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From the Center for Health Disparities Research and Department of Biochemistry and Microbiology and Department of Medicine, Loma Linda University School of Medicine, Loma Linda, California 92350 and the || W. M. Keck Autoimmune Disease Center, Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California 92037
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ABSTRACT |
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Presently there is a growing enthusiasm for applying proteomic approaches to the identification of serum biomarkers for the early, non-invasive diagnosis of cancer and for monitoring tumor progression. These approaches include direct profiling of human sera, using two-dimensional (2D) gel electrophoresis and MS to identify distinctive protein signatures characteristic of different tumor types (4–7), and the exploitation of the serum autoantibody repertoire from cancer patients for the identification of TAAs and the design of TAA panels or arrays (2, 3, 7–12). This review emphasizes the utility of such arrays for serological diagnosis in cancer. The first part will focus on the biological and clinical significance of serum autoantibodies to TAAs and novel approaches for the identification and validation of these antigens. This will be followed by a discussion of recent advances and current issues in the design of TAA arrays for cancer diagnosis.
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CANCER-ASSOCIATED AUTOANTIBODIES AS REPORTERS OF TUMORIGENESIS |
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 TOPABSTRACT CANCER-ASSOCIATED AUTOANTIBODIES... IDENTIFICATION AND VALIDATION OF...AUTOANTIGEN ARRAYS: LESSONS FROM...TAA ARRAYS FOR AUTOANTIBODY...TAA ARRAYS IN PROSTATE...CONCLUSIONS AND FUTURE...REFERENCES |
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It is not entirely clear how intracellular proteins become targets of autoantibodies, but it has been suggested that posttranslational modifications (e.g. proteolytic cleavage, phosphorylation, and oxidation) associated with aberrant cell death may enhance their immunogenicity under a proinflammatory environment (39–43). Other possibilities are that specific autoantigens are fetal proteins aberrantly expressed in tumor cells (44) or expressed in abnormally high amounts in tissues affected by autoimmune disease, inflammatory disease, or cancer, contributing to loss of immune tolerance to these antigens (45).
The elicitation of serum autoantibodies to autologous cellular antigens expressed in tumors is a well recognized form of cancer-related autoimmunity. These autoantibodies have been detected in several human cancers, and significant advances have been made in the identification of their target antigens, particularly in lung cancer (28, 30), colorectal cancer (36), breast cancer (29), prostate cancer (27, 37), leukemia (26), non-Hodgkin lymphoma (24), hepatocellular carcinoma (25, 32, 34), ovarian cancer (31), pancreatic cancer (33, 38), and paraneoplastic neurological syndromes (35). Although the mechanisms leading to autoantibody production in cancer patients are not clearly understood, emerging evidence indicates that most TAAs are cellular proteins whose aberrant regulation or function could be linked to malignancy (3). For instance, TAAs include known oncoproteins such as HER-2/Neu and c-MYC (46–49); tumor suppressor proteins such as p53 (50); survival proteins such as survivin and lens epithelium-derived growth factor (LEDGF/p75) (27, 51); cell cycle regulatory proteins such as cyclin B1 (52); mitosis-associated proteins such as centromere protein F (CENP-F) (25, 53, 54); chromatin-associated proteins such as topoisomerases (29, 55); mRNA-binding proteins such as p62, IMP1, and Koc (56, 57); and differentiation and cancer testis antigens such as NY-ESO-1 (58).
The oncoprotein nature of most TAAs has led to the hypothesis that cancer-associated autoantibodies are immunological "reporters" or "sentinels" identifying aberrant cellular mechanisms associated with tumorigenesis (2, 3). This was first hinted by the observation that these autoantibodies often arise during the transition from conditions that pose a high risk for cancer to the development of malignancy (59, 60). For instance, the transition from chronic liver disease to hepatocellular carcinoma is associated with the appearance of autoantibodies to proteins linked to cell survival and proliferation that are also highly expressed in tumors, such as CENP-F and p62; the latter is a developmentally regulated protein that binds the mRNA of insulin-like growth factor II (IGF-II) (60). This transition is also associated with increasing titers and changes in the specificities of antibodies to specific nuclear proteins (59).
Additional evidence that cancer-associated autoantibodies function as "red flags" indicating the presence of a malignant transformation process can be found in studies on the autoantibody response to p53, one of the best characterized in cancer. p53 is a multifunctional transcription factor that promotes tumor cell death by regulating the expression of genes involved in cell cycle control, apoptosis, DNA repair, and angiogenesis (61). Mutation or down-regulation of p53 prevents it from exerting its tumor suppressor function, contributing to cancer development and progression (61). p53 mutations in cancer patients are often associated with poor prognosis (61). The presence of serum autoantibodies to p53 has been reported in patients with various types of cancers at frequencies ranging from 4 to 30% depending on the cancer type (50), and in some cancers, autoantibodies to p53 are associated with poor prognosis (62). A strong correlation between the presence of these autoantibodies and p53 mutation and/or accumulation has been reported (50). Only missense mutations have been associated with this autoantibody response (63, 64). Although these mutations could alter the native conformation of p53, which may result in aberrant processing and presentation by antigen-presenting cells, there is some controversy with regard to whether the anti-p53 autoantibodies are driven by the p53 mutations themselves or by accumulation of non-functional p53 protein in the nucleus.
An emerging concept is that autoantibodies associated with a specific type of cancer are directed against aberrantly regulated or activated protein components of molecular pathways involved in the malignant transformation process in that particular type of cancer. For example, the rapamycin-sensitive mTOR (mammalian target of rapamycin) phosphorylation pathway has been implicated in the pathogenesis of breast cancer, and members of this pathway are targeted by autoantibodies in patients with breast cancer (29). These include ribosomal protein S6, eukaryotic elongation factor 2, eukaryotic elongation factor 2 kinase, and heat shock protein 90 (HSP90). Autoantibodies in breast cancer also recognize components of the DNA repair pathway such as the Ku protein, topoisomerase I, and the 32-kDa subunit of replication protein A (29). It should be noted that replication protein A interacts with the DNA repair and tumor suppressor proteins BRCA1 and BRCA2 (65, 66). Loss of BRCA function has been linked to development of breast cancer (67).
The autoantibody response in prostate cancer may also reflect the activation of genes and their protein products that play an important role in prostate tumorigenesis, including several proteins that participate in the cellular response to oxidative stress (Table I). There is growing evidence that an augmented state of cellular oxidative stress (ASCOS), associated with a proinflammatory environment, plays a major role in prostate carcinogenesis (68, 69). This evidence is derived from the following observations: (a) Clinical trials with antioxidants such as lycopene, vitamin E, and selenium suggest that antioxidants may be beneficial in limiting the progression of prostate cancer (70–73), (b) androgens stimulate prostate cell growth by increasing oxidative stress (74, 75), (c) prostate cancer tissues manifest increased oxidative stress (76, 77), and (d) prostate cancer development is associated with early loss of the antioxidant enzyme glutathione S-transferase protein 1 (GSTP1), leaving prostate cells vulnerable or tolerant to oxidative DNA damage (68, 69). The cellular response to increased oxidative stress can lead to either activation of the apoptotic machinery if oxidative stress levels are too high or activation of a protective stress/survival response if the levels are not excessive enough to induce massive damage and rapid cell death but sufficient to induce sublethal oxidative damage (78). Cancer cells appear to develop a high threshold of resistance to oxidative stress-induced cell death as evidenced by their ability to survive under conditions of increased oxidative stress and other stressors such as hypoxia, heat, and lack of growth and survival factors (78–81). This resistance is associated with overexpression of survival and antioxidant proteins in tumors, which could lead to loss of immune tolerance and provoke autoantibody responses.
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20% of patients with prostate cancer compared with a frequency of 5% in age- and gender-matched individuals with no history of prostate cancer (27). Although originally identified as a transcription co-activator and a growth and survival factor in lens epithelial cells (82, 83), LEDGF/p75 is now known to be ubiquitously expressed in tumor cells and is emerging as a key regulator of the cellular response to stress (84, 85). LEDGF/p75 promotes protection against oxidative stress-induced cell death by transcriptionally activating stress proteins, such as HSP27, 
B-crystallin, and anti-oxidant protein 2 (AOP2; also known as peroxiredoxin 6 (PRDX6)) (85–88). This transactivation is facilitated by the binding of LEDGF/p75 to heat shock elements and stress-related elements in promoter regions of target genes (88). LEDGF/p75 has been also identified as a target autoantigen in leukemia by serological analysis of recombinant expression libraries (SEREX) (89). Consistent with this finding, the LEDGF gene is involved in chromosomal translocations in patients with various types of leukemia, resulting in a fusion with nucleoprotein-98 that retains the C terminus of LEDGF/p75 (90–92). This domain has been implicated in DNA binding and transcription function (93), survival function (93, 94), binding to human immunodeficiency virus integrase (95), and autoantibody recognition (96). It should be emphasized that anti-LEDGF/p75 antibodies are not specific for prostate cancer because they have been also detected, albeit at relatively low frequencies and titers, in "healthy individuals" and patients with various human inflammatory conditions (97). We suggested that autoantibodies to LEDGF/p75 could be considered as reporters of the up-regulation of this protein by inflammation and ASCOS in cancer and other inflammatory conditions (27, 97).
Using a combination of proteomic and immunological approaches, Ronquist et al. (98) identified various proteins present in prostasomes (submicron prostate-derived particles) that were capable of eliciting autoantibody responses in prostate cancer patients. Intriguingly, several of these candidate TAAs are proteins that, like LEDGF/p75, regulate the cellular redox environment and protect cancer cells against the increased oxidative stress and other stresses present within the tumor microenvironment. Among these candidate TAAs were PRDX6/AOP2, clusterin, DJ-1, superoxide dismutase, alcohol dehydrogenase, heat shock proteins, and lactoylglutathione lyase. As mentioned above, PRDX6/AOP2 is transcriptionally regulated by LEDGF/p75 and plays an important role in antioxidant defense (86, 99). Interestingly, loss of the homeobox gene Nkx3.1 in mice is associated with loss of PRDX6/AOP2 and promotes increased oxidative damage in prostate carcinogenesis (100). Clusterin (also known as apolipoprotein J) is a ubiquitously expressed secreted glycoprotein whose overexpression in prostate cancer cells protects against death induced by high levels of oxidative stress (101, 102). DJ-1 is a novel oncoprotein that is the causative gene for the familial form of Parkinson disease and antagonizes oxidative stress by eliminating reactive oxygen species (103). Superoxide dismutase is an antioxidant defense enzyme that plays an important role in protecting the prostate against oxidative damage (104). Heat shock protein 70 is known to inhibit stress-induced apoptosis in prostate cancer cells (105). HSP27 (also called HSPB1) is also a stress response antiapoptotic protein that is regulated by LEDGF/p75 (88, 105–107). B-crystallin, another heat shock protein implicated in resistance to cell death in various cancer types, is also regulated by LEDGF/p75 (88, 106), but to our knowledge the presence of autoantibodies to this protein in prostate cancer sera has not been reported. Another stress response protein, glucose-regulated protein-78 kDa (GRP78), was shown to be a target of autoantibodies in advanced prostate cancer but not in lung, breast, and ovarian cancers (108). Overexpression of this protein was detected in metastatic prostate cancer but not in normal prostate (108). We have also demonstrated the presence of serum autoantibodies in prostate cancer patients against p62, Koc, and IMP1, proteins that bind the mRNA of IGF-II, an important global regulator of cell survival in breast and prostate cancers (109–111). It has been suggested that overexpression of these proteins in prostate and other tumors may stabilize IGF-II mRNA, leading to its up-regulation and, consequently, to tumor cell survival (57). -Methylacyl-coenzyme A racemase and 5--reductase are two enzymes involved in redox reactions that are also targeted by autoantibodies in prostate cancer (112, 113); however, their role in prostate tumor cell survival is not clear.
Based on the limited information available, it seems that the autoantibody response to TAAs in prostate cancer appears to be directed preferentially against proteins that participate in redox and stress/survival pathways and are highly expressed in prostate tumors. However, further studies are needed to confirm this association. It would be also important to determine whether different cancers have unique signature antibody repertoires that target TAAs preferentially associated with specific molecular or metabolic pathways activated during malignant transformation.
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IDENTIFICATION AND VALIDATION OF TAA |
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TOPABSTRACTCANCER-ASSOCIATED AUTOANTIBODIES...IDENTIFICATION AND VALIDATION OF...AUTOANTIGEN ARRAYS: LESSONS FROM...TAA ARRAYS FOR AUTOANTIBODY...TAA ARRAYS IN PROSTATE...CONCLUSIONS AND FUTURE...REFERENCES |
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A second approach for the serological identification of putative TAAs with the possibility of high throughput analysis of patient sera and tumor libraries is the cDNA or peptide phage surface display technology (130). This approach involves the construction of a cDNA phage display library from a specific tumor or cancer cell line. The candidate TAAs in these libraries are expressed and displayed on the surface of a phage, such as the T7 phage, as peptides fused to the capsid protein of the phage. This surface complex is then used as a bait to capture autoantibodies to TAAs using pools of cancer and control sera. After several rounds of biopanning to enrich the library for peptides that bind specifically to autoantibodies in the cancer sera, phage clones are selected, and their cDNA inserts are sequenced to identify the encoded candidate TAAs. This approach has been used for the identification of candidate TAAs in various cancers, including colorectal (131), breast (132), prostate (133), and ovarian cancers (9). Recently Chatterjee et al. (9) designed protein microarrays containing 480 antigen clones derived from biopanning a T7 phage display cDNA library from an ovarian cancer cell line and used them for immunoscreening against sera from ovarian cancer patients, healthy women, and women with other gynecologic diseases. This approach yielded 65 clones that interacted with autoantibodies in sera from the ovarian cancer patients but not in the control sera. Sequence analysis of the 65 clones revealed 62 different antigens, including several known TAAs.
The combination of standard proteomic tools of 2D gels and MS with serological screening of 2D gel-derived Western blots is an emerging approach that promises to yield a high number of candidate TAAs (8, 134, 135). This approach has been termed "serological proteome analysis" (SERPA) (134). In this approach, proteins in lysates prepared from tumors or cancer cell lines are separated by 2D gel electrophoresis and transferred to membranes that are then incubated with individual or pooled sera from cancer patients or healthy controls. Immunoreactive proteins that exhibit specific reactivity with cancer sera are then identified by MS. Various groups have used SERPA to identify candidate TAAs associated with breast cancer, including the RNA-binding protein regulatory subunit (RS), DJ-1 oncogene, glucose-6-phosphate dehydrogenase, heat shock 70-kDa protein 1 (HS71), and dihydrolipoamide dehydrogenase (8, 29, 134, 135). Interestingly, although glucose-6-phosphate dehydrogenase is widely expressed in normal cells, the breast cancer sera reacted preferentially with some isoforms of the protein detected by MS, suggesting that autoantibodies in these sera might be driven by posttranslational modifications (8), which is consistent with previous observations that intracellular proteins targeted by autoantibodies in systemic autoimmune diseases often undergo these modifications (41). The SERPA approach has also been used to identify calreticulin and DEAD-box protein 48 (DDX48) as target autoantigens in pancreatic cancer (33, 38) and the Rho GDP dissociation inhibitor 2 as a major candidate TAA in leukemia (26). Although a powerful approach, SERPA also presents some drawbacks, including the difficulty in generating reproducible gels, which leads to variations in the migration of spots of interest from gel to gel; the difficulty in resolving certain classes of proteins; the requirement of highly expensive and sophisticated MS and imaging equipment and appropriately trained personnel; and the dependence on Western blotting for the serological screening, which has an inherent bias toward the detection of denatured epitopes (8, 135, 136).
As these approaches continue to be optimized for enhancing the identification of candidate TAAs, it is also necessary to develop methods to optimize the validation of these TAAs. For a candidate TAA to be considered useful in diagnosis, it has to be preferentially recognized by sera from patients with the particular tumor type in which the antigen was identified when compared with sera from control individuals, other tumors, or autoimmune diseases. A caveat, however, is that some TAAs such as p53 and survivin lack specificity with regard to their tumor association because they are almost universally involved in the process of malignant transformation and are targeted by the immune system not only in various cancer types but also in autoimmune diseases (50, 51). In our experience, once a TAA is identified, the combined use of various immunoassays (e.g. ELISA, immunoblotting, and immunoprecipitation) is required to enhance the detection of specific autoantibodies to that particular TAA in the cancer of interest and determine more accurately the frequency of these autoantibodies in patient sera (27). The reason for this is that there is often an incomplete correlation in the detection of autoantibodies to TAAs between the different immunoassays (27, 51). This divergence could be caused by differences in the sensitivity of the various assays and in changes in antigen conformation from one assay platform to another. Cancer-associated autoantibodies might also display heterogeneity in epitope recognition within a given antigen. Consequently some patients would produce autoantibodies against non-denatured epitopes, whereas others may produce antibodies against denatured epitopes. It is also important to establish that candidate TAAs are indeed expressed in the tumor type associated with the anti-TAA autoantibody response. This could be facilitated by the availability of tissue microarrays specific for various kinds of tumors (Fig. 1).
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AUTOANTIGEN ARRAYS: LESSONS FROM AUTOIMMUNE DISEASES |
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TOPABSTRACTCANCER-ASSOCIATED AUTOANTIBODIES...IDENTIFICATION AND VALIDATION OF...AUTOANTIGEN ARRAYS: LESSONS FROM...TAA ARRAYS FOR AUTOANTIBODY...TAA ARRAYS IN PROSTATE...CONCLUSIONS AND FUTURE...REFERENCES |
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30%, indicating that a relatively large subset of patients would be missed if the presence of these autoantibodies were to be used as a diagnostic test (137). Other autoantibodies primarily associated with a particular autoimmune disease may also appear in other diseases. For instance, autoantibodies to the SSA/Ro and SSB/La ribonucleoprotein particles, which are present in patients with Sjögren syndrome, are also present in SLE and other autoimmune diseases (138). It has been recognized that to achieve the highest degree of sensitivity and specificity in the diagnosis of systemic autoimmune diseases, multiple autoantibody reactivities, not a single autoantibody reactivity, should be used in a diagnostic test consisting of a well defined antigen array or panel (136, 139). The protein microarray technology was first adapted for the profiling of serum autoantibodies by Joos et al. (140) and Robinson et al. (141). Joos et al. (140) used an ELISA-based microarray containing serial dilutions of 18 known autoantigens targeted in systemic autoimmune diseases for accurate determination of autoantibody titer using minimal amounts of serum. These microarrays were very sensitive and showed little cross-reactivity with nonspecific proteins. The array constructed by Robinson et al. (141) used the conventional DNA microarray technology and consisted of 196 known autoantigens that were spotted onto derivatized microscope glass slides. Using over 100 different serum samples from patients with eight different autoimmune diseases, these investigators demonstrated that comprehensive autoantigen microarrays could be used to profile autoantibodies in these diseases with high sensitivity and antigen specificity. These autoantigen microarrays also provided a tool for the identification of multiple cellular targets of the autoimmune response in individual patients. The results were validated using traditional immunoassays such as ELISA and immunoblotting. In a recent study, Robinson and co-workers (142) used this technology to develop a synovial proteome microarray containing 225 peptides and proteins that represent candidate and control antigens. These microarrays were used to profile autoantibodies in over 60 patients with RA and 38 controls. The results from this study provided useful diagnostic and prognostic information, which allowed the stratification of patients with early RA into clinically relevant disease subsets. For example, autoantibodies targeting citrunilated epitopes were present in a subset of patients with early stage RA who had features predictive of the development of severe RA. In contrast, autoantibodies targeting autoantigens native to the synovium, such as gp39 and type II collagen, delineated a subpopulation of RA patients who had laboratory and clinical features predictive of less severe RA.
The development of autoantigen microarrays for autoantibody profiling in systemic autoimmune diseases has paved the way for the use of this technology in other diseases, including cancer, multiple sclerosis, infectious diseases, and diabetes (136, 139). The potential applications of autoantigen microarrays have been discussed recently by Balboni et al. (136) and include (a) improved diagnosis, (b) monitoring disease progression and response to therapy, (c) development of individualized therapies, (d) identification of autoantibody signatures that might be associated with a particular subgroup within the disease or have prognostic value, (e) development of antigen-specific therapies, and (f) identification of new autoantigens that could serve as disease biomarkers. Current challenges in the use of this technology include (a) establishing optimal conditions to minimize variations observed when using different slide surfaces for autoantigen spotting and printing conditions, (b) minimizing epitope alterations following attachment of antigens to a planar surface, which may result in inefficient or no detection of autoantigens in the array, (c) development of efficient internal controls, and (d) validation of the arrays with well characterized serum samples before this technology is ready for use in clinical settings (136, 139). An issue that needs further attention is whether the use of microarrays comprised of hundreds of autoantigens provides an advantage for disease diagnosis and prognosis over smaller arrays consisting of carefully selected antigen biomarkers.
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TAA ARRAYS FOR AUTOANTIBODY PROFILING IN CANCER |
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Several TAA arrays have been designed and evaluated for autoantibody profiling in cancer patients using a variety of formats, including glass slide-based microarrays, ELISA multiplex, and membrane-based immunoassays. Using an ELISA system, Stockert et al. (58) evaluated autoantibody responses in patients with various cancers to seven protein cancer testis and differentiation antigens (NY-ESO-1, Melan-A, SSX2, MAGE-1, MAGE-3, tyrosinase, and carbonic anhydrase). A survey of 234 cancer sera showed frequencies of antibodies specific to NY-ESO-1 of 10% in melanoma and ovarian cancer; however, the majority of the other antigens were recognized by the cancer sera at very low frequencies (<2%). Normal serum reactivity was not observed for any of the antigens. Although the main finding in this study was that NY-ESO-1 is an important autoantigen in melanoma, the study also provided the first survey of the human immune response to a panel of candidate TAAs. Subsequently Scanlan et al. (143) screened colon cancer sera against a panel of 77 SEREX-defined TAAs using nitrocellulose-based spot immunoassays and identified a panel of 13 TAAs that reacted exclusively with sera from colon cancer patients but not with sera from normal blood donors. In this study, it was found that 46% of sera from colon cancer patients detected one or more of these 13 antigens, whereas sera from normal blood donors were not reactive with this subset of antigens. This study confirmed the need for using TAA panels for increasing specific autoantibody detection in cancer patients.
Our group further refined a miniarray comprised of multiple TAAs to enhance antibody detection and explore their utility in cancer detection and diagnosis (111). This miniarray of TAAs included full-length recombinant proteins from cDNA encoding c-MYC, p53, cyclin B1, p62, Koc, IMP1, and survivin. These TAAs were selected based on previous observations that they were targeted by autoantibodies in cancer patients and not in normal controls or in other disease conditions. They were evaluated in an ELISA multiplex system to detect antibodies in 527 sera from six different types of cancer: breast, lung, colorectal, gastric, hepatocellular, and prostate. Consistent with previous observations, we observed that although antibody frequencies to any individual TAA were variable and rarely exceeded 15–20%, the successive addition of TAAs to the array to a total of seven antigens was associated with a stepwise increase of positive antibody reactions up to a range of 44–68% (Table II). However, there was no increase in positive antibody reactions in sera from normal blood donors or from autoimmune diseases, indicating that the miniarray had high specificity for the detection of cancer-associated antibodies. These results provided evidence that detection of autoantibodies in cancer sera could be enhanced if TAA miniarrays, rather than individual TAAs, were used. We also observed that there was preferential reactivity against certain TAAs in some cancers, with some TAAs targeted at higher frequencies or increasing antibody reactivity when added to the array. Interestingly, for some cancers addition of a particular TAA to the array did not further increase the antibody reactivity. This was consistent with the notion that not all cellular proteins recognized as antigens by autoantibodies in cancer sera are specific for cancer or for a particular type of cancer. This study underscored a general principle that should be given serious consideration when applying the TAA microarray technology for autoantibody profiling in cancer. That is, to optimize sensitivity and specificity, it is extremely important to evaluate first different combinations of TAAs against sera from both normal matched controls and other disease conditions. This would determine whether the inclusion of a particular TAA in the array would enhance or diminish the effectiveness of the array.
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TAA ARRAYS IN PROSTATE CANCER DIAGNOSIS |
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Prostate cancer is the most frequently diagnosed cancer in men and the second leading cause of male cancer deaths in the United States with an estimated 234,460 cases and 27,350 deaths in 2006 (1). It is also becoming evident that prostate cancer presents the greatest racial disparity of any cancer in the United States with an incidence and mortality in African-American men that is 3 times higher than in White and Latino men (147). An alarming fact is that African-American men are more likely to present with advanced stage, hormone-refractory prostate cancer than men from other ethnic/racial groups (147). Reducing these disparities would require a multifaceted approach that includes developing more effective screening interventions for the early detection of prostate cancer. Early detection of prostate cancer using the PSA blood test has increased the proportion of patients with lower tumor stage at the time of diagnosis (148). However, although the sensitivity of PSA testing is exceptional, its specificity, particularly at lower PSA levels, remains controversial (148, 149). The gradual lowering over the past few years of the cutoff PSA level, which has lead to recommendations for prostate biopsy, has resulted in confusion as to how to interpret low PSA values, leading to an increasing number of unnecessary biopsies (149). Furthermore serum PSA is not specific for prostate cancer but rather a marker of prostate disease given that it can be also be detected in serum from patients with benign prostatic hyperplasia (BPH) and prostatitis (149). It was suggested that because of the heterogeneity of prostate cancer and other diseases of the prostate, multiple biomarkers would be needed to discriminate between the various stages of prostate cancer and differentiate prostate cancer from BPH and prostatitis (149). TAA arrays could be useful tools for supplementing PSA screening in the diagnosis of prostate cancer.
We tested our miniarray of seven TAAs (Table II) in 206 patients diagnosed with prostate cancer at Loma Linda University Medical Center and observed that the frequency of serum autoantibodies reacting with the TAAs increased in relationship to the number of TAAs in the array, going from 8.7% using IMP1 alone to 46% using the seven TAAs (111). We noticed that addition of just p62 to IMP1 in a two-antigen array increased the total frequency to 31.1% due to the fact that the frequency of autoantibodies to p62 was the highest among the seven TAAs. However, the addition of the remaining five antigens to the array gradually increased the frequency from 31.1 to 46.1% (Table II). In a subsequent study, we were able to dramatically increase the frequency of positively reacting sera to 92.5% by deleting from the miniarray three TAAs that were targeted by autoantibodies in prostate cancer patients at frequencies below 5% (p53, MYC, and survivin) and adding a new candidate TAA, p90, which was targeted at a frequency of 30.8% (Table III) (150). It should be noted that this miniarray was relatively specific for cancer because the total autoantibody frequencies against the combined TAAs were less than 15% for sera from normal controls, patients with autoimmune diseases, and patients with BPH (111).
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In a recent study, Wang et al. (120) used a phage display library derived from prostate cancer tissue to develop a phage protein microarray for the analysis of serum samples from 119 patients with prostate cancer and 138 individuals with no history of prostate cancer. These investigators identified a panel of 22 phage-peptide clones that could distinguish serum samples from patients with prostate cancer from those of controls with high sensitivity and specificity, 81.6 and 88.2%, respectively. An interesting finding was that the peptide array not only performed better than PSA in discriminating between the two groups but also provided additional discriminatory power (120). It should be noted, however, that of the 22 phage-peptide clones, only five peptides were derived from in-frame coding sequences corresponding to known proteins: bromo domain-containing protein 2 (BRD2), eukaryotic translation initiation factor eIF4G1, ribosomal proteins L22 and L13a, and a hypothetical protein designated XP_373908 (120). The remaining 17 peptides could be mimotopes, i.e. epitopes that are structurally similar to other peptides expressed in proteins but are unrelated or weakly related at the protein sequence level. Although this was a promising study that confirmed the power of TAA arrays for discriminating between cancer and non-cancer populations, it had some limitations that prevent its adoption in a screening program for prostate cancer in the near future. For instance, it was not established whether this peptide array is specific for prostate cancer when evaluated against sera from patients with other cancers, other diseases of the prostate, and autoimmune diseases. Some of the antigens identified in this peptide array, such as eIF4G1, might be overexpressed in other tumor tissues (151) and elicit autoantibody responses in various cancer cell types. Another limitation, which is prevalent in most studies using TAA arrays for cancer diagnosis, including our own studies, was the lack of biopsies in the control group to rule out the absence of prostate cancer (120). As pointed out by Wang et al. (120), prospective and multi-institutional studies need to be conducted to determine the utility of this and other TAA arrays in evaluating autoantibody signatures associated with prostate cancer, a required step to establish these arrays as screening tools for prostate cancer.
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CONCLUSIONS AND FUTURE PERSPECTIVES |
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In conclusion, TAA arrays provide promising and powerful tools for enhancing cancer detection and treatment, but their utility in a clinical setting is currently in its infancy. Before TAA arrays could be widely implemented in screening programs for cancer diagnosis or as tools for monitoring cancer progression and guiding therapeutic interventions, it would be important to maximize their sensitivity and specificity by defining systematically the optimal combination of TAAs. Different array platforms (e.g. glass slide microarray, spot-based membranes, and multiplex ELISA) would also have to be evaluated to determine which one yields the highest sensitivity with minimal experimental variation. Prospective studies in multiple centers would then be required to ensure the reproducibility of these arrays.
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Published, MCP Papers in Press, May 29, 2006, DOI 10.1074/mcp.R600010-MCP200
1 The abbreviations used are: TAA, tumor-associated antigen; AOP2, antioxidant protein 2; ASCOS, augmented state of cellular oxidative stress; BPH, benign prostatic hyperplasia; BRCA, breast cancer; CENP-F, centromere protein F; DFS70, dense fine speckled protein of 70 kDa; eIF4G1, eukaryotic translation initiation factor 4 
1; HSP, heat shock protein; IGF-II, insulin-like growth factor 2; IMP1, IGF-II mRNA-binding protein 1; LEDGF/p75, lens epithelium-derived growth factor p75; PRDX6, peroxiredoxin 6; PSA, prostate-specific antigen; RA, rheumatoid arthritis; SEREX, serological analysis of recombinant expression libraries; SERPA, serological proteome analysis; SLE, systemic lupus erythematosus; 2D, two-dimensional. 
* This work was supported by grants from the National Institutes of Health (NCMHD 5P20MD001632, NIGMS 2R25GM60507, and NCI CA56956), and the National Medical Technology Test Bed/United States Army Medical Research and Materiel Command (Subagreement DAMD 17-97-2-7016).
¶ To whom correspondence should be addressed. Tel.: 909-558-1000 (ext. 42759); Fax: 909-558-0177; E-mail: ccasiano{at}llu.edu
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