Toward an early diagnosis of lung cancer: an autoantibody signature for squamous cell lung carcinoma

Measuring serum human epididymis secretory protein autoantibody as an early biomarker of lung cancer

BACKGROUND

Lung cancer (LC) is one of the most common types of malignant tumors and is the most prominent cause of tumor-related death worldwide. LC is a heterogeneous disease caused by somatic cell mutations and dysregulation in several signaling pathways. Understanding these pathways provides the basis for detecting LC. LC screening and diagnosis in current clinic still rely on computed tomography (CT), but its high false positive rates and cost may prevent it from being a routine screening method. Therefore, the discovery of new non-invasive and more valuable biomarkers may present an improved diagnostic approach for LC, and potentially provide more useful information for the prognosis and treatment of LC in patients. This study investigated the potential of detecting serum autoantibodies produced against human epididymis secretory protein 4 (HE4) for LC diagnosis in high-risk groups.

METHODS

Serum samples from 61 patients with LC were included in this study, and another 53 serum samples from healthy donors or benign lung diseases (BLD) patients were collected as the control group. The samples were analyzed with enzyme-linked immunosorbent assays (ELISA).

RESULTS

ELISA results showed significantly higher levels of serum autoantibodies against HE4 in samples from LC patients compared to the control group (P<0.001). Analysis of HE4 autoantibodies showed a receiver operating characteristic (ROC) curve indicating 67.21% sensitivity, 96.23% specificity, and an area under the curve (AUC) of 0.848. Levels of HE4 autoantibodies can discriminate early-stage LC patients from the control group with a 54.76% sensitivity.

CONCLUSIONS

Detecting serum HE4 autoantibody levels may be a potential biomarker in high-risk groups of LC. We present a new method for the diagnosis of LC in the clinic.

Autoantibodies as diagnostic biomarkers for lung cancer: A systematic review

Lung cancer (LC) accounts for the largest number of tumor-related deaths worldwide. As the overall 5-year survival rate of LC is associated with its stages at detection, development of a cost-effective and noninvasive cancer screening method is necessary. We conducted a systematic review to evaluate the diagnostic values of single and panel tumor-associated autoantibodies (TAAbs) in patients with LC. This review included 52 articles with 64 single TAAbs and 19 with 20 panels of TAAbs. Enzyme-linked immunosorbent assays (ELISA) were the most common detection method. The sensitivities of single TAAbs for all stages of LC ranged from 3.1% to 92.9% (mean: 45.2%, median: 37.1%), specificities from 60.6% to 100% (mean: 88.1%, median: 94.9%), and AUCs from 0.416 to 0.990 (mean: 0.764, median: 0.785). The single TAAb with the most significant diagnostic value was the autoantibody against human epididymis secretory protein (HE4) with the maximum sensitivity 91% for NSCLC. The sensitivities of the panel of TAAbs ranged from 30% to 94.8% (mean: 76.7%, median: 82%), specificities from 73% to 100% (mean: 86.8%, median: 89.0%), and AUCs from 0.630 to 0.982 (mean: 0.821, median: 0.820), and the most significant AUC value in a panel (M13 Phage 908, 3148, 1011, 3052, 1000) was 0.982. The single TAAb with the most significant diagnostic calue for early stage LC, was the autoantibody against Wilms tumor protein 1 (WT1) with the maximum sensitivity of 90.3% for NSCLC and its sensitivity and specificity in a panel (T7 Phage 72, 91, 96, 252, 286, 290) were both above 90.0%. Single or TAAbs panels may be useful biomarkers for detecting LC patients at all stages or an early-stage in high-risk populations or health people, but the TAAbs panels showed higher detection performance than single TAAbs. The diagnostic value of the panel of six TAAbs, which is higher than the panel of seven TAAbs, may be used as potential biomarkers for the early detection of LC and can probably be used in combination with low-dose CT in the clinic.

Comparative Network Analysis of Patients with Non-Small Cell Lung Cancer and Smokers for Representing Potential Therapeutic Targets

Cigarette smoking is the leading cause of lung cancer worldwide. In this study, we evaluated the serum autoantibody (AAb) repertoires of non-small cell lung cancer (NSCLC) patients and smokers (SM), leading to the identification of overactivated pathways and hubs involved in the pathogenesis of NSCLC. Surface- and solution-phase biopanning were performed on immunoglobulin G purified from the sera of NSCLC and SM groups. In total, 20 NSCLC- and 12 SM-specific peptides were detected, which were used to generate NSCLC and SM protein datasets. NSCLC- and SM-related proteins were visualized using STRING and Gephi, and their modules were analyzed using Enrichr. By integrating the overrepresented pathways such as pathways in cancer, epithelial growth factor receptor, c-Met, interleukin-4 (IL-4) and IL-6 signaling pathways, along with a set of proteins (e.g. phospholipase D (PLD), IL-4 receptor, IL-17 receptor, laminins, collagens, and mucins) into the PLD pathway and inflammatory cytokines network as the most critical events in both groups, two super networks were made to elucidate new aspects of NSCLC pathogenesis and to determine the influence of cigarette smoking on tumour formation. Taken together, assessment of the AAb repertoires using a systems biology approach can delineate the hidden events involved in various disorders.

Serum tumor-associated autoantibodies as diagnostic biomarkers for lung cancer: A systematic review and meta-analysis

OBJECTIVE

We performed a comprehensive review and meta-analysis to evaluate the diagnostic values of serum single and multiplex tumor-associated autoantibodies (TAAbs) in patients with lung cancer (LC).

METHODS

We searched the MEDLINE and EMBASE databases for relevant studies investigating serum TAAbs for the diagnosis of LC. The primary outcomes included sensitivity, specificity and accuracy of the test.

RESULTS

The systematic review and meta-analysis included 31 articles with single autoantibody and 39 with multiplex autoantibodies. Enzyme-linked immunosorbent assay (ELISA) was the most common detection method. For the diagnosis of patients with all stages and early-stage LC, different single or combinations of TAAbs demonstrated different diagnostic values. Although individual TAAbs showed low diagnostic sensitivity, the combination of multiplex autoantibodies offered relatively high sensitivity. For the meta-analysis of a same panel of autoantibodies in patients at all stages of LC, the pooled results of the panel of 6 TAAbs (p53, NY-ESO-1, CAGE, GBU4-5, Annexin 1 and SOX2) were: sensitivity 38% (95% CI 0.35-0.40), specificity 89% (95% CI 0.86-0.91), diagnostic accuracy 65.9% (range 62.5-81.8%), AUC 0.52 (0.48-0.57), while the summary estimates of 7 TAAbs (p53, CAGE, NY-ESO-1, GBU4-5, SOX2, MAGE A4 and Hu-D) were: sensitivity 47% (95% CI 0.34-0.60), specificity 90% (95% CI 0.89-0.92), diagnostic accuracy 78.4% (range 67.5-88.8%), AUC 0.90 (0.87-0.93). For the meta-analysis of the same panel of autoantibodies in patients at early-stage of LC, the sensitivities of both panels of 7 TAAbs and 6 TAAbs were 40% and 29.7%, while their specificities were 91% and 87%, respectively.

CONCLUSIONS

Serum single or combinations of multiplex autoantibodies can be used as a tool for the diagnosis of LC patients at all stages or early-stage, but the combination of multiplex autoantibodies shows a higher detection capacity; the diagnostic value of the panel of 7 TAAbs is higher than the panel of 6 TAAbs, which may be used as potential biomarkers for the early detection of LC.

Molecular biomarkers for future screening of lung cancer

The Landmark National Lung Screening Trial established the potential for low dose CT screening (LDCT) to reduce lung cancer-specific mortality in high-risk patients as defined by smoking history and age. However, the prevalence of lung cancer in asymptomatic smokers selected based on the NLST criteria is low. Recent advances have facilitated biomarker discovery for early diagnosis of lung cancer through the analysis of surrogate tissues, including airway epithelium, sputum, exhaled breath, and blood. Although a number of candidate diagnostic biomarkers have been described, none have been validated for use in the clinical setting. The NLST ACRIN biomarker repository is a valuable resource of annotated biological specimens that were collected during the NLST trial, which has the potential to facilitate validation of candidate biomarkers for early diagnosis identified in discovery trials. It will be important to perform retrospective and prospective analysis of biomarkers to screen for lung cancer. The review below summarizes some of our understanding of biomarkers in screening.

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

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

Anti-idiotypic monobodies for immune response profiling

A major goal in the study of autoimmune disease is the identification of biomarkers of disease to allow early diagnosis and initiation of treatment. The production of autoantibodies is the key feature of most autoimmune disease, so much effort has focused on characterizing the antigens reactive with these antibodies. However, even for the most well understood autoimmune diseases like rheumatoid arthritis and systemic lupus erythematosus, identification of antigens that detect autoantibodies in all patients have yet to be discovered. We describe a novel strategy for deriving mimotopes to disease-specific serum antibodies by selecting anti-idiotypic monobodies from a large molecular diversity library. Monobodies are derived by partial randomization of two surface exposed loops of a fibronectin domain scaffold in a phage display vector. The phage library is selected for binding to serum antibodies using a subtractive panning strategy. We evaluated this strategy by selecting the monobody library on a pool of serum immunoglobulin derived from a group of rheumatoid arthritis patients and evaluated selected clones for multi-patient reactivity and specificity for rheumatoid arthritis. The use of the fibronectin scaffold to derive stable, easy to produce molecular probes for diagnosis of autoimmune disease could be of significant value in improving diagnostic assays for virtually any disease that exhibits a characteristic immune response.

Autoantibody signatures: progress and perspectives for early cancer detection

Becoming invasive is a crucial step in cancer development, and the early spread of tumour cells is usually undetected by current imaging technologies. In patients with cancer and no signs of overt metastases, sensitive methods have been developed to identify circulating autoantibodies and their antigen counterparts in several cancers. These technologies are often based on proteomic approaches, and recent advances in protein and antibody microarrays have greatly facilitated the discovery of new antibody biomarkers in sera from cancer patients. Interestingly, in a clinical application setting, combinations of multiple autoantibody reactivities into panel assays have recently been proposed as relevant screening tests and validated in several independent trials. In addition, autoantibody signatures seem to be particularly relevant for early detection of cancer in high-risk cancer patients. In this review, we highlight the concept that immunogenic epitopes associated with the humoural response and key pathogenic pathways elicit serum autoantibodies that can be considered as relevant cancer biomarkers. We outline the proteomic strategies employed to identify and validate their use in clinical practice for cancer screening and diagnosis. We particularly emphasize the clinical utility of autoantibody signatures in several cancers. Finally, we discuss the challenges remaining for clinical validation.

Autoantibody signature differentiates Wilms tumor patients from neuroblastoma patients

Several studies report autoantibody signatures in cancer. The majority of these studies analyzed adult tumors and compared the seroreactivity pattern of tumor patients with the pattern in healthy controls. Here, we compared the autoimmune response in patients with neuroblastoma and patients with Wilms tumor representing two different childhood tumors. We were able to differentiate untreated neuroblastoma patients from untreated Wilms tumor patients with an accuracy of 86.8%, a sensitivity of 87.0% and a specificity of 86.7%. The separation of treated neuroblastoma patients from treated Wilms tumor patients' yielded comparable results with an accuracy of 83.8%. We furthermore identified the antigens that contribute most to the differentiation between both tumor types. The analysis of these antigens revealed that neuroblastoma was considerably more immunogenic than Wilms tumor. The reported antigens have not been found to be relevant for comparative analyses between other tumors and controls. In summary, neuroblastoma appears as a highly immunogenic tumor as demonstrated by the extended number of antigens that separate this tumor from Wilms tumor.

Multicenter study identified molecular blood-born protein signatures for Wilms Tumor

Wilms Tumor (WT) is the most common renal childhood tumor. Recently, we reported a cDNA microarray expression pattern that varied between WTs with different risk histology. Since the Societé Internationale d'Oncologie Pédiatrique (SIOP) in Europe initiates treatment without a histological confirmation, it is important to identify blood-born markers that indicate WT development. In a multicenter study, we established an autoantibody signature by using an array with 1,827 recombinant E. coli clones. This array was screened with sera of patients with WT recruited by SIOP or the Children's Oncology Group (COG). We report an extended number of antigens that are reactive with autoantibodies present in sera from patients with WT. We established an autoantibody signature that separates untreated patients with WT recruited in SIOP from non-WT controls with a specificity of 0.83 and a sensitivity of 0.82 at standard deviations of 0.02 and 0.04, respectively. Likewise, patients recruited in the COG in the United States were separated from the controls with an accuracy of 0.83 at a standard deviation of 0.02. Proteins that were most significant include zinc finger proteins (e.g., ZFP 346), ribosomal proteins and the protein fascin that has been associated with various types of cancer including renal cell carcinoma. Our study provides first evidence for autoantibody signatures for WTs and suggests that these may be most informative before chemotherapy. We present the first multicenter study of autoantibody signatures in patients with WT. We established an autoantibody signature that separates patients with WT from controls.

Immunogenicity of autoantigens

Background

Autoantibodies against self-antigens have been associated not only with autoimmune diseases, but also with cancer and are even found in healthy individuals. The mechanism causing the autoantibody response remains elusive for the majority of the immunogenic antigens. To deepen the understanding of autoantibody responses, we ask whether natural-occurring, autoimmunity-associated and tumor-associated antigens have structural or biological features related to the immune response. To this end, we have carried out the most comprehensive in-silicio study of different groups of autoantigens including large antigen sets identified by our groups combined with publicly available antigen sets.

Results

We found evidence for an enrichment of genes with a larger exon length increasing the probability of the occurrence of potential immunogenic features such as mutations, SNPs, immunogenic sequence patterns and structural epitopes, or alternative splicing events. While SNPs seem to play a more central role in autoimmunity, somatic mutations seem to be stronger enriched in tumor-associated antigens. In addition, antigens of autoimmune diseases are different from other antigen sets in that they appear preferentially secreted, have frequently an extracellular location, and they are enriched in pathways associated with the immune system. Furthermore, for autoantibodies in general, we found enrichment of sequence-based properties including coiled-coils motifs, ELR motifs, and Zinc finger DNA-binding motifs. Moreover, we found enrichment of proteins binding to proteins or nucleic acids including RNA and enrichment of proteins that are part of ribosome or spliceosome. Both, homologies to proteins of other species and an enrichment of ancient protein domains indicate that immunogenic proteins are evolutionary conserved and that mimicry might play a central role.

Conclusions

Our results provide evidence that proteins which i) are evolutionary conserved, ii) show specific sequence motifs, and iii) are part of cellular structures show an increased likelihood to become autoimmunogenic.

Screening and early detection of lung cancer

Lung cancer with an estimated 342,000 deaths in 2008 (20% of total) is the most common cause of death from cancer, followed by colorectal cancer (12%), breast cancer (8%), and stomach cancer (7%) in Europe. In former smokers, the absolute lung cancer risk remains higher than in never-smokers; these data therefore call for effective secondary preventive measures for lung cancer in addition to smoking cessation programs. This review presents and discusses the most recent advances in the early detection and screening of lung cancer.An overview of randomized controlled computerized tomography-screening trials is given, and the role of bronchoscopy and new techniques is discussed. Finally, the approach of (noninvasive) biomarker testing in the blood, exhaled breath, sputum, and bronchoscopic specimen is reviewed.

Usage of cancer associated autoantibodies in the detection of disease

Efforts toward deciphering the complexity of the tumor specific proteome by profiling immune responses generated against tumor associated antigens (TAAs) holds great promise for predicting the presence of cancer long before the development of clinical symptoms. The immune system is capable of sensing aberrant expression of certain cellular components involved in tumorigenesis and the resultant autoantibody response provides insights to the targets that are responsible for eliciting immunogenicity to these cellular components. Analysis of the cancer-specific humoral immune response has led to panels of biomarkers that are specific and sensitive biomarkers of disease. Using multianalyte-based in vitro analytical discovery platforms which can be easily adapted into clinical diagnostic screening tests, body fluids such as serum, plasma saliva, or urine can be interrogated to detect autoantibodies against natural or recombinant antigens, which may possess etiologic significance to cancer. Non-invasive screening tests exhibiting high specificity and sensitivity to detect early stage cancer in the heterogeneous population of cancer patients potentially have the greatest impact in decreasing mortality rates. Overall, this review summarizes different experimental approaches in the development of diagnostic screening tests for the early detection of cancer and their implementation in the development of clinical multianalyte biomarker assays.

Sequencing and quantifying IgG fragments and antigen-binding regions by mass spectrometry

In cancer and autoimmune diseases, immunoglobulins with a specific molecular signature that could potentially be used as diagnostic or prognostic markers are released into body fluids. An immunomics approach based on this phenomenon relies on the ability to identify the specific amino acid sequences of the complementarity-determining regions (CDR) of these immunoglobulins, which in turn depends on the level of accuracy, resolution, and sensitivity that can be achieved by advanced mass spectrometry. Reproducible isolation and sequencing of antibody fragments (e.g., Fab) by high-resolution mass spectrometry (MS) from seven healthy donors revealed 43 217 MS signals: 225 could be associated with CDR1 peptides, 513 with CDR2 peptides, and 19 with CDR3 peptides. Seventeen percent of the 43 217 MS signals did not overlap between the seven donors. The Fab isolation method used is reproducible and fast, with a high yield. It provides only one Fab sample fraction for subsequent characterization by high-resolution MS. In 17% and 4% of these seven healthy donors, qualitative (presence/absence) and quantitative (intensity) differences in Fab fragments could be demonstrated, respectively. From these results, we conclude that the identification of a CDR signature as biomarker for autoimmune diseases and cancer without prior knowledge of the antigen is feasible.

Immune responses are characterized by specific shared immunoglobulin peptides that can be detected by proteomic techniques

In the adaptive immune response, immunoglobulins develop that bind specifically to the antigens to which the organism was exposed. Immunoglobulins may bind to known or unknown antigens in a variety of diseases and have been used in the past to identify novel antigens for use as a biomarker. We propose that the immunoglobulins themselves could also be used as biomarkers in antibody-mediated disease. In this proteomic study, rats were immunized with one of two purified antigens, and immunoglobulins from pre- and postimmune sera were analyzed with nano-LC coupled mass spectrometry. It was found that the two treatment groups could be distinguished based on cluster analysis of the immunoglobulin peptides from the immune sera. In addition, we identified 684 specific peptides that were differentially present in one of the two treated groups. We could find an amino acid sequence for 44% of the features in the mass spectra by combining database-driven and de novo sequencing techniques. The latter were essential for sequence identification, as the more common database-driven approach suffers from a poor representation of immunoglobulins in the available databases. Our data show that the development of immunoglobulins during an immune response is not a fully random process, but that instead selection pressures exist that favor the best binding amino acid sequences, and that this selection is shared between different animals. This finding implies that immunoglobulin peptides could indeed be a powerful and easily accessible class of biomarkers.

Improving seroreactivity-based detection of glioma

Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM) that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.

Identification of lung cancer with high sensitivity and specificity by blood testing

Background

Lung cancer is a very frequent and lethal tumor with an identifiable risk population. Cytological analysis and chest X-ray failed to reduce mortality, and CT screenings are still controversially discussed. Recent studies provided first evidence for the potential usefulness of autoantigens as markers for lung cancer.

Methods

We used extended panels of arrayed antigens and determined autoantibody signatures of sera from patients with different kinds of lung cancer, different common non-tumor lung pathologies, and controls without any lung disease by a newly developed computer aided image analysis procedure. The resulting signatures were classified using linear kernel Support Vector Machines and 10-fold cross-validation.

Results

The novel approach allowed for discriminating lung cancer patients from controls without any lung disease with a specificity of 97.0%, a sensitivity of 97.9%, and an accuracy of 97.6%. The classification of stage IA/IB tumors and controls yielded a specificity of 97.6%, a sensitivity of 75.9%, and an accuracy of 92.9%. The discrimination of lung cancer patients from patients with non-tumor lung pathologies reached an accuracy of 88.5%.

Conclusion

We were able to separate lung cancer patients from subjects without any lung disease with high accuracy. Furthermore, lung cancer patients could be seprated from patients with other non-tumor lung diseases. These results provide clear evidence that blood-based tests open new avenues for the early diagnosis of lung cancer.

Identifying autoantibody signatures in cancer: a promising challenge

Biomarkers that show high sensitivity and specificity are needed for the early diagnosis and prognosis of cancer. An immune response to cancer is elicited in humans, as demonstrated, in part, by the identification of autoantibodies against a number of tumor-associated antigen (TAAs) in sera from patients with different types of cancer. Identification of TAAs and their cognate autoantibodies is a promising strategy for the discovery of relevant biomarkers. During the past few years, three proteomic approaches, including serological identification of antigens by recombinant expression cloning (SEREX), serological proteome analysis (SERPA) and, more recently, protein microarrays, have been the dominant strategies used to identify TAAs and their cognate autoantibodies. In this review, we aim to describe the advantages, drawbacks and recent improvements of these approaches for the study of humoral responses. Finally, we discuss the definition of autoantibody signatures to improve sensitivity for the development of clinically relevant tests.

Systems biology coupled with label-free high-throughput detection as a novel approach for diagnosis of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a treatable and preventable disease state, characterised by progressive airflow limitation that is not fully reversible. Although COPD is primarily a disease of the lungs there is now an appreciation that many of the manifestations of disease are outside the lung, leading to the notion that COPD is a systemic disease. Currently, diagnosis of COPD relies on largely descriptive measures to enable classification, such as symptoms and lung function. Here the limitations of existing diagnostic strategies of COPD are discussed and systems biology approaches to diagnosis that build upon current molecular knowledge of the disease are described. These approaches rely on new 'label-free' sensing technologies, such as high-throughput surface plasmon resonance (SPR), that we also describe.

Detecting tumor-specific autoantibodies for cancer diagnosis: a technology overview

BACKGROUND

Studies on the autoantibody (humoral immune) response to tumor-associated aberrant cellular components have provided critical information about an individual's disease state. Tumor-specific autoantibodies detected in human serum samples are being studied extensively to determine their utility in developing serological diagnostic assays for cancer. The development of accurate panels of diagnostic markers for cancer detection is now being pursued using new technologies and tailored computational methods capable of analyzing data generated from large-scale biomarker discovery projects.

OBJECTIVE

In this technical overview, current methodologies being applied to the identification and characterization of tumor-specific autoantibodies in cancer patients' sera are reviewed.

METHODS

A variety of research approaches are presented that are now being used or have the potential to evaluate large numbers of patient sera for the presence of tumor-specific autoantibodies. Each approach is discussed regarding its primary attributes (advantages and limitations) that could lead to serological diagnostic assays for the early detection of cancer.

CONCLUSION

Preliminary results in the development of serological diagnostic assays have demonstrated that the basic experimental tools to accomplish this goal exist. In the future, autoantibody patterns against tumor-specific proteins may achieve high specificity and sensitivity for diagnosing disease in screening populations. The development of highly accurate reliable assays is a prerequisite for this technology to be integrated into clinically applicable strategies in patient care.