Identification and Validation of a Blood-Based 18-Gene Expression Signature in Colorectal Cancer

Published OnlineFirst March 27, 2013; DOI: 10.1158/1078-0432.CCR-12-3851

Clinical Cancer Imaging, Diagnosis, Prognosis Research

Identiﬁcation and Validation of a Blood-Based 18-Gene Expression Signature in Colorectal Cancer

Ye Xu1,4, Qinghua Xu1,6,7, Li Yang1,4, Xun Ye6,7, Fang Liu6,7, Fei Wu6,7, Shujuan Ni1,2,3,5, Cong Tan1,2,3,5, Guoxiang Cai1,4, Xia Meng6,7, Sanjun Cai1,4, and Xiang Du1,2,3,5

Abstract Purpose: The early detection of colorectal cancer (CRC) is crucial for successful treatment and patient survival. However, compliance with current screening methods remains poor. This study aimed to identify an accurate blood-based gene expression signature for CRC detection. Experimental Design: Gene expression in peripheral blood samples from 216 patients with CRC tumors and 187 controls was investigated in the study.Wefirstconductedamicroarrayanalysistoselect candidate genes that were significantly differentially expressed between patients with cancer and controls. A quantitative reverse transcription PCR assay was then used to evaluate the expression of selected genes. A gene expression signature was identified using a training set (n ¼ 200) and then validated using an independent test set (n ¼ 160). Results: We identified an 18-gene signature that discriminated the patients with CRC from controls with 92% accuracy, 91% sensitivity, and 92% specificity. The signature performance was further validated in the independent test set with 86% accuracy, 84% sensitivity, and 88% specificity. The area under the receiver operating characteristics curve was 0.94. The signature was shown to be enriched in genes related to immune functions. Conclusions: This study identified an 18-gene signature that accurately discriminated patients with CRC from controls in peripheral blood samples. Our results prompt the further development of blood- based gene expression biomarkers for the diagnosis and early detection of CRC. Clin Cancer Res; 1–11. 2013 AACR.

Introduction (FOBT), stool DNA tests, flexible sigmoidoscopy, computed Colorectal cancer (CRC) is the third most common tomographic (CT) colonography, and colonoscopy. Differ- malignancy and the fourth most common cause of can- ent screening strategies are preferred in various countries. cer-related mortality worldwide (1). In 2008, more than 1 However, compliance with current CRC screening recom- million cases were newly diagnosed and more than 600,000 mendations remains poor. The most reliable screening tool, people died from the disease (2). Given its slow develop- colonoscopy, is invasive, costly, and conducted infrequent- ment from removable precancerous lesions and curable ly. In contrast, the currently most widely used noninvasive early stages, screening for CRC has the potential to reduce screening option, FOBT, has important limitations, includ- both the incidence and mortality of the disease (3). The ing inconvenience and low sensitivity. available screening tools include fecal occult blood testing The discovery of novel biomarkers based on the analysis of blood samples has become a focus of current research. A novel blood biomarker may offer several practical advan- Authors' Affiliations: 1Department of Oncology, Shanghai Medical Col- lege; 2Institute of Biomedical Sciences; 3Institute of Pathology, Fudan tages compared with the currently used screening University; Departments of 4Colorectal Surgery and 5Pathology, Fudan approaches. First, in vitro blood tests are safe and minimally 6 University Shanghai Cancer Center; Fudan University Shanghai Cancer invasive. Second, no dietary restriction, colon cleansing, or Center—Institut Merieux Laboratory; and 7bioMerieux (Shanghai) Co., Ltd., Shanghai, China sedation is required. Third, the sample collection and processing procedures may be easier and more convenient. Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). Furthermore, there is no microflora that could degrade the biomarker or hamper the analysis. Thus, a sensitive and Y. Xu and Q. Xu contributed equally to this work. specific in vitro blood test that detects CRC in the currently Corresponding Authors: Xiang Du, Fudan University Shanghai Cancer noncompliant patient population will improve participa- Center, 270 Dong'an Road, Shanghai 200032, China. Phone: 86-21- 64175590-8911; Fax: 86-21-64174774; E-mail: [email protected]; tion in screening, increase the accuracy of detection, and and Sanjun Cai. Phone: 86-21-6417590-1108; Fax: 86-21-64035387; therefore save lives. E-mail: [email protected] Several studies have shown that molecular biomarkers in doi: 10.1158/1078-0432.CCR-12-3851 the peripheral blood can be used to develop in vitro tests for 2013 American Association for Cancer Research. the detection of CRC. The DNA methylation biomarker

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toms of inflammatory bowel diseases, polyps, or CRC, Translational Relevance which had been confirmed by colonoscopy, were enrolled Colorectal cancer (CRC) is the leading cause of cancer- from a population-based screening program. Figure 1 related deaths worldwide. The early detection of CRC is depicts 3 different phases of the study design and Table crucial for successful treatment and patient survival. 1 summarizes the clinical characteristics of the samples in However, compliance with current screening methods the study. remains poor and there is a clear need for an accurate in In the discovery set, we analyzed the whole-blood gene vitro blood test to increase participation in CRC screen- expression profiles of 100 patients with CRC and 100 ing. This study shows that the gene expression profiles of controls using GeneChip U133plus2 microarrays (Affy- peripheral blood samples can be used to distinguish metrix). There were no significant differences in the between patients with CRC and controls. An 18-gene distribution of age or gender between the CRC and signature was identified and validated as highly sensitive control groups. The CRC group included 41 patients with and specific for detecting CRC in blood samples. These colon cancer and 59 with rectal cancer. Sixteen of the results open an avenue for the further development of patients with cancer were stage I, 36 were stage II, 24 were blood-based gene expression biomarkers for the diag- stageIII,and24werestageIV.Thesignificanceanalysisof nosis and early detection of CRC. microarray (SAM) method was used to identify genes that were differentially expressed between the CRC and control groups (10). The list of genes was further refined by expression signal intensity, fold change, biologic anno- methylated Septin 9 (mSEPT9) previously showed a sensi- tation, and probe set grade. Finally, 52 unique genes were tivity of 70% and a specificity of 90% for discriminating selected for further testing by quantitative reverse tran- patients with CRC from controls (4, 5). Recently, the scription PCR (qRT-PCR). performance of mSEPT9 was determined in a prospective The training set consisted of 100 patients with CRC and study of 7,940 average-risk individuals undergoing colo- 100 controls, including 71 patients with CRC and 86 con- noscopy for CRC screening. The sensitivity in the 45 patients trols that were also used in the discovery set to assess the with CRC was 67%, with a specificity of 88% (6). DNA correspondence between the microarray and qRT-PCR mea- microarray technology can quantify the expression of sev- surements. The remaining 43 samples from the discovery set eral thousand genes simultaneously and may be able to were not applicable for qRT-PCR experiments due to low capture the complex biology that underlies colorectal RNA concentrations and, thus, were replaced these with tumorigenesis and progression better than single gene new samples. The distributions of age and gender were markers. A number of studies have been published, using balanced between the CRC and control groups. The CRC DNA microarray technology to identify blood-based gene group included 51 patients with colon cancer and 49 with expression signatures for CRC detection. Han and collea- rectal cancer. Eleven of the patients with cancer were stage I, gues reported a 5-gene signature with 88% sensitivity and 40 were stage II, 22 were stage III, and 27 were stage IV. These 64% specificity (7). Marshall and colleagues recently pub- samples were used as the training set to identify the gene lished a 7-gene signature with a sensitivity of 72% and a expression signature for differentiation between the CRC specificity of 70% (8). Rosenthal and colleagues also group and the control group. reported a panel of 202 genes with 90% sensitivity and In the test set, we used an independent cohort of 87 88% specificity (9). patients with CRC and 73 controls. The CRC group included In this study, we tested the hypothesis that gene expression 27 patients with colon cancer and 60 with rectal cancer. profiling of peripheral blood cells could yield diagnostic Eleven of the patients with cancer were stage I, 31 were stage information in a cohort of Chinese patients. An 18-gene II, 31 were stage III, and 13 were stage IV. The TNM variables signature was identified and validated as highly sensitive and of one patient were not available. The fully specified gene specific for detecting CRC in blood samples. expression signature from the training set was applied to the test set for validating the signature performance in the Materials and Methods independent samples. Study design and patients The study was approved by the Institutional Review Board Study participants were recruited from the Fudan Uni- of Fudan University Shanghai Cancer Center and written versity Shanghai Cancer Center and Shanghai Qibao informed consent was obtained from all participants. Community Hospital from 2006 through 2010 (Shang- hai, China). All the participants were Chinese. For the Blood collection and RNA extraction CRC group, all patients had blood collected before sur- For each participant, 2.5 mL of peripheral blood was gery. None of the patients with cancer had received collected into PAXgene Blood RNA tubes, and the total preoperative radiotherapy or chemotherapy before blood RNA was extracted with the PAXgene Blood RNA System collection. The tumors were staged according to the (PreAnalytiX). The quantity of total RNA was measured tumor–node–metastasis (TNM) system. Patients suffering with a spectrophotometer at an optical density of 260 nm, from hereditary CRC were excluded. For the control and the quality was assessed using the RNA 6000 Nano group, FOBT-positive participants without any symp-- LabChip Kit on an Agilent 2100 Bioanalyzer (Agilent

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Figure 1. Study design. The blood gene expression profiles of 216 patients with CRC and 187 controls were investigated in 3 different phases. We first conducted a microarray analysis to select candidate genes that were significantly differentially expressed between patients with cancer and controls. qRT-PCR assays were then applied to evaluate the expression of selected genes. A gene expression signature was identified using a training set (n ¼ 200) and then validated using an independent test set (n ¼ 160). , TNM stage was not available for 1 patient in the test set. FUSCC, Fudan University Shanghai Cancer Center.

Technologies). All samples met the quality criterion: RNA tics) and GeneChip DNA Labeling Reagent. The labeled integrity number > 7.0. cDNA was hybridized onto the GeneChip U133plus2 microarray in a Hybridization Oven 640 (Agilent Technol- Microarray hybridization ogies) at 60 rpm at 50C for 18 hours. After hybridization, For each sample, 50 ng of total RNA were reversely the arrays were washed and stained according to the Affy- transcribed and linearly amplified as single-stranded cDNA metrix protocol EukGE-WS2v4 using a GeneChip Fluidics using Ribo-SPIA technology with the WT-Ovation RNA Station 450. The arrays were scanned with a GeneChip Amplification System (NuGEN Technologies), and the pro- Scanner 3000. ducts were purified using the QIAquick PCR Purification Kit (Qiagen). A total of 2 mg of amplified and purified cDNA qRT-PCR were subsequently fragmented with RQ1 RNase-Free DNase For the training set and the test set, qRT-PCR using (Promega) and labeled with biotinylated deoxynucleoside SYBR Green assays was conducted according to the man- triphosphates using Terminal Transferase (Roche Diagnos- ufacturer’s instructions. For each sample, a total of 320 ng

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Table 1. Characteristics of the CRC and control populations

Discovery set Training set Test set Variable CRC Control CRC Control CRC Control (n ¼ 100) (n ¼ 100) (n ¼ 100) (n ¼ 100) (n ¼ 87) (n ¼ 73) Age, y Mean 57.6 56.5 56.3 56.3 56.2 56.3 Range 27–78 38–74 27–78 38–74 26–78 32–74 Sex, no. (%) Male 50 (50.0) 50 (50.0) 49 (49.0) 48 (48.0) 50 (57.5) 17 (23.3) Female 50 (50.0) 50 (50.0) 51 (51.0) 52 (52.0) 37 (42.5) 56 (76.7) Tumor site, no. (%) Colon 41 (41.0) — 51 (51.0) — 27 (31.0) — Rectum 59 (59.0) 49 (49.0) 60 (69.0) Tumor stage, no. (%)a Stage I 16 (16.0) — 11 (11.0) — 11 (12.6) — Stage II 36 (36.0) 40 (40.0) 31 (35.6) Stage III 24 (24.0) 22 (22.0) 31 (35.6) Stage IV 24 (24.0) 27 (27.0) 13 (14.9)

aTNM stage was not available for 1 patient in the test set.

of RNA were reversely transcribed into single-stranded genes among the candidate list. Therefore, these 3 genes cDNA using the QuantiTect Reverse Transcription Kit were selected as reference genes and their geometric mean (Qiagen). The cDNA was amplified using the SYBR Pre- was used as a normalization factor for qRT-PCR data mix DimerEraser (Perfect Real Time) Kit (Takara Biotech- normalization. nology). The amplification was detected in real time using the Applied Biosystems 7900HT Fast Real-Time PCR Statistical analysis System (Life Technologies). The primers were designed Microarray data were analyzed using R software and primarily within the target sequences of the selected packages from the Bioconductor project (17, 18). Raw data Affymetrix probe sets with Beacon Designer software had been deposited in the ArrayExpress public repository (Premier Biosoft). The primer sequences are provided in and were accessible through the accession number Supplementary Table S1. The primer pairs were experi- E-MTAB-1532. Raw data were normalized using the robust mentally validated with the following criteria: (i) a single multichip average (RMA) method (19). The probe set level gene-specific product was produced; (ii) the amplification data were log2-transformed. In addition, we applied a efficiency ranged between 90% and 110%; and (iii) the bioinformatics-based filtering approach using information cycle threshold (Ct) value of the no-template control was in the Entrez Gene Database (20). Probe sets without more than 35. Entrez Gene ID annotation were removed. For multiple Six candidate reference genes (ACTB, CRY2, CSNK1G2, probe sets mapping to the same Entrez Gene ID, only probe DECR1, FARP1,andTRAP1) that have been reported to be sets showing the largest interquantile range were kept, consistently expressed in human whole-blood samples and the rest were excluded. The analysis of differentially were selected for investigation (11). The expression sta- expressed genes was conducted using the SAM method bility of the 6 reference genes in the training set were implemented in the "samr" package (10). estimated using 4 commonly used algorithms: geNorm The qRT-PCR–based gene expression levels were estimat- (12), NormFinder (13), BestKeeper (14), and the com- ed using the comparative DCt method of relative quantifi- parative cycle threshold (DCt) method (15). Each algo- cation (21), normalizing the Ct values relative to the nor- rithm ranked the reference genes from most stable (rank malization factor. The relative fold change was represented DDCt #1) to least stable (rank #6). The overall ranking of as 2 , where DDCt ¼ mean DCt CRC mean DCt Control. candidate reference genes was calculated according to the We used the minimum redundancy maximum relevance RefFinder method described by Chen and colleagues (mRMR) algorithm for gene selection (22), the support (16). Briefly, the geometric means of the 4 ranking num- vector machine (SVM) algorithm for classification (23), bers of each gene were calculated, and then candidate and the leave-one-out-cross-validation (LOOCV) proce- reference genes were ranked according to the geometric dure for sampling. Gene Ontology and Kyoto Encyclo- mean, the gene with the smaller geometric mean being pedia of Genes and Genomes (KEGG) pathway analysis the most stable reference gene. As a result, CSNK1G2, were conducted using the GeneCodis bioinformatics tool DECR1,andFARP1 were shown to be the most stable (24–26).

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Results Microarray analysis and candidate gene selection The recent release of the U133plus2 array contains more than 54,000 probe sets, which represent approximately 38,500 human genes. Confronted with such an overwhelm- ing amount of information, it was necessary to reduce the total number of genes analyzed to a manageable number of genes with well-characterized biologic information and use visualization schemes to facilitate the recognition of pat- terns in the data (27). We thus conducted a bioinformatics- based filtering procedure to summarize the probe sets at the gene level and exclude those probe sets with low-grade biologic annotations. After filtering, the expression profiles of 8,662 unique genes in 100 patients with CRC and 100 controls were retained for downstream analysis. Using the SAM method, we identified 263 genes that were differentially expressedbetweentheCRCgroupand the control group, among which 179 genes were upregulated and 84 genes were downregulated in the patients with cancer. Using the 263 differentially expressed genes, a hierarchical clustering analysis showed that 78 of 100 controls and 76 of 100 patients with CRC were correctly classified (Supplementary Fig. S1). The gene list was further refined by multiple criteria: (i) an average expression intensity of greater than 64; (ii) a fold change in mean expression intensity of greater than 1.2; (iii) genes with known biologic function; and (iv) a high-grade probe design. This analysis resulted in 52 candidate biomarkers for further qRT-PCR study.

Identification of an 18-gene signature in the training set The training set included 100 patients with CRC and 100 fi controls, among which 71 patients with CRC and 86 controls Figure 2. Gene signature identi cation process. Our process could be conceptually broken down into 6 steps: 1, the samples were divided into were also included in the discovery set. First, we compared an inner training set and an inner test set. 2, using only the inner training the expression profiles of the 52 selected genes measured by set, the mRMR algorithm returned a subset of n genes that have both microarray and qRT-PCR. For each gene, the fold maximum relevance with the clinical status and minimum redundancy change between the 71 patients with CRC and 86 controls within the gene set. 3, the n-gene–based SVM classification model was built using the linear kernel and fitted to the inner training set. 4, the was calculated. Spearman correlation analysis showed that developed model was used to predict the class of the test sample. The the fold change of candidate genes measured by microarray predicted class was compared with the true class label of the sample. If and qRT-PCR was highly comparable [r ¼ 0.94; 95% con- they disagreed, the prediction was in error. 5, the process was repeated fidence interval (CI), 0.90–0.98; P < 0.001], although a few leaving each of the 200 samples out of the training set, one at a time. A exceptions were observed. Three genes were considered out- total of 200 different models was created, and each model was used to predict the class of the test sample. Eventually, the number of prediction liers because their expression profiles were not consistent errors was summed up and reported as the LOOCV estimate of the between the microarray and qRT-PCR analyses. prediction error. 6, steps 1 to 5 were repeated 52 times starting with Signature identification was subsequently conducted n ¼ 1, increasing one at time until n ¼ 52. The LOOCV performance using the SVM classification model, with the selection of for each of the n-gene signatures was estimated and compared to determine the optimal size of the signature. significant genes based on the mRMR method, through repetitions of the LOOCV process. As shown in Fig. 2, our process could conceptually be broken into 6 steps: 2. Using only the inner training set, the mRMR algorithm was used to search for a subset of n genes that had 1. The samples were divided into an inner training set maximum relevance with the clinical status and and an inner test set. The inner test set consisted of minimum redundancy within the gene set. only a single sample; the remaining 199 samples were 3. The n-gene based SVM model was built using the placed in the inner training set. The sample in the test linear kernel and was fitted to the inner training set. set was placed aside and not used in the development 4. The developed model was used to predict the class of of the class prediction model. the test sample. The prediction was based on the

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expression profile of the test sample, without using genes (signature size from 18 to 48), this input:output ratio knowledge of the true class of the sample. The was not truly effective. predicted class was compared with the true class label Ultimately, we considered a signature size of 18 to be of the sample. If they disagreed, the prediction was in ideal. As shown in Fig. 3A, an estimated performance of error. 91.5% accuracy, 91% sensitivity, and 92% specificity was 5. Then, a new training set–test set partition was created. achievable with a signature composed of the top 18 genes. This time another sample was placed in the inner test We then used a consensus method to assess the stability of set, and all of the other samples were placed in the candidate genes across the LOOCV process. Through the inner training set. A new classification model was LOOCV process specifying n ¼ 18, we identified 200 differ- constructed using the samples in the new training set. ent top 18 gene lists. The appearance rate of each gene among Although the same algorithm for gene selection and all of the top 18 gene lists was recorded. The maximum parameter estimation was used, because the new appearance rate was 100% (i.e., some genes were included in model was constructed on the basis of the new training all of the top 18 gene lists during the cross-validation set, it would in general not select exactly the same gene process). In contrast, the minimum occurrence rate was 0, set as the previous model. Again, the model was indicating that those genes were never included in the top 18 applied to the expression profile of the test sample. If gene lists during the cross-validation process. The genes were the predicted class did not agree with the true class ranked according to their appearance rates. The 18 genes label of the test sample, then the prediction was in with the highest appearance rates were selected for the final error. The process was repeated leaving each of the 200 gene expression signature. The entire training set was used to biologically independent samples out of the training specify the parameters of the final 18-gene signature. set, one at a time. During the steps, 200 different Table 2 shows the descriptions of the 18 selected genes models were created and each model was used to and their statistical significance across control and CRC predict the class of the test sample. Eventually, the samples in the training set. Eight and 10 genes were up- and number of prediction errors was totaled and reported downregulated, respectively, in patients with CRC com- as the LOOCV-based error rate. pared with controls (Fig. 3B); NEAT1 (nuclear paraspeckle 6. The number of genes (n) to be selected by mRMR assembly transcript 1) was the most significantly upregu- algorithm was set as a predefined variable. Steps 1 to 5 lated gene; and DUSP2 (dual-specificity phosphatase 2) was were repeated 52 times, starting with n ¼ 1, adding 1 the most significantly downregulated gene. Gene Ontology gene at time until the n ¼ 52. The LOOCV-based and KEGG pathway analyses showed that several of the 18 performance for each of n-gene signature was genes are involved in apoptosis (e.g., GZMB and IL1B), cell estimated and used to determine the optimal size of adhesion (e.g., CD36 and ITGAM), the mitogen-activated the signature. protein kinase (MAPK) signaling pathway (e.g., DUSP2 and IL1B), signal transduction (e.g., SH2D2A, PDE4D, and At the end of the process, 10,400 different classification IL1B), and some are hematopoietic cell markers (e.g., CD36, models were constructed to assess signature performance in ITGAM, and IL1B; Supplementary Table S2). association with the number of genes included in the signature. As shown in Fig. 3A, the classification accuracy Validation of the 18-gene signature in an independent was 53.5% when only 1 gene was included in the signature. test set The performance accuracy increased to 90.0% when 9 genes The 18-gene signature was then applied to an indepen- were included in the signature. However, the classification dent test set of 87 patients with CRC and 73 controls. These accuracy decreased to 85.5% when another gene was added, samples had been excluded from the training set and were implying that the observed performance might not be truly not used in the development of the 18-gene signature. For stable at a signature size of approximately 10. The perfor- each sample, a probability score was calculated and a mance increased continuously and reached 90.5% and threshold of 50% was used to classify samples. Samples 91.5% accuracy at signature sizes of 15 and 18, respectively. with a probability score less than 50% were classified as Subsequently, the signature performance increased to controls, whereas samples with a probability score more 92.5% and 93.0% accuracy with signature sizes of 38 and than 50% were classified as CRC (Fig. 4). For the controls, 48, respectively. 64 samples were correctly classified and 9 samples were Not surprisingly, the classification performance increased misclassified. For the patients with CRC, 73 samples were continuously as more genes were added to the signature. correctly classified and 14 samples were misclassified. In the However, signature size determination also needs to con- test set, the 18-gene signature had 85.6% accuracy (95% CI, sider signature complexity; when more genes are included 0.79–0.90), 83.9% sensitivity (95% CI, 0.74–0.90), and in the signature, the signature becomes more complex and 87.7% specificity (95% CI, 0.77–0.94). When the sensitivity less generalizable. Our strategy was to identify a signature was plotted against the specificity in a receiver operating that showed satisfactory performance but maintained a characteristic curve, the area under the curve (AUC) was compact signature size and reasonable complexity for future 0.94 (95% CI, 0.91–0.98). development. Although the prediction accuracy could be Of the 9 misclassified controls, 4 were female and 5 were improved from 91.5% to 93% by adding 30 additional male; 3 were over 60 years old. Of the 14 misclassified

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Figure 3. Identification of the 18- gene signature. A, the classification accuracy during the LOOCV process for each n-gene signature was estimated and was used to determine the optimal size of the signature. The size determination took into account the classification accuracy and signature complexity. Ultimately, we considered a signature size of 18 to be ideal. The results of LOOCV process showed that an estimated performance of 91.5% accuracy was achievable with a signature composed of the top 18 genes. B, univariate fold changes of 18 genes across 100 patients with CRC and 100 controls in the training set. Positive fold changes indicate genes that are upregulated in CRC, whereas negative fold changes indicate genes that are downregulated in CRC.

patients with cancer, 5 were female and 9 were male; 4 were screening effectiveness. In this study, we aimed to identify over 60 years old. In addition, 4 of 14 misclassified patients and validate a blood-based gene signature that could dis- with cancer were stage I, 4 were stage II, 5 were stage III, and tinguish patients with CRC from controls with high accu- 1 was stage IV; 2 had colon cancer and 12 had rectal cancer. racy. Our work followed Biomarker Development for Early The results obtained from Fisher exact test suggested that Detection of Cancer guidelines (28). We first conducted a clinical and pathologic variables, such as age, gender, tumor microarray study to select genes that were significantly site, and stage, did not obviously affect the prediction out- differentially expressed between the controls and patients comes (Supplementary Table S3). with CRC. The selected genes were then transferred to a qRT- PCR platform. The qRT-PCR study was conducted for sig- Discussion nature identification and validation using 2 independent The early detection of CRC is crucial for successful treat- cohorts. We presented a blood-based 18-gene signature that ment and patient survival. However, the lack of compliance can be used as a biomarker to discriminate between patients remains the greatest challenge currently limiting CRC with CRC and controls with a sensitivity and specificity of

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Table 2. Composition of the 18-gene signature

Gene Description Cytoband UniGene P value CD36 CD36 molecule (thrombospondin receptor) 7q11.2 Hs.120949 8.76E-07 DHRS13 Dehydrogenase/reductase (SDR family) member 13 17q11.2 Hs.631760 2.96E-04 DUSP2 Dual-speciﬁcity phosphatase 2 2q11 Hs.1183 1.69E-10 FAM198B Family with sequence similarity 198, member B 4q32.1 Hs.567498 7.57E-08 FKBP5 FK506-binding protein 5 6p21.31 Hs.407190 4.70E-05 GLT25D2 Glycosyltransferase 25 domain containing 2 1q25 Hs.387995 2.39E-07 GZMB Granzyme B (granzyme 2, CTL-associated serine esterase 1) 14q11.2 Hs.1051 9.68E-09 IL1B Interleukin 1, b 2q14 Hs.126256 2.09E-04 ITGAM Integrin, a M (complement component 3 receptor 3 subunit) 16p11.2 Hs.172631 6.27E-09 ITPRIPL2 Inositol 1,4,5-trisphosphate receptor interacting protein-like 2 16p12.3 Hs.530899 7.72E-05 MYBL1 v-myb Myeloblastosis viral oncogene homolog (avian)-like 1 8q22 Hs.445898 2.42E-09 NEAT1 Nuclear paraspeckle assembly transcript 1 (nonprotein coding) 11q13.1 Hs.523789 3.48E-10 NUDT16 Nudix (nucleoside diphosphate linked moiety X)-type motif 16 3q22.1 Hs.282050 1.08E-05 P2RY10 Purinergic receptor P2Y, G-protein coupled, 10 Xq21.1 Hs.296433 8.54E-09 PDE4D Phosphodiesterase 4D, cAMP-speciﬁc 5q12 Hs.117545 4.32E-08 PDZK1IP1 PDZK1-interacting protein 1 1p33 Hs.431099 4.02E-05 SH2D2A SH2 domain containing 2A 1q21 Hs.103527 3.01E-08 VSIG10 V-set and immunoglobulin domain containing 10 12q24.23 Hs.187624 1.60E-06

84% and 88%, respectively. Fisher exact test showed no shown to activate the BCL2 P2 promoter through a Cdx- association between clinical variables (age, gender, tumor binding site, promoting resistance to apoptosis (38). sites, and stages) and the prediction outcomes assigned to PDE4D was reported to be overexpressed in human prostate each sample, suggesting that the 18-gene signature con- cancers and associated with increased tumor growth and ducted equally well for different categories of samples. cell migration (39). PDE4D is also expressed in lung cancer, An understanding of the function of the genes comprising interacting with hypoxia-inducible factor (HIF) signaling the signature could provide mechanistic insight into the and promoting lung cancer progression (40). PDZK1IP1 diagnostic effect of this gene panel. Functional annotations was shown to be overexpressed in a variety of human revealed that many of the selected genes were related to cancers in the kidney, colon, lung, and breast (41). The immune function. GZMB, which was significantly down- overexpression of the PDZK1IP1 protein was correlated regulated in patients with CRC, is crucial for the rapid with tumor progression in prostate and ovarian carcinomas induction of target cell apoptosis by CTL and natural killer (42). cells in the cell-mediated immune response (29). IL1B,an Interestingly, a long noncoding RNA, NEAT1, was the important mediator of the inflammatory response, is most significantly upregulated gene in our analysis. Recent involved in different mechanisms leading to tumorigenesis studies have shown that NEAT1 plays an essential role in via tumor-associated inflammation and neovascularization the assembly and architecture of nuclear paraspeckles and (30). IL1B has been shown to upregulate COX2 expression colocalizes with the paraspeckle-associated proteins p54nrb, in human CRC cells (31), which may contribute to the PSP1, and PSF (43–45). NEAT1 may therefore play an growth and metastatic potential of CRC by increasing important role in regulating gene expression by governing the expression of the antiapoptotic factor BCL-2 and upre- the nuclear export of mRNAs. Our study is the first to report gulating specific angiogenic factors (32, 33). Furthermore, the deregulation of DHRS13, FAM198B, GLT25D2, polymorphisms in IL1B have been associated with tumor ITPRIPL2, NUDT16, P2RY10, and VSIG10 mRNA expres- recurrence in stage II colon cancer (34). The expression of sion in the peripheral blood in association with colorectal SH2D2A is limited in immune system tissues, particularly carcinoma. activated T cells. SH2D2A has been reported as a positive Recently, several studies have suggested that the gene regulator of proximal T-cell receptor signal transduction expression signatures identified in peripheral blood are (35). CD36 is expressed by various types of cells that are likely not conventional tumor-derived cancer biomarkers associated with the blood and the immune system. High but rather reflect subtle alterations in blood gene expres- CD36 expression is related to decreased stromal vascular- sion serving as a systemic immune response to tumori- ization and is a predictor of good prognosis in colon cancer genesis (8, 46–49). Our results are consistent with these (36). A polymorphism in CD36 (A52C) has been associated findings as evidenced by the fact that the signature was with an increased risk for CRC (37). enriched in genes related to immune functions. Recent In addition, several of these genes have been associated work has elucidated the role of distinct immune cells, with other human carcinomas. In lymphoma, MYBL1 was cytokines, and other immune mediators in virtually all

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Figure 4. Validation of the 18-gene signature in the test set. A, for each sample, the probability score was calculated. The dashed line at 50% indicates the CRC versus control decision threshold. A sample was classified as CRC if the probability score was more than 50%. For controls, 64 samples were correctly classified, and 9 samples were misclassified. For patients with cancer, 73 of 87 samples were correctly classified. Of those misclassified patients with cancer, 4 were stage I, 4 were stage II, 5 were stage III, and 1 was stage IV. B, the area under the receiver operating characteristics curve reached 0.94, and the 95% CI was 0.91 to 0.98.

steps of colorectal tumorigenesis, including initiation, understand the mechanistic relationship and the biologic promotion, progression, and metastasis (50). Although meaning of this complex blood gene expression signature we cannot entirely exclude the fact that metastatic tumor in CRC. cells or circulating cell-free tumor nucleic acids can affect Although the 18-gene signature could be developed as the gene expression proﬁles of peripheral blood, we an in vitro blood test for general population screening, postulate that the distinct gene expression observed in the initial clinical use of our biomarkers is more likely controls and patients with CRC is most likely attributed to to serve as a complementary test to existing screening the interactions between the immune system and tumors. methods. In clinical practice, only 30% of high-risk On the basis of these results, future research is needed to individuals with FOBT-positive results eventually undergo

www.aacrjournals.org Clin Cancer Res; 2013 OF9

Xu et al.

colonoscopy. Our biomarker may therefore provide an such as polyps and adenomas, also needs to be evaluated. additional risk assessment for noncompliant populations. Finally, a large prospective study in the target screening Because a higher probability score by the 18-gene signature population is required to fully determine the clinical increases the likelihood that a patient has cancer, the performance of the blood-based gene signature in com- probability score may provide clinically actionable risk parison to colonoscopy and to assess the practical feasi- information. For patients with a probability score less than bility of implementing the blood test in a screening 50%, patients may decide by themselves whether to under- program. go colonoscopy. For those patients who resist undergoing In conclusion, our study describes the development and colonoscopy, repeat blood tests would be recommended at validation of a blood-based 18-gene signature that differ- intervals consistent with practice guidelines, for example, entiates patients with CRC from controls with a high degree annually. For patients with a probability score more than of accuracy in a large number of participants. Our results 50%, they should be strongly recommended to undergo open an avenue for the further development of blood-based colonoscopy for confirmation. The combination of blood gene expression biomarkers for the diagnosis and early biomarkers with existing screening methods can provide detection of CRC. several major advantages. It addresses the greatest challenge currently limiting CRC screening effectiveness, name- Disclosure of Potential Conflicts of Interest ly, the lack of compliance. Blood biomarkers may have Q. Xu, F. Wu, and X. Meng are employed as Senior research scientist, valuable use for reaching the segment of the screening Technician, and Asia Pacific Scientific Director, respectively, at bioMerieux (Shanghai) Co., Ltd. No potential conflicts of interest were disclosed by the population that is resistant to the currently recommended other authors. methods as well as providing screening to underserved patients with limited access to endoscopy centers. In addi- Authors' Contributions tion, blood biomarkers may help to minimize false-pos- Conception and design: Y. Xu, Q. Xu, L. Yang, G. Cai, X. Meng, S. Cai, X. Du itive FOBTs, and thus reduce the number and cost of Development of methodology: Y. Xu, Q. Xu, L. Yang, X. Ye Acquisition of data (provided animals, acquired and managed patients, colonoscopies. provided facilities, etc.): Y. Xu, L. Yang, X. Ye, F. Liu, S. Ni, C. Tan, S. Cai Considerable research effort continues for the devel- Analysis and interpretation of data (e.g., statistical analysis, biosta- opment of an accurate, reliable, and minimally invasive tistics, computational analysis): Q. Xu Writing, review, and/or revision of the manuscript: Y. Xu, Q. Xu, L. Yang, blood test for the detection of CRC. Han and colleagues X. Meng, X. Du and Marshall and colleagues have identified a 5- and 7- Administrative, technical, or material support (i.e., reporting or orga- gene signatures, respectively, by analyzing gene expres- nizing data, constructing databases): Y. Xu, X. Ye, F. Liu, F. Wu, G. Cai, S. Cai sion profiles in whole-blood samples from patients with Study supervision: Y. Xu, G. Cai, X. Meng, S. Cai, X. Du CRC and controls using qRT-PCR assays (7, 8). Although similar approaches were used for biomarker identifica- Acknowledgments tion, the reported 2 signatures share no genes in common The authors thank Wencui Huang at the Fudan University Shanghai with our 18-gene signature. The absence of concordant Cancer Center and Ying Jin, Ling Zhang, and Aijun Ye at the Shanghai Qibao Community Clinic for their excellent work in blood sample collection. genes could be related to many different issues, including differences in the study populations, the gene quantifi- cation technologies, and the statistical approaches used Grant Support This study was supported by Grants from bioMerieux (Shanghai) Co., Ltd. to generate the gene signatures, highlighting the need for This study was also supported by the Chinese National Clinical Key Disci- extensive validation before the clinical implementation pline (2011–2012) and the Shanghai Science and Technology Commission of these promising biomarkers. Our results represent of Shanghai Municipality (no. 10DJ1400500). The costs of publication of this article were defrayed in part by the an encouraging primary step, but several issues remain payment of page charges. This article must therefore be hereby marked to be addressed. Additional external validation studies advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate are being conducted to establish a standard testing pro- this fact. tocol and to confirm the signature accuracy. The use of Received December 28, 2012; revised March 4, 2013; accepted March 14, the 18-gene signature for detecting precancerous lesions, 2013; published OnlineFirst March 27, 2013.

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www.aacrjournals.org Clin Cancer Res; 2013 OF11

Identification and Validation of a Blood-Based 18-Gene Expression Signature in Colorectal Cancer

Ye Xu, Qinghua Xu, Li Yang, et al.

Clin Cancer Res Published OnlineFirst March 27, 2013.

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