Usefulness of Transcriptional Blood Biomarkers as a Non-invasive Surrogate Marker of Mucosal Healing and Endoscopic Response in Ulcerative Colitis

Núria Planell, M Carme Masamunt, Raquel Franco Leal, Lorena Rodríguez, Miriam Esteller, Juan J Lozano, Anna Ramírez, Maria de Lourdes Setsuko Ayrizono, Claudio Saddy Rodrigues Coy, Ignacio Alfaro, Ingrid Ordás, Sudha Visvanathan, Elena Ricart, Jordi Guardiola, Julián Panés, Azucena Salas, Núria Planell, M Carme Masamunt, Raquel Franco Leal, Lorena Rodríguez, Miriam Esteller, Juan J Lozano, Anna Ramírez, Maria de Lourdes Setsuko Ayrizono, Claudio Saddy Rodrigues Coy, Ignacio Alfaro, Ingrid Ordás, Sudha Visvanathan, Elena Ricart, Jordi Guardiola, Julián Panés, Azucena Salas

Abstract

Background and aims: Ulcerative colitis [UC] is a chronic inflammatory disease of the colon. Colonoscopy remains the gold standard for evaluating disease activity, as clinical symptoms are not sufficiently accurate. The aim of this study is to identify new accurate non-invasive biomarkers based on whole-blood transcriptomics that can predict mucosal lesions and response to treatment in UC patients.

Methods: Whole-blood samples were collected for a total of 152 UC patients at endoscopy. Blood RNA from 25 UC individuals and 20 controls was analysed using microarrays. Genes that correlated with endoscopic activity were validated using real-time polymerase chain reaction in an independent group of 111 UC patients, and a prediction model for mucosal lesions was evaluated. Responsiveness to treatment was assessed in a longitudinal cohort of 16 UC patients who started anti-tumour necrosis factor [TNF] therapy and were followed up for 14 weeks.

Results: Microarray analysis identified 122 genes significantly altered in the blood of endoscopically active UC patients. A significant correlation with the degree of endoscopic activity was observed in several genes, including HP, CD177, GPR84, and S100A12. Using HP as a predictor of endoscopic disease activity, an accuracy of 67.3% was observed, compared with 52.4%, 45.2%, and 30.3% for C-reactive protein, erythrocyte sedimentation rate, and platelet count, respectively. Finally, at 14 weeks of treatment, response to anti-TNF therapy induced alterations in blood HP, CD177, GPR84, and S100A12 transcripts that correlated with changes in endoscopic activity.

Conclusions: Transcriptional changes in UC patients are sensitive to endoscopic improvement and appear to be an effective tool to monitor patients over time.

Keywords: Gene expression; anti-TNF-α; blood biomarkers; ulcerative colitis.

© European Crohn’s and Colitis Organisation (ECCO) 2017.

Figures

Figure 1.
Figure 1.
Whole-blood transcriptional profile associated with the presence of endoscopic mucosal lesions in UC. [A] Heatmap representation of differentially expressed genes in peripheral blood from UC and non-IBD controls according to microarray analysis [Table 1, Group 1]. A total of 122 differentially expressed genes in active UC patients [endoscopic Mayo score ≥ 1] compared with non-IBD controls and UC patients in remission [endoscopic Mayo score = 0] are represented and the 15 genes with a fold-change above 2 are highlighted. Up-regulated genes are shown in red and down-regulated genes are shown in green. Each row shows one individual gene and each column an experimental sample. Unsupervised hierarchical cluster method using Pearson distance and average linkage method was applied for gene and sample classification. Samples from non-IBD controls [shown in skyblue, n = 20], remission UC patients [shown in gold, n = 8], and active UC patients [shown in indianred, n = 18] are shown. [B] Bar plot representation [mean ± MSE] of microarray data for the eight genes that have been validated by RT-PCR [Table 1, Group 1]. Samples from non-IBD controls [shown in skyblue, n = 20], remission UC patients [shown in gold, n = 8], and active UC patients [shown in indianred, n = 18] are shown. [C] Bar plot representations [mean ± MSE] of the eight genes validated by RT-PCR in peripheral blood from UC patients [Table 1, Group 2]. Samples from remission UC patients [shown in gold, n = 25] and active UC patients [shown in indianred, n = 86] are shown; *P < 0.05, **P < 0.01 by pairwise Wilcoxon test. UC, ulcerative colitis; IBD, inflammatory bowel disease; MSE, mean squared error; RT-PCR, real-time polymerase chain reaction.
Figure 2.
Figure 2.
Association between blood transcripts and serological biomarkers with the degree of endoscopic inflammation. [A] Representations of probability density function of Modified Score [MS] for each endoscopic Mayo score. Density function of UC patients from Group 2 [Table 1] with endoscopic Mayo score equals 0 [n = 25, shown in gold], patients with endoscopic Mayo score = 1 [n = 22; shown in skyblue], patients with endoscopic Mayo score = 2 [n = 19; shown in seagreen], and patients with endoscopic Mayo score = 3 [n = 29; shown in darkblue] are shown. [B] Bar plot representation [mean ± MSE] of HP, CD177, GPR84, and S100A12 gene expression from RT-PCR analysis. Patients are categorised by endoscopic Mayo score; patients with endoscopic Mayo score = 0 [n = 25, shown in gold], patients with endoscopic Mayo score = 1 [n = 24; shown in skyblue], patients with endoscopic Mayo score = 2 [n = 21; shown in seagreen], and patients with endoscopic Mayo score = 3 [n = 41] shown in darkblue] are shown. Additionally, the data point of each observation is represented in the plot; *p < 0.05, **p < 0.01 by pairwise Wilcoxon test. The Spearman’s rho and p-value are also shown. [C] Correlation between MS and gene expression of HP, CD177, GPR84, and S100A12 [RT-PCR data]. The linear regression model and Spearman’s rho and p-value are shown. [D] Bar plot representation [mean ± MSE] of CRP, ESR, and platelet count by endoscopic Mayo score [same observations as panel B]; *p < 0.05, **p < 0.01 by pairwise Wilcoxon test. The Spearman’s rho and p-value are also shown. [E] Correlation between MS and CRP, ESR, and platelet count [same observations as panel C]. The linear regression model and Spearman’s rho and p-value are shown. UC, ulcerative colitis; MSE, mean squared error; RT-PCR, real-time polymerase chain reaction; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate.
Figure 3.
Figure 3.
Predictive power of haptoglobin [HP] gene for disease activity in UC patients. The receiver operating characteristic curve [ROC] is shown for different models. Red line shows the ROC curve from Model 1, where the binary response is defined as inactive [endoscopic Mayo score 0] versus active [endoscopic Mayo score 1–3]. The darkblue dashed line shows the 10-fold cross-validation from Model 1. Green line shows the ROC curve from Model 2, where the binary response is defined as inactive [endoscopic Mayo score 0–1] versus active [endoscopic Mayo score 2–3]. The skyblue dashed line shows the 10-fold cross-validation from Model 2. The cut-off points associated with the best specificity and sensitivity are shown [dots] for each Model. The AUC of each ROC curve are shown. UC, ulcerative colitis; AUC, area under the curve.
Figure 4.
Figure 4.
Changes in whole-blood transcriptional profiles are associated with endoscopic progression in UC patients after 14 weeks of anti-TNFα treatment. [A] Distribution of MS in patients with UC before and after anti-TNFα treatment [n = 16, Table 2]. Each dot represents an individual patient at Week 0 or 14. Dot colour corresponds to endoscopic Mayo score and the colour of the connecting line to the endoscopic progression [assessed by MS]. Blue connecting line corresponds to mucosal improvement [n = 10] and red connecting line to no improvement or worsening [n = 6]. [B] Dot plot representation of HP, CD177, GPR84, and S100A12 gene expression changes after 14 weeks of anti-TNFα treatment. Y-axis shows the difference between gene expression at Weeks 14 and 0. [C] Dot plot representation of CRP, ESR, platelet count, and serological HP changes after 14 weeks of anti-TNFα treatment. Y-axis shows the difference between biomarker levels at Weeks 14 and 0. Black dashed line at point 0 denotes no changes in gene expression. Based on MS, individuals with mucosal improvement [shown in blue, n = 10] and those without improvement or worsening [shown in red, n = 6] are shown. Black arrows show two samples without mucosal improvement [based on MS], but decreased gene expression after treatment. Median ± IQR are represented. UC, ulcerative colitis; MS, Modified Score; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; TNF, tumour necrosis factor; IQR, interquartile range.

References

    1. Ordás I, Eckmann L, Talamini M, Baumgart DC, Sandborn WJ. Ulcerative colitis. Lancet 2012;380:1606–19.
    1. Solberg IC, Lygren I, Jahnsen J et al. ; IBSEN Study Group. Clinical course during the first 10 years of ulcerative colitis: results from a population-based inception cohort [IBSEN Study]. Scand J Gastroenterol 2009;44:431–40.
    1. Schroeder KW, Tremaine WJ, Ilstrup DM. Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. A randomized study. N Engl J Med 1987;317:1625–9.
    1. Rosenberg L, Lawlor GO, Zenlea T et al. . Predictors of endoscopic inflammation in patients with ulcerative colitis in clinical remission. Inflamm Bowel Dis 2013;19:779–84.
    1. Colombel JF, Rutgeerts P, Reinisch W et al. . Early mucosal healing with infliximab is associated with improved long-term clinical outcomes in ulcerative colitis. Gastroenterology 2011;141:1194––201..
    1. Stidham RW, Higgins PD. Value of mucosal assessment and biomarkers in inflammatory bowel disease. Expert Rev Gastroenterol Hepatol 2010;4:285–91.
    1. D’Haens G, Ferrante M, Vermeire S et al. . Fecal calprotectin is a surrogate marker for endoscopic lesions in inflammatory bowel disease. Inflamm Bowel Dis 2012;18:2218–24.
    1. Lemmens B, Arijs I, Van Assche G et al. . Correlation between the endoscopic and histologic score in assessing the activity of ulcerative colitis. Inflamm Bowel Dis 2013;19:1194–201.
    1. Boland BS, Boyle DL, Sandborn WJ et al. . Validation of gene expression biomarker analysis for biopsy-based clinical trials in Crohn’s disease. Inflamm Bowel Dis 2015;21:323–30.
    1. Leal RF, Planell N, Kajekar R et al. . Identification of inflammatory mediators in patients with Crohn’s disease unresponsive to anti-TNFα therapy. Gut 2015;64:233–42.
    1. Planell N, Lozano JJ, Mora-Buch R et al. . Transcriptional analysis of the intestinal mucosa of patients with ulcerative colitis in remission reveals lasting epithelial cell alterations. Gut 2013;62:967–76.
    1. Román J, Planell N, Lozano JJ et al. . Evaluation of responsive gene expression as a sensitive and specific biomarker in patients with ulcerative colitis. Inflamm Bowel Dis 2013;19:221–9.
    1. Wu F, Dassopoulos T, Cope L et al. . Genome-wide gene expression differences in Crohn’s disease and ulcerative colitis from endoscopic pinch biopsies: insights into distinctive pathogenesis. Inflamm Bowel Dis 2007;13:807–21.
    1. Kabakchiev B, Turner D, Hyams J et al. . Gene expression changes associated with resistance to intravenous corticosteroid therapy in children with severe ulcerative colitis. PLoS One 2010;5:e13085.
    1. Peyrin-Biroulet L, Panes J, Sandborn WJ et al. . Defining disease severity in inflammatory bowel diseases: Current and future directions. Clin Gastroenterol Hepatol 2016;14:348–54.e17.
    1. Lobatón T, Bessissow T, De Hertogh G et al. . The Modified Mayo Endoscopic Score [MMES]: a new index for the assessment of extension and severity of endoscopic activity in ulcerative colitis patients. J Crohns Colitis 2015;9:846–52.
    1. Gentleman RC, Carey VJ, Bates DM et al. . Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5:R80.
    1. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, 2014.
    1. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004;20:307–15.
    1. Dai M, Wang P, Boyd AD et al. . Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 2005;33:e175.
    1. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118–27.
    1. Ritchie ME, Phipson B, Wu D et al. . limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B 1995;57:289–300.
    1. Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30:207–10.
    1. Vandesompele J, De Preter K, Pattyn F et al. . Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002;3:RESEARCH0034.
    1. Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann Stat 2004;32:407–99.
    1. Hu N, Mora-Jensen H, Theilgaard-Mönch K et al. . Differential expression of granulopoiesis related genes in neutrophil subsets distinguished by membrane expression of CD177. PLoS One 2014;9:e99671.
    1. Theilgaard-Mönch K, Jacobsen LC, Nielsen MJ et al. . Haptoglobin is synthesized during granulocyte differentiation, stored in specific granules, and released by neutrophils in response to activation. Blood 2006;108:353–61.
    1. Suzuki M, Takaishi S, Nagasaki M et al. . Medium-chain fatty acid-sensing receptor, GPR84, is a proinflammatory receptor. J Biol Chem 2013;288:10684–91.
    1. Rijksen G, Staal GE, Beks PJ, Streefkerk M, Akkerman JW. Compartmentation of hexokinase in human blood cells. Characterization of soluble and particulate enzymes. Biochim Biophys Acta 1982;719:431–7.
    1. Federzoni EA, Valk PJ, Torbett BE et al. . PU.1 is linking the glycolytic enzyme HK3 in neutrophil differentiation and survival of APL cells. Blood 2012;119:4963–70.
    1. Munder M, Mollinedo F, Calafat J et al. . Arginase I is constitutively expressed in human granulocytes and participates in fungicidal activity. Blood 2005;105:2549–56.
    1. Le Cabec V, Maridonneau-Parini I. Annexin 3 is associated with cytoplasmic granules in neutrophils and monocytes and translocates to the plasma membrane in activated cells. Biochem J 1994;303[Pt 2]:481–7.
    1. Mirsaeidi M, Gidfar S, Vu A, Schraufnagel D. Annexins family: insights into their functions and potential role in pathogenesis of sarcoidosis. J Transl Med 2016;14:89.
    1. Foell D, Kucharzik T, Kraft M et al. . Neutrophil derived human S100A12 [EN-RAGE] is strongly expressed during chronic active inflammatory bowel disease. Gut 2003;52:847–53.
    1. Lill M, Kõks S, Soomets U et al. . Peripheral blood RNA gene expression profiling in patients with bacterial meningitis. Front Neurosci 2013;7:33.
    1. Hu X, Yu J, Crosby SD, Storch GA. Gene expression profiles in febrile children with defined viral and bacterial infection. Proc Natl Acad Sci U S A 2013;110:12792–7.
    1. Kam SH, Singh A, He JQ et al. . Peripheral blood gene expression changes during allergen inhalation challenge in atopic asthmatic individuals. J Asthma 2012;49:219–26.
    1. Galamb O, Sipos F, Solymosi N et al. . Diagnostic mRNA expression patterns of inflamed, benign, and malignant colorectal biopsy specimen and their correlation with peripheral blood results. Cancer Epidemiol Biomarkers Prev 2008;17:2835–45.
    1. Kaiser T, Langhorst J, Wittkowski H et al. . Faecal S100A12 as a non-invasive marker distinguishing inflammatory bowel disease from irritable bowel syndrome. Gut 2007;56:1706–13.
    1. Kalla R, Kennedy NA, Ventham NT et al. . Serum calprotectin: a novel diagnostic and prognostic marker in inflammatory bowel diseases. Am J Gastroenterol 2016;111:1796–805.
    1. de Bruyn M, Arijs I, Wollants WJ et al. . Neutrophil gelatinase B-associated lipocalin and matrix metalloproteinase-9 complex as a surrogate serum marker of mucosal healing in ulcerative colitis. Inflamm Bowel Dis 2014;20:1198–207.
    1. Quaye IK. Haptoglobin, inflammation and disease. Trans R Soc Trop Med Hyg 2008;102:735–42.
    1. Stroncek D. Neutrophil-specific antigen HNA-2a [NB1, CD177]: serology, biochemistry, and molecular biology. Vox Sang 2002;83[Suppl 1]:359–61.
    1. Wang L, Ge S, Agustian A, Hiss M, Haller H, von Vietinghoff S. Surface receptor CD177/NB1 does not confer a recruitment advantage to neutrophilic granulocytes during human peritonitis. Eur J Haematol 2013;90:436–7.
    1. Göhring K, Wolff J, Doppl W et al. . Neutrophil CD177 [NB1 gp, HNA-2a] expression is increased in severe bacterial infections and polycythaemia vera. Br J Haematol 2004;126:252–4.
    1. Shahabi V, Berman D, Chasalow SD et al. . Gene expression profiling of whole blood in ipilimumab-treated patients for identification of potential biomarkers of immune-related gastrointestinal adverse events. J Transl Med 2013;11:75.
    1. Dupont S, Arijs I, Blanque R et al. . Gpr84 inhibition as a novel therapeutic approach in IBD: Mechanistic and translational studies. In: 10th Congress of the European Crohn’s and Colitis Organisation [ECCO]; February 1821, 2015, Barcelona, Spain.
    1. Vanhoutte F, Dupont S, Van Kaem T et al. . Human safety, pharmacokinetics and pharmacodynamics of the gpr84 antagonist glpg1205, a potential new approach to treat IBD. J Crohns Colitisournal of Crohns & Colitis 2015;9[Suppl 1]:5387.
    1. Reinisch W, Panés J, Khurana S et al. . Anrukinzumab, an anti-interleukin 13 monoclonal antibody, in active UC: efficacy and safety from a phase IIa randomised multicentre study. Gut 2015;64:894–900.
    1. Sandborn WJ, Panés J, Zhang H, Yu D, Niezychowski W, Su C. Correlation between concentrations of fecal calprotectin and outcomes of patients with ulcerative colitis in a phase 2 trial. Gastroenterology 2016;150:96–102.
    1. Kugathasan S, Saubermann LJ, Smith L et al. . Mucosal T-cell immunoregulation varies in early and late inflammatory bowel disease. Gut 2007;56:1696–705.
    1. Desreumaux P, Brandt E, Gambiez L et al. . Distinct cytokine patterns in early and chronic ileal lesions of Crohn’s disease. Gastroenterology 1997;113:118–26.
    1. Veny M, Esteller M, Ricart E, Piqué JM, Panés J, Salas A. Late Crohn’s disease patients present an increase in peripheral Th17 cells and cytokine production compared with early patients. Aliment Pharmacol Ther 2010;31:561–72.

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