molecular patterns in human ulcerative colitis and correlation with response to infliximab

ORIGINAL ARTICLE

Molecular Patterns in Human Ulcerative Colitis and Correlationwith Response to InfliximabBrendan Halloran, BSc, MD,* Jessica Chang, BSc,‡ David Q. Shih, MD, PhD,† Dermot McGovern, MD, PhD,†

Konrad Famulski, PhD, DSc,‡,§ Chad Evaschesen, BSc, MD,* Richard N. Fedorak, MD,*Aducio Thiesen, MSc, MD, PhD,§ Stephan Targan, MD,† and Philip F. Halloran, MD, PhD‡,k

Background: As a T cell–mediated disease of the colonic epithelium, ulcerative colitis (UC) is likely to share pathogenic elements with other T cell–mediated inflammatory diseases. Recently, microarray analysis revealed large-scale molecular changes in T cell–mediated rejection of kidney and hearttransplants. We hypothesized that similar disturbances might be operating in UC and could provide insights into responsiveness to therapy.

Methods: We studied 56 colon biopsies from patients with colitis characterizing the clinical and histological features and using microarrays to definethe messenger RNA phenotype. We expressed the microarray results using previously defined pathogenesis-based transcript sets. We also studied 48published microarray files from human colon biopsies downloaded from the Gene Expression Omnibus database, classified by response to infliximabtherapy, to examine whether the molecular measurements derived from our studies correlated with nonresponsiveness to treatment.

Results: UC biopsies manifested coordinate transcript changes resembling rejecting transplants, with effector T cell, IFNG-induced, macrophage, andinjury transcripts increasing while parenchymal transcripts decreased. The disturbance in gene expression, summarized as principal component 1 (PC1),correlated with conventional clinical and histologic assessments. When assessed in microarray results from published studies, the disturbance (PC1)predicted response to infliximab: patients with intense disturbance did not achieve clinical response, although quantitative improvement was seen even inmany clinical nonresponders. Similar changes were seen in Crohn’s colitis.

Conclusions: The molecular phenotype of UC manifests a large-scale coordinate disturbance reflecting changes in inflammatory cells and parenchymalelements that correlates with conventional features and predicts response to infliximab.

(Inflamm Bowel Dis 2014;20:2353–2363)

Key Words: T cell–mediated rejection, ulcerative colitis, microarrays, infliximab, dedifferentiation

A s a chronic T cell–mediated inflammatory disease of thecolonic epithelium, ulcerative colitis (UC) represents a con-

tinuing challenge to mechanistic understanding and clinical man-agement. The treatment of UC, once dominated by steroids,mesalamine, and immune modulators, has evolved with the intro-duction of biologic agents. Antitumor necrosis factor therapies

such as infliximab have proven to be effective for induction andmaintenance of remission in moderate-to-severe UC.1–3 Despitetheir success, these biological agents are expensive, often ineffec-tive, and associated with adverse events.4,5 Prediction of resis-tance to infliximab and other biological drugs would improvemanagement and potential outcomes,6,7 as would an earlier delin-eation of true nonresponse from partial primary response. Molec-ular assessment of the disease state in the mucosa offers potentialin this regard, as illustrated by the work of Arijs et al8–10 usingmicroarrays to elucidate which genes are associated with responseto infliximab in both Crohn’s disease (CD) and UC.

Although the molecular studies of UC have usually taken theform of studies of specific molecules in relationship to paradigmssuch as TH1/TH211–13 and TH17,14 a new approach is to define thelarge-scale organization using methods that reveal the internalstructure and interrelationships of the molecular changes. Inflam-matory diseases have unique disease-specific features but also sharemany elements that reflect the stereotyped responses to injury in thetissue. Notwithstanding the importance of discovering the disease-specific mechanisms, insights in UC may be derived from studyingthe elements that are shared with other T cell–mediated humandiseases, particularly T cell–mediated rejection (TCMR) of organtransplants. The molecular phenotype of human TCMR in kidney

Supplemental digital content is available for this article. Direct URL citationsappear in the printed text and are provided in the HTML and PDF versions of thisarticle on the journal’s Web site (www.ibdjournal.org).

Received for publication July 13, 2014; Accepted September 3, 2014.

From the *Division of Gastroenterology, Department of Medicine, University ofAlberta, Edmonton, AB, Canada; †F. Widjaja Foundation, Inflammatory Bowel andImmunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles,California; ‡Alberta Transplant Applied Genomics Centre, Edmonton, AB, Canada;§Department of Laboratory Medicine and Pathology, University of Alberta, Edmon-ton, AB, Canada; and kDivision of Nephrology and Transplant Immunology, Depart-ment of Medicine, University of Alberta, Edmonton, AB, Canada.

P. F. Halloran holds shares in Transcriptome Sciences Inc., a company with aninterest in molecular diagnostics. The other authors have no conflicts of interest todisclose.

Reprints: Brendan Halloran, BSc, MD, Division of Gastroenterology, Depart-ment of Medicine, University of Alberta, Zeidler Ledcor Building, 8540-112st,Edmonton, AB T6G 2X8, Canada (e-mail: [email protected]).

Copyright © 2014 Crohn’s & Colitis Foundation of America, Inc.

DOI 10.1097/MIB.0000000000000239

Published online 5 November 2014.

Inflamm Bowel Dis � Volume 20, Number 12, December 2014 www.ibdjournal.org | 2353

and heart transplants has been deconstructed as a large-scale coor-dinate biologic disturbance in the expression of thousands ofgenes.15,16 The trigger is the cognate interaction of effector T cellswith antigen-presenting cells, leading to a massive recruitment andactivation of infiltrating cells such as effector T cells, macrophages,and dendritic cells, IFNG-induced transcripts, microcirculationchanges, and the injury–repair responses of the parenchyma.Injury–repair is represented by increased injury related transcriptsand loss of the transcripts associated with the normal differentiatedepithelium. This large-scale molecular disturbance, like the classicsigns of “inflammation” and wound repair, is not disease- or organ-specific, instead reflecting a general structured response withinwhich specific features of diseases and organ-specific elementscan be mapped. It is similar in heart and kidney transplants withrejection but can also be seen in organ injury and recurrent dis-ease.15,16 In this coordinate process, the increase in burden ofinflammation, the increase in parenchymal injury–repair genes,and the loss of transcripts associated with parenchymal metabolismand function such as expression of solute carriers (SLC)15,17,18 arestrongly correlated.

This study examined how the extent of these generaldisturbances described in other organ diseases could offer a usefulnew dimension for assessing UC. The hypothesis was that the generaldisturbance in UC would correlate with clinical and histological stateand provide an accurate predictor of response to therapy. Wesummarized the microarray changes as scores for gene sets, whichhad been worked out in experimental systems to measure biologicalchanges that might be shared between TCMR and UC, pathogenesis-based transcript sets (PBTs) (http://www.atagc.med.ualberta.ca).These had been derived largely in mouse systems and in cultureand verified and refined in human organ transplant biopsies. Weassembled a cohort of biopsies from consented patients; characterizedtheir conventional phenotype—clinical findings, laboratory medicine,and histology; compared these features with the molecular changes inUC biopsies using the predefined PBT measurements of biologicalchanges; and determined the potential of such quantitative measure-ments for predicting and measuring response to therapy. In particular,we studied whether this approach would provide insights intoresponse to treatment in the dataset developed by Arijs et al. Giventhat the changes should be conserved between diseases, we alsoexamined whether the findings in UC would be conserved in Crohnsdisease of the colon (CDc).

METHODS

Data CollectionThe patient cohort was derived from populations at the

Center of Excellence for Gastrointestinal Inflammation andImmunity Research, University of Alberta Hospital, Edmonton,Canada, and the F. Widjaja Foundation, Inflammatory Bowel andImmunobiology Research Institute, Cedars-Sinai Medical Center(CSMC), Los Angeles, CA. Thirty-one patients were selectedfrom the Center of Excellence for Gastrointestinal Inflammation

and Immunity Research biobank and 25 from the F. WidjajaInflammatory Bowel Disease Center, all collected under approvedInstitutional Review Board protocol no. 3358, and full consentwas obtained from all patients at the time of enrollment. All UCpatients have a verified diagnosis based on accepted criteria. Thefollowing data were collected: demographics (sex, age at biopsy,age at disease diagnosis, disease duration at time of biopsy),disease activity (determined by the Mayo score),19,20 currentmedications, endoscopic data (gross disease extent, Mayo scoreat biopsy site and endoscopic Mayo score), and biochemical data(CBC, Albumin, CRP). Of note, the reason that endoscopic Mayoscore and endoscopic Mayo score at the biopsy site were recordedas separate categories was that the 2 were not always the same(i.e., the Mayo score at the biopsy site was less that the Mayoscore in the worst portion of colitis).

All biopsies have been analyzed by a gastrointestinalpathologist with interest in inflammatory bowel disease (IBD).A model for histologic score was developed based on the typicalinflammatory features observed in IBD with the specific goal ofquantifying the changes and correlating with gene expression. Thehistologic score represented the most severely affected area of theavailable biopsies of a particular patient. The score contained 8different parameters: number of intraepithelial neutrophils perhigh-power field, number of crypt abscess, number of ulcers,number of neutrophils within the lamina propria per high-powerfiled, lymphoplasmacytic inflammatory infiltrate within thelamina propria, number of branched crypts, number of shortcrypts, and number of spaced crypts. The only categoricalparameter was the lymphoplasmacytic inflammatory infiltrate thatwas categorized as normal, mild, and extensive.

Once data had been collected, patients were reclassified toscreening/no major abnormalities, IBD unclassified, or UC. Sixbiopsies previously labeled as UC were switched to IBDU afterreview but kept in the study for analysis.

Pathogenesis-based Transcript SetsWe used the previously developed set of PBTs to permit

biological interpretation of microarray results from biopsies(Table 1) (http://atagc.med.ualberta.ca/Research/GeneLists/). PBTsrepresent biological processes during rejection and other types ofinjury in renal allografts.16,21 The PBTs include cytotoxic T cell–associated transcripts (QCAT),22 interferon-g inducible transcripts(GRIT1),23 macrophage transcripts (QCMAT),18,24 alternative mac-rophage activation transcripts (AMAT1),25 injury and repair tran-scripts (IRRAT),26 and kidney parenchymal transcripts (KT1 andKT2).27 We also included a PBT derived from analysis of TCMR.28

New sets of colon parenchymal transcripts (CT1 and CT2)were derived as colon-specific analogous to the previouslydefined kidney transcript sets KT1 and KT2. Using the rationalethat KT1 and KT2 were anticorrelated with the injury–repair tran-scripts (IRRAT), we identified the probe sets that were anticorre-lated with the IRRAT in colon biopsies from GSE16879, rankedby Spearman’s correlation and P-value. SLC genes were removedfrom CT1 and defined as a separate set, CT2. The top 1333 probe

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sets negatively correlated with IRRAT were selected as CT1because the kidney counterparts, KT1, have 1333 probe sets, andthe top 64 SLC probe sets were selected for CT2 because KT2 has64 probe sets. The top 30 CT1 and CT2 are listed in Table, Sup-plemental Digital Content 1, http://links.lww.com/IBD/A641. CT1shared 148/1333 probe sets with KT1, and CT2 shared 19/64 probesets with KT2.

Abbreviations for all genes conform to the gene namesat http://www.ncbi.nlm.nih.gov/sites/entrez. Standardized PBTscores were calculated (geometric mean of the standardizedexpression of the individual scores) across all the biopsies.

Microarray Processing and AnalysisMicroarray processing was previously described.28 For

analysis, we used robust multiarray averaging as implementedin the Bioconductor “RefPlus” package. All expression valuesare in log2 units. All analyses and graphics were performedusing the “R” software package, version 2.13.1 (64-bit), withvarious libraries from Bioconductor 2.8.4. The details of micro-array expression data will be posted on the Gene ExpressionOmnibus (GEO) website (http://www.ncbi.nlm.nih.gov/geo/;accession number GSE51785).

Statistical AnalysisPBT scores were calculated as the mean of the log2 values

across transcripts for a particular PBT set. The standardized PBTscores were calculated by subtracting the mean PBT score acrossall samples in a set from the PBT score of a given sample anddividing by the SD of the PBT scores for the set. Unsupervisedprincipal component analysis (PCA) was performed on the PBTscores for the populations of colon biopsies.

Correlations between PBTs or genes were calculated usingSpearman’s correlation. Statistical analysis of the mean PBT sig-nals and PC1 in responders and nonresponders was performedusing paired and unpaired Student’s t tests.

Studies of CEL Files from GEOWe downloaded CEL files from 48 human colon biopsy

microarrays from the GEOs database (http://www.ncbi.nlm.nih.gov/geo/; accession number GSE16879). Colonic biopsies wereobtained from 24 patients with treatment-refractory UC, 1 weekbefore and 6 to 8 weeks after initial treatment with infliximab.10

Response to infliximab was defined as Mayo score of 0 or 1 withcomplete mucosal healing. The microarrays were analyzed usingthe previously established gene sets (Table 1). Statistical analysisof the principal component 1 (PC1) and of the mean TCMR,injury–repair, and colon transcript signals in responders and non-responders were performed using paired and unpaired Student’s ttests. For the CDc analysis, we downloaded the files for GEO asfor the UC biopsies.

Ethical ConsiderationsFull Institutional Review Board approval was obtained for

studies at both the University of Alberta (UA) and Cedars-SinaiMedical Center. All patients were fully consented at the time oftissue and data collection, and all procedures were performed asper standard clinical care. All data were de-identified at the timeof collection.

RESULTS

Demographics of the UC PopulationsThe demographics and clinical characteristics of the 56

patients from 2 centers whose colon biopsies are included in thisanalysis are shown in Table 2. The populations at the 2 centerswere similar, including 43 UC, and for comparison, 6 with IBDUand 7 with no major abnormalities. The therapies used were sim-ilar in the 2 centers except that 5 patients from CSMC werereceiving antitumor necrosis factor therapy, whereas none of thepatients from UA were on biologics.

TABLE 1. Pathogenesis-based Transcript Sets

Transcript Set Biologic Descriptiona Interpretation

QCAT Quantitative cytotoxic T cell–associated transcripts Burden of effector T cells

GRIT IFNG-induced transcripts induced in rejecting tissue Burden of IFNG effectsTop 30 TCMR transcripts Transcripts most specific for TCMR in human kidney

transplantsTranscripts best reflecting cognate interactions between

effector T cells and antigen-presenting cells in tissue

QCMAT Quantitative constitutive macrophage associated transcripts Burden of macrophages

IRRAT Injury- and repair-induced transcripts in acutelyinjured parenchyma

Parenchymal response to acute injury with epithelialrepair and remodeling

KT1 Kidney parenchymal transcripts Normal kidney parenchymal functionsCT1 Colon parenchymal transcripts Normal colon parenchymal functions

KT2 Kidney parenchymal SLC transcripts Normal kidney parenchymal solute transport

CT2 Colon parenchymal SLC transcripts Normal colon parenchymal solute transport

aSee Materials and Methods for references to transcript sets.

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The age at biopsy correlated with duration of disease(P ¼ 0.003), and both anticorrelated with the disease severity/total Mayo score (see Table, Supplemental Digital Content 2,http://links.lww.com/IBD/A642), indicating an association betweenyounger age, shorter disease duration, and more active disease. Thetotal Mayo score correlated with the histology features, the highestcorrelation being with lamina propria neutrophilic infiltrate. Thelamina propria lymphoplasmacytic infiltrate and, to a lesser extent,the neutrophil infiltrate correlated with the distribution of disease.The CRP results were not assessable because they were performedby different methods at UA versus CSMC.

Assessing the Large-scale Disturbance inColon Biopsies

We used microarray analysis of the transcripts expressed in thecolon biopsies to study the disturbance in gene expression previouslydefined in kidney and heart transplant biopsies, summarizing the

microarray results as the means of predefined transcript sets, thePBTs15 (Fig. 1). The PBT disturbance in the 56 colon biopsies isvisualized by aligning each biopsy on the x-axis in order of increas-ing injury–repair transcript (IRRAT) scores. The biopsies with lowexpression of the injury–repair transcripts (left side) had also lowexpression of the inflammation-related transcript sets representing thetop transcripts in TCMR, as well as transcripts annotated in effectorT cells (QCAT), macrophages (QCMAT), alternative macrophageactivation (AMAT), and IFNG-inducible transcript sets (GRIT),and high expression of parenchymal transcripts annotated in colon(CT1 and CT2) and kidney (CT1, CT2, KT1, and KT2). The biop-sies with highest injury–repair transcripts had high expression of theinflammation- and injury-/repair-related transcripts and had decreasedexpression of parenchymal transcripts. This is similar to the distur-bance previously described in rejecting kidney and heart transplants,reflecting the derivation of most of these transcript sets in experi-mental conditions independent of these tissues.

TABLE 2. Demographics

Patient Demographics All (n ¼ 56) UA (n ¼ 31) Cedars-Sinai Medical Center (n ¼ 25) Pa

Age at diagnosis; mean (range), yr 30 (7–64) 29 (7–64) 31 (12–57) 0.67

Age at biopsy; mean (range), yr 43 (20–74) 44 (20–66) 42 (24–74) 0.67Disease duration; mean (range), yr 12 (0–38) 15 (1–38) 8 (0–29) 0.03

Total Mayo score

Remission (0–2) 27 17 10 0.42

Mild (3–5) 10 4 6

Moderate (6–10) 18 10 8

Severe (11–12) 1 0 1

Mayo score at biopsy site

Remission (0–2) 19 11 8 0.62Mild (3–5) 19 9 10

Moderate (6–10) 15 10 5

Severe (11–12) 3 1 2

Disease extent

Left-sided colitis 18 8 10 0.52

Pancolitis 19 13 6

Proctitis 4 2 2

None 15 8 7Diagnosis

IBDU 6 4 2 0.31

UC 43 25 18

No major abnormalities/screening colonoscopy 7 2 5

Drug therapy

5-ASA 33 21 12 0.01

6-MP/Aza/mtx 11 7 4

Tumor necrosis factor 5 0 5Steroid 16 5 11

Antibiotics 1 0 1

aChi-square test.Bold values indicate a significance of P,0.05.



The change in the colon transcripts (CT1 and CT2) wassimilar to the change in the kidney transcripts (KT1 and KT2)originally derived in experimental mouse kidney transplants, despitemany differences in the transcripts summarized in each tissue.

The ribbons at the top of Figure 1A show that the biopsieswith highest disturbance in transcript sets (right side) also hadmore disease activity as judged by extent of disease and by endo-scopic and total Mayo scores in general. Thus in each biopsy, themean expression each PBT (y-axis) was related to the changes inall other PBTs.

PCA of the Transcript Set ScoresPCA is a method for summarizing the main changes in large

data sets. The large-scale disturbance in the PBTs was translatedinto an overall score by PCA in “sample space” (Fig. 1B). PC1accounted for 76% of the variance in the PBT scores across thesamples (x-axis) and PC2 accounted for 11% (y-axis) (The outlierwith maximum PC2 is unexplained but included. Its exclusion hadno major effect on the results.). The samples from both centers are

evenly distributed, indicating no major differences between the UAand CSMC biopsies.

The individual transcript sets correlated with PC1 and PC2as shown in Figure 1C, with the inflammation-related and injury–repair transcript sets positively correlated (left side) and the nor-mal parenchyma transcripts negatively correlated (right side).Thus, the positive correlations with inflammation-related andinjury-related transcripts and the anticorrelation with normalparenchymal transcripts, represents most variance across thesamples and can be summarized by PC1.

In addition to the individual transcript sets, the meanclinical, endoscopic, and histologic scores across the samplesPBT scores are plotted on the PCA to visualize their relationshipto the molecular changes shown in Figure 1D (These measure-ments were not included in the calculation of the PCA). Themarkers of disease activity and the histologic features of diseaseactivity and disease extent correlated with the extent of the dis-turbance as expressed by PC1. Measures of disease activity suchas endoscopic Mayo score and lamina propria lymphoplasmacytic

FIGURE 1. PBTs in UC biopsies. A, The molecular phenotype in colon biopsies lined up in order of increasing acute injury-repair (IRRAT) score tovisualize correlation among PBTs. The ribbons at the top show the total and endoscopic Mayo score and disease extent. B, PCA of 56 colonicbiopsies in sample space using the 9 interquartile range (IQR)-filtered PBTs, showing principal components, PC1 and PC2. C, PCA in PBT spaceusing the 9 IQR filtered PBTs, showing PC1 and PC2. D, PCA in PBT space using the 9 IQR filtered PBTs, showing PC1 and PC2. Clinical, endoscopic(blue), and histologic (green) scores were then superimposed onto the PCA plot to show their relative correlation with PBT scores.



infiltrate correlated positively, whereas age at biopsy, diseaseduration, and albumin correlated negatively, i.e., greater distur-bance was observed in biopsies from younger patients with short-er disease duration and lower serum albumin. The number ofbiopsies assessed showed no correlation with PC1, nor did thepresence of pseudopolyps, providing an internal control.

Correlations of Molecular Scores and PrincipalComponents with Clinical, Endoscopic,Laboratory, and Histology Changes

We calculated the Spearman’s correlation coefficients of thePBT scores plus PC1 and PC2 across all biopsies (Table 3). Thestrongest correlations in each column are in bold and the correla-tions significant at P , 0.01 are marked with a superscript a. PC1and all the individual molecular measurements correlated with theclinical features and selected histology features. PC2, which bydefinition is orthogonal to PC1, showed no correlations and is notshown.

The total Mayo score and its components, the endoscopicMayo score and PGA, and disease extent were strongly correlatedwith PC1 and with the positive and negative PBT changes. Rectalbleeding and pseudopolyps were not significantly correlated withPC1. PC1 anticorrelated with duration of disease, albumin, andage at biopsy.

The histologic feature that correlated most strongly with themolecular phenotype/PC1 was lamina propria lymphoplasmacyticinfiltrate and, to a lesser extent, the neutrophilic infiltrate. Focal

epithelial injury (intraepithelial neutrophils and crypt abscess) andchronic epithelial architectural changes (branched crypts) did notcorrelate with PC1. Thus, PC1 summarizes the transcript setchanges in UC biopsies and correlates with many clinical,endoscopic, and histology features of each biopsy.

Relating the Disturbance in PBTs to theResponse to Infliximab

We studied the relationship of the molecular disturbance toresponse to therapy in the microarray files from the biopsies fromthe published studies of Arijs et al.10 U133 2.0 microarray resultsfrom 48 human colon biopsies from 24 patients, taken in 2 differentcohorts preinfliximab and postinfliximab treatment and classifiedby response to therapy, were downloaded from the GEO database(http://www.ncbi.nlm.nih.gov/geo/; accession number GSE16879).

The molecular disturbance in the PBT scores for theinterquartile range-filtered transcripts in biopsies with UC beforetreatment (Fig. 2A) was similar to that described in the our biopsyset, with increasing loss of parenchymal transcripts (CT1 and CT2)as inflammation and injury transcripts increased (QCMAT, QCAT,IRRAT, GRIT1, and TCMR). Six control biopsies from this set areshown at the left. The ribbon in Figure 2A labels the pretreatmentsample by the patient response to treatment, showing that greaterpretreatment disturbance was associated with resistance to treatment.

Table 4 analyzes the differences between UC biopsies from8 responders and 16 nonresponders (clinically defined), pretreat-ment and posttreatment. Response was defined as total Mayo

TABLE 3. Spearman Correlation

Type of Data Feature Studied PC1 TCMR QCAT GRIT1 QCMAT IRRAT KT1 CT1

Clinical and endoscopic Mayo score at biopsy site 0.74a 0.59a 0.56a 0.70a 0.55a 0.72a 20.57a 20.73a

Endoscopic Mayo score 0.70a 0.54a 0.55a 0.68a 0.49b 0.69a 20.55a 20.70a

Total Mayo score 0.70a 0.56a 0.57a 0.72a 0.49a 0.60a 20.55a 20.67a

Disease extent 0.68a 0.71a 0.67a 0.69a 0.44 0.58a 20.58a 20.60a

Physicians’ global assessment 0.65a 0.43b 0.45 0.58a 0.41b 0.67a 20.50b 20.67a

Stool frequency 0.61a 0.47b 0.59a 0.64a 0.40b 0.50a 20.40b 20.53a

Rectal bleeding 0.35 0.29 0.36 0.43b 0.35 0.22 20.26 20.32Pseudopolyps 20.20 20.25 20.22 20.11 20.08 20.13 0.16 0.12

Age at biopsy 20.46 20.35 20.36 20.39b 20.44b 20.41b 0.39 0.46

Disease duration 20.58a 20.56a 20.56a 20.45b 20.39 20.51a 0.42b 0.53a

Albumin 20.67a 20.53a 20.57a 20.65a 20.69a 20.58a 0.54a 0.62a

Histology Lamina propria lymphoplasmacytic infiltrate 0.81a 0.82a 0.78a 0.72a 0.47 0.69a 20.79a 20.80a

Lamina propria neutrophils 0.59a 0.52a 0.56a 0.57a 0.43b 0.49b 20.46b 20.52a

Intra epithelial neutrophils 0.44 0.46b 0.54a 0.40b 0.26 0.33 20.40b 20.37

Crypt abcsesses 0.40 0.40b 0.44b 0.40b 0.27 0.38 20.40b 20.42Branched crypts 0.30 0.30 0.26 0.14 0.03 0.32 20.25 20.33

Biopsy pieces assessed in location 0.08 0.05 0.02 0.05 0.27 0.11 20.07 20.11

aP , 0.01.bP , 0.05.Bold values indicate highest correlations within each type of data.



FIGURE 2. Microarray analysis of Crohn’s and UC colonic biopsies preinfliximab and postinfliximab therapy. A, The molecular phenotype inpretreatment UC biopsies lined up in order of increasing acute injury-repair (IRRAT) score to visualize correlation among PBTs. The ribbon at thetop shows response or nonresponse to infliximab as determined by clinical and endoscopic parameters. B, PCA in pretreatment and posttreatmentUC biopsies from patients using the 9 interquartile range (IQR) filtered PBTs, showing PC1 and PC2. Blue triangles are responders and orangetriangles are nonresponders. The arrow points toward the posttreatment biopsy. Samples which had increasing disturbance (PC1), postinfliximabtherapy are labeled with asterisks. C, PCA in pretreatment and posttreatment Crohn’s colitis biopsies from patients using the 9 IQR filtered PBTs,showing PC1 and PC2. Blue triangles are responders and orange triangles are nonresponders. The arrow points toward the posttreatment biopsy.Samples which had increasing disturbance (PC1), postinfliximab therapy are labeled with asterisks.



score of 0 or 1, and the posttreatment biopsies were taken at week6 or 8 depending on the cohort. PC1 and 3 representative PBTs(TCMR, injury–repair score, and colon transcripts) showed similarpatterns. The disturbance was higher in the nonresponder patientsbefore treatment than in the responders (P , 0.03). The biopsiestaken after treatment showed improvement in PC1in responders(P , 0.004) but also in nonresponders (P , 0.05). However, thedisturbance in the nonresponders was so great before treatment thatit was still higher after treatment (PC1 ¼ 0.16 6 2.30) than it wasin the responders pretreatment (PC1 ¼ 20.65 6 0.85).

The results for individual patients (Fig. 2B) showed themovement to the left (lower PC1/disturbance) in most patientsafter infliximab therapy. Before treatment, the responders (greensymbols) generally began with less disturbance than the nonres-ponders (red symbols), i.e., higher PC1 scores). After treatment(arrow head), the nonresponders generally showed improvementbut only into the range where the responders began. Only 5 of 18samples (marked with asterisk) in the nonresponders worsened,and many labeled clinically as nonresponders markedly improvedafter infliximab therapy.

Thus, greater disturbance in the biopsy before treatmentwas associated with nonresponse as defined clinically, but manynonresponders actually showed improvement in molecular fea-tures during treatment, whether summarized as PC1 or expressedby individual PBT scores.

Crohn’s Colitis Manifests DisturbanceChanges and Responses toInfliximab Therapy

Since our hypothesis was that the disturbance was stereo-typed across disease states, we examined colon biopsies from 19patients with CDc from the data of Arijs et al.8 As in the UCbiopsies, the disturbance was greater in the pretreatment biopsiesof nonresponders (P , 0.001) and was reduced in the posttreat-ment biopsies of responders (P , 0.001) (Table 5). Unlike theobservations in UC, the CDc nonresponders did not consistentlymanifest molecular improvement (P ¼ 0.605), but the number ofpatients was small (n ¼ 7). Most individuals manifested somenumerical improvement (Fig. 2C). Thus, the molecular distur-bance in CDc is similar to that in UC and is associated withclinical nonresponsiveness to treatment.

DISCUSSIONThis study examined how a general structure of an

inflammatory disorder in tissue previously mapped in humankidney and heart transplants with TCMR is found in UC tissuesand correlates with clinical and histologic features. This approachis based on analysis of previously defined “metagenes”: transcriptsets derived to reflect a biologic change within a tissue, mainly inexperimental systems. As a complement to the focus on individualmolecules, this approach studies the global change in the messen-ger RNA landscape within which the individual molecularchanges can be interpreted. We selected a group of patients

undergoing assessment for UC at 2 centers. We found that sum-marizing microarray results from UC biopsies using previouslydefined PBTs reflecting inflammation, tissue injury, and dediffer-entiation mapped a large-scale organization in UC biopsies thatcorrelated with the clinical, endoscopic, and histologic parame-ters. This disturbance was also present in the microarray resultsfrom UC biopsies in a published study of infliximab therapy andthat the extent of the disturbance (summarized as PC1) wasgreater in pretreatment biopsies from patients who were resistantto therapy. The extent of the disturbance showed improvementduring therapy in both groups (responders and nonresponders).However, after therapy, the disturbance in the nonresponders hadonly reached the levels found in the responders before therapy.The disturbance was similar in CDc and also was greater in pa-tients’ resistant to therapy and improved after therapy. The resultssuggest that the large-scale changes in mucosal biopsies fromcolons of patients with IBDs can provide objective informationthat may permit a more personalized approach to the predictionand management of patients with these conditions, particularly inpredicting response to expensive therapies such as infliximab.

The emergence of genome-wide transcript assessment inbiopsies permits study of disease not only at the single gene levelbut also at the level of the large-scale organization of disease-driven changes that reflect biological programs that cross diseasestates, tissue differences, and patient groups. Although researchtends to focus on disease-specific and organ-/tissue-specificfeatures, the sharing of features across disease types and tissuesand organs is also a fundamental concept rooted in studies ofwound healing and inflammation, originating in classical descrip-tions of the features of inflammation that transcend diseases andtissues. For example, the old concept of “functio laesa” (loss offunction) is a feature of inflammatory diseases across all tissuesand corresponds with the loss of the transcripts associated with thefunctions of differentiated tissues, and in some cases sharedbetween tissues, like the sharing between the CT and KT tran-scripts in this study. Studying the shared features of the tissueresponses to injury, the rules that all tissues tend to obey whenthreatened with injury and disease, offers a new dimension thatcomplements and contextualizes the disease and organ-/tissue-specific changes.

Our initial data set, selected to reflect the generality of thedisturbance independent of local differences, showed that thetissues from UC manifest a structure disturbance that, asrepresented by PC1, correlated well with several clinical andhistopathology features. The groups were not separated in termsof center, clinical phenotype, and distributed similarly in PCA oftheir PBT scores. The structure of the disturbance in UC biopsieswas similar to that in kidney transplants and in heart transplants:injury–repair transcripts were accompanied by inflammatoryfeatures—an increase in effector T cell, macrophage, and IFNG-induced transcripts, and a decrease in expression of parenchymaltranscripts, including SLCs. Although the strongest correlateswere seen with the endoscopic Mayo scoring and the total Mayoscore, all of the other components of the Mayo score correlated



with PC1, except for rectal bleeding. It is possible that rectalbleeding is unreliably reported or is less reliable in partially trea-ted patients. In addition, 6 of the 49 patients included werebelieved to be indeterminant colitis when rephenotyping for thisstudy. This blended phenotype of UC and CD may have alsoplayed a role in bleeding not correlating with the molecular signal.

The strongest histologic correlation with PC1 was thelamina propria lymphoplasmacytic infiltrate, and to a lesserdegree neutrophilic lamina propria infiltrate, consistent withrecent publications of Feagin et al29 where lymphoplasmacytosiswas an important predictor of relapse in patients with UC,whereas acute findings of inflammation were not useful. Thissubstantiated similar results from Bitton et al.30 The objectivityand quantitation of PC1 measurements may prove to be an advan-tage of the microarray approach, particularly in the context ofclinical trials, correlating well with clinical and histologic featuresbut offering a more granular assessment.

By assessing the disturbance in microarray results from theACT trials and a second cohort from Leuven,1,10 we were ableapply the system to a more severe disease phenotype and to

demonstrate the ability of this system to predict and to measureresponse to therapy in biopsies from patients with UC and CDc.Both responders and nonresponders showed a significant decreasein the tissue disturbance. Strikingly, the disturbance was greater inthe preinfliximab and post nonresponders compared with respond-ers, indicating that the inflammatory burden and the parenchymalinjury and dedifferentiation plays a major role in predicting resis-tance to antitumor necrosis factor therapy. Moreover, despitea demonstrable response to therapy, the mean signals in the non-responders’ posttreatment did not reach the pretreatment meansignal in the responders: the posttreatment nonresponders werestill more inflamed and injured than the pretreatment responders.

These results provide a basis for developing molecularphenotyping to help assess IBD biopsies, particularly in relation-ship to resistance to biologic therapy, but also provide opportu-nities for understanding the organization of the response to injuryin the tissue affected by these diseases and illustrate how generalfeatures can complement disease and tissue specific features. Inour analysis of the Arjis UC cohort, molecular nonresponse (noimprovement in PC1 after therapy) was far less frequent than

TABLE 5. Crohn’s Colitis (n ¼ 37)

Test (P)

Mean 6 SD

PC1 TCMR transcripts IRRAT CT1

Pretreatment responders (n ¼ 12) 20.10 6 1.08 0.15 6 0.40 0.08 6 0.23 20.09 6 0.19

Posttreatment responders (n ¼ 11) 22.85 6 0.97 20.60 6 0.32 20.40 6 0.25 0.52 6 0.27

P (pretreatment versus posttreatment) (paired Student’s t test) ,0.001 ,0.001 ,0.001 ,0.001

Pretreatment nonresponders (n ¼ 7) 2.61 6 1.04 0.71 6 0.50 0.68 6 0.30 20.75 6 0.47

Posttreatment nonresponders (n ¼ 7) 2.04 6 2.69 0.62 6 0.68 0.45 6 0.45 20.62 6 0.77

P (pretreatment versus posttreatment) (paired Student’s t test) 0.605 0.815 0.065 0.495P (pretreatment responders versus nonresponders) (Student’s t test) ,0.001 0.016 ,0.001 ,0.001

P (posttreatment responders versus nonresponders) (Student’s t test) ,0.001 ,0.001 ,0.001 ,0.001

Bold values indicate a significance of P,0.05.

TABLE 4. Ulcerative Colitis Biopsies (n ¼ 48)

Test (P)

Mean 6 SD

PC1 TCMR Transcripts IRRAT CT1

Pretreatment responders (n ¼ 8) 20.65 6 0.85 20.12 6 0.56 20.03 6 0.26 20.07 6 0.35

Posttreatment responders (n ¼ 8) 23.52 6 0.93 20.60 6 0.34 20.44 6 0.21 0.82 6 0.28

P (pretreatment versus posttreatment) (paired Student’s t test) ,0.001 0.004 ,0.001 ,0.001

Pretreatment nonresponders (n ¼ 16) 1.92 6 2.02 0.59 6 0.72 0.45 6 0.30 20.62 6 0.51

Posttreatment nonresponders (n ¼ 16) 0.16 6 2.30 0.08 6 0.63 0.11 6 0.47 20.16 6 0.59

P (pretreatment versus posttreatment) (paired Student’s t test) 0.051 0.041 0.042 0.045

P (pretreatment responders versus nonresponders) (Student’s t test) 0.002 0.023 0.001 0.013

P (posttreatment responders versus nonresponders) (Student’s t test) ,0.001 0.010 ,0.001 ,0.001

Bold values indicate a significance of P,0.05.



clinically indicated (5 in 16 patients), opening the possibility ofa new definition of response and resistance, potentially identifying“resistant” patients for whom therapy should be initiated at higherdose and/or escalated rapidly rather than abandoned. Conversely,those which truly showed no clinical improvement in their distur-bance during induction can be checked to see if in fact they areundergoing a partial response at the molecular level beforemaking the decision to switch them to other therapy.

Our findings are consistent with current literature, whichshows that mucosal healing and decreased inflammatory markersare both associated with better outcomes in IBD treatment.6,31,32

Arjis et al showed restoration of aberrant mucosal function byinfliximab.33 The ability to potentially quantitate mucosal distur-bance would allow for evaluation of all patients with quantitativemeasurements, and as mucosal healing is now an establishedimportant endpoint, using a “treat-to-target” paradigm, biopsyprocurement is very feasible.34

The strength of our study is that it takes a well-establisheddiagnostic system developed in experimental systems and appliesit to a well-characterized and varied patient cohort. We examinea group of patients that is diverse in its disease phenotype,activity, and geographic location, and then characterized bya single gastroenterologist and pathologist before molecularanalysis. Correlation with the Mayo score validated the relevanceof the molecular findings. All patients included were biopsied forclinical cause without “cherry picking,” and the use of unsuper-vised analysis allows for the examination of large-scale changewithin the tissue without “overtraining.”

The limitations of our study include the retrospective natureof the data collection for the patients gathered from the UA andCSMC cohorts, and the fact that limited data were available forthe cohorts from Arijs et al. We did not have data on fecalcalprotectin because it is not currently standard of care at UA andnot being performed routinely on all patients at CSMC. CRP datawere inconsistently collected and used a number of differentassays and could not be analyzed. Furthermore, our initial cohortwas comprised largely mild disease phenotype. For future studies,a carefully phenotyped larger patient group with full inflammatoryparameters and long-term follow-up will provide greater insightsinto prognostic factors and into molecular phenotyping.

Like many areas of medicine, therapies for IBD currentlyare applied in a “trial and error” strategy that would beimproved by the greater predictability by quantitative molecu-lar “theranostic” tests. Predicting and assessing response totherapy with greater accuracy can avoid costly and wastefulbiologic therapy with unnecessary risks to the patient andcan direct expensive therapies to patients with high chance ofbenefit. Understanding the basis of resistance can guide trialsof new strategies for the resistant patients. Our studies speak tothe possibility that many patients with clinical nonresponse toinfliximab may actually benefit from extended therapy or moreintense therapy, even if a subgroup shows complete nonre-sponse. True molecular nonresponse was uncommon in thispopulation but detectable by our assay.

Although it may seem surprising that tissues share manyfeatures of their large-scale organization, including their parenchy-mal transcript changes, it is actually expected given the greatoverlap in the genes expressed in most tissues. Tissue studies tendto focus on the differences, but the conserved features are also ofinterest, e.g., related to DNA repair, metabolism, electrolyte trans-port, and internal structures and organelles. The injured parenchymamanifests a response to wounding program that aims to mitigateharm, taking the damaged tissue out of service and attempting torestore homeostasis, including activation and recruitment of innateimmunity and preparation for an adaptive immune response,clearance of damaged cells, and remodeling of the matrix andmicrocirculation. By reading these changes, the clinician canquantitate the functional disturbances that ultimately translate intosymptoms.

If these results can be confirmed in larger prospectivestudies, the therapeutic implications of the disturbance measure-ment are considerable as a step toward personalized medicine inIBD. Reliable prediction of response of individuals to therapy hasthe potential to help all patients, leading to better and more cost-effective protocols for the sensitive and avoiding the resistant. Forthe resistant, who are improving and moving toward remission,a trial of longer courses would be reasonable, with assessment ofdurability and net benefit afterward. Alternatively, anotherdimension of therapy might be added to bring the resistant patientinto the therapeutic range for response. For both the sensitive andthe resistant, monitoring of the molecular phenotype could bea guide for when to reduce therapy (and risk and expense). Beingable to say 90% responsiveness versus 90% resistance would bea major step in the clinical trial process, separate trials by riskgroup, and ultimately in management. The goal of definingprecisely the patient phenotype and delivering and monitoringtherapy in relation to that phenotype hold strong promise forbetter care for all patients.

ACKNOWLEDGMENTSSupported in part by funding and/or resources from

Novartis Pharma AG, and in the past by Genome Canada, theUniversity of Alberta Hospital Foundation, Roche MolecularSystems, Hoffmann-La Roche Canada Ltd., the Alberta Ministryof Advanced Education and Technology, the Roche OrganTransplant Research Foundation, and Astellas. DQS andCedars-Sinai biopsies are supported by the F. Widjaja Founda-tion, Inflammatory Bowel and Immunobiology Research Institute.Dr. Halloran held a Canada Research Chair in TransplantImmunology until 2008 and currently holds the Muttart Chair inClinical Immunology.

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molecular patterns in human ulcerative colitis and correlation with response to infliximab

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