WO2019087200A1 - Prognostic methods for anti-tnfa treatment - Google Patents

Prognostic methods for anti-tnfa treatment Download PDF

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Publication number
WO2019087200A1
WO2019087200A1 PCT/IL2018/051187 IL2018051187W WO2019087200A1 WO 2019087200 A1 WO2019087200 A1 WO 2019087200A1 IL 2018051187 W IL2018051187 W IL 2018051187W WO 2019087200 A1 WO2019087200 A1 WO 2019087200A1
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Prior art keywords
therapy
subject
tnfα
expression
trem1
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PCT/IL2018/051187
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French (fr)
Inventor
Yehuda Chowers
Sigal PRESSMAN
Haggai BAR YOSEPH
Shiran VAINBERG
Shai S. Shen-Orr
Renaud Gilles GAUJOUX
Elina STAROVETSKY
Naama MAIMON
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Rambam Med-Tech Ltd.
Technion Research & Development Foundation Limited
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Publication of WO2019087200A1 publication Critical patent/WO2019087200A1/en

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    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/24—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against cytokines, lymphokines or interferons
    • C07K16/241—Tumor Necrosis Factors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P37/00—Drugs for immunological or allergic disorders
    • A61P37/02—Immunomodulators
    • A61P37/06—Immunosuppressants, e.g. drugs for graft rejection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/566—Immunoassay; Biospecific binding assay; Materials therefor using specific carrier or receptor proteins as ligand binding reagents where possible specific carrier or receptor proteins are classified with their target compounds
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention is in the field of anti-TNF ⁇ therapy diagnostics.
  • IBDs Inflammatory Bowel Diseases
  • UC ulcerative colitis
  • CD Crohn’s disease
  • anti-TNF ⁇ therapy remains suboptimal for several reasons.
  • Treatment choice and administration is empiric and based on data obtained from the“average patient” in clinical trials.
  • This practice is associated with insufficient remission rates, that result from primary nonresponse (PNR) (20-40% in clinical trials; 10-20% in real life cohorts), and from loss of response commonly due to immunogenicity and increased anti-TNF ⁇ clearance in 13-24% of patients at 12 months.
  • PNR primary nonresponse
  • the treatment is associated with adverse side effects, is expensive and each therapeutic attempt requires waiting the anticipated time for response during which the disease is active, and damage accumulates.
  • PNR primary nonresponse
  • the present invention provides methods of determining suitability of a subject to be treated with anti-TNF ⁇ therapy comprising measuring blood TREM1 levels or intestinal plasma cell number, intestinal inflammatory macrophage number or intestinal CCL7 or CCR2 levels. Kits for doing same are also provided.
  • a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy comprising:
  • a obtaining a blood sample from the subject; b. measuring expression levels of Triggering Receptor Expressed on Myeloid cells 1 (TREM1) in the blood sample; and c. determining the suitability of the subject for anti-TNF ⁇ therapy according to the expression levels of TREM1, wherein expression below a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy and expression at or above the predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy; thereby determining suitability of a subject to be treated with anti-TNF ⁇ therapy.
  • TREM1 Triggering Receptor Expressed on Myeloid cells 1
  • a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy comprising
  • determining the suitability of the subject for anti-TNF ⁇ therapy according to the plasma cell or inflammatory macrophage number wherein a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy and a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both below the predetermined threshold indicates the subject is suitable for the anti-TNFa therapy; thereby determining suitability of a subject to be treated with anti-TNF ⁇ therapy.
  • the blood sample is a peripheral blood sample.
  • a number of plasma cells above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy and a number of plasma cells below the predetermined threshold indicates the subject is suitable for the anti-TNFa therapy.
  • expression levels of CCL7 and CCR2 above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy and expression levels of CCL7 and CCR2 below the predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • the method further comprises measuring in an intestinal biopsy from the subject expression levels of TREM1, wherein intestinal expression levels of TREM1 above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy and intestinal expression levels of TREM 1 below the predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • the subject suffers from inflammation.
  • the subject suffers from an autoimmune disease.
  • the autoimmune disease is selected from inflammatory bowel disease (IBD) and rheumatoid arthritis (RA).
  • the IBD comprises colitis, ulcerative colitis, and Crohn’s disease.
  • the anti-TNF ⁇ therapy comprises administration of an anti-TNF ⁇ antibody.
  • the anti-TNF ⁇ antibody is selected from infliximab and adalimumab.
  • the method further comprises administering the anti- TNFa therapy to the subject indicated to be suitable for the anti-TNF ⁇ therapy.
  • the method further comprises administering to the subject indicated to be unsuitable for the anti-TNF ⁇ therapy an increased dose of the anti-TNF ⁇ therapy above the standard dose.
  • the method further comprises administering a non-anti-TNF ⁇ anti-inflammation therapy to the subject indicated to be unsuitable for anti-TNFa therapy.
  • the non-anti-TNF ⁇ anti-inflammation therapy comprises blocking at least one of CCR2, CCR5 and CXCR3.
  • the predetermined threshold is determined from blood levels of TREM 1 , intestinal numbers of plasma cells or inflammatory macrophages or intestinal expression of CCL7, CCR2 or both in subjects that responded to anti-TNF ⁇ therapy, wherein the blood levels, the intestinal numbers and the intestinal expression are from before the subjects received the anti-TNF ⁇ therapy.
  • measuring plasma cell number comprises measuring the number of cells positive for surface expression of CD 138.
  • measuring expression levels comprises measuring mRNA levels, protein levels or both. [019] According to some embodiments, measuring expression levels comprises contacting the blood sample or intestinal biopsy with a molecule that detects TREM1, CCL7, or CCR2 mRNA or protein, and wherein the molecule is connected to an artificial solid support.
  • a TREM1 detecting agent for determining serum TREM1 levels in a subject and determining suitability of the subject for treatment with an anti-TNF ⁇ therapy.
  • the detecting agent is an anti-TREMl antibody or a nucleic acid molecule that selectively binds and hybridizes to TREM1 mRNA.
  • kits comprising a plurality of molecules selected from:
  • Figure 1A Predictive gene signatures from previous gene expression differential analysis. Heatmaps showing the gene expression of the 6 previously reported predictive gene signatures in their respective discovery cohort(s). The top strip annotates responders (r) and nonresponder (n) samples. Panel title and subtitle indicate the corresponding gene signature and cohort names respectively. The heat map goes from high in responder t o high in nonresponder. The row annotation indicates the group in which each gene was up-regulated. All but 4 genes (all from IRRAT) were up-regulated in non-responders.
  • Figure IB Characteristic sorted cell type expression of gene signatures of anti- TNFa response reported from heterogeneous tissue biopsy show contributions from distinct immune cell subsets. Analysis of the immune contribution of 109 unique signature genes mapped to a compendium of sorted cell expression profiles, spanning 17 immune cell subpopulations as well as colon tissue samples. Shown are the contributions (x-axis), i.e. number of genes assigned, for 8 major cell lineages (y-axis), highlighted into resting and activated/memory subpopulations. Lineages are ordered by decreasing total contribution, with most signature genes are expressed in the myeloid, B and T-cell lineages (68%, 13% and 19% of the genes respectively). 85% of signature genes are expressed in low abundance in bulk healthy colon.
  • Figure 2A Computational deconvolution of cell subset proportions identifies higher proportions of inflammatory macrophages and plasma cells in non-responders. Estimated immune cell subset proportions in the UC cohorts. Boxplot shows the baseline estimated proportions of each immune cell subset in each cohort for responders (left) and non-responders (right) to anti-TNF ⁇ therapy. Only cell types with at least 75% non-zero values are shown.
  • FIG. 2B-D Meta-analysis of computationally deconvolved cell subset proportions identifies consistently higher proportions of inflammatory macrophages and plasma cells in non-responders.
  • FIG. 2E Multi-cohort analysis of estimated cell proportions identifies consistent baseline differences in inflammatory macrophages and plasma cells. Each panel shows estimated group proportion differences (pseudo median) and 95% confidence interval for a given cell subset, across all discovery cohorts. Missing data is due to cell type/cohort pairs not included in the meta-analysis due to too many zero estimated proportions.
  • the x-axis represents the log2 proportion fold change (i.e. log2(Responders/Non-Responders)).
  • Statistical significance was calculated using Wilcoxon rank sum test (nominal p-value ⁇ 0.05) and is shown for significantly higher proportions in non-responders (NR) and responders (R) respectively.
  • NS indicates nonsignificant differences.
  • FIG. 3A-C Abundance of plasma cells and macrophage subtypes in biopsies of IBD patients predicts anti-TNFa treatment outcome.
  • the x and y axis represent the specificity (true negative fraction) and sensitivity (true positive fraction) respectively.
  • FIG. 3D Adjusting gene expression for abundances of inflammatory macrophages and plasma cells significantly breaks the association between gene signatures and treatment response status. Heatmaps similar to those in Figure 1A, showing the association between the signatures scores UC-A (left), UC-B (middle) and CDc (right) in their respective cohorts, generated from the original gene expression data (top row) and after adjustment for estimated abundances of inflammatory macrophages and plasma cells (bottom row).
  • FIG. 3E-M Differences in cell subset is the driving force of the reported gene signatures for predicting anti-TNF ⁇ non-response from baseline.
  • AUC was calculated for each gene signature score in their respective discovery cohort. Densities represent AUC null distributions obtained by adjusting gene expression data with random proportions, in each cohort (panels). Solid line shows achieved AUC in original (unadjusted) data, dashed line indicates AUC obtained after adjustment for estimated proportions of inflammatory and plasma cells.
  • 3G-H 3G Plasma cells were immunostalned with CD 138, in an independent set of IBD biopsies. Example staining slides showing visual differences between responders and non-responders these populations. CD138+ plasma cells are colored in brown, showing a clear increased staining in non-responsive patients.
  • (3L) ROC curve showing analysis of a cohort of 52 IBD patients collected from two medical centers whose biopsies were stained by CD 138+ IHC staining. Plasma cell abundance classifies non-response at baseline (AUC 71% and 74% by the pathologist and quantitative scores respectively).
  • (3M) The predictive power increases when restricting to highly inflamed tissues according to the pathologist score (AUC 82% and 84% by the pathologist and quantitative scores respectively).
  • FIG. 4A-G Adjusting samples for cell subset variation unmasks upregulated pathways in biopsies of anti-TNF non-responders.
  • (4D) GSEA enrichment score are driven by consistent leading-edge genes across cohorts. Curves show GSEA enrichment scores for the 3 pathways (rows) that have the most consistent leading- edge genes across cohorts (>25% of their genes in leading-edge in all cohorts).
  • FIG. 5A-B Treml expression in blood predicts anti-TNF non-response at baseline in Crohn's patients.
  • 5A Boxplots showing TREM1, CCL7, CCR2, and TNFR1 mRNA expression as measured in whole blood of 29 responding and non-responding CD patients, prior to initiation of infliximab therapy.
  • 5B ROC curve of classifier of anti-TNF response at baseline based on TREM1 expression in whole blood.
  • FIG. 6A-C Figures 6A-C. Treml expression in blood predicts anti-TNF non-response at baseline in rheumatoid arthritis patients.
  • the present invention provides methods and kits for determining suitability for treatment with an anti-TNF ⁇ therapy, and also for treatment. Means and methods for identifying altered abundance of plasma cells and inflammatory macrophages in pretreatment intestinal biopsies of anti-TNF ⁇ responders versus non-responders are also provided. The present invention provides means and methods for identifying altered expression of certain genes in the blood wherein the altered expression is predictive of non- responsiveness to anti-TNF ⁇ therapy in patients suffering from inflammation.
  • the invention is based on the surprising finding that measuring blood levels of TREM1 alone can accurately predict if a subject will be responsive to anti-TNF ⁇ therapy.
  • This finding is highly surprising in that elevated levels of blood TREM1 are actually indicative of a subject who is responsive.
  • TREM1 levels in biopsies from the intestines were found to have the inverse relationship, i.e. increased levels of TREM1 were indicative of a subject who is non-responsive.
  • the invention is further based on the unexpected finding that elevated levels of CCR2, CCL7 plasma cell number and inflammatory macrophage number in intestine biopsies could each independently accurately predict that a subject would not be responsive to anti-TNF ⁇ therapy.
  • a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy comprising:
  • TNFa is a well characterized cytokine, and therapies that target TNFa are well known in the art.
  • the anti-TNF ⁇ therapy comprises reduction of TNFa expression.
  • anti- TNFa therapy comprises inhibition of TNFa function.
  • the anti-TNF ⁇ therapy comprises reduced TNFa protein, and/or mRNA expression.
  • anti-TNF ⁇ therapy comprises administration of an anti-TNFa antibody.
  • the antibody is an antigen binding fragment of an antibody.
  • anti-TNF ⁇ therapy comprises reducing TNFa mRNA expression.
  • anti-TNF ⁇ therapy comprises administering a nucleic acid molecule that binds to TNFa mRNA and inhibits its translation or leads to its degradation.
  • the nucleic acid molecule is a siRNA, miRNA or the like.
  • anti-TNF ⁇ therapy comprises administering a TNFa antagonist.
  • the term“antagonists” refer to substances which inhibit or neutralize the biologic activity of TNFa. Such antagonists accomplish this effect in a variety of ways.
  • One class of antagonists will bind to the gene product protein with sufficient affinity and specificity to neutralize the biologic effects of the protein. Included in this class of molecules are antibodies and antibody fragments (such as, for example, F(ab) or F(ab')2molecules).
  • Another class of antagonists comprises fragments of the gene product protein, muteins or small organic molecules, i.e., peptidomimetics, that will bind to the cognate binding partners or ligands of the gene product, thereby inhibiting the biologic activity of the specific interaction of the gene product with its cognate ligand or receptor.
  • the TNFa antagonist can be of any of these classes as long as it is a substance that inhibits a biological activity of the gene product.
  • Antagonists include antibodies directed to one or more regions of the gene product protein or fragments thereof, antibodies directed to the cognate ligand or receptor, and partial peptides of the gene product or its cognate ligand which inhibit a biological activity of the gene product.
  • Another class of antagonists includes siRNAs, shRNAs, antisense molecules and DNAzymes targeting the gene sequence as known in the art are disclosed herein.
  • the TNFa antagonist is an anti-TNF ⁇ antibody.
  • the anti-TNF ⁇ antibody is selected from infliximab, golimumab, Enbrel, certolizumab pegol, and adalimumab.
  • the anti-TNF ⁇ antibody is selected from infliximab and adalimumab.
  • the anti-TNF ⁇ antibody is infliximab.
  • the anti-TNF ⁇ antibody is adalimumab.
  • the subject suffers from inflammation. In some embodiments, the subject suffers from an inflammatory disease. In some embodiments, the subject suffers from a disease characterized by inflammation. In some embodiments, the subject suffers from a disease that can be treated with anti-TNF ⁇ therapy. In some embodiments, the subject suffers from a disease that can be treated with ana anti-TNF antibody. In some embodiments, the subject suffers from autoimmune inflammation. In some embodiments, the subject suffers from an autoimmune disease. In some embodiments, the subject suffers from an autoimmune inflammatory disease. In some embodiments, the autoimmune inflammatory disease is selected from inflammatory bowel disease (IBD) and rheumatoid arthritis (RA).
  • IBD inflammatory bowel disease
  • RA rheumatoid arthritis
  • the subject suffers from a disease selected from IBD, RA, psoriatic arthritis, ankylosing spondylitis, chronic psoriasis, hidradentisis suppurativa, and juvenile idiopathic arthritis.
  • the subject suffers from a disease selected from IBD, and RA.
  • the subject suffers from arthritis.
  • the subject suffers from RA.
  • the subject suffers from inflammatory bowel disease (IBD).
  • IBD comprises colitis, ulcerative colitis and Crohn’s disease.
  • IBD comprises colitis, ulcerative colitis, Behcet’s disease and Crohn’s disease.
  • the IBD is colitis.
  • the IBD is ulcerative colitis.
  • the IBD is Crohn’s disease.
  • the anti-TNF ⁇ therapy is an anti-IBD therapy.
  • the IBD is characterized by increased TNFa expression.
  • the increased TNFa expression is in the intestines.
  • the sample is a blood sample.
  • the blood sample is a peripheral blood sample.
  • the blood sample is an intestinal blood sample.
  • the blood sample is from a clinical blood draw.
  • the sample is an intestinal sample.
  • the intestinal sample is an intestinal biopsy.
  • the intestine is the small intestine.
  • the intestine is the large intestine.
  • the biopsy is acquired during a colonoscopy.
  • the sample includes cells.
  • the sample includes protein.
  • the sample includes nucleic acids.
  • the sample includes mRNA.
  • the term“anti-TNF ⁇ therapy predicative factor” refers to a measurable biological readout that is predictive of whether a subject will respond to anti-TNF ⁇ therapy.
  • the predicative factor is Triggering Receptor Expressed on Myeloid cells 1 (TREM1) expression.
  • the predicative factor is Chemokine (C-C motif) ligand 7 (CCL7) expression.
  • the predicative factor is C-C chemokine receptor type 2 (CCR2) expression.
  • the predicative factor is CCL7 and CCR2 expression.
  • the predicative factor is CCL7 and/or CCR2 expression and TREM1 expression.
  • the predicative factor is plasma cell (PC) number. In some embodiments, the predictive factor is inflammatory macrophage (IM) number. In some embodiments, the predicative factor is selected from TREM1 expression, CCL7 expression, CCR2 expression, PC number, IM number and a combination thereof.
  • TREM1 is serum TREM1. In some embodiments, TREM1 is soluble TREM1. In some embodiments, TREM1 is membranal TREM1. In some embodiments, the serum TREM1 is soluble TREM1, membranal TREM1 or both. In some embodiments, TREM1 is a variant of TREM1.
  • TREM1 expression is measured in a blood sample. In some embodiments, TREM1 expression is measured in an intestinal sample. In some embodiments, CCR2, CCL7, PC number, and/or IM number is measured in an intestinal sample. In some embodiments, TREM1 expression is measured in blood to determine suitability of a subject suffering from a non-intestinal disease. In some embodiments, the non-intestinal disease is RA.
  • measuring comprises measuring mRNA levels. In some embodiments, measuring comprises measuring protein levels. In some embodiments, measuring comprises counting cells. In some embodiments, measuring comprises automated cell counting. In some embodiments, measuring comprises PCR of a target mRNA. In some embodiments, measuring comprises hybridization with a nucleic acid-based array (for example, a microarray). In some embodiments, measuring comprises immuno-detection. In some embodiments, the immuno-detection is immunohistochemistry. In some embodiments, the immuno-detection is ELISA. In some embodiments, immuno-detection is immunoblotting. In some embodiments, immuno-detection of PC cells comprises detection of CD 138.
  • measuring PC number comprises measuring the number of cells positive for surface expression of CD 138.
  • immuno-detection of IM cells comprises detection of at least one of CD86, CD80, CD68, MHCII, IL-1R, TLR2, TLR4, iNOS and SOCS3.
  • a combination of markers is immuno-detected.
  • measuring IM number comprises measuring the number of cells positive for surface expression of CD68 and CD86.
  • measuring expression levels comprises contacting the sample with a molecule that detects the predicative factor.
  • the molecule that detects the predicative factor is connected to an artificial solid support.
  • the molecule that detects the predicative factor is an artificial molecule.
  • the molecule that detects the predicative factor is an engineered molecule.
  • the sample is processed before measuring.
  • the processing comprises extracting protein.
  • the processing comprises extracting nucleic acids.
  • the processing comprises purifying the extracted protein and/or nucleic acid molecule.
  • the processing comprises embedding tissue.
  • the tissue is a biopsy.
  • the embedding is paraffin embedding.
  • the detecting comprises contacting the molecule that detects the predicative factor with a second detecting molecule.
  • the second detecting molecule is a fluorescent molecule.
  • the second detecting molecule is a colored molecule. Detection of protein, cells and/or mRNA is well known in the art and some examples of such are provided hereinbelow. Any method of such detection however may be employed for the performance of the invention disclosed herein.
  • the predetermined threshold is determined from healthy subjects. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD but respond to therapy. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD but do respond to therapy. In some embodiments, the predetermined threshold is the highest or lowest level/number from subjects that respond to therapy. In some embodiments, the predetermined threshold is the highest or lowest levels/number from subjects that do not respond to therapy. In some embodiments, the predetermined threshold is the level/number that best distinguishes subjects that respond and don’t respond to therapy. In some embodiments, the predetermined threshold is from subject naive to anti-TNF ⁇ therapy. In some embodiments, the predetermined threshold is baseline level/number from subjects before receiving therapy.
  • TREM1 expression is measured in a blood sample and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the blood TREM1 levels.
  • blood TREM1 expression below a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • blood TREM1 expression above a predetermined threshold indicates the subject is suitable for the anti- TNFa therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician.
  • expression at or about at the threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • TREM1 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the biopsy TREM1 levels.
  • biopsy TREM1 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • biopsy TREM1 expression below a predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician.
  • expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy.
  • intestinal expression or cell numbers are used for determining suitability of subjects suffering from an intestinal disease.
  • the intestinal disease is IBD.
  • CCR2 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the biopsy CCR2 levels.
  • biopsy CCR2 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • biopsy CCR2 expression below a predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician.
  • expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy.
  • CCL7 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the biopsy CCL7 levels.
  • biopsy CCL7 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • biopsy CCL7 expression below a predetermined threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician.
  • expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy.
  • PC number is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the number of PCs in the biopsy.
  • biopsy PC number above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • biopsy PC number below a predetermined threshold indicates the subject is suitable for the anti-TNFa therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • IM number is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNF ⁇ therapy is determined according to the number of IMs in the biopsy.
  • biopsy IM number above a predetermined threshold indicates the subject is unsuitable for the anti-TNF ⁇ therapy.
  • biopsy IM number below a predetermined threshold indicates the subject is suitable for the anti-TNFa therapy.
  • Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNF ⁇ therapy and may be determined by a physician.
  • expression at or about at the threshold indicates the subject is suitable for the anti-TNF ⁇ therapy.
  • PC or IM number is measured. In some embodiments, PC and IM number is measured. In some embodiments, both numbers must be below the predetermined threshold for the subject to be suitable. In some embodiments, at least one of the numbers must be below the threshold for the subject to be suitable. In some embodiments, CCL7 or CCR2 is measure. In some embodiments, CCL7 and CCR2 is measured. In some embodiments, both expression levels must be below the predetermined threshold for the subject to be suitable. In some embodiments, at least one of the expression levels must be below the threshold for the subject to be suitable.
  • a combination of the predicative factors described herein is measured. In some embodiments, all the measured factors must indicate a subject is suitable for the subject to be determined to be suitable. In some embodiments, at least a plurality of factors must indicate a subject is suitable for the subject to be determined to be suitable. In some embodiments, blood TREM1 and at least one other predicative factor is measured and at least blood TREM1 and one other predicative factor must indicate a subject is suitable for the subject to be determined to be suitable.
  • the factors listed herein are the only factors measured. In some embodiments, the measuring is devoid of measuring any other factors. In some embodiments, only TREM1, CCR2 and/or CCL7 levels are measured. In some embodiments, only TREM1, CCR2 and/or CCL7 levels and the levels of a control gene or protein are measured. In some embodiments, only CCR2 and/or CCL7 levels and the levels of another gene from the CCR2/CCL7 axis are measured.
  • genes/proteins from the CCR2/CCL7 axis are selected from the group consisting of: AIM2, AP2B1, ARAF, BAG2, BCAT1, CCNF, DDX25, DESI2, DIABLO, EPHB2, ERK1/2, GPC1, HAVCR2, HOMER 1, IgG, IL19, IL12 (complex), IL4I1, IL7R, Interferon alpha, Jnk, MY06, P38 MAPK, PIK3CB, RIPK1, RNF13, S1PR1, SOX17, STAT2, TCR, TRIO, TSPYL2, and WNK2.
  • expression of any one of Soxl7, RNF13, TSPYL2, S1PR1, BAG2, HOMER 1, IL4I1, AIM2, STAT2, BCAT1, HAVCR2, BCAT1, and IL7R above the predetermined threshold indicates the subject is unsuitable for anti-TNF ⁇ therapy.
  • expression of any one of WNK2, DESI2, GPC1, IL19, DIABLO, DDX25, ARAF, RIPK1, TRIO, CCNF, EPHB2, MY06, PIK3CB and AP2B1 below the predetermined threshold indicate the subject is unsuitable for anti-TNFa therapy.
  • a predicative factor is combined with the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 genes from the above list of genes/proteins for determined suitability of the subject for anti-TNFa therapy.
  • a predicative factor is combined with the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 genes from the above list of genes/proteins for determined suitability of the subject for anti-TNFa therapy.
  • control, or housekeeping genes and proteins are well known in the art and may be used for normalization of expression levels in sample. Examples of control genes/proteins include, but are not limited to B-actin, GAPDH, and ribosomal components. In some embodiments, the expression levels are normalized to cell types percentage in the biopsy. [063] In one embodiment, significantly high plasma cell numbers in anti-TNF ⁇ non-responders were validated in IBD patients naive to anti-TNF ⁇ therapy. In one embodiment, significantly is at least 10% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 20% higher plasma cell numbers in anti-TNF ⁇ non-responders.
  • significantly is at least 30% higher plasma cell numbers in anti-TNF ⁇ nonresponders. In one embodiment, significantly is at least 40% higher plasma cell numbers in anti- TNFa non-responders. In one embodiment, significantly is at least 50% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 75% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 100% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 150% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 200% higher plasma cell numbers in anti-TNF ⁇ nonresponders.
  • significantly is at least 300% higher plasma cell numbers in anti- TNFa non-responders. In one embodiment, significantly is at least 350% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 400% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 450% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 500% higher plasma cell numbers in anti-TNF ⁇ non-responders.
  • the above the threshold is at least 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100% above the threshold.
  • the below the threshold is at least 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100% below the threshold.
  • Each possibility represents a separate embodiment of the invention.
  • a method for identifying an IBD patient nonresponding to anti-TNF ⁇ therapy comprising, analyzing intestinal specimen from the IBD patient, wherein significantly high plasma cell numbers in the intestinal specimen compared to a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative for IBD non -responding to anti- TNFa therapy, thereby identifying an IBD patient non -responding to anti-TNF ⁇ therapy.
  • intestinal specimen comprises intestinal biopsy.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein significantly high plasma cell numbers in the intestinal specimen compared to a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti- TNFa therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein a higher plasma cell numbers in the intestinal specimen compared to the range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFa therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein plasma cell numbers in the intestinal specimen below a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti-TNF ⁇ therapy is effective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein plasma cell numbers in the intestinal specimen within a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti-TNF ⁇ therapy is effective, thereby determining the effectiveness of an anti-TNFa therapy to an IBD patient.
  • an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen within a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFa therapy, is indicative that the anti-TNF ⁇ therapy is effective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
  • an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen beyond a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient.
  • an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen higher than the threshold values for number of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti- TNFa therapy is ineffective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient.
  • intestinal biopsy showing plasma cells is an intestinal biopsy stained for plasma cells.
  • intestinal biopsy showing plasma cells is an intestinal biopsy wherein the number of plasma cells can be quantified.
  • analyzing the intestinal biopsy for plasma cell number and/or abundance is staining an intestinal biopsy and identifying plasma cells. In one embodiment, analyzing the intestinal biopsy for plasma cell number and/or abundance is treating an intestinal biopsy with immunohistochemical reagent for identifying plasma cells. In one embodiment, staining or immunohistochemical reagents for identifying plasma cells are known to a person of skill in the art. In one embodiment, determining, analyzing and/or staining is providing means for counting the number of plasma cell within an intestinal specimen.
  • “threshold value of plasma cell numbers” is indicative to the upper threshold of cell numbers or abundance of cells in an IBD patient responding to an anti- TNFa therapy.
  • “a range of plasma cell numbers in control intestinal specimen” is indicative to the range of cell numbers or abundance in an IBD patient responding to an anti- TNFa therapy.
  • “cell numbers or abundance” include the concentration of cells.
  • “cells” are plasma cells.
  • “cell number” is plasma cell number.
  • intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy is the mean and/or median values of a plurality of specimens as described herein.
  • “IBD non-responding to anti-TNF ⁇ therapy” is a patient afflicted with IBD wherein anti-TNF ⁇ therapy has little or no therapeutic effect.
  • “IBD non-responding to anti-TNF ⁇ therapy” is a patient afflicted with IBD wherein anti- TNFa therapy has insufficient therapeutic effect.
  • the abundance, concentration and/or the total number of plasma cells within an intestinal biopsy is indicative for the efficiency of anti- TNFa therapy in an IBD patient.
  • an intestinal biopsy of an IBD patient as described herein is assessed as described herein, prior to determining the actual treatment or prior to prescribing anti-TNF ⁇ therapy.
  • an intestinal biopsy of an IBD patient as described herein is assessed as described herein, upon failure of anti-TNF ⁇ therapy.
  • an intestinal biopsy of an IBD patient as described herein is assessed as described herein, during anti-TNF ⁇ therapy.
  • plasma cells comprise macrophages.
  • plasma cells comprise inflammatory macrophages.
  • the present invention provides means for identifying altered abundance of plasma cells and inflammatory macrophages in pretreatment intestinal biopsies of anti-TNF ⁇ responders versus non-responders.
  • significantly high plasma cell numbers in anti-TNF ⁇ non -responders were validated in IBD patients naive to anti-TNF ⁇ therapy. In one embodiment, significantly is at least 10% higher plasma cell numbers in anti-TNF ⁇ non-responders. In one embodiment, significantly is at least 20% higher plasma cell numbers.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an elevated expression of TREM1 and/or CCR2-CCL7 beyond a threshold value of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an expression of TREM1 and/or CCR2-CCL7 beyond the range of expression of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti- TNFa therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an elevated expression of TREM1 and/or CCR2-CCL7 beyond a threshold value of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti-TNF ⁇ therapy, is indicative that the anti-TNF ⁇ therapy is ineffective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to an IBD patient.
  • a method for determining the effectiveness of an anti-TNF ⁇ therapy to a patient suffering from inflammation comprising obtaining from the patient a blood sample and measuring the expression of TREM1, wherein an expression of TREM1 below the range or threshold of expression of TREM1 expression in control blood specimen derived from patients responding to anti-TNF ⁇ therapy, is indicative that the anti- TNFa therapy is ineffective, thereby determining the effectiveness of an anti-TNF ⁇ therapy to a patient suffering from inflammation.
  • analyzing the expression is quantifying the expression, measuring the expression and/or assessing the expression.
  • a blood sample is a peripheral blood sample.
  • plasma cell numbers are quantified by staining the intestinal biopsy by IHC staining.
  • the intestinal biopsy is a paraffin-embedded intestinal biopsy.
  • the intestinal biopsy is obtained during colonoscopy.
  • the method of the invention further comprises administering the anti-TNF ⁇ therapy to a subject indicated to be suitable for the anti-TNF ⁇ therapy.
  • the method of the invention further comprises administering to a subject indicated to be unsuitable for the anti-TNF ⁇ therapy an increased dose of the anti-TNF ⁇ therapy.
  • the increased dose is above the standard dose.
  • the method of the invention further comprises administering a non-anti-TNF ⁇ anti-inflammation therapy to a subject suffering from inflammation and indicated to be unsuitable for anti-TNF ⁇ therapy.
  • the non-anti-TNF ⁇ anti-inflammation therapy is an anti-IBD therapy.
  • the non- anti-TNF ⁇ anti-inflammation therapy is an anti-RA therapy.
  • inflammation therapies that are not anti-TNFa therapies include, but are not limited to, steroids, NSAIDs, disease-modifying antirheumatic drugs, azathioprine, 4-aminosalicylic acid and derivatives thereof, anti-IL-23 therapy, anti-IL- 17therapy, JAK inhibitors, anti-IL-6R therapy, methotrexate, thiopurines and fecal microbiota transplant.
  • the non-anti-TNF ⁇ anti-IBD therapy comprises blocking at least one of CCR2, CCR5 and CXCR3.
  • the non-anti-TNF ⁇ anti-IBD therapy comprises administering a CCR2 and/or CCL7 antagonist. In some embodiments, the non-anti-TNF ⁇ anti-inflammation therapy comprises blocking at least one of CCR2, CCR5 and CXCR3. In some embodiments, the non-anti-TNF ⁇ anti-inflammation therapy comprises administering a CCR2 and/or CCL7 antagonist.
  • the TREM1 detecting agent for determining serum TREM1 levels in a subject and determining suitability of the subject for treatment with an anti-TNF ⁇ therapy.
  • the TREM1 detecting agent is a serum TREM1 detecting agent.
  • the detecting agent is an anti-TREMl antibody.
  • the detecting agent is a nucleic acid molecule that selectively binds and hybridizes to TREM1 mRNA or a TREM1 gene product.
  • the detecting agent is primers for PCR amplification of TREM1 or a portion thereof.
  • the detecting agent is a nucleic acid probe for TREM1 or a portion thereof. In some embodiments, the detecting agent is part of a chip, bead or array.
  • the terms“array” or“microarray” or “biochip” or“chip” as used herein refer to articles of manufacture or devices comprising an immobilized target elements, each target element comprising a“clone,”“feature,”“spot” or defined area comprising a particular composition, such as a biological molecule, e.g., a nucleic acid molecule or polypeptide, immobilized to a solid surface.
  • kits comprising a detecting agent for detecting at least one predicative factor disclosed herein.
  • the kit comprises a detecting agent for TREM1, a detecting agent for CCR2, a detecting agent for CCL7, a detecting agent for PCs, a detecting agent for IMs and combinations thereof.
  • the agents are labeled as for prognostic assessment of responsiveness to anti-TNF ⁇ therapy.
  • the kit comprises antibodies against TREM1, CCR2, CCL7, CD138, and/or CD86 and CD68. Each possibility represents a separate embodiment of the invention.
  • the kit comprises nucleic acid molecules for measuring TREM1, CCR2, CCL7 or another gene from the CCR2/CCL7 axis.
  • TREM1 is serum TREM1.
  • TREM1 is blood TREM1.
  • Nucleic acid sequences of the genes and amino acid sequences of the proteins provided herein can be found on numerous websites known to those skilled in the art, including pubmed.org, ncbi.nlm.nih.gov/genbank, and uniport.org among others.
  • a length of about 1000 nanometers (nm) refers to a length of 1000 nm+- 100 nm.
  • Table 4 Compendium of sorted cell expression profiles. Immune contribution to signature gene expression. Results of the preliminary analysis that derived overall cellular origin of all previously reported signature genes.
  • Signature UC-AB was defined as the overlap between all differentially expressed genes found in the same UC cohorts A and B, and comprised a total of 53 unique genes (Kaplan, IBID); Signature UC-B-knn was also derived from UC cohort B, but using a different methodology based on a k-nearest-neighbor classifier; Signature CDc was identified in CD patients from colon biopsies (Targan et ak, A short-term study of chimeric monoclonal antibody cA2 to tumor necrosis factor alpha for Crohn’s disease. Crohn’s Disease cA2 Study Group. N. Engl. J. Med., 2997) (cohort CDc). The remaining signature named IRRAT was taken from the kidney transplant study (Ben-Horin et ak, Optimizing anti-TNF treatments in inflammatory bowel disease, Autoimmun. Rev., 2014). Cell type expression pattern of predictive gene signatures
  • CEL files from sorted cell type samples from IRIS (GSE22886) and the Human body index (GSE7307) were normalized separately using frma.
  • GSE7307 the profiles from all immune cells (32 profiles from monocyte, T cell and B cell lineages) and colon tissues (2 profiles) were extracted.
  • a combined cell type gene expression matrix was created, it was corrected for dataset of origin effects using combat, and subsequently probe-sets were averaged into genes. This resulted in the creation of an expression matrix of 130 expression profiles (Table 5), which were standardized using z-scores and averaged into major cell lineages split into resting and activation/memory state.
  • Table 5 Compendium of sorted cell expression profiles. Description of the GEO data used to compute the immune contribution to previously reported signature genes
  • the gene expression data for each IBD cohort used in the deconvolution meta-analysis were obtained from 3 GEO datasets: UC-A from GSE14580, UC-B form GSE12251 and CDc from GSE 16879. These datasets contain biopsy gene expression profiles generated from 2 cohorts of UC patients (Cohort A and B in GSE14580 and GSE12251 respectively), and 1 cohort of CD patients (part of GSE16879). They were originally designed for the discovery of gene signatures that can predict, at baseline, if a patient is likely to respond to an anti-TNF ⁇ treatment (Infliximab).
  • signatures In terms of signatures, all signatures were identified from baseline gene expression differential analysis between responders and non-responders to Infliximab treatment in the same set of 3 IBD cohorts of UC (cohorts UC-A and UC-B) or CD (cohort CDc) patients, exception being the IRRAT signature which, subsequent to the study it originated with, was found to correlate with anti-TNF ⁇ response at baseline in the UC-B cohort.
  • ROC analyses were performed on signature expression scores that summarize, for each sample, the expression level of all the genes in a predictive gene set. Given a gene expression dataset (including data adjusted for proportion variations) and a gene signature/set, the signature score Sj for sample j was computed as:
  • g is the expression level of the z-th gene of the signature in sample j
  • d is the sign of the difference between its mean expression in non-responders and responders.
  • g L' g L - m s + 1, where m s is the minimum expression value amongst the signature genes.
  • ROC curve analysis of cellular biomarkers was computed either directly on estimated proportions for individual cell subsets or on the average standardized proportions (centered, unit-variance) for combined signatures. AUC values were computed using the R package pROC.
  • IFX levels and antibodies to IFX (ATI) measurements were available for 28 of the patients. After reviewing these patients, the responders for which 2 subsequent measurements of IFX level ⁇ 3 (pg/ml) were observed prior to week 26 were excluded, assuming their response status was less likely to be IFX related, as were non-responders with measurements of ATI level > 15 (pg/mL), assuming they had secondary loss of response, not related to susceptibility to TNFa blockade. These criteria left 29 responders and 23 non-responders from the two centers. Immuno-histochemistry markers
  • Plasma cell frequencies were examined by CD 138+ IHC staining.
  • in-silico deconvolution analysis herein relied on a gene expression signature of monocyte derived macrophages bearing typical macrophage morophology and phagocytic activity.
  • Ml inflammatory macrophage phenotype
  • the expert pathologist performing the IHC assessed the co-expression of the CD68 and CD86 as well as cell morphology, as these markers are co-expressed by monocytes and CD86 also in other cell subsets (e.g B and T).
  • mononuclear cells showing broad cytoplasm and oval nucleus were considered as“inflammatory macrophages”, while CD68 and CD 86 -positive monocytes were ignored.
  • Example 1 Gene expression signatures for anti-TNFa non-response show contributions from distinct immune cell subsets
  • Fig. IB Clustering the expression profiles of these signature genes across sorted cell subsets suggested that three distinct lineages contribute to non-response to anti-TNF ⁇ treatment.
  • Fig. IB First, myeloid lineage cell subsets expressing 70% of signature genes; second, B-cell lineage cell subsets in which 30% of signature genes were expressed; third, T and NK cells’ genes, which together comprised 30% of genes in the collective signature (Table 4).
  • Table 4 the majority of signature genes were denoted as highly expressed in the bulk colon samples and of these, the majority were also noted to be highly expressed in the B-cell lineage.
  • Example 2 Meta-analysis identifies cell type proportion differences between response groups at baseline and following treatment
  • Example 3 Baseline plasma cell proportions are predictive of anti-TNFa non-response
  • Example 4 A dysregulated gene network masked by cell proportion variation
  • DEG differentially expressed genes
  • IPA Ingenuity Pathway Analysis
  • One of the top most enriched networks included 28 genes, including the ligand-receptor pair CCL7-CCR2 which was found to be upregulated in non-responders (Fig. 4E, Table 9).
  • the CCL7-CCR2 axis has been associated with inflammation and upregulation in IBD.
  • CCL7 is produced by inflammatory lymphocytes, including plasma cells, whereas CCR2 is expressed primarily on monocytes, and mediates their recruitment to inflamed tissues.
  • TREM1 Triggering Receptor Expressed on Myeloid cells 1 (TREM1) as an upstream regulator of six of the adjustment-derived DEG, including CCL7.
  • TREM1 is expressed on myeloid lineage cells including monocytes and macrophages, has well- documented pro-inflammatory functions, and its blockade has shown promising results in attenuation of symptoms in IBD models.
  • Meta-analysis of TREM1, CCL7 and CCR2 gene expression across the public data biopsy cohorts showed all these genes to be consistently up- regulated in the non-responder group in the original measured data (meta-FDR ⁇ 0.037).
  • TREM1 pathway in anti-TNF ⁇ mechanism we searched for TNF-related genes and found TNFa and TNFR2 as up-regulated in non-responders in the original data as well (Fig. 4F), probably due to TREM-l activation via synergism with TLR signaling, which leads to TNFa secretion from the inflammatory macrophages. Unlike CCL7 and CCR2, their expression difference post-adjustment was lost (Fig. 4G). Taken together, these results suggest that the plasma cells may be responsible for the recruitment of inflammatory macrophages to the inflamed area, which ultimately impact response potential. [0127] Table 8: List of differentially expressed genes when adjusting for inflammatory macrophage and plasma cell proportions.
  • TREM1 expression is a predictive biomarker in blood
  • TREM1 was overall highly expressed (average log2-expression > 11.9), providing further confidence in the measured signal.
  • TREM-l and CCR2 gene expression levels in blood were correlated with endoscopic activity in an additional cohort of patients with UC (see Materials and Methods), further supporting monitoring of the axis in blood as an important clinical non-invasive biomarker and their potential for reproducibility as a clinical non-invasive biomarker of anti-TNF ⁇ response status at baseline.
  • TREM1 expression is predictive in rheumatoid arthritis
  • Anti- TNFa therapy is also used to treat other forms of autoimmune inflammation besides IBD.
  • Rheumatoid arthritis RA
  • RA Rheumatoid arthritis
  • Infliximab therapeutic anti-TNF ⁇ antibodies, including Infliximab.
  • IBD blood expression data from RA patients before anti-TNFa therapy was collected from the GEO, and the expression of TREM1 was examined in eventual responders and non-responders.
  • the first cohort examined contained 37 subjects that were responsive to IFX and seven that were not.
  • Microarray analysis of blood expression of TREM1 found higher levels of TREM1 mRNA in responders both before and after adjustment for plasma cell and inflammatory macrophage number (Fig. 6A), as had been seen for IBD subjects.
  • non-responders may represent a phenotypic niche which is difficult to resolve therapeutically via anti-TNF ⁇ treatments, in which case based on baseline detection of non-response one may elect an alternative therapeutic.

Abstract

Methods of determining suitability of a subject to be treated with anti-TNFα therapy comprising measuring blood TREM1 levels or intestinal plasma cell number, intestinal inflammatory macrophage number or intestinal CCL7 or CCR2 levels are provided. Kits for doing same are also provided.

Description

PROGNOSTIC METHODS FOR ANTI-TNFA TREATMENT
CROSS REFERENCE TO RELATED APPLICATIONS
[001] This application claims the benefit of priority of U.S. Provisional Patent Application No. 62/581,960, filed November 6, 2017, the contents of which are all incorporated herein by reference in their entirety.
FIELD OF INVENTION
[002] The present invention is in the field of anti-TNFα therapy diagnostics.
BACKGROUND OF THE INVENTION
[003] Inflammatory Bowel Diseases (IBDs) consist primarily of ulcerative colitis (UC) and Crohn’s disease (CD) and are prevalent in the industrialized world with increasing incidence in both western and developing countries. IBD is characterized by chronic intestinal inflammation, driven both by the innate and adaptive immune systems, although their pathogenesis is not completely understood. The advent of anti-TNFα therapy was a major breakthrough for IBD treatment and was shown to be effective for treating various forms of disease.
[004] However, anti-TNFα therapy remains suboptimal for several reasons. Treatment choice and administration is empiric and based on data obtained from the“average patient” in clinical trials. This practice is associated with insufficient remission rates, that result from primary nonresponse (PNR) (20-40% in clinical trials; 10-20% in real life cohorts), and from loss of response commonly due to immunogenicity and increased anti-TNFα clearance in 13-24% of patients at 12 months. Beyond that, the treatment is associated with adverse side effects, is expensive and each therapeutic attempt requires waiting the anticipated time for response during which the disease is active, and damage accumulates. Taken together, there is an urgent unmet need for predicting response prior to treatment initiation to reduce healthcare costs and avoid unnecessary treatment. [005] Several attempts have been made to define a baseline signature of anti-TNF response in IBD patients using genetics, microbiome, and gene expression data. Yet, no predictive biomarker is in clinical practice. Thus, finding predictive biomarkers of non-response prior to commencing anti-TNFα therapy is of high-value.
SUMMARY OF THE INVENTION
[006] The present invention provides methods of determining suitability of a subject to be treated with anti-TNFα therapy comprising measuring blood TREM1 levels or intestinal plasma cell number, intestinal inflammatory macrophage number or intestinal CCL7 or CCR2 levels. Kits for doing same are also provided.
[007] According to a first aspect, there is provided a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy, the method comprising:
a. obtaining a blood sample from the subject; b. measuring expression levels of Triggering Receptor Expressed on Myeloid cells 1 (TREM1) in the blood sample; and c. determining the suitability of the subject for anti-TNFα therapy according to the expression levels of TREM1, wherein expression below a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy and expression at or above the predetermined threshold indicates the subject is suitable for the anti-TNFα therapy; thereby determining suitability of a subject to be treated with anti-TNFα therapy.
[008] According to another aspect, there is provided a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy, the method comprising
a. obtaining an intestinal biopsy from the subject; b. measuring in the intestinal biopsy at least one of: i. plasma cell number; ii. inflammatory macrophage; and iii. expression levels of CCL7, CCR2 or both; c. determining the suitability of the subject for anti-TNFα therapy according to the plasma cell or inflammatory macrophage number, wherein a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy and a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both below the predetermined threshold indicates the subject is suitable for the anti-TNFa therapy; thereby determining suitability of a subject to be treated with anti-TNFα therapy.
[009] According to some embodiments, the blood sample is a peripheral blood sample.
[010] According to some embodiments, a number of plasma cells above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy and a number of plasma cells below the predetermined threshold indicates the subject is suitable for the anti-TNFa therapy.
[011] According to some embodiments, expression levels of CCL7 and CCR2 above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy and expression levels of CCL7 and CCR2 below the predetermined threshold indicates the subject is suitable for the anti-TNFα therapy.
[012] According to some embodiments, the method further comprises measuring in an intestinal biopsy from the subject expression levels of TREM1, wherein intestinal expression levels of TREM1 above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy and intestinal expression levels of TREM 1 below the predetermined threshold indicates the subject is suitable for the anti-TNFα therapy.
[013] According to some embodiments, the subject suffers from inflammation. According to some embodiments, the subject suffers from an autoimmune disease. According to some embodiments, the autoimmune disease is selected from inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). According to some embodiments, the IBD comprises colitis, ulcerative colitis, and Crohn’s disease.
[014] According to some embodiments, the anti-TNFα therapy comprises administration of an anti-TNFα antibody. According to some embodiments, the anti-TNFα antibody is selected from infliximab and adalimumab.
[015] According to some embodiments, the method further comprises administering the anti- TNFa therapy to the subject indicated to be suitable for the anti-TNFα therapy. According to some embodiments, the method further comprises administering to the subject indicated to be unsuitable for the anti-TNFα therapy an increased dose of the anti-TNFα therapy above the standard dose. According to some embodiments, the method further comprises administering a non-anti-TNFα anti-inflammation therapy to the subject indicated to be unsuitable for anti-TNFa therapy. According to some embodiments, the non-anti-TNFα anti-inflammation therapy comprises blocking at least one of CCR2, CCR5 and CXCR3.
[016] According to some embodiments, the predetermined threshold is determined from blood levels of TREM 1 , intestinal numbers of plasma cells or inflammatory macrophages or intestinal expression of CCL7, CCR2 or both in subjects that responded to anti-TNFα therapy, wherein the blood levels, the intestinal numbers and the intestinal expression are from before the subjects received the anti-TNFα therapy.
[017] According to some embodiments, measuring plasma cell number comprises measuring the number of cells positive for surface expression of CD 138.
[018] According to some embodiments, measuring expression levels comprises measuring mRNA levels, protein levels or both. [019] According to some embodiments, measuring expression levels comprises contacting the blood sample or intestinal biopsy with a molecule that detects TREM1, CCL7, or CCR2 mRNA or protein, and wherein the molecule is connected to an artificial solid support.
[020] According to another aspect, there is provided a use of a TREM1 detecting agent for determining serum TREM1 levels in a subject and determining suitability of the subject for treatment with an anti-TNFα therapy.
[021] According to some embodiments, the detecting agent is an anti-TREMl antibody or a nucleic acid molecule that selectively binds and hybridizes to TREM1 mRNA.
[022] According to another aspect, there is provided a kit comprising a plurality of molecules selected from:
a. a TREM1 detecting molecule; b. a CCL7 detecting molecule; c. a CCR2 detecting molecule; d. a plasma cell detecting molecule; and e. an inflammatory macrophage detecting molecule.
[023] Further embodiments and the full scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
[024] Figure 1A: Predictive gene signatures from previous gene expression differential analysis. Heatmaps showing the gene expression of the 6 previously reported predictive gene signatures in their respective discovery cohort(s). The top strip annotates responders (r) and nonresponder (n) samples. Panel title and subtitle indicate the corresponding gene signature and cohort names respectively. The heat map goes from high in responder t o high in nonresponder. The row annotation indicates the group in which each gene was up-regulated. All but 4 genes (all from IRRAT) were up-regulated in non-responders.
[025] Figure IB: Characteristic sorted cell type expression of gene signatures of anti- TNFa response reported from heterogeneous tissue biopsy show contributions from distinct immune cell subsets. Analysis of the immune contribution of 109 unique signature genes mapped to a compendium of sorted cell expression profiles, spanning 17 immune cell subpopulations as well as colon tissue samples. Shown are the contributions (x-axis), i.e. number of genes assigned, for 8 major cell lineages (y-axis), highlighted into resting and activated/memory subpopulations. Lineages are ordered by decreasing total contribution, with most signature genes are expressed in the myeloid, B and T-cell lineages (68%, 13% and 19% of the genes respectively). 85% of signature genes are expressed in low abundance in bulk healthy colon.
[026] Figure 2A. Computational deconvolution of cell subset proportions identifies higher proportions of inflammatory macrophages and plasma cells in non-responders. Estimated immune cell subset proportions in the UC cohorts. Boxplot shows the baseline estimated proportions of each immune cell subset in each cohort for responders (left) and non-responders (right) to anti-TNFα therapy. Only cell types with at least 75% non-zero values are shown.
[027] Figures 2B-D. Meta-analysis of computationally deconvolved cell subset proportions identifies consistently higher proportions of inflammatory macrophages and plasma cells in non-responders. (2B) Plasma cell and macrophage log2 proportion fold change between response groups, across three cohorts (p-value < 0.05 by Wilcoxon Rank Sum are shown for significantly higher proportions in non -responders; no significant change is also shown). (2C-D) Cellular signature abundance decreases while differences persist after anti-TNFα treatment. Deconvolution derived frequencies of inflammatory macrophages (2C) and plasma cells (2D) pre and post- anti-TNFα treatment (x-axis) are shown.
[028] Figure 2E. Multi-cohort analysis of estimated cell proportions identifies consistent baseline differences in inflammatory macrophages and plasma cells. Each panel shows estimated group proportion differences (pseudo median) and 95% confidence interval for a given cell subset, across all discovery cohorts. Missing data is due to cell type/cohort pairs not included in the meta-analysis due to too many zero estimated proportions. The x-axis represents the log2 proportion fold change (i.e. log2(Responders/Non-Responders)). Statistical significance was calculated using Wilcoxon rank sum test (nominal p-value < 0.05) and is shown for significantly higher proportions in non-responders (NR) and responders (R) respectively. NS indicates nonsignificant differences.
[029] Figure 2F. Differences in cellular biomarkers increase between response groups following treatment. Deconvolution derived estimates of plasma cells and inflammatory macrophages proportions in responders and non-responder groups in the UC-A and CDc cohorts. Both cell subsets are significantly lower in responders in both cohorts p-values<0.0l BH- adjusted). Strikingly, inflammatory macrophages were undetectable (zero values) in all responders, except for two patients from the CDc cohort. R=responders, NR=non-responders.
[030] Figures 3A-C. Abundance of plasma cells and macrophage subtypes in biopsies of IBD patients predicts anti-TNFa treatment outcome. (3A-C) ROC curves and AUC computed in the (3A) CDc, (3B) UC-A and (3C) UC-B cohorts from the estimated proportions of inflammatory macrophages and plasma cells or combined, with 89%, 88% and 95% AUCs respectively. The x and y axis represent the specificity (true negative fraction) and sensitivity (true positive fraction) respectively.
[031] Figure 3D. Adjusting gene expression for abundances of inflammatory macrophages and plasma cells significantly breaks the association between gene signatures and treatment response status. Heatmaps similar to those in Figure 1A, showing the association between the signatures scores UC-A (left), UC-B (middle) and CDc (right) in their respective cohorts, generated from the original gene expression data (top row) and after adjustment for estimated abundances of inflammatory macrophages and plasma cells (bottom row).
[032] Figures 3E-M. Differences in cell subset is the driving force of the reported gene signatures for predicting anti-TNFα non-response from baseline. (3E) Predictive power of reported gene signatures drops after correction for variations in cellular biomarkers. ROC curves for each original gene signature score in its respective discovery cohort UC-A, UC-B, and CDc on the original data (left) and the data following adjustment for variations in the abundances of both cellular biomarkers (right). Respective AUCs are reduced from 93%, 97% and 100% on the original data, to 57%, 68% and 79% on the adjusted data. (3F) The drop in AUC due to cell subset proportions differences is significant. AUC was calculated for each gene signature score in their respective discovery cohort. Densities represent AUC null distributions obtained by adjusting gene expression data with random proportions, in each cohort (panels). Solid line shows achieved AUC in original (unadjusted) data, dashed line indicates AUC obtained after adjustment for estimated proportions of inflammatory and plasma cells. (3G-H) (3G) Plasma cells were immunostalned with CD 138, in an independent set of IBD biopsies. Example staining slides showing visual differences between responders and non-responders these populations. CD138+ plasma cells are colored in brown, showing a clear increased staining in non-responsive patients. (3H) ROC curve showing the predictive power of plasma cell and inflammatory macrophages proportions as quantified by a pathologist categorical score (solid line, AUC = 71% and 67% respectively) and a quantitative score for plasma cells (dashed line, AUC = 81%). (31) Boxplots of plasma cell quantitative score in the preliminary cohort (left panel, n=20) and main cohort (right panel, n=52). P values < 0.001 for both cohorts (by Student T-test). (3J-K) Distribution of responding and non-responding patients in the preliminary cohort (top panels) and main cohort (bottom panels) as shown by patient proportions and patient counts in each score. (3J) Histograms of pathologist plasma cell score, (3K) Histograms of inflammation severity scores as determined by an expert pathologist. (3L) ROC curve showing analysis of a cohort of 52 IBD patients collected from two medical centers whose biopsies were stained by CD 138+ IHC staining. Plasma cell abundance classifies non-response at baseline (AUC = 71% and 74% by the pathologist and quantitative scores respectively). (3M) The predictive power increases when restricting to highly inflamed tissues according to the pathologist score (AUC = 82% and 84% by the pathologist and quantitative scores respectively).
[033] Figures 4A-G. Adjusting samples for cell subset variation unmasks upregulated pathways in biopsies of anti-TNF non-responders. (4A) Meta-analysis of non-response associated biological pathways by GSEA on cell subset adjusted expression data supports up- regulation of TLR2/4, IL-6 and B cell receptor signaling, as well as other inflammatory pathways. (4B) Venn diagram showing overlap of cellular biomarker-adjusted differentially expressed pathways across cohorts identified by GSEA. The fifteen pathways differentially expressed in all three cohorts were all upregulated in non-responders. (4C) Venn diagrams showing overlap of differentially expressed genes across cohorts. Upregulated genes (left), downregulated (right). (4D) GSEA enrichment score are driven by consistent leading-edge genes across cohorts. Curves show GSEA enrichment scores for the 3 pathways (rows) that have the most consistent leading- edge genes across cohorts (>25% of their genes in leading-edge in all cohorts). (4E) Top enriched IPA network identified from meta-analysis of cell subset adjusted gene expression data. The CCL7-CCR2 axis is upregulated in non -responders (4F) Boxplots showing the expression of TNFa and its receptors, TNFR1 and TNFR2 in the three biopsy cohorts in the original unadjusted data. (4G) Box plots showing the adjusted expression of TNFa and its receptors, TNFR1 and TNFR2 in the three biopsy cohorts.
[034] Figures 5A-B. Treml expression in blood predicts anti-TNF non-response at baseline in Crohn's patients. (5A) Boxplots showing TREM1, CCL7, CCR2, and TNFR1 mRNA expression as measured in whole blood of 29 responding and non-responding CD patients, prior to initiation of infliximab therapy. (5B) ROC curve of classifier of anti-TNF response at baseline based on TREM1 expression in whole blood.
[035] Figures 6A-C. Treml expression in blood predicts anti-TNF non-response at baseline in rheumatoid arthritis patients. (6A-C) Boxplots showing TREM1 mRNA expression before (left plot) and after adjustment for cell frequency of PCs and inflammatory macrophages (right plot) in whole blood of RA patients from (6A) GEO 12051 and (6B) GSE58795, prior to initiation of infliximab therapy and from (6C) GE033377 prior to initiation of infliximab and adalimumab.
DETAILED DESCRIPTION OF THE INVENTION
[036] The present invention provides methods and kits for determining suitability for treatment with an anti-TNFα therapy, and also for treatment. Means and methods for identifying altered abundance of plasma cells and inflammatory macrophages in pretreatment intestinal biopsies of anti-TNFα responders versus non-responders are also provided. The present invention provides means and methods for identifying altered expression of certain genes in the blood wherein the altered expression is predictive of non- responsiveness to anti-TNFα therapy in patients suffering from inflammation.
[037] The invention is based on the surprising finding that measuring blood levels of TREM1 alone can accurately predict if a subject will be responsive to anti-TNFα therapy. This finding is highly surprising in that elevated levels of blood TREM1 are actually indicative of a subject who is responsive. TREM1 levels in biopsies from the intestines were found to have the inverse relationship, i.e. increased levels of TREM1 were indicative of a subject who is non-responsive. The invention is further based on the unexpected finding that elevated levels of CCR2, CCL7 plasma cell number and inflammatory macrophage number in intestine biopsies could each independently accurately predict that a subject would not be responsive to anti-TNFα therapy. Thus, several new and highly accurate methods of determining subject suitability for anti-TNFa therapy are provided.
[038] By a first aspect, there is provided a method of determining suitability of a subject in need thereof to be treated with anti-Tumor Necrosis Factor Alpha (TNFa) therapy, the method comprising:
a. obtaining a sample from the subject;
b. measuring an anti-TNFa therapy predicative factor in the sample;
c. determining the suitability of the subject for anti-TNFα therapy according to the predictive factor, wherein a value of the predicative factor above/below a predetermined threshold indicates that the subject is suitable for anti-TNFa therapy, and a value of the predicative factor on the other side of the threshold indicates the subject is unsuitable for the anti-TNFα therapy; thereby determining suitability of the subject to be treated with anti-TNFα therapy.
[039] TNFa is a well characterized cytokine, and therapies that target TNFa are well known in the art. In some embodiments, the anti-TNFα therapy comprises reduction of TNFa expression. In some embodiments, anti- TNFa therapy comprises inhibition of TNFa function. In some embodiments, the anti-TNFα therapy comprises reduced TNFa protein, and/or mRNA expression. In some embodiments, anti-TNFα therapy comprises administration of an anti-TNFa antibody. In some embodiments, the antibody is an antigen binding fragment of an antibody. In some embodiments, anti-TNFα therapy comprises reducing TNFa mRNA expression. In some embodiments, anti-TNFα therapy comprises administering a nucleic acid molecule that binds to TNFa mRNA and inhibits its translation or leads to its degradation. In some embodiments, the nucleic acid molecule is a siRNA, miRNA or the like. In some embodiments, anti-TNFα therapy comprises administering a TNFa antagonist.
[040] As used herein, the term“antagonists” refer to substances which inhibit or neutralize the biologic activity of TNFa. Such antagonists accomplish this effect in a variety of ways. One class of antagonists will bind to the gene product protein with sufficient affinity and specificity to neutralize the biologic effects of the protein. Included in this class of molecules are antibodies and antibody fragments (such as, for example, F(ab) or F(ab')2molecules). Another class of antagonists comprises fragments of the gene product protein, muteins or small organic molecules, i.e., peptidomimetics, that will bind to the cognate binding partners or ligands of the gene product, thereby inhibiting the biologic activity of the specific interaction of the gene product with its cognate ligand or receptor. The TNFa antagonist can be of any of these classes as long as it is a substance that inhibits a biological activity of the gene product.
[04] ] Antagonists include antibodies directed to one or more regions of the gene product protein or fragments thereof, antibodies directed to the cognate ligand or receptor, and partial peptides of the gene product or its cognate ligand which inhibit a biological activity of the gene product. Another class of antagonists includes siRNAs, shRNAs, antisense molecules and DNAzymes targeting the gene sequence as known in the art are disclosed herein.
[042] In some embodiments, the TNFa antagonist is an anti-TNFα antibody. In some embodiments, the anti-TNFα antibody is selected from infliximab, golimumab, Enbrel, certolizumab pegol, and adalimumab. In some embodiments, the anti-TNFα antibody is selected from infliximab and adalimumab. In some embodiments, the anti-TNFα antibody is infliximab. In some embodiments, the anti-TNFα antibody is adalimumab.
[043] In some embodiments, the subject suffers from inflammation. In some embodiments, the subject suffers from an inflammatory disease. In some embodiments, the subject suffers from a disease characterized by inflammation. In some embodiments, the subject suffers from a disease that can be treated with anti-TNFα therapy. In some embodiments, the subject suffers from a disease that can be treated with ana anti-TNF antibody. In some embodiments, the subject suffers from autoimmune inflammation. In some embodiments, the subject suffers from an autoimmune disease. In some embodiments, the subject suffers from an autoimmune inflammatory disease. In some embodiments, the autoimmune inflammatory disease is selected from inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). In some embodiments, the subject suffers from a disease selected from IBD, RA, psoriatic arthritis, ankylosing spondylitis, chronic psoriasis, hidradentisis suppurativa, and juvenile idiopathic arthritis. In some embodiments, the subject suffers from a disease selected from IBD, and RA. In some embodiments, the subject suffers from arthritis. In some embodiments, the subject suffers from RA.
[044] In some embodiments, the subject suffers from inflammatory bowel disease (IBD). In some embodiments, IBD comprises colitis, ulcerative colitis and Crohn’s disease. In some embodiments, IBD comprises colitis, ulcerative colitis, Behcet’s disease and Crohn’s disease. In some embodiments, the IBD is colitis. In some embodiments, the IBD is ulcerative colitis. In some embodiments, the IBD is Crohn’s disease. In some embodiments, the anti-TNFα therapy is an anti-IBD therapy. In some embodiments, the IBD is characterized by increased TNFa expression. In some embodiments, the increased TNFa expression is in the intestines.
[045] In some embodiments, the sample is a blood sample. In some embodiments, the blood sample is a peripheral blood sample. In some embodiments, the blood sample is an intestinal blood sample. In some embodiments, the blood sample is from a clinical blood draw. In some embodiments, the sample is an intestinal sample. In some embodiments, the intestinal sample is an intestinal biopsy. In some embodiments, the intestine is the small intestine. In some embodiments, the intestine is the large intestine. In some embodiments, the biopsy is acquired during a colonoscopy. In some embodiments, the sample includes cells. In some embodiments, the sample includes protein. In some embodiments, the sample includes nucleic acids. In some embodiments, the sample includes mRNA.
[046] As used herein, the term“anti-TNFα therapy predicative factor” refers to a measurable biological readout that is predictive of whether a subject will respond to anti-TNFα therapy. In some embodiments, the predicative factor is Triggering Receptor Expressed on Myeloid cells 1 (TREM1) expression. In some embodiments, the predicative factor is Chemokine (C-C motif) ligand 7 (CCL7) expression. In some embodiments, the predicative factor is C-C chemokine receptor type 2 (CCR2) expression. In some embodiments, the predicative factor is CCL7 and CCR2 expression. In some embodiments, the predicative factor is CCL7 and/or CCR2 expression and TREM1 expression. In some embodiments, the predicative factor is plasma cell (PC) number. In some embodiments, the predictive factor is inflammatory macrophage (IM) number. In some embodiments, the predicative factor is selected from TREM1 expression, CCL7 expression, CCR2 expression, PC number, IM number and a combination thereof.
[047] In some embodiments, TREM1 is serum TREM1. In some embodiments, TREM1 is soluble TREM1. In some embodiments, TREM1 is membranal TREM1. In some embodiments, the serum TREM1 is soluble TREM1, membranal TREM1 or both. In some embodiments, TREM1 is a variant of TREM1.
[048] In some embodiments, TREM1 expression is measured in a blood sample. In some embodiments, TREM1 expression is measured in an intestinal sample. In some embodiments, CCR2, CCL7, PC number, and/or IM number is measured in an intestinal sample. In some embodiments, TREM1 expression is measured in blood to determine suitability of a subject suffering from a non-intestinal disease. In some embodiments, the non-intestinal disease is RA.
[049] In some embodiments, measuring comprises measuring mRNA levels. In some embodiments, measuring comprises measuring protein levels. In some embodiments, measuring comprises counting cells. In some embodiments, measuring comprises automated cell counting. In some embodiments, measuring comprises PCR of a target mRNA. In some embodiments, measuring comprises hybridization with a nucleic acid-based array (for example, a microarray). In some embodiments, measuring comprises immuno-detection. In some embodiments, the immuno-detection is immunohistochemistry. In some embodiments, the immuno-detection is ELISA. In some embodiments, immuno-detection is immunoblotting. In some embodiments, immuno-detection of PC cells comprises detection of CD 138. In some embodiments, measuring PC number comprises measuring the number of cells positive for surface expression of CD 138. In some embodiments, immuno-detection of IM cells comprises detection of at least one of CD86, CD80, CD68, MHCII, IL-1R, TLR2, TLR4, iNOS and SOCS3. In some embodiments, a combination of markers is immuno-detected. In some embodiments, measuring IM number comprises measuring the number of cells positive for surface expression of CD68 and CD86.
[050] In some embodiments, measuring expression levels comprises contacting the sample with a molecule that detects the predicative factor. In some embodiments, the molecule that detects the predicative factor is connected to an artificial solid support. In some embodiments, the molecule that detects the predicative factor is an artificial molecule. In some embodiments, the molecule that detects the predicative factor is an engineered molecule. In some embodiments, the sample is processed before measuring. In some embodiments, the processing comprises extracting protein. In some embodiments, the processing comprises extracting nucleic acids. In some embodiments, the processing comprises purifying the extracted protein and/or nucleic acid molecule. In some embodiments, the processing comprises embedding tissue. In some embodiments, the tissue is a biopsy. In some embodiments, the embedding is paraffin embedding. In some embodiments, the detecting comprises contacting the molecule that detects the predicative factor with a second detecting molecule. In some embodiments, the second detecting molecule is a fluorescent molecule. In some embodiments, the second detecting molecule is a colored molecule. Detection of protein, cells and/or mRNA is well known in the art and some examples of such are provided hereinbelow. Any method of such detection however may be employed for the performance of the invention disclosed herein.
[051] In some embodiments, the predetermined threshold is determined from healthy subjects. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD but respond to therapy. In some embodiments, the predetermined threshold is determined from subjects suffering from IBD but do respond to therapy. In some embodiments, the predetermined threshold is the highest or lowest level/number from subjects that respond to therapy. In some embodiments, the predetermined threshold is the highest or lowest levels/number from subjects that do not respond to therapy. In some embodiments, the predetermined threshold is the level/number that best distinguishes subjects that respond and don’t respond to therapy. In some embodiments, the predetermined threshold is from subject naive to anti-TNFα therapy. In some embodiments, the predetermined threshold is baseline level/number from subjects before receiving therapy.
[052] In some embodiments, TREM1 expression is measured in a blood sample and suitability of the subject to be treated with anti-TNFα therapy is determined according to the blood TREM1 levels. In some embodiments, blood TREM1 expression below a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, blood TREM1 expression above a predetermined threshold indicates the subject is suitable for the anti- TNFa therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti-TNFα therapy. [053] In some embodiments, TREM1 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNFα therapy is determined according to the biopsy TREM1 levels. In some embodiments, biopsy TREM1 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, biopsy TREM1 expression below a predetermined threshold indicates the subject is suitable for the anti-TNFα therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy.
[054] In some embodiments, intestinal expression or cell numbers are used for determining suitability of subjects suffering from an intestinal disease. In some embodiments, the intestinal disease is IBD.
[055] In some embodiments, CCR2 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNFα therapy is determined according to the biopsy CCR2 levels. In some embodiments, biopsy CCR2 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, biopsy CCR2 expression below a predetermined threshold indicates the subject is suitable for the anti-TNFα therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy.
[056] In some embodiments, CCL7 expression is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNFα therapy is determined according to the biopsy CCL7 levels. In some embodiments, biopsy CCL7 expression above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, biopsy CCL7 expression below a predetermined threshold indicates the subject is suitable for the anti-TNFα therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti- TNFa therapy. [057] In some embodiments, PC number is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNFα therapy is determined according to the number of PCs in the biopsy. In some embodiments, biopsy PC number above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, biopsy PC number below a predetermined threshold indicates the subject is suitable for the anti-TNFa therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti-TNFα therapy.
[058] In some embodiments, IM number is measured in an intestinal biopsy and suitability of the subject to be treated with anti-TNFα therapy is determined according to the number of IMs in the biopsy. In some embodiments, biopsy IM number above a predetermined threshold indicates the subject is unsuitable for the anti-TNFα therapy. In some embodiments, biopsy IM number below a predetermined threshold indicates the subject is suitable for the anti-TNFa therapy. Expression at or about at the threshold may mean the subject is either suitable or unsuitable for anti-TNFα therapy and may be determined by a physician. In some embodiments, expression at or about at the threshold indicates the subject is suitable for the anti-TNFα therapy.
[059] In some embodiments, PC or IM number is measured. In some embodiments, PC and IM number is measured. In some embodiments, both numbers must be below the predetermined threshold for the subject to be suitable. In some embodiments, at least one of the numbers must be below the threshold for the subject to be suitable. In some embodiments, CCL7 or CCR2 is measure. In some embodiments, CCL7 and CCR2 is measured. In some embodiments, both expression levels must be below the predetermined threshold for the subject to be suitable. In some embodiments, at least one of the expression levels must be below the threshold for the subject to be suitable.
[060] In some embodiments, a combination of the predicative factors described herein is measured. In some embodiments, all the measured factors must indicate a subject is suitable for the subject to be determined to be suitable. In some embodiments, at least a plurality of factors must indicate a subject is suitable for the subject to be determined to be suitable. In some embodiments, blood TREM1 and at least one other predicative factor is measured and at least blood TREM1 and one other predicative factor must indicate a subject is suitable for the subject to be determined to be suitable.
[061] In some embodiments, the factors listed herein are the only factors measured. In some embodiments, the measuring is devoid of measuring any other factors. In some embodiments, only TREM1, CCR2 and/or CCL7 levels are measured. In some embodiments, only TREM1, CCR2 and/or CCL7 levels and the levels of a control gene or protein are measured. In some embodiments, only CCR2 and/or CCL7 levels and the levels of another gene from the CCR2/CCL7 axis are measured. In some embodiments, genes/proteins from the CCR2/CCL7 axis are selected from the group consisting of: AIM2, AP2B1, ARAF, BAG2, BCAT1, CCNF, DDX25, DESI2, DIABLO, EPHB2, ERK1/2, GPC1, HAVCR2, HOMER 1, IgG, IL19, IL12 (complex), IL4I1, IL7R, Interferon alpha, Jnk, MY06, P38 MAPK, PIK3CB, RIPK1, RNF13, S1PR1, SOX17, STAT2, TCR, TRIO, TSPYL2, and WNK2. In some embodiments, genes/proteins selected from AIM2, AP2B1, ARAF, BAG2, BCAT1, CCNF, DDX25, DESI2, DIABLO, EPHB2, ERK1/2, GPC1, HAVCR2, HOMER 1, IgG, IL19, IL12 (complex), IL4I1, IL7R, Interferon alpha, Jnk, MY06, P38 MAPK, PIK3CB, RIPK1, RNF13, S1PR1, SOX17, STAT2, TCR, TRIO, TSPYL2, and WNK2 are also measured. In some embodiments, expression of any one of Soxl7, RNF13, TSPYL2, S1PR1, BAG2, HOMER 1, IL4I1, AIM2, STAT2, BCAT1, HAVCR2, BCAT1, and IL7R above the predetermined threshold indicates the subject is unsuitable for anti-TNFα therapy. In some embodiments, expression of any one of WNK2, DESI2, GPC1, IL19, DIABLO, DDX25, ARAF, RIPK1, TRIO, CCNF, EPHB2, MY06, PIK3CB and AP2B1 below the predetermined threshold indicate the subject is unsuitable for anti-TNFa therapy. In some embodiments, a predicative factor is combined with the expression levels of at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 genes from the above list of genes/proteins for determined suitability of the subject for anti-TNFa therapy. Each possibility represents a separate embodiment of the invention.
[062] Control, or housekeeping genes and proteins are well known in the art and may be used for normalization of expression levels in sample. Examples of control genes/proteins include, but are not limited to B-actin, GAPDH, and ribosomal components. In some embodiments, the expression levels are normalized to cell types percentage in the biopsy. [063] In one embodiment, significantly high plasma cell numbers in anti-TNFα non-responders were validated in IBD patients naive to anti-TNFα therapy. In one embodiment, significantly is at least 10% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 20% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 30% higher plasma cell numbers in anti-TNFα nonresponders. In one embodiment, significantly is at least 40% higher plasma cell numbers in anti- TNFa non-responders. In one embodiment, significantly is at least 50% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 75% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 100% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 150% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 200% higher plasma cell numbers in anti-TNFα nonresponders. In one embodiment, significantly is at least 300% higher plasma cell numbers in anti- TNFa non-responders. In one embodiment, significantly is at least 350% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 400% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 450% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 500% higher plasma cell numbers in anti-TNFα non-responders.
[064] In some embodiments, the above the threshold is at least 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100% above the threshold. Each possibility represents a separate embodiment of the invention. In some embodiments, the below the threshold is at least 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100% below the threshold. Each possibility represents a separate embodiment of the invention.
[065] In one embodiment, provided herein a method for identifying an IBD patient nonresponding to anti-TNFα therapy, comprising, analyzing intestinal specimen from the IBD patient, wherein significantly high plasma cell numbers in the intestinal specimen compared to a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative for IBD non -responding to anti- TNFa therapy, thereby identifying an IBD patient non -responding to anti-TNFα therapy. In one embodiment, intestinal specimen comprises intestinal biopsy. [066] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein significantly high plasma cell numbers in the intestinal specimen compared to a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti- TNFa therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti-TNFα therapy to an IBD patient.
[067] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein a higher plasma cell numbers in the intestinal specimen compared to the range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFa therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
[068] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein plasma cell numbers in the intestinal specimen below a threshold value of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti-TNFα therapy is effective, thereby determining the effectiveness of an anti-TNFα therapy to an IBD patient.
[069] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for plasma cell number and/or abundance, wherein plasma cell numbers in the intestinal specimen within a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti-TNFα therapy is effective, thereby determining the effectiveness of an anti-TNFa therapy to an IBD patient.
[070] In one embodiment, provided herein the use of an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNFα therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen within a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFa therapy, is indicative that the anti-TNFα therapy is effective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient. In one embodiment, provided herein the use of an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNFα therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen beyond a range of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti-TNFα therapy to an IBD patient. In one embodiment, provided herein the use of an intestinal biopsy showing plasma cells within the biopsy for method determining the effectiveness of an anti-TNFα therapy to an IBD patient, wherein plasma cell numbers in the intestinal specimen higher than the threshold values for number of plasma cell numbers in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti- TNFa therapy is ineffective, thereby determining the effectiveness of an anti-TNFα therapy to an IBD patient.
[071] In one embodiment, intestinal biopsy showing plasma cells is an intestinal biopsy stained for plasma cells. In one embodiment, intestinal biopsy showing plasma cells is an intestinal biopsy wherein the number of plasma cells can be quantified.
[072] In one embodiment, analyzing the intestinal biopsy for plasma cell number and/or abundance is staining an intestinal biopsy and identifying plasma cells. In one embodiment, analyzing the intestinal biopsy for plasma cell number and/or abundance is treating an intestinal biopsy with immunohistochemical reagent for identifying plasma cells. In one embodiment, staining or immunohistochemical reagents for identifying plasma cells are known to a person of skill in the art. In one embodiment, determining, analyzing and/or staining is providing means for counting the number of plasma cell within an intestinal specimen.
[073] In one embodiment,“threshold value of plasma cell numbers” is indicative to the upper threshold of cell numbers or abundance of cells in an IBD patient responding to an anti- TNFa therapy.
[074] In one embodiment,“a range of plasma cell numbers in control intestinal specimen” is indicative to the range of cell numbers or abundance in an IBD patient responding to an anti- TNFa therapy. In one embodiment,“cell numbers or abundance” include the concentration of cells. In one embodiment,“cells” are plasma cells. In one embodiment,“cell number” is plasma cell number. In one embodiment, intestinal specimen derived from IBD patient responding to anti-TNFα therapy is the mean and/or median values of a plurality of specimens as described herein. In one embodiment,“IBD non-responding to anti-TNFα therapy” is a patient afflicted with IBD wherein anti-TNFα therapy has little or no therapeutic effect. In one embodiment,“IBD non-responding to anti-TNFα therapy” is a patient afflicted with IBD wherein anti- TNFa therapy has insufficient therapeutic effect. In one embodiment, the abundance, concentration and/or the total number of plasma cells within an intestinal biopsy is indicative for the efficiency of anti- TNFa therapy in an IBD patient. In one embodiment, an intestinal biopsy of an IBD patient as described herein is assessed as described herein, prior to determining the actual treatment or prior to prescribing anti-TNFα therapy. In one embodiment, an intestinal biopsy of an IBD patient as described herein is assessed as described herein, upon failure of anti-TNFα therapy. In one embodiment, an intestinal biopsy of an IBD patient as described herein is assessed as described herein, during anti-TNFα therapy. In one embodiment, plasma cells comprise macrophages. In one embodiment, plasma cells comprise inflammatory macrophages.
[075] The present invention provides means for identifying altered abundance of plasma cells and inflammatory macrophages in pretreatment intestinal biopsies of anti-TNFα responders versus non-responders.
[076] In one embodiment, significantly high plasma cell numbers in anti-TNFα non -responders were validated in IBD patients naive to anti-TNFα therapy. In one embodiment, significantly is at least 10% higher plasma cell numbers in anti-TNFα non-responders. In one embodiment, significantly is at least 20% higher plasma cell numbers.
[077] Pathway analysis of the cell-adjusted differentially expressed genes in biopsy between responders and non-responders suggests an upregulation of the TREM 1 and CCR2-CCL7 axes in non-responders.
[078] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an elevated expression of TREM1 and/or CCR2-CCL7 beyond a threshold value of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
[079] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an expression of TREM1 and/or CCR2-CCL7 beyond the range of expression of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti- TNFa therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti- TNFa therapy to an IBD patient.
[080] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to an IBD patient, comprising obtaining from the IBD patient an intestinal biopsy and analyzing the intestinal biopsy for the expression TREM1 and/or CCR2-CCL7, wherein an elevated expression of TREM1 and/or CCR2-CCL7 beyond a threshold value of TREM1 and/or CCR2-CCL7 expression in control intestinal specimen derived from IBD patient responding to anti-TNFα therapy, is indicative that the anti-TNFα therapy is ineffective, thereby determining the effectiveness of an anti-TNFα therapy to an IBD patient.
[081] In one embodiment, provided herein a method for determining the effectiveness of an anti-TNFα therapy to a patient suffering from inflammation, comprising obtaining from the patient a blood sample and measuring the expression of TREM1, wherein an expression of TREM1 below the range or threshold of expression of TREM1 expression in control blood specimen derived from patients responding to anti-TNFα therapy, is indicative that the anti- TNFa therapy is ineffective, thereby determining the effectiveness of an anti-TNFα therapy to a patient suffering from inflammation. In one embodiment, analyzing the expression is quantifying the expression, measuring the expression and/or assessing the expression. In one embodiment, a blood sample is a peripheral blood sample.
[082] In one embodiment, plasma cell numbers are quantified by staining the intestinal biopsy by IHC staining. In one embodiment, the intestinal biopsy is a paraffin-embedded intestinal biopsy. In one embodiment, the intestinal biopsy is obtained during colonoscopy. [083] In some embodiments, the method of the invention further comprises administering the anti-TNFα therapy to a subject indicated to be suitable for the anti-TNFα therapy. In some embodiments, the method of the invention further comprises administering to a subject indicated to be unsuitable for the anti-TNFα therapy an increased dose of the anti-TNFα therapy. In some embodiments, the increased dose is above the standard dose. In some embodiments, increased dose is increased over the doses delivered in the studies whose data was compiled in the analyses described herein. In some embodiments, the increased dose is increased by at least 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100%. Each possibility represents a separate embodiment of the invention. In some embodiments, the method of the invention further comprises administering a non-anti-TNFα anti-inflammation therapy to a subject suffering from inflammation and indicated to be unsuitable for anti-TNFα therapy. In some embodiments, the non-anti-TNFα anti-inflammation therapy is an anti-IBD therapy. In some embodiments, the non- anti-TNFα anti-inflammation therapy is an anti-RA therapy. As the subject has now been determined to be unsuitable for anti-TNFα therapy an alternative inflammation therapy is indicated and may be administered. Examples of inflammation therapies that are not anti-TNFa therapies include, but are not limited to, steroids, NSAIDs, disease-modifying antirheumatic drugs, azathioprine, 4-aminosalicylic acid and derivatives thereof, anti-IL-23 therapy, anti-IL- 17therapy, JAK inhibitors, anti-IL-6R therapy, methotrexate, thiopurines and fecal microbiota transplant. In some embodiments, the non-anti-TNFα anti-IBD therapy comprises blocking at least one of CCR2, CCR5 and CXCR3. In some embodiments, the non-anti-TNFα anti-IBD therapy comprises administering a CCR2 and/or CCL7 antagonist. In some embodiments, the non-anti-TNFα anti-inflammation therapy comprises blocking at least one of CCR2, CCR5 and CXCR3. In some embodiments, the non-anti-TNFα anti-inflammation therapy comprises administering a CCR2 and/or CCL7 antagonist.
[084] By another aspect, there is provided a use of a TREM1 detecting agent for determining serum TREM1 levels in a subject and determining suitability of the subject for treatment with an anti-TNFα therapy. In some embodiments, the TREM1 detecting agent is a serum TREM1 detecting agent. In some embodiments, the detecting agent is an anti-TREMl antibody. In some embodiments, the detecting agent is a nucleic acid molecule that selectively binds and hybridizes to TREM1 mRNA or a TREM1 gene product. In some embodiments, the detecting agent is primers for PCR amplification of TREM1 or a portion thereof. In some embodiments, the detecting agent is a nucleic acid probe for TREM1 or a portion thereof. In some embodiments, the detecting agent is part of a chip, bead or array. The terms“array” or“microarray” or “biochip” or“chip” as used herein refer to articles of manufacture or devices comprising an immobilized target elements, each target element comprising a“clone,”“feature,”“spot” or defined area comprising a particular composition, such as a biological molecule, e.g., a nucleic acid molecule or polypeptide, immobilized to a solid surface.
[085] By another aspect, there is provided a kit comprising a detecting agent for detecting at least one predicative factor disclosed herein. In some embodiments, the kit comprises a detecting agent for TREM1, a detecting agent for CCR2, a detecting agent for CCL7, a detecting agent for PCs, a detecting agent for IMs and combinations thereof. In some embodiments, the agents are labeled as for prognostic assessment of responsiveness to anti-TNFα therapy. In some embodiments, the kit comprises antibodies against TREM1, CCR2, CCL7, CD138, and/or CD86 and CD68. Each possibility represents a separate embodiment of the invention. In some embodiments, the kit comprises nucleic acid molecules for measuring TREM1, CCR2, CCL7 or another gene from the CCR2/CCL7 axis. In some embodiments, TREM1 is serum TREM1. In some embodiments, TREM1 is blood TREM1. Nucleic acid sequences of the genes and amino acid sequences of the proteins provided herein can be found on numerous websites known to those skilled in the art, including pubmed.org, ncbi.nlm.nih.gov/genbank, and uniport.org among others.
[086] As used herein, the term "about" when combined with a value refers to plus and minus 10% of the reference value. For example, a length of about 1000 nanometers (nm) refers to a length of 1000 nm+- 100 nm.
[087] It is noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a polynucleotide" includes a plurality of such polynucleotides and reference to "the polypeptide" includes reference to one or more polypeptides and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements or use of a "negative" limitation. [088] In those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."
[089] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.
[090] Additional objects, advantages, and novel features of the present invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. Additionally, each of the various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples.
[091] Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.
EXAMPLES
[092] Generally, the nomenclature used herein, and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, "Molecular Cloning: A laboratory Manual" Sambrook et al., (1989); "Current Protocols in Molecular Biology" Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols in Molecular Biology", John Wiley and Sons, Baltimore, Maryland (1989); Perbal, "A Practical Guide to Molecular Cloning", John Wiley & Sons, New York (1988); Watson et ah, "Recombinant DNA", Scientific American Books, New York; Birren et al. (eds) "Genome Analysis: A Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531 ; 5,192,659 and 5,272,057; "Cell Biology: A Laboratory Handbook", Volumes I-III Cellis, J. E., ed. (1994); "Culture of Animal Cells - A Manual of Basic Technique" by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; "Current Protocols in Immunology" Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), "Basic and Clinical Immunology" (8th Edition), Appleton & Lange, Norwalk, CT (1994); Mishell and Shiigi (eds), "Strategies for Protein Purification and Characterization - A Laboratory Course Manual" CSHL Press (1996); all of which are incorporated by reference. Other general references are provided throughout this document.
MATERIALS AND METHODS
Public IBD and RA gene expression data and reported gene signatures
[093] Colon biopsy gene expression data and patient Infliximab (IFX, an anti-TNFa agent) responsiveness was obtained from published studies (Arijs et ah, Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis, Gut 2009, and Arijs, et ah, Mucosal gene signatures to predict response to infliximab in patient with ulcerative colitis, Gut 2009) available in Gene Expression Omnibus (GEO) and consist of two cohorts of UC patients (UC-A and UC-B) and one CD patient cohort (CDc). In addition, six reported signatures predictive of IFX response consisting in total of 109 unique genes were gathered from (Arijs et ah, IBID 2009; Arijs et ah, Predictive value of epithelial gene expression profiles for response to infliximab in Crohn’s disease, Inflamm. Bowel Dis., 2010; W02010/044952; Halloran et ah, Molecular patterns in human ulcerative colitis and correlation with response to infliximab, Inflamm. Bowel Dis., 2014) (Table 1, Table 3). Blood gene expression data from RA patients was from GEO 12051, GE033377 and GSE58795. [094] Table 1. Colon biopsy discovery cohorts (in-silico)- IBD predictive gene signatures and patient cohorts used in the immune contribution analysis and cell type meta-analysis respectively.
Figure imgf000029_0001
* (Details of the previous y reported gene signatures, grouped according to the cohort in which they were identified to be predictive of IFX response). Underlined names being used as cohort names.
** R/NR- Responder/Non-responder number in each cohort
Λ Signature defined from overlap between cohort UC-A and UC-B
[095] Table 3. Previously reported gene signatures predictive of response to anti-TNFa.
List of the gene signatures used in the analysis shown in Figure 2. For each signature, we provide its member genes and their respective annotations.
Figure imgf000030_0001
Figure imgf000031_0001
[096] For initial assessment of cell-type expression pattern of these signature genes a combined cell type gene expression matrix was created from 130 sorted cell-type samples from IRIS and the Human body index (GSE7307), each dataset was normalized, batch corrected and standardized by z-score. Signature genes were enumerated to cell-types and lineages by assigning the expression of each gene to the top three most expressing subpopulations (Table 4).
[097] Table 4. Compendium of sorted cell expression profiles. Immune contribution to signature gene expression. Results of the preliminary analysis that derived overall cellular origin of all previously reported signature genes.
Figure imgf000033_0001
Figure imgf000034_0001
Figure imgf000035_0001
Figure imgf000036_0001
Computational deconvolution and meta-analysis
[098] To estimate cell subset proportions, a linear regression framework was used, in which individual samples were regressed based on a characteristic expression of marker genes expressed in 17 cell-types (See Supplementary Table S4 of the priority document US provisional application 62/581960 and of Gaujoux et ah, 2018, Cell-centered meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBP, Gut, April 4). The resulting output of this procedure was an estimated frequency of each cell subset in each sample. These were then re-scaled into proportions, asinh -transformed and compared between groups within each cohort using Wilcoxon rank sum test. Meta-analysis across three cohorts was performed using Fisher’s combined probability test and corrected for multiple comparisons. Cell types having nominal p-values <0.05 in at least 2 cohorts and a combined FDR <0.05 were selected for further analysis. ROC analyses were performed by scoring individuals based on the mean gene expression scores per signature, whereas for cellular biomarkers we used estimated proportions for individual cell subsets or the average standardized proportions for combined signatures.
Patients in the validation cohorts
[099] Analyses was performed of archival slides of IBD patients in two cohorts: First, in a preliminary cohort 20 biopsies from 16 IBD patients (8 Crohn's Disease, 7 Ulcerative Colitis, 1 IBDU, 3 patients had more than one biopsy) treated at the gastroenterology department of the Rambam Health Care Campus (RHCC) were included. Patient characteristics are represented in Table 2. Clinical response was defined by the attending physician as clinical and/or endoscopic improvement of IBD-related symptoms coupled with a decision to continue IFX therapy, at least 14 weeks after treatment initiation. Non-response was defined by lack of improvement or aggravation of clinical or endoscopic presentation or disease symptoms coupled with therapy change. Colonic biopsies were obtained during endoscopy performed prior to first IFX treatment. Biopsies were taken from inflamed and/or uninflamed areas of the colon or ileum.
[0100] Second, in a primary validation cohort, biopsies from 61 IBD patient from RHCC and Tel-Aviv Sourasky Medical Center (TASMC) were obtained. For this cohort, a decision algorithm was used to determine response (Materials and Methods). [0101] For blood validation by gene expression analysis, whole blood was obtained at baseline from 28 IBD patients treated with IFX in PaxGene tubes. Response was defined using the same algorithm. RNA was extracted and assayed using Affymetrix Clariom D chips.
[0102] Table 2. Clinical and demographic characteristics of patients
included in the study
Figure imgf000038_0001
Figure imgf000039_0001
Figure imgf000040_0001
* According to response classification as detailed in "Methods"
** Ascending colon/transverse colon/descending colon/sigmoid/rectum/non-informed colonic segment/small intestine
*** For cohorts 1-2- at the time when biopsy was taken. For cohort 3- concurrent with IFX
**** At baseline
Λ Montreal classification - Age <16 years/ 17-40 years/>40 years.
ΛΛ Montreal classification - Ileal/colonic/ileocolonic
ΛΛΛ Montreal classification - Proctitis/left-side/pancolitis
ΛΛΛΛ Montreal classification- Inflammatory/structuring/penetrating
Immuno-histochemistry (IHC)
[0103] Formalin-fixed slides of paraffin-embedded colon or ileum tissues, sectioned at 4 pm, were immunostained for the expression of plasma cells (CD138+, obtained from Serotec, clone B-A38, dilution 1 :250) in both cohorts and for inflammatory macrophages (CD68+ and CD86+ from Abeam, clone EP1158Y, 1 : 100) in the preliminary cohort. Slides were deparaffinized and Polink-l HRP Broad Spectrum DAB Detection Kit (GBI labs) was used for detection according to the manufacturer’s instructions.
Staining data analysis
[0104] Slides were interpreted by an expert pathologist blind to response attributes. A specific cell abundance categorical index between 0 and 3 was determined by the pathologist for plasma cells and inflammatory macrophages. A minimal number of cells was scored as‘O’, the highest abundance which was seen within all slides was scored as‘3’. In addition, slides were coded and scanned by an automatic slide scanner 250 Flash. Intestinal crypts, muscles and lymphatic follicles were excluded in each tissue to evaluate cell proportions in the stroma only. Whole tissue area quantification was performed using ImagePro Premier software V.9.3. CD138+ staining Area was determined with the same color cut-off for all patients, and the ratio between CD 138+ staining and the whole-tissue area (in pixels) was evaluated by a researcher blind to response attributes.
Predictive gene signatures
[0105] 6 reported gene signatures were gathered from their respective original publication (Table 1) and mapped to official gene symbols using annotation package org.Hs.eg.db (version 3.3.0). Altogether, the signatures counted 126 unique genes, and were named according to the patient cohort from which they were derived (Table 3). Briefly, signatures UC-A and UC-B were defined as the top 20 differentially expressed genes found in two independent cohorts of UC patients, which were labelled A and B as per the original publication (Kaplan, The global burden of IBD: from 2015 to 2025, Nat. Rev. Gastroenterol Hepatol., 2015); Signature UC-AB was defined as the overlap between all differentially expressed genes found in the same UC cohorts A and B, and comprised a total of 53 unique genes (Kaplan, IBID); Signature UC-B-knn was also derived from UC cohort B, but using a different methodology based on a k-nearest-neighbor classifier; Signature CDc was identified in CD patients from colon biopsies (Targan et ak, A short-term study of chimeric monoclonal antibody cA2 to tumor necrosis factor alpha for Crohn’s disease. Crohn’s Disease cA2 Study Group. N. Engl. J. Med., 2997) (cohort CDc). The remaining signature named IRRAT was taken from the kidney transplant study (Ben-Horin et ak, Optimizing anti-TNF treatments in inflammatory bowel disease, Autoimmun. Rev., 2014). Cell type expression pattern of predictive gene signatures
[0106] CEL files from sorted cell type samples from IRIS (GSE22886) and the Human body index (GSE7307) were normalized separately using frma. In GSE7307, the profiles from all immune cells (32 profiles from monocyte, T cell and B cell lineages) and colon tissues (2 profiles) were extracted. A combined cell type gene expression matrix was created, it was corrected for dataset of origin effects using Combat, and subsequently probe-sets were averaged into genes. This resulted in the creation of an expression matrix of 130 expression profiles (Table 5), which were standardized using z-scores and averaged into major cell lineages split into resting and activation/memory state.
[0107] Table 5. Compendium of sorted cell expression profiles. Description of the GEO data used to compute the immune contribution to previously reported signature genes
Figure imgf000042_0001
Figure imgf000043_0001
Figure imgf000044_0001
Figure imgf000045_0001
[0108] Previously, others assign signature genes to the most likely contributing cell subpopulations they were detected from in the samples; here each gene was assigned to the three most expressing cell subsets and how many unique genes were assigned to each of 8 major functional cell lineages was counted (Table 4). Since enrichment of cell type expression was looked for, the analysis was restricted to the 122 signatures genes up-regulated in non-responders, of which 109 genes could be mapped to probe-sets in the sorted cell compendium data.
IBD cohorts' gene expression data
[0109] The gene expression data for each IBD cohort used in the deconvolution meta-analysis were obtained from 3 GEO datasets: UC-A from GSE14580, UC-B form GSE12251 and CDc from GSE 16879. These datasets contain biopsy gene expression profiles generated from 2 cohorts of UC patients (Cohort A and B in GSE14580 and GSE12251 respectively), and 1 cohort of CD patients (part of GSE16879). They were originally designed for the discovery of gene signatures that can predict, at baseline, if a patient is likely to respond to an anti-TNFα treatment (Infliximab). In terms of signatures, all signatures were identified from baseline gene expression differential analysis between responders and non-responders to Infliximab treatment in the same set of 3 IBD cohorts of UC (cohorts UC-A and UC-B) or CD (cohort CDc) patients, exception being the IRRAT signature which, subsequent to the study it originated with, was found to correlate with anti-TNFα response at baseline in the UC-B cohort. In addition, blood gene expression data of IBD patients for whom endoscopic activity was available was obtained (GSE107865, Gaujoux et ah, Cell-centred meta-analysis reveals baseline predictors of anti-TNFa non-response in biopsy and blood of patients with IBD, Gut, 2018) in which we assessed the relation of the CCL7-CCR2-TREM1 axis to monitor disease activity.
Signature scores and ROC analysis
[0110] ROC analyses were performed on signature expression scores that summarize, for each sample, the expression level of all the genes in a predictive gene set. Given a gene expression dataset (including data adjusted for proportion variations) and a gene signature/set, the signature score Sj for sample j was computed as:
Figure imgf000046_0001
where g, is the expression level of the z-th gene of the signature in sample j, and d, is the sign of the difference between its mean expression in non-responders and responders. For adjusted data where negative expression values occurred, we shifted the data by gL' = gL- ms+ 1, where ms is the minimum expression value amongst the signature genes. ROC curve analysis of cellular biomarkers was computed either directly on estimated proportions for individual cell subsets or on the average standardized proportions (centered, unit-variance) for combined signatures. AUC values were computed using the R package pROC.
[0111] To assess whether the observed drops in AUC could result from the reduction in degrees of freedom incurred by the adjustment procedure itself, the data was repeatedly adjusted with random pairs of cell subset proportions and compared the derived "random" AUCs with the ones obtained using actual estimated proportions (Fig. 3F). This showed that all observed AUC differences were statistically significant (all p-values<0.0l8). The adjusted gene expression datasets were also visualized in order according to their respective signature score data, which confirmed that the association between signature scores and treatment response status was lost after adjustment.
Response classification by a decision algorithm
[0112] In patients with CD, clinical remission was defined as cessation of diarrhea and abdominal cramping or, in the cases of patients with fistulas, cessation of fistula drainage and complete closure of all draining fistulas at week 14, coupled with a decision of the treating physician to continue IFX therapy at the current dosing and schedule. Partial response was defined as a reduction in the amount of diarrhea and abdominal cramping, or, in the case of fistula patients, a decrease in the drainage, size, or number of fistulas at last follow-up. In patients with UC, clinical remission was defined as cessation of diarrhea, rectal bleeding, and abdominal cramping at week 14 as indicated in the patient’s chart by the treating physician, coupled with a decision of the treating physician to continue IFX therapy at the current dosing and schedule, whereas partial response was defined as a reduction in the amount of diarrhea, rectal bleeding, and abdominal cramping. Outcomes not meeting one of the above definitions were classified as non-response.
[0113] To further stratify the response, patients deemed as partial responders were allocated to a decision tree following these steps: failure to withdraw steroid treatment at week 14 was deemed as therapeutic failure. In patients not treated with steroids, a substantial decrease (>50%) in biomarker dynamics (fecal calprotectin if available and serum CRP when calprotectin was not available), as an indicator of response to treatment. For subjects who were not steroid dependent and exhibited no substantial biomarker dynamics, response was defined according to the clinical state at week 26.
[0114] IFX levels and antibodies to IFX (ATI) measurements were available for 28 of the patients. After reviewing these patients, the responders for which 2 subsequent measurements of IFX level < 3 (pg/ml) were observed prior to week 26 were excluded, assuming their response status was less likely to be IFX related, as were non-responders with measurements of ATI level > 15 (pg/mL), assuming they had secondary loss of response, not related to susceptibility to TNFa blockade. These criteria left 29 responders and 23 non-responders from the two centers. Immuno-histochemistry markers
[0115] Plasma cell frequencies were examined by CD 138+ IHC staining. For inflammatory macrophages, in-silico deconvolution analysis herein relied on a gene expression signature of monocyte derived macrophages bearing typical macrophage morophology and phagocytic activity. Given the disease context, this suggested a bias towards inflammatory macrophage phenotype (Ml), as such, the expert pathologist performing the IHC, assessed the co-expression of the CD68 and CD86 as well as cell morphology, as these markers are co-expressed by monocytes and CD86 also in other cell subsets (e.g B and T). Specifically, to account for morphological differences between macrophages and monocytes, mononuclear cells showing broad cytoplasm and oval nucleus were considered as“inflammatory macrophages”, while CD68 and CD 86 -positive monocytes were ignored.
Example 1: Gene expression signatures for anti-TNFa non-response show contributions from distinct immune cell subsets
[0116] Reported gene expression signatures of anti-TNFα response from biopsy show an enrichment for broad immune response categories but have not directly been implicated with particular immune cell subsets. The likely cellular origin of expression of 109 genes extracted from six different reported signatures associated with baseline anti-TNFα non-response was checked (Table 1, Table 3, Fig. 1A). To do so, we analyzed the relative expression pattern of these genes across a compendium of assembled profiles from sorted immune cell subsets and normal bulk colon tissue biopsies available in the public domain.
[0117] Clustering the expression profiles of these signature genes across sorted cell subsets suggested that three distinct lineages contribute to non-response to anti-TNFα treatment (Fig. IB): First, myeloid lineage cell subsets expressing 70% of signature genes; second, B-cell lineage cell subsets in which 30% of signature genes were expressed; third, T and NK cells’ genes, which together comprised 30% of genes in the collective signature (Table 4). Of note, only 15% of signature genes were denoted as highly expressed in the bulk colon samples and of these, the majority were also noted to be highly expressed in the B-cell lineage. Taken together, the majority of anti-TNF response signature genes are more highly expressed by immune cell subsets, with non-overlapping gene set contributions stemming from myeloid and B -lineage associated cell subsets. This further suggested that resident or infiltrating leukocyte populations within biopsy tissues could constitute a good baseline predictor of non-response.
Example 2: Meta-analysis identifies cell type proportion differences between response groups at baseline and following treatment
[0118] Bulk gene expression measurements of a tissue may be strongly confounded by variation in cell subsets. Given the distinct immune cell subset associated genes in anti-TNFα response gene signatures, it was hypothesized that the signature-identified genes may be due to variations in cell subset proportions between individuals. To test this hypothesis, a computational gene expression deconvolution approach was used to estimate the relative composition of 17 immune cell subsets in each sample of three publicly available IBD cohorts (Table 1, cohorts UC-A, UC- B and CDc, 65 samples total), from which the previously reported predictive gene signatures were individually derived (Table 4). Differences in cellular proportions between anti-TNFa therapy responders and non-responders were investigated (Fig. 2A, Table 6). To ensure robustness of the downstream analyses, only those cell types for which at least 75% of the samples had non-zero estimated proportions were considered and a meta-analysis of cellular proportion differences across all three cohorts was performed by combining p-values for cell subset differences between response groups across cohorts. The analysis identified two cell subsets, inflammatory macrophages and plasma cells, which were significantly different in at least two out of the three discovery cohorts (nominal p-value<0.05) and passed a combined false discovery rate of 5% (Fig. 2B, E). The proportions of both cell subsets were significantly higher in non-responders than in responders. Repeating the same analysis in a second time point, available for UC-A and CDc for 4-6 weeks post-treatment, a significant decrease in both cell subset abundances was observed, which was independent of response in UC, but statistically significant only in responders in CD (FDR<0.05, Fig. 2C-D). Importantly, both cell subset proportions were still significantly higher in non -responders post-treatment (FDR<0.05, Fig. 2F). Overall, this showed that the differences observed at baseline in inflammatory macrophages and plasma cell abundances were maintained after treatment initiation, and that successful response to anti-TNFα was associated with a sharp decrease in inflammatory macrophage and plasma cell abundances. [0119] Table 6: Estimated immune cell subset proportions. Estimated proportions in all cohorts at baseline and after treatment. The following abbreviations are used: B, Baseline; PT, Post-Treatment; R, Responder; NR, Non-Responder.
Figure imgf000051_0001
Figure imgf000052_0001
Figure imgf000053_0001
Figure imgf000054_0001
Example 3: Baseline plasma cell proportions are predictive of anti-TNFa non-response
[0120] The significant differences observed in immune cell subset proportions between response groups suggested they may serve as clinically feasible predictive biomarkers of non-response. To test this, each subset's predictive power was assessed in the training data, and a mean AUC of 83% and 71% respectively was observed for the estimated proportion of inflammatory macrophages and plasma cells predicting non-response (Fig. 3A-C for CDc, UC-A and UC-B respectively, Table 7, inflammatory macrophages and plasma cells AUC: (77%, 79%), (82%, 45%) and (89%, 88%) for UC-A, UC-B and CDc respectively). Combining the two cell subset proportions into a single score (average of standardized proportions) an increase in predictive power was observed over each individual biomarker in the UC-A and CDc cohorts, achieving AUCs of 90% and 95% respectively (Fig. 3A-C), whereas UC-B, where tissue samples included both normal and inflamed biopsies, did not yield an improved combined score. Conversely, adjusting the expression data of each cohort for variations in the cellular biomarker proportions, showed a significant decrease (a mean drop of 32%) in the ability of gene signature scores to discriminate responders from non-responders (Fig. 3D-F). These results suggest that the predictive power of reported gene signatures is largely based on cell subset proportion differences, whose increase in colon biopsies on non-responders may serve for predictive purposes.
[0121] Table 7: Results of the cell type proportion meta-analysis
Figure imgf000055_0001
[0122] Next, an independent set of 20 paraffin embedded colon biopsies was analyzed. The biopsies were collected as part of standard clinical care from IBD patients for which anti-TNFa response status was available. These were stained for inflammatory macrophages and for plasma cells using IHC staining and assessed for cell type abundance using a discrete scoring (low/medium/high) performed by an expert pathologist who was blind to patient response status (see Materials and Methods). In agreement with the computational analysis, both plasma cells and inflammatory macrophages were observed as associated with non-response to therapy (Fig. 3G-H, plasma cell AUC = 71%, 81% by pathologist and quantification respectively, inflammatory macrophages AUC=67% by pathologist), while total macrophages abundance was not predictive (AUC 48%).
[0123] For clinical feasibility, a biomarker must exhibit reproducibility and ease of use. Plasma cells have a unique morphology and can be sufficiently defined by a single marker, CD 138. Therefore, plasma cells were focused on a second cohort consisting of 61 patients from two medical centers was collected (35 responders and 26 non-responders, Table 2). Plasma cell frequencies were examined by CD 138+ IHC staining and again a significantly higher frequency of plasma cells in non-responders as evaluated by an expert pathologist and by automated quantification was observed (p=0.02 and 0.0005 by Student’s T-test, Fig. 31- J). This difference enabled predicting non-response class at baseline (Fig. 3L, 71% and 74% AUC, n=52, Materials and Methods), which was further increased when the analyses was restricted only to highly inflamed tissues (Fig. 3M, AUC = 82% and 84% by an expert pathologist and quantitatively, n= 20, p-0.005/0.002 by Student T-test, Fig, 3K). Taken together, the computational deconvolution predictions and external validations confirmed that pretreatment plasma cell abundance are associated with non-response to anti-TNF treatment and suggested that relevant immune pathways should be further studied to understand non-response pathophysiology.
Example 4: A dysregulated gene network masked by cell proportion variation
[0124] Given the differential abundance of inflammatory macrophages and plasma cells between the two response groups, it was reasoned that underlying biological signals may be masked by this difference. To characterize the biological processes associated with non-response that cannot be explained by cell subsets differences, pathways and genes differentially expressed were searched for while adjusting each individual's gene expression profile for variations in the two cell subsets (see Materials and Methods). Fifteen pathways that were dysregulated across all cohorts (FDR=0.05) were found using Gene Set Enrichment Analysis (GSEA), all of which were up-regulated in non-responders and related to immune signaling and inflammation (Fig. 4A-C,). Most prominent was the association of non-response with the MyD88 deficiency (which impairs Toll Like Receptors (TLR2/4) function), Interleukin-6 signaling, and antigen activation of B Cell Receptor, which had a substantial fraction of genes in the leading edge (64%, 30% and 26% of genes respectively (Fig. 4D.)).
[0125] At the gene level, 166 differentially expressed genes (DEG) were identified in at least two cohorts across post cellular biomarker adjustment (62 up-regulated and 104 down-regulated, p-value < 0.05, Table 8). This set of genes was analyzed using Ingenuity Pathway Analysis (IPA), in particular, searching for links to the predictive cellular biomarkers. One of the top most enriched networks included 28 genes, including the ligand-receptor pair CCL7-CCR2 which was found to be upregulated in non-responders (Fig. 4E, Table 9). The CCL7-CCR2 axis has been associated with inflammation and upregulation in IBD. In the mucosa of IBD patients, CCL7 is produced by inflammatory lymphocytes, including plasma cells, whereas CCR2 is expressed primarily on monocytes, and mediates their recruitment to inflamed tissues.
[0126] Interestingly, IPA analysis identified Triggering Receptor Expressed on Myeloid cells 1 (TREM1) as an upstream regulator of six of the adjustment-derived DEG, including CCL7. TREM1 is expressed on myeloid lineage cells including monocytes and macrophages, has well- documented pro-inflammatory functions, and its blockade has shown promising results in attenuation of symptoms in IBD models. Meta-analysis of TREM1, CCL7 and CCR2 gene expression across the public data biopsy cohorts showed all these genes to be consistently up- regulated in the non-responder group in the original measured data (meta-FDR<0.037). To find the role of TREM1 pathway in anti-TNFα mechanism we searched for TNF-related genes and found TNFa and TNFR2 as up-regulated in non-responders in the original data as well (Fig. 4F), probably due to TREM-l activation via synergism with TLR signaling, which leads to TNFa secretion from the inflammatory macrophages. Unlike CCL7 and CCR2, their expression difference post-adjustment was lost (Fig. 4G). Taken together, these results suggest that the plasma cells may be responsible for the recruitment of inflammatory macrophages to the inflamed area, which ultimately impact response potential. [0127] Table 8: List of differentially expressed genes when adjusting for inflammatory macrophage and plasma cell proportions.
Figure imgf000058_0001
Figure imgf000059_0001
Figure imgf000060_0001
[0128] Table 9: Results from IPA analysis: common regulators, enriched biofunctions and networks
Figure imgf000060_0002
Figure imgf000061_0001
Example 5: TREM1 expression is a predictive biomarker in blood
[0129] From the point of view of clinical application, prognostic tests performed on blood samples are non-invasive and hence of high value. Whether these findings could lead towards cellular or molecular biomarkers in blood samples was investigated. To do so, whole blood gene expression from a cohort of 28 CD patients prior to anti-TNFα therapy, whose subsequent response status has been determined similarly to the primary biopsy validation cohort criteria was profiled. The TREM1-CCL7-CCR2 axis was focused on and the genes were tested in a hypothesis directed manner only. Of these, TREM1 was the only gene differentially expressed between responders and non-responders (down-regulated in non-responders, adjusted p-value = 0.007, Fig. 5A) in blood, and notably showed a very high prediction accuracy (Fig. 5B, AUC = 94%). Importantly, TREM1 was overall highly expressed (average log2-expression > 11.9), providing further confidence in the measured signal. In addition, it was observed that TREM-l and CCR2 gene expression levels in blood were correlated with endoscopic activity in an additional cohort of patients with UC (see Materials and Methods), further supporting monitoring of the axis in blood as an important clinical non-invasive biomarker and their potential for reproducibility as a clinical non-invasive biomarker of anti-TNFα response status at baseline.
Example 6: TREM1 expression is predictive in rheumatoid arthritis
[0130] Anti- TNFa therapy is also used to treat other forms of autoimmune inflammation besides IBD. Rheumatoid arthritis (RA) is also treated with therapeutic anti-TNFα antibodies, including Infliximab. As was done for IBD, blood expression data from RA patients before anti-TNFa therapy was collected from the GEO, and the expression of TREM1 was examined in eventual responders and non-responders. The first cohort examined (GEO12051) contained 37 subjects that were responsive to IFX and seven that were not. Microarray analysis of blood expression of TREM1 found higher levels of TREM1 mRNA in responders both before and after adjustment for plasma cell and inflammatory macrophage number (Fig. 6A), as had been seen for IBD subjects. Similar results were observed with a second cohort (GSE58795, 23 responders and 7 non-responders) (Fig. 6B). Lastly, a mixed cohort of subjects (GE033377) treated with IFX and another anti-TNFα antibody, Adalimumab, was examined. Once again responders (18 subjects) had elevated blood TREM1 levels as compared to non-responders (24) (Fig. 6C).
[0131] During IFX therapy, circulating monocytes undergo apoptosis, and a shift towards proresolving macrophages occurs in the lamina propria, yielding reduced inflammation, tissue repair and remission. This analysis post-treatment suggests this process occurs to a lesser extent in non-responding individuals implying that the immune milieu observed in patients lies along a continuous immune-phenotypic inflammatory space, in which non-responders represent an extreme. If a non-responsive profile is merely a more exacerbated form of inflammation, then an increased drug dose may bring effective disease remission. In support of this, patients with acute severe colitis showed increased efficacy when treated with higher drug doses.
[0132] Conversely, non-responders may represent a phenotypic niche which is difficult to resolve therapeutically via anti-TNFα treatments, in which case based on baseline detection of non-response one may elect an alternative therapeutic. These findings suggest that blocking the mechanism that draws increased numbers of plasma cells and inflammatory macrophages to intestinal mucosa would be of benefit. Indeed, experiments in mice showed that blocking CCR2, simultaneously with CCR5 and CXCR3, prevents experimental colitis.
[0133] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

Claims

Claims
1. A method of determining suitability of a subject in need thereof to be treated with anti- Tumor Necrosis Factor Alpha (TNFa) therapy, the method comprising:
a. obtaining a blood sample from said subject;
b. measuring expression levels of Triggering Receptor Expressed on Myeloid cells 1 (TREM1) in said blood sample; and
c. determining said suitability of said subject for anti-TNFα therapy according to said expression levels of TREM1, wherein expression below a predetermined threshold indicates said subject is unsuitable for said anti-TNFα therapy and expression at or above said predetermined threshold indicates said subject is suitable for said anti- TNFa therapy;
thereby determining suitability of a subject to be treated with anti-TNFα therapy.
2. The method of claim 1 , wherein said blood sample is a peripheral blood sample.
3. A method of determining suitability of a subject in need thereof to be treated with anti- Tumor Necrosis Factor Alpha (TNFa) therapy, the method comprising
a. obtaining an intestinal biopsy from said subject;
b. measuring in said intestinal biopsy at least one of:
i. plasma cell number;
ii. inflammatory macrophage; and
iii. expression levels of CCL7, CCR2 or both;
c. determining said suitability of said subject for anti-TNFα therapy according to said plasma cell or inflammatory macrophage number, wherein a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both above a predetermined threshold indicates said subject is unsuitable for said anti-TNFα therapy and a number of plasma cells, a number of inflammatory macrophages or expression levels CCL7, CCR2 or both below said predetermined threshold indicates said subject is suitable for said anti-TNFα therapy; thereby determining suitability of a subject to be treated with anti-TNFα therapy.
4. The method of claim 3, wherein a number of plasma cells above a predetermined threshold indicates said subject is unsuitable for said anti-TNFα therapy and a number of plasma cells below said predetermined threshold indicates said subject is suitable for said anti- TNFa therapy.
5. The method of claim 3, wherein expression levels of CCL7 and CCR2 above a predetermined threshold indicates said subject is unsuitable for said anti-TNFα therapy and expression levels of CCL7 and CCR2 below said predetermined threshold indicates said subject is suitable for said anti-TNFα therapy.
6. The method of any one of claims 1 to 5, further comprising measuring in an intestinal biopsy from said subject expression levels of TREM1, wherein intestinal expression levels of TREM1 above a predetermined threshold indicates said subject is unsuitable for said anti-TNFα therapy and intestinal expression levels of TREM1 below said predetermined threshold indicates said subject is suitable for said anti-TNFα therapy.
7. The method of any one of claims 1-6, wherein said subject suffers from inflammation.
8. The method of claim, 7 wherein said subject suffers from an autoimmune disease.
9. The method of claim 8, wherein said autoimmune disease is selected from inflammatory bowel disease (IBD) and rheumatoid arthritis (RA).
10. The method of claim 9, wherein said IBD comprises colitis, ulcerative colitis, and Crohn’s disease.
11. The method of any one of claims 1 to 10, wherein said anti-TNFα therapy comprises administration of an anti-TNFα antibody.
12. The method of claim 11, wherein said anti-TNFα antibody is selected from infliximab and adalimumab.
13. The method of any one of claims 1 to 12, further comprising administering said anti-TNFa therapy to said subject indicated to be suitable for said anti-TNFα therapy.
14. The method of any one of claims 1 to 13, further comprising administering to said subject indicated to be unsuitable for said anti-TNFα therapy an increased dose of said anti-TNFa therapy above the standard dose.
15. The method of any one of claims 7 to 14, further comprising administering a non-anti- TNFα anti-inflammation therapy to said subject indicated to be unsuitable for anti-TNFa therapy.
16. The method of claim 15, wherein said non-anti-TNFα anti-inflammation therapy comprises blocking at least one of CCR2, CCR5 and CXCR3.
17. The method of any one of claims 1 to 16, wherein said predetermined threshold is determined from blood levels of TREM1, intestinal numbers of plasma cells or inflammatory macrophages or intestinal expression of CCL7, CCR2 or both in subjects that responded to anti-TNFα therapy, wherein said blood levels, said intestinal numbers and said intestinal expression are from before said subjects received said anti-TNFa therapy.
18. The method of any one of claims 3 to 17, wherein measuring plasma cell number comprises measuring the number of cells positive for surface expression of CD 138.
19. The method of any one of claims 1 to 18, wherein measuring expression levels comprises measuring mRNA levels, protein levels or both.
20. The method of any one of claims 1 to 19, wherein said measuring expression levels comprises contacting said blood sample or intestinal biopsy with a molecule that detects TREM1, CCL7, or CCR2 mRNA or protein, and wherein said molecule is connected to an artificial solid support.
21. Use of a TREM1 detecting agent for determining serum TREM1 levels in a subject and determining suitability of said subject for treatment with an anti-TNFα therapy.
22. The use of claim 21, wherein said detecting agent is an anti-TREMl antibody or a nucleic acid molecule that selectively binds and hybridizes to TREM1 mRNA.
23. A kit comprising a plurality of molecules selected from:
a. a TREM1 detecting molecule;
b. a CCL7 detecting molecule;
c. a CCR2 detecting molecule;
d. a plasma cell detecting molecule; and
e. an inflammatory macrophage detecting molecule.
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