An Integrated Genome-Wide Approach to Discover Tumor- Speciﬁc Antigens As Potential Immunologic and Clinical Targets in Cancer

Published OnlineFirst November 7, 2012; DOI: 10.1158/0008-5472.CAN-12-1656

Cancer Integrated Systems and Technologies Research

An Integrated Genome-Wide Approach to Discover Tumor- Speciﬁc Antigens as Potential Immunologic and Clinical Targets in Cancer

Qing-Wen Xu1, Wei Zhao1, Yue Wang8,9, Maureen A. Sartor11, Dong-Mei Han2, Jixin Deng10, Rakesh Ponnala8,9, Jiang-Ying Yang3, Qing-Yun Zhang3, Guo-Qing Liao4, Yi-Mei Qu4,LuLi5, Fang-Fang Liu6, Hong-Mei Zhao7, Yan-Hui Yin1, Wei-Feng Chen1,†, Yu Zhang1, and Xiao-Song Wang8,9

Abstract Tumor-specific antigens (TSA) are central elements in the immune control of cancers. To systematically explore the TSA genome, we developed a computational technology called heterogeneous expression profile analysis (HEPA), which can identify genes relatively uniquely expressed in cancer cells in contrast to normal somatic tissues. Rating human genes by their HEPA score enriched for clinically useful TSA genes, nominating candidate targets whose tumor-specific expression was verified by reverse transcription PCR (RT-PCR). Coupled with HEPA, we designed a novel assay termed protein A/G–based reverse serological evaluation (PARSE) for quick detection of serum autoantibodies against an array of putative TSA genes. Remarkably, highly tumor-specific autoantibody responses against seven candidate targets were detected in 4% to 11% of patients, resulting in distinctive autoantibody signatures in lung and stomach cancers. Interrogation of a larger cohort of 149 patients and 123 healthy individuals validated the predictive value of the autoantibody signature for lung cancer. Together, our results establish an integrated technology to uncover a cancer-specific antigen genome offering a reservoir of novel immunologic and clinical targets. Cancer Res; 72(24); 1–11. 2012 AACR.

Introduction normal somatic cells, making them difficult to target. For this Unlike microorganisms, whose molecular characteristics reason, a central aim of cancer research is to identify molecular are distinct from human cells, cancer cells greatly resemble targets distinguishing cancer cells from normal somatic cells. Tumor-specific antigens (TSA) are normal human gene products that are relatively uniquely expressed in cancer cells in Authors' Affiliations: 1Department of Immunology, Peking University contrast to normal somatic tissues, and thus often evoke 2 Health Science Center; Department of Hematology, People's Liberation specific antitumor immune responses. TSAs often have impor- Army Air Force General Hospital; 3Department of Clinical Laboratory, Peking University School of Oncology, Beijing Cancer Hospital and Insti- tant functions in embryonic or germ line cells but are silenced tute; 4Department of Oncology, People's Liberation Army 309 Hospital; in most somatic tissues. In contrast to "overexpression" in 5Department of Cardiothoracic Surgery, People's Liberation Army 306 fi Hospital; 6Department of Pathology, Peking University People's Hospital; cancer, this "tumor-speci c" expression pattern implies crucial 7Department of Surgery, Peking University Third Hospital, Beijing, China; clinical significance of these targets for cancer management. 8Lester & Sue Smith Breast Center, 9Dan L. Duncan Cancer Center, and Indeed, TSAs are pivotal targets in the management of 10Human Genome Sequencing Center, Baylor College of Medicine, Hous- ton, Texas; and 11National Center for Integrative Biomedical Informatics, human cancers. Assays detecting TSAs are commonly used The Center for Computational Medicine and Bioinformatics, University of in the clinic for the early detection or monitoring of human Michigan, Ann Arbor, Michigan cancers, such as tests for prostate-specific antigen (PSA; ref. 1), Note: Supplementary data for this article are available at Cancer Research a-fetoprotein (AFP; ref. 2), and CA19-9 (3). In recent years, Online (http://cancerres.aacrjournals.org/). autoantibody signatures against TSAs have been adopted as a Q.-W. Xu and W. Zhao share first authorship. new type of immunologic biomarker (4, 5). By combining the responses against multiple immunogenic TSAs, autoantibody Y. Zhang and X.-S. Wang share senior authorship. signatures provide considerably higher sensitivity and speci- †Deceased. ficity than a single biomarker, implying the high potential for – Corresponding Authors: Yu Zhang, Department of Immunology, Peking early cancer detection (6 8). In the therapeutic perspective, University Health Science Center, 38 Xue Yuan Road, Beijing 100191, substantial progresses are being made in the development of China. Phone: 86-10-8280-5055; Fax: 86-10-8280-1436; E-mail: active and adoptive immunotherapy against TSAs for human [email protected]; and Xiao-Song Wang, Lester & Sue Smith – Breast Center, Dan L. Duncan Cancer Center, Department of Medicine, cancers (9 14), and a prostatic acid phosphatase (or ACPP) Baylor College of Medicine, One Baylor Plaza, MS600, Houston, TX 77030. vaccine has recently been approved by the U.S. Food and Drug Phone: 713-798-1624; Fax: 713-798-1642; E-mail: [email protected] Administration (FDA; ref. 15). doi: 10.1158/0008-5472.CAN-12-1656 In this study, we designed a novel, integrated, computation- 2012 American Association for Cancer Research. al, and experimental approach for the discovery and validation

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of TSA genes, as well as translation toward cancer detection Supplementary Methods and Fig. S2. The cutoff for a mean- using autoantibody signatures against candidate TSAs. ingful HEPA score is determined on the basis of optimal detection of known TSAs from http://www.cancerimmunity. Materials and Methods org/CTdatabase/. In our compendia of datasets, a cutoff of 6 is found to be the highest cutoff offering optimal detection of Meta-analysis of Affymetrix microarray gene expression known TSAs (Supplementary Fig. S2C). In addition, a HEPA data score of more than 8 is empirically considered as high and more We compiled the publicly available Affymetrix U133 plus 2.0 than 10 as very high. microarray data for 34 normal tissue types from the human The HEPA analysis ranks the putative antigens using the body index dataset (HBI, GSE7307), and 28 cancer types individual HEPA scores for different cancer types. The repre- profiled by the Expression Project of Oncology (EXPO, sentative probe generating the greatest average HEPA score for GSE2109; Supplementary Table S1). To increase the coverage each gene is selected in this ranking. For each probe, the of cancer types poorly represented by EXPO, we integrated a average HEPA score is calculated from the different HEPA hepatocellular carcinoma dataset (GSE6764; ref. 16), a lym- scores computed for different tumor entities. Lead protein- phoma dataset (GSE6338; ref. 17), and a melanoma dataset coding gene candidates (according to the Consensus Coding (GSE7127; ref. 18). Gene expression values were extracted with Sequence Database; ref. 19) were selected and subjected to a the MAS5 algorithm and were scaled to a reference sample, manual inspection of expression profiles. The putative TSA of using a housekeeping gene probe set provided by Affymetrix. 16 major cancer types are supplied in the following website: These normalized expression signals are directly applied to http://hepa.cagenome.org. heterogeneous expression profile analysis (HEPA) analysis, which represent "absolute expression level" as apposed to Kolmogorov–Smirnov test for HEPA score "relative expression level" in mean- or median-centered data. To determine the optimal range of the power r in the To depict gene expressions in a similar scale in heat map, the calculation of Depreciatory Penalty score, we tested the enrich- gene expression values were median-centered, and then divid- ment of the 8 prototype antigens (AFP, CTAG1B, MAGEA3, ed by their median absolute deviation (MAD). ACPP, PSA, MLANA, PMEL, and TYR) in top-scoring genes using Kolmogorov–Smirnov rank statistics (20). The resulting fi Heterogeneous expression pro le analysis P value was plotted against the power r ranging between 1 and fi HEPA analysis rst calibrates the outlier expression of a gene 2 (Supplementary Fig. S2A). X in a specific cancer k by taking the adjusted upper quartile k mean of its expression signals in cancer . Let Tissue and serum samples

x ¼ MeanfP75 P95g Total RNA from human normal tissues was purchased from Clontech. Tumor tissues and paired normal tissues were Then, the Beneficial Bonus (BB) of gene X in cancer type k is obtained from People's Hospital, First Hospital, and Third given as Hospital of Peking University (Beijing, China). The diagnosis was confirmed by independent pathologic review. Serum x BBk ¼ log2ð Þ samples from patients with bladder, renal, gastric, colon, liver, and lung cancer were collected at People's Hospital, Next, a Depreciatory Penalty (DP) score for gene X is First Hospital, Oncology School of Peking University, PLA 306 calculated on the basis of its expression signals across different Hospital, and 309 Hospital. Healthy donor sera with matched types of normal human tissues. Let age and sex were collected at Third Hospital of Peking yj ¼ Meanfðxi ’Þ I ðxi ’Þ=ðk ’Þg University from volunteers without known disease. All sera ½Þ0;¥ were stored in aliquots at 80C until use. This study was approved by the Peking University's Ethics Committee. All Here, xi is the expression signal of ith sample in tissue type j, clinical samples were collected with informed consent of yj is the tissue j–specific ratio. The Depreciatory Penalty score patients. was then estimated from the weighted power sum of tissue- specific ratios yj across all normal tissues: Reverse transcription PCR and semiquantification Xn Reverse transcription was conducted using the Reverse r fi DP ¼ vjyj Transcription System (Promega). The expression pro le of j¼1 putative TSA genes was examined using a 35-cycle endpoint PCR with primers listed in Supplementary Table S4. For Here, vj is the weight of tissue type j, n is the total number of semiquantification, band intensities were quantitated using normal tissue types. The power r penalizes the expression ImageJ software (NIH) and normalized to respective glycer- signals in somatic tissues. aldehyde-3-phosphate dehydrogenase (GAPDH) controls. A The HEPA score for gene X in cancer k was then calculated as signal value less than 10% of the saturated signal was HEPAk ¼ BBk DP, to highlight genes with the most remark- considered as no expression, between 10% and 30% as able heterogeneous expression profiles (Supplementary Fig. median/weak expression, and more than 30% as strong S1A and S1B). The parameters of HEPA score are detailed in expression.

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Gene cloning, protein expression, and in vitro on a rotator at 4C overnight. After 3 washes, 5 mL in vitro– translation translated protein was added. After incubation for 1 hour at The open reading frames (ORF) of the TSA genes were room temperature, the beads were washed with PBSN, eluted cloned into the pGEM-T easy vector (Promega) and subcloned by sampling buffer, separated on SDS-PAGE gel, and analyzed into pET-28a (Novagen). For a gene encoding a large protein by autoradiograph. product (IQGAP3, CLCA2, and IGF2BP3), the ORF was cloned as fragments. Preparation of TSPY and IGF2BP3 proteins was Statistical analysis conducted as previously described (21). 35S-labeled proteins Receiver-operating curves (ROC) were generated and the were prepared using the TnT T7 Coupled Transcription/ areas under the ROC curves (AUC) were calculated to assess Translation System (Promega) and 35S-methionine (PerkinEl- the predictive value of each antigen as well as the combination mer). Unincorporated 35S-methionine was removed by a of the 4 selected TSAs for detection of lung cancer. desalting column (Pierce). Results ELISA and Western blot analysis Developing the computational model for genome-wide ELISA and Western blot analysis to detect serum autoanti- detection of tumor-specific antigens bodies were conducted as previously described (21, 22). To analyze the expression profiles of human genes under normal and cancer status, we compiled Affymetrix U133 plus 2 PARSE assay microarray datasets for 34 normal tissues and 28 cancer types To conduct the protein A/G–based reverse serological eval- (Supplementary Table S1). By analysis of expression profiles for uation (PARSE) assay, the black-wall high-binding 96-well plate 8 well-established TSA genes widely accepted as clinical tar- (Greiner) was used to minimize the cross-talk between wells gets, AFP (23), CTAG1B (13), MAGEA3 (12), ACPP (24, 25), PSA during scintillation counting. The plate was coated with protein (26), MLANA (27), PMEL (28) and TYR (29), we observed that A/G (Sigma, 5 mg/mL each), diluted in Na2CO3/NaHCO3 buffer these prototype TSA genes usually exhibit distinctive expres- (pH 9.5), and incubated at 4C overnight. After 3 washes with sion profiles (Fig. 1 and Supplementary Fig. S1A). First, instead PBS-0.05% NP-40 (PBSN), the plate was blocked with PBS of being generally upregulated in cancer cells, an exceptional containing 1% bovine serum albumin (BSA) for 1 hour. Then overexpression profile is most often noted in a relatively small the plate was incubated with 1:100 diluted serum samples, or subset of tumors, whereas the rest subset of tumors maintains with anti-His (Sigma, 1:1,000) or anti-TSPY (1:1,000) antibody expression silence. The level of this outlier overexpression may (21) in PBS/BSA at 4C overnight. After 3 washes, the plate was increase the likelihood of clinical benefits (beneficial outliers). incubated with in vitro translated protein (2 mL/well diluted in Conventional statistical tests (e.g., Student t test) that search PBS/BSA) for 1 hour at room temperature. After washing, for ubiquitously upregulated genes across a panel of cancer radioactivity was read on a liquid scintillation counter (Perki- samples would fail to accentuate this profile, whereas rank- nElmer). Serum samples with a radioactivity value exceeding based nonparametric tests, such as the Mann–Whitney U test, the median detected value (m) by 3 MADs (s) were considered to would ignore the strength of overexpression in a small subset be autoantibody positive. Each radioactivity value was therefore of patients. Second, the expressions of most TSA genes in normalized with the equation: (xi m)/s 3, in which xi is the normal tissues present exclusively in embryonic and germ-line readout of the ith well. Negative resulting values were set to 0. tissues (exemplified by fetal, placenta, and testis tissues), or are restricted to specific lineages. Such a restricted expression Immunoprecipitation pattern discriminates normal somatic cells from cancer cells, For each sample, 4 mL serum was diluted in 400 mL PBSN and implying potential clinical applications. Furthermore, this incubated with protein A/G Sepharose CL-4B (GE Healthcare) expression privilege may presumably decrease the likelihood

CER HCC GA HEPA 34 Normal tissues BRE CA CAP END LYM MEL NSCLC OVA RCC SAR TCC (Max) AFP 13.9 CTAG1B 12.5 MAGEA3 13.4 ACPP 11.0 10 PSA 15.2 20 MLANA 14.0 30 PMEL 11.3 40 TYR 13.6

Figure 1. The gene expression proﬁles of 8 prototype TSAs widely adopted as clinical targets. Gene expression proﬁles are analyzed using Affymetrix U133 plus 2.0 microarray datasets for 34 normal tissues and 28 cancer types (see Materials and Methods). The maximum HEPA score for each antigen is labeled on the right side. BRE, breast cancer; CA, colon adenocarcinoma; CAP, prostate cancer; CER, cervical cancer; END, endometrium carcinoma; HCC, hepatocellular carcinoma; LYM, lymphoma; MEL, melanoma; NSCLC, non–small cell lung carcinoma; OVA, ovary carcinoma; GA, gastric adenocarcinoma; SAR, sarcoma.

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that recirculating lymphocytes will access these antigens. Confirming the cancer-specific expression profile of Lymphocytes responsive to them, therefore, may be well candidate targets preserved in the mature repertoire (30). These observations HEPA analysis also revealed a number of highly heteroge- lay the foundation for discovering TSA genes as immunologic neously expressed genes in a variety of cancer types whose and clinical targets by gene expression profile analysis. Toward implication in cancer immunology and management has not this end, we developed a novel analysis called the HEPA, yet been well established (Fig. 3A). Reverse transcription PCR incorporating the key expression features of clinically adopted (RT-PCR) detecting these gene products were conducted using TSA genes described earlier. 16 normal tissues and 154 paired cancer and noncancerous The scoring scheme of HEPA takes into account both the tissues covering 8 common human cancers. A 35-cycle end- "beneficial" outlier profile in cancers and the "depreciatory" point RT-PCR assay was applied as a strict test to assess expression profile in normal tissues (Supplementary Fig. S1B). expression silence in normal somatic tissues. Among the 60 In this approach, a positive score is given for a signal measure genes examined (Supplementary Table S4), 19 were found to be of increased beneficial outliers in a specific cancer (Beneficial highly expressed in a variety of human cancers while silent in Bonus), and a negative penalty score is given in proportion to most normal somatic tissues (Fig. 3B, Supplementary Fig. S3A, increased expression signals across normal tissues (Deprecia- and Table 1). The expression of each of these TSA genes was tory Penalty). The scoring scheme of Beneficial Bonus takes examined against a panel of tumor tissues from 154 patients advantage of the adjusted upper quartile mean to feature the with cancer (Supplementary Fig. S3B). beneficial outliers in cancer (see Materials and Methods). This This validation leads to several interesting new findings. function highlights the genes overexpressed in only a subset of VGLL1, a poorly characterized X chromosome gene, was found cancer samples, while avoiding the misrepresentation caused to exhibit a dramatic tumor-specifi c expression pattern. Its by extremely high outlier expression signals (Supplementary expression was strictly restricted to the placenta in normal Fig. S1C). tissues but highly represented in bladder transitional cell The algorithm for the Depreciatory Penalty penalizes the carcinoma (TCC). Up to 50% of patients with TCC have strong expression signals that are above an expression silence thresh- expression of VGLL1, whereas it is completely absent in normal old w in normal tissues, so that differences in the expression bladder and adult soma (Fig. 3B and Supplementary Fig. S3B). signals lower than this threshold will not affect the Deprecia- VGLL1 encodes a transcriptional coactivator that binds to tory Penalty score (Supplementary Fig. S2B). The average of transcriptional enhancer factor domain containing transcrip- these adjusted expression signals in each normal tissue is then tion factors (31). Our result calls for future studies of the compared with the estimated threshold of expression signals, functional and translational significance of VGLL1 in human below which the gene products would not be able to educate cancers. Like VGLL1, SNX31 is upregulated in more than 50% of the developing immune system for central tolerance specificto TCCs and absent in normal tissues (Fig. 3B and Supplementary these antigens (k). The ratios from different tissues are then Fig. S3B). SNX31 belongs to the sorting nexins family (32); its weighted and summarized on a power scale to generate the role in cancer is poorly understood. KRT81, whose normal Depreciatory Penalty score, such that the moderately to highly expression was restricted to placenta and testis, was found to expressed genes will be contrasted from genes with little or no be overexpressed in 67% of renal cell carcinoma (RCC), sug- expression in normal tissues (see Materials and Methods and gesting that KRT81 may be an attractive immunologic target. Supplementary Table S2). The final HEPA score is then SLC6A3, a multipass transmembrane dopamine transporter obtained by subtracting the Depreciatory Penalty from the (33), was absent in adult soma but overexpressed in 67% of Beneficial Bonus score to estimate the potential clinical value patients with RCC (Fig. 3B and Supplementary Fig. S3B), which of human genes. could be a potential target for monoclonal antibody therapy. Although further investigation of the literature revealed recent Detecting tumor-specific antigen genes as potential reports of some of the validated TSA genes as tumor biomar- clinical targets kers [CDH17 (34), KRT20 (35), REG4 (36), CLCA2 (37), CST1 (38), We first tested the feasibility of HEPA by determining its UPK2 (39), and TNFSF7 (40)], their substantial tumor specificity performance in detecting known clinically important TSAs. revealed in this study calls for further studies about their role in Rating the previously documented TSAs by HEPA score tumor immunology or serologic detection of human cancers. resulted in a substantial separation of TSAs active in cancer In addition, IGF2BP3 was reported as a TSA in colorectal diagnostics and therapeutics from those with less clinical cancers during the course of this study (41). applicability based on literature reports (Fig. 2A and Supple- mentary Table S3A). We then ranked all human genes in our Detecting autoantibody responses against the candidate compendium of datasets by their HEPA score to highlight TSAs in cancer patients those showing the best heterogeneous expression profiles (Fig. Next, we sought to evaluate the immunologic significance of 2B and Supplementary Table S3B). It is notable that we are able the candidate tumor-specific targets in patients with cancer. to isolate the clinically useful TSA genes including the 8 The presence of specific autoantibodies was taken as a sur- prototype TSAs directly from the human genome, which rogate marker of immunogenicity. Conventionally, serum comprises tens of thousands of genes, simply by this single autoantibodies are detected by Western blot analysis or ELISA, parameter. This shows the strength of the HEPA score in which require recombinant proteins with high purity and thus prioritizing TSA genes with immunologic and clinical value. pose a great challenge for antigen preparation when evaluating

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A B BRECAP CA HCC RCC NSCLC(A)(S)MELTCC (max) CA KLK2 BRE CAP HCCRCC NSCLC(A)NSCLC(S) MEL TCC HEPA score * KLK3 KLK3 **SCGB2A2 **SCGB2A2 * SLC26A3 MLANA MLANA * TGM4 * AFP * AFP **DCT OR51E2 * TYR **DCT * MAGEA3 * TYR * GAGE1,2,4-8,7B * ANKRD30A PRAME RLN1 CTAG1B SCGB1D2 * FMO9P * CTAG2 * NOX1 CEACAM7 PRAC * MAGEA2 MAGEA3 * GPR143 * GAGE12E GAGE3 TOP 100 C15orf21 MAGEA12 APOB XAGE1D * IL13RA2 SPINK4 MAGEA1 MGC39715 * SILV PRAME * CSAG2 * CTAG1B Figure 2. HEPA analysis MAGEA4 * CDH17 ACPP * CTAG2 preferentially identiﬁes clinically * TRPM8 * SSX2 NQO1 ﬁ SSX3 KRT20 important tumor-speci c targets. A, KLK4 NTS MAGEB2 CFHR5 the heat map of HEPA scores for GPA33 MAGEA2 * MAGEA5 * MMP10 reported TSA genes across 9 tumor SSX1 HOXB13 GPR143 entities. TSA genes reported to have FOLH1 CEACAM7 * MAGEC1 * MAGEA12 clinical use are marked with colored MAGEA9 * CYP4Z1 TYRP1 * TRPM1 asterisks. B, separation of TSA genes * CASC5 SLC45A2 CCDC110 IL13RA2 active in cancer diagnostics and DDX43 IGF2BP3 * COL10A1 therapeutics from the rest of the SPANXB1 COL11A1 MORC1 MUC13 MAGEA8 VGLL1 human genome by HEPA scoring. MAGEB1 ABCB5 BRDT SLC39A6 Left, all 16,435 coding genes 16435 coding genes RAB38 LDHC * DHRS2 included in the Affymetrix U133 plus LUZP4 REG4 CTAGE1 PBK 2 arrays are ranked by their maximum MGAT5 SEMG1 DDX53 CT45-4 HEPA score. Right, the HEPA scores NLRP4 CCL20 TDRD6 C6orf218 of the top 100 TSA genes in distinct IL24 CRISP2 CALCA tumor entities. Each point represents SYCP1 UPK3A SLCO6A1 OCA2 the HEPA score of 1 gene in 1 cancer THEG MAGEA1 MAGEA11 * ASCL2 TPTE PI15 type. Different cancer types are SLC38A4 MAGEA10 MEP1A depicted by different colors. The 8 * ADAM2 SILV (PMEL) SPACA3 MS4A12 prototype TSAs are highlighted in ZNF165 * CLCA1 NXF2 ZNF718 1 1 red. The complete list of related 0 FCRLA XAGE3 5 HORMAD1 0 5 PTHLH literature is summarized in BTBD16 CT45A3 HEPA score DLX2 Supplementary Table S3. SPANXA1 (Max) MAGEA4 MAGEE1 MMP1 MAGEC3 * SLC30A10 XAGE2 CDX1 SSX4 GAPDHS TRPM4 DKKL1 ITGB3 SPA17 TMPRSS2 * SAGE1 KISS1R TEX15 CYP3A7 SPANXC MMP3 FTHL17 CENPN MAGEC2 SPO11 DLX1 BAGE ACPP ARL14 * SSX2 0 1 5 9 ≥13 STC2 ST6GAL1 HEPA score * CXCL11 SERHL2 UPK1A Cancer vaccine target KRT23 * FST CDH3 * Tumor biomarker OTC 6 8 10 12 14 16 HEPA score (Max)

a large number of candidates. To accelerate the process, we therefore retaining antigen-binding activity, which may be developed a novel assay called PARSE for rapid and easy attenuated when antibodies are directly immobilized on plates detection of the autoantibodies against putative TSAs. Small by passive adsorption (Fig. 4A, left). amounts of radiolabeled protein were prepared by in vitro Using a TSPY antigen we previously identified (21), the translation, and used as a probe for detection of autoantibodies feasibility of PARSE was evaluated with serum samples from in patients' sera. To remove the complex repertoire of serum patients with hepatocellular carcinoma, which yielded a result proteins, which may result in high background readings, we comparable with ELISA (Fig. 4B and C). To verify the specificity coated the plates with protein A/G before the addition of the of the PARSE assay, unlabeled TSPY protein was added in 50- serum samples. Because of the specific binding of immuno- fold excess to compete with 35S-labeled TSPY protein and the globulins to protein A/G, most of the serum proteins and other positive signal was completely abolished (Fig. 4C). The positive components are presumably removed in subsequent washes. serum from the PARSE assay was further validated using a Moreover, protein A/G binds to the Fc region of antibodies, specially designed immunoprecipitation assay (Fig. 4A, right),

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A Gene Normal tissues BRE CA HCC MEL NSCLC RCC GA TCC HAS3 IGF2BP3 KRT23 IQGAP3 KRT81 VGLL1 CDH17 KRT20 MMP3 REG4 SPINK4 REG3A ≤0 CST1 10 CLCA2 NTS 20 SLC6A3 30 TNFSF7 SNX31 40 UPK2 ≥50

B Normal tissues Tumor normal pairs

Figure 3. Tumor-specific gene expression profile of newly identified TSA genes. A, heat map of normalized gene expression data depicting the tumor-specific expression profile of these TSA genes based on the compendium of microarray datasets from normal and cancer tissues. B, expression profile of TSA genes in 16 normal tissues (left) and tumor- normal pairs (right). GAPDH was used as a control. PBMC, peripheral blood mononuclear cell.

in which immune complexes were allowed to form between the studies as a confirmation test for the PARSE assay. The 35S-labeled antigen and the specific autoantibodies, and then performance of PARSE in comparison with ELISA was further pulled down by protein A/G beads. The result from this evaluated using another TSA, IGF2BP3 (Supplementary Fig. immunoprecipitation assay correlated well with Western blot S4A and S4B), which yielded similar results. analysis but with a clearer background (Fig. 4D and E). Immu- To apply the PARSE assay to the newly identified TSA genes, noprecipitation of radioactive 35S-labeled TSPY protein was 12 putative antigen proteins were successfully expressed using also abolished by excess unlabeled TSPY protein (Fig. 4E). This in vitro translation (Table 1). To show the general applicability modified immunoprecipitation was thus used in subsequent of HEPA–PARSE approach in cancer, we have attempted to

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Table 1. Summary of mRNA and autoantibody proﬁle of TSAs validated in this study

mRNA expression (%) Cancer patients Healthy donors

Subcellular Cancer PARSEþ IPcþ/total PARSEþ IPþ/total Gene symbol localizationa type þþþIVTb /total (%) tested /total (%) tested HAS3 Multipass membrane LC 8.3 50 IGF2BP3 Cytoplasm; nucleus LC 25 66.7 Y 5/72 (6.9) 4/72 0/70 (0) 0/48 IQGAP3 Cytoplasm LC 25 50 Y 5/72 (6.9) 0/70 (0) 0/8 GA 20 66.7 2/48 (4.2) 2/9 KRT23 Cytoplasm LC 8.3 58.3 Y 8/72 (11.1) 3/36 0/70 (0) 0/18 GA 26.7 33.3 2/48 (4.2) 1/16 KRT81 Cytoplasm RCC 33.3 33.3 Y 3/48 (6.3) 1/6 1/46 (2.2) 0/18 VGLL1 Nucleus TCC 11.1 48.1 Y 3/42 (7.1) 1/6 0/48 (0) 0/8 CDH17 Type I membrane CA 16.1 48.4 KRT20 Cytoplasm CA 12.9 35.5 Y 0/48 (0) 0/46 (0) MMP3 Secreted CA 6.5 45.2 Y 0/48 (0) 0/46 (0) REG4 Secreted CA 3.2 16.1 SPINK4 Secreted CA 6.5 93.5 REG3A Cytoplasm; secreted GA 6.7 33.3 Y 2/48 (4.2) 1/6 0/46 (0) 0/8 CST1 Secreted GA 20 33.3 CLCA2 Type II membrane LC 8.3 41.7 Y 5/72 (6.9) 1/48 (2.1) 0/18 TCC 14.8 55.6 2/42 (4.8) 1/6 NTS Secreted LC 8.3 33.3 Y 0/72 (0) 0/70 (0) SLC6A3 Multipass membrane RCC 16.7 66.7 Y 0/72 (0) 0/70 (0) TNFSF7 Type II membrane RCC 16.7 66.7 SNX31 Soluble, nonsecreted TCC 18.5 33.3 UPK2 Type I membrane TCC 25.9 40.7 Y 0/42 (0) 0/48 (0)

aPredicted by the protein subcellular localization database—LOCATE (http://locate.imb.uq.edu.au/). bIVT, protein that successfully in vitro translated; Y, yes. cIP, immunoprecipitation. include multiple cancer types with available serum samples in TSA targets into diagnostic autoantibody signatures in specific our laboratory. These include lung cancer (n ¼ 72), gastric cancers. Among 72 patients with lung cancer, for example, the cancer (n ¼ 48), colon cancer (n ¼ 48), RCC (n ¼ 48), and TCC numbers of patients showing positive responses against each (n ¼ 42) patients. We therefore interrogated each antigen of the 4 individual antigens, IGF2BP3, KRT23, IQGAP3, and against 1 or 2 of these 5 cancer types with the highest CLCA2, were 5, 8, 5, and 5, respectively, but the total number expression level and positive rate for that antigen (according reached 21 when results for the 4 antigens were combined. to RT-PCR results). Serum samples from 72 healthy donors are Similarly, 11 of 48 patients with gastric adenocarcinoma con- used as control. Spontaneously arising antibodies against 7 of tained autoantibodies against at least 1 of the 3 tumor antigens, these TSAs were detected in patients with cancer (Fig. 5A KRT23, IQGAP3, and REG3A (Fig. 5A). To further assess the and Table 1). The results obtained from the PARSE assay were diagnostic value of the antibody signature in lung cancer, ROC subsequently verified by the specially designed immunopre- curves were generated on the basis of the normalized PARSE cipitation assay noted earlier (Fig. 5B). The radiolabeled tumor scores. In a cohort of 72 patients with lung cancer and 70 antigens were immunoprecipitated by PARSE-positive sera healthy donors, the AUC ranged from 0.614 to 0.667 for each of from patients with cancer, but not by sera from PARSE- the 4 TSAs alone, and increased to 0.711 when combined (Fig. negative patients or healthy donors. 5C). The same analysis was conducted against an independent, but larger cohort containing 149 cases of patients with lung Translation toward diagnostic autoantibody signatures cancer and 123 healthy individuals (Supplementary Fig. S4C), in cancer which yielded an AUC of 0.768 when the 4 antigens were The overall positivity of autoantibodies specific for each of combined (Fig. 5D). In both cohorts, the age and sex distribu- the TSAs varies from 4.2% to 11.1% in distinct cancer subtypes tions are comparable between cancer and healthy donor (Table 1), and in the same tumor entity there was limited groups (Supplementary Table S5A). Furthermore, the positive overlap in the spectrum of antibodies against different anti- rates of autoantibodies are similar between patients with early- gens (Fig. 5A). This raised the possibility of combining these and late-stage lung cancer (Supplementary Table S5B). These

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ficity, over the statistical methods that we previously used in A PARSE assay Immunoprecipitation a putative tumor antigen target database (ref. 42; data not Protein A/G shown). Of note, the parameters of the HEPA analysis applied Autoantibodies in this study have been optimized to discover TSAs whose expressions are absent in most somatic tissues, as shown by 35S-labeled protein strict endpoint RT-PCR assays. However, some of the over- Sepharose beads expressed antigens may also be important in the therapeutic BD setting, such as EGF receptor (EGFR) and HER-2. Interestingly, kDa we observed that such therapeutically relevant overexpressed 70 * antigens also possess some degree of tumor-specificity (Sup- plementary Fig. S5A). With adjusted parameters, these ther- 40 apeutically relevant overexpressed antigens can also be OD 405 nm * detected by HEPA analysis (Supplementary Fig. S5B). The list of putative overexpressed antigens is provided in the website: Anti-TSPY N21 N22 H90 * http://hepa.cagenome.org. To quickly evaluate the immunologic significance of the C E kDa large number of candidate TSA genes in patients with cancer, 1,000 62 we developed a novel experimental approach called the PARSE in vitro 25 assay. PARSE leverages the mammalian translation system as a quick and efficient way to produce a small amount of TSAs with natural conformations and posttranslational Anti-TSPYN21 N22 H90 H90+TSPYInput modifications. Thus, it presumably can generate a precise picture of autoantibody responses in patients with cancer by detecting the full spectrum of epitopes on these antigen proteins. Furthermore, the in vitro translation system enables the expression of toxic proteins not producible in live cells and Figure 4. The principle and feasibility of PARSE and immunoprecipitation assays. A, schematic depiction of the principle of PARSE (left) and avoids aggregation of proteins in inclusion bodies as occurs in immunoprecipitation assays (right). B, detection of autoantibodies bacteria. Our data suggest that the output of PARSE is com- against a known cancer-testis antigen, TSPY, by ELISA. Representative parable with conventional ELISA (Fig. 4B and C and Supple- results are shown with serum samples from healthy individuals (N21, N22) mentary Fig. S4A and S4B), with greatly improved throughput and patients with HCC (H89, H90, and H91). The anti-TSPY antibody in detecting autoantibodies against new TSAs. served as a positive control and PBS as a negative control. , positive fi serum. C, detection of TSPY-specific autoantibodies using the PARSE Identifying gene targets highly speci c for a malignant assay. D, confirmation of TSPY-specific autoantibodies by Western blot condition is a great challenge for developing a serologic cancer analysis. The specific band for TSPY is indicated with arrowhead. , detection assay. Careful choices are required when considering fi fi fi nonspeci c bands. E, con rmation test for TSPY-speci c autoantibodies which antigen genes should be selected for assembling an by immunoprecipitation. Input, radiolabeled TSPY. B and C, error bars, – SD; n ¼ 3; C and E, to verify the binding specificity, unlabeled TSPY autoantibody signature. The HEPA PARSE approach showed protein was added in 50-fold excess to compete with 35S-labeled TSPY improved specificity and greater speed in the discovery of TSAs (H90 þ TSPY). OD, optical density. CPM, counts per minute. and validation of autoantibody signatures. In contrast to frequent autoantibody responses observed in patients with cancer, specific autoantibodies against these TSAs were found – results support the feasibility of applying the HEPA PARSE to be extremely rare in a comparable number of healthy approach to develop autoantibody signatures for the detection individuals (Fig. 5A and B). This remarkable tumor specificity of human cancers. may be attributable to the highly restricted expression of the TSAs in normal somatic tissues and the improved background Discussion readout of the PARSE assay. Of note, 5 of 12 candidate antigens Here, we have reported the development and application of a tested do not elicit autoantibody response in patients of mathematical scheme for genome-wide detection of TSA genes selected cancer types. Interestingly, 2 of 3 membrane proteins, as immunologic and clinical targets. Rather than relying solely 2 of 3 secreted proteins, and 2 of 2 colon-specific proteins are on traditional statistical tests based on distribution estimation found to be negative. Further studies will be needed to eluci- or ranks, our novel approach incorporates the distinctive date the impact of subcellular localization, lineage specificity, biologic features within a panel of established TSA genes and other factors on the natural choice of an antigen. Fur- widely adopted in clinics. Applying HEPA analysis to large thermore, we observed a high degree of mutual exclusivity in gene expression datasets for multiple cancer types and a the patient autoantibody spectrum for different antigens. For spectrum of normal tissues revealed arrays of TSA genes with example, while specific autoantibodies against IGF2BP3, diagnostic and therapeutic potential. Profound enrichment of KRT23, IQGAP3, and CLCA2 were detected in 6.9%, 11.1%, known clinically useful TSA genes was observed in these lists. 6.9%, and 6.9% of patients with lung cancer, respectively, their Endpoint RT-PCR validation revealed that this approach has combination covered up to 29.2% of patients (21 of 72), greatly improved predictive power in terms of tumor speci- resulting in a distinctive autoantibody signature (Fig. 5A).

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Genome-Wide Discovery of TSAs as Clinical Cancer Targets

A Sera from cancer patients Sera from healthy donors PBS Anti-His IGF2BP3 KRT23 LC IQGAP3 CLCA2 KRT23 GA IQGAP3 REG3A RCC KRT81 VGLL1 TCC CLCA2 10 2 3 4 5 60 70 10 20 30 40 50 60 70 1 1 0 0 0 0

≥5 ≥4 ≥3

B Lung cancer Healthy His Input C D L37 L67 L68 L69 N1 N2 kDa IGF2BP3 IGF2BP3 _ IGF2BP3 42 _ Total IgG 50

Gastric cancer Healthy Input AUC:0.614 AUC:0.645 G1 G2 G3 G14 G15 G16 N61 N62 _ IQGAP3 55 KRT23 KRT23 _ Total IgG 50

Lung cancer Gastric cancer Healthy His Input L12 L40 G2 G3 G4 N17 N18 _ AUC:0.755 KRT23 52 AUC:0.625 _ Total IgG 50 IQGAP3 IQGAP3 Renal cell carcinoma Healthy Input R1 R2 R3 R4 R5 R6 N15 N16 KRT81 _ 55 _ AUC:0.643 AUC:0.684 Total IgG 50

Gastric cancer Healthy Input G21 G22 G23 G24 G25 G26 N63 N64 CLCA2 CLCA2 _ REG3A 25 _ Total IgG 50 Transitional cell carcinoma Healthy Input AUC:0.667 AUC:0.567 T13 T14 T15 T16 T17 T18 N7 N8 VGLL1 _ 34 Combine Combine _ Total IgG 50

Transitional cell carcinoma Healthy Input T7 T8 T9 T10 T11 T12 N9 N10 _ AUC:0.711 AUC:0.768 CLCA2 40

_ Sensitivity 50 Total IgG 1-Specificity

Figure 5. Tumor-specific autoantibodies against candidate TSAs are detected by PARSE assay. A, PARSE assay reveals autoantibodies against 7 newly identified TSAs in serum samples from patients with cancer or healthy individuals. The results are normalized and presented as a heat map in red color scales. Anti-His antibody is used as a positive control and PBS as a negative control. Gray color shows that no serum sample is available at the specific number. B, a subset of positive and negative sera revealed by the PARSE assay was subjected to immunoprecipitation evaluation. PARSE-positive sera are highlighted in red. The sample numbers indicated below each gel picture are matched to those shown in A. C, diagnostic values of the panel of the 4 tumor antigens in a cohort of 72 patients with lung cancer and 70 healthy individuals. The AUC is indicated in each chart. D, validation using an independent cohort of 149 patients with lung cancer and 123 healthy individuals. IgG, immunoglobulin G.

To further evaluate the value of this autoantibody signature plementary Fig. S4C). Comparable AUC of ROC curves were in the detection of lung cancer, we conducted the PARSE assays obtained from these 2 independent cohorts, conﬁrming the using sera from an independent and larger cohort of 149 diagnostic value of this autoantibody signature (Fig. 5C and D). patients with lung cancer and 123 healthy individuals (Sup- Future studies will be needed to elucidate the translational

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significance of this autoantibody signature and to compare gene expression technology, we expect that interest in HEPA with other related but noncancer conditions. More TSAs analysis will increase as an approach to interrogate these new should be identified by the HEPA–PARSE approach and com- datasets. bined with the current antigen panel to increase sensitivity. Furthermore, the level of mutual exclusiveness in the gene Disclosure of Potential Conflicts of Interest expression profiles of candidate antigens can be analyzed using No potential conflicts of interest were disclosed. microarray datasets to identify the best combination of TSAs in the detection of specific tumors. Authors' Contributions Conception and design: Q.-W. Xu, W.-F. Chen, Y. Zhang, X.-S. Wang It is notable that most of the TSAs validated in this study are Development of methodology: Q.-W. Xu, W. Zhao, M.A. Sartor, X.-S. Wang related to multiple tumor entities except SLC6A3 (exclusive to Acquisition of data (provided animals, acquired and managed patients, RCC), or UPK2 and SNX31 (exclusive to TCC). Those antigens provided facilities, etc.): Q.-W. Xu, W. Zhao, D.-M. Han Analysis and interpretation of data (e.g., statistical analysis, biostatistics, expressed in multiple tumor entities may be used for the computational analysis): Q.-W. Xu, W. Zhao, Y. Wang, J. Deng, Y. Zhang, X.-S. detection of cancer in general, whereas others exclusive to Wang certain cancer types can be used to identify the specific tumor Writing, review, and/or revision of the manuscript: Q.-W. Xu, M.A. Sartor, Y. Zhang, X.-S. Wang entity. This could be important for determining treatment Administrative, technical, or material support (i.e., reporting or orga- options, which is often difficult when the original tumor is nizing data, constructing databases): Y. Wang, R. Ponnala, J.-Y. Yang, Q.-Y. Zhang, G.-Q. Liao, Y.-M. Qu, L. Li, F.-F. Liu, H.-M. Zhao, Y.-H. Yin undetectable. The beauty of HEPA analysis is that it provides a Study supervision: Y.-H. Yin, W.-F. Chen, Y. Zhang, X.-S. Wang quantitative value for each gene in each tumor entity (as shown in Fig. 2 and Supplementary Fig. S3A), so that the preference of Acknowledgments expression for each TSA in different cancers can be apparent at The authors thank Dr. Jun Zhang for helpful discussion and critical reading of a glance. the article and Xuewen Pang, Xiao-Ping Qian, and Yan Li for technical assistance. Together, the HEPA–PARSE technology provides a generally applicable approach for genome-wide dissection of the cancer- Grant Support specific antigen genome, and for the quick development of This study was supported by grants from National Natural Science Founda- tion of China (no. 30525044 and 30830091), National High-tech R&D Program of autoantibody signatures in the detection of epithelial tumors. China (863 Program, no. 2007AA021103), National Basic Research Program of By optimizing the parameters, HEPA technology can be readily China (973 Program, no. 2011CB946100), Congressionally Directed Medical adapted to many specific applications, such as the identifica- Research Programs (#W81XWH-12-1-0166), NIH grants P30-125123-06, and NCRR S10RR02950. tion of autoantibody signatures, biomarkers, tumor vaccine The costs of publication of this article were defrayed in part by the payment of targets, or membrane antigen targets. Its performance could be page charges. This article must therefore be hereby marked advertisement in further enhanced by combining it with other datasets, such as accordance with 18 U.S.C. Section 1734 solely to indicate this fact. cancer plasma or membrane proteomics datasets. With the Received May 7, 2012; revised August 29, 2012; accepted September 25, 2012; development of deep transcriptome sequencing and digital published OnlineFirst November 7, 2012.

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An Integrated Genome-Wide Approach to Discover Tumor-Specific Antigens as Potential Immunologic and Clinical Targets in Cancer

Qing-Wen Xu, Wei Zhao, Yue Wang, et al.

Cancer Res Published OnlineFirst November 7, 2012.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-12-1656

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