Cancer Letters 328 (2013) 160–167

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Cancer Letters

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A cancer/testis antigen microarray to screen autoantibody biomarkersof non-small cell lung cancer

Qiang Shan a,b, Xiaomin Lou a, Ting Xiao c, Ju Zhang a, Huiying Sun a, Yanning Gao c, Shujun Cheng c,Lin Wu a, Ningzhi Xu d,⇑, Siqi Liu a,⇑a Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 101318, Chinab Graduate University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, Chinac State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute and Cancer Hospital, Peking Union Medical College and Chinese Academyof Medical Sciences, Beijing 100021, Chinad Laboratory of Cell and Molecular Biology, Cancer Institute and Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

a r t i c l e i n f o

Article history:Received 6 April 2012Received in revised form 8 August 2012Accepted 15 August 2012

Keywords:Cancer/testis antigen (CTA)Protein microarrayNon-small cell lung cancer (NSCLC)Biomarker

0304-3835/$ - see front matter � 2012 Elsevier Irelanhttp://dx.doi.org/10.1016/j.canlet.2012.08.019

⇑ Corresponding authors. Addresses: Laboratory ofCancer Institute and Cancer Hospital, Chinese AcadePeking Union Medical College, No. 17 Panjiayuan100021, China. Tel./fax: +86 10 6773 8220 (N. Xu), BChinese Academy of Sciences, Beijing Airport IndustrBeijing 101318, China. Tel.: +86 10 8048 5325; fax: +

E-mail addresses: xunzh@genomics.org.cn (N. Xu),

a b s t r a c t

Cancer/testis antigens (CTAs) are highly immunogenic in many tumors, especially in non-small cell lungcancer (NSCLC). A low-density protein microarray, which consisted of 72 CTAs and six non-CTAs, wasused to screen for lung cancer-related autoantibodies. The CTA panel of NY-ESO-1, XAGE-1, ADAM29and MAGEC1, had sensitivity and specificity values of 33% and 96%, respectively. When examined in a testset, this panel of markers had sensitivity and specificity values of 36% and 89%, respectively. This array ofmarkers preferentially detected NSCLC, but did not detect breast cancer, and non-cancer lung disease.

� 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction teins. High false positive or false negative signals often impact data

The immune system produces autoantibodies against novelproteins or proteins that are secreted in large amounts by abnor-mal cells. Because autoantibodies are produced in response to tu-mor-associated antigens, the detection of serum autoantibodiesis used in tumor biomarker assays [1–3]. Protein microarrays arewell recognized as powerful tools to search autoantibody panelsusing various tumor samples [4]. Using a protein microarray of5005 human proteins expressed in insect cells, Hudson et al. iden-tified 94 antigens with enhanced immune reactivity in the serumof ovarian cancer patients [5]. Chen et al. employed a high-densitymicroarray with 2304 phage peptides to explore adenocarcinoma-related biomarkers. A panel of 22 discriminating peptides with 85%sensitivity and 86% specificity was identified [6]. Although proteinmicroarrays with high density appear to be widely selective forscreening, this approach has limitations. Due to non-specific inter-actions between serum substances and the test proteins, high‘‘background noise’’ is common when loading high-density pro-

d Ltd. All rights reserved.

Cell and Molecular Biology,my of Medical Sciences and, Chaoyang District, Beijingeijing Institute of Genomics,ial Zone B-6, Shunyi District,86 10 8048 5324 (S. Liu).siqiliu@genomics.cn (S. Liu).

interpretation, and the cost of performing protein microarrays re-mains much higher than that of DNA microarrays. Therefore,large-scale screening of disease-related autoantibodies remains asubstantial burden for most research laboratories. These factsprompted us to seek an alternative approach to protein microarraytechnology in the search for autoantibody biomarkers. Herein, wedescribe a low-density protein microarray using a protein familythat is both highly autoimmune-responsive and is associated withcancers. This targeted protein microarray is expected to displaylower background noise, which should reduce the number of falsepositive results and be less costly.

Lung cancer is the leading cause of cancer-related death world-wide and is associated with a 5-year survival rate of only 15%.Expression of the cancer/testes antigen (CTA) protein family ishighest in cases of non-small cell lung cancer (NSCLC), which isthe most common type of lung cancer. CTAs are normally ex-pressed in the testis but not in other somatic tissues [7]. Approxi-mately 110 members of the CTA family have been identified todate, and the expression of a large percent of these proteins is tu-mor cell-dependent and autoimmune-driven [8]. At the mRNA le-vel, Shigematsu et al. employed reverse transcription-polymerasechain reaction (RT-PCR) to screen 239 NSCLC patients for four CTAsand reported that each of these four candidates was detectable in10–30% of the samples [9]. Glazer et al. also used RT-PCR in 151NSCLC patients and found that 60% of the significant candidates

Q. Shan et al. / Cancer Letters 328 (2013) 160–167 161

belonged to the CTA family [10]. At protein level, Tureci et al. em-ployed an ELISA-based technique to examine the expression of theNY-ESO-1 autoantibody in the serum of lung cancer patients andfound that 20% of patients were seropositive [11]. Using a proteinmicroarray, Zhong et al. observed that a CTA protein (GAGE7) incombination with four other proteins could be used to diagnoseNSCLC, with 88.9% accuracy [12]. Chapman et al. detected anotherCTA autoantibody (CAGE), which was prevalent in 28% of lung can-cer patients [13]. However, few systematic investigations havebeen performed to survey multiple CTAs in lung cancer sera. Webelieve that the development of a low-density protein microarrayfor CTAs represents a novel approach to further investigate CTAauto-immune behavior in lung cancer.

In the current study, we generated a low-density protein micro-array containing 72 CTAs. Serum samples from both normal andNSCLC patients were screened using this CTA microarray. A statis-tical analysis using Fisher’s exact test was then used to identify anNSCLC-related autoantibody panel. Our study is the first to use aCTA microarray to identify an autoantibody panel with four CTAsin lung cancer sera.

2. Materials and methods

2.1. Source of the clinical samples

A total of 377 sera samples were collected from the Cancer Hospital of PekingUnion Medical College and Beijing Chao-Yang Hospital. These sera included 179NSCLC samples, 39 breast cancer samples, 20 gastric cancer samples, 36 non-cancerlung disease samples, and 103 samples from healthy controls. All cancers werediagnosed by histopathological examination, and the clinical information for thepatients studied is listed in Supplemental Table 1A. The ethics committees fromthe corresponding hospitals and institutes approved the collection protocols usedfor the clinical samples.

2.2. Generation of the CTA recombinant proteins

A CTA databank was generated based on previous publications [7,14–26]. Poly-clonal autoantibodies produced by cancer patients are difficult to distinguish fromCTAs due to high sequence similarity. Thus, we limited CTA families of proteinsbased on the following criteria: (1) if the sequence identity was greater than 90%among certain CTAs, only one CTA was chosen as the representative, and (2) ifCTA family members were generated by splicing, the longest form was regardedas the representative. To facilitate the generation of recombinant proteins withhigher antigenicity, we analyzed the CTA epitopes using the online tool, PredictingAntigenic Peptides (http://imed.med.ucm.es/Tools/antigenic.html). Based upon thisanalysis, full-length or truncated CTAs were selected, and these CTA candidates con-tained antigenic region(s) with polypeptides from 200 to 500 amino acids in length.DNAMAN (version 5.2.2, LynnonBiosoft) was employed for homology analysis of theCTA genes. An affinity tag, such as GST, His or NusA-His, was added at the N-terminiof the CTA recombinant constructs. Following purification by affinity chromatogra-phy, the purity, and concentration of the purified proteins were examined by so-dium dodecyl sulfate polyacrylamide gel electrophoresis and bicinchoninic acidassay (Pierce), respectively.

2.3. Preparation of the CTA microarray

For each epoxy-coated slide (CapitalBio), the recombinant proteins were addedin duplicate onto twelve identical blocks using a SpotArray-72 Microarray printer(Perkin–Elmer). All of the CTA microarrays utilized for serum screening wereprinted in the same batch. To guarantee the spotting quality, an antibody againstthe tag was routinely incubated with the microarrays, followed by the addition ofa polyclonal secondary antibody labeled with Cy-5 (CoWin Biotech). The microarraywas scanned with a microarray scanner (CapitalBio), and the signals were extractedusing GenePix Pro 6.0 software (Molecular Devices). To estimate the limit of detec-tion (LOD) for the microarray, a series dilution of human IgG was dotted onto theepoxy-coated slides, followed by incubation with anti-human IgG labeled withCy-5 (Jackson Laboratory). Bovine serum albumin (BSA) was used as a negativecontrol.

2.4. Serum screening against CTA microarrays

A 12-sample grid (CapitalBio) was applied to each CTA microarray to form 12individual chambers. Each serum sample was diluted 200-fold with 3% w/v BSAin phosphate buffered saline (PBS) and then reacted with each individual block ofthe protein microarray at 37 �C for 1 h. After three 5 min washes with PBST (0.1%

v/v Tween 20 in PBS), the microarrays were subsequently probed with 1000-fold di-luted goat anti-human IgG antibody labeled with Cy-5 in the dark at 37�Cfor 1 h.After three 5-min washes with PBST, the protein microarrays were scanned. Thecoefficient of variations (CV) of signals from human IgG and mouse IgG were usedto evaluate the quality of the serum screening results.

2.5. Data analyses

For each protein spot, the signal intensity was calculated by subtracting themedian of the foreground from the median of the background at 635 nm. For a gi-ven antigen, the signal intensity, Si,j (i = sample, j = antigen), was calculated as themean signal of duplicate spots. If the signal for an antigen was negative, Si,j was as-signed as ‘‘1’’ for further mathematical treatment. The antigen signals were con-verted to log2 transformation, followed by normalization using proCAT, a proteinmicroarray analysis approach [27]. The adjusted signal of antigens Ai,j was calcu-lated using the following formula:

Ai;j ¼ MEDþ ðSi;j �MEDiÞMAD

MADi

; ði ¼ sample; j ¼ antigenÞ

where MED and MAD represent the median and the median absolute deviation of thesignal intensities from the individual samples, respectively. In the training set, we seta cutoff value equal to the mean + 2 standard deviation (SD) of the normal control,and a signal above the cutoff was considered to be seropositive. The numbers andpercentages were presented as the 95% exact confidence interval (95% CI) for bino-mial proportions. Fisher’s exact tests or v2 tests were employed to define NSCLC-re-lated CTAs and to determine whether the CTA panel was specific to NSCLC. All of theNSCLC samples with their clinical parameters were combined to increase sample sizeand to assess the correlation between CTAs and the clinical information, using Fish-er’s exact tests or v2 tests. For all statistical analyses, p values < 0.05 were regardedas significant.

2.6. Validation of the microarray results by ELISA

Each well of a 96-well microtiter plate was coated with 100 ng antigen. Eachsample was diluted 200-fold with 1% BSA in PBS, and then the samples were addedto individual wells, washed, and incubated with 3000-fold diluted anti-humanhorseradish peroxidase-conjugated secondary antibody. Assays were developedusing a TMB/H2O2 (3,30 ,5,50-tetramethylbenzidine) substrate (BD Pharmingen,)and stopped with 1 M phosphoric acid. The absorbance signal was measured at450 nm. A cutoff value equal to the mean + 2 SD of the normal control was set,and a signal above the cutoff was considered to represent seropositive samples.

3. Results

3.1. Preparation of the CTA microarray

Based on the analysis of antigen sequence similarity and immu-nogenicity, 82 CTAs were selected. Due to poor expression of somerecombinant proteins, a total of 72 CTAs were successfully ex-pressed in E. coli or yeast, including three CTAs consisting of six re-combinant proteins (two recombinant proteins for each CTA) and69 unique CTA recombinant proteins. The details are provided inSupplemental Table 2. To verify the accuracy of our microarray,we selected six non-CTAs that are known to be associated withNSCLC, such as p53, K-ras, and c-Myc. All of the non-CTAs were re-garded as putatively positive reference antigen markers becausethese antigens have been reported to produce autoantibodies,and have been characterized in NSCLC sera [28]. All of the recom-binant proteins were at least 90% pure and qualified as microarrayantigens. Representative results are shown in Supplemental Fig. 1.

To evaluate the LOD, we tested 16 spots of human IgG, consist-ing of eight varied amounts of protein ranging from 1.56 to 200 pg/spot. Meanwhile, four spots of BSA with 5 ng/spot were spottedand their signals were taken as background. As shown in Fig. 1,the coefficient of determination (R2) was 0.999, indicating a gooddose-dependent relationship between signal intensities and hu-man IgG loaded. By setting signal intensity mean + 2 SD of BSA asthe maximum background noise, the LOD of IgG upon its calibra-tion curve was estimated 0.9 pg. To determine whether loadinghigh amounts of protein led to signal ‘‘smear’’, we tested the pro-tein loading on the spots and found a maximum of approximately5 ng/spot (data not shown). Therefore, we adjusted the antigen

Fig. 1. Limit of detection (LOD) estimation for the microarray. (A) Typical image of the fluorescent signals from the microarray. A series of diluted human IgG samples werespotted in duplicate onto epoxy slides at 5 nl/spot, followed by the addition of the Cy-5-labeled secondary antibody against human IgG. Bovine serum albumin (BSA) wasspotted onto the upper left of the slide to serve as a background. (B) Linear regression analyses between the fluorescent intensities and the IgG quantities loaded onto themicroarray. The regression curve was generated based on triplicate data, which resulted in the following equation: y = ax + b; a = 119.500, b = 9.001, and R2 = 0.999.

162 Q. Shan et al. / Cancer Letters 328 (2013) 160–167

concentrations, which ranged from 0.1 to 1 mg/ml (0.5–5 ng/spot).The positive control proteins, human and mouse IgG were dilutedto a concentration of 0.05 ng/spot to avoid signal saturation.

The CTA microarray was designed as a 13 � 14 matrix in ablock. After a visualization test using GST and NusA as antigens,we observed that the diameter of the spots was approximately200 lm, which was acceptable for the detection of sufficient fluo-rescent signal and maintenance a sharp boundary between eachspot (Fig. 2A). To check the spotting quality, a correlation curvewas generated based on the fluorescent intensities of duplicatespots. As shown in Fig. 2B, the R2 value was 0.998 in the curve,which indicated highly reproducible spotting.

3.2. Serum screening with the CTA microarrays

Considering that most of the recombinant antigens were taggedwith GST, we first tested whether the serum samples would have anon-specific reaction with the GST tag. If a serum sample reactedpositively with most GST-CTA fused proteins, resulting in similarfluorescent intensities, the sample was excluded from the assaygroup. Of the 377 human serum samples, only two serum samplesreacted with the GST tag. To evaluate the reproducibility of theblocks within a microarray, the signals of human and mouse IgGin each block were acquired and their CV values were estimated.Approximately 80% of these signals had low intra-microarray CVvalues (9% and 14% for human and mouse IgG, respectively). A sim-ilar study was conducted to evaluate the comparable inter-micro-array values, and the CV values were also acceptable (28% and 33%for human and mouse IgG, respectively). ProCAT was used to com-pensate for variability between individual microarrays. Followingdata analyses, all samples, regardless of the blocks or microarrays,shared a similar median and absolute median deviation, whichindicated that the data treatment to the quantitative differencesbetween the individual serum samples was accepted.

The clinical samples were divided into two groups, consisting ofsera from NSCLC cases and normal controls, and each group wasrandomly divided into training and test set (Table 1). To ensurethe grouped samples to be well randomized, we checked the clin-ical parameters for the samples in the two groups. As shown inSupplemental Table 1B, the clinical parameters in the trainingand test sets were not significantly different. The CTA microarrayswere first screened against serum from the training set, and thenthe NSCLC-related antigen panel elicited from the training setscreening was applied to the sera in the test set to further evaluatevalidity. The immune reactivity of the CTA panel was further exam-ined against serum from patients with breast cancer, gastric can-cer, and non-cancer lung diseases, to assess specificity.

Typical screening images from the CTA microarray are depictedin Fig. 2C�F, and the immune responsive signals were further ana-lyzed with a statistical algorithm. An antigen with signal intensityabove the mean + 2 SD of normal samples was defined as serum-positive in the training set. Of the antigens examined, only ADAM2was serum-negative. Furthermore, 69 antigens were detected inthe normal sera with prevalence values ranging from 2% to 6%,whereas 76 antigens were found in NSCLC sera with prevalencevalues ranging from 1% to 13%. As the prevalence values for anti-gens from either normal or NSCLC sera were largely variable, Fish-er’s exact test was used to compare the frequency of eachautoantibody between normal and NSCLC sera. As shown in Table 2,statistical analyses (p < 0.05) demonstrated that NY-ESO-1 andMAGEC1 were significantly elevated in squamous cell carcinoma(SCC), while XAGE-1 and ADAM29 were elevated in adenocarci-noma (ADC). In NSCLC, the prevalence values of XAGE-1, ADAM29,NY-ESO-1, and MAGEC1, were 7%, 7%, 13%, and 10%, respectively. Inaddition, as a control for autoantigen, p53 displayed a positive rateof 13%. Combining different and individual autoantigen is expectedto enhance diagnostic accuracy. Therefore, we further analyzedcombinations of individual CTAs to determine if CTA combinationscould improve sensitivity for use in NSCLC screening. CTA candi-date antigens, XAGE-1, ADAM29, NY-ESO-1, and MAGEC1, usedin combination greatly increased the accuracy for NSCLC samplesto 33%, whereas the reaction remained at 4% for normal samples.When p53 antigen was added to the CTA panel, CTA-p53 panel,the results improved to 38% for the NSCLC samples, and 7% forthe normal samples, and these changes resulted in a 5% increasein sensitivity and a 3% loss in specificity. The observed gain in sen-sitivity and the loss in specificity were similar, indicating that thep53 autoantibody contributed little to the NSCLC panel. Therefore,p53 was excluded from further assays.

The CTA panel was further employed to test set consisting of 94serum samples. The prevalence of individual CTA positives variedfrom 5–19% to 0–3% in NSCLC and normal control samples, respec-tively. The data are depicted in Supplemental Fig. 2, which indi-cated that the individual CTA signals in NSCLC samples weredifferent from that of the normal controls (NCs). When we appliedthe CTA panel to the test set of serum samples, the prevalence ofthe CTA improved to 36% for NSCLC samples, and 11% for normalsamples (the data are listed in Supplemental Table 3). The CTA pa-nel results for the NSCLC and normal samples had sensitivity andspecificity values of 33% and 96% for the training set and 36%and 89% for the test set, respectively. These data indicate that theCTA panel demonstrates good generality. We then determined ifthe CTA panel could be used in an autoimmune marker assay todistinguish NSCLC from other disease cases. We tested the CTA pa-nel assay with sera from patients with breast cancer, gastric cancer,

Fig. 2. Quality control and serum screening of non-small cell lung cancer (NSCLC) biomarkers using the cancer/testis antigens (CTAs) microarray. (A) A typical image forquality control of the CTA microarray. The CTA microarray was incubated with the antibody cocktail containing poly anti-NusA and anti-GST and was then incubated with Cy-5-labeled goat anti-rabbit polyclonal antibody. The green and red boxes represent human and mouse IgG, respectively. The white and pink boxes represent the GST and NusAproteins, respectively. (B) Estimation of the correlation between the duplicate spots. The two sets of fluorescent intensities were analyzed by linear regression, which resultedin the following equation: y = ax + b; a = 0.998, b = 94.020 and R2 = 0.998. (C), (D), (E), and (F) Images of CTA microarrays generated from serum screening. The images C, D, andE were derived from NSCLC sera, and the images F were obtained from normal sera. The positive responses for antigens were labeled with yellow boxes.

Table 1The sample information of the non-small cell lung cancer patients.

Group Total Gender Age Stages Differentiation

Male, n (%) Mean ± SD (median) I-II n (%) III n (%) IV n (%) Unknown n (%) H n (%) M n (%) L n (%)

Training set 188ADCa 60 29 (48) 58 ± 11 (61) 22 (37) 12 (20%) 3 (5) 23 (38) 9 (15) 27 (45) 24 (40)SCCb 60 55 (92) 61 ± 9 (60) 24 (40) 12 (20%) 2 (3) 22 (37) 1 (2) 32 (53) 27 (45)Normal 68 35 (51) 52 ± 5 (51) NAc NA NA NA NA NA NA

Test set 94ADCa 30 20 (67) 61 ± 9 (62) 11 (37) 7 (23%) 1 (3) 11 (37) 4 (13) 11 (37) 15 (50)SCCb 29 28 (97) 60 ± 9 (57) 12 (41) 9 (31%) 0 (0) 8 (28) 1 (3) 16 (55) 12Normal 35 13 (37) 51 ± 6 (49) NA NA NA NA NA NA NA

Sum 282

a Adenocarcinoma.b Squamous cell carcinoma.c Not available.

Q. Shan et al. / Cancer Letters 328 (2013) 160–167 163

or non-cancer lung diseases, and observed that the prevalence val-ues of positive reactions varied from 0% to 15% for individual CTAs,as illustrated in Supplemental Fig. 2. With the CTA panel, the prev-alence values for positive reactions were 10%, 15%, and 12% for the

breast cancer, gastric cancer, and non-cancer lung disease samples,respectively. Fisher’s exact tests indicated that the prevalence ofthe CTA panel for NSCLC was significantly higher than for breastcancer or non-cancer lung disease (p < 0.05). However, the preva-

Table 2Prevalence of the autoantibodies against CTA-p53 panel.

Group Total Number and percentage positive (with 95% exact confidence interval)

NY-ESO-1 XAGE-1 MAGEC1 ADAM29 p53 CTA panel CTA-p53 panel

NSCLCn 120 16b 8 12b 8 16b 40c 45c

% +ve (95% CI)a 13 (8–21) 7 (3–13) 10 (6–17) 7 (3–13) 13 (8–21) 33 (26–42) 38 (29–46)

ADCn 60 6 7b 4 5b 9b 21c 23c

% +ve (95% CI) a 10 (5–20) 12 (6–22) 7 (3–16) 8 (4–18) 15 (8–26) 35 (24–48) 38 (27–51)

SCCn 60 10c 1 8b 3 7 19c 22c

% +ve (95% CI) a 17 (9–28) 2 (0–9) 13 (7–24) 5 (2–14) 12 (6–22) 32 (21–44) 37 (26–49)

Normaln 68 1 1 1 0 2 3 5% +ve (95% CI) a 1 (0–8) 1 (0–8) 1 (0–8) 5 (2–14) 3 (1–10) 4 (2–12) 7 (3–16)

a Number (n) and percentage positive (% +ve) with 95% confidence interval probabilities.b P < 0.05 relative to normal control.c P < 0.01 relative to normal control.

164 Q. Shan et al. / Cancer Letters 328 (2013) 160–167

lence of the CTA panel for NSCLC was not significantly differentfrom that of the gastric cancer samples. This is likely due to thelimited number of gastric cancer serum samples available in thisstudy. Therefore, the CTA panel may be useful to determine theautoimmune responses in NSCLC, which shows that the responsediffers from breast cancer and non-cancer lung disease.

3.3. Correlation analyses of the CTA panel and clinical characteristics

The statistical values derived from the v2 test were used toevaluate the correlation between each CTA autoantibody and theclinical parameters. For each autoantibody on the CTA panel, nocorrelation was found between autoantibody prevalence and otherclinical characteristics, such as gender, age, and TNM stage. How-ever, the prevalence of the NY-ESO-1 autoantibody was signifi-cantly higher in cases of SCC than of ADC (p = 0.011), whereas,the prevalence of the XAGE-1 autoantibody was significantly lowerin cases of SCC than of ADC (p = 0.014). In addition, for all of theNSCLC samples, the prevalence of the MAGEC1 autoantibody wassignificantly higher for patients in stage I compared to other stages(p = 0.007). Thus, the individual CTA may serve as an autoantibodybiomarker to distinguish the status (stages) of NSCLC. Further de-tails are provided in Supplemental Table 4.

We further sought to determine whether the CTA panel couldbe used to characterize the stages of NSCLC. As clinical informationrelated to NSCLC staging was not well documented for the samplestested, we collected only the samples (n = 115) containing clinical

Table 3Sensitivity analysis to the CTA panel at different stages of NSCLC.

Group Samples wi

I

ADCa n = 56 Number +ve% 5/26+ve (95% CI)d 19 (9–38)

SCCb n = 59 Number +ve% +ve (95% CI)d 9/21

43 (24–63)

NSCLCc n = 115 Number +ve% +ve (95% CI)d 14/47

30 (19–44)

a Adenocarcinoma.b Squamous cell carcinoma.c Non-small cell lung cancer.d Number (n) and percentage positive (% +ve) with 95% confidence intervals.

information regarding the status of NSCLC stages from both thetraining and test set, and conducted a correlation analysis for theCTA panel with NSCLC staging. As shown in Table 3, for all of theNSCLC cases, the prevalence of the autoantibody panel was insig-nificant in relation to disease stage. However, when the cases ofNSCLC were grouped into SCC and ADC, the prevalence of theCTA panel for SCC was independent of the stage (although the va-lue for stage I SCC was relatively high, at approximately 43%),whereas the CTA panel for ADC was stage-dependent. The detec-tion rates of CTA panel autoantibodies during later stages of ADC(III/IV) were significantly higher than those in early stages (I/II).

3.4. ELISA-based comparison of the microarray results

We tested 138 samples, including 87 NSCLC and 51 normal con-trol samples, against three CTAs, ADAM29, NY-ESO-1, and XAGE-1.Based on the cutoff value mentioned above, eight samples (sixNSCLC, two NC), 11 samples (nine NSCLC, two NC), and eight sam-ples (eight NSCLC, 0 NC) were serum-positive against ADAM29,NY-ESO-1, and XAGE-1, respectively (Fig. 3A, C, and E). Moreover,we observed similar results when comparing the microarrays, asfour samples (four NSCLC, 0 NC), 13 samples (12 NSCLC, one NC),and seven samples (seven NSCLC, 0 NC) were serum-positiveagainst ADAM29, NY-ESO-1, and XAGE-1, respectively (Fig. 3B, D,and F). Thus, the detection rates for the three CTAs by ELISA werebasically comparable to that of protein microarray.

th autoantibody positivity against CTA panel

II III IV

0/7 11/19 1/40 (0–35) 58 (36–77) 25 (1–70)

5/15 8/21 0/233 (15–58) 38 (21–59) 0 (0–66)

5/22 19/40 1/623 (10–43) 48 (33–63) 17 (1–56)

Fig. 3. Comparison between microarray and ELISA results. (A), (C), and (E) The ELISA results from serum screening against ADAM29, NY-ESO-1, and XAGE-1, respectively. (B),(D), and (F) Microarray results from serum screening against ADAM29, NY-ESO-1, and XAGE-1, respectively. Dashed lines indicate the cutoff values.

Q. Shan et al. / Cancer Letters 328 (2013) 160–167 165

4. Discussion

It is well accepted that disease biomarkers are found amongprotein families. For example, several S-100 members are usefulmarkers for certain cancers as well as for epidermal cell differenti-ation. Yao et al. used nucleotide microarrays to survey the involve-ment of the S100 gene family in bladder cancer and found that 10genes were up-regulated and three genes were down-regulated[29]. The abundance and activities of matrix metalloproteinases(MMPs) are of major interest for understanding the matrix physi-ology of disease processes. Rosell et al. developed an MMP arrayto evaluate MMP levels after hyper acute stroke in the ischemic

brain and reported that high levels of MMP-9 and MMP-13 wereinvolved in diffusion-weighted image lesion expansion duringthe neuroinflammatory response [30].

Recently, several efforts have been made to explore cancer bio-markers using protein microarrays. Gnjatic et al. generated an ar-ray containing 329 full-length proteins identified by serologicalanalysis of recombination cDNA expression libraries (SEREX)[31]. The comparisons between the autoantibody signals fromNSCLC and normal serum samples revealed that the sensitivityand specificity of the arrays displayed 94% concordance with stan-dard ELISA techniques. Kilic et al. constructed a protein microarraycontaining 51 cancer-related proteins to detect the levels of serum

166 Q. Shan et al. / Cancer Letters 328 (2013) 160–167

autoantibodies in 18 patients with esophageal adenocarcinomaand 14 patients with gastro-esophageal reflux disease [32]. Theseresults demonstrated that Fas ligand was the most discriminatingmarker and was associated with 83.3% sensitivity and 100% speci-ficity for cancer patients.

In the current study, we designed a CTA microarray with sev-eral additional components. First, the autoimmune antigen candi-dates of interest were not general, cancer-related proteins, butrather were a protein family that has been shown to be highlyautoimmune enhancing and relevant to lung cancer. Second, weconducted a careful analysis of CTA amino acid sequences tosearch for homologues and to evaluate antigenicity, and we se-lected 82 CTAs that were representative of the CTA family with in-creased antigenicity. Third, considering the importance of properprotein folding for antibody recognition, we attempted to producesoluble forms of the recombinant CTAs. Our study is the first toevaluate a CTA microarray to screen for CTA autoantibodies inlung cancer sera. After assessing quality control and screeningpatient sera with the CTA microarray, our CTA microarray wasvalidated.

Proper statistical tests are important for microarray data analy-ses, whereas data analyses for DNA-based versus protein-basedmicroarrays are different somehow. DNA-based microarrays con-tain a high density of test genes which produce a large numberof different signals for analyses of two cohorts. The consistent sig-nals are easily evaluated by t-test, although small yet significantdifferences can be acquired. In contrast, protein-based microarraysare tested at a relatively lower density (less sample proteins) andthis generates signals that are fewer in number but have a widerdistribution. Thus, the t-test is not proper to evaluate these scat-tered signals, and Fisher’s exact test is preferred for such a datapattern. To evaluate autoantibody signals, Gnjatic et al. found thatthe reactivity of autoantibodies with protein microarrays pre-sented a discrete pattern responding to individual serum. Theauthors proposed that the individual cutoff values for each antigenshould enable the judgment of corresponding serum-positive reac-tivity [33]. Li et al. set cutoff values for several antigens to defineserum-positive reactivity in sera from normal and liver cancer pa-tients. Using the v2 test to treat the microarray data, these investi-gators found six antigens that were significantly different betweennormal and liver cancer sera [34]. In the current study, we em-ployed a similar statistical analysis to evaluate autoantibodies re-lated to NSCLC cases.

For the CTA panel associated with NSCLC, CTAs were dividedinto two groups, consisting of autoantibodies that had been previ-ously identified and novel autoantibodies. As an autoantigen, NY-ESO-1 possesses extremely high immunogenicity and inducesautoantibody production in various cancers at different frequen-cies. In NSCLC, the frequency of autoantibodies against NY-ESO-1was reported to be between 4% and 22% [28]. Moreover, the sero-positive reaction to NY-ESO-1 was shown to correlate with histo-logical type in SCC. This antigen was also found to induce animmune response at a higher level comparable to ADC. In our re-sults, NY-ESO-1 was the most frequent autoantibody identified ata prevalence of 8–21%, which is similar to data previously obtainedby ELISA [11].

XAGE-1 is located on the X chromosome and is highly expressedin normal testis and in patients with Ewing’s sarcoma [35]. Wanget al. found that an XAGE-1 isoform was expressed in both theearly and late stages of lung adenocarcinoma [36]. Watanabeet al. reported an increase in autoantibodies targeting the splicedforms of XAGE-1 in lung cancer [37]. Using a CTA microarray, ourresults are similar to those of previous reports. As 12% of patientswith ADC, but only 2% of patients with SCC, were positive forXAGE-1, XAGE-1 may represent a histologically specific biomarkerfor NSCLC.

MAGEC1 is expressed by a majority of myeloma patients, andelicits an immune response in most patients with MAGEC1 (+)myelomas. Immunohistochemical evidence also suggests that MA-GEC1 is abundant in several cancers, although autoimmune re-sponses targeting this protein have not yet been reported in thesera of cancer patients [38,39]. Our results demonstrated that theMAGEC1 autoantibody was detected in approximately 10% of theNSCLC serum samples, and the positive detection rate of this auto-antibody was significantly correlated with stage I of NSCLC.

ADAM29, a member of the disintegrin and metalloproteinasefamily of proteins, may participate in cell-to-cell or cell-to-matrixinteractions as well as in the proteolytic shedding of the cell sur-face. Oppezzo et al. quantified ADAM29 mRNA levels in a cohortof 127 patients with chronic lymphocytic leukemia and found thatits expression was a good prognostic indicator for disease progres-sion [17], although whether ADAM29 is able to induce immune re-sponses remains unclear. Our data clearly demonstrated thatautoantibodies against ADAM29 were detected in NSCLC samplesat a low incidence of approximately 8%.

Due to the low specificity of NSCLC microarrays, a single CTAautoantibody provides limited information for diagnosing NSCLC.To overcome this limitation, we efficiently generated a CTA panelfor detecting multiple signals of CTA autoantibodies. The conceptof autoantibody panel is widely accepted by several investigatorsto look for the biomarkers related with cancers. Chapman et al.developed a similar panel with seven autoantibodies that achieved76% sensitivity and 92% specificity for NSCLC [13], while Lam et al.created an autoantibody panel with six cancer-related antigensthat exhibited approximately 30% sensitivity and 85% specificityfor NSCLC, after screening 517 lung cancer samples [40]. In the cur-rent study, the CTA microarray containing NY-ESO-1, XAGE-1,ADAM29, and MAGEC1 resulted in 33% sensitivity and 96% speci-ficity in the training set and 36% sensitivity and 89% specificity inthe test set for NSCLC samples. Moreover, these results were con-firmed by ELISA, which represents a widely used tool in clinicaldiagnoses. Thus, the results of this study offer convincing evidencethat this CTA panel can be used for developing serum-based diag-nostic assays for NSCLC cancer patients.

Acknowledgments

We are grateful for the valuable suggestions to the manuscriptfrom Dr. Ying Hu and for help with sample collections from Ms. JiaGao and Yazhe Fang. This work was supported by the Major StateBasic Research Development Program of China (2011CB910704),the National High Technology Research and Development Programof China (2012AA020206), and the National Natural Science Foun-dation of China (81172084).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.canlet.2012.08.019.

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