarker, CD11c antigen, set forth by name in Table 9 as being increased in 100% of responders vs. non-responders, as described above). In each of these embodiments, increased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another embodiment of the above-described methods and computer programs, the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81 (or the biomarkers set forth by name in Table 9 as being decreased in ≧80% of responders vs. non-responders, as described above), more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 11, 29, 65, 73 and 74 (or the biomarkers set forth by name in Table 9 as being decreased in ≧90% of responders vs. non-responders, as described above), and even more preferably the one or more biomarkers is encoded by a nucleic acid molecule comprising the nucleotide sequence of SEQ ID NO: 11 or SEQ ID NO: 74 (or the biomarkers set forth by name in Table 9 as being decreased in 100% of responders vs. non-responders, as described above). In each of these embodiments, decreased expression of the one or more biomarkers is predictive of responsiveness of the subject to a TNFα inhibitor.
In another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 83-133 (or the biomarkers set forth by name in Table 3, as described above).
In yet another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 134-177, 110, 112, 118, 123 and 131 (or the biomarkers set forth by name in Table 4, as described above), more preferably SEQ ID NO: 134-150, 112, 118 (or the biomarkers set forth by name in Table 4 as having increased expression, as described above), wherein expression of the one or more biomarkers is increased in the subject, and/or more preferably SEQ ID NO: 151-177, 110 and 123 (or the biomarkers set forth by name in Table 4 as having decreased expression, as described above), wherein expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 178-292, 91 and 97 (or the biomarkers set forth by name in Table 5, as described above), more preferably SEQ ID NO: 178-185; 187-200, 202, 203, 205-207; 211, 213, 214, 216, 220, 221, 226, 228, 229, 231, 232, 234, 235, 238-247, 249, 250, 253, 262-265, 268-282 and 285-288 (or the biomarkers set forth by name in Table 5 as having increased expression, as described above), wherein expression of the one or more biomarkers is increased in the subject, and/or more preferably SEQ ID NO: 186, 201, 204, 208, 209, 91, 210, 212, 97, 215, 217-219, 222-225, 227, 230, 233, 236, 237, 248, 251, 252, 254-261, 266, 267, 283, 284 and 289-292 (or the biomarkers set forth by name in Table 5 as having decreased expression, as described above), wherein expression of the one or more biomarkers is decreased in the subject.
In yet another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 87, 97, 101, 293, 92, 272, 93, 107, 108, 121 and 123 (or the biomarkers set forth by name in Table 6 as pre-treatment biomarkers, as described above), wherein the subject is monitored prior to treatment with a TNFα inhibitor. In yet another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 290, 209, 98, 112, 116, 121, 130, 155, 92, 289, 216 and 131 (or the biomarkers set forth by name in Table 6 as post-treatment biomarkers, as described above), wherein the subject is monitored after treatment with a TNFα inhibitor.
In yet another embodiment of the above-described methods and/or computer programs, the biomarker expression pattern(s) can be for one or more biomarkers selected from the group consisting of SEQ ID NO: 97, 102, 29, 294, 295, 91, 131, 290, 100, 134, 296, 297, 298, 299, 136, 174 and 300 (or the biomarkers set forth by name in Table 7, as described above).
Computer systems and database software well established in the art can be adapted for use in the methods and computer program products of the invention for building and searching a database for use in selecting or monitoring a treatment regimen for a subject having an autoimmune disorder or for selecting a particular autoimmune disorder subject for treatment with a TNFα inhibitor.
The present invention is further illustrated by the following examples which should not be construed as further limiting. The contents of all references, patents and published patent applications cited throughout this application are expressly incorporated herein by reference in their entirety.
EXAMPLES Example 1 Materials and MethodologiesIn this example, the materials and methodologies used in the subsequent Examples are described.
PatientsPurified monocytes (MO) derived from a total of 84 RA patients were used. The clinical data for the RA patients, pre- and post-anti-TNFα treatment, is summarized in Table 1. All patients fulfilled the revised American College of Rheumatology (ACR) criteria (Arnett, F. C. et al. (1988) Arthritis Rheum. 31:315-324). Patients were defined as responders (≧continuous ACR score 40) or non-responders (≦continuous ACR score 30) to anti-TNFα monotherapy, MTX monotherapy or combination therapy. In order to account for a gradual transition from clinical responders to non-responders to therapy, a continuous ACR response evaluation was performed by applying the ACR criteria, but by defining 10% response steps instead of the usual 20, 50, and 70% steps. This was also done to allow more detailed analyses of the correlation between the ACR response and the mRNA expression levels of the predictive marker CD11c.
For Affymetrix® microarray analysis, MO samples from 7 RA patients (RA1-RA7; all females) undergoing the Abbott DE011 trial were used (ReACT; monoclonal antibody Adalimumab®). In this multicenter, double-blinded study patients received Adalimumab® (20 mg weekly or 40 mg biweekly) in the outpatient Rheumatology Clinic of the Charite University Hospital in Berlin, Germany, for a total of 2 years. Non-steroidal anti-inflammatory drugs and 10 mg prednisolone-equivalent per week were allowed in addition to the anti-TNFα therapy. For TaqMan® real-time PCR, 26 patients (RA8-RA84 from the Abbott DE013 trial were used (ATTRACT). In this study Adalimumab®, methotrexate (MTX; 10-20 mg/week) or a combination of both therapeutics was applied.
The mean time interval between the two MO samples (blood sampling before and during therapy) was 9.4±1.8 months (mean±SEM; range 4-13.5 months), the mean age 50.3±3.9 years (range 39-70), and the mean disease duration 18.6±5.3 years (range 4-38). As controls, healthy individuals were recruited (n=7; 6 females, 1 male; mean age 36.1±7.4 years). RA patients RA1-RA84 were recruited from the Rheumatology Clinic of the Charité University Clinic, the Rheumaklinik of the Charité in Berlin-Buch, and from the Schlossparklinik in Berlin.
Separation of Peripheral Blood Mononuclear Cells, Purification of Monocytes, RNA IsolationPeripheral blood (30 to 35 ml) was obtained by venopuncture, immediately stored in heparin-containing vacutainers (Beckton-Dickinson, Rutherford, N.J., USA), and cooled to 4° C. Blood samples were subjected to a Ficoll-Hypaque gradient (d=1.077 g/ml; Biochrom, Berlin, Germany). To enrich MO, negative selection magnetic cell sorting (MACS; Miltenyi; Bergisch Gladbach, Germany) was subsequently applied. Successful purification of MO (purity of 83-90%; 1.3-3.2×106 MO/patient) was validated by FACS analysis using CD14 and CD45 antibodies (Beckman-Coulter, Krefeld, Germany). In all cases, purified MO showed >98% vitality using propidiumiodide staining (Pharmingen, San-Diego, Calif., USA). MO preparation was performed at 4° C. After purification, MO were lysed in RLT lysis buffer and total RNA was isolated using the RNeasy mini elute kit (Quiagen, Düsseldorf, Germany; yield 1.5-3.2 μg/sample). Quantification and quality control of RNA was performed at 260/280 nm using a Bioanalyzer 2100 unit (Agilent, Palo Alto, Calif., USA).
RNA Amplification and LabelingFor target synthesis, 500 ng of total RNA were amplified using the standard protocol of the manufacturer (Affymetrix®, Palo Alto, Calif., USA) and the Megascript kit (Ambion, Camhridgeshire, UK).
Biotin-Labeling of cRNA and Gene-Chip Hybridization
Biotinylated cRNA target was generated from amplified cRNAs using the Bioarray high-yield transcription kit (Enzo, New York, N.Y., USA). Samples were hybridized to Affymetrix® test and HG-U133A GeneChip arrays.
Following washing and staining, arrays were scanned twice at 3 μm resolution using a confocal scanner with an argon laser instrument (Agilent® G2500A GeneArray Scanner; Agilent, CA, USA).
Bioinformatic Analysis of Differentially Expressed Genes in ND and RA Patients Pre- and Post Anti-TNFα TreatmentAll GeneChips were analyzed for signal calculation and pairwise comparisons using the GCOS 1.4 software package with standard settings provided by Affymetrix®. Scaling was performed to a target value of 150 and normalization was set to “1”. Pairwise comparison data were grouped to generate a percentage level of increased and decreased comparisons. Fold-changes were calculated from the mean of the SLR values in pairwise comparisons. Filtering was performed on the basis of “increased/decreased” comparisons with a percentage cutoff as indicated in the results. For hierarchical clustering, the software tool “Gene Expression Similarity Investigation Suite” (Genesis; Sturn, A. et al. (2002) Bioinformatics 18:207-208; http://genome.tugraz.at/Software/GenesisCenter.html) was applied using normalized signal intensities, Pearson distance correlation, and complete linkage clustering. Prediction analysis was performed using the PAM software (http://www.bioconductor.org; Khan, J. et al. (2001) Nat. Med. 7:673-679).
Data Sets of PublicationThe complete ASCI-file datasets have been deposited in the microarray GEO database (http://www.ncbi.nlm.nih.gov/geo/).
TaqMan® Real Time PCRReal-time PCR (RT-PC) was performed using a TaqMan® 7500 system and pre-designed TaqMan® low density gene expression primers (Applied Biosystems; Foster City, Calif., USA) or, in the case of CD11c, the primer Hs01015072_g1 (commercially available from Applied Biosystems) in a Bio-Rad iQ real time PCR system (Icycler; Bio-Rad; München, Germany). The housekeeping gene GAPDH was used for normalization of the cDNA content. Quantification was performed using the SDS 2.2.0 software (Applied Biosystems); results were expressed as relative quantities of the logarithm of the ΔΔCT values (log RQ), as the relative quantity of expression (RQ; fold-change in comparison to normal donor expression), or as the % expression as normalized to GAPDH.
Literature-Associated Pathway Analysis Using IngenuityGene ontology and gene interaction analyses were executed using the Ingenuity® Pathway analysis tool v.4.0 (Ingenuity, Redwood City, Calif., USA; Jenssen, T. K. et al. (2001) Nat. Genet. 28:21-28; http://www.ingenuity.com). Highest scoring neighborhood analysis of literature-based gene connections was performed by comparing up- and downregulated genes in anti-TNFα responders and non-responders (pre- and post-treatment). Significantly regulated genes in both comparisons were merged from the networks 1 to 5, complemented by transcription factors and finally overlayed with their relative expression values.
Expression-Based Pathway Analysis Using Kyoto Encyclopedia of Genes and Genomes (KEGG)The KEGG pathway analysis (Kanehisa, M. and Bork, P. (2003) Nat. Genet. 33:305-310) was performed using selected genes from the comparison of microarray data in responders and non-responders either pre- or post-treatment with anti-TNFα. Upregulated genes and downregulated genes within the illustrated pathways were color-coded in a gradient fashion (SLR 0.5 to ≧1.5). A total of 4 pathways out of the 8 most highly ranked pathways were selected for illustration.
Statistical AnalysisThe non-parametric Mann-Whitney U test was applied to analyze differences between data from RA patients and normal donors, from untreated and anti-TNFα-treated RA patients, and from responders and non-responders to anti-TNFα-therapy. Correlation analyses between experimental and clinical/laboratory parameters of the patients were performed using the Pearson test and the software SPSS 13.0™ (SPSS Inc., Chicago, Ill., USA). For the U test, statistically significant differences were accepted for P≦0.05; for correlation analyses, the acceptance level was reduced to P≦0.01 to account for multiple comparisons.
Example 2 Clinical and Laboratory Assessments for RA Patients and Normal DonorsTwo of the seven anti-TNFα-treated RA patients used for microarray analysis, i.e., patients RA4 and RA6, were non-responders to therapy according to the ACR improvement criteria (≦continuous ACR 30 score). In general, this was also reflected in the respective percent-reduction of other clinical parameters of local or systemic inflammation. The group of seven RA patients employed for microarray analysis in the present study constituted a representative RA cohort, as demonstrated by well-known correlations among clinical parameters pre- and post-anti-TNFα treatment, as summarized in Table 2.
The identification of patients RA4 and RA6 as non-responders was also confirmed by hierarchical clustering of clinical parameters.
Example 3 Gene Expression Profiling and Analysis Differential Gene Expression in Responders Versus Non-Responders to Anti-TNFα-TherapyA total of 119 differentially-expressed genes was identified by comparing RA and normal donors (ND; n=7 each; total of 49 comparisons). Hierarchical clustering of ND, as well as RA patients pre- and post-treatment with these genes also identified 5 responders and 2 non-responders (RAantiTNF4 and RAantiTNF6).
In order to select therapeutically relevant genes, a subpopulation of 51 differentially-expressed genes was then identified by the simultaneous comparison between RA versus normal donors (ND; total of 49 comparisons) and RA responders (n=5) pre- versus post-anti-TNFα therapy (25 comparisons). These genes showed an increase or decrease of the signal log ratio (SLR; between −4.36 and 4.61) in >70% of the pair-wise comparisons between RA and ND. These genes are summarized in Table 3.
Hierarchical clustering with these genes resulted in precise (100%) classification of ND, RA patients pre-treatment, and clinically-defined responders (≧continuous ACR score 40; clustered as ND) or non-responders (≦continuous ACR score 30; clustered as RA; note RAantiTNF4 and RAantiTNF6). This was confirmed by supervised pattern discovery using prediction analysis of microarrays (PAM analysis) at a threshold value of 4.3 in order to minimize the misclassification error. Table 4 summarizes the results of the PAM analysis, showing 49 selected genes, five of them overlapping with genes listed in Table 3. In this case, both non-responders (RAantiTNF4 and RAantiTNF6) were classified as RA patients, whereas the responders, RAantiTNF1-3, RAantiTNF5, and RAantiTNF7, were classified either as normals or as anti-TNFα-treated RA patients. Notably, ND showed the lowest misclassification error at the threshold value (0.00; i.e., the highest similarity among individuals), followed by pre-treatment RA patients (0.14), and anti-TNFα-treated RA patients (0.42).
Identification of responders/non-responders was also confirmed by hierarchical clustering of RA patients post-treatment. A total of 117 genes differentially expressed in RA responders versus RA non-responders was identified in post-anti-TNFα therapy samples, which showed an increase or decrease in 100% of the respective pair-wise comparisons (total of 10 comparisons). The 117 genes identified in this analysis are summarized in Table 5, three of which genes overlap with the genes summarized in Table 3. Hierarchical clustering with these genes resulted in precise (100%) classification of responders and non-responders to therapy.
A number of the differentially-expressed genes showed highly significant correlations with clinical or laboratory parameters pre- and/or post-anti-TNFα treatment, indicating a potential clinical relevance of the genes and contributing to the selection of genes for validation with TaqMan® real-time RT-PCR. These genes are summarized in Table 6.
Example 4 Real-Time RT-PCR Validation of Genes Differentiating Responders and Non-RespondersSixteen genes with a likely pathogenetic importance in RA (and 6 control genes) from Affymetrix® gene expression profiling were selected for validation by TaqMan® real-time RT-PCR (n=10 anti-TNFα-treated RA patients prior to therapy; n=14 ND). The RT-PCR results confirmed the results of Affymetrix® gene expression profiling for 17 of 22 genes (approx. 77%), summarized in Table 7. This applied to genes regarded as differentially expressed in RA versus ND by Affymetrix® analysis (decreased: <−70%; increased: >70%) and to equally expressed control genes. By real-time RT-PCR, 18 genes showed significantly differential expression (p≦0.000 for 7 genes; p≦0.041 for the remaining) in MO from RA patients responding to anti-TNFα therapy versus ND, including genes 10 upregulated in RA (Amphiregulin, Charcot-Leyden crystal protein, TNFα-induced protein 6, thrombomodulin, membrane-spanning 4-domains, subfamily A—member 4, S100 calcium binding protein A1, TNFα, IL-1β, lipoyltransferase 1, and interferon regulatory protein 1), as well as 8 genes downregulated in RA (Uncharacterized hypothalamus protein HT007, MHC class II HLA-DR-alpha, hypothetical protein L0054103, proteasome subunit beta type 7 precursor, protein KIAA0174, microsomal signal peptidase 18 kDa subunit, ring zinc finger protein 361, and protein phosphatase 1, catalytic subunit, beta isoform). However, the expression for these genes showed no significant differences for the direct comparison between RA responders and non-responders to anti-TNFα therapy.
The potential clinical relevance of some of the differentially expressed genes is underlined by a significant correlation with the ACR response at different time points during anti-TNFα therapy, as summarized in Table 8.
Validation of the 22 selected genes by TaqMan® real-time RT-PCR confirmed the results of Affymetrix® gene expression profiling for 17 of 22 genes (approx. 77% true positives or negatives), in accordance with the rates reported in other gene expression studies and therefore underlining the validity of the present data. The 18 genes showing significantly differential expression in MO from RA patients responding to anti-TNFα therapy versus ND included several genes with a likely pathogenetic importance in RA (e.g., amphiregulin, TNFα-induced protein 6, thrombomodulin, membrane-spanning 4-domains, subfamily A—member 4, S100 calcium binding protein A1, TNFα, IL-1β, lipoyltransferase 1, interferon regulatory protein 1, MHC class II HLA-DR-alpha).
Example 5 Gene Expression Profiling and Analysis Differential Gene Expression in Responders Versus Non-Responders Prior to Anti-TNFα-Therapy (Predictive Genes)Using a threshold of ≧80% for the pairwise comparisons between future RA responders and future RA non-responders prior to anti-TNFα therapy, a total of 82 predictive genes was identified (11 genes for a threshold of ≧90%; 3 genes for 100%). These genes are summarized in Table 9. The latter group (100%) consisted of 2 known proteins (Homo sapiens predicted osteoblast protein (GS3786); integrin alpha-X (antigen CD11c) and an unknown protein (Homo sapiens hypothetical protein FLJ10134). In particular the antigen CD11e appears highly interesting, since it is a surface molecule on human MO (and other cells of the myelomonocytic lineage), and since it has known inflammatory functions. Hierarchical clustering with the selected genes resulted again in precise (100%) predictive classification of future responders and non-responders to therapy. Marginal co-clustering of the patient RA5 with the non-responders RA4 and RA6 at the threshold values 80% and 100% possibly identified RA5 as a ‘weak’ responder, as also indicated by a marginal position in the hierarchical clustering of clinical parameters.
Interestingly, future responders to anti-TNFα-therapy (when directly compared to non-responders) showed a pattern shift from the recently described ‘inflammatory’ MO to ‘resident’ MO subsets (Gordon, S. et al. (2005) Nat. Immunol. 5:953-963) both prior to therapy and post-treatment. Although most of the individual molecules showed an identical pattern shift in both pre-treatment and post-treatment comparisons, in particular the activating (CD16a,b) and inhibiting Fcγ-receptors (CD32) displayed an opposite behavior. This pattern shift was observed despite the fact that all pre-treatment or post-treatment comparisons between RA patients (all, future responders, future non-responders) and ND indicated a dominance of ‘inflammatory’ MO in the respective RA groups. Post-treatment, strikingly, responders to anti-TNFα-therapy became barely distinguishable from ND in contrast to non-responders, which still showed a clear ‘inflammatory’ predominance. These results indicate that successful anti-TNFα-therapy acts by blocking TNFα signaling via TNFα-receptors 1 and 2 and by subsequent induction of a major change in the composition of pro-inflammatory and other MO subsets.
Example 6 Real-Time RT-PCR Validation of Predictive CD11cTaqMan® real-time RT-PCR confirmed the discrimination of future RA responders (n=15) from future nonresponders (n=12) to anti-TNF monotherapy (at the level of continuous ACR score 30) on the basis of their CD11c mRNA expression in monocytes.
TaqMan® real-time RT-PCR confirmed the separation of future RA responders n=55 from future non-responders n=12 to anti-TNFα monotherapy on the basis of their CD11c mRNA expression in MO. The results are summarized in the bar graph of
Of the 3 genes identified by pairwise comparison between RA responders and RA non-responders prior to treatment at the level of 100% (and confirmed by TaqMan® real-time RT-PCR), the antigen CD11c appears of particular interest, since it is expressed on the surface of human MO (and other cells of the myelomonocytic lineage, e.g. dendritic cells), and since it has known functions in inflammatory reactions (e.g., as the complement receptor 4) and cell adhesion.
The validity of CD11c as a predictive biomarker is further underlined by a significant correlation (r=0.651, P=<0.0001, n=27) between the CD11c expression prior to therapy and the future percentage of the ACR response, illustrated in the graph of
Except for 1 RA patient with a borderline ACR response of 30 and a CD11c mRNA level directly at the distinction threshold (who was therefore classified as a false negative), the threshold level (40%) almost fully distinguished future responders from nonresponders (100% specificity, 94% sensitivity, and 96% power). This clear separation was lost in the case of combination therapy with anti-TNF/methotrexate (MTX), possibly owing to a differential importance of the CD11c mRNA expression for the anti-TNF and MTX treatment components. This was further underlined by the fact that: i) future responders to MTX monotherapy did not significantly differ in their CD11c mRNA expression level from future nonresponders to MTX monotherapy; and ii) there was no significant correlation between the future ACR response of RA patients treated with MTX monotherapy and their CD11c mRNA expression (data not shown).
Strikingly, this was true not only for the continuous ACR score, but also for the clinically applied, discrete ACR criteria.
A significant correlation (r=0.656, P=<0.0001, n=27) was observed between CD11c expression and the future percentage of strict ACR response, illustrated in the graph in
Ingenuity® pathway analysis of the genes in Tables 3 and 5 indicated a direct or indirect influence of anti-TNFα therapy on several molecules thought to be relevant for the pathogenesis and/or severity of RA, e.g., HLA-DMA/B (Morel, J. et al. (2004) Ann. Rheum. Dis. 63:1581-1586), CD69 (Marzio, R. et al. (1999) Immunopharmacol. Immunotoxicol. 2:565-582) thrombomodulin (Cobankara, V. et al. (2004) Clin. Rheumatol. 23:430-434), membrane-spanning 4-domains, subfamily A—member 4 (Fujikado, N. et al. (2006) Arthritis Res. Ther. 8:1-13), and forkhead box 03a (Jonsson, H. et al. (2005) Nat. Med. 11:666-671). The present approach, therefore, represents a powerful tool to identify gene regulation patterns applicable for diagnosis, as well as for therapy stratification and monitoring in rheumatic diseases, in particular in view of the fact that blood MO are much more easily available than synovial tissue samples. This is further supported by the fact that a high number of individual genes show significant correlations with clinical parameters in RA patients pre- and/or post-anti-TNFα treatment (see Table 6).
Pairwise comparison between RA responders and RA non-responders prior to treatment yielded a number of genes suitable for the prediction of a future response to anti-TNFα therapy (82 genes for a threshold of ≧80%; 11 genes for ≧90%; 3 genes for 100%; summarized in Table 9), resulting in exact classification of future responders and non-responders upon hierarchical clustering. Using all genes differentially expressed in the above comparison at a threshold level of 70% (256 pre-treatment; 1295 post-treatment) for Ingenuity® pathway analysis, the differences between responders and non-responders either pre-treatment or post-treatment were concentrated in the functional gene ontology terms cellular movement, haematological system development, immune response, cell-to-cell signaling and interaction, as well as immunological disease.
In particular, numerous relevant mediators were identified: i) pro-inflammatory cytokines (e.g., interleukin-8 [IL8], chemokine (C—C motif) ligand 5 [CCL5], chemokine (C—X—C motif) ligand 5 [CXCL5], and chemokine (C—X—C motif) ligand 10 [CXCL10]); ii) pro-destructive enzymes (e.g., matrix metalloproteinase 9 [MMP9]); iii) adhesion molecules and Fcγ-receptors (galectin-8 [LGALS8], integrin alpha-X [ITGAX] or CD11c, Fc-gamma receptor IIb [FCGR2B] or CD32, CD86 [CD86] and platelet/endothelial cell adhesion molecule 1 [PECAM1] or CD31); iv) signal transduction molecules (e.g., protein kinase B [AKT1], apoptosis regulator Bel-2 [BCL2], p21-activated protein kinase 1 [PAK1]); and v) transcription factors (e.g., Mad-related protein 2 [SMAD2], interferon regulatory factor 1 [IRF1], c-myb [MYB], early growth response protein 1 [EGR1], signal transducer and activator of transcription 1 [STAT1], and nuclear factor NF-κB 1 [NFKB1]; for the remaining abbreviations see the respective gene cards [http://www.genecards.org/index.shtml]). These molecules were differentially expressed between RA responders and RA non-responders either pre-treatment or post-treatment and in some cases even inverted their expression upon anti-TNFα-therapy (see interleukin-8 receptor beta [IL8RB], Amyloid-beta A4 precursor [APP]; overexpressed in RA responders pre-treatment and underexpressed post-treatment). Similar opposite variations in transcript levels between RA responders and RA non-responders have recently been reported when comparing baseline to 3-month results (Lequerre, T. et al. (2006) Arthritis Res. Ther. 8:R105).
Most strikingly, the transcription of TNFα itself, as well the transcription of members of the subsequent NFKB-pathway (NFKB1 and inhibitor of NF-κB [IKBKB]), was upregulated in RA responders post-treatment. This previously unreported finding at first sight questions the central pro-inflammatory role of TNFα in RA. However, several caveats should be considered: i) The mRNA expression levels of TNFα measured in the present study may not be proportional to the levels of circulating TNFα protein and/or bioactivity, the latter apparently predictive of the clinical response to TNFα inhibition (Marotte, H. et al. (2005) Arthritis Res. Ther. 7:R149-155); ii) TNFα, in addition to its well-established pro-inflammatory properties, may also exhibit phase-dependent immunosuppressive properties (Kassiotis, G. et al. (2001) J. Exp. Med. 193:427-434).
Several interesting pathways with potential importance for the mechanisms underlying susceptibility to anti-TNFα-therapy were identified by KEGG pathway analysis; including Apotosis and the MAPK pathways.
Claims
1. A method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising: (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder, and (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-10, 12-28, 30-43, 45-61, 63, 64, 66-73, and 75-82,
- wherein decreased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 12-14, 18, 20, 21, 23, 25, 26, 28, 30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 66-69, 71-73, 76-78, 80 and 81 is predictive of responsiveness of the subject to a TNFα inhibitor; and/or
- wherein increased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43, 45-47, 49, 51, 54, 55, 57, 59, 61, 64, 70, 75, 79 and 82 is predictive of responsive of the subject to a TNFα inhibitor.
2. A method for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the method comprising:
- (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder, and
- (ii) predicting responsiveness of the subject to the TNFα inhibitor based on expression of the one or more biomarkers in the subject, wherein the one or more biomarkers is selected from the group consisting of Aquaporin 3 (Genbank Accession No. NM—004925); Similar to ribosomal protein S24, clone MGC:8595 (Genbank Accession No. NM—033022); Transmembrane emp24 domain trafficking protein 2 (Genbank Accession No. NM—006815); Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (Genbank Accession No. NM—000454); Calmodulin 1 (phosphorylase kinase, delta) (Genbank Accession No. NM—006888); Guanine nucleotide binding protein (G protein), beta polypeptide 1 (Genbank Accession No. NM—002074); Prothymosin, alpha (gene sequence 28) (Genbank Accession No. NM—002823); Homo sapiens isocitrate dehydrogenase 1 (NADP+) soluble (IDH1) (Genbank Accession No. NM—005896); Tumor protein D52 (Genbank Accession Nos. NM—001025252, NM—001025253, NM—005079); Early growth response 1 (Genbank Accession No. NM—001964); Cytochrome c oxidase subunit VIIb (Genbank Accession No. NM—001866); CUG triplet repeat, RNA binding protein 2 (Genbank Accession No. NM—001025077, NM—001025076, NM—006561); Ubiquinol-cytochrome c reductase hinge protein (Genbank Accession No. NM—006004); Homo sapiens leptin receptor gene-related protein (HS0BRGRP) (Genbank Accession No. NM—017526); Wiskott-Aldrich syndrome protein interacting protein (Genbank Accession Nos. NM—001077269, NM—003387); CD97 antigen (Genbank Accession Nos. NM—001025160, NM—001784, NM—078481); Glutamate-cysteine ligase, catalytic subunit (Genbank Accession No. NM—001498); Crystallin, zeta (quinone reductase) (Genbank Accession No. NM—001889); Rap guanine nucleotide exchange factor (GEF) 2 (Genbank Accession No. NM—014247); Ataxin 1 (Genbank Accession No. NM—000332); Adaptor-related protein complex 1, sigma 2 subunit (Genbank Accession No. NM—003916); Ectonucleotide pyrophosphatase/phosphodiesterase 4 (Genbank Accession No. NM—014936); Desmocollin 2 (Genbank Accession Nos. NM—024422, NM—004949); MAL, T-cell differentiation protein (Genbank Accession Nos. NM—002371, NM—022438, NM—022439, NM—022440); Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (Genbank Accession No. NM—005476); Chemokine (C—C motif) ligand 3 (Genbank Accession Nos. NM—001001437, NM—021006); Carboxypeptidase A3 (Genbank Accession No. NM—001870); NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 1, 7 kDa (Genbank Accession No. NM—004545); Interleukin 8 receptor, beta (Genbank Accession No. NM—001557); Platelet factor 4 variant 1 (Genbank Accession No. NM—002620); Poly(A) binding protein interacting protein 1 (Genbank Accession No. NM—006451); ATP-binding cassette, sub-family C (CFTR/MRP), member 3 (Genbank Accession No. NM—003786); Actinin, alpha 1 (Genbank Accession No. NM—001102); NAD kinase (Genbank Accession No. NM—023018); Platelet/endothelial cell adhesion molecule (CD31 antigen) (Genbank Accession No. NM—000442); Esterase D/formylglutathione hydrolase (Genbank Accession No. NM—001984); Chromosome 20 open reading frame 111 (Genbank Accession No. NM—016470); Sterol-C4-methyl oxidase-like (Genbank Accession Nos. NM—001017369, NM—006745); PIM-1 oncogene (Genbank Accession No. NM—002648); GATA binding protein 2 (Genbank Accession No. NM—032638); Cathepsin Z (Genbank Accession No. NM—001336); Lectin, galactoside-binding, soluble, 8 (galectin 8) (Genbank Accession Nos. NM—006499, NM—201545); CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) (Genbank Accession Nos. NM—006889, NM—175862); Interleukin 8 (Genbank Accession No. NM—000584); Fc fragment of IgE, high affinity I, receptor for alpha polypeptide (Genbank Accession No. NM—002001); Actin, gamma 1 (Genbank Accession No. NM—001614); KIAA0746 protein (Genbank Accession No. NM—015187); Glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID) (Genbank Accession No. NM—002076); Transcription factor 4 (Genbank Accession Nos. BF592782, CR612521); Major histocompatibility complex, class II, DQ alpha 1 (Genbank Accession Nos. NM—002122, NM—020056); Cell division cycle 2-like 6 (CDK8-like) (Genbank Accession No. NM—015076); Major histocompatibility complex, class II, DQ beta 1 (Genbank Accession No. XM—942240); Phospholipase C-like 2 (Genbank Accession No. NM—015184); Coagulation factor II (thrombin) receptor-like 1 (Genbank Accession No. NM—005242); TM2 domain containing 1 (Genbank Accession No. NM—032027); Splicing factor 3b, subunit 1, 155 kDa (Genbank Accession No. NM—012433); SUB1 homolog (S. cerevisiae) (Genbank Accession No. NM—006713); MRNA, cDNA DKFZp564O0862 (Genbank Accession No. AI278204); Cytochrome b-5 (Genbank Accession Nos. NM—001914, NM—148923); Cold autoinflammatory syndrome 1 (Genbank Accession No. NM—183395); Ribosomal protein S26, 40S ribosomal protein (Genbank Accession No. XM—941927); CCR4-NOT transcription complex, subunit 6 (Genbank Accession No. NM—015455); Ubiquinol-cytochrome c reductase complex (7.2 kD) (Genbank Accession Nos. NM—013387, NM—001003684); Hepatocellular carcinoma-associated antigen 112 (Genbank Accession No. NM—018487); Kruppel-like factor 11 (Genbank Accession No. XM—001129527); GGA binding partner (Genbank Accession No. NM—018318); Cornichon homolog 4 (Drosophila) (Genbank Accession No. NM—014184); Hypothetical protein FLJ21616 (Genbank Accession No. NM—024567); Nuclear prelamin A recognition factor (Genbank Accession Nos. NM—012336, NM—001038618); Erythroblast membrane-associated protein (Genbank Accession Nos. NM—018538, NM—001017922); LR8 protein (Genbank Accession No. NM—014020); Likely ortholog of mouse limb-bud and heart gene (LBH) (Genbank Accession No. NM—030915); Calmin (calponin-like, transmembrane) (Genbank Accession No. NM—024734); Chromosome 14 open reading frame 156 (Genbank Accession No. NM—031210); Guanine nucleotide binding protein (G protein) alpha 12 (Genbank Accession No. NM—007353); and SRY (sex determining region Y)-box 18 (Genbank Accession No. NR—003287).
3. A method for predicting responsiveness to a TNFα inhibitor, which TNFα inhibitor is adalimumab, in a subject having an autoimmune disorder, the method comprising:
- (i) assaying the subject for expression of one or more biomarkers predictive of responsiveness to adalimumab in an autoimmune disorder, and
- (ii) predicting responsiveness of the subject to adalimumab based on expression of the one or more biomarkers in the subject,
- wherein decreased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 12-14, 18, 20, 21, 23, 25, 26, 28, 30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 66-69, 71-73, 76-78, 80 and 81 is predictive of responsiveness of the subject to a TNFα inhibitor; and/or
- wherein increased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43, 45-47, 49, 51, 54, 55, 57, 59, 61, 64, 70, 75, 79 and 82 is predictive of responsive of the subject to a TNFα inhibitor.
4. The method of any of claims 1-3, wherein a sample from the subject is assayed for expression of mRNA encoding the one or more biomarkers.
5. The method of any of claims 1-3, wherein a sample from the subject is assayed for protein expression of the one or more biomarkers.
6. The method of any of claims 1-3, which further comprises selecting a treatment regimen with the TNFα inhibitor based upon expression of the one or more biomarkers in the subject.
7. The method of claim 6, which further comprises administering the TNFα inhibitor to the subject according to the treatment regimen such that the autoimmune disorder is inhibited in the subject.
8. The method of any of claims 1-3, wherein the TNFα inhibitor is an anti-tumor necrosis factor-alpha (TNFα) antibody, or antigen-binding portion thereof.
9. The method of claim 8, wherein the anti-TNFα antibody, or antigen-binding portion thereof, is a human antibody, a humanized antibody, a chimeric antibody or a multivalent antibody.
10. The method of claim 8, wherein the anti-TNFα antibody, or antigen-binding portion thereof, is adalimumab.
11. The method of claim 9, wherein the anti-TNFα antibody, or antigen-binding portion thereof, is selected from the group consisting of
- i) an isolated human antibody that dissociates from human TNFα with a Kd of 1×10−8 M or less and a Koff rate constant of 1×10−3 s−1 or less, both determined by surface plasmon resonance, and neutralizes human TNFα cytotoxicity in a standard in vitro L929 assay with an IC50 of 1×10−7 M or less;
- ii) an isolated human antibody with the following characteristics: a) dissociates from human TNFα with a Koff rate constant of 1×10-3 s-1 or less, as determined by surface plasmon resonance; b) has a light chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 305, or modified from SEQ ID NO: 305 by a single alanine substitution at position 1, 4, 5, 7 or 8 or by one to five conservative amino acid substitutions at positions 1, 3, 4, 6, 7, 8 and/or 9; c) has a heavy chain CDR3 domain comprising the amino acid sequence of SEQ ID NO: 306, or modified from SEQ ID NO: 306 by a single alanine substitution at position 2, 3, 4, 5, 6, 8, 9, 10 or 11 or by one to five conservative amino acid substitutions at positions 2, 3, 4, 5, 6, 8, 9, 10, 11 and/or 12; and
- iii) an isolated human antibody with a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 303 and a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 304.
12. The method of claim 1, wherein the TNFα inhibitor is etanercept.
13. The method of any one of claims 1-3, wherein the subject is a human.
14. The method of any one of claims 1-3, wherein the autoimmune disorder is rheumatoid arthritis.
15. A kit for predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, the kit comprising:
- a) means for isolating monocytes;
- b) means for measuring expression in the subject of one or more biomarkers predictive of responsiveness to a TNFα inhibitor in an autoimmune disorder;
- c) means for measuring expression of at least one housekeeping gene; and
- d) instructions for use of the kit to predicting responsiveness to a TNFα inhibitor in a subject having an autoimmune disorder, wherein decreased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-5, 8, 9, 11-14, 18, 20, 21, 23, 25, 26, 28-30, 32, 33, 40-42, 48, 50, 52, 53, 56, 58, 60, 63, 65-69, 71-74, 76-78, 80 and 81 is predictive of responsiveness of the subject to a TNFα inhibitor and/or wherein increased expression of the one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NOs: 6, 7, 10, 15-17, 19, 22, 24, 27, 31, 34-39, 43, 44-47, 49, 51, 54, 55, 57, 59, 61, 62, 64, 70, 75, 79 and 82 is predictive of responsive of the subject to a TNFα inhibitor.
16. A method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 83-133, thereby monitoring the autoimmune disorder in the subject.
17. A method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 134-177, 110, 112, 118, 123 and 131, thereby monitoring the autoimmune disorder in the subject.
18. A method of monitoring an autoimmune disorder in a subject having an autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 178-292, 91 and 97, thereby monitoring the autoimmune disorder in the subject.
19. A method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 87, 97, 101, 293, 92, 272, 93, 107, 108, 121 and 123, and wherein the subject is monitored prior to treatment with a TNFα inhibitor, thereby monitoring the autoimmune disorder in the subject.
20. A method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 290, 209, 98, 112, 116, 121, 130, 155, 92, 289, 216 and 131, and wherein the subject is monitored after treatment with a TNFα inhibitor, thereby monitoring the autoimmune disorder in the subject.
21. A method of monitoring an autoimmune disorder in a subject having the autoimmune disorder, the method comprising: assaying the subject for expression of one or more biomarkers, wherein the one or more biomarkers is encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 97, 102, 29, 294, 295, 91, 131, 290, 100, 134, 296, 297, 298, 299, 136, 174 and 300, thereby monitoring the autoimmune disorder in the subject.
22. A method of selecting an autoimmune disorder subject for a treatment with a TNFα inhibitor, the method comprising:
- (i) identifying, in a database comprising a plurality of autoimmune disorder subjects, a subject whose database entry is associated with a biomarker expression pattern that is predictive of responsiveness to treatment with a TNFα inhibitor, and
- (ii) selecting the subject for treatment with a TNFα inhibitor, wherein the biomarker expression pattern reports expression in the subject of one or more biomarkers encoded by a nucleic acid molecule comprising a nucleotide sequence selected from the group consisting of SEQ ID NO: 1-82.
Type: Application
Filed: Oct 27, 2011
Publication Date: Feb 16, 2012
Applicant: Abbott Laboratories (Abbott Park, IL)
Inventors: Bruno Stuhlmüller (Berlin), Gerd-Reudiger Burmester (Berlin)
Application Number: 13/282,850
International Classification: A61K 39/395 (20060101); C12Q 1/68 (20060101); A61P 37/06 (20060101); G01N 33/573 (20060101); C40B 40/06 (20060101); A61P 29/00 (20060101); C40B 30/04 (20060101); G01N 33/566 (20060101);
