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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Proteome Res. Author manuscript; available in PMC Aug 26, 2008.
Published in final edited form as:
PMCID: PMC2522321

The sentinel within: exploiting the immune system for cancer biomarkers


The release of proteins from tumors triggers an immune response in cancer patients. These tumor antigens arise from several mechanisms including tumor-specific alterations in protein expression, mutation, folding, degradation, or intracellular localization. Responses to most tumor antigens are rarely observed in healthy individuals, making the response itself a biomarker that betrays the presence of underlying cancer. Antibody immune responses show promise as clinical biomarkers because antibodies have long half lives in serum, are easy to measure, and are stable in blood samples. However, our understanding of the specificity and the impact of the immune response in early stages of cancer is limited. The immune response to cancer, whether endogenous or driven by vaccines, involves highly specific T lymphocytes (which target tumor-derived peptides bound to self-MHC proteins) and B lymphocytes (which generate antibodies to tumor-derived proteins). T cell target antigens have been identified either by expression cloning from tumor cDNA libraries, or by prediction based on patterns of antigen expression (“reverse immunology”). B cell targets have been similarly been identified using the antibodies in patient sera to screen cDNA libraries derived from tumor cell lines. This review focuses on the application of recent advances in proteomics for the identification of tumor antigens. These advances are opening the door for targeted vaccine development, and for using immune response signatures as biomarkers for cancer diagnosis and monitoring.

Keywords: Tumor antigen, antibody, protein array, proteomics, tumor immunology, biomarkers


The challenge faced by our immune system resembles that of an intelligent security system, which must continually monitor for the presence of foreign invaders, while recognizing and disregarding normal self. Like a vigilant sentry, immunologic memory persists long after exposure to the threat has abated. Recognizing the value of this persistent response, clinicians have exploited it for years to test individuals for current or past exposure to a wide variety of infections. Compared to other serum-derived proteins, antibodies are stable, highly specific, and readily detected with well-validated secondary reagents, making them ideal for such tests. Indeed, the traditional “blood test” required of couples before obtaining a marriage license is nothing more than a test for antibodies to the spirochete T. pallidum that causes syphilis. Thus, assessing immune responses is one of the oldest and most successful forms of biomarkers in medicine.

The immune system employs complex mechanisms to distinguish between self and non-self. It deletes or renders tolerant any cells which react to the constant stream of benign macromolecules in routine circulation. The system is not foolproof, however, and in certain diseases, the immune system responds to self-derived antigens, perhaps because their location, abundance, modified form or other features appear unfamiliar. Cancer patients often produce responses to self-proteins that are expressed by their tumors, called tumor antigens, most of which are altered in some form that renders them immunogenic. These proteins may be unique to cancer and germ cells (the “cancer-testis” antigens), found only in specific tumors (prostate-specific antigen)1 or in most tumors (telomerase)2.

They may be mutated (p53)3, misfolded4, overexpressed (NY-ESO-1) 5, aberrantly degraded6 or aberrantly glycosylated (MUC-1)7. The magnitude of the immune response to cancer, in general, is lower than the immune response to infectious agents and the potential number of tumor antigens encompasses the entire tumor proteome in all its variations. At present, we have a limited understanding of the breadth, extent, impact, and dynamic variation of the immune response to cancer (the “cancer immunome”). Identifying the specific targets of B- and T- lymphocyte immunity to cancer may 1) identify potential biomarkers for cancer diagnosis, classification, and monitoring of response, 2) determine the impact of immune regulation on cancer progression, and 3) identify potential antigens and mechanisms for immunotherapy development.

The natural immune response is achieved through a tightly regulated, yet flexible network including antibodies, antigen presenting cells, T lymphocytes, cytokines, chemokines, regulatory systems, as well as microenvironmental signals (Figure 1). Of these responses, the targeted responses to protein (and carbohydrate) antigens relies on the development of antibodies and/or T lymphocytes to target epitopes. T lymphocytes can respond to antigens derived from within cells and without. They primarily recognize short peptides (8-22mer) derived from intracellular proteins (i.e., viral antigens) bound to self-MHC molecules for presentation to CD8+ T lymphocytes. Exogenous antigens are endocytosed, degraded, and presented to CD4+ lymphocytes (Figure 2). Antibody responses increase antigen presentation by enhancing uptake through the Fcγ receptors on antigen presenting cells. As a result, antibody targets may contain epitopes that are also recognized by T lymphocytes. This has formed the basis for using antibody responses to identify T cell antigens for immunotherapy.

Figure 1
The application of cancer proteomics to tumor immunology
Figure 2
Classical model of antigen presentation

Lessons from Autoimmune Diseases

Spontaneous autoantibodies were first identified in a series of clinical disorders in which the patients' immune systems mount a vigorous response to self antigens, in some cases leading to debilitating symptoms. Systemic lupus, myasthenia gravis, rheumatoid arthritis and others all involve this process, called autoimmunity. In these illnesses, the titers of autoantibodies often track with the severity of the illness and thus have long been used as serum biomarkers (reviewed in8, 9).

Although post hoc arguments can usually be constructed explaining why proven autoantigens may have appeared unfamiliar to the immune system; in general, we do not understand enough about the characteristics that determine whether a protein will act as an autoantigen to predict them a priori. To date, only one or two percent of proteins have been identified as autoantibody targets10. In autoimmune diseases, the autoantigens identified, such as Ro/SSA, La/SSB, Sm, RNP, Scl-70 and Jo-1 are primarily intracellular antigens that function in large complexes with nucleic acids involved in protein synthesis8, and are thought to arise as a result of overexpression, apoptosis, reduced degradation, or similarity to cross-reacting foreign antigens (mimicry). It is often not clear whether these autoantibodies have any direct role in disease pathophysiology. In contrast, antibodies directed at membrane proteins, such as the target antigens of hemolytic anemia or the acetocholine receptor in myasthenia gravis may contribute to the disease process through complement activation, antibody-dependent cytotoxicity, or interference with receptor/ligand interactions. Certain structural motifs enhance antigenicity, such as carbohydrates, multivalency, epitope repetition, and coiled-coils11. Some of these antigens are linked to degradation during apoptosis12, or are targets of granzyme B cleavage during T lymphocyte-induced cytolysis13, 14. Further elucidation of the mechanisms underlying autoantibody generation will assist in predicting and identifying target antigens.

p53 as a Model Tumor Autoantigen

Perhaps the most well-studied autoantigen in cancer is the p53 protein (reviewed in 3). In 1979, DeLeo et al15 demonstrated that autoantibodies to some tumor cells in mice were directed against the p53 protein. Subsequent studies have demonstrated that the half-life of mutated p53 (several hours) was markedly increased compared to wild-type (several minutes) resulting in accumulation in the cell nucleus. p53 autoantibodies are dependent on the type of p53 mutation16-18. Notably, the immunogenic epitopes have been mapped primarily to both the N- and C-terminal portions of the molecule, which are heavily glycosylated, but not to the central portion of the molecule, which harbors the mutations, suggesting that the accumulation of protein, rather than the mutations per se, results in autoantibody generation. Multiple assays using recombinant antigens and p53-derived peptides have shown that some antigenic determinants are dependent on full-length antigen, conformation, or phosphorylation3, 19.

The detection of p53 autoantibodies in serum correlates strongly with cancer (p<10-4). p53 autoantibodies have been studied in over 9489 patients with a wide variety of tumors3. Despite the strong specificity of the response, only 20-40% of patients with cancers harboring p53 missense mutations will have p53 antibodies in their sera3. Therefore, there are additional unknown factors beyond antigen accumulation that impact the development of autoantibodies. This illustrates one of the major challenges facing the use of single autoantibody tests for cancer detection. Although the appearance of such antibodies is highly suggestive of cancer, many patients will be missed because they do not respond to p53. p53 autoantibodies have been detected in body fluids, such as ascites, pleural effusions, and saliva, and these correlate with serum autoantibodies19-21. Interestingly, gliomas, which are associated with p53 mutations, are not associated with the development of p53 antibodies22, 23, suggesting either that development of tumors in the CNS leads to immunologic privilege, or the use of steroids in this patient population could dampen the serologic immune response.

Anti-tumor Effects of Autoantibodies in Cancer

It is not known if autoantibodies reflect underlying immunosurveillance of cancer or have an impact on the clinical outcome of the disease. For example, multiple studies have attempted to correlate p53 autoantibodies with prognosis showing mixed results3. As the development of B cell immunity often depends on concordant T cell immunity, autoantibody identification can lead to the identification of relevant T cell antigens24-26. In patients who have undergone autologous tumor vaccination, certain autoantibodies correlate with tumor response to treatment25, 27. In bone marrow transplantation, the development of autoantibodies may identify minor histocompatibility antigens associated with graft versus host disease28 or tumor antigens associated with the graft-vs-leukemia (GVL) effects of donor lymphocyte infusion29, 30. In murine tumor models, it is the coordinated activities of CD4+ and CD8+ T cells, CD1d-restricted invariant NKT cells, and antibodies that accomplish protective immunity 31, 32 33 34.

The serologic identification of tumor antigens has directly led to the development of a number of cancer vaccines currently in clinical trials. The proteinase-3 antigen, first identified as the target antigen of the ANCA antibody assay for Wegener's granulomatosis, is being tested as a target for vaccination in CML35. NY-ESO-1 has been used as an immunogen in multiple clinical trials36. Antibody response to tumor vaccines have been shown to correlate with improved disease-free and overall survival in stage II melanoma patients vaccinated with a polyvalent vaccine37. Melanoma patients vaccinated with autologous tumor cells may develop highly-individualized antibody responses38. The identification of these autoantibodies has directly led to mapping of CD4+ and CD8+ T lymphocyte epitopes. The clinical benefit of antigen-specific tumor vaccination strategies is just beginning to emerge, with the recent demonstration of a survival benefit of a dendritic cell-based vaccine in hormone-refractory prostate cancer39.

Autoantibodies as Biomarkers of Cancer

In 2003, it is estimated that 1,334,100 people in the US were newly diagnosed with cancer, with an estimated 556,500 patients dying of the disease40. In this setting, there is intense effort in the search for biomarkers that can predict disease, identify biologic subtypes of disease, track response to therapeutic interventions, predict side effect profiles, and monitor for disease progression and recurrence41.

There are a limited number of serum protein biomarkers that are widely used in clinical oncology for prognosis and treatment monitoring, and their use in early cancer diagnosis has been hampered by false-positive rates in the normal population. The identification and development of these biomarkers took decades of research and required large prospective trials. Carcinoembryonic antigen (CEA), widely used for the monitoring of adenocarcinomas, was originally identified from tumor lysates because of its immunogenicity in rabbits42. The subsequent identification of the prostate specific antigen (PSA)43 and the ovarian cancer biomarker CA-12544, among others, have demonstrated both the validity and limitations of serum biomarkers in the diagnosis of cancer. Although PSA is now routinely used for screening healthy populations for prostate cancer diagnosis, most serum biomarkers are used for monitoring treatment or for screening highly selected high-risk populations. More recent developments in serum proteomics have been reviewed in this issue and elsewhere45.

Compared with other polypeptides, autoantibodies have many appealing features as biomarkers. First, although tumor antigens may circulate only briefly or in low concentration, perhaps due to transient shedding by tumors, rapid degradation in the serum, or rapid clearance, the corresponding antibody response is likely to be persistent. Second, antibodies are highly stable in serum samples and are not subject to the types of proteolysis that are commonly observed for other polypeptides (discussed elsewhere in this issue), making sample handling much easier. Third, the t1/2 of antibodies in circulation is >7 days, so hourly or daily fluctuations are expected to be minimal, simplifying sample collection. Finally, the biochemical properties of antibodies are well understood and there are many available reagents for their detection, simplifying assay development. Given these advantages, the challenge that remains is demonstrating that antibodies can be sufficiently informative to reliably detect cancer.

Early Disease Diagnosis

Very limited data are available on the use of autoantibodies to reveal early disease. Antibodies have been detected as early as several years before the clinical appearance of cancer46, 47 and in patients with preneoplastic disease48. Anecdotal studies have detected p53 autoantibodies in heavy smokers prior to the diagnosis of lung cancer47. Another study showed p53 autoantibodies in individuals exposed to vinyl chloride, a risk factor for the development of angiosarcoma of the liver46. The p53 autoantibodies predated the diagnosis of angiosarcoma by several years. Although encouraging, these few studies are not yet sufficient to endorse the value of antibodies in the early detection setting.

Monitoring treatment response and predicting recurrence

Antibodies to tumor antigens have been detected in early stages of disease and fluctuate with tumor response. In patients with both stage III and stage IV neuroblastoma, 10% have evidence of antibodies to the NY-ESO-1 antigen, but the antibody is not seen in the sera of patients in clinical remission or in earlier stages of disease49. In melanoma, antibodies to TA90 antigen have been detected in only 12% of patients with 1-2mm primary, node negative melanoma who subsequently relapsed, but 62% of case-controlled patients who did not50.

Antibodies to the her2/neu tumor antigen have been detected in the sera of 20% of patients with her2+ early-stage breast cancer51 but only 7% of late-stage breast and ovarian cancer patients52. Some of the early-stage patients had titers>1:5000, suggesting a strong, possibly protective immune response. In patients with both stage III and stage IV neuroblastoma, 10% have evidence of antibodies to the NY-ESO-1 antigen, but the antibody is not seen in the sera of patients in clinical remission or in earlier stages of disease49.

The titer of antibodies to NY-ESO-1 have been shown to correlate with disease progression, as well as with disease response/resection53. The titer of anti-p53 antibodies have been shown to increase in a subset of patients with early-stage breast cancer prior to disease relapse54, and disappearance of anti-p53 antibodies has been observed in 27 patients after resection of colorectal cancer55. Tumor antigen/antibody immune complexes have been detected up to 19 months prior to the development of clinical relapse and correlate with survival in patients with early-stage melanoma56.

Methods for assessing autoantibodies

The development process for autoantibodies as biomarkers is outlined in Figure 3. Historically, this cycle begins with identifying antigens that are detected by the sera of cancer patients. Antigens must then be selected and tested by comparing sera from both patients and healthy donors to determine if the antigens are “informative”, i.e., responses are limited to patients. As part of this process, the sensitivity and specificity of the antigens should be determined. Promising antigens are then tested on a set of samples separate from the training set to validate their usefulness. Ultimately prospective trials will establish their value as biomarkers. The cycle is completed by using the sera from patients not detected by the test to screen for new antigens. In some of the newer proteomic approaches, two of these steps may combine into a single step.

Figure 3
The development of autoantibody biomarkers in cancer

Identification of novel tumor antigens


A variety of techniques have been developed that use patient sera as probes against candidate antigens derived from tumor cells and tumor cell lysates to find novel autoantigens. Prominent among these, serologic expression cloning (SEREX) was developed ten years ago (Figure 4). This method uses patient sera to probe blotted phage expression libraries derived from tumor cells and has resulted in the identification of over 2000 autoantigens recognized by patient sera5, 57-79. Through a database established by the SEREX collaborative group, sequences from over 1390 genes have been deposited 80 and http://www2.licr.org/CancerImmunomeDB/. As SEREX relies on immunoblotting, these antigens are limited to linear epitopes and those gene products that can be expressed in bacteria.

Figure 4
Methods of antigen identification

As with the autoimmune targets, most of the antigens identified with SEREX are nuclear or intracellular antigens, many of which are upregulated, mutated, or specifically expressed in tumors. However, altered degradative pathways, such as sensitivity to granzyme B cleavage, has been shown to have a marked impact on the immunogenicity of a hepatocellular tumor antigen6. In addition, frameshifts and alternative reading frames, can result in autoantibodies. Using colon cancer sera, the antigen ADO34 was identified with a frameshift insertion81, and CDX2 had a frameshift mutation82. Antibodies to the OGFr protein can recognize an alternative-reading frame of the molecule79.

The SEREX method identifies autoantigens whether or not they are informative. At present only a handful of antigens have undergone comparison testing, in part because of the technical challenges associated with historical assays for validating autoantigens.

Phage display

Combinatorial phage display is a new and attractive approach for the identification of tumor antigens. By expressing antigens as fusions with phage proteins, antigens can be detected more rapidly and with less serum. Rather than immunoblotting as with SEREX, phage display relies on successive rounds of immunoprecipitation of phage libraries using patient serum. This powerful approach has been used in the field of rheumatology to identify autoantigens in multiple sclerosis, lupus, rheumatoid arthritis and others83-85. For tumor antigens, one study showed that only 4 of 13 antigens identified with prostate cancer sera had already been identified by SEREX technologies, demonstrating that novel tumor antigens may be identified with different screening approaches 86. Phage display has been successfully used to identify tumor autoantigens in cancer86-89. However, antigens expressed by phage display may not be in native conformation, do not have mammalian post-translational modifications, and each positive phage clone must be individually sequenced. Yeast display systems are also being investigated90.

Validating autoantibodies

To be useful as biomarkers, the tumor antigens must distinguish between individuals with and without cancer. Thus once a candidate autoantigen is identified, comparisons must be made regarding the responses of individuals from both groups. The measurement of an autoantibody response is most often accomplished using the enzyme linked immunosorbent assay (ELISA), in which purified protein (the autoantigen) is immobilized in the wells of a microtiter dish and exposed to serum samples (Fig. 4). After adequate washing, any bound antibody is revealed using standard anti-human antibody reagents linked to enzymatic markers. If the response is very strong, it may be useful to perform serial dilutions of the serum to determine the titer of the antibody. By comparing multiple patient and normal donor samples it is possible to determine the frequency of response. For example, antibodies to dsDNA are present in only 40-60% of SLE patients, but antibodies to histones are present in >95% of patients with drug-induced SLE. To compute the sensitivity and specificity of the test, a threshold value must be set, such that the test is considered positive if the antibody response achieves that value or above. Typically, this threshold is either 2 or 3 standard deviations above the average response in the normal population.

The development of clinical ELISAs require the use of recombinant proteins or peptides, which may be complicated by batch-to-batch variations, loss of conformational epitopes or lack of mammalian post-translational processing91, 92. Antigenic epitopes from proteins such as Ro, La, SmB, SmD, Scl-70, can be linear epitopes of 10-22 amino acids conformational, or cryptic. Furthermore, epitopes may be naturally post-translationally modified by multiple means, including phosphorylation, glycosylation, acetylation, and methylation (SmD1, SmD3, fibrillarin, nucleolin)93, 94, all of which must be considered prior to the development of a clinical diagnostic assay. Of the many candidate antigens identified so far, only a handful have gone through the validation process to determine their sensitivity and specificity as cancer predictors, in part because of the challenges associated with setting up ELISAs.

Proteomic Approaches for the Identification of Tumor Antigens

The Power of Multiplexing

With all of their potential advantages, the Achilles heel of autoantibodies as biomarkers is their sensitivity, i.e., the fraction of true positives that have a positive test. In large part, this may reflect the nature of cancer. Unlike infections in which the vast majority of patients respond to the same immunodominant antigens, even cancers of the same type represent a mix of different biological subtypes. Thus, patients are likely to mount immune responses to different tumor antigens, and no single antigen is likely to detect all cancers. Typically, only 15-20% of patients demonstrate a response to any given antigen. However, proteomics may hold the key to success because it provides the means to multiplex. By linking the responses to several antigens together, the sensitivity and specificity of the test increases considerably, presumably because the chance that a patient will respond to at least one of the antigens is increased76, 95-102. Koziol et al demonstrated that a panel of just seven tumor-associated antigens (myc, cyclin B1, p62, IMP-1, Koc, p53, and survivin) could be used to segregate sera from patients with cancers from healthy donor sera95. In that study, sera from breast, colorectal, gastric, hepatocellular, lung, and prostate cancers were distinguished with sensitivities from 0.77-0.92 and specificities from 0.85-0.91 using no more than 3 tumor antigens for any cancer cohort. Scanlan et. al. studied the serologic responses to 13 defined tumor antigens in sera from colon cancer patients. Of these, 46% of patients, but not healthy donors, had antibodies to at least 1 of these antigens103.

These results demonstrate the potential power of simultaneously analyzing multiple autoantigens. It provides a greater likelihood of detecting and diagnosing the appropriate cancer presumably because the sensitivity will improve (increased chance that the patient will have a response to at least one of the tested antigens) and the specificity improves (positive responses on multiple antigens increases certainty). By coupling this with the appropriate statistical modeling, it is likely that patterns or weighted schemes of antibody responses, rather than individual responses, will have the greatest utility in clinical assays. The early experiments above were performed by using recursive partitioning. Given the technical challenges inherent in that method for large numbers of antigens, newer technologies are needed for high throughput analysis. To this end, recent development of several proteomic technologies have been adapted for tumor antigen identification and biomarker development. These include probing fractionated tumor cell lysates, phage display, and protein microarrays (See Table 1).

Table 1
Current Methodologies for the Detection of Autoantigens

Probing fractionated tumor cell lysate blots with serum

In this approach, tumor cell lysates are fractionated to separate the various protein species and blotted onto a membrane or microarray and then probed with patient or control sera. Response patterns are then analyzed to differentiate between the two (Fig. 4). This has been used to demonstrate antibodies in the sera of lung cancer patients96, 104. A similar approach was used to distinguish sera from prostate cancer patients from healthy donor sera with 98% accuracy105. The development of automated separation systems using liquid-based chromatography with subsequent microarray spotting of lysate fractions enhances the reproducibility and speed of immunogenic fraction identification. Advantages of this approach include the ability to query a large fraction of the tumor cell proteome and the preservation of the post translational modifications of proteins. Moreover, when this is executed using a microarray format, only small amounts of serum are required. However, the reproducibility demanded by a clinical assay requires an identified and validated antigen. Thus, as with other chromatographic separation systems, the identity of the proteins in the fraction must eventually be determined, presumably using sensitive mass spectrometric analysis. As each lysate fraction may contain many proteins at very different concentrations, of which the minor component may be the immunogen, antigen identification can be difficult.

Probing Candidate Antigen Arrays

Known or predicted tumor antigens may be directly spotted on microarrays and probed with human sera, with the advantage of reproducibility and more rapid screening of small amounts of sera (Figure 4). In this case, the identity of a single protein at each feature on the array is known a priori. This approach has been successfully used to screen autoimmune patient sera100, 101. A powerful advantage of this approach is that it offers the ability to screen for informative autoantigens by comparing responses of patients to controls. Because each feature of the array represents a single protein, whose identity is known, it is possible to calculate sensitivity and specificity values for the response to each candidate antigen during the screening phase. This allows the rapid determination of which antigens are informative for cancer detection. Ultimately, focused arrays of tumor antigens previously identified by other means may have the greatest utility for immunodiagnostics.

Their theoretical advantages notwithstanding, protein microarrays have still not found widespread use, in part because producing them is challenging. Historically it has required the high-throughput production and purification of protein, which then must be spotted on the arrays. Once printed, concerns remain about the shelf life of proteins on the arrays. Recently, programmable protein microarrays that consist of anchored cDNA's and in situ transcription and translation of tagged proteins have been shown to result in highly reproducible protein arrays without the requirement of heterologous protein expression and purification106. As the proteins are synthesized at the time of the assay, shelf life is not an issue and the proteins are translated with a mammalian reticulocyte lysate, which reproduces the folding and some of the post-translational processing of antigenic epitopes. Unlike tumor lysate fractionation, antigen identification is automatic, although epitopes that depend on abnormal processing by tumor cells would not be identified, and transmembrane proteins have not yet been tested in this system. Like most antigen presentation methods (SEREX, ELISA, phage display, etc.), these programmable arrays require access to cDNAs to express the antigens. Fortunately, nearly all known tumor antigen genes are available because they were identified with methods that involved cDNA identification. Moreover, libraries of cDNA clones representing most of the human proteome are increasingly available.

Proteomics and the Identification of T cell Antigens

In contrast to the rapid identification of B cell antigens, the identification of T cell antigens remains much more difficult. Since T lymphocytes specifically recognize peptides derived from protein antigens, the isolation and confirmation of these antigens have traditionally relied on laborious T cell isolation and cloning. Alternatively, direct isolation and sequencing of MHC-associated peptides from cells has been limited by the overall low concentration of specific peptide bound to MHC molecules, and the highly polymorphic nature of the MHC molecules themselves. These challenges are being overcome by advances in high-throughput MHC-peptide binding studies, the bioinformatics of epitope prediction, and in particular, mass spectrometry.

With high-throughput peptide synthesis, direct MHC-peptide binding assays may now be performed on peptides that span target antigens, or using combinatorial peptide libraries107. Detailed analysis of peptide binding to common HLA alleles has resulted in the development of bioinformatics tools for epitope prediction108, 109. Further limitations imposed by proteasomal cleavage patterns can enhance prediction of peptide epitopes. Multiple prediction models are available, primarily for MHC Class I-binding peptides, due to the more restrictive nature of the peptide binding groove. These systems include BIMAS110, SYFPEITHI111, MHCPEP112, and RANKPEP113, among others.

Advances in mass spectrometry have also greatly aided antigen identification. Original studies of direct peptide sequencing from purified MHC molecules from cell lines114-116 demonstrated that the majority of peptides within the MHC grooves are derived from self-proteins, but required billions of cells for the source of antigen. More recent approaches to identify tumor-specific T cell epitopes have used capillary-scale chromatography and tandem mass spectrometry (Figure 5) (nanoLC-MS/MS)117-120. To enhance sensitivity, nanospray quadrupole-TOF combined with Poisson algorithms121 to specifically evaluate the likelihood of predicted peptides in unfractionated mixtures has markedly increased the sensitivity of the assay to the range of 1 copy number of peptide/cell in a murine influenza viral model, but is limited to predicted MHC-binding peptide epitopes.

Figure 5
Identification of HLA-binding peptides by mass spectrometry


The development of proteomic-based methods of cell lysate fractionation, phage display, protein microarrays, bioinformatics, and mass spectrometry is resulting in the rapid identification of both B and T cell tumor antigens. These have the potential for clinical diagnosis, identifying targets for immunotherapies, monitoring disease response, and to understand the breadth, scope, and impact of the immune response to cancer. As each new technology leads to the discovery of novel antigenic targets, systematic approaches of target validation and assessment of clinical applications, especially in the area of biomarkers and diagnostic testing, will need to be developed.


We would like to thank Drs. Glenn Dranoff, Ellis Reinherz, and Niroshan Ramachandran for critical review of this manuscript.

Support: This work has been supported by NCI K08 CA88444-03 (K.S.A.) and NCI P50 CA89393-05, NCI R33 CA099191-02 and the Breast Cancer Research Foundation (J.L.)


1. Pavlenko M, Roos AK, Lundqvist A, Palmborg A, Miller AM, Ozenci V, Bergman B, Egevad L, Hellstrom M, Kiessling R, Masucci G, Wersall P, Nilsson S, Pisa P. A phase I trial of DNA vaccination with a plasmid expressing prostate-specific antigen in patients with hormone-refractory prostate cancer. Br J Cancer. 2004;91:688–694. [PMC free article] [PubMed]
2. Vonderheide RH, Hahn WC, Schultze JL, Nadler LM. The telomerase catalytic subunit is a widely expressed tumor-associated antigen recognized by cytotoxic T lymphocytes. Immunity. 1999;10:673–679. [PubMed]
3. Soussi T. p53 Antibodies in the sera of patients with various types of cancer: a review. Cancer Res. 2000;60:1777–1788. [PubMed]
4. Schubert U, Anton LC, Gibbs J, Norbury CC, Yewdell JW, Bennink JR. Rapid degradation of a large fraction of newly synthesized proteins by proteasomes. Nature. 2000;404:770–774. [PubMed]
5. Chen YT, Scanlan MJ, Sahin U, Tureci O, Gure AO, Tsang S, Williamson B, Stockert E, Pfreundschuh M, Old LJ. A testicular antigen aberrantly expressed in human cancers detected by autologous antibody screening. Proc Natl Acad Sci U S A. 1997;94:1914–1918. [PMC free article] [PubMed]
6. Ulanet DB, Torbenson M, Dang CV, Casciola-Rosen L, Rosen A. Unique conformation of cancer autoantigen B23 in hepatoma: a mechanism for specificity in the autoimmune response. Proc Natl Acad Sci U S A. 2003;100:12361–12366. [PMC free article] [PubMed]
7. von Mensdorff-Pouilly S, Petrakou E, Kenemans P, van Uffelen K, Verstraeten AA, Snijdewint FG, van Kamp GJ, Schol DJ, Reis CA, Price MR, Livingston PO, Hilgers J. Reactivity of natural and induced human antibodies to MUC1 mucin with MUC1 peptides and n-acetylgalactosamine (GalNAc) peptides. Int J Cancer. 2000;86:702–712. [PubMed]
8. Routsias JG, Tzioufas AG, Moutsopoulos HM. The clinical value of intracellular autoantigens B-cell epitopes in systemic rheumatic diseases. Clin Chim Acta. 2004;340:1–25. [PubMed]
9. Lernmark A. Autoimmune diseases: are markers ready for prediction? J Clin Invest. 2001;108:1091–1096. [PMC free article] [PubMed]
10. Plotz PH. The autoantibody repertoire: searching for order. Nat Rev Immunol. 2003;3:73–78. [PubMed]
11. Brendel V, Dohlman J, Blaisdell BE, Karlin S. Very long charge runs in systemic lupus erythematosus-associated autoantigens. Proc Natl Acad Sci U S A. 1991;88:1536–1540. [PMC free article] [PubMed]
12. Utz PJ, Hottelet M, Le TM, Kim SJ, Geiger ME, van Venrooij WJ, Anderson P. The 72-kDa component of signal recognition particle is cleaved during apoptosis. J Biol Chem. 1998;273:35362–35370. [PubMed]
13. Casciola-Rosen L, Andrade F, Ulanet D, Wong WB, Rosen A. Cleavage by granzyme B is strongly predictive of autoantigen status: implications for initiation of autoimmunity. J Exp Med. 1999;190:815–826. [PMC free article] [PubMed]
14. Raben N, Nichols R, Dohlman J, McPhie P, Sridhar V, Hyde C, Leff R, Plotz P. A motif in human histidyl-tRNA synthetase which is shared among several aminoacyl-tRNA synthetases is a coiled-coil that is essential for enzymatic activity and contains the major autoantigenic epitope. J Biol Chem. 1994;269:24277–24283. [PubMed]
15. DeLeo AB, Jay G, Appella E, Dubois GC, Law LW, Old LJ. Detection of a transformation-related antigen in chemically induced sarcomas and other transformed cells of the mouse. Proc Natl Acad Sci U S A. 1979;76:2420–2424. [PMC free article] [PubMed]
16. Casey G, Lopez ME, Ramos JC, Plummer SJ, Arboleda MJ, Shaughnessy M, Karlan B, Slamon DJ. DNA sequence analysis of exons 2 through 11 and immunohistochemical staining are required to detect all known p53 alterations in human malignancies. Oncogene. 1996;13:1971–1981. [PubMed]
17. Winter SF, Minna JD, Johnson BE, Takahashi T, Gazdar AF, Carbone DP. Development of antibodies against p53 in lung cancer patients appears to be dependent on the type of p53 mutation. Cancer Res. 1992;52:4168–4174. [PubMed]
18. Dowell SP, Wilson PO, Derias NW, Lane DP, Hall PA. Clinical utility of the immunocytochemical detection of p53 protein in cytological specimens. Cancer Res. 1994;54:2914–2918. [PubMed]
19. Vennegoor CJ, Nijman HW, Drijfhout JW, Vernie L, Verstraeten RA, von Mensdorff-Pouilly S, Hilgers J, Verheijen RH, Kast WM, Melief CJ, Kenemans P. Autoantibodies to p53 in ovarian cancer patients and healthy women: a comparison between whole p53 protein and 18-mer peptides for screening purposes. Cancer Lett. 1997;116:93–101. [PubMed]
20. Angelopoulou K, Yu H, Bharaj B, Giai M, Diamandis EP. p53 gene mutation, tumor p53 protein overexpression, and serum p53 autoantibody generation in patients with breast cancer. Clin Biochem. 2000;33:53–62. [PubMed]
21. Angelopoulou K, Stratis M, Diamandis EP. Humoral immune response against p53 protein in patients with colorectal carcinoma. Int J Cancer. 1997;70:46–51. [PubMed]
22. Rainov NG, Dobberstein KU, Fittkau M, Bahn H, Holzhausen HJ, Gantchev L, Burkert W. Absence of p53 autoantibodies in sera from glioma patients. Clin Cancer Res. 1995;1:775–781. [PubMed]
23. Weller M, Bornemann A, Stander M, Schabet M, Dichgans J, Meyermann R. Humoral immune response to p53 in malignant glioma. J Neurol. 1998;245:169–172. [PubMed]
24. Zorn E, Miklos DB, Floyd BH, Mattes-Ritz A, Guo L, Soiffer RJ, Antin JH, Ritz J. Minor Histocompatibility Antigen DBY Elicits a Coordinated B and T Cell Response after Allogeneic Stem Cell Transplantation. J Exp Med. 2004;199:1133–1142. [PMC free article] [PubMed]
25. Schmollinger JC, Vonderheide RH, Hoar KM, Maecker B, Schultze JL, Hodi FS, Soiffer RJ, Jung K, Kuroda MJ, Letvin NL, Greenfield EA, Mihm M, Kutok JL, Dranoff G. Melanoma inhibitor of apoptosis protein (ML-IAP) is a target for immune-mediated tumor destruction. Proc Natl Acad Sci U S A. 2003;100:3398–3403. [PMC free article] [PubMed]
26. Molldrem JJ, Lee PP, Wang C, Felio K, Kantarjian HM, Champlin RE, Davis MM. Evidence that specific T lymphocytes may participate in the elimination of chronic myelogenous leukemia. Nat Med. 2000;6:1018–1023. [PubMed]
27. Hodi FS, Schmollinger JC, Soiffer RJ, Salgia R, Lynch T, Ritz J, Alyea EP, Yang J, Neuberg D, Mihm M, Dranoff G. ATP6S1 elicits potent humoral responses associated with immune-mediated tumor destruction. Proc Natl Acad Sci U S A. 2002;99:6919–6924. [PMC free article] [PubMed]
28. Miklos DB, Kim HT, Zorn E, Hochberg EP, Guo L, Mattes-Ritz A, Viatte S, Soiffer RJ, Antin JH, Ritz J. Antibody response to DBY minor histocompatibility antigen is induced after allogeneic stem cell transplantation and in healthy female donors. Blood. 2004;103:353–359. [PMC free article] [PubMed]
29. Bellucci R, Wu CJ, Chiaretti S, Weller E, Davies FE, Alyea EP, Dranoff G, Anderson KC, Munshi NC, Ritz J. Complete response to donor lymphocyte infusion in multiple myeloma is associated with antibody responses to highly expressed antigens. Blood. 2004;103:656–663. [PubMed]
30. Yang XF, Wu CJ, Chen L, Alyea EP, Canning C, Kantoff P, Soiffer RJ, Dranoff G, Ritz J. CML28 is a broadly immunogenic antigen, which is overexpressed in tumor cells. Cancer Res. 2002;62:5517–5522. [PubMed]
31. Dranoff G, Jaffee E, Lazenby A, Golumbek P, Levitsky H, Brose K, Jackson V, Hamada H, Pardoll D, Mulligan RC. Vaccination with irradiated tumor cells engineered to secrete murine granulocyte-macrophage colony-stimulating factor stimulates potent, specific, and long-lasting anti-tumor immunity. Proc Natl Acad Sci U S A. 1993;90:3539–3543. [PMC free article] [PubMed]
32. Huang AY, Golumbek P, Ahmadzadeh M, Jaffee E, Pardoll D, Levitsky H. Role of bone marrow-derived cells in presenting MHC class I-restricted tumor antigens. Science. 1994;264:961–965. [PubMed]
33. Hung K, Hayashi R, Lafond-Walker A, Lowenstein C, Pardoll D, Levitsky H. The central role of CD4(+) T cells in the antitumor immune response. J Exp Med. 1998;188:2357–2368. [PMC free article] [PubMed]
34. Gillessen S, Naumov YN, Nieuwenhuis EE, Exley MA, Lee FS, Mach N, Luster AD, Blumberg RS, Taniguchi M, Balk SP, Strominger JL, Dranoff G, Wilson SB. CD1d-restricted T cells regulate dendritic cell function and antitumor immunity in a granulocyte-macrophage colony-stimulating factor-dependent fashion. Proc Natl Acad Sci U S A. 2003;100:8874–8879. [PMC free article] [PubMed]
35. Heslop HE, Stevenson FK, Molldrem JJ. Immunotherapy of hematologic malignancy. Hematology (Am Soc Hematol Educ Program) 2003:331–349. [PubMed]
36. Davis ID, Chen W, Jackson H, Parente P, Shackleton M, Hopkins W, Chen Q, Dimopoulos N, Luke T, Murphy R, Scott AM, Maraskovsky E, McArthur G, MacGregor D, Sturrock S, Tai TY, Green S, Cuthbertson A, Maher D, Miloradovic L, Mitchell SV, Ritter G, Jungbluth AA, Chen YT, Gnjatic S, Hoffman EW, Old LJ, Cebon JS. Recombinant NY-ESO-1 protein with ISCOMATRIX adjuvant induces broad integrated antibody and CD4(+) and CD8(+) T cell responses in humans. Proc Natl Acad Sci U S A. 2004;101:10697–10702. [PMC free article] [PubMed]
37. DiFronzo LA, Gupta RK, Essner R, Foshag LJ, O'Day SJ, Wanek LA, Stern SL, Morton DL. Enhanced humoral immune response correlates with improved disease-free and overall survival in American Joint Committee on Cancer stage II melanoma patients receiving adjuvant polyvalent vaccine. J Clin Oncol. 2002;20:3242–3248. [PubMed]
38. Ehlken H, Schadendorf D, Eichmuller S. Humoral immune response against melanoma antigens induced by vaccination with cytokine gene-modified autologous tumor cells. Int J Cancer. 2004;108:307–313. [PubMed]
39. Small EJ, S PF, Higano C, Neumanaitis J, Valone F, Herschberg RM. Immunotherapy (APC8015) for androgen independent prostate cancer (AIPC): Final survival data from a phase 3 randomized placebo-controlled trial; 2005 ASCO Prostate Cancer Symposium; 2005.
40. Cancer Facts and Figures. 2004.
41. Srinivas PR, Kramer BS, Srivastava S. Trends in biomarker research for cancer detection. Lancet Oncol. 2001;2:698–704. [PubMed]
42. Gold P, Freedman SO. Specific carcinoembryonic antigens of the human digestive system. J Exp Med. 1965;122:467–481. [PMC free article] [PubMed]
43. Wang MC, Valenzuela LA, Murphy GP, Chu TM. Purification of a human prostate specific antigen. Invest Urol. 1979;17:159–163. [PubMed]
44. Niloff JM, Knapp RC, Schaetzl E, Reynolds C, Bast RC., Jr CA125 antigen levels in obstetric and gynecologic patients. Obstet Gynecol. 1984;64:703–707. [PubMed]
45. Petricoin E, Wulfkuhle J, Espina V, Liotta LA. Clinical proteomics: revolutionizing disease detection and patient tailoring therapy. J Proteome Res. 2004;3:209–217. [PubMed]
46. Trivers GE, Cawley HL, DeBenedetti VM, Hollstein M, Marion MJ, Bennett WP, Hoover ML, Prives CC, Tamburro CC, Harris CC. Anti-p53 antibodies in sera of workers occupationally exposed to vinyl chloride. J Natl Cancer Inst. 1995;87:1400–1407. [PubMed]
47. Trivers GE, De Benedetti VM, Cawley HL, Caron G, Harrington AM, Bennett WP, Jett JR, Colby TV, Tazelaar H, Pairolero P, Miller RD, Harris CC. Anti-p53 antibodies in sera from patients with chronic obstructive pulmonary disease can predate a diagnosis of cancer. Clin Cancer Res. 1996;2:1767–1775. [PubMed]
48. Suzuki H, Graziano DF, McKolanis J, Finn OJ. T cell-dependent antibody responses against aberrantly expressed cyclin B1 protein in patients with cancer and premalignant disease. Clin Cancer Res. 2005;11:1521–1526. [PubMed]
49. Rodolfo M, Luksch R, Stockert E, Chen YT, Collini P, Ranzani T, Lombardo C, Dalerba P, Rivoltini L, Arienti F, Fossati-Bellani F, Old LJ, Parmiani G, Castelli C. Antigen-specific immunity in neuroblastoma patients: antibody and T-cell recognition of NY-ESO-1 tumor antigen. Cancer Res. 2003;63:6948–6955. [PubMed]
50. Litvak DA, Gupta RK, Yee R, Wanek LA, Ye W, Morton DL. Endogenous immune response to early- and intermediate-stage melanoma is correlated with outcomes and is independent of locoregional relapse and standard prognostic factors. J Am Coll Surg. 2004;198:27–35. [PubMed]
51. Disis ML, Pupa SM, Gralow JR, Dittadi R, Menard S, Cheever MA. High-titer HER-2/neu protein-specific antibody can be detected in patients with early-stage breast cancer. J Clin Oncol. 1997;15:3363–3367. [PubMed]
52. Disis ML, Knutson KL, Schiffman K, Rinn K, McNeel DG. Pre-existent immunity to the HER-2/neu oncogenic protein in patients with HER-2/neu overexpressing breast and ovarian cancer. Breast Cancer Res Treat. 2000;62:245–252. [PubMed]
53. Jager E, Stockert E, Zidianakis Z, Chen YT, Karbach J, Jager D, Arand M, Ritter G, Old LJ, Knuth A. Humoral immune responses of cancer patients against “Cancer-Testis” antigen NY-ESO-1: correlation with clinical events. Int J Cancer. 1999;84:506–510. [PubMed]
54. Regele S, Vogl FD, Kohler T, Kreienberg R, Runnebaum IB. p53 autoantibodies can be indicative of the development of breast cancer relapse. Anticancer Res. 2003;23:761–764. [PubMed]
55. Takeda A, Shimada H, Nakajima K, Imaseki H, Suzuki T, Asano T, Ochiai T, Isono K. Monitoring of p53 autoantibodies after resection of colorectal cancer: relationship to operative curability. Eur J Surg. 2001;167:50–53. [PubMed]
56. Kelley MC, Gupta RK, Hsueh EC, Yee R, Stern S, Morton DL. Tumor-associated antigen TA90 immune complex assay predicts recurrence and survival after surgical treatment of stage I-III melanoma. J Clin Oncol. 2001;19:1176–1182. [PubMed]
57. Tajima K, Obata Y, Tamaki H, Yoshida M, Chen YT, Scanlan MJ, Old LJ, Kuwano H, Takahashi T, Mitsudomi T. Expression of cancer/testis (CT) antigens in lung cancer. Lung Cancer. 2003;42:23–33. [PubMed]
58. Lee SY, Obata Y, Yoshida M, Stockert E, Williamson B, Jungbluth AA, Chen YT, Old LJ, Scanlan MJ. Immunomic analysis of human sarcoma. Proc Natl Acad Sci U S A. 2003;100:2651–2656. [PMC free article] [PubMed]
59. Ayyoub M, Hesdorffer CS, Montes M, Merlo A, Speiser D, Rimoldi D, Cerottini JC, Ritter G, Scanlan M, Old LJ, Valmori D. An immunodominant SSX-2-derived epitope recognized by CD4+ T cells in association with HLA-DR. J Clin Invest. 2004;113:1225–1233. [PMC free article] [PubMed]
60. Sugita Y, Wada H, Fujita S, Nakata T, Sato S, Noguchi Y, Jungbluth AA, Yamaguchi M, Chen YT, Stockert E, Gnjatic S, Williamson B, Scanlan MJ, Ono T, Sakita I, Yasui M, Miyoshi Y, Tamaki Y, Matsuura N, Noguchi S, Old LJ, Nakayama E, Monden M. NY-ESO-1 expression and immunogenicity in malignant and benign breast tumors. Cancer Res. 2004;64:2199–2204. [PubMed]
61. Odunsi K, Jungbluth AA, Stockert E, Qian F, Gnjatic S, Tammela J, Intengan M, Beck A, Keitz B, Santiago D, Williamson B, Scanlan MJ, Ritter G, Chen YT, Driscoll D, Sood A, Lele S, Old LJ. NY-ESO-1 and LAGE-1 cancer-testis antigens are potential targets for immunotherapy in epithelial ovarian cancer. Cancer Res. 2003;63:6076–6083. [PubMed]
62. Scanlan MJ, Gout I, Gordon CM, Williamson B, Stockert E, Gure AO, Jager D, Chen YT, Mackay A, O'Hare MJ, Old LJ. Humoral immunity to human breast cancer: antigen definition and quantitative analysis of mRNA expression. Cancer Immun. 2001;1:4. [PubMed]
63. Jager D, Unkelbach M, Frei C, Bert F, Scanlan MJ, Jager E, Old LJ, Chen YT, Knuth A. Identification of tumor-restricted antigens NY-BR-1, SCP-1, and a new cancer/testis-like antigen NW-BR-3 by serological screening of a testicular library with breast cancer serum. Cancer Immun. 2002;2:5. [PubMed]
64. Koroleva EP, Lagarkova MA, Mesheryakov AA, Scanlan MJ, Old LJ, Nedospasov SA, Kuprash DV. Serological identification of antigens associated with renal cell carcinoma. Russ J Immunol. 2002;7:229–238. [PubMed]
65. Scanlan MJ, Gure AO, Jungbluth AA, Old LJ, Chen YT. Cancer/testis antigens: an expanding family of targets for cancer immunotherapy. Immunol Rev. 2002;188:22–32. [PubMed]
66. Forti S, Scanlan MJ, Invernizzi A, Castiglioni F, Pupa S, Agresti R, Fontanelli R, Morelli D, Old LJ, Pupa SM, Menard S. Identification of breast cancer-restricted antigens by antibody screening of SKBR3 cDNA library using a preselected patient's serum. Breast Cancer Res Treat. 2002;73:245–256. [PubMed]
67. Scanlan MJ, Gordon CM, Williamson B, Lee SY, Chen YT, Stockert E, Jungbluth A, Ritter G, Jager D, Jager E, Knuth A, Old LJ. Identification of cancer/testis genes by database mining and mRNA expression analysis. Int J Cancer. 2002;98:485–492. [PubMed]
68. Jager D, Stockert E, Gure AO, Scanlan MJ, Karbach J, Jager E, Knuth A, Old LJ, Chen YT. Identification of a tissue-specific putative transcription factor in breast tissue by serological screening of a breast cancer library. Cancer Res. 2001;61:2055–2061. [PubMed]
69. Gure AO, Stockert E, Scanlan MJ, Keresztes RS, Jager D, Altorki NK, Old LJ, Chen YT. Serological identification of embryonic neural proteins as highly immunogenic tumor antigens in small cell lung cancer. Proc Natl Acad Sci U S A. 2000;97:4198–4203. [PMC free article] [PubMed]
70. Scanlan MJ, Altorki NK, Gure AO, Williamson B, Jungbluth A, Chen YT, Old LJ. Expression of cancer-testis antigens in lung cancer: definition of bromodomain testis-specific gene (BRDT) as a new CT gene, CT9. Cancer Lett. 2000;150:155–164. [PubMed]
71. Gure AO, Stockert E, Arden KC, Boyer AD, Viars CS, Scanlan MJ, Old LJ, Chen YT. CT10: a new cancer-testis (CT) antigen homologous to CT7 and the MAGE family, identified by representational-difference analysis. Int J Cancer. 2000;85:726–732. [PubMed]
72. Jager D, Stockert E, Scanlan MJ, Gure AO, Jager E, Knuth A, Old LJ, Chen YT. Cancer-testis antigens and ING1 tumor suppressor gene product are breast cancer antigens: characterization of tissue-specific ING1 transcripts and a homologue gene. Cancer Res. 1999;59:6197–6204. [PubMed]
73. Scanlan MJ, Gordan JD, Williamson B, Stockert E, Bander NH, Jongeneel V, Gure AO, Jager D, Jager E, Knuth A, Chen YT, Old LJ. Antigens recognized by autologous antibody in patients with renal-cell carcinoma. Int J Cancer. 1999;83:456–464. [PubMed]
74. Scanlan MJ, Williamson B, Jungbluth A, Stockert E, Arden KC, Viars CS, Gure AO, Gordan JD, Chen YT, Old LJ. Isoforms of the human PDZ-73 protein exhibit differential tissue expression. Biochim Biophys Acta. 1999;1445:39–52. [PubMed]
75. Scanlan MJ, Chen YT, Williamson B, Gure AO, Stockert E, Gordan JD, Tureci O, Sahin U, Pfreundschuh M, Old LJ. Characterization of human colon cancer antigens recognized by autologous antibodies. Int J Cancer. 1998;76:652–658. [PubMed]
76. Stockert E, Jager E, Chen YT, Scanlan MJ, Gout I, Karbach J, Arand M, Knuth A, Old LJ. A survey of the humoral immune response of cancer patients to a panel of human tumor antigens. J Exp Med. 1998;187:1349–1354. [PMC free article] [PubMed]
77. Gure AO, Altorki NK, Stockert E, Scanlan MJ, Old LJ, Chen YT. Human lung cancer antigens recognized by autologous antibodies: definition of a novel cDNA derived from the tumor suppressor gene locus on chromosome 3p21.3. Cancer Res. 1998;58:1034–1041. [PubMed]
78. Gure AO, Tureci O, Sahin U, Tsang S, Scanlan MJ, Jager E, Knuth A, Pfreundschuh M, Old LJ, Chen YT. SSX: a multigene family with several members transcribed in normal testis and human cancer. Int J Cancer. 1997;72:965–971. [PubMed]
79. Mollick JA, Hodi FS, Soiffer RJ, Nadler LM, Dranoff G. MUC1-like tandem repeat proteins are broadly immunogenic in cancer patients. Cancer Immun. 2003;3:3. [PubMed]
80. Chen YT, S M, Obata Y, Old LJ. Identification of human tumor antigens by serological expression cloning. In: SA R, editor. Principles and practice of the biologic therapy of cancer. 3rd. Lippincott Williams & Wilkins; Philadelphia: 2000. pp. 557–570.
81. Line A, Slucka Z, Stengrevics A, Silina K, Li G, Rees RC. Characterisation of tumour-associated antigens in colon cancer. Cancer Immunol Immunother. 2002;51:574–582. [PubMed]
82. Ishikawa T, Fujita T, Suzuki Y, Okabe S, Yuasa Y, Iwai T, Kawakami Y. Tumor-specific immunological recognition of frameshift-mutated peptides in colon cancer with microsatellite instability. Cancer Res. 2003;63:5564–5572. [PubMed]
83. Kemp EH, Herd LM, Waterman EA, Wilson AG, Weetman AP, Watson PP. Immunoscreening of phage-displayed cDNA-encoded polypeptides identifies B cell targets in autoimmune disease. Biochem Biophys Res Commun. 2002;298:169–177. [PubMed]
84. Jensen LB, Riise E, Nielsen LK, Dziegiel M, Fugger L, Engberg J. Efficient purification of unique antibodies using peptide affinity-matrix columns. J Immunol Methods. 2004;284:45–54. [PubMed]
85. Sioud M, Hansen M, Dybwad A. Profiling the immune responses in patient sera with peptide and cDNA display libraries. Int J Mol Med. 2000;6:123–128. [PubMed]
86. Fossa A, Alsoe L, Crameri R, Funderud S, Gaudernack G, Smeland EB. Serological cloning of cancer/testis antigens expressed in prostate cancer using cDNA phage surface display. Cancer Immunol Immunother. 2004;53:431–438. [PubMed]
87. Sioud M, Hansen MH. Profiling the immune response in patients with breast cancer by phage-displayed cDNA libraries. Eur J Immunol. 2001;31:716–725. [PubMed]
88. Minenkova O, Pucci A, Pavoni E, De Tomassi A, Fortugno P, Gargano N, Cianfriglia M, Barca S, De Placido S, Martignetti A, Felici F, Cortese R, Monaci P. Identification of tumor-associated antigens by screening phage-displayed human cDNA libraries with sera from tumor patients. Int J Cancer. 2003;106:534–544. [PubMed]
89. Fernandez-Madrid F, Tang N, Alansari H, Granda JL, Tait L, Amirikia KC, Moroianu M, Wang X, Karvonen RL. Autoantibodies to Annexin XI-A and Other Autoantigens in the Diagnosis of Breast Cancer. Cancer Res. 2004;64:5089–5096. [PubMed]
90. Mischo A, Wadle A, Watzig K, Jager D, Stockert E, Santiago D, Ritter G, Regitz E, Jager E, Knuth A, Old L, Pfreundschuh M, Renner C. Recombinant antigen expression on yeast surface (RAYS) for the detection of serological immune responses in cancer patients. Cancer Immun. 2003;3:5. [PubMed]
91. Yan SC, Grinnell BW, Wold F. Post-translational modifications of proteins: some problems left to solve. Trends Biochem Sci. 1989;14:264–268. [PubMed]
92. St Clair EW, Kenan D, Burch JA, Jr, Keene JD, Pisetsky DS. The fine specificity of anti-La antibodies induced in mice by immunization with recombinant human La autoantigen. J Immunol. 1990;144:3868–3876. [PubMed]
93. Doyle HA, Mamula MJ. Post-translational protein modifications in antigen recognition and autoimmunity. Trends Immunol. 2001;22:443–449. [PubMed]
94. Doyle HA, Mamula MJ. Posttranslational protein modifications: new flavors in the menu of autoantigens. Curr Opin Rheumatol. 2002;14:244–249. [PubMed]
95. Koziol JA, Zhang JY, Casiano CA, Peng XX, Shi FD, Feng AC, Chan EK, Tan EM. Recursive partitioning as an approach to selection of immune markers for tumor diagnosis. Clin Cancer Res. 2003;9:5120–5126. [PubMed]
96. Zhang JY, Casiano CA, Peng XX, Koziol JA, Chan EK, Tan EM. Enhancement of antibody detection in cancer using panel of recombinant tumor-associated antigens. Cancer Epidemiol Biomarkers Prev. 2003;12:136–143. [PubMed]
97. Joos TO, Stoll D, Templin MF. Miniaturised multiplexed immunoassays. Curr Opin Chem Biol. 2002;6:76–80. [PubMed]
98. Stoll D, Templin MF, Schrenk M, Traub PC, Vohringer CF, Joos TO. Protein microarray technology. Front Biosci. 2002;7:c13–32. [PubMed]
99. Templin MF, Stoll D, Schwenk JM, Potz O, Kramer S, Joos TO. Protein microarrays: promising tools for proteomic research. Proteomics. 2003;3:2155–2166. [PubMed]
100. Robinson WH, DiGennaro C, Hueber W, Haab BB, Kamachi M, Dean EJ, Fournel S, Fong D, Genovese MC, de Vegvar HE, Skriner K, Hirschberg DL, Morris RI, Muller S, Pruijn GJ, van Venrooij WJ, Smolen JS, Brown PO, Steinman L, Utz PJ. Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat Med. 2002;8:295–301. [PubMed]
101. Robinson WH, Steinman L, Utz PJ. Protein arrays for autoantibody profiling and fine-specificity mapping. Proteomics. 2003;3:2077–2084. [PubMed]
102. Shin BK, Wang H, Hanash S. Proteomics approaches to uncover the repertoire of circulating biomarkers for breast cancer. J Mammary Gland Biol Neoplasia. 2002;7:407–413. [PubMed]
103. Scanlan MJ, Welt S, Gordon CM, Chen YT, Gure AO, Stockert E, Jungbluth AA, Ritter G, Jager D, Jager E, Knuth A, Old LJ. Cancer-related serological recognition of human colon cancer: identification of potential diagnostic and immunotherapeutic targets. Cancer Res. 2002;62:4041–4047. [PubMed]
104. Qiu J, Madoz-Gurpide J, Misek DE, Kuick R, Brenner DE, Michailidis G, Haab BB, Omenn GS, Hanash S. Development of natural protein microarrays for diagnosing cancer based on an antibody response to tumor antigens. J Proteome Res. 2004;3:261–267. [PubMed]
105. Bouwman K, Qiu J, Zhou H, Schotanus M, Mangold LA, Vogt R, Erlandson E, Trenkle J, Partin AW, Misek D, Omenn GS, Haab BB, Hanash S. Microarrays of tumor cell derived proteins uncover a distinct pattern of prostate cancer serum immunoreactivity. Proteomics. 2003;3:2200–2207. [PubMed]
106. Ramachandran N, Hainsworth E, Bhullar B, Eisenstein S, Rosen B, Lau AY, Walter JC, LaBaer J. Self-assembling protein microarrays. Science. 2004;305:86–90. [PubMed]
107. Lawendowski CA, Giurleo GM, Huang YY, Franklin GJ, Kaplan JM, Roberts BL, Nicolette CA. Solid-phase epitope recovery: a high throughput method for antigen identification and epitope optimization. J Immunol. 2002;169:2414–2421. [PubMed]
108. Rammensee HG, Weinschenk T, Gouttefangeas C, Stevanovic S. Towards patient-specific tumor antigen selection for vaccination. Immunol Rev. 2002;188:164–176. [PubMed]
109. Sette A, Keogh E, Ishioka G, Sidney J, Tangri S, Livingston B, McKinney D, Newman M, Chesnut R, Fikes J. Epitope identification and vaccine design for cancer immunotherapy. Curr Opin Investig Drugs. 2002;3:132–139. [PubMed]
110. Parker KC, Bednarek MA, Coligan JE. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol. 1994;152:163–175. [PubMed]
111. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50:213–219. [PubMed]
112. Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 1998;26:368–371. [PMC free article] [PubMed]
113. Reche PA, Glutting JP, Zhang H, Reinherz EL. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics. 2004;56:405–419. [PubMed]
114. Hunt DF, Henderson RA, Shabanowitz J, Sakaguchi K, Michel H, Sevilir N, Cox AL, Appella E, Engelhard VH. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science. 1992;255:1261–1263. [PubMed]
115. Wei ML, Cresswell P. HLA-A2 molecules in an antigen-processing mutant cell contain signal sequence-derived peptides. Nature. 1992;356:443–446. [PubMed]
116. Rotzschke O, Falk K, Wallny HJ, Faath S, Rammensee HG. Characterization of naturally occurring minor histocompatibility peptides including H-4 and H-Y. Science. 1990;249:283–287. [PubMed]
117. Kao H, Marto JA, Hoffmann TK, Shabanowitz J, Finkelstein SD, Whiteside TL, Hunt DF, Finn OJ. Identification of cyclin B1 as a shared human epithelial tumor-associated antigen recognized by T cells. J Exp Med. 2001;194:1313–1323. [PMC free article] [PubMed]
118. Hogan KT, Coppola MA, Gatlin CL, Thompson LW, Shabanowitz J, Hunt DF, Engelhard VH, Slingluff CL, Jr, Ross MM. Identification of a shared epitope recognized by melanoma-specific, HLA-A3-restricted cytotoxic T lymphocytes. Immunol Lett. 2003;90:131–135. [PubMed]
119. Lemmel C, Stevanovic S. The use of HPLC-MS in T-cell epitope identification. Methods. 2003;29:248–259. [PubMed]
120. Weinschenk T, Gouttefangeas C, Schirle M, Obermayr F, Walter S, Schoor O, Kurek R, Loeser W, Bichler KH, Wernet D, Stevanovic S, Rammensee HG. Integrated functional genomics approach for the design of patient-individual antitumor vaccines. Cancer Res. 2002;62:5818–5827. [PubMed]
121. Zhong W, Reche PA, Lai CC, Reinhold B, Reinherz EL. Genome-wide characterization of a viral cytotoxic T lymphocyte epitope repertoire. J Biol Chem. 2003;278:45135–45144. [PubMed]


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