WO2005009339A2 - Maladies intestinales inflammatoires - Google Patents

Maladies intestinales inflammatoires Download PDF

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WO2005009339A2
WO2005009339A2 PCT/US2004/017024 US2004017024W WO2005009339A2 WO 2005009339 A2 WO2005009339 A2 WO 2005009339A2 US 2004017024 W US2004017024 W US 2004017024W WO 2005009339 A2 WO2005009339 A2 WO 2005009339A2
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levels
proteins
patient
disease
population
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PCT/US2004/017024
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WO2005009339A3 (fr
Inventor
Ebenezer Satyaraj
Velizar T. Tchernev
Serguei Lejnine
Gregory Kotler
Dhavalkumar D. Patel
Stephen F. Kingsmore
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Pathway Diagnostics Corporation, Inc. (a Delaware Corporation)
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Publication of WO2005009339A3 publication Critical patent/WO2005009339A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/06—Gastro-intestinal diseases
    • G01N2800/065—Bowel diseases, e.g. Crohn, ulcerative colitis, IBS

Definitions

  • the invention relates in some of its aspects to diagnosis of inflammatory bowel disease. More particularly, it relates in some of its aspects to Ulcerative Colitis and Crohn's Disease.
  • IBD Inflammatory bowel disease
  • GI gastrointestinal
  • CD Crohn's disease
  • UC ulcerative colitis
  • CD represents a nonspecific chronic transmural inflammatory disease that most commonly affects the distal ileum and colon but may occur in any part ofthe GI tract.
  • UC is defined as a chronic, inflammatory, and ulcerative disease arising in the colonic mucosa, characterized most often by bloody diarrhea.
  • the Western-Eastern discrepancy can be attributed to a difference in life styles.
  • the incidence of the disease has been increasing worldwide, but its spread has been slowing down in highly affected countries.
  • Racial and ethnic relations in different populations and immigration studies offer interesting data, which can reflect genetic, environmental and behavioral factors (Karlinger et al. 2000).
  • the disease seems to have a characteristic racial-ethnic distribution: the Jewish population is highly susceptible everywhere, but its prevalence in that population nears that of the domestic society in which they live. In Hungary, the Roma (Gypsies) have a considerably lower prevalence than the average population. This can be attributed to a genetic or environmental influence.
  • the onset of the disease occurs more often in the second or the third decade of life, but there also is another peak in the 60s.
  • IBD infectious bacteria
  • Yersinia Campylobacter
  • Clostridium Clomidia
  • herpesvirus rotavirus
  • the primary measles virus Karlinger et al. 2000. None of them has been proven as a real and exclusively pathogenic factor.
  • Irnmunological background has an important function in the manifestation of the disease. If an individual has a genetic susceptibility to infections, the down regulation of an inflammation in the bowel wall may not occur in a proper way and may initiate an autoimmune process.
  • ulcerative colitis and Crohn's disease are heterogeneous disorders of mutifactorial etiology in which hereditary (genetic) and environmental (microbial, behavior) factors interact to produce the disease.
  • IL-10 inhibits the production of proinflammatory cytokines such as IL-1, tumor necrosis factor-alpha (TNF-a), interferon-gamma (EFN-gamma) and IL-6 through inhibitory action on Thl cells and macrophages, and it is thought to be a suppressor type cytokine.
  • IL-10 is elevated in serum of patients with active CD and UC, suggesting that IL-10 acts as a naturally occurring damper in the acute inflammatory process of IBD.
  • M-CSF Macrophage-colony stimulating factor
  • salivary M-CSF influences monocyte/macrophage proliferation, differentiation, and activation.
  • Serum M- CSF levels were found to be increased in active IBD, and compared to normal intestine, in active IBD the frequency of M-CSF-expressing cells was significantly increased and their distribution markedly altered, although no increase in mucosal M-CSF mRNA levels in intestinal tissue was observed (Klebl et al. 2001). However, the changes were not specific to IBD, as there were similar findings in intestinal tuberculosis and ischaemic colitis.
  • TNF-alpha and IL-6 were significantly increased in the CD group only.
  • Chemokines, together with key cytokines that promote their release are elevated in mucosal tissues from patients with IBD. It is likely that these chemokines play an important role in the perpetuation of tissue destructive inflammatory processes.
  • antisense ohgonucleotides (anti-ICAM), anti- cytokine antibodies (anti-TNF) or recombinant human cytokines (IL-10 or IL-11) are effective in some patients with Crohn's disease refractory to steroids (Heresbach et al. 1999). However, these data need to be confirmed and the potential side effects of these treatments must be further considered.
  • Figures 1-15 provide data which were generated in identifying the biomarkers of the present invention.
  • cytokine profiles of IBD patients and normal controls were able to build cytokine classifiers for most of the 11 comparisons and to correctly categorize both original and cross-validated cases with a high success rate.
  • the cytokines, identified as classifiers represent biomarkers that can be used to diagnose IBD, and biomarkers that correlate with disease activity.
  • the ability to diagnose IBD early, based on biomarkers, will result in timely therapeutic intervention and improved outcome.
  • the ability to evaluate and predict the disease activity will enable the design of more appropriate treatments, adapted to the precise molecular mechanisms that are dysregulated in EBD.
  • Anti-TNF therapy has been shown to be effective against rheumatoid arthritis and Crohn's disease (Raza 2000; Emery et al. 2001).
  • the identification of additional cytokines, involved in the pathogenesis of IBD, may allow the design of combination therapies v/ith several anti-cytokine antibodies.
  • Antibody microarrays were printed using a Packard Biosciences (Downers Grove, IL) BCA-II piezoelectric microarray dispenser on cyanosilane-coated glass slides divided by Teflon boundaries into sixteen 0.5 cm diameter circular subarrays. Monoclonal antibodies for 78 cytokines (see Supplementary Material for listing of antibodies and vendors) were dispensed in quadruplicate at a concentration of 0.5 mg/mL. Printed slides were blocked as described (Schweitzer et al. 2002) and stored at 4oC until use. Batches of slides were subjected to a quality control consisting of incubation with a fluorescently-labeled anti-mouse antibody, followed by washing, scanning and quantitation. Typically, the coefficient of variability (CV) of antibody deposition in printing was ⁇ 5%.
  • CV coefficient of variability
  • the assay was performed by a liquid-handhng robot (Biomek 2000, Beckman Instruments, Fullerton, CA), which was enclosed in an 80% humidified, HEPA- filtered, plexiglass chamber. 15 mL sample was applied to each subarray, and immunoassays with RCA signal amplification were performed as described (Schweitzer et al., 2002). Slides were scanned (GenePix, Axon Instruments Inc., Foster City, CA) at 10-mm resolution with laser setting of 100 and PMT setting of 550. Mean pixel fluorescence were quantified using the fixed circle method in GenePix Pro 3.0 (Axon instruments, Foster City, CA). The fluorescence intensity of quadruplicate microarray features was averaged for each feature and sample, and the resulting cytokine values were determined. For every slide, a set of blanks was run and the intensity values were used to correct for background signal.
  • Antibody microarrays were printed using a Packard Biosciences (Downers Grove, EL) BCA-II piezoelectric microarray dispenser on cyanosilane-coated glass slides divided by Teflon boundaries into sixteen 0.5 cm diameter circular subarrays. Monoclonal antibodies for 78 cytokines were dispensed in quadruplicate at a concentration of 0.5 mg/mL. Printed slides were blocked as described (Schweitzer et al. 2002) and stored at 4°C until use. Batches of slides were subjected to a quality control consisting of incubation with a fluorescently-labeled anti-mouse antibody, followed by washing, scanning and quantitation. Typically, the coefficient of variability (CV) of antibody deposition in printing was ⁇ 5%.
  • the assay was performed by a liquid-handling robot (Biomek 2000, Beckman Instruments, Fullerton, CA), which was enclosed in an 80% humidified, HEPA- filtered, plexiglass chamber. 15 ⁇ L sample was applied to each subarray, and immunoassays with RCA signal amplification were performed as described (Schweitzer et al., 2002). Slides were scanned (GenePix, Axon instruments Inc., Foster City, CA) at 10- ⁇ m resolution with laser setting of 100 and PMT setting of 550. Mean pixel fluorescence were quantified using the fixed circle method in GenePix Pro 3.0 (Axon Instruments, Foster City, CA). The fluorescence intensity of quadruplicate microarray features was averaged for each feature and sample, and the resulting cytokine values were determined. For every slide, a set of blanks was run and the intensity values were used to correct for background signal.
  • Cross-validation a method for testing the robustness of a prediction model, was then carried out.
  • To cross-validate a prediction model one removes and sets aside one sample, uses the remaining samples to build a prediction model based on the preselected cytokine predictors, and determines whether the new model is able to predict the one sample not used in building the new model correctly. This process is repeated for all samples one at a time, and a cumulative cross-validation rate can then be calculated.
  • the final list of cytokine predictors was determined by manually entering and removing cytokines to maximize the cross-validation rate, using information obtained from the univariate analyses and cross-validations.
  • the final cytokine classifier is then defined as the set of cytokine predictors that gives the highest cross-validation rate.
  • the aim of our study is to find biomarkers for IBD and for disease activity.
  • Serum samples from IBD patients and from age-matched normal controls were analyzed using the cytokine chip (see Materials and Methods). After QC, data from 61 IBD patients and 63 normal controls were used in statistical analyses. Univariate analysis (Kruskal-Wallis test) was carried out to detect differences in the serum cytokine levels between the groups and to identify potential predictors. Multivariate analysis (linear discriminant analysis) was then performed to determine differences between the cytokine profiles of the groups. Prediction models were built and found to be capable of predicting IBD with high success rate in both original and cross-validation cases (see below).
  • CD patients (clinical remission) vs. CD patients (active disease) - to identify biomarkers that correlate with Crohn's disease activity 2. All CD patients vs. all normal controls - to identify biomarkers that can be used to diagnose CD 3. CD patients (active disease) vs. all normal controls - to identify biomarkers that can be used to diagnose active CD 4. All UC patients vs. all CD patients - to identify biomarkers that can be used to differentiate UC from CD 5. All UC patients vs.
  • Figure 1 shows the serum PLGF (placenta growth factor) levels in CD patients in clinical remission and in CD patients with active disease.
  • the mean intensity of PLGF in the remission group (556+233) was higher than the active group (336 ⁇ 250).
  • the Kruskal-Wallis test identified PLGF as the marker with highest statistical significance in Comparison 1 (CD remission vs. CD active) (Table 1), suggesting that PLGF is a potential class predictor.
  • Table 1 shows that PLGF is a potential class predictor.
  • Table 1 the ranges of PLGF levels for the two groups overlap significantly ( Figure 1).
  • serum PLGF level by itself is not a good predictor of Crohn's disease activity.
  • LDA linear discriminant analysis
  • PLGF placenta growth factor
  • placenta growth factor
  • NEGF vascular endothelial growth factor
  • NEGF nerve growth factor receptor
  • monocytes express the VEGF receptor Flt-1, and this receptor specifically binds also the NEGF homolog PLGF. Both NEGF and PLGF stimulate tissue factor production and chemotaxis in monocytes at equivalent doses.
  • Flt-1 is a functional receptor for VEGF and PLGF in monocytes and endothelial cells and a mediator of monocyte recruitment and procoagulant activity (Clauss et al. 1996). Blood monocytes are recruited to the inflammatory bowel disease mucosa, and the phenotype of the recently-arrived monocytes indicates their susceptibility to stimulation by lipopolysaccharide, suggesting a mechanism for the continuing inflammation in the bacterial product-rich milieu of IBD (Grimm et al. 1995). If used individually, PLGF will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 10- cytokine classifier, the correct classification rate is considerably improved.
  • PLGF was also identified as a classifier cytokine in Comparison 8, all CD patients vs. normal controls ( ⁇ 9 years of age). [49] For Comparison 2, all CD patients vs. all normal controls, we were able to build a 22-cytokine classifier that correctly categorized 96.2% of original grouped cases and 93.3% of cross-validated grouped cases. The number of cytokines used in this classifier is within the range of maximum confidence level, as shown in Figure 12. This 22-cytokine classifier was almost equally successful in correctly categorizing individuals from both groups (Table 13-2).
  • GM-CSF was also one of the cytokines in the classifier. If used individually, G-CSF or other cytokines will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 22-cytokine classifier, the correct classification rate is considerably improved. G-CSF was also identified as a classifier cytokine in Comparisons 4 (all UC patients vs. all CD patients), 5 (all UC patients vs. all normal controls ) and 6 (CD patients ⁇ 9 years of age vs. normal controls ⁇ 9 years of age).
  • MIG MIG, ENA-78
  • mRNA levels encoding MIG and other chemokines were significantly increased in chronically inflamed colons when compared with wild type mice.
  • reversal of colitis by anti-IL-12 mAb was accompanied by the inhibition in the expression of MIG and other chemokines (Scheerens et al. 2001).
  • ENA-78 and other chemokines are highly expressed in the intestinal mucosa in areas of active Crohn's disease and ulcerative colitis (MacDermott 1999). Chemokines, including ENA-78, have been proposed to play a central role of in the immunopathogenesis of IBD (MacDermott et al. 1998). If used individually, MIG and ENA-78 will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 12-cytokine classifier, the correct classification rate is considerably improved. ENA-78 was also identified as a classifier cytokine in Comparisons 2 (all CD patients vs. all normal controls) and 9 (all CD patients vs.
  • FIG. 13 A Venn diagram representing the overlap of the best cytokine classifiers from Comparisons 1, 2 and 3 is shown in Figure 13. Although there are no cytokines common for all 3 classifiers, there are 2 common cytokines (BDNF and sCD23) between the classifiers from Comparisons 1 and 2, one cytokine (1-309) is shared between the classifiers from Comparisons 1 and 3, and 4 cytokines (ENA-78, MSP, NT3 and PARC) are common between the classifiers from Comparisons 2 and 3.
  • BDNF and sCD23 common cytokines
  • EDA-78, MSP, NT3 and PARC 4 cytokines
  • CD45RO+CD8+ T cells Fas mediated apoptosis of CD45RO+CD8+ T cells was reported to be higher in UC patients than the controls, while the number of apoptotic CD45RO+CD4+ T cells from UC mucosa was not (Suzuki et al. 2000).
  • CD45RO+CD4+ T cells are less sensitive to apoptotic signals mediated by Fas, which may contribute to the pathogenesis of UC. If used individually, FAS or other cytokines will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 6-cytokine classifier, the correct classification rate is considerably improved. [53] For Comparison 5, all UC patients vs.
  • HCC-4 Three of them (HCC-4, IL-7, G-CSF) were identified also by Kruskal-Wallis test to be significantly different between the 2 groups (Table 5). It has been shown that the serum IL-7 concentration was significantly increased in UC patients (Watanabe et al. 1997). IL-7 mRNA expression is increased in the thymus tissues from patients but decreased in the colonic mucosa. Since IL-7 is a crucial cytokine for proliferation and differentiation of T cells in the thymus, these results indicate that TL-7 may contribute to the disturbance of immune regulatory T cells in ulcerative colitis.
  • IL-7 transgenic mice develop acute and chronic colitis with histopathological similarity to ulcerative colitis in humans (Watanabe et al. 1999).
  • IL-7 stimulates the proliferation of inactivated mucosal lymphocytes but eliminates activated lymphocytes in the inflamed mucosa of human ulcerative colitis.
  • These findings suggest that chronic inflammation in the colonic mucosa is mediated by dysregulation of epithelial cell-derived IL-7 system. If used individually, IL-7 or other cytokines will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 7-cytokine classifier, the correct classification rate is considerably improved.
  • FIG. 14 A Venn diagram representing the overlap of the best cytokine classifiers from Comparisons 4 and 5 is shown in Figure 14. There is 1 common cytokine (G- CSF) between the classifiers from Comparisons 4 and 5. The relevance of G-CSF to IBD was discussed above.
  • G- CSF common cytokine
  • EGF epidermal growth factor family
  • TGF alpha transforming growth factor alpha
  • EGF beta transforming growth factor beta
  • IGF insulinlike growth factors
  • FGF fibroblast growth factors
  • HGF hepatocyte growth factor
  • VEGF vascular endothelial growth factor
  • Intestinal barrier dysfunction concomitant with high levels of reactive oxygen metabolites (ROM) in the inflamed mucosa have been observed in IBD (Banan et al. 2000b).
  • the cytoskeletal network has been suggested to be involved in the regulation of barrier function.
  • Growth factors epidermal growth factor (EGF) and transforming growth factor alpha (TGF- ' alpha)
  • EGF epidermal growth factor
  • TGF- ' alpha transforming growth factor alpha
  • Caco-2 monolayers were preincubated with EGF, TGF-alpha, or vehicle before incubation with ROM (H(2)O(2) or HOC1).
  • Growth factor pretreatment decreased actin oxidation and enhanced the stable F-actin, while in concert prevented actin disruption and restored normal barrier function of monolayers exposed to ROM.
  • Cytochalasin-D an inhibitor of actin assembly, not only caused actin disassembly and barrier dysfunction but also abolished the protective action of EGF and TGF-alpha.
  • an actin stabilising agent, phalloidin mimicked the protective actions ofthe EGF and TGF-alpha (Banan et al. 2000b).
  • Organization and stability ofthe microtubule cytoskeleton appears to be critical to both oxidant-induced mucosal barrier dysfunction and protection of intestinal barrier mediated by growth factors. Therefore, microtubules may be useful targets for development of drugs for the treatment of IBD (Banan et al. 2000a).
  • Epidermal growth factor, and its human homologue urogastrone are secreted by the gut-associated salivary and Brunner's glands.
  • Recombinant EGF/URO is a powerful stimulator of cell proliferation and differentiation in the rodent and neonatal human intestine (Wright et al. 1990). Ulceration of the epithelium anywhere in the human gastrointestinal tract induces the development of a novel cell lineage from gastrointestinal stem cells. This lineage initially appears as a bud from the base of intestinal crypts, adjacent to the ulcer, and grows locally as a tubule, ramifying to form a new small gland, and ultimately emerges onto the mucosal surface.
  • the lineage produces neutral mucin, shows a unique lectin-binding profile and immunophenotype, is nonproliferative, and contains and secretes abundant immunoreactive EGF/URO. It has been proposed that all gastrointestinal stem cells can produce this cell lineage after mucosal ulceration, secreting EGF/URO to stimulate cell proliferation, regeneration and ulcer healing. This cell lineage is very commonly associated with gastrointestinal mucosal ulceration, and a principal in vivo role for EGF/URO is to stimulate ulcer healing throughout the gut through induction of this cell lineage in the adjacent mucosa (Wright et al. 1990). The relevance of G-CSF to IBD was discussed above (see Comparison 2).
  • Thl T-helper type 1 cytokines IL-18 may play a key pathogenetic role in Thl -mediated disorders, such as CD. Regulation and expression of IL-18 appears to differ between CD and UC, and serum IL-18 may be a useful clinical marker for CD (Furuya et al. 2002). Macrophages, and the macrophage-derived IL-18, play a pivotal role in the establishment of TNBS-induced colitis in mice (Kanai et al. 2001), highlighting the potential use of therapy directed against IL-18 in the treatment of patients with CD.
  • EGF EGF, G-CSF, IL-18 or other cytokines will not be able to correctly classify samples with high success rate, but when used in combination with other cytokines in the 6-cytokine classifier, the correct classification rate is considerably improved.
  • the SDs for each analyte for all UC patients were summed, and analytes were sorted according to the sums, with the lowest sums shown on the left and the highest sums shown on the right.
  • the sample IDs are shown on the left, with the top 20 rows representing UC patients, and the rest of the rows representing normal controls.
  • the analyte abbreviations are shown at the bottom of the figure.
  • VEGF vascular endothelial growth factor
  • P1GF placenta growth factor
  • the serum factor from patients with ulcerative colitis that induces T cell proliferation in the mouse thymus is interleukin-7. J Clin Immunol 17:282-92 Wright NA, Pike C, Elia G (1990) Induction of a novel epidermal growth factor-secreting cell lineage by mucosal ulceration in human gasfrointestinal stem cells.

Abstract

La présente invention concerne des biomarqueurs et des combinaisons de biomarqueurs utilisés pour diagnostiquer et surveiller des maladies intestinales inflammatoires.
PCT/US2004/017024 2003-05-30 2004-06-01 Maladies intestinales inflammatoires WO2005009339A2 (fr)

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US8852581B2 (en) 2005-08-31 2014-10-07 Ith Immune Therapy Holdings Ab Treatment of inflammatory bowel disease
US7943328B1 (en) 2006-03-03 2011-05-17 Prometheus Laboratories Inc. Method and system for assisting in diagnosing irritable bowel syndrome
US8463553B2 (en) 2006-08-15 2013-06-11 Nestec S.A. Methods for diagnosing irritable bowel syndrome
WO2009017444A3 (fr) * 2007-08-02 2009-03-19 Iss Immune System Stimulation Diagnostic, stadification et surveillance de l'affection abdominale inflammatoire
CN101815945A (zh) * 2007-08-02 2010-08-25 Iss免疫系统刺激股份公司 炎性肠病的诊断、分期和监测
US10086072B2 (en) 2011-05-10 2018-10-02 Nestec S.A. Methods of disease activity profiling for personalized therapy management
US11160863B2 (en) 2011-05-10 2021-11-02 Prometheus Laboratories Inc. Methods of disease activity profiling for personalized therapy management
WO2014186728A2 (fr) * 2013-05-17 2014-11-20 Genentech, Inc. Procédés pour le diagnostic et le traitement d'une affection abdominale inflammatoire
CN105209643A (zh) * 2013-05-17 2015-12-30 豪夫迈·罗氏有限公司 用于诊断和治疗炎性肠病的方法
JP2016521540A (ja) * 2013-05-17 2016-07-25 ジェネンテック， インコーポレイテッド 炎症性腸疾患の診断及び治療方法
EP2997162A4 (fr) * 2013-05-17 2017-02-01 F. Hoffmann-La Roche AG Procédés pour le diagnostic et le traitement d'une affection abdominale inflammatoire
WO2014186728A3 (fr) * 2013-05-17 2015-01-22 Genentech, Inc. Procédés pour le diagnostic et le traitement d'une affection abdominale inflammatoire
WO2015067913A1 (fr) 2013-11-07 2015-05-14 Diagnodus Limited Biomarqueurs
JP2017511131A (ja) * 2014-03-27 2017-04-20 ジェネンテック， インコーポレイテッド 炎症性腸疾患の診断及び治療のための方法
US10669587B2 (en) 2014-03-27 2020-06-02 Genentech, Inc. Methods for diagnosing and treating inflammatory bowel disease
US11261493B2 (en) 2014-03-27 2022-03-01 Genentech, Inc. Methods for diagnosing and treating inflammatory bowel disease
US11162943B2 (en) 2017-05-31 2021-11-02 Prometheus Biosciences Inc. Methods for assessing mucosal healing in Crohn's disease patients
US20220082563A1 (en) * 2017-05-31 2022-03-17 Prometheus Laboratories Inc. Methods for assessing mucosal healing in crohn's disease patients
US11796541B2 (en) * 2017-05-31 2023-10-24 Prometheus Laboratories Inc. Methods for assessing mucosal healing in ulcerative colitis disease patients

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