Methods and uses of inflammatory bowel disease biomarkers

Abstract

In various aspects of the present disclosure, methods for diagnosing and treating inflammatory bowel disease (IBD), including ulcerative colitis (UC) or Crohn's disease (CD), are provided. Specifically, the present disclosure provides, in part, a panel of IBD biomarkers that can be used for diagnosis and treatment decisions. In addition, the present disclosure provides methods for treating IBD with plasminogen activator inhibitor-1 (PAI-1) inhibitors or tissue plasminogen activator (tPA).

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 533,982, filed July 18, 2017, which is hereby incorporated by reference in its entirety. Field of the Invention

[0003] The present disclosure generally relates to methods and uses of markers of inflammatory bowel disease activity for diagnosis, prognosis, or treatment of the disease.

[0004] background

[0005] Inflammatory bowel disease (IBD) is a general term for chronic diseases that cause inflammation of the gastrointestinal tract of unknown cause. It is a difficult-to-treat disease with long-term diarrhea and bloody stools, including ulcerative colitis and Crohn's disease. Unlike common food poisoning, this medical condition is long-lasting and repeatedly remits and worsens.

[0006] Treatments for inflammatory bowel disease include nutritional therapy, medical therapy, surgical treatment, and granulocyte apheresis (whereby granulocytes recruited to the site of inflammation are selectively removed). Among medical therapies, sulfasalazine, 5-aminosalicylic acid (mesalazine-type preparations), steroidal anti-inflammatory agents, and immunosuppressants are used. However, there are problems with side effects, such as headaches and gastritis caused by sulfapyridine, a metabolite of sulfasalazine, and infections and adrenal insufficiency caused by the excessive immunosuppressive effects of steroidal anti-inflammatory agents.

[0007] Expensive biologics are approved for the treatment of moderate to severe IBD, but it is currently unclear who should be treated with which. Current markers of IBD activity for disease diagnosis and prognosis are insufficient (including the widely used fecal calprotectin). This is largely because the disease is heterogeneous, and identifying biomarkers downstream of all key proinflammatory pathways that are variably enhanced in IBD patients is challenging.

[0008] Therefore, what is needed is a biomarker signature of IBD activity to guide diagnosis, prognosis, and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This application document contains at least one drawing executed in color. Copies of this patent application disclosure with color drawings will be provided by the Office upon request and payment of the necessary fee. Those skilled in the art will understand that the drawings described below are for illustrative purposes only. The drawings are not intended to limit the scope of this teaching in any way.

[0011] Figure 1 A model showing an in vitro culture system (Kaiko G and Ryu S et al., Cell, 2016).

[0012] Figure 2 A PCA plot is depicted showing that IL-17 has a subtle effect on stem cell differentiation.

[0013] Figure 3 Delineation of the identification of gene candidates downstream of IL-17A in the epithelium.

[0014] Figure 4 Delineating the identification of genes conservedly dysregulated in IBD patients.

[0015] Figure 5 qPCR validation and dose curves are shown: colon.

[0016] Figure 6 Dose curve for the ileum was plotted.

[0017] Figure 7 A textbook view depicting tPA and its inhibitor PAI-1.

[0018] Figure 8 A plasminogen-mediated pathway hypothesis is shown.

[0019] Figure 9A 、 Figure 9B 、 Figure 9C 、 Figure 9D and Figure 9E tPA was shown to be induced by inflammation and to originate from both epithelial and non-epithelial cells in mice. Figure 9A Unprocessed tissue is shown. Figure 9B Showing DSS epithelial ulceration. Figure 9C DSS is shown adjacent to the inflamed area. Figure 9D Showing simulated infection. Figure 9E Shown is day 10 post-infection.

[0020] Figure 10A Figure 10G shows that tPA is induced by inflammation and originates from both epithelial and non-epithelial cells in mice. In the ileum, there was no tPA in any CRF het at day 14 and none in any mouse at day 0. Figure 10A Shown are 11-10R2+ / - controls + at day 14 post-infection. Figure 10B dnKO at day 14 post-infection is shown.

[0021] Figure 11 tPA was shown to be low to absent in the absence of inflammation and to originate from both epithelial and non-epithelial cells in mice.

[0022] Figure 12A and Figure 12B Data are presented indicating that tPA protects against colitis.

[0023] Figure 13A 、 Figure 13B and Figure 13C Describes that a novel PAI-1 inhibitor increases tPA levels in the blood and colon. Figure 13A Active tPA and total tPA in plasma are shown. Figure 13B Active tPA and total tPA in the colon are shown. Figure 13C The ratios of active tPA to total tPA in plasma and colon are shown.

[0024] Figure 14A and Figure 14B Targeting PAI-1 as therapy (rather than prevention) in DSS colitis was shown to suppress disease. Figure 14A The percent weight change of the control and PAI-1 inhibitor is shown. Figure 14B Shown are the colon lengths under control-treated and PAI-1-treated conditions.

[0025] Figure 15A 、 Figure 15B 、 Figure 15C 、 Figure 15D 、 Figure 15E 、 Figure 15F and Figure 15G Targeting PAI-1 as therapy (rather than prevention) in DSS colitis was shown to suppress disease. Figure 15A Stool consistency scores in control and PAI-1 inhibitor-treated subjects are shown. Figure 15B Fecal blood scores in control and PAI-1 inhibitor-treated subjects are shown. Figure 15C H&E staining of a control subject is shown. Figure 15D H&E staining in PAI-1 inhibitor-treated subjects is shown. Figure 15E Shown are the percentage lengths of colon with normal epithelium / goblet cells in control and PAI-1 inhibitor treated subjects. Figure 15F Hyperplastic crypt heights in control and PAI-1 inhibitor-treated subjects are shown. Figure 15G Shown are the mean muscle thicknesses in control and PAI-1 inhibitor treated subjects.

[0026] Figure 16A Figure 16B and Figure 16C PAI-1 inhibition is shown to suppress neutrophil influx. Figure 16A shows inflamed tissue adjacent to an ulcer in a control-treated subject. Figure 16B Shown are inflamed tissue adjacent to ulcers in subjects treated with a PAI-1 inhibitor. Figure 16C Shown are the numbers of Ly6G+ neutrophils per high-power field in control and PAI-1 inhibitor-treated subjects.

[0027] Figure 17PAI-1 inhibition was shown to suppress IL-6.

[0028] Figure 18A and Figure 18B Trends are depicted for reduced weight loss and bacterial load with PAI-1 inhibition. Figure 18A The percent weight changes in control and PAI-1 inhibitor treated subjects are shown. Figure 18B Shown are the CFU per gram of feces in control and PAI-1 inhibitor-treated subjects.

[0029] Figure 19A 、 Figure 19B 、 Figure 19C 、 Figure 19D and Figure 19E PAI-1 inhibition was shown to suppress crypt proliferation. Figure 19A and Figure 19B Depicted is H&E staining of control treated subjects. Figure 19C and Figure 19D Depicts H&E staining of PAI-1 inhibitor treated subjects. Figure 19E Hyperplastic crypt heights in control and PAI-1 inhibitor-treated subjects are shown.

[0030] Figure 20A 、 Figure 20B and Figure 20C PAI-1 inhibition was shown to suppress IL-6, MPO activity, and Ly6G+ neutrophils. Figure 20A Shown are the amounts of IL-6 in the colon of control and PAI-1 inhibitor-treated subjects. Figure 20B The amount of MPO activity in control and PAI-1 inhibitor treated subjects is shown. Figure 20C Shown are the numbers of Ly6G+ neutrophils per high-power field in control and PAI-1 inhibitor-treated subjects.

[0031] Figure 21A Schematic diagram showing IL-17RA signaling. Figure 21B Shown are the fold changes of Plat in control, IL-17A, IL=17a + NFKBi, IL=17A + p38i, and IL-17A + CEBPi treated subjects.

[0032] Figure 22A and Figure 22B showed evidence that tPA can directly and indirectly cleave latent TGFβ in a cell-free assay.

[0033] Figure 23 Schematic diagram depicting the TGF-β pathway. In cancer cell lines, the most highly upregulated gene is serpin 1 / PAI-1.

[0034] Figure 24 The construction of TGFβ-Smad-luciferase reporter is shown.

[0035] Figure 25 We show that TGFβ drives SERPINE1 / PAI-1 expression in colonospheres (a negative feedback loop).

[0036] Figure 26 IL-17A has been shown to be induced to combat infection and maintain a barrier to commensals. It also limits tissue damage by tPA. Increased PAI-1 in IBD patients may limit the tissue-protective function of IL-17A-tPA. 2. PAI-1, long known to be the most responsive gene to TGFβ, may act as a negative feedback regulator of TGFβ by tPA. PAI-1 dysregulation in IBD may explain their hyperinflammatory state.

[0037] Figure 27 IF staining of sections from surgically resected cases showed that tPA was unchanged in UC patients. Therefore, tPA is not a biomarker.

[0038] Figure 28 Shown are highly upregulated SERPINE1 / PAI-1 in inflamed tissues analyzed from CD and UC patients (4 cohorts) from raw data deposited in GEO NCBI.

[0039] Figure 29 IF staining of sections from surgically resected cases showing highly upregulated PAI-1 protein in inflamed tissues from UC patients.

[0040] Figure 30A and Figure 30B Data are shown for responder vs. non-responder subjects. Figure 30A Shown are responders vs. non-responders before vedolizumab and infliximab. Figure 30B Shown are responders vs. non-responders prior to infliximab in CD colon and UC colon.

[0041] Figure 31 Data are shown for responder vs. non-responder subjects.

[0042] Figure 32 Graph showing data for responders and non-responders using PAI as an indicator.

[0043] Figure 33A showed a positive correlation between PAI-1 and IL-6. Figure 33B showed a positive correlation between PAI-1 and TNF-α.

[0044] Figure 34AA positive correlation between PAI-1 and oncostatin M was shown. Figure 34B showed a positive correlation between PAI-1 and Ptgs2.

[0045] Figure 35 Conserved responses to IL-17A and IBD downstream predictions are shown.

[0046] Figure 36A IPA comparative pathway analysis showing the top ten overlapping pathways in UC / CD and IL-17A treatment in vitro. Figure 36B Demonstrates acute phase response pathways.

[0047] Figure 37 All datasets for the combination of the 2-biomarker signature prior to infliximab are shown.

[0048] Figure 38 The sensitivity vs specificity of the five genes that overlap are shown.

[0049] Figure 39A 、 Figure 39B 、 Figure 39C 、 Figure 39D 、 Figure 39E and Figure 39F The density of the first three PCs (PC1, PC2, PC3) from the principal component (PC) analysis, plotted diagonally, and the paired scatter plots between them are shown. Black, red, and green points indicate individual patient samples from cohorts 1, 2, and 3, respectively. Based on the first three PCs, cohort 3 samples are well mixed with cohorts 1 and 2 samples.

[0050] Figure 40 Shown is a multidimensional scaling (MDS, a dimensionality reduction technique similar to PCA) plot for proximity visualization of the original high-dimensional samples on a 2-dimensional plane (MDS dimension 1 vs. MDS dimension 2), with non-responders in black circles and responders in green triangles.

[0051] Figure 41A 、 Figure 41B 、 Figure 41C 、 Figure 41D 、 Figure 41E 、 Figure 41F 、 Figure 41G 、 Figure 41H 、 Figure 41I 、 Figure 41J 、 Figure 41K 、 Figure 41L 、 Figure 41M 、 Figure 41N 、 Figure 41O 、 Figure 41P 、 Figure 41Q 、 Figure 41R 、 Figure 41S 、 Figure 41T 、 Figure 41U 、 Figure 41V 、 Figure 41W 、 Figure 41X 、 Figure 41Y 、 Figure 41Z and Figure 41ZA Shown is the ROC plot for a subset of the top 100 genes using the optimal cutoff point. Figure 41A Displays PRNP. Figure 41B ILR13RA2 is shown. Figure 41C KLHL5 is shown. Figure 41D Display PTX3. Figure 41E Displays GPX8. Figure 41F Show IKBIP. Figure 41G Display TXNDC15. Figure 41H LY96 is shown. Figure 41I RNF144B is shown. Figure 41J PDE4B is shown. Figure 41K Display C1S. Figure 41L Display ST8SIA4. Figure 41M Display EDNRB. Figure 41N Displays ENTPD1. Figure 41O WNT5A is shown. Figure 41P Displays SAMSN1. Figure 41Q Display MTMR11. Figure 41R TLR1 is shown. Figure 41S Displays MME. Figure 41T Display CACFD1. Figure 41U CD69 is shown. Figure 41V Display SNAPC1. Figure 41W PRICKLE2 is displayed. Figure 41X SLAMF7 is displayed. Figure 41Y Displays TSPAN2. Figure 41Z CXCL6 is shown. Figure 41ZA TNFRSF11B is shown.

[0052] Figure 42A 、 Figure 42B 、 Figure 42C 、 Figure 42D 、 Figure 42E 、 Figure 42F 、 Figure 42G 、 Figure 42H 、 Figure 42I 、 Figure 42J 、 Figure 42K 、 Figure 42L 、 Figure 42M 、 Figure 42N 、 Figure 42O 、 Figure 42P 、 Figure 42Q 、 Figure 42R 、 Figure 42S 、 Figure 42T 、 Figure 42U 、 Figure 42V 、 Figure 42W 、 Figure 42X 、 Figure 42Y and Figure 42Z Shown is the ROC plot for a subset of the top 100 genes using the optimal cutoff point. Figure 42A Displays ACSL4. Figure 42B CSGALNACT2 is displayed. Figure 42C Displays DRAM1. Figure 42D Display LILRB2. Figure 42E Display PAPPA. Figure 42F AKR1B1 is shown. Figure 42G GPR183 is shown. Figure 42H Display SGTB. Figure 42I GLIPR1 is shown. Figure 42J Displays PDPN. Figure 42K Display RBMS1. Figure 42L Display SMARCA1. Figure 42M ANGPT2 is displayed. Figure 42N Display PLAU. Figure 42O TMEM55A is shown. Figure 42P IGFBP5 is shown. Figure 42Q ASAP1 is displayed. Figure 42R Display SGCE. Figure 42S HGF is shown. Figure 42T Display CEBPB. Figure 42U DCBLD1 is displayed. Figure 42V Display MCTP1. Figure 42W STAT4 is shown. Figure 42X ROBO1 is displayed. Figure 42Y ARL13B is shown. Figure 42Z Displays AAED1.

[0053] Figure 43A 、 Figure 43B 、 Figure 43C 、 Figure 43D 、 Figure 43E 、 Figure 43F 、 Figure 43G 、 Figure 43H 、 Figure 43I 、 Figure 43J 、 Figure 43K 、 Figure 43L 、 Figure 43M 、 Figure 43N 、 Figure 43O 、 Figure 43P 、 Figure 43Q 、 Figure 43R 、 Figure 43S 、 Figure 43T 、 Figure 43U 、 Figure 43V 、 Figure 43W 、 Figure 43X 、 Figure 43Y 、 Figure 42Z and Figure 43ZA Shown is the ROC plot for a subset of the top 100 genes using the optimal cutoff point. Figure 43A Display RGS5. Figure 43B TOR1AIP1 is shown. Figure 43C Display CCL18. Figure 43D FERMT2 is displayed. Figure 43E Display BPGM. Figure 43F Display NR3C1. Figure 43G Display QKI. Figure 43H Display STX11. Figure 43I Display DEGS1. Figure 43J Show THBD. Figure 43K Displays CCL2. Figure 43L Display HS3ST3B1. Figure 43M Display SDC2. Figure 43N Display SLC16A10. Figure 43O Display VCAN. Figure 43P Display PXDN. Figure 43Q Displays SRGN. Figure 43R Display DSE. Figure 43S Display CAV1. Figure 43T FGFR3 is shown. Figure 43U Display ANGPTL2. Figure 43V Display CLEC2B. Figure 43W Display IL7R. Figure 43X Displays CCR1. Figure 43Y Displays LAMC1. Figure 43Z Display LOX. Figure 43ZA Display CFL2.

[0054] Figure 44A 、 Figure 44B 、 Figure 44C 、 Figure 44D 、 Figure 44E 、 Figure 44F 、 Figure 44G 、 Figure 44H 、 Figure 44I 、 Figure 44J 、 Figure 44K 、 Figure 44L 、 Figure 44M 、 Figure 44N 、 Figure 44O 、 Figure 44P 、 Figure 44Q 、 Figure 44R 、 Figure 44S and Figure 44TShown is the ROC plot for a subset of the top 100 genes using the optimal cutoff point. Figure 44A Display RDX. Figure 44B Display SERPINE1. Figure 44C Show CLIC2. Figure 44D Display CLMP. Figure 44E SNX10 is displayed. Figure 44F The TNC is displayed. Figure 44G FAM49A is shown. Figure 44H Display S100A9. Figure 44I Displays STC1. Figure 44J ZNF57 is shown. Figure 44K Display PPT1. Figure 44L Show CYTIP. Figure 44M Display CTSL. Figure 44N Display GNB4. Figure 44O LDLRAD3 is displayed. Figure 44P Display RGS18. Figure 44Q Show THEMIS2. Figure 44R BICC1 is displayed. Figure 44S Displays HSPA13. Figure 44T IL10RA is shown.

[0055] Figure 45A 、 Figure 45B 、 Figure 45C 、 Figure 45D 、 Figure 45E 、 Figure 45F 、 Figure 45G 、 Figure 45H 、 Figure 45I 、 Figure 45J 、 Figure 45K 、 Figure 45L 、 Figure 45M 、 Figure 45N 、 Figure 45O 、 Figure 45P 、 Figure 45Q 、 Figure 45R 、 Figure 45S 、 Figure 45T 、 Figure 45U 、 Figure 45V 、 Figure 45W 、 Figure 45X 、 Figure 45Y and Figure 45Z The corresponding sensitivity and specificity at the cutoff points are shown for a subset of the top 100 genes. Figure 45A Displays PRNP. Figure 45B ILR13RA2 is shown. Figure 45C KLHL5 is shown. Figure 45D Display PTX3. Figure 45E Displays GPX8. Figure 45F Show IKBIP. Figure 45G Display TXNDC15. Figure 45H LY96 is shown. Figure 45I RNF144B is shown. Figure 45J PDE4B is shown. Figure 45K Display C1S. Figure 45L Display ST8SIA4. Figure 45M Display EDNRB. Figure 45N Displays ENTPD1. Figure 45O WNT5A is shown. Figure 45P Displays SAMSN1. Figure 45Q Display MTMR11. Figure 45R TLR1 is shown. Figure 45S Displays MME. Figure 45T Display CACFD1. Figure 45U CD69 is shown. Figure 45V Display SNAPC1. Figure 45W PRICKLE2 is displayed. Figure 45X SLAMF7 is displayed. Figure 45Y Displays TSPAN2. Figure 45Z CXCL6 is shown.

[0056] Figure 46A 、 Figure 46B 、 Figure 46C 、 Figure 46D 、 Figure 46E 、 Figure 46F 、 Figure 46G 、 Figure 46H 、 Figure 46I 、 Figure 46J 、 Figure 46K 、 Figure 46L 、 Figure 46M 、 Figure 46N 、 Figure 46O 、 Figure 46P 、 Figure 46Q 、 Figure 46R 、 Figure 46S 、 Figure 46T 、 Figure 46U 、 Figure 46V 、 Figure 46W 、 Figure 46X 、 Figure 46Y and Figure 46Z The corresponding sensitivity and specificity at the cutoff points are shown for a subset of the top 100 genes. Figure 46A TNFRSF11B is shown. Figure 46B Displays ACSL4. Figure 46C CSGALNACT2 is displayed. Figure 46D Displays DRAM1. Figure 46E Display LILRB2. Figure 46F Display PAPPA. Figure 46GAKR1B1 is shown. Figure 46H GPR183 is shown. Figure 46I Display SGTB. Figure 46J GLIPR1 is shown. Figure 46K Displays PDPN. Figure 46L Display RBMS1. Figure 46M Display SMARCA1. Figure 46N ANGPT2 is displayed. Figure 46O Display PLAU. Figure 46P TMEM55A is shown. Figure 46Q IGFBP5 is shown. Figure 46R ASAP1 is displayed. Figure 46S Display SGCE. Figure 46T HGF is shown. Figure 46U Display CEBPB. Figure 46V DCBLD1 is displayed. Figure 46W Display MCTP1. Figure 46X STAT4 is shown. Figure 46Y ROBO1 is displayed. Figure 46Z ARL13B is shown.

[0057] Figure 47A 、 Figure 47B 、 Figure 47C 、 Figure 47D 、 Figure 47E 、 Figure 47F 、 Figure 47G 、 Figure 47H 、 Figure 47I 、 Figure 47J 、 Figure 47K 、 Figure 47L 、 Figure 47M 、 Figure 47N 、 Figure 47O 、 Figure 47P 、 Figure 47Q 、 Figure 47R 、 Figure 47S 、 Figure 47T 、 Figure 47U 、 Figure 47V 、 Figure 47W 、 Figure 47X 、 Figure 47Y and Figure 47Z The corresponding sensitivity and specificity at the cutoff points are shown for a subset of the top 100 genes. Figure 47A Displays AAED1. Figure 47B Display RGS5. Figure 47C TOR1AIP1 is shown. Figure 47D Display CCL18. Figure 47E FERMT2 is displayed. Figure 47F Display BPGM. Figure 47G Display NR3C1. Figure 47H Display QKI. Figure 47I Display STX11. Figure 47J Display DEGS1. Figure 47K Show THBD. Figure 47L Displays CCL2. Figure 47M Display HS3ST3B1. Figure 47N Display SDC2. Figure 47O Display SLC16A10. Figure 47P Display VCAN. Figure 47Q Display PXDN. Figure 47R Displays SRGN. Figure 47S Display DSE. Figure 47T Display CAV1. Figure 47U FGFR3 is shown. Figure 47V Display ANGPTL2. Figure 47W Display CLEC2B. Figure 47X Display IL7R. Figure 47Y Displays CCR1. Figure 47Z Displays LAMC1.

[0058] Figure 48A 、 Figure 48B 、 Figure 48C 、 Figure 48D 、 Figure 48E 、 Figure 48F 、 Figure 48G 、 Figure 48H 、 Figure 48I 、 Figure 48J 、 Figure 48K 、 Figure 48L 、 Figure 48M 、 Figure 48N 、 Figure 48O 、 Figure 48P 、 Figure 48Q 、 Figure 48R 、 Figure 48S 、 Figure 48T 、 Figure 48U and Figure 48V The corresponding sensitivity and specificity at the cutoff points are shown for a subset of the top 100 genes. Figure 48A Display LOX. Figure 48B Display CFL2. Figure 48C Display RDX. Figure 48D Display SERPINE1. Figure 48E Show CLIC2. Figure 48F Display CLMP. Figure 48G SNX10 is displayed. Figure 48H The TNC is displayed. Figure 48I FAM49A is shown. Figure 48J Display S100A9. Figure 48K Displays STC1. Figure 48L ZNF57 is shown. Figure 48MDisplay PPT1. Figure 48N Show CYTIP. Figure 48O Display CTSL. Figure 48P Display GNB4. Figure 48Q LDLRAD3 is displayed. Figure 48R Display RGS18. Figure 48S Show THEMIS2. Figure 48T BICC1 is displayed. Figure 48U Displays HSPA13. Figure 48V IL10RA is shown.

[0059] Figure 49 Displays a CV plot.

[0060] Figure 50 Shown are plots of sensitivity and specificity using 9 genes selected from the top 100 genes.

[0061] Figure 51 Displays a CV plot.

[0062] Figure 52 The ROC curve shows that the linear predictor constructed using only 5 genes resulted in an AUC of 1 and increased the sensitivity to 0.96.

[0063] Figure 53 The prediction tree for IL13RA2 is shown.

[0064] Details

[0065] The present disclosure is based, at least in part, on the discovery that the plasminogen activation pathway plays a key role in driving colitis.

[0066] Because more and more expensive biologics are approved for moderate to severe IBD, and it is currently unknown who should be treated with which one, the methods described herein are extremely valuable. Since anti-TNF therapy is still the first line, we disclose here a biomarker signature for identifying subjects who do not respond to anti-TNF, which may be extremely attractive for the medical field and personalized medicine where many alternative drugs now exist.

[0067] There is an urgent need for plasma or tissue biomarkers of active disease in IBD to help physicians assess prognosis. Biomarkers are also needed to predict response to expensive biologic therapies and to select subsets of subjects for clinical trials to improve outcomes.

[0068] IL-17A is one of the most important and best-studied cytokines in intestinal inflammation (IBD or infection). However, IL-17A may not be the causative agent it was hoped to be. Based on mouse colitis models and human clinical trials with anti-IL-17A and anti-IL-17RA (which resulted in more severe disease), IL-17A appears to have both positive and negative effects, suggesting that IL-17A is a poor drug target.

[0069] Despite the importance of IL-17 in autoimmunity and inflammatory bowel disease (IBD), its function in the mucosa during disease remains unclear. In IBD, IL-17 is produced in part by mucosal pro-inflammatory Th17 cells. However, multiple clinical trials using monoclonal therapies that block IL-17 have shown that this cytokine actually plays a protective role in the disease. To investigate this possibility, we treated primary cultured intestinal epithelial cells with IL-17 and performed transcriptomic analysis. Comparison of this IL-17-induced epithelial signature with transcriptomic analysis of biopsy samples from patients with active versus inactive ulcerative colitis (UC) revealed possible dysregulation of the coagulation pathway during active disease. We found that IL-17 induces epithelial cells to produce tissue plasminogen activator (tPA), and that most UC patients had a significant upregulation of a direct tPA inhibitor called plasminogen activator inhibitor 1 (PAI-1). Building on these findings, we used both genetic and chemical inhibitor models to show that tPA is protective against lesions caused by dextran sulfate sodium and Citrobacter rodentium infections, while PAI-1 exacerbates the lesion response. We found that tPA suppresses inflammation in these models, and this is due to activation of the immunosuppressive molecule TGF-β. tPA cleaves the ubiquitous blood factor plasminogen, which in turn activates latent TGF-β to its mature form. This process is inhibited by PAI-1. Finally, we demonstrated that colonic PAI-1 levels in UC patients predict both disease activity and response to biologic therapy. This study identifies a novel pathway in UC whereby dysregulated PAI-1 leads to exacerbated inflammation and disease activity by blocking the IL-17-tPA-TGF-β axis.

[0070] Various aspects of these methods are described in more detail below.

[0071] I. Methods

[0072] In one aspect, the present disclosure provides a method for classifying a subject with inflammatory bowel disease. The method generally comprises detecting nucleic acid of one or more biomarkers selected from the group consisting of: PAI-1 / SERPINE, TNC, IL13RA2, CCL2, PRNP, GPX8, DRAM1, STAT4, IL24, IL6, PI15, PTGS2, SELE, SMR3A, SLC23A2, HDGFRP3, HIF1A, IKBIP, KLHL5, PTX3, TXNDC15, PDE4B, C1S, TLR1, MME, TSPAN2, TNF RSF11B, ACSL4, CSGALNACT2, SGTB, PDPN, RBMS1, ANGPT2, TMEM55A, HGF, RGS5, ROBO1, TOR1AIP1, CCL18, HS3ST3B1, SDC2, PXDN, DSE, SNX10, TNC, CLIC2, PPT1, RGS18 or THEMIS2, and classifying the subject as a responder or non-responder to treatment by determining the log2 expression of the biomarker relative to a reference value. With reference to the above biomarkers, sequence names can be identified in public databases such as NCBI or UniProt, and by using gene names, the markers are not limited to a specific species, but when the biomarkers are used in the methods described herein, the source of the biomarker should match the species of the subject. In some embodiments, the detection biomarker is selected from one or more of the group consisting of: PAI-1 / SERPINE, TNC, IL13RA2, CCL2, PRNP, GPX8, DRAM1, STAT4, IL24, IL6, PI15, PTGS2, SELE, SMR3A, SLC23A2, HDGFRP3, HIF1A, IKBIP, or KLHL5. In some embodiments, the detection biomarker is selected from one or more of the group consisting of PRNP, IL13RA2, GPX8, IKBIP, KLHL5, PTX3, TXNDC15, PDE4B, C1S, TLR1, MME, TSPAN2, TNFRSF11B, ACSL4, CSGALNACT2, DRAM1, SGTB, PDPN, RBMS1, ANGPT2, TMEM55A, HGF, STAT4, RGS5, ROBO1, TOR1AIP1, CCL18, HS3ST3B1, SDC2, PXDN, DSE, SNX10, TNC, CLIC2, PPT1, RGS18, or THEMIS2.

[0073] The Log2 expression values ​​of the genes studied herein may be from about 0 to about 20. For example, the log2 expression values ​​may be 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9; 1; 1.1; 1.2; 1.3; 1.4; 1.5; 1.6; 1.7; 1.8; 1.9; 2; 2.1; 2.2; 2.3; 2.4; 2.5; 2.6; 2.7; 2.8; 2.9; 3; 3.1; 3.2; 3.3; 3.4; 3.5; 3.6; 3.7; 3.8; 3.9; 4; 4.1; 4.2; 4.3; 4.4; 4.5; 4.6; 4.7; 4.8; 4.9; 5; 5.1; 5.2; 5.3; 5.4 ;5.5;5.6;5.7;5.8;5.9;6;6.1;6.2;6.3;6.4;6.5;6.6;6.7;6.8;6.9;7;7.1;7.2;7.3;7.4;7.5;7.6;7.7;7.8;7.9;8;8.1;8.2;8.3;8.4;8.5;8.6;8.7;8.8;8.9;9;9.1;9.2;9.3;9.4;9.5;9.6;9.7;9.8;9.9;10;10.1;10.2;10.3;10.4;10.5;10.6;10.7;10.8;10. 9;11;11.1;11.2;11.3;11.4;11.5;11.6;11.7;11.8;11.9;12;12.1;12.2;12.3;12.4;12.5;12.6;12.7;12.8;12.9;13;13.1;13.2;13.3;13.4;13.5;13.6;13.7;13.8;13.9;14;14.1;14.2;14.3;14.4;14.5;14.6;14.7;14.8;14.9;15;15.1;15.2;15.3;15.4;15 .5;15.6;15.7;15.8;15.9;16;16.1;16.2;16.3;16.4;16.5;16.6;16.7;16.8;16.9;17;17.1;17.2;17.3;17.4;17.5;17.6;17.7;17.8;17.9;18;18.1;18.2;18.3;18.4;18.5;18.6;18.7;18.8;18.9;19;19.1;19.2;19.3;19.4;19.5;19.6;19.7;19.8;19.9;or 20.

[0074] Plasminogen activator inhibitor-1 (PAI-1) (UniProt accession number P05121) (also known as endothelial plasminogen activator inhibitor or serpin E1) is a protein encoded by the SERPINE1 gene in humans. Elevated PAI-1 is a risk factor for thrombosis and atherosclerosis. PAI-1 is a serine protease inhibitor (serpin) that functions as an activator of tissue plasminogen activator (tPA) and urokinase (uPA), plasminogen and therefore a major inhibitor of fibrinolysis (physiological decomposition of blood clots). It is a serine protease inhibitor (serpin) protein (SERPINE1). The PAI-1 gene is SERPINE1 located on chromosome 7 (7q21.3-q22).

[0075] In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of PAI-PAI-1 / SERPINE is less than about 6.5. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of TNC is less than about 6.3. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of IL13RA2 is less than about 5.5. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of CCL2 is less than about 7.5. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of PRNP is less than about 7.75. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of GPX8 is less than about 5.5. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of DRAM1 is less than about 7.5. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of STAT4 is less than about 6.45. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of IKBIP is less than about 4.65. In some embodiments, a subject is classified as a responder to anti-TNFα therapy if the log2 expression value of KLHL5 is less than about 5.25.

[0076] In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of PAI-1 / SERPINE is greater than about 6.5. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of TNC is greater than about 6.3. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of IL13RA2 is greater than about 5.5. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of CCL2 is greater than about 7.5. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of PRNP is greater than about 7.75. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of GPX8 is greater than about 5.5. In some embodiments, a subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of DRAM1 is greater than about 7.5. In some embodiments, the subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of STAT4 is greater than about 6.45. In some embodiments, the subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of IKBIP is greater than about 4.65. In some embodiments, the subject is classified as a non-responder to anti-TNFα therapy if the log2 expression value of KLHL5 is greater than about 5.25.

[0077] In another aspect, the present disclosure provides methods for treating a subject with inflammatory bowel disease. The methods generally comprise (i) detecting the amount of one or more of PAI-1 / SERPINE, TNC, IL13RA2, CCL2, PRNP, GPX8, DRAM1, STAT4, IKBIP, or KLHL5 in a biological sample obtained from the subject, (ii) determining a fold2 expression value relative to a reference value, (iii) classifying the subject as a responder or a non-responder to anti-TNFα therapy, and (iv) if the subject is classified as a responder, treating the subject with an anti-TNFα therapy, or if the subject is classified as a non-responder, treating the subject with a PAI-1 inhibitor.

[0078] In yet another aspect, the present disclosure provides a method for treating a subject in need thereof. The method generally includes (i) detecting the amount of PAI-1 / SERPINE in a biological sample obtained from the subject, (ii) when PAI-1 is raised relative to a reference value or if the PAI-1 log2 expression value is greater than about 4.5, the subject is diagnosed to have IBD, and (iii) if the PAI-1 level has a log2 expression value of 7.5 or less, an effective amount of anti-TNF or anti-α4β7 antibodies is applied to the subject, or if the PAI-1 level has a log2 expression value of about 9.5 or greater, an effective amount of a PAI-1 inhibitor is administered. In some embodiments, the anti-TNF antibody is infliximab. In some embodiments, the anti-α4β7 antibody is vedolizumab. In some embodiments, the PAI-1 inhibitor is CDE-268. In some embodiments, the subject has or is suspected of having IBD.

[0079] In yet another aspect, the present disclosure provides a method for treating a subject in need thereof. The method generally includes (i) detecting the amount of PAI-1 / SERPINE in a biological sample obtained from the subject, (ii) if the number of PAI-1 positive cells per high-power field is about 25 or greater, the subject is diagnosed to have active ulcerative colitis, and (iii) if the PAI-1 level has a log2 expression value of 7.5 or less, an effective amount of an anti-TNF or anti-α4β7 antibody is administered to the subject, or if the PAI-1 level has a log2 expression value of about 9.5 or greater, an effective amount of a PAI-1 inhibitor is administered. In some embodiments, the anti-TNF antibody is infliximab. In some embodiments, the anti-α4β7 antibody is vedolizumab. In some embodiments, the PAI-1 inhibitor is CDE-268. In some embodiments, the subject has or is suspected of having IBD. As used herein, the term "high-power field" (HPF) is used relative to a microscope and refers to the area visible under the maximum magnification of the objective lens used. In some embodiments, this represents a 400-fold magnification.

[0080] In another aspect, the present disclosure provides a method for diagnosing or treating a subject in need thereof. The method generally comprises (i) obtaining a biological sample from a subject; (ii) detecting the levels of PAI-1 and CCL2 in the sample; (iii) diagnosing the subject with IBD when PAI-1 is upregulated or the presence of PAI-1 detected in the sample is greater than the level of PAI-1 in the control; diagnosing the subject with active ulcerative colitis if the number of PAI-1 positive cells per high power field is about 25 or greater; or diagnosing the subject with active ulcerative colitis if PAI-1 is upregulated or the presence of PAI-1 is greater than the level of PAI-1 in the control. log2 value is greater than 4.5, diagnosing the subject with IBD; (iv) if the PAI-1 level has a log2 fold expression value of about 7.4 or less, administering an effective amount of an anti-TNF or anti-α4β7 antibody (e.g., anti-TNFα (infliximab) and anti-α4β7 (vedolizumab)) to the diagnosed subject; (v) if the PAI-1 level has a log2 fold expression value of about 9.2 or greater, administering an effective amount of a PAI-1 inhibitor (e.g., CDE-268); (vi) if the CCL2 level has a log2 fold expression value of about 9.2 or less, administering an effective amount of an anti-TNF or anti-α4β7 antibody (e.g., anti-TNFα (infliximab) and anti-α4β7 (vedolizumab)) to the diagnosed subject; or (v) if the CCL2 level has a log2 fold expression value of about 9.2 or greater, administering an effective amount of a PAI-1 inhibitor (e.g., CDE-268). In some embodiments, the subject has or is suspected of having IBD.

[0081] In yet another aspect, the present disclosure provides a method for diagnosing or treating inflammatory bowel disease. The method generally comprises (i) obtaining a biological sample from a subject; (ii) detecting the level of PAI-1 in the sample; (iii) diagnosing the subject with IBD when PAI-1 is upregulated or the presence of PAI-1 detected in the sample is greater than the PAI-1 level in the control; diagnosing the subject with active ulcerative colitis if the number of PAI-1-positive cells per high-power field is about 25 or greater; or diagnosing the subject with IBD if the PAI log2 value is greater than 4.5. In some embodiments, the method comprises (iv) administering to the diagnosed subject an effective amount of an anti-TNF or anti-α4β7 antibody (e.g., anti-TNFα (infliximab) and anti-α4β7 (vedolizumab)) if the PAI-1 level has a log2-fold expression value of about 7.5 or less; or (v) administering an effective amount of a PAI-1 inhibitor (e.g., CDE-268) if the PAI-1 level has a log2-fold expression value of about 9.5 or greater.

[0082] In yet another aspect, the present disclosure provides a method for screening for a PAI-1 inhibitor capable of treating inflammatory bowel disease. The method generally comprises (i) obtaining a biological sample from a subject; (ii) contacting the biological sample with a test compound; (iii) contacting a second biological sample with a lead compound; (ii) detecting the level of PAI-1 in the first or second biological sample; (iii) detecting the interaction of chemicals or chemical moieties; or (iv) comparing the interaction of the test compound with the lead compound. In some embodiments, if the test compound reduces the level of PAI-1 or increases the level of tPA, the test compound is identified as a PAI-1 inhibitor capable of treating inflammatory bowel disease. In one aspect, the present disclosure provides a method for identifying an inhibitor of the PAI-1 pathway. In one embodiment, the inhibitor of the PAI-1 pathway is a PAI-1 antagonist. The activity of the test agent (as described in the present disclosure) can be assessed based on its effect on any step of the PAI-1 pathway. It can be compared to the effect in the absence of the test compound, or it can be compared to the effect of PAI-1 or a known antagonist thereof.

[0083] The assay for evaluating an agent for inhibiting PAI-1 can be performed by using purified or recombinant PAI-1 in vitro. PAI-1-expressing cells, such as intestinal epithelial or non-epithelial cells, can also be used to perform assays in vitro. Furthermore, animal models can be used to perform screening tests in vivo. The cells of the culture can be primary cells or can be secondary cells or cell lines. The cells can be enriched from a source such as the intestine. For example, a tissue biopsy sample can be obtained from an individual, and well-known techniques or commercially available test kits can be used to separate the cells of the desired type. In one embodiment, the cells can be modified cells. For example, cells can be engineered to express or overexpress PAI-1. Cells in culture can be maintained using conventional cell culture reagents and procedures. In one embodiment, the assay can be performed in animals (including mice).

[0084] The compound used to test can be a part of a library, or can be newly synthesized. In addition, the compound can be purified, partially purified or can exist as a cell extract, a crude mixture, etc. (i.e., not purified). Although it is desirable to test each compound separately, it is also possible to test the combination of compounds.

[0085] As used herein, the term "biological sample" refers to a sample obtained from a subject. Any biological sample containing IBD biomarkers is suitable. Many types of biological samples are known in the art. Suitable biological samples may include, but are not limited to, tissue samples or body fluids. In some embodiments, the biological sample is a tissue sample, such as a tissue biopsy sample. The biopsy tissue can be fixed, embedded in paraffin or plastic, and sliced, or the biopsy tissue can be frozen and cryosectioned. Alternatively, the biopsy tissue can be processed into single cells or explants, or processed into homogenates, cell extracts, membrane fractions, or IBD biomarker extracts. In other embodiments, the sample can be a body fluid. Non-limiting examples of suitable body fluids include blood, plasma, serum, urine, and saliva. In a specific embodiment, the biological sample is blood, plasma, or serum. In a specific embodiment, the biological sample is plasma. The fluid can be used "as is", cellular components can be separated from the fluid, or standard techniques can be used to separate IBD biomarker fractions from the fluid.

[0086] As will be appreciated by those skilled in the art, the method of collecting a biological sample can and will vary depending on the nature of the biological sample and the type of analysis to be performed. Any of a variety of methods generally known in the art can be used to collect a biological sample. In general, the method preferably maintains the integrity of the sample so that IBD biomarkers can be accurately detected and their amounts measured according to the present disclosure.

[0087] In some embodiments, a single sample is obtained from a subject to detect IBD biomarkers in a sample. Alternatively, IBD biomarkers can be detected in a sample obtained from a subject over time. Therefore, more than one sample can be collected from a subject over time. For example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more samples can be collected from a subject over time. In some embodiments, 2, 3, 4, 5 or 6 samples are collected from a subject over time. In other embodiments, 6, 7, 8, 9 or 10 samples are collected from a subject over time. In other embodiments, 10, 11, 12, 13 or 14 samples are collected from a subject over time. In other embodiments, 14, 15, 16 or more samples are collected from a subject over time.

[0088] When collecting more than one sample from a subject over time, samples can be collected every 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more hours. In some embodiments, samples are collected every 0.5, 1, 2, 3 or 4 hours. In other embodiments, samples are collected every 4, 5, 6 or 7 hours. In yet other embodiments, samples are collected every 7, 8, 9 or 10 hours. In other embodiments, samples are collected every 10, 11, 12 or more hours. In addition, samples can be collected every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days. In some embodiments, samples are collected about every 6 days. In some embodiments, samples are collected every 1, 2, 3, 4 or 5 days. In other embodiments, samples are collected every 5, 6, 7, 8 or 9 days. In yet other embodiments, samples are collected every 9, 10, 11, 12 or more days.

[0089] In some embodiments, once a sample is obtained, it is subjected to in vitro treatment to detect and measure the amount of IBD biomarkers. All suitable methods known to those skilled in the art for detecting and measuring the amount of IBD biomarkers are considered within the scope of the present invention. In some embodiments, IBD biomarkers can be detected at the nucleic acid level. In some embodiments, IBD biomarkers can be detected at the protein level. For example, epitope binding agent determination (i.e., antibody determination), enzymatic determination, electrophoresis, chromatography and / or mass spectrometry can be used. Non-limiting examples of epitope binding agent determination include ELISA, lateral flow determination, sandwich immunoassay, radioimmunoassay, immunoblot or Western blot, flow cytometry, immunohistochemistry and array. In one embodiment, PCR or qPCR is used to detect IBD biomarkers. IBD biomarkers can be detected by direct injection into a mass spectrometer. In another embodiment, chromatography is used to detect IBD biomarkers. Specifically, the technology of connecting a chromatographic step with a mass spectrometry step can be used. The chromatographic step can be liquid chromatography, gas chromatography or thin layer chromatography (TLC). In general, the presence of IBD biomarkers can be determined using liquid chromatography, followed by mass spectrometry. In some embodiments, the liquid chromatography is high performance liquid chromatography (HPLC). Non-limiting examples of HPLC include partition chromatography, normal phase chromatography, displacement chromatography, reverse phase chromatography, size exclusion chromatography, ion exchange chromatography, bioaffinity chromatography, aqueous normal phase chromatography, or ultrafast liquid chromatography. Non-limiting examples of mass spectrometry include constant neutral loss mass spectrometry, tandem mass spectrometry (MS / MS), matrix-assisted laser desorption / ionization (MALDI), electrospray ionization mass spectrometry (ESI-MS).

[0090] Any suitable reference value as known in the art can be used.For example, a suitable reference value can be the amount of the IBD biomarker in the biological sample obtained from a subject or subject group of the same species without detectable IBD.In another example, a suitable reference value can be the amount of the IBD biomarker in the biological sample obtained from a subject or subject group of the same species with detectable IBD (as measured via a standard method).In another example, a suitable reference value can be the measured value of the amount of the IBD biomarker in the reference sample obtained from the same subject.The reference sample comprises the biological fluid of the same type as the test sample, and may or may not be obtained from the subject when IBD is not suspected.Technicians will understand that when the subject is otherwise healthy, it is not always possible or desirable to obtain a reference sample from the subject.For example, in an acute environment, a reference sample can be the first sample obtained from the subject when presenting.In another example, when monitoring the effectiveness of therapy, a reference sample can be a sample obtained from the subject before therapy starts.In such an example, the subject may have suspected IBD, but may not have other symptoms of IBD, or the subject may have suspected IBD and one or more other symptoms of IBD. In a specific embodiment, a suitable reference value may be a threshold value provided in the Examples.

[0091] In another aspect, the present disclosure provides a method for treating IBD in a subject in need thereof. The method generally includes (i) administering a therapeutically effective amount of tissue plasminogen activator (tPA). Tissue plasminogen activator (UniProt accession number P00750) (abbreviated as tPA or PLAT) is a protein involved in the decomposition of blood clots. It is a serine protease (EC 3.4.21.68) found on endothelial cells (cells lining blood vessels). As an enzyme, it catalyzes the conversion of plasminogen to plasmin (the main enzyme responsible for clot decomposition). Human tPA has a molecular weight of ~70 kDa and is in single-chain form.

[0092] tPA can be manufactured using recombinant biotechnology; tPA produced in this manner is called recombinant tissue plasminogen activator (rtPA). Specific rtPAs include alteplase, reteplase, and tenecteplase. They are used in clinical medicine to treat embolic or thrombotic strokes. This protein is contraindicated in hemorrhagic stroke and head trauma. In cases of toxicity, the antidote for tPA is aminocaproic acid. In a medical treatment called thrombolysis, tPA is used in some cases of diseases characterized by blood clots (such as pulmonary embolism, myocardial infarction, and stroke). The most common use is for ischemic stroke.

[0093] Method described herein is usually carried out to the subject in need.The subject of the methods described herein can be a subject with IBD, diagnosed with IBD, suspected of having IBD or being at risk of IBD. The determination of treatment needs will usually be evaluated by a medical history and physical examination consistent with the disease or the patient's condition in question. The diagnosis of various patient's conditions treatable by the methods described herein is within the art. The subject can be an animal subject, including mammals, such as horses, cattle, dogs, cats, sheep, pigs, mice, rats, monkeys, hamsters, guinea pigs and chickens and people. For example, the subject can be a human subject.

[0094] Generally, a safe and effective amount of a therapeutic agent is one that will, for example, elicit the desired therapeutic effect in a subject while minimizing undesirable side effects. In various embodiments, an effective amount of a therapeutic agent described herein can substantially inhibit or alleviate IBD and / or related symptoms.

[0095] According to the methods described herein, administration can be parenteral, pulmonary, oral, topical, intradermal, intramuscular, intraperitoneal, intravenous, subcutaneous, intranasal, epidural, ophthalmic, buccal, or rectal.

[0096] When used in the treatments described herein, a therapeutically effective amount of the therapeutic agent can be used in pure form or (where such a form exists) in the form of a pharmaceutically acceptable salt, and with or without a pharmaceutically acceptable excipient. For example, the compounds of the present disclosure can be administered in an amount sufficient to inhibit or alleviate IBD or related symptoms, at a reasonable benefit / risk ratio applicable to any drug treatment.

[0097] The amount of the compositions described herein that can be combined with a pharmaceutically acceptable carrier to produce a single dosage form will vary depending on the host being treated and the particular mode of administration. Those skilled in the art will understand that the unit content of the agent contained in a single dose of each dosage form does not necessarily constitute a therapeutically effective amount in itself, as the necessary therapeutically effective amount can be achieved by administering multiple separate doses.

[0098] Toxicity and therapeutic efficacy of the compositions described herein can be determined by standard pharmaceutical procedures in cell cultures or experimental animals to determine the LD50 (the dose lethal to 50% of the population) and the ED50 (the dose therapeutically effective in 50% of the population). The dose ratio between toxic and therapeutic effects is the therapeutic index which can be expressed as the ratio LD50 / ED50, with larger therapeutic indices generally considered optimal in the art.

[0099] The specific therapeutically effective dosage level for any particular subject will depend on a variety of factors, including the condition being treated and the severity of the condition; the activity of the specific compound employed; the specific composition employed; the age, weight, general health, sex, and diet of the subject; the time of administration; the route of administration; the rate of excretion of the compound employed; the duration of the treatment; the drugs used in combination with or concurrently with the specific compound employed; and like factors well known in the medical arts (see, e.g., Koda-Kimble et al. (2004) Applied Therapeutics: The Clinical Use of Drugs, Lippincott Williams & Wilkins, ISBN 0781748453; Winter (2003) Basic Clinical Pharmacokinetics, 4th ed., Lippincott Williams & Wilkins, ISBN 0781741475; Sharqel (2004) Applied Biopharmaceutics & Pharmacokinetics, McGraw-Hill / Appleton & Lange, ISBN 0071375503). For example, it is well known to those skilled in the art to start the dosage of the composition at a level lower than that required to achieve the desired therapeutic effect and gradually increase the dosage until the desired effect is achieved. If desired, the effective daily dose can be divided into multiple doses for the purpose of administration. Therefore, a single dose composition may contain such an amount or a submultiple thereof to constitute a daily dose. However, it should be understood that the total daily dosage of the compounds and compositions of the present disclosure will be determined by the attending physician within the scope of sound medical judgment.

[0100] Again, each state, disease, disorder and condition described herein, as well as others, can benefit from the compositions and methods described herein. Typically, treating a state, disease, disorder or condition includes preventing or delaying the appearance of clinical symptoms in a mammal that may be suffering from or susceptible to the state, disease, disorder or condition, but has not yet experienced or exhibited its clinical or subclinical symptoms. Treatment can also include inhibiting the state, disease, disorder or condition, for example, preventing or reducing the development of the disease or at least one of its clinical or subclinical symptoms. In addition, treatment can include alleviating the disease, for example, causing the disappearance of at least one of the state, disease, disorder or condition or its clinical or subclinical symptoms. The benefit to the subject being treated can be statistically significant, or at least perceptible to the subject or physician.

[0101] Administration of the therapeutic agent can occur as a single event or over the course of treatment. For example, the therapeutic agent can be administered daily, weekly, biweekly, or monthly. For the treatment of acute conditions, the time course of treatment will generally be at least a few days. Certain conditions may extend treatment from a few days to several weeks. For example, treatment may extend beyond one, two, or three weeks. For more chronic conditions, treatment may last from a few weeks to several months or even a year or longer.

[0102] Treatment according to the methods described herein can be performed prior to, concurrently with, or after conventional treatment modalities for cardiovascular diseases, disorders, or conditions.

[0103] Therapeutic agent can be used simultaneously or sequentially with another medicament, such as the standard therapeutic agent of IBD or another medicament.For example, therapeutic agent can be used simultaneously with another medicament (such as standard IBD therapeutic agent).Using simultaneously can be carried out by the use of independent composition, each composition contains one or more in therapeutic agent or another medicament.Using simultaneously can be carried out by the use of a kind of composition, described composition contains two or more in therapeutic agent or another medicament.Therapeutic agent can be used sequentially with antibiotic, anti-inflammatory agent or another medicament.For example, therapeutic agent can be used before or after using antibiotic, anti-inflammatory agent or another medicine.

[0104] definition

[0105] When introducing elements of the present disclosure or one or more preferred aspects thereof, the articles "a," "an," "the," and "said" mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements.

[0106] As used herein, the following definitions shall apply unless otherwise indicated. For purposes of the present invention, chemical elements are identified according to the Periodic Table of the Elements, CAS version, and Handbook of Chemistry and Physics, 75th edition, 1994. In addition, the general principles of organic chemistry are described in "Organic Chemistry," Thomas Sorrell, University Science Books, Sausalito: 1999, and "March's Advanced Organic Chemistry," 5th edition, Smith, MB and March, J., eds. John Wiley & Sons, New York: 2001, the entire contents of which are hereby incorporated by reference.

[0107] In some embodiments, the numbers representing the amounts of ingredients, characteristics such as molecular weight, reaction conditions, etc., used to describe and claim certain embodiments of the present disclosure should be understood to be modified by the term "about" in some cases. In some embodiments, the term "about" is used to indicate that the value includes the standard deviation of the mean value of the device or method for measuring the value. In some embodiments, the numerical parameters set forth in the written description and the appended claims are approximate values, which can vary depending on the desired characteristics to be attempted to be obtained by the specific embodiment. In some embodiments, the numerical parameters should be interpreted based on the number of reported significant figures and by applying ordinary rounding techniques. Although the numerical ranges and parameters setting forth the wide range of some embodiments of the present disclosure are approximate values, the numerical values ​​set forth in the specific examples are reported as accurately as possible. The numerical values ​​presented in some embodiments of the present disclosure may contain certain errors, which are inevitably caused by the standard deviation found in their corresponding test measurements. The description of the range of values ​​herein is intended only to serve as a shorthand method for referring to each separate numerical value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually cited herein.

[0108] In some embodiments, the terms "a," "an," and "the," and similar references used in the context of describing specific embodiments (especially in the context of certain of the following claims) may be interpreted to cover both the singular and the plural, unless specifically noted otherwise. In some embodiments, the term "or," as used herein (including the claims), is used to mean "and / or," unless explicitly stated to refer only to alternatives or that the alternatives are mutually exclusive.

[0109] The terms "comprise," "have," and "include" are open-ended linking verbs. Any form or tense of one or more of these verbs, such as "comprises," "comprising," "has," "having," "includes," and "including," are also open-ended. For example, any method that "comprises," "has," or "includes" one or more steps is not limited to having only those one or more steps, and may also encompass other, unlisted steps. Similarly, any composition or device that "comprises," "has," or "includes" one or more features is not limited to having only those one or more features, and may cover other, unlisted features.

[0110] All methods described herein can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by the context. The use of any and all examples or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended solely to better illustrate the present disclosure and does not constitute a limitation on the scope of the present disclosure otherwise requested. Any language in this specification should not be construed as indicating any unclaimed element essential to the practice of the present disclosure. Example

[0111] The following examples are included to demonstrate various embodiments of the present disclosure. It will be understood by those skilled in the art that the technology disclosed in the following examples represents the technology that the inventors have found to function well in implementing the present invention, and therefore can be considered to constitute the preferred mode of its implementation. However, in view of this disclosure, it will be understood by those skilled in the art that many changes can be made in the disclosed specific embodiments, and still obtain similar or similar results without departing from the spirit and scope of the present invention.

[0112] introduction

[0113] Example 1: IL-17A added to the intestinal epithelium

[0114] What happens when you add IL-17A to the intestinal epithelium? We performed more than six functional assays with IL-17A (not shown), but shown here are the microarray analyses.

[0115] Figure 1 Shows in vitro culture system (Kaiko G and Ryu S et al., Cell, 2016).

[0116] Microarray Experiment Setup: Primary Epithelial Cells

[0117] From colon epithelial cell line, n=4; stem cells; stem cells (2 days); DM; colonocytes; with and without 20 ng / ml IL-17A.

[0118] Figure 2 PCA plot: IL-17 has subtle effects on stem cell differentiation.

[0119] Figure 3 Identification of gene candidates downstream of IL-17A in the epithelium.

[0120] Figure 4 Identification of genes conservedly dysregulated in IBD patients.

[0121] PlatAre the most up-regulated or down-regulated genes in the coagulation pathway altered by IL-17? Why are members of the coagulation cascade altered by immune activation of IL-17 on colonic epithelial cells? Does this pathway provide previously overlooked insights into disease pathogenesis?

[0122] Array analysis: ImmGen database

[0123] Cross-reference identification with ImmGen Plat These genes are upregulated by endothelial cells and fibroblasts following immune stimulation, suggesting a previously unknown inflammatory role. Most of the other co-regulated genes are innate immune molecules.

[0124] Figure 5 qPCR validation and dose curve: colon.

[0125] Figure 6 Dose curve: ileum.

[0126] IL-17A has a conservative association with tPA. GEO dataset mining shows that tPA is strongly associated with both colitis disease state and IL-17A levels in humans and mice. By searching the GEO data array sets of skin epidermis / keratinocytes and lung epithelium treated with IL-17A, it is clear that Plat upregulation is a conservative epithelial response to IL-17A. In IL-17-dominated intestinal models (such as DSS and Citrobacter rodentium infection), Plat mRNA is upregulated (~4-fold). SERPINE1 mRNA is upregulated ~7-fold in DSS, but not in Citrobacter rodentium. However, no one has studied why IL-17A is associated with IL-17A in any organ system. Plat So Plat What is tissue plasminogen activator (tPA)

[0127] Figure 7 A textbook view of tPA and its inhibitor PAI-1. tPA and PAI-1 are far more than just coagulation factors. tPA is a serine protease with both plasmin-dependent and -independent functions. Inhibition of PAI-1 enhances these novel functions of tPA (see, e.g., Figure 8). tPA and PAI-1 (a pathway not functionally studied in IBD). PAI-1 is a direct binding inhibitor of tPA. IBD patients are at a much greater risk (3 times) for thrombosis and hypercoagulability (~90% of IBD patients have abnormal coagulation parameters - Kohoutova D et al., Scand J Gastro, 2014). tPA / PAI-1 has been extensively studied in the neuronal and cardiovascular systems, with potential roles in remodeling / cell migration.

[0128] Assumptions

[0129] tPA is an anti-inflammatory, pro-repair molecule that acts as a positive downstream effector of IL-17A. Increasing tPA levels (e.g., by inhibiting PAI-1) may have the potential to be a novel drug therapy in IBD, not only improving disease outcomes but also reducing the risk of thrombosis. tPA is expressed in vivo in response to an IL-17-induced colitis model.

[0130] FIG. 9 tPA is induced by inflammation and originates from epithelial and non-epithelial cells in mice.

[0131] Figure 10 tPA is induced by inflammation and originates from both epithelial and non-epithelial cells in mice. In the ileum, there was no tPA in any CRF het at day 14 and none in any mouse at day 0.

[0132] Figure 11 tPA is low to non-existent in the absence of inflammation and is derived from both epithelial and non-epithelial cells in mice.

[0133] Figures 12A-12B . Data suggest that tPA protects against colitis.

[0134] Example 2: Novel PAI-1 inhibitors

[0135] Development of a PAI-1 inhibitor (CDE-268) from small molecule screening.

[0136] Figure 13. Novel PAI-1 inhibitors increase tPA levels in blood and colon.

[0137] Using DSS colitis, we investigate the role of tPA in the disease and PAI-1 inhibitors as novel therapeutics.

[0138] Figure 14 shows that targeting PAI-1 as therapy (rather than prevention) in DSS colitis inhibits disease.

[0139] This is better than the treatment results achieved with prednisone or anti-IL-6 in mice, and is comparable to the treatment results of anti-TNFα in mice.

[0140] Figure 15 shows that targeting PAI-1 as therapy (rather than prevention) in DSS colitis inhibits disease.

[0141] Figure 16 PAI-1 inhibition suppresses neutrophil influx.

[0142] Figure 17 PAI-1 inhibition suppresses IL-6.

[0143] Using Citrobacter rodentium colitis, we investigated the role of tPA in disease and PAI-1 inhibitors as novel therapeutics.

[0144] Figure 18: PAI-1 inhibition showed a trend toward reduced weight loss and bacterial burden. However, importantly, the inhibitor did not exacerbate bacterial infection, which has been a detrimental effect and a major concern of anti-IL-17 therapy in clinical trials and mouse models.

[0145] Figure 19. PAI-1 inhibition suppresses crypt proliferation.

[0146] Figure 20. PAI-1 inhibition suppresses IL-6 and MPO activity.

[0147] Mechanism of action

[0148] Which upstream signaling pathway drives Plat / tPA through IL-17A?

[0149] Figure 21. IL-17RA signaling. Cebpd is also one of the Venn diagram genes upregulated by IL-17.

[0150] Downstream signaling pathways of tPA

[0151] TGF-β is an immunosuppressive / repair regulatory molecule located in the ECM and requires cleavage by proteases for activation.

[0152] Figure 22. Evidence that tPA can directly and indirectly cleave latent TGFβ in a cell-free assay.

[0153] Figure 23 TGF-β pathway. In cancer cell lines, the most highly upregulated gene was serpin 1 / PAI-1.

[0154] Figure 24Construction of a TGFβ-Smad-luciferase reporter. Twelve clones were isolated and tested for responsiveness to TGFβ (mouse and human). Clones #8 and 10 were selected and expanded to create stable lines for testing supernatants and colon homogenates for mature TGFβ activity. TGF-β reporter activity assay results were confirmed by western blot. Data were collected (I-ling).

[0155] Figure 25 . TGFβ drives serpin 1 / PAI-1 expression in colonospheres (negative feedback loop).

[0156] Figure 26 . 1. Induces IL-17A to fight infection / maintain a barrier to commensals. It also limits tissue damage by tPA. Increased PAI-1 in IBD patients may limit the tissue protective function of IL-17A-tPA. 2. PAI-1, long known to be the most responsive gene to TGFβ, may act as a negative feedback regulator of TGFβ by tPA. PAI-1 dysregulation in IBD may explain their hyperinflammatory state. Target models include dnKO colitis model, PlatKO mice, DSS colitis model (remainder of endpoints). Test whether immune cells replicate the effects induced by recombinant IL-17A-tPA in vitro (Th17 cells + colonic epithelial cells) (Australia). Using PAI-1KO mice in DSS, test whether genetic increase of tPA improves colitis prognosis. Downstream mechanisms: Test whether tPA acts as a protease to cleave and activate a latent form of TGFb. We replaced TGFb in PlatKO mice subjected to DSS or inhibited TGFb in PAI-1 inhibitor-treated mice subjected to DSS to reveal functional roles downstream of tPA.

[0157] Example 3: PAI-1 is elevated in IBD patients

[0158] How does one, and what might go wrong in this pathway in IBD It is hypothesized that patients with active IBD have elevated PAI-1 due to inflammation and tissue damage, which disrupts the tPA / TGF-β axis. In this paper, we show that PAI-1 is elevated in IBD patients. There is a great need for: 1. Biomarkers of disease activity in human IBD patients. 2. Predictors of response to biologic therapy

[0159] tPA and PAI-1 (a pathway not functionally studied in IBD).

[0160] PAI-1 is a direct binding inhibitor of tPA. IBD patients are at a much greater risk (3-fold) of thrombosis and hypercoagulable disorders (~90% of IBD patients have abnormal coagulation parameters - Kohoutova D et al., Scand J Gastro, 2014).

[0161] Figure 27 tPA was unchanged in UC patients, as determined by IF staining of sections from surgically resected cases. Therefore, tPA is not a biomarker.

[0162] Figure 28 Highly upregulated serpin 1 / PAI-1 in inflamed tissues from CD and UC patients (4 cohorts) analyzed from raw data deposited in GEO NCBI.

[0163] Figure 29 PAI-1 protein is highly upregulated in inflamed tissues from UC patients, IF staining of sections from surgically resected cases.

[0164] Thus, serpin 1 / PAI-1 expression in colonic tissue indicates disease activity in UC (diagnostic / prognostic potential).

[0165] Example 4: Predictors of which patients will respond to biologic therapy with anti-TNFα (infliximab) and anti-α4β7 (vedolizumab)

[0166] The process of identifying UC predictive features to indicate response to biologics. Colon biopsy sample mRNA microarray raw data deposited in GEO NCBI. Here, we target mining raw data deposited in GEO NCBI. ~300 patients in 3 separate cohorts. See, e.g., Figures 30 and Figure 31 .

[0167] Before starting therapy with a monoclonal biologic drug, biopsy samples were taken from multiple cohorts of patients with moderate to severe IBD. Microarrays were performed on these biopsy samples. We compared various genes altered before treatment in patients who subsequently responded to therapy versus those who did not. Therefore, these genes predicted how the patient was likely to respond to the drug.

[0168] We then compiled these comparisons and developed an 8-gene colon signature to predict patient response to drugs (see, e.g., Figure 31 ).

[0169] Table 1: 8-gene biomarker signature

[0170] 8-Gene Biomarker Signature SERPINE1 / PAI-1 CCL2 IL24 IL6 PI15 PTGS2 SELE TNC

[0171] Patients with high SERPINE1 expression are less likely to respond to infliximab or vedolizumab (see, e.g., Figure 32 ). When all genes from the 8-gene signature were used, the accuracy of the predictions improved. This study is believed to be the largest UC transcriptional analysis ever conducted by us (10 independent studies across different continents over 8 years on different array platforms). PAI-1 was found to be consistently upregulated in active UC biopsy samples across all studies, and PAI-1 was strongly correlated with inflammatory molecules in routine biopsy samples from UC patients.

[0172] FIG33 shows the positive correlation between PAI-1 and IL-6 / TNF-α.

[0173] FIG34 shows the positive correlation between PAI-1 and oncostatin M / Cox2.

[0174] Figure 35 Conserved responses downstream of IL-17A and IBD predictions were shown. One of the top ten canonical pathways predicted to be associated with the UC / CD colon gene signature was acute phase response signaling.

[0175] Figure 36A Comparative pathway analysis by IPA of the top ten overlapping pathways in UC / CD and IL-17A treatment in vitro.

[0176] Figure 36B Acute phase response pathway. If we zoom in on this pathway, we can see that it involves classic inflammatory mediators like TNF, IL-1, and IL-6, which drive the activation of the acute response.

[0177] However, this includes our target gene serpin 1 / PAI-1 as well as many other closely related members of the serpin family highlighted here in purple (see e.g., Figure 36B What probably occurs in IBD is that a state of acute inflammation in the colon drives PAI-1 expression, and in susceptible individuals this PAI-1 process becomes chronic and highly elevated, which deregulates the mechanisms of immunosuppression mediated by TGFb.

[0178] As shown herein, the present disclosure has shown the discovery of PAI-1 (gene name Serpin 1) as a biomarker for active inflammatory bowel disease and a predictor of response to biologic therapy (i.e., anti-TNF therapy) using colon biopsy samples and / or plasma. The present disclosure has shown that: (1) PAI-1 levels can be used to diagnose IBD in patients with IBD; (2) they can predict treatment outcomes based on PAI-1 levels; and (3) a PAI-1 inhibitor (CDE-268, a known PAI-1 inhibitor used to treat heart conditions) can successfully treat colitis.

[0179] We seek markers of intestinal inflammation in IBD, which are downstream of multiple inflammatory pathways. We first performed RNA microarray analysis on primary mouse intestinal epithelial cells and processed these cells with IL-17 (known important inflammatory cytokines in IBD). We cross-referenced a list of 23 molecules with enhanced mRNA production in these cells grown in multiple states with a list of molecules with enhanced expression in IBD colon biopsy samples. It is enriched that we identify the Plat / serpin 1 pathway. We found that in the IL-17-dominated intestinal model (such as DSS and Citrobacter rodentium infection), Plat and serpin mRNA were upregulated. The Plat mRNA and protein (protein name tissue plasminogen activator; tPA) rise in the mouse model is functional, because the loss of tPA's function worsens the disease, and the reduction of PAI-1 activity improves the disease results in multiple mouse models. PAI-1 is a direct binding inhibitor of Plat. We found that PAI-1 expression in the disease model rises at the site of inflammation. The inhibition of PAI-1 raises active Plat, and it rescues disease activity.Compared with similar UC sections and non-IBD cases (n=34 total samples) without active disease, in the immunofluorescence analysis of the section of resected case from ulcerative colitis (UC), PAI-1 protein expression significantly raises.The mRNA data of colon biopsy samples from 6 independent groups of ulcerative colitis and colon Crohn's disease (CD) show that, compared with inactive disease or non-IBD control, only the PAI-1 expression in the patient with active disease significantly increases.In the group of taking biopsy samples before and after treatment, we find that the level prediction of PAI-1 is to the response of anti-TNF therapy (patients with high levels of PAI-1 are unlikely to respond).

[0180] Plasma-PAI-1 protein levels were tested in plasma to confirm that our observations using mRNA in colon biopsy samples also applied to protein levels in blood. PAI-1 is readily detected in plasma and has been used as a biomarker for other diseases, including cardiovascular disease. PAI-1 is unique in that it is induced downstream of multiple inflammatory factors associated with ulcerative colitis and CD, and plasma levels correlate with levels in tissues in other disease states.

[0181] We have shown the ability of PAI-1 expression levels to indicate disease activity in ulcerative colitis and Crohn's disease. This has included analysis of the following patient samples: 1. Resection cases of patients (n=34) with ulcerative colitis, which showed an increase in PAI-1 in inflamed areas of the colon. 2. Microarray analysis of >500 patient colon biopsy samples showing serpin 1 expression predicts disease activity, and using a smaller subset of patients to show that serpin 1 expression can also help predict whether a patient will respond to biologic therapy (e.g., anti-TNF therapy). 3. Plasma studies are underway to analyze the ability of PAI-1 protein levels in blood (~150 patients) to predict disease activity and response to biologic therapy (e.g., anti-TNF therapy).

[0182] abbreviation:

[0183] tPA = tissue plasminogen activator; gene name Plat

[0184] PAI-1 = plasminogen activator inhibitor 1; gene name Serpine1

[0185] UC = ulcerative colitis

[0186] CD = Crohn's disease.

[0187] Example 5: Additional biomarkers to increase the predictability of treatment response

[0188] Detection of additional biomarkers may increase the predictive power of treatment efficacy in patients with IBD.The following examples describe gene expression signatures that predict IBD responders vs. non-responders to anti-TNF therapy.

[0189] (I) 2-way biomarkers tested (CCL2 and SERPINE1 / PAI-1)

[0190] The following data show that the two biomarkers (CCL2 and PAI-1 / SERPINE ) features to improve the prediction of responders vs. non-responders to biological therapy. SERPINE , TNC and IL13RA2, and similar results were found.

[0191] Figure 37 . 2 biomarker signature combinations for all datasets.

[0192] (II) Panel of multiple biomarkers tested

[0193] This example describes biomarker results from a standard analysis and a higher powered analysis from a statistical collaborator. The analysis was from 3 cohorts, totaling 66 patients.

[0194] Set 1) The transcriptional signature from the results (lower statistical power than Set 2, but prioritized genes with larger fold changes between responders and non-responders, as this was envisioned as a quantitative PCR assay on colon biopsy samples) was: SERPINE 1, CCL2, TNC and IL13RA2.

[0195] Set 2). From the random forest test (the gold standard for gene expression biomarker analysis with very high power statistics, but without specifying high or low fold changes), the final features were: PRNP, IL13RA2, GPX8, DRAM1, and STAT4.

[0196] The ROC curve AUC achieved 96% sensitivity and 97% specificity for predicting which IBD patients would continue to respond or not respond to anti-TNFα.

[0197] Set 2 is currently more statistically robust than set 1, but when the method was developed as a PCR-based test on a pre-admission cohort, the fold change for set 2 was much lower than set 1, even though it predicted a higher % of patients. Therefore, from an assay perspective, set 1 may prove to be superior.

[0198] Figure 38 Data are shown for a 5-biomarker signature demonstrating diagnostic predictive ability to distinguish responders vs non-responders to anti-TNFα.

[0199] Table 2: Frequency summary of samples by group and respondent

[0200] Non-respondents Respondent Total number of rows Group 1 16 8 24 Group 2 7 12 19 Group 3 15 8 23 Total number of columns 38 28 66

[0201] The gene expression data of group 1 and group 2 are merged with the gene expression data of group 3.To carry out group 1 and group 2 of profile analysis on AffymetrixHgu133 plus, normalize together from raw data and be folded into unique gene by meansigma methods (by Gerard), it contains 23520 kinds of genes altogether.Group 3 on Hgu1.0ST version 1 is separated normalization with group 1 and 2, and be folded into gene by meansigma methods (by Gerard), it contains 20475 kinds of genes.17272 kinds of genes overlap (data not shown) between two normalization gene data sets.

[0202] Using the COMBAT method [reference] implemented in the Bioconductor software package "sva" [reference], the gene expression data of groups 1, 2, and 3 were merged together while removing batch effects. Principal component (PC) analysis was performed using the densities of the first three PCs (PC1, PC2, PC3) drawn diagonally, as well as paired scatter plots between them. Black, red, and green dots indicate individual patient samples from groups 1, 2, and 3, respectively. Based on the first three PCs, group 3 samples were well mixed with group 1 and 2 samples. (PCA diagram is Figure 39).

[0203] A heat map was generated on the merged gene expression matrix with patient samples in columns and genes in rows, each of which was clustered based on similarity measured by Pearson's correlation coefficient using a hierarchical clustering method with average linkage. The heat map also showed that batch effects were negligible in the patient samples.

[0204] Supervised classification method, random forest (RF), is used to classify responders vs. non-responders. RF is a tree-based machine learning classification algorithm using resampling technology. RF repeats and randomly extracts a set (here, 66 samples) of samples of the original data identical with the original sample size. The resampled data is used to build a set of trees (here, 5000 trees) to classify patient samples into responders and non-responders. Each tree is allowed to have a maximum number of terminal nodes (here, 5), and at each tree branch split, multiple trials (here, 10) are carried out to select the best split gene. Then, the majority vote of the set tree set up by RF is used to predict the missed samples. Then, the classification error rate can be finally robustly assessed by tabulating the true state and the predicted state. In addition, several important measurements will be reported for each gene by assessing the average reduction of the Gini coefficient (purity measure of the tree node) and the overall classification accuracy (keeping other genes unaffected simultaneously) after only replacing the gene.

[0205] Table 3: The individual true and predicted states for each of the 66 samples are provided in the table below.

[0206] PID RF.Prediction status Real state GSM364633 Previous infliximab responders Previous infliximab responders GSM364634 Previous infliximab responders Previous infliximab responders GSM364635 Previous infliximab responders Previous infliximab responders GSM364636 Previous infliximab responders Previous infliximab responders GSM364637 Previous infliximab responders Previous infliximab responders GSM364638 Previous infliximab responders Previous infliximab responders GSM364639 Previous infliximab responders Previous infliximab responders GSM364640 Previous infliximab responders Previous infliximab responders GSM364641 Previous infliximab non-responders Patients who did not respond to infliximab before GSM364642 Previous infliximab non-responders Patients who did not respond to infliximab before GSM364643 Previous infliximab non-responders Previous infliximab non-responders GSM364644 Previous infliximab non-responders Previous infliximab non-responders GSM364645 Patients who did not respond to infliximab before Previous infliximab non-responders GSM364646 Patients who did not respond to infliximab before Previous infliximab non-responders GSM364647 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364648 Previous infliximab non-responders Previous infliximab non-responders GSM364649 Previous infliximab non-responders Patients who did not respond to infliximab before GSM364650 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364651 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364652 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364653 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364654 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364655 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM364656 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423010 Previous infliximab responders Previous infliximab responders GSM423012 Previous infliximab responders Previous infliximab responders GSM423013 Previous infliximab responders Previous infliximab responders GSM423015 Previous infliximab responders Previous infliximab responders GSM423017 Previous infliximab responders Previous infliximab responders GSM423019 Previous infliximab responders Previous infliximab responders GSM423021 Previous infliximab responders Previous infliximab responders GSM423023 Previous infliximab responders Previous infliximab responders GSM423025 Previous infliximab responders Previous infliximab responders GSM423027 Previous infliximab responders Previous infliximab responders GSM423029 Previous infliximab responders Previous infliximab responders GSM423031 Previous infliximab responders Previous infliximab responders GSM423033 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423035 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423037 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423039 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423041 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423043 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM423045 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900148 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900154 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900155 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900158 Previous infliximab responders Previous infliximab responders GSM1900172 Previous infliximab responders Previous infliximab responders GSM1900175 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900176 Patients who did not respond to infliximab before Patients who did not respond to infliximab before GSM1900180 Previous infliximab responders Previous infliximab responders GSM1900181 Previous infliximab non-responders Previous infliximab non-responders GSM1900184 Previous infliximab non-responders Previous infliximab non-responders GSM1900185 Patients who did not respond to infliximab before Previous infliximab non-responders GSM1900186 Previous infliximab responders Previous infliximab responders GSM1900192 Previous infliximab responders Previous infliximab responders GSM1900195 Previous infliximab non-responders Previous infliximab non-responders GSM1900202 Previous infliximab non-responders Patients who did not respond to infliximab before GSM1900204 Previous infliximab responders Previous infliximab responders GSM1900206 Previous infliximab non-responders Previous infliximab non-responders GSM1900208 Previous infliximab non-responders Previous infliximab non-responders GSM1900210 Previous infliximab responders Previous infliximab responders GSM1900213 Previous infliximab responders Previous infliximab responders GSM1900214 Previous infliximab non-responders Previous infliximab non-responders GSM1900215 Previous infliximab non-responders Previous infliximab non-responders GSM1900217 Previous infliximab non-responders Previous infliximab non-responders

[0207] Classification error matrix.

[0208] Due to the random sampling nature of the algorithm, the confusion matrix may vary slightly between runs. Of the 66 patient samples, 42 were predicted as non-responders and 24 were predicted as responders. 33 of the 38 true non-responders were correctly predicted, while only 19 of the 28 true responders were predicted as responders, corresponding to a class error rate of 13.16% among true non-responders and 32.14% among true responders. The overall classification accuracy was (34 + 19) / 66 = 80.30%.

[0209] Table 4.

[0210] True respondent status Total samples with true state Predicted non-responders Predicted Respondents Category classification error Non-respondents 38 34 4 0.105263 Respondent 28 9 19 0.3214 Total samples with predicted states 66 43 23

[0211] Multidimensional scaling (MDS, a dimensionality reduction technique similar to PCA) plots were used to visualize the proximity of the original high-dimensional samples on a 2-dimensional plane (MDS dimension 1 vs. MDS dimension 2), with non-responders in black circles and responders in green triangles (see, e.g., Figure 40 ).

[0212] The importance measure for all genes was sorted by reducing the average Gini coefficient. Due to the random sampling nature of the algorithm, the order of genes may vary between runs, except for the overall importance (which should be stable within the top 100, for example, data not shown).

[0213] ROC analysis was performed on each individual gene to estimate the area under the ROC (AUC) with a 95% confidence interval and an optimal cutoff corresponding to the coordinates of (1-specificity, sensitivity) that is closest to the perfect classification coordinates (0, 1) (i.e., 100% specificity and 100% sensitivity). The top five genes with AUC estimates >= 0.9 were: PRNP, IL13RA2, GPX8, IKBIP, KLHL5. Box plots of the top 100 genes with the highest AUC were plotted by response (see, e.g., Figures 41-45). ROC plots were plotted for the top 100 genes with the optimal cutoff and the corresponding sensitivity and specificity at that cutoff (see, e.g., Figures 41-45, 46-48).

[0214] Table 5: 37 genes that overlap between the top 100 genes with the largest average reduced Gini coefficient and the top 100 genes with the highest AUC.

[0215]

[0216] From the RF analysis using R software package "cart", a tree was built using the first 100 genes (based on average reduction Gini). The logistic regression model lasso penalty with the first 100 genes resulted in the maximum reduction in the average Gini coefficient from the RF analysis using R software package "glmnet". For the logistic regression model fitting of penalty, the gene expression data of every gene were standardized. By cross validation (CV), 9 genes were finally maintained in the logistic regression model of penalty, and the penalty parameter λ was 0.1042963 (the rightmost vertical line in the figure below). It is the maximum penalty within 1 standard error corresponding to the optimal penalty parameter for minimum deviation. Note that the penalty parameter corresponding to minimum CV error maintains 12 genes, see, for example, Figure 49 .

[0217] Table 6: Coefficients for the 9 genes (and intercept) are shown below.

[0218] variable coefficient (intercept) 12.66847 SMR3A 1.482112 DRAM1 -0.14616 SLC23A2 -0.28982 HDGFRP3 -0.00719 IL13RA2 -0.59576 GPX8 -0.70709 PRNP -0.41885 STAT4 -0.29415 HIF1A -0.24065

[0219] A linear predictor constructed using a penalized logistic regression model based on 9 genes improved the AUC to 0.99 compared to 0.93 for the best gene from individual gene ROC analysis (see, e.g., Figure 49 ), and more importantly, increased both sensitivity and specificity to >0.9. This model will be validated in an independent cohort. Similarly, a lasso penalized logistic regression model was performed using the gene expression data of the top 100 genes with the highest AUC. 9 genes were also selected based on a penalty parameter of 0.05438 (see CV plot, Figure 45).

[0220] Table 7: Their coefficients are shown below (see e.g., Figure 50 ).

[0221] variable coefficient (intercept) 22.97536 PRNP -0.17088 IL13RA2 -0.74211 GPX8 -1.95242 DRAM1 -0.59201 STAT4 -0.8049 TOR1AIP1 -0.05234 CCL18 -0.00022 S100A9 -0.03044 ZNF57 0.423264

[0222] Note that 5 genes (DRAM1, GPX8, IL13RA2, PRNP, STAT4) overlap with the analysis starting with the top 100 RF genes. The linear predictor derived based on these 9 genes also resulted in the same improvement in AUC, sensitivity, and specificity. Since the penalized logistic regression models starting with the top 100 RF genes or top AUC genes ultimately shared 5 genes, it is suspected that using these 5 genes may be sufficient. The expression of the 5 genes (at their original scale) was used in the logistic regression model.

[0223] Table 8: The coefficients are shown below (see e.g., Figure 52 ).

[0224] variable coefficient (intercept) 132.9813 PRNP 1.963612 IL13RA2 -2.71323 GPX8 -12.5419 DRAM1 -2.43214 STAT4 -7.03906

[0225] The ROC curve based on the linear predictor constructed using only 5 genes resulted in an AUC of 1 and increased the sensitivity to 0.96 (see, e.g., Figure 52 Finally, we provide some insights into how well a tree can predict responses. The top 100 RF genes were further constructed into a single tree using the R package “rpart” (see, e.g., Figure 53 ), where the tree is shown below. A single tree first separated all 66 patients (38 / 28 nonresponders / responders) by IL13RA2 with a cutoff of 5.777, which identified 21 nonresponders with IL13RA2 above the threshold (leftmost node). The remaining 35 patients (7 nonresponders / 28 responders) were separated based on GPX8 with a cutoff of 5.706. 27 of the 28 responders were identified as having GPX8 < 5.706 (rightmost node). This left an intermediate node with 7 nonresponders and 1 responder having GPX8 >= 5.706.

Claims

1. Use of an agent for detecting PAI-1 / SERPINE in the preparation of a kit for treating a human subject with inflammatory bowel disease (IBD) by a method comprising: a) identifying a human subject with IBD as a likely responder to treatment with an anti-TNF or anti-α4β7 antibody based on a detected level of PAI-1 / SERPINE protein and / or mRNA in a sample from said human subject being lower than a corresponding protein or mRNA reference value, when said detected level of PAI-1 / SERPINE protein or mRNA has a log2 expression value of about 6.5 or less relative to said reference value; and b) administering an effective amount of the anti-TNF or anti-α4β7 antibody to the human subject.

2. The method of claim 1, wherein the kit further comprises a reagent for detecting CCL2, and the method further comprises: The human subject having IBD is identified as a potential responder to the treatment with an anti-TNF or anti-α4β7 antibody based on the detected level of CCL2 in the sample from the human subject having a log2 expression value relative to a reference value of about 9.2 or less.

3. The use of claim 1, wherein the anti-TNF antibody comprises infliximab. The use of claim 1 , wherein the anti-α4β7 antibody comprises vedolizumab.

5. The use of claim 1, wherein the administration is with the anti-TNF antibody. The use of claim 1 , wherein the administration is performed using the anti-α4β7 antibody.

7. The use of claim 1, wherein the sample comprises intestinal tissue.

8. The use of claim 1, wherein the sample comprises saliva.

9. The use of claim 1, wherein the sample comprises blood.

10. The use of claim 1, wherein the sample is a plasma sample.

11. The use according to claim 1, wherein the sample is a serum sample.

12. The use of claim 1, wherein the sample comprises urine.

13. Use of a reagent for detecting PAI-1 / SERPINE in the preparation of a kit for classifying a subject with IBD as a responder or non-responder to anti-TNFα therapy by the following method, the method comprising: a) detecting the level of PAI-1 / SERPINE protein and / or mRNA in a sample from a human subject, wherein the human subject has inflammatory bowel disease (IBD); and b) Classify the subjects: i) classifying the subject as a responder to anti-TNFα treatment for the IBD when the PAI-1 / SERPINE protein and / or mRNA level is lower than the corresponding protein or mRNA reference value; or ii) classifying the subject as a non-responder to anti-TNFα treatment for the IBD when the PAI-1 / SERPINE protein and / or mRNA level is higher than the corresponding protein or mRNA reference value.

14. The use of claim 13, wherein the subject is classified as a responder to anti-TNFα treatment when the log2 expression value of PAI-1 / SERPINE mRNA and / or protein is less than 6.

5.

15. Use of tissue plasminogen activator (tPA) in the preparation of a pharmaceutical composition for treating IBD in a subject.

16. The use of claim 15, wherein the inflammatory bowel disease (IBD) comprises ulcerative colitis (UC) or Crohn's disease (CD).

17. The use of claim 15, wherein the subject is a human.

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