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

Author Manuscript Published OnlineFirst on November 7, 2012; DOI: 10.1158/0008-5472.CAN-12-1656 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

An integrated genome-wide approach to discover tumor specific antigens as potential immunological and clinical targets in cancer

Authors and Affiliations:

Qing-Wen Xu1,*, Wei Zhao1,*, Yue Wang2, Maureen A. Sartor3, Dong-Mei Han4, Jixin Deng5, Rakesh Ponnala2, Jiang-Ying Yang6, Qing-Yun Zhang6, Guo-Qing Liao7, Yi-Mei Qu7, Lu Li8, Fang-Fang Liu9, Hong-Mei Zhao10, Yan-Hui Yin1, Wei-Feng Chen1,11, Yu Zhang1,#, Xiao-Song Wang2,#

1. Department of Immunology, Peking University Health Science Center, Beijing 100191, China. 2. Lester & Sue Smith Breast Center and Dan L. Duncan Cancer Center, Baylor College of Medicine. 3. National Center for Integrative Biomedical Informatics, CCMB, University of Michigan, MI, 48109, USA. 4. Department of Hematology, PLA Air Force General Hospital, Beijing 100036, China. 5. Human Genome Sequencing Center, Baylor College of Medicine. 6. Department of Clinical Laboratory, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, China. 7. Department of Oncology, PLA 309 Hospital, Beijing, China. 8. Department of Cardiothoracic Surgery, the 306th Hospital of PLA, Beijing, China. 9. Department of Pathology, Peking University People's Hospital, Beijing 100044, China. 10. Department of Surgery, Peking University Third Hospital, Beijing 100191, China. 11. Deceased.

Note: * These authors share first authorship. # These authors share senior authorship.

Running Title: Genome-wide discovery of TSAs as clinical cancer targets

Key words: Tumor specific antigen; TSA; Gene expression profiling; New algorithims; Immunodiagnosis; Autoantibody signatures.

Disclosure of Potential Conflicts of Interest: No potential conflicts of interest were disclosed.

Grant Support: This study was supported by grants from National Natural Science Foundation of China (No. 30525044 and 30830091), National High-tech R&D Program of China (863 Program, No. 2007AA021103), National Basic Research Program of China (973 Program, No. 2011CB946100), Congressionally Directed Medical Research Programs (#W81XWH-12-1-0166), NIH grants P30-125123-06, and NCRR S10RR02950.

Corresponding Authors:

Yu Zhang, Department of Immunology, Peking University Health Science Center, 38 Xue Yuan Road, Beijing 100191, China. Phone: (8610)8280-5055; Fax: (8610)8280-1436; E-Mail: [email protected] and Xiao-Song Wang, Lester & Sue Smith Breast Center and Dan L. Duncan Cancer Center, Department of Medicine, Baylor College of Medicine, One Baylor Plaza, MS600, Houston, TX, 77030. Phone: 713-798-1624; Fax: - 1 -

713-798-1642; Email: [email protected]

Total word count: 4974 words Figures and Tables: 6

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Abstract

Tumor-specific antigens (TSAs) 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 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 4-11% 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 immunological and clinical targets.

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Introduction

Unlike microorganisms, whose molecular characteristics are distinct from human cells, cancer cells greatly resemble normal somatic cells, making them difficult to target. For this reason, a central aim of cancer research is to identify molecular targets distinguishing cancer cells from normal somatic cells. Tumor-specific antigens

(TSAs) are normal human gene products that are relatively uniquely expressed in cancer cells in contrast to normal somatic tissues, and thus often evoke specific anti-tumor immune responses. TSAs often have important functions in embryonic or germ line cells but are silenced in most somatic tissues. In contrast to ‘overexpression’ in cancer, this ‘tumor-specific’ expression pattern implies crucial clinical significance of these targets for cancer management.

Indeed, TSAs are pivotal targets in the management of human cancers. Assays detecting TSAs are commonly used in the clinic for the early detection or monitoring of human cancers, such as tests for PSA(1), alpha-fetoprotein (AFP)(2), and CA19-9(3). In recent years, autoantibody signatures against TSAs have been adopted as a new type of immunologic biomarker (4, 5). By combining the responses against multiple immunogenic TSAs, autoantibody signatures provide considerably higher sensitivity and specificity than a single biomarker, implying the high potential for early cancer detection (6-8). In the therapeutic perspective, substantial progresses are being made in the development of active and adoptive immunotherapy against TSAs for human cancers (9-14), and a Prostatic Acid Phosphatase (or ACPP) vaccine has recently been approved by the FDA (15).

In this study, we designed a novel, integrated, computational and experimental approach for the discovery and validation of TSA genes, as well as translation towards cancer detection using autoantibody signatures against candidate TSAs.

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Materials and Methods

Meta-analysis of Affymetrix microarray gene expression data

We compiled the publicly available Affymetrix U133 plus 2.0 microarray data for 34 normal tissue types from the Human Body Index dataset (HBI, GSE7307), and 28 cancer types profiled by the Expression Project of

Oncology (EXPO, GSE2109) (Table S1). To increase the coverage of cancer types poorly represented by EXPO, we integrated a hepatocellular carcinoma dataset (GSE6764)(16), a lymphoma dataset (GSE6338)(17), and a melanoma dataset (GSE7127)(18). Gene expression values were extracted with the MAS5 algorithm and were scaled to a reference sample, using a house-keeping gene probe set provided by Affymetrix. These normalized expression signals are directly applied to HEPA analysis, which represent “absolute expression level” as apposed to “relative expression level” in mean- or median-centered data. To depict gene expressions in a similar scale in heat-map, the gene expression values were median-centered, and then divided by their median absolute deviation

(MAD).

Heterogeneous Expression Profile Analysis

HEPA analysis first calibrates the outlier expression of a gene X in a specific cancer k by taking the adjusted upper quartile mean of its expression signals in cancer k. Let

Then the Beneficial Bonus of gene X in cancer type k is given as

Next, a Depreciatory Penalty (DP) score for gene X is calculated on the basis of its expression signals across different types of normal human tissues. Let

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Here xi is the expression signal of ith sample in tissue type j, yj is the tissue j specific ratio. The DP score was then estimated from the weighted power sum of tissue-specific ratios yj across all normal tissues:

Here˶M is the weight of tissue type j, n is the total number of normal tissue types. The power r penalizes the expression signals in somatic tissues.

The HEPA score for gene X in cancer k was then calculated as HEPAk=BBk-DP, to highlight genes with the most remarkable heterogeneous expression profiles (Figure S1A-B). The parameters of HEPA score are detailed in Supplementary Methods and Figure S2. The cut-off for a meaningful HEPA score is determined based on optimal detection of known TSAs from http://www.cancerimmunity.org/CTdatabase/. In our compendia of datasets, a cut-off of 6 is found to be the highest cut-off offering optimal detection of known TSAs (Figure S2C).

In addition, a HEPA score >8 is empirically considered as high and >10 as very high.

The HEPA analysis ranks the putative antigens using the individual HEPA scores for different cancer types.

The representative probe generating the greatest average HEPA score for each gene is selected in this ranking. For each probe, the average HEPA score is calculated from the different HEPA scores computed for different tumor entities. Lead protein-coding gene candidates (according to the Consensus Coding Sequence Database (19)) were selected and subjected to a manual inspection of expression profiles. The putative tumor specific antigens of 16 major cancer types are supplied in the following website: http://hepa.cagenome.org.

Kolmogorov-Smirnov test for HEPA-score

To determine the optimal range of the power r in the calculation of DP score, we tested the enrichment of the

8 prototype antigens (AFP, CTAG1B, MAGEA3, ACPP, PSA, MLANA, PMEL, and TYR) in top-scoring genes using Kolmogorov-Smirnov Rank statistics (K-S, Hollander and Wolfe, 1999). The resulting P value was plotted against the power r ranging between 1 and 2 (Figure S2A). - 6 -

Tissue and serum samples

Total RNA from human normal tissues was purchased from Clontech. Tumor tissues and paired normal tissues were obtained from People’s Hospital, First Hospital, and Third Hospital of Peking University. The diagnosis was confirmed by independent pathologic review. Serum samples from bladder, renal, gastric, colon, liver, and lung cancer patients were collected at People’s Hospital, First Hospital, Oncology School of Peking

University, PLA 306 Hospital, and 309 Hospital. Healthy donor sera with matched age and sex were collected at

Third Hospital of Peking University from volunteers without known disease. All sera were stored in aliquots at

-80|& until use. This study was approved by the Peking University ethics committee. All clinical samples were collected with informed consent of patients.

Reverse transcription PCR and semi-quantification

Reverse transcription was performed using the Reverse Transcription System (Promega). The expression profile of putative TSA genes was examined using a 35-cycle endpoint PCR with primers listed in Table S4. For semi-quantification, band intensities were quantitated using ImageJ software (National Institutes of Health) and normalized to respective GAPDH controls. A signal value less than 10% of the saturated signal was considered as no expression, between 10% and 30% as median/weak expression; more than 30% as strong expression.

Gene cloning, protein expression, and in vitro translation

The open reading frames (ORFs) of the TSA genes were cloned into the pGEM-T easy vector (Promega) and subcloned into pET-28a (Novagen). For a gene encoding a large protein product (IQGAP3, CLCA2, and

IGF2BP3), the ORF was cloned as fragments. Preparation of TSPY and IGF2BP3 proteins was performed as previously described (20). 35S-labeled proteins were prepared using the TNT Coupled Transcription/Translation

System (Promega) and 35S-methionine (Perkin-Elmer). Unincorporated 35S-methionine was removed by a desalting column (Pierce).

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ELISA and Western blot

ELISA and Western blot to detect serum autoantibodies were performed as previously described (20, 21).

PARSE assay

To perform the PARSE assay, the black-wall high-binding 96-well plate (Greiner) was used to minimize the crosstalk between wells during scintillation counting. The plate was coated with Protein A / G (Sigma, 5 g/ml

each), diluted in Na2CO3/NaHCO3 buffer (pH 9.5), and incubated at 4|& overnight. After three washes with

PBS-0.05% NP-40 (PBSN), the plate was blocked with PBS containing 1% BSA for 1 hour. Then the plate was incubated with 1:100 diluted serum samples, or with anti-His (Sigma, 1:1,000) or anti-TSPY (1:1,000) antibody

(20) in PBS/BSA at 4|& overnight. After three washes, the plate was incubated with in vitro translated protein (2

l per well diluted in PBS/BSA) for 1 hour at RT. After washing, radioactivity was read on a liquid scintillation counter (Perkin Elmer). Serum samples with a radioactivity value exceeding the median detected value (˩) by 3 median absolute deviations (MAD) (˰) were considered to be autoantibody positive. Each radioactivity value was therefore normalized with the equation: (xi-˩)/˰-3, where xi is the readout of the ith well. Negative resulting values were set to zero.

Immunoprecipitation

For each sample, 4 L serum was diluted in 400 L PBSN and incubated with protein A/G Sepharose CL-4B

(GE healthcare) on a rotator at 4°C overnight. After three washes, 5 L in vitro translated protein was added. After incubation for 1 hour at RT, the beads were washed with PBSN, eluted by sampling buffer, separated on

SDS-PAGE gel, and analyzed by autoradiograph.

Statistical analysis

Receiver operating curves (ROC) were generated and the areas under the ROC curves (AUC) were calculated to assess the predictive value of each antigen as well as the combination of the four selected TSAs for detection of

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Results

Developing the computational model for genome-wide detection of tumor specific antigens

To analyze the expression profiles of human genes under normal and cancer status, we compiled Affymetrix

U133 plus 2 microarray datasets for 34 normal tissues and 28 cancer types (Table S1). By analysis of expression profiles for eight well-established TSA genes widely accepted as clinical targets, AFP(22), CTAG1B(13),

MAGEA3(12), ACPP(23, 24), PSA(25), MLANA(26), PMEL(27) and TYR(28), we observed that these prototype TSA genes usually exhibit distinctive expression profiles (Figure 1, Figure S1A). First, instead of being generally up-regulated in cancer cells, an exceptional over-expression profile is most often noted in a relatively small subset of tumors, whereas the rest subset of tumors maintains expression silence. The level of this outlier overexpression may increase the likelihood of clinical benefits (beneficial outliers). Conventional statistical tests (e.g. Student’s t-test) that search for ubiquitously up-regulated genes across a panel of cancer samples would fail to accentuate this profile, whereas rank-based non-parametric tests, such as the

Mann-Whitney U test, would ignore the strength of over-expression in a small subset of patients. Second, the expressions of most TSA genes in normal tissues present exclusively in embryonic and germ-line tissues

(exemplified by fetal, placenta, and testis tissues), or are restricted to specific lineages. Such a restricted expression pattern discriminates normal somatic cells from cancer cells, implying potential clinical applications.

Further, this expression privilege may presumably decrease the likelihood that recirculating lymphocytes will access these antigens. Lymphocytes responsive to them, therefore, may be well preserved in the mature repertoire

(29). These observations lay the foundation for discovering TSA genes as immunological and clinical targets by gene expression profile analysis. Toward this end, we developed a novel analysis called the Heterogeneous

Expression Profile Analysis (HEPA), incorporating the key expression features of clinically adopted TSA genes described above.

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The scoring scheme of HEPA takes into account both the “beneficial” outlier profile in cancers and the

“depreciatory” expression profile in normal tissues (Figure S1B). In this approach, a positive score is given for a signal measure of increased beneficial outliers in a specific cancer (Beneficial Bonus, BB), and a negative penalty score is given in proportion to increased expression signals across normal tissues (Depreciatory Penalty, DP). The scoring scheme of BB takes advantage of the adjusted upper quartile mean to feature the beneficial outliers in cancer (see Materials and Methods). This function highlights the genes over-expressed in only a subset of cancer samples, while avoiding the misrepresentation caused by extremely high outlier expression signals

(Figure S1C).

The algorithm for the Depreciatory Penalty penalizes the expression signals that are above an expression silence threshold ϕ in normal tissues, so that differences in the expression signals lower than this threshold will not affect the DP score (Figure S2B). The average of these adjusted expression signals in each normal tissue is then compared with the estimated threshold of expression signals below which the gene products would not be able to educate the developing immune system for central tolerance specific to these antigens (κ ). The ratios from different tissues are then weighted and summarized on a power scale to generate the DP score, such that the moderately to highly expressed genes will be contrasted from genes with little or no expression in normal tissues

(see Materials and Methods and Table S2). The final HEPA score is then obtained by subtracting the DP from the BB score to estimate the potential clinical value of human genes.

Detecting tumor-specific antigen genes as potential clinical targets

We first tested the feasibility of HEPA by determining its performance in detecting known clinically important TSAs. Rating the previously documented TSAs by HEPA score resulted in a substantial separation of

TSAs active in cancer diagnostics and therapeutics from those with less clinical applicability based on literature reports (Figure 2A, Table S3A). We then ranked all human genes in our compendium of datasets by their HEPA

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Confirming the cancer-specific expression profile of candidate targets

HEPA analysis also revealed a number of highly heterogeneously expressed genes in a variety of cancer types whose implication in cancer immunology and management has not yet been well established (Figure 3A).

Reverse transcription polymerase chain reactions (RT-PCR) detecting these gene products were performed using

16 normal tissues and 154 paired cancer and non-cancerous tissues covering eight common human cancers. A

35-cycle endpoint RT-PCR assay was applied as a strict test to assess expression silence in normal somatic tissues.

Among the 60 genes examined (Table S4), 19 were found to be highly expressed in a variety of human cancers while silent in most normal somatic tissues (Figure 3B, Figure S3A, Table 1). The expression of each of these

TSA genes was examined against a panel of tumor tissues from 154 cancer patients (Figure S3B).

This validation leads to several interesting new findings. VGLL1, a poorly characterized X chromosome gene, was found to exhibit a dramatic tumor-specific expression pattern. Its expression was strictly restricted to the placenta in normal tissues but highly represented in bladder transitional cell carcinoma (TCC). Up to 50% of

TCC patients have strong expression of VGLL1, while it is completely absent in normal bladder and adult soma

(Figure 3B, Figure S3B). VGLL1 encodes a transcriptional co-activator, that binds to TEA-domain containing transcription factors (30). Our result calls for future studies of the functional and translational significance of

VGLL1 in human cancers. Like VGLL1, SNX31 is up-regulated in more than 50% of TCCs and absent in normal tissues (Figure 3B, Figure S3B). SNX31 belongs to the sorting nexins family (31); its role in cancer is poorly understood. KRT81, whose normal expression was restricted to placenta and testis, was found to be - 12 -

UPK2(38), and TNFSF7(39)], their substantial tumor specificity revealed in this study calls for further studies about their role in tumor immunology or serological detection of human cancers. In addition, IGF2BP3 was reported as a TSA in colorectal cancers during the course of this study (40).

Detecting autoantibody responses against the candidate TSAs in cancer patients

Next, we sought to evaluate the immunological significance of the candidate tumor-specific targets in cancer patients. The presence of specific autoantibodies was taken as a surrogate marker of immunogenicity.

Conventionally, serum autoantibodies are detected by Western blot or ELISA, which require recombinant proteins with high purity and thus pose a great challenge for antigen preparation when evaluating a large number of candidates. To accelerate the process, we developed a novel assay called Protein A/G based Reverse

Serological Evaluation (PARSE) for rapid and easy detection of the autoantibodies against putative TSAs. Small amounts of radio-labeled protein were prepared by in vitro translation, and used as a probe for detection of autoantibodies in patients’ sera. To remove the complex repertoire of serum proteins, which may result in high background readings, we coated the plates with protein A/G before the addition of the serum samples. Because of the specific binding of immunoglobulins to protein A/G, most of the serum proteins and other components are presumably removed in subsequent washes. Moreover, protein A/G binds to the Fc region of antibodies, therefore retaining antigen-binding activity, which may be attenuated when antibodies are directly immobilized on plates by passive adsorption (Figure 4A, left). - 13 -

Using a TSPY antigen we previously identified (20), the feasibility of PARSE was evaluated with serum samples from hepatocellular carcinoma patients, which yielded a result comparable to ELISA (Figure 4B-C). To verify the specificity of the PARSE assay, unlabeled TSPY protein was added in 50-fold excess to compete with

35S-labeled TSPY protein and the positive signal was completely abolished (Figure 4C). The positive serum from the PARSE assay was further validated using a specially designed immunoprecipitation (IP) assay (Figure 4A, right), in which immune complexes were allowed to form between the 35S-labeled antigen and the specific autoantibodies, and then pulled down by protein A/G beads. The result from this IP assay correlated well with

Western blot, but with a clearer background (Figure 4D and E). Immunoprecipitation of radioactive 35S labeled

TSPY protein was also abolished by excess unlabeled TSPY protein (Figure 4E). This modified IP was thus used in subsequent studies as a confirmation test for the PARSE assay. The performance of PARSE in comparison with

ELISA was further evaluated using another TSA, IGF2BP3 (Figure S4A-B), which yielded similar results.

To apply the PARSE assay to the newly identified TSA genes, 12 putative antigen proteins were successfully expressed using in vitro translation (Table 1). To demonstrate the general applicability of HEPA-PARSE approach in cancer, we have attempted to include multiple cancer types with available serum samples in our laboratory. These include lung cancer (n=72), gastric cancer (n=48), colon cancer (n=48), renal cell carcinoma

(n=48) and transitional cell carcinoma (n=42) patients. We therefore interrogated each antigen against 1 or 2 of these 5 cancer types with the highest expression level and positive rate for that antigen (according to RT-PCR results). Serum samples from 72 healthy donors are used as control. Spontaneously arising antibodies against seven of these TSAs were detected in cancer patients (Figure 5A, Table 1). The results obtained from the PARSE assay were subsequently verified by the specially designed IP assay noted above (Figure 5B). The radio-labeled tumor antigens were immunoprecipitated by PARSE-positive sera from cancer patients, but not by sera from

PARSE-negative patients or healthy donors.

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Translation toward diagnostic autoantibody signatures in cancer

The overall positivity of autoantibodies specific for each of the TSAs varies from 4.2% to 11.1% in distinct cancer subtypes (Table 1), and in the same tumor entity there was limited overlap in the spectrum of antibodies against different antigens (Figure 5A). This raised the possibility of combining these TSA targets into diagnostic autoantibody signatures in specific cancers. Among 72 lung cancer patients, for example, the numbers of patients showing positive responses against each of the four individual antigens, IGF2BP3, KRT23, IQGAP3, and CLCA2, were 5, 8, 5 and 5, respectively, but the total number reached 21 when results for the four antigens were combined.

Similarly, 11 out of 48 patients with gastric adenocarcinoma contained autoantibodies against at least one of the three tumor antigens, KRT23, IQGAP3, and REG3A (Figure 5A). To further assess the diagnostic value of the antibody signature in lung cancer, ROC curves were generated based on the normalized PARSE scores. In a cohort with 72 lung cancer patients and 70 healthy donors, the area under the curve (AUC) ranged from 0.614 to

0.667 for each of the four TSAs alone, and increased to 0.711 when combined (Figure 5C). The same analysis was performed against an independent, but larger cohort containing 149 cases of lung cancer patients and 123 healthy individuals (Figure S4C), which yielded an AUC of 0.768 when the 4 antigens were combined (Figure

5D). In both cohorts, the age and sex distributions are comparable between cancer and healthy donor groups

(Table S5A). Furthermore, the positive rates of autoantibodies are similar between early and late stage lung cancer patients (Table S5B). These results support the feasibility of applying the HEPA-PARSE approach to develop autoantibody signatures for the detection of human cancers.

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Discussion

Here we have reported the development and application of a mathematical scheme for genome-wide detection of TSA genes as immunological and clinical targets. Rather than relying solely on traditional statistical tests based on distribution estimation or ranks, our novel approach incorporates the distinctive biological features within a panel of established TSA genes widely adopted in clinics. Applying HEPA analysis to large gene expression datasets for multiple cancer types and a spectrum of normal tissues revealed arrays of TSA genes with diagnostic and therapeutic potential. Profound enrichment of known clinically useful TSA genes was observed in these lists. Endpoint RT-PCR validation revealed that this approach has greatly improved predictive power in terms of tumor specificity, over the statistical methods that we previously used in a putative tumor antigen target database (41) (data not shown). Of note, the parameters of the HEPA analysis applied in this study have been optimized to discover TSAs whose expressions are absent in most somatic tissues, as shown by strict end-point

RT-PCR assays. However, some of the overexpressed antigens may also be important in the therapeutic setting, such as EGFR and HER-2. Interestingly, we observed that such therapeutically relevant overexpressed antigens also possess some degree of tumor-specificity (Figure S5A). With adjusted parameters, these therapeutically relevant overexpressed antigens can also be detected by HEPA analysis (Figure S5B). The list of putative overexpressed antigens is provided in the website: http://hepa.cagenome.org.

To quickly evaluate the immunological significance of the large number of candidate TSA genes in cancer patients, we developed a novel experimental approach called the PARSE assay. PARSE leverages the mammalian in vitro translation system as a quick and efficient way to produce a small amount of TSAs with natural conformations and post-translational modifications. Thus it presumably can generate a precise picture of autoantibody responses in cancer patients by detecting the full spectrum of epitopes on these antigen proteins.

Further, the in vitro translation system enables the expression of toxic proteins not producible in live cells, and

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PARSE is comparable to conventional ELISA (Figure 4B-C, Figure S4A-B), with greatly improved throughput in detecting autoantibodies against new TSAs.

Identifying gene targets highly specific for a malignant condition is a great challenge for developing a serological cancer detection assay. Careful choices are required when considering which antigen genes should be selected for assembling an autoantibody signature. The HEPA-PARSE approach showed improved specificity and greater speed in the discovery of tumor-specific antigens and validation of autoantibody signatures. In contrast to frequent autoantibody responses observed in cancer patients, specific autoantibodies against these TSAs were found to be extremely rare in a comparable number of healthy individuals (Figure 5A-B). This remarkable tumor specificity may be attributable to the highly restricted expression of the TSAs in normal somatic tissues, and the improved background readout of the PARSE assay. Of note, 5 out of 12 candidate antigens tested do not elicit autoantibody response in patients of selected cancer types. Interestingly, 2 out of 3 membrane proteins, 2 out of 3 secreted proteins, and 2 out of 2 colon specific proteins are found to be negative. Further studies will be needed to elucidate the impact of subcellular localization, lineage specificity and other factors on the natural choice of an antigen. Further, we observed a high degree of mutual exclusivity in the patient autoantibody spectrum for different antigens. For example, while specific autoantibodies against IGF2BP3, KRT23, IQGAP3, and CLCA2 were detected in 6.9%, 11.1%, 6.9%, and 6.9% of lung cancer patients, respectively, their combination covered up to 29.2% of patients (21 out of 72), resulting in a distinctive autoantibody signature (Figure 5A).

To further evaluate the value of this autoantibody signature in the detection of lung cancer, we performed the

PARSE assays using sera from an independent and larger cohort of 149 lung cancer patients and 123 healthy individuals (Figure S4C). Comparable AUC of ROC curves were obtained from these two independent cohorts, confirming the diagnostic value of this autoantibody signature (Figure 5C and D). Future studies will be needed

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It is notable that most of the TSAs validated in this study are related to multiple tumor entities except

SLC6A3 (exclusive to RCC), or UPK2 and SNX31 (exclusive to TCC). Those antigens expressed in multiple tumor entities may be used for the detection of cancer in general, while others exclusive to certain cancer types can be used to identify the specific tumor entity. This could be important for determining treatment options, which is often difficult when the original tumor is undetectable. The beauty of HEPA analysis is that it provides a quantitative value for each gene in each tumor entity (as shown in Figure 2 and Figure S3A), so that the preference of expression for each TSA in different cancers can be apparent at a glance.

Together, the HEPA-PARSE technology provides a generally applicable approach for genome-wide dissection of the cancer-specific antigen genome, and for the quick development of autoantibody signatures in the detection of epithelial tumors. By optimizing the parameters, HEPA technology can be readily adapted to many specific applications, such as the identification of autoantibody signatures, biomarkers, tumor vaccine targets, or membrane antigen targets. Its performance could be further enhanced by combining it with other datasets such as cancer plasma or membrane proteomics datasets. With the development of deep transcriptome sequencing and digital gene expression technology, we expect that interest in HEPA analysis will increase as an approach to interrogate these new datasets.

Acknowledgments: The authors thank Dr. Jun Zhang for helpful discussion and critical reading of the manuscript and Xuewen Pang, Xiao-Ping Qian, and Yan Li for technical assistance.

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

mRNA Cancer patients Healthy donors Gene Subcellular Cancer expression (%) IVT

Symbol Localizationa type b PARSE+ / IPc+ / Total PARSE+ / IP+ / Total + ++ 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

LC 25 50 5/72 (6.9) IQGAP3 Cytoplasm Y 0/70 (0) 0/8 GA 20 66.7 2/48 (4.2) 2/9

LC 8.3 58.3 8/72 (11.1) 3/36 KRT23 Cytoplasm Y 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

LC 8.3 41.7 5/72 (6.9) CLCA2 Type II membrane Y 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, non-secreted 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.

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Figure Legends

Figure 1. The gene expression profiles of 8 prototype tumor-specific antigens 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 Methods). The maximum HEPA score for each antigen is labeled in the right side.

Figure 2. HEPA analysis preferentially identifies clinically important tumor-specific targets. A. The heat map of HEPA scores for reported tumor-specific antigen genes across 9 tumor entities. TSA genes reported to have clinical use are marked with colored asterisks. B. Separation of tumor-specific antigen genes active in cancer diagnostics and therapeutics from the rest of the human genome by HEPA scoring. Left: all 16435 coding genes included in the Affymetrix U133 plus 2 arrays are ranked by their maximum HEPA score. Right: the HEPA scores of the top 100 TSA genes in distinct tumor entities. Each point represents the HEPA score of one gene in one cancer type. Different cancer types are depicted by different colors. The eight prototype TSAs are highlighted in red. The complete lists of related literature are summarized in Tables S3 and S4.

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.

Figure 4. The principle and feasibility of PARSE and immunoprecipitation (IP) assays. A. Schematic depiction of the principle of PARSE (left) and IP assays (right). B. Detection of autoantibodies against a known cancer-testis antigen, TSPY, by ELISA. Representative results are shown with serum samples from healthy

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PARSE assay. D. Confirmation of TSPY-specific autoantibodies by Western blot. The specific band for TSPY is indicated with arrowhead. *, non-specific bands. E. Confirmation test for TSPY-specific autoantibodies by immunoprecipitation. Input, radio-labeled TSPY. B and D: error bars, SD; n=3; C and E: to verify the binding specificity, unlabeled TSPY protein was added in 50-fold excess to compete with 35S -labeled TSPY (H90 +

TSPY).

Figure 5. Tumor specific autoantibodies against candidate TSAs are detected by PARSE assay. A. PARSE assay reveals autoantibodies against seven newly identified TSAs in serum samples from cancer patients 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. Grey 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 IP evaluation. PARSE-positive sera are highlighted in red. The sample numbers indicated below each gel picture are matched to those shown in panel A. C. Diagnostic values of the panel of the four tumor antigens in a cohort with

72 lung cancer patients and 70 healthy individuals. The area under the ROC curve (AUC) is indicated in each chart. D. Validation using an independent cohort of 149 lung cancer patients and 123 healthy individuals.

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Figure 1

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

Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2012 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 7, 2012; DOI: 10.1158/0008-5472.CAN-12-1656 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Figure 2 a b BRECAP CA HCC RCC NSCLC(A)(S)MELTCC KLK2 CA (max) BRE CAP HCCRCC NSCLC(A)NSCLC(S) MEL TCC HEPA Score KLK3 KLK3 * SCGB2A2 SCGB2A2 **SLC26A3 **MLANA * MLANA * AFP TGM4 * * 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 * MAGEA4 * CDH17 ACPP * CTAG2 * SSX2 * TRPM8 * NQO1 SSX3 KRT20 KLK4 NTS MAGEB2 CFHR5 GPA33 MAGEA2 MAGEA5 MMP10 * SSX1 * HOXB13 GPR143 FOLH1 CEACAM7 * MAGEC1 MAGEA12 MAGEA9 * CYP4Z1 TYRP1 * TRPM1 CASC5 * SLC45A2 * CCDC110 IL13RA2 IGF2BP3 DDX43 COL10A1 SPANXB1 * COL11A1 MORC1 MUC13 MAGEA8 VGLL1 MAGEB1 ABCB5 BRDT SLC39A6 LDHC 16435 coding genes RAB38 * DHRS2 LUZP4 REG4 CTAGE1 PBK MGAT5 SEMG1 DDX53 CT45-4 NLRP4 CCL20 TDRD6 C6orf218 IL24 CRISP2 CALCA SYCP1 UPK3A SLCO6A1 OCA2 THEG MAGEA1 MAGEA11 ASCL2 TPTE * PI15 SLC38A4 MAGEA10 MEP1A * ADAM2 SILV (PMEL) SPACA3 MS4A12 ZNF165 * CLCA1 NXF2 ZNF718 FCRLA 1 1

XAGE3 0 5 HORMAD1 0 5 PTHLH BTBD16 CT45A3 HEPA score DLX2 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 STC2 0 1 5 9 ≥13 ST6GAL1 HEPA score * CXCL11 SERHL2 UPK1A KRT23 * Cancer vaccine target FST CDH3 * Tumor biomarker OTC 6 8 10 12 14 16 HEPA Score (Max)

A PARSE assay Immunoprecipitation Protein A/G Autoantibodies

35S labeled protein

Sepharose beads B D kDa

70 *

40 * Anti-TSPY N21 N22 H90 *

C E kDa 62

Anti-TSPYN21 N22 H90 H90+TSPYInput

Anti-His Sera from Cancer Patients Sera from Healthy Donors 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 Sensitivity 50 Total IgG 1-Specificity

An integrated genome-wide approach to discover tumor specific antigens as potential immunological 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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