ORIGINAL ARTICLE

Gene expression profiling-derivedimmunohistochemistry signature with highprognostic value in colorectal carcinoma

Wenjun Chang,1 Xianhua Gao,2 Yifang Han,1 Yan Du,1 Qizhi Liu,2 Lei Wang,3

Xiaojie Tan,1 Qi Zhang, Yan Liu,1,1 Yan Zhu,4 Yongwei Yu,4 Xinjuan Fan,3

Hongwei Zhang,1 Weiping Zhou,5 Jianping Wang,3 Chuangang Fu,2 Guangwen Cao1

▸ Additional material ispublished online only. To viewplease visit the journal online(http://dx.doi.org/10.1136/gutjnl-2013-305475).1Department of Epidemiology,Second Military MedicalUniversity, Shanghai, China2Department of ColorectalSurgery, Changhai Hospital,Second Military MedicalUniversity, Shanghai, China3Department of ColorectalSurgery, The Sixth AffiliatedHospital, Sun Yat-senUniversity, Guangzhou, China4Department of Pathology,Changhai Hospital, SecondMilitary Medical University,Shanghai, China5Department of Surgery,Eastern Hepatobiliary SurgeryHospital, Second MilitaryMedical University, Shanghai,China

Correspondence toProfessor Guangwen Cao,Department of Epidemiology,Second Military MedicalUniversity, 800 Xiangyin Rd.,Shanghai 200433, China;gcao@smmu.edu.cn

Received 18 June 2013Revised 9 October 2013Accepted 11 October 2013Published Online First30 October 2013

To cite: Chang W, Gao X,Han Y, et al. Gut2014;63:1457–1467.

ABSTRACTObjective Gene expression profiling provides anopportunity to develop robust prognostic markers ofcolorectal carcinoma (CRC). However, the markers havenot been applied for clinical decision making. We aimedto develop an immunohistochemistry signature usingmicroarray data for predicting CRC prognosis.Design We evaluated 25 CRC gene signatures inindependent microarray datasets with prognosisinformation and constructed a subnetwork usingsignatures with high concordance and repeatableprognostic values. Tumours were examinedimmunohistochemically for the expression of network-centric and the top overlapping molecules. Prognosticvalues were assessed in 682 patients from Shanghai,China (training cohort) and validated in 343 patientsfrom Guangzhou, China (validation cohort). Medianfollow-up duration was 58 months. All p values aretwo-sided.Results Five signatures were selected to construct asubnetwork. The expression of GRB2, PTPN11, ITGB1and POSTN in cancer cells, each significantly associatedwith disease-free survival, were selected to construct animmunohistochemistry signature. Patients weredichotomised into high-risk and low-risk subgroups withan optimal risk score (1.55). Compared with low-riskpatients, high-risk patients had shorter disease-specificsurvival (DSS) in the training (HR=6.62; 95% CI 3.70 to11.85) and validation cohorts (HR=3.53; 95% CI 2.13to 5.84) in multivariate Cox analyses. The signaturebetter predicted DSS than did tumour-node-metastasisstaging in both cohorts. In those who receivedpostoperative chemotherapy, high-risk score predictedshorter DSS in the training (HR=6.35; 95% CI 3.55 to11.36) and validation cohorts (HR=5.56; 95% CI 2.25to 13.71).Conclusions Our immunohistochemistry signature maybe clinically practical for personalised prediction of CRCprognosis.

INTRODUCTIONColorectal carcinoma (CRC) is the third most com-monly diagnosed cancer in men and the second inwomen worldwide.1 Surgery remains the mainstayof curative treatment. However, a subset of patientswill develop local recurrences and metachronousmetastases after resection of the primary tumour.To properly address postoperative surveillance and

treatment, it is necessary to develop prognosticmarkers to characterise the heterogeneity of CRC.The tumour-node-metastasis (TNM) stagingsystem, based on the anatomical extent of thedisease, is a well-established prognostic system.However, its prognostic value has been recentlychallenged.2 There have been many ‘prognosticmarker’ studies aiming to improve the prognosticprediction of the TNM system. However, most

Significance of this study

What is already known on this subject?▸ Gene expression signatures predicting the

prognosis of colorectal cancer (CRC) have beencontinually developed in the past 10 years.

▸ None of the previously reported CRC signatureshas been applied in clinical settings possiblybecause of low reproducibility.

▸ Although immunohistochemistry (IHC)signatures have been tested for CRC prognosis,none of them was developed by integratingcurrent gene signatures.

What are the new findings?▸ Of 25 gene signatures evaluated, five were

verified to have higher consistency and/orbetter performance in three independentcombined microarray cohorts.

▸ The five signatures were integrated to constructa functional subnetwork, from which threenetwork-centric molecules were identified tohave prognostic value in CRC.

▸ A four-molecule IHC signature derived from theabove analysis had a higher prognostic valuethan tumour-node-metastasis staging in ourtraining and validation cohorts.

How might it impact on clinical practice inthe foreseeable future?▸ Our four-molecule IHC signature will be more

clinically practicable than previously reportedgene signatures due to its convenience andhigh reproducibility in the clinical settings.

▸ Signature genes identified might be potentialtherapeutic targets for CRC recurrence.

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proposed biomarkers for CRC are not clinically implementeddue to lack of reproducibility and/or standardisation.3 Geneexpression profiling has provided an opportunity to understandthe diversity of cancers and shown great promise in accurateprediction of prognosis and therapeutic response, paving theway for personalised medicine.4 In CRC, a substantial numberof pioneering studies have been conducted on prognosis-relatedglobal gene expression in tumours.5–27 These studies have iden-tified gene signatures that are prognostic, predictive or both forCRC patients. However, none of these signatures has beenadopted in clinical practice, possibly because of low reproduci-bility. A meta-analysis indicated that most signatures showed asignificant association with prognosis in their training datasetsbut none of the signatures performed satisfactorily when theprediction ability was assessed in independent datasets.28 Theheterogeneity in cell populations of tumour mass might dilutethe prognosis molecular signal. These gene signatures onlyslightly overlap in gene identity, which was perplexing withregard to their clinical application. This lower-than-expectedoverlap is likely due to differences in patient cohort, microarrayplatform and data-mining method. Therefore, it is beneficial toknow if these gene signatures with little overlap can be connectedtogether to form a molecular network with robust stratificationpower and high stability in various CRC cohorts. Currentmethods of gene expression profiling usually require frozentissues for analysis. However, most available samples areformalin-fixed paraffin-embedded (FFPE), which has been thestandard storage method for decades. Immunohistochemistry(IHC) using FFPE specimens is highly available in medicalcentres because of its convenience, low cost and high reproduci-bility. Hence, IHC using FFPE specimens could be an importantstep in translating the findings of current gene expression profil-ing. Here, we applied a systematic approach to evaluate therobustness of CRC-related signatures and construct a subnetworkby comparing and integrating currently available microarraydata. Based on the network-derived and top overlapping mole-cules, we developed a powerful IHC signature with high prog-nostic value in CRC.

METHODSSelection of candidate prognostic biomarkersWe searched PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) forgene expression signatures concerning CRC prognosis, publishedup to 31 December 2012. We also constructed an inhouse CRCprognosis-related gene signature (see online supplementary tableS1) using microarray datasets GSE28702 and GSE5206.29 30 CRCmicroarray datasets with prognosis information were retrievedfrom GEO (http://www.ncbi.nlm.nih.gov/geo/) and ArrayExpress(http://www.ebi.ac.uk/arrayexpress/). After excluding datasets withsmall sample size and duplications, we combined the datasets ofthe same platforms. After gene identity mapping, prognosticvalues of gene signatures were reassessed separately in each com-bined microarray dataset using a modified nearest template predic-tion method.31 Genes from robust signatures were mapped andimported to NetBox (http://cbio.mskcc.org/tools/netbox/) thatqueried the human protein–protein interaction network for inter-actions between effective linkers and seeds to construct a func-tional subnetwork. This procedure is detailed in online datasupplement. The top overlapping molecule and the network-centric molecules were selected for subsequent study.

Study patientsWe obtained pathologically proven FFPE specimens of 1097stages I–III CRC patients with typical adenocarcinoma histology.

Of these, 706 received curative surgery in Changhai Hospital,Second Military Medical University (Shanghai, China) betweenJanuary 2001 and December 2009 and 391 received curativesurgery in The Sixth Affiliated Hospital, Sun Yet-Sen University(Guangzhou, China) between January 2000 and January 2006.Baseline information for each specimen donor, including age,gender, disease location, and TNM staging at surgery and rule-based postoperative chemotherapy (FOLFOX regimen), wasdocumented. TNM staging was reclassified according to theAmerican Joint Committee on Cancer staging manual (seventhedition). Flow diagram and selection criteria of study patientsfor survival analysis are presented in online supplementaryfigure S1. We also acquired FFPE specimens of mucosa from 51haemorrhoids patients, of adenoma from 51 polyps patients andfreshly frozen tumour tissues from 96 stage I–III CRC patientswho received surgery in Changhai Hospital. All participants areself-reported Han Chinese. This study was approved by theinstitutional review boards of Changhai Hospital and The SixthAffiliated Hospital. A written informed consent was obtainedfrom each patient.

Quantitative RT-PCR, RNA silencing and western blotRelative mRNA levels of GRB2 (growth factor receptor-boundprotein 2), PTPN11 encoded SHP2 (Src homology phosphotyr-osine phosphatase 2), ITGB1 (integrin β1) and POSTN (perios-tin) in freshly frozen CRC tissues were measured usingquantitative reverse transcription (RT)-PCR. The primers andPCR condition are presented in online supplementary table S2.The frozen CRC tissues with mRNA concentration gradients ofthe four molecules were randomly selected for protein analysis.Relative protein levels of the four molecules in the frozen CRCtissues and human CRC cell lines were measured using westernblot. Western blot with siRNA transfection was also performedto evaluate the specificity of antibodies. These assays aredetailed in online data supplement.

ImmunohistochemistryTissue microarrays (TMAs) containing the specimens fromChanghai Hospital were commercially developed (OutdoBiotech, Shanghai, China) and those from The Sixth AffiliatedHospital were developed conforming to the guidelines.32 33 Theconstruction of tissue microarrays is detailed in online data sup-plement. IHC was carried out in Pathology Core Laboratory ofChanghai Hospital. Rabbit monoclonal antibodies to GRB2(1:150, #1517-1, Epitomics, Burlingame, California, USA),mouse monoclonal antibody to ITGB1 (1:50, ab3167, Abcam,Cambridge, UK), and rabbit polyclonal antibodies to humanPOSTN (1:500, ab14041, Abcam) and SHP2 (1:100,#AP8471e, Abgent, San Diego, California, USA) were used forIHC according to protocols provided by the manufacturers.Intratumoural heterogeneity of these antibodies was assessed byexamining eight randomly selected spots of each whole mountfrom the remaining blocks of specimens originally used for thedevelopment of TMA from 10 patients. The procedures of IHCand the intratumoural heterogeneity assays are presented inonline data supplement. Scores were independently assessed byeight researchers including two pathologists (YZ, YY) blinded toclinical data as previously described.34 Briefly, staining intensitywas graded as 0 (negative), 1 (weak), 2 (moderate) and 3(strong); staining extent was graded as 0 (0%–4%), 1 (5%–

24%), 2 (25%–49%), 3 (50%–74%) and 4 (>75%). Values ofthe intensity and the extent were multiplied as an immunoreac-tive score. The criteria of IHC scoring for the four proteinswere first determined by the two pathologists and followed by

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the others. There was a close agreement on staining intensity(81%) and staining extent (85%) between the two pathologists.Disagreements were resolved by consensus.

Follow-up and survival analysisFollow-up exam was performed at our outpatient clinics every3–6 months or outpatient clinics of local hospitals whenever thepatients had related symptoms and/or syndromes. Medianfollow-up time was 58 months (IQR 30–78). At the follow-upexam, serum levels of carcinoembryonic antigen (CEA) andcarbohydrate antigen 19-9 (CA19-9) were measured and anabdominal ultrasonography was performed for all patients. Forthose suspected of CRC relapse, CT, MRI and/or colonoscopywere conducted to confirm the diagnosis. The final date offollow-up was 10 January 2012 for patients from ChanghaiHospital (the Shanghai cohort) and 5 August 2010 for patientsfrom The Sixth Affiliated Hospital (the Guangzhou cohort).

Patients with intact IHC data were included in survival ana-lysis. Our primary outcome of interest was disease-free survival(DFS). DFS was defined as months from the date of receivingsurgery to the date of first relapse. Patients who experienced

second primary tumours of other histotypes were counted ascensored in the DFS analysis. Disease-specific survival (DSS) wasmeasured in months from the date of receiving surgery to thedate that patient died of CRC. We identified each biomarkerwhose immunoreactive score was significantly associated withDFS, then selected an optimal combination of molecules withhigh prognostic values to form an IHC signature using thesamples of patients from the Shanghai cohort as a training set.Prognostic value of the signature was subsequently validated inthe patients from the Guangzhou cohort as an external valid-ation set.

Statistical analysisPearson’s r test was applied to evaluate the correlations ofmRNA levels with protein levels of selected molecules in freshlyfrozen CRC tissues. Spearman’s r test was used to evaluate thecorrelations of immunoreactive score with TNM stage.Intratumoural heterogeneity of IHC scores was assessed separ-ately for each marker by calculating the coefficient of variation(CV). To evaluate the effects of each marker and their combina-tions on the prediction of DFS in the training cohort, lasso

Figure 1 Structure of functional subnetwork constructed with molecules of five colorectal carcinoma prognosis-related microarray signatures.Molecules in the five robust signatures (Oh’s, Jorissen’s, Laiho’s, Popovici’s and inhouse) were used to construct a subnetwork which was connectedby 24 linker (rectangle) genes and 118 seed (round circle) genes. Bigger size and red colour direction indicate higher degree and higherbetweenness, respectively.

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penalised multivariate Cox proportional hazards model wasemployed to perform the variable selection and shrinkage and aleave-one-out cross-validation method was used to calculateregression coefficient of each marker and cross-validation partiallikelihood of each formula.35 Only the marker with non-zeroregression coefficient was used to form a formula of risk scorewith the regression coefficient as a weight. Receiver operatingcharacteristics (ROC) curve was also used to compare the valid-ity of each formula in predicting the 5-year DFS. A formulawith maximal partial likelihood and area under ROC curve(AUC) was considered as the best formula. The cut-off point ofthe best formula, which could partition patients into high-riskand low-risk subgroups, was optimised using X-tile software

(http://medicine.yale.edu/lab/rimm/research/software.aspx).36 χ2

Test, Fisher’s exact test, Student t test, and Mann–Whitney Utest were used to determine differences in clinicopathogical vari-ables between high-risk and low-risk subgroups in both cohorts.Kaplan–Meier analysis with log-rank test was used to estimateDFS and DSS. Multivariate Cox regression analysis was per-formed to determine contribution of the IHC signature to thesurvivals, adjusting for age, gender, disease location, TNMstage, tumour differentiation grade, lymph nodes examined,serum CEA, serum CA19-9 and adjuvant chemotherapy. All stat-istical analyses were two-sided and conducted using R (http://www.r-project.org/) and SPSS V.16.0.2 for Windows (SPSS,Chicago, Illinois, USA). Significance was defined as p<0.05.

Figure 2 Immunohistochemistry expression pattern of GRB2, ITGB1, SHP2 and POSTN in normal mucosa, adenoma and stages I–III colorectalcarcinoma (CRC). The representative immunohistochemistry visual fields in this figure were scored as follows. Immunoreactive scores for GRB2 were12 in epithelial nuclei, 8 in epithelial cytoplasm and 8 in parenchyma of normal mucosa; 12 in epithelial nuclei, 8 in epithelial cytoplasm and 8 inparenchyma of adenoma tissues; 4 in cancer cell nuclei, 8 in cancer cytoplasm and 6 in parenchyma of stage I CRC; 3 in cancer cell nuclei, 12 incancer cytoplasm and 6 in parenchyma of stage II CRC; and 3 in cancer cell nuclei, 12 in cancer cytoplasm and 6 in parenchyma of stage III CRC.Immunoreactive scores for ITGB1 were 0 in epithelial cytoplasm of normal mucosa; 0 in epithelial cytoplasm of adenoma tissues; 12 in cancercytoplasm of stage I CRC; 12 in cancer cytoplasm of stage II CRC; and 12 in cancer cytoplasm of stage III CRC. Immunoreactive scores for SHP2were 12 in epithelial nuclei of normal mucosa; 12 in epithelial nuclei of adenoma tissues; 8 in cancer nuclei of stage I CRC; 6 in cancer cell nucleiof stage II CRC; and 0 in cancer cell nuclei of stage III CRC. Immunoreactive scores for POSTN were 4 in fibroblast cytoplasm and 3 in epithelialcytoplasm of normal mucosa; 9 in fibroblast cytoplasm and 3 in epithelial cytoplasm of adenoma tissues; 8 in fibroblast cytoplasm and 8 inepithelial cytoplasm of stage I CRC; 12 in fibroblast cytoplasm and 9 in epithelial cytoplasm of stage II CRC; and 12 in fibroblast cytoplasm and 12in epithelial cytoplasm of stage III CRC. Different proteins might have different immunostaining patterns. The grading for staining intensity wasbased on individual protein. Bar, 50 μm.

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RESULTSThe top overlapping and network-centric moleculesWe initially included 24 published signatures (see onlinesupplementary table S3)5–27 and an inhouse signature. The 25signatures contained a total of 1708 unique genes and none ofthem appeared in all signatures. The top overlapping genes inthe 25 signatures were POSTN (six times), followed by CYP1B1and SPP1 (five times), 23 genes (three times), and 155 genes(twice). We excluded five signatures without information ofgene expression direction.7 9 11 12 26 The remaining 20 gene sig-natures were subjected for nearest template prediction analysesin each of four combined microarray datasets from Affymetrixplatforms (see online supplementary tables S4 and S5). Twelveof the 20 signatures had positive prevalent prediction in at leastone combined dataset, six (Laiho’s, Oh’s, Jorissen’s, Popovici’s,Arango’s and inhouse) had a high prevalent prediction (>8%)in three combined datasets (see online supplementary table S6).The concordance analysis indicated that five signatures( Jorissen’s, Oh’s, Popovici’s, Laiho’s and inhouse) correlatedwith each other (Cramer’s V values of >0.40) in the three com-bined datasets (see online supplementary table S7). Laiho’s,Oh’s, Popovici’s and inhouse signatures significantly predictedpoor survival(s) in the U133Plus2 dataset, while Jorissen’s signa-ture significantly predicted poor survival(s) in U133A dataset

(see online supplementary figure S2). Multivariate Cox analysiswith age, gender and tumour stage as covariates showed thateach of the five signatures ( Jorissen’s, Oh’s, Popovici’s, Laiho’sand inhouse) significantly predicted poor survival(s) of CRC(see online supplementary table S8).

The five signatures contained 508 unique genes, of which one(POSTN) overlapped four times. With these genes, we con-structed a subnetwork consisting of 24 linkers and 118 seedsconnected by 270 edges with the shortest path threshold of twoand p<0.05. Gene function enrichment analysis showed thatthe subnetwork was enriched with molecules functionallyrelated to cancer invasiveness (see online supplementary tableS9). GRB2, PTPN11, ITGB1, FN1, VEGFA and CD4 withhigher degrees (>12 neighbours) and betweennesses than otherswere network-centric proteins (figure 1). FN1 is an extracellularmatrix protein whose expression differs among normal tissuesfrom the right colon, left colon and rectum as well as betweenadenoma and normal mucosa.37 VEGFA, a major proangiogenicfactor, expresses prominently in adjacent parenchyma.38 CD4 isthe hallmark of T helper cells. Thus, FN1, VEGFA and CD4 arenot ideal for the establishment of CRC prognosis-related IHCsignature with molecules predominately expressed in cancercells. We then selected GRB2, PTPN11, ITGB1 and POSTN forsubsequent study.

Table 1 Clinical characteristics of colorectal carcinoma patients dichotomised by immunohistochemistry signature at the cut-off in the trainingand validation cohorts

Shanghai cohort (n=682) Guangzhou cohort (n=343)

CharacteristicsHigh-risk group(n=205)

Low-risk group(n=477) p Value*

High-risk group(n=69)

Low-risk group(n=274) p Value*

Age (years), mean (SD) 59.79 (12.67) 60.09 (12.78) 0.777† 58.42 (14.42) 59.11 (14.09) 0.718†

Sex (n (%))

Women 80 (39.0) 211 (44.2) 0.602 38 (55.1) 118 (43.1) 0.073

Men 125 (61.0) 266 (55.8) 31 (44.9) 156 (56.9)

Disease location (n (%))

Colon 88 (42.9) 217 (45.5) 0.537 26 (37.7) 127 (46.4) 0.195

Rectum 117 (57.1) 260 (54.5) 43 (62.3) 147 (53.6)

Differentiation grade (n (%))

Well 1 (0.5) 24 (5.0) 0.152‡ 5 (7.2) 24 (8.8) 0.796‡

Moderately 123 (60.0) 264 (55.3) 58 (84.1) 226 (82.5)

Poorly 75 (36.6) 153 (32.1) 6 (8.7) 24 (8.8)

Missing 6 (2.9) 36 (7.5) 0 (0) 0 (0)

Number of lymph nodes examined (n (%))

<12 85 (41.5) 224 (47.0) 0.186 38 (55.1) 150 (54.7) 0.965

≥12 120 (58.5) 253 (53.0) 30 (43.5) 117 (42.7)

Missing 0 (0) 0 (0) 1 (1.4) 7 (2.6)

TNM stage (n (%))

I 3 (1.5) 49 (10.3) <0.006‡ 13 (18.8) 36 (13.1) 0.654‡

II 95 (46.3) 216 (45.3) 30 (43.5) 136 (49.6)

III 107 (52.2) 212 (44.4) 26 (37.7) 102 (37.2)

Adjuvant chemotherapy (n (%))

Yes 188 (91.7) 386 (80.9) <0.0001 12 (17.4) 77 (28.1) 0.072

No 17 (8.3) 91 (19.1) 54 (78.3) 187 (68.2)

Missing 0 (0.0) 0 (0.0) 3 (4.3) 10 (3.6)

Serum CEA (ng/mL), median (range) 4.03 (0–303.40) 3.25 (0–577.80) 0.077‡ 2.95 (0.2–656.0) 2.85 (0.1–203.0) 0.687‡

Serum CA19-9 (U/mL), median(range)

12.88 (0–1000.0) 12.05 (0–1000.0) 0.160‡ 22.00 (0.01–272.0) 2055 (0–275.2) 0.785‡

*χ2 test or Fisher’s exact test.†Student t test.‡Mann–Whitney U test (non-parametric). Missing values are excluded for all statistic tests.CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; TNM, tumour-node-metastasis.

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Correlation between mRNA and protein expression levelsof the selected genes in CRC tissuesIn the 30 frozen CRC tissues with mRNA concentration gradi-ents of the four selected molecules, protein expression levels ofthese molecules significantly correlated to their correspondingmRNA levels (GRB2: r=0.631, p<0.001; ITGB1: r=0.425,p=0.019; POSTN: r=0.502, p=0.005; PTPN11: r=0.654,p<0.001). The expression patterns of the four molecules in thefrozen CRC tissues were generally consistent with those inhuman CRC cell lines as detected using western blot. Westernblot and siRNA assays supported the specificities of antibodiesto the four markers in CRC cell lines. These data are shown inonline supplementary figure S3.

Expression patterns of candidate molecules in colorectalspecimensTypical immunostainings are shown in figure 2. POSTNexpressed in extracellular matrix and nuclear of epithelial andmesenchymal cells in mucosa and adenoma tissues but expressedstrongly in cytoplasm of cancer cells and infiltrated fibroblasts inCRC specimens. GRB2 immunoreactivity was mainly nuclear innormal epithelial cells and cytoplasmic and nuclear in cancercells in CRC specimens. Strong ITGB1 immunoreactivity wascell membrane and cytoplasmic in cancer cells in CRC speci-mens although positive staining of ITGB1 was also observed onthe membrane of some epithelial cells in the benign samples.SHP2 immunoreactivity was mainly nuclear in normal epithelialcells and weakly expressed in epithelial cells in adenoma andCRC tissues. The CV (IQR) for the intratumoural heterogeneityin immunoreactive scores of ITGB1, POSTN, GRB2 and SHP-2in whole mounts were 15.5% (10.8%–21.5%), 15.0% (11.5%–

18.8%), 21.0% (3.3%–28.3%) and 14.0% (10.3%–26.5%),respectively.

Construction of an IHC signatureIHC data of all the four proteins were available for 1025(93.4%) patients (682 in the Shanghai cohort and 343 in theGuangzhou cohort). In the Shanghai cohort, univariate Coxregression analysis indicated that TNM stage, differentiationgrade, postoperative chemotherapy and immunoreactive scores(as continuous variables) of GRB2 in cancer cytoplasm (HR,1.14; 95% CI 1.08 to 1.20; p<0.0001), ITGB1 in cancer cyto-plasm (HR 1.21; 95% CI 1.14 to 1.30; p<0.0001) and POSTNin cancer epithelial (HR 1.13; 95% CI 1.08 to 1.18;p<0.0001), were significantly associated with increased DFS;whereas the score of SHP2 in cancer cells was significantly asso-ciated with decreased DFS (HR, 0.90; 95% CI, 0.86 to 0.94;p<0.0001). A formula composed of tumour POSTN, tumourcytoplasm GRB2, tumour ITGB1 and tumour SHP2 had thehighest cross-validated partial likelihood and the maximal AUCvalue among the tested formulae (see online supplementarytable S10), indicating the four-protein panel was the best one.We then derived this formula to calculate a risk score for eachpatient based on immunoreactive scores of the four markers inthe training set, weighted by regression coefficients:

Riskscore ¼ð0:1467 � ITGB1scoreÞ þ ð0:1132

� cytoplasmGRB2 scoreÞ

þ ð0:0759 � tumour POSTN scoreÞ-ð0:1150

� SHP2 scoreÞ

Table

2Cox

regression

analysisof

immunohistochem

istrysignaturescoreandclinicopathologicalcovariates

withsurvivalsintheShanghaicohort

Disease-freesurvival

Disease-specificsurvival

Univariateanalysis

Multivariateanalysis

Univariateanalysis

Multivariateanalysis

HR(95%

CI)

pValue

HR(95%

CI)

pValue

HR(95%

CI)

pValue

HR(95%

CI)

pValue

Signaturescore(high-risk

vslow-risk)

4.50

(3.10to

6.54)

<0.001

4.55

(3.07to

6.76)

<0.001

6.47

(3.72to

11.26)

<0.001

6.62

(3.70to

11.85)

<0.001

TNM

stage(IIIvs

I+II)

2.62

(1.78to

3.85)

<0.001

1.69

(1.10to

2.60)

0.018

2.11

(1.25to

3.55)

0.005

1.40

(0.78to

2.50)

0.262

Differentiationgrade(poorlyvs

well+moderately)

1.99

(1.37to

2.90)

<0.001

1.47

(0.99to

2.19)

0.057

1.95

(1.17to

3.28)

0.011

1.52

(0.88to

2.63)

0.132

Adjuvantchem

otherapy

(yes

vsno)

3.70

(1.63to

8.41)

0.002

1.81

(0.75to

4.42)

0.189

2.77

(1.00to

7.64)

0.049

1.54

(0.51to

4.67)

0.448

Age

(≥50

vs<50

years)

0.71

(0.47to

1.06)

0.096

0.79

(0.50to

1.23)

0.292

1.16

(0.60to

2.22)

0.663

1.23

(0.61to

2.46)

0.561

Sex(men

vswom

en)

1.18

(0.81to

1.71)

0.386

1.25

(0.84to

1.85)

0.277

1.34

(0.79to

2.27)

0.275

1.36

(0.78to

2.36)

0.278

Disease

location

(rectum

vscolon)

1.05

(0.73to

1.51)

0.788

1.25

(0.85to

1.84)

0.254

1.22

(0.73to

2.02)

0.446

1.33

(0.78to

2.27)

0.293

Lymph

nodesexam

ined

(≥12

vs<12)

2.09

(1.42to

3.07)

<0.001

1.90

(1.26to

2.85)

0.002

1.88

(1.11to

3.20)

0.020

1.95

(1.12to

3.40)

0.019

Serum

CEA

(ng/ml)(≥

5vs

<5)

1.35

(0.94to

1.95)

0.110

1.02

(0.68to

1.53)

0.939

1.36

(0.81to

2.26)

0.242

1.01

(0.58to

1.76)

0.968

Serum

CA19-9

(U/ml)(≥

37vs

<37)

1.85

(1.21to

2.83)

0.004

1.63

(1.00to

2.64)

0.049

1.71

(0.94to

3.10)

0.080

1.50

(0.77to

2.92)

0.230

CA19-9,carbohydrate

antigen19-9;CEA

,carcinoembryonicantigen;

TNM,tumourto

node

tometastasis.

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Prognostic values of the signatureUsing the formula, patients in the Shanghai cohort were dichoto-mised into high-risk and low-risk subgroups with an optimal riskscore (1.55) as the cut-off. Distribution of demographic and clin-ical characteristics such as age, gender, disease location, tumourdifferentiation grade, and serum levels of CEA and CA19-9 didnot vary significantly between the high-risk and the low-risk sub-groups (table 1). In the multivariate Cox regression analyses, high-risk score, TNM stage (III vs I+II) and tumour cell involvement inlymph nodes were independently associated with unfavourable

DFS while high-risk score and the lymph node involvement wereindependently associated with poor DSS (table 2). Compared withpatients with low-risk scores, those with high-risk scores hadshorter DFS and DSS. Importantly, high-risk score significantlypredicted poor DFS and DSS for stage II patients (figure 3).

We then applied the same cut-off to dichotomise the studypatients in the Guangzhou cohort. Clinical variables includingthe TNM stage, tumour differentiation grade and postoperativechemotherapy did not vary significantly between the high-riskand low-risk subgroups (table 1). In the multivariate Cox

Figure 3 High-risk immunohistochemistry signature score and poor survivals of colorectal carcinoma patients in the Shanghai cohort and theGuangzhou cohort. Stage I–III patients were dichotomised into high-risk subgroup and low-risk subgroup at the cut-off point (1.55) of the signaturescore, respectively. Disease-free survival and disease-specific survival are presented. p Values are shown. Green line represents the high-risksubgroup. Blue line indicates the low-risk subgroup. Log-rank p values are from Kaplan–Meier analysis with log-rank test.

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regression analyses, high-risk signature score, rather than TNMstage, was significantly associated with an unfavourable DSS(table 3). Patients with high-risk scores had shorter DFS andDSS than did those with low-risk scores. Similarly, high-riskscore significantly predicted a poor DSS for stage II patients(figure 3).

Taking signature risk score as a continuous variable, we alsoperformed the multivariate Cox regression analysis, adjustingfor these covariates. The same conclusions were obtained with acontinuous risk score in both cohorts. The signature scoresincreased with increasing TNM stage in the training cohort(Spearman’s r=0.181, p<0.001) but not in the validationcohort (Spearman’s r=0.008, p=0.884).

The signature predicts the prognosis of the patients whoreceived postoperative chemotherapyWe assessed prognostic value of the signature for the patientswith or without postoperative chemotherapy. In the Shanghaicohort, high-risk signature score was significantly associatedwith shorter DFS and DSS in the patients with postoperativechemotherapy whereas this effect was not observed in thosewithout chemotherapy. In the Guangzhou cohort, high-riskscore significantly predicted an unfavourable DSS in those withand without postoperative chemotherapy. These data are pre-sented in figure 4.

DISCUSSIONIn this study, we applied a systematic approach to evaluate theconcordance and robustness of 25 signatures in predicting CRCpostoperative prognosis. Of the 20 published signatures withinformation of gene expression direction, 15 without sufficientconcordance in the validation process were excluded. These 15studies were originally designed to answer different prognosis-related questions, for example, risk for metastases. The fiveselected signatures had high prevalence prediction and were atleast moderately correlated, indicating that these genes could beinvolved in an intrinsic network. Of the five signatures, Oh’shas been proven to have robust performance in identifying CRCpatients with poor prognosis in two independent cohorts.39 Assignatures with sufficient concordance contained less genes incommon, we constructed a functional subnetwork by integratingthe five signatures. The molecules enriched in this subnetworkwere functionally related to cancer invasiveness, indicating theimportance of this network in CRC progression.

Besides gene transcription, other factors may influenceprotein expression levels. Therefore, we compared mRNA andproteins levels and confirmed that mRNA levels significantlycorrelated to protein levels of the four molecules in CRCtissues, which bridges gene signatures and protein profiling.High mRNA levels of ITGB1 and POSTN in tumours predicteda poor prognosis of CRC patients7 10 15 19 26 while low mRNAlevels of PTPN11 also predicted a poor prognosis (GSE28702and GSE5206), which was quite consistent with their proteins’performance.

We developed an IHC signature by examining expression pat-terns of the network-centric and the top overlapping proteins inthe training set. This signature efficiently discriminated CRCpatients with distinct prognosis in the validation set. CRC prog-nosis is stage and grade dependent. Our signature efficiently par-titioned the patients into the high-risk and low-risk subgroupswith balanced grade in both cohorts and TNM stage in the val-idation cohort (table 1) and had a better survival predictivepower than TNM staging in both cohorts (table 2). Importantly,our signature significantly predicted poor postoperative progno-sis of stage II CRC patients in both cohorts (figure 3).Dichotomisation of stage II CRC patients by markers is the areaof greatest clinical need for prognostic assays.

Our IHC signature exhibited stronger stratification powerthan did single-marker studies,3 34 40 in terms of HR or validity,possibly because the four molecules were selected via our sys-temic approach and their effects in predicting CRC prognosiswere complementary. This signature also has advantages overreported gene signatures and other quantitative approaches suchas reverse-phase protein array (RPPA).41 The stratificationpower is generally higher than previously reported signatures.5 9

10 14 15 18 20–23 25–27 IHC provides cell-type localisation infor-mation, whereas gene signatures and the RPPA do not. Severalprotein signatures composed of key proteins in some signallingpathways and differentially expressed proteins between CRCtissues and normal mucosa have been reported.42–44 However,none of them were validated in external cohorts. Our approachmay be more clinically useful at least in the short-term as infor-mation technology and IHC capacities now exist in majormedical centres. To our knowledge, this is the first study report-ing an IHC signature derived from gene signatures for predict-ing CRC prognosis.

The expression pattern of signature proteins may present newinsights into the mechanisms that underlie cancer metastasis or

Table 3 Cox regression analysis of immunohistochemistry signature score and clinicopathological covariates with disease-specific survival in theGuangzhou cohort

Univariate analysis Multivariate analysis

HR (95% CI) p Value HR (95% CI) p Value

Signature score (high-risk vs low-risk) 3.54 (2.30 to 5.43) <0.001 3.53 (2.13 to 5.84) <0.001

TNM stage (III vs I+II) 1.87 (1.22 to 2.87) 0.004 1.63 (0.99 to 2.66) 0.053

Differentiation grade (poorly vs well+moderately) 1.59 (0.82 to 3.07) 0.171 1.97 (0.87 to 4.44) 0.104

Adjuvant chemotherapy (yes vs No) 0.89 (0.54 to 1.47) 0.642 1.05 (0.60 to 1.84) 0.864

Age (≥50 vs <50 years) 1.01 (0.61 to 1.67) 0.972 1.34 (0.75 to 2.41) 0.327

Sex (men vs women) 1.03 (0.67 to 1.58) 0.885 1.04 (0.64 to 1.69) 0.882

Disease location (rectum vs colon) 1.82 (1.15 to 2.88) 0.010 1.91 (1.12 to 3.25) 0.017

Lymph nodes examined (≥12 vs <12) 0.94 (0.60 to 1.46) 0.767 0.72 (0.43 to 1.21) 0.216

Serum CEA (ng/ml) (≥5 vs <5) 1.84 (1.16 to 2.92) 0.010 1.65 (0.98 to 2.79) 0.059

Serum CA19-9 (U/ml) (≥37 vs <37) 2.19 (1.33 to 3.62) 0.002 1.73 (0.99 to 3.03) 0.054

CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; TNM, tumour to node to metastasis.

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recurrence. GRB2 is critical for cell cycle progression and angio-genesis.45 Blocking the GRB2 signalling inhibits CRC cell motil-ity.46 POSTN has been found to be highly upregulated in CRCtissues and sera of CRC patients.47 A study reported thatPOSTN was positive in cancer-associated fibroblasts, not incolon cancer cells.48 Our data indicated that POSTN was highlyexpressed in human CRC cell line HCT116 and most CRCtissues (see online supplementary figure S3). This differencemay be explained by the different antibodies used. Expressionof POSTN can be solely found in cancer cells of colon cancer

tissues and promote metastasis by augmenting cell survival viathe Akt/PKB pathway.49 POSTN is required for cancer stem cellmaintenance.50 Anti-POSTN antibody activates apoptosis ofCRC cells and potentiates the effects of 5-fluorouracil-basedchemotherapy.51 Based on these evidence, we believe thatPOSTN promotes CRC aggressiveness and aberrant expressionof POSTN in CRC cells and tumour-infiltrating cells predicate apoor postoperative prognosis. The roles of GRB2 and POSTNexpression in primary tumours for the prediction of CRC prog-nosis have not been reported. SHP2 acts as either tumour

Figure 4 The association between the scores of immunohistochemistry signature and survivals of the colorectal carcinoma (CRC) patients with orwithout postoperative chemotherapy in both cohorts. The CRC patients from each cohort were dichotomised into high-risk subgroup and low-risksubgroup at the cut-off point (1.55) of the signature score. Disease-free survival and disease-specific survival of the patients with and withoutpostoperative chemotherapy (FOLFOX) are presented. p Values are shown. Green line represents the high-risk subgroup. Blue line indicates thelow-risk subgroup. Log-rank p values are from Kaplan-Meier analysis with log-rank test.

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promoter or suppressor in the malignancies of differentorigin.52 53 SHP2 expression in CRC correlated to a good prog-nosis, indicating SHP2 tends to be a tumour suppressor in CRC.Integrin β1, an important extracellular matrix-interactingnetwork hub, is essential for oncogenesis and angiogenesis.54

However, the effect of integrin β1 expression on CRC prognosisremains unknown. These signature proteins may affect CRCrecurrence through inflammation and/or metastasis from disse-minated cancer cells after surgery. Our signature predicted anunfavourable prognosis of those with postoperative chemother-apy in both cohorts (figure 4), indicating that some signaturemolecules might promote the ‘stemness’ of disseminated cancercells and contribute to poor therapeutic outcome. Our signaturecould allow clinicians to identify the patients who need targetedtreatments. Function of these molecules in CRC progressionneeds to be further studied.

Our study remains to be improved on several aspects.Although we carried out this study following the guidelines forREMARK55 and enrolled consecutive patients in the trainingcohort, some patients were lost to follow-up. This might intro-duce a bias. Further randomised clinical trials are needed to val-idate this signature. Relapse monitoring was incomplete for 70of the 343 patients in the validation cohort, resulting in loss ofDFS data. Inflammation is important in CRC metastasis. Theexpression of some network-centric molecules in non-cancercells may also be of prognostic value. We presented preliminarydata of intratumoural heterogeneity in the expression of thefour molecules in whole mounts of 10 patients. However, effectof the intratumoural heterogeneity on prognosis prediction ofour signature remains to be systemically evaluated using largecohorts before clinical translation.

In conclusion, this study presents a powerful IHC signature bycomparing and integrating currently available microarray data.Although further prospective studies are necessary to validate therobustness of this signature, our approach represents an innov-ation toward clinical applications of current gene expression pro-filing in CRC, contributing to personalised prediction of CRCprognosis. Furthermore, the roles of these proteins in CRCmetastasis and targeted therapy warrant further investigation.

Acknowledgements We are grateful to Professor Timothy C Thompson (Universityof Texas MD Anderson Cancer Center, Texas, USA) for critical reading of thismanuscript.

Contributors WC, XG, YH, YD, QL, LW, JW, CF and GC had full access to all thedata in the study and take responsibility for the integrity of the data and accuracy ofthe data analysis. WC, XG, YH, YD, QL and LW contributed equally to this work andshare cofirst authorship; GC, CF and JW are equal corresponding authors. GC, WC,CF and JW developed the study concept and design. WC, XG, YH, YD, QL, LW, XT,QZ, YL, YZ, YY, XF, HZ, WZ, JW, CF and GC were involved in acquisition of thedata. GC wrote the manuscript. WC, XG, YH, YD, QL, LW, XT, QZ, YL, HZ, JW, CFand GC proofread the manuscript for important intellectual content. WC, YH, YDand HZ performed statistical analysis. GC, CF, XG and WC obtained funding. LW,XF, HZ, JW and CF provided administrative, technical or material support. GCsupervised the study.

Funding This work was sponsored by the National Science Fund for DistinguishedYoung Scholar (81025015 to Cao) and regular funds (81272561 to Fu, 81201936to Gao and 81372671 to Chang) from the National Natural Scientific Foundation ofChina. The funding agencies had no role in the design and conduct of the study;collection, management, analysis and interpretation of the data; and preparation,review or approval of the manuscript.

Competing interests None.

Patient consent Obtained.

Ethics approval The study was approved by the committees for ethics review forresearch involving human subjects at Changhai Hospital, Secondary Military MedicalUniversity and The Sixth Affiliated Hospital, Sun Yet-Sen University.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement The gene expression data in this study can be foundonline at the Gene Expression Omnibus under accession numbers GSE28702 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28702) and GSE5206 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5206).

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doi: 10.1136/gutjnl-2013-305475 2014 63: 1457-1467 originally published online October 30, 2013Gut

Wenjun Chang, Xianhua Gao, Yifang Han, et al. prognostic value in colorectal carcinomaimmunohistochemistry signature with high Gene expression profiling-derived

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