Epitomics: Global Profiling of Immune Response to Disease Using Protein Microarrays

Proteomics and Its Applications in Cancers 2.0

Considering the success of our previous Special Issue (SI) "Proteomics and Its Applications in Cancers", we aimed to attract more publications where cancer proteomics is involved [...].

Protein Microarrays for Ocular Diseases

The eye is a multifaceted organ organized in several compartments with particular properties that reflect their diverse functions. The prevalence of ocular diseases is increasing, mainly because of its relationship with aging and of generalized lifestyle changes. However, the pathogenic molecular mechanisms of many common eye pathologies remain poorly understood. Considering the unquestionable importance of proteins in cellular processes and disease progression, proteomic techniques, such as protein microarrays, represent a valuable approach to analyze pathophysiological protein changes in the ocular environment. This technology enables to perform multiplex high-throughput protein expression profiling with minimal sample volume requirements broadening our knowledge of ocular proteome network in eye diseases.In this review, we present a brief summary of the main types of protein microarrays (antibody microarrays, reverse-phase protein microarrays, and protein microarrays) and their application for protein change detection in chronic ocular diseases such as dry eye, age-related macular degeneration, diabetic retinopathy, and glaucoma. The validation of these specific protein changes in eye pathologies may lead to the identification of new biomarkers, depiction of ocular disease pathways, and assistance in the diagnosis, prognosis, and development of new therapeutic options for eye pathologies.

Protein Microarrays: Valuable Tools for Ocular Diseases Research

The eye is a complex organ comprised of several compartments with exclusive and specialized properties that reflect their diverse functions. Although the prevalence of eye pathologies is increasing, mainly because of its correlation with aging and of generalized lifestyle changes, the pathogenic molecular mechanisms of many common ocular diseases remain poorly understood. Therefore, there is an unmet need to delve into the pathogenesis, diagnosis, and treatment of eye diseases to preserve ocular health and reduce the incidence of visual impairment or blindness. Proteomics analysis stands as a valuable tool for deciphering protein profiles related to specific ocular conditions. In turn, such profiles can lead to real breakthroughs in the fields of ocular science and ophthalmology. Among proteomics techniques, protein microarray technology stands out by providing expanded information using very small volumes of samples. In this review, we present a brief summary of the main types of protein microarrays and their application for the identification of protein changes in chronic ocular diseases such as dry eye, glaucoma, age-related macular degeneration, or diabetic retinopathy. The validation of these specific protein alterations could provide new biomarkers, disclose eye diseases pathways, and help in the diagnosis and development of novel therapies for eye pathologies.

High-throughput screening of T7 phage display and protein microarrays as a methodological approach for the identification of IgE-reactive components

Olive pollen and yellow mustard seeds are major allergenic sources with high clinical relevance. To aid with the identification of IgE-reactive components, the development of sensitive methodological approaches is required. Here, we have combined T7 phage display and protein microarrays for the identification of allergenic peptides and mimotopes from olive pollen and mustard seeds. The identification of these allergenic sequences involved the construction and biopanning of T7 phage display libraries of mustard seeds and olive pollen using sera from allergic patients to both biological sources together with the construction of phage microarrays printed with 1536 monoclonal phages from the third/four rounds of biopanning. The screening of the phage microarrays with individual sera from allergic patients enabled the identification of 10 and 9 IgE-reactive unique amino acid sequences from olive pollen and mustard seeds, respectively. Five immunoreactive amino acid sequences displayed on phages were selected for their expression as His6-GST tag fusion proteins and validation. After immunological characterization, we assessed the IgE-reactivity of the constructs. Our results show that protein microarrays printed with T7 phages displaying peptides from allergenic sources might be used to identify allergenic components -peptides, proteins or mimotopes- through their screening with specific IgE antibodies from allergic patients.

Serum prognostic biomarkers in head and neck cancer patients

OBJECTIVES/HYPOTHESIS

A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC).

STUDY DESIGN

Prospective cohort study.

METHODS

A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models.

RESULTS

Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival.

CONCLUSIONS

The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted.

An optimized predictor panel for colorectal cancer diagnosis based on the combination of tumor-associated antigens obtained from protein and phage microarrays

Humoral response in cancer patients appears early in cancer progression and can be used for diagnosis, including early detection. By using human recombinant protein and T7 phage microarrays displaying colorectal cancer (CRC)-specific peptides, we previously selected 6 phages and 6 human recombinant proteins as tumor-associated antigens (TAAs) with high diagnostic value. After completing validation in biological samples, TAAs were classified according to their correlation, redundancy in reactivity patterns and multiplex diagnostic capabilities. For predictor model optimization, TAAs were reanalyzed with a new set of samples. A combination of three phages displaying peptides homologous to GRN, NHSL1 and SREBF2 and four proteins PIM1, MAPKAPK3, FGFR4 and ACVR2B, achieved an area under the curve (AUC) of 94%, with a sensitivity of 89.1% and specificity of 90.0%, to correctly predict the presence of cancer. For early colorectal cancer stages, the AUC was 90%, with a sensitivity of 88.2% and specificity of 82.6%. In summary, we have defined an optimized predictor panel, combining TAAs from different sources, with highly improved accuracy and diagnostic value for colorectal cancer. This article is part of a Special Issue entitled: Translational Proteomics.

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.

Identification of MST1/STK4 and SULF1 proteins as autoantibody targets for the diagnosis of colorectal cancer by using phage microarrays

The characterization of the humoral response in cancer patients is becoming a practical alternative to improve early detection. We prepared phage microarrays containing colorectal cancer cDNA libraries to identify phage-expressed peptides recognized by tumor-specific autoantibodies from patient sera. From a total of 1536 printed phages, 128 gave statistically significant values to discriminate cancer patients from control samples. From this, 43 peptide sequences were unique following DNA sequencing. Six phages containing homologous sequences to STK4/MST1, SULF1, NHSL1, SREBF2, GRN, and GTF2I were selected to build up a predictor panel. A previous study with high-density protein microarrays had identified STK4/MST1 as a candidate biomarker. An independent collection of 153 serum samples (50 colorectal cancer sera and 103 reference samples, including healthy donors and sera from other related pathologies) was used as a validation set to study prediction capability. A combination of four phages and two recombinant proteins, corresponding to MST1 and SULF1, achieved an area under the curve of 0.86 to correctly discriminate cancer from healthy sera. Inclusion of sera from other different neoplasias did not change significantly this value. For early stages (A+B), the corrected area under the curve was 0.786. Moreover, we have demonstrated that MST1 and SULF1 proteins, homologous to phage-peptide sequences, can replace the original phages in the predictor panel, improving their diagnostic accuracy.

Colorectal cancer proteomics, molecular characterization and biomarker discovery

Colorectal cancer (CRC) is a widespread disease, whose major genetic changes and mutations have been well characterized in the sporadic form. Much less is known at the protein and proteome level. Still, CRC has been the subject of multiple proteomic studies due to the urgent necessity of finding clinically relevant markers and to elucidate the molecular mechanisms underlying the progression of the disease. These proteomic approaches have been limited by different technical issues, mainly related with sensitivity and reproducibility. However, recent advances in proteomic techniques and MS systems have rekindled the quest for new biomarkers in CRC and an improved molecular characterization. In this review, we will discuss the application of different proteomic approaches to the identification of differentially expressed proteins in CRC. In particular, we will make a critical assessment about the use of 2-D DIGE, MS and protein microarray technologies, in their different formats, to identify up- or downregulated proteins and/or autoantibodies profiles that could be useful for CRC characterization and diagnosis. Despite a wide list of potential biomarkers, it is clear that more scientific efforts and technical advances are still needed to cover the range of low-abundant proteins, which may play a key role in CRC diagnostics and progression.

Identification of tumor-associated autoantigens for the diagnosis of colorectal cancer in serum using high density protein microarrays

There is a mounting evidence of the existence of autoantibodies associated to cancer progression. Antibodies are the target of choice for serum screening because of their stability and suitability for sensitive immunoassays. By using commercial protein microarrays containing 8000 human proteins, we examined 20 sera from colorectal cancer (CRC) patients and healthy subjects to identify autoantibody patterns and associated antigens. Forty-three proteins were differentially recognized by tumoral and reference sera (p value <0.04) in the protein microarrays. Five immunoreactive antigens, PIM1, MAPKAPK3, STK4, SRC, and FGFR4, showed the highest prevalence in cancer samples, whereas ACVR2B was more abundant in normal sera. Three of them, PIM1, MAPKAPK3, and ACVR2B, were used for further validation. A significant increase in the expression level of these antigens on CRC cell lines and colonic mucosa was confirmed by immunoblotting and immunohistochemistry on tissue microarrays. A diagnostic ELISA based on the combination of MAPKAPK3 and ACVR2B proteins yielded specificity and sensitivity values of 73.9 and 83.3% (area under the curve, 0.85), respectively, for CRC discrimination after using an independent sample set containing 94 sera representative of different stages of progression and control subjects. In summary, these studies confirmed the presence of specific autoantibodies for CRC and revealed new individual markers of disease (PIM1, MAPKAPK3, and ACVR2B) with the potential to diagnose CRC with higher specificity and sensitivity than previously reported serum biomarkers.

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.

Proteomics and cancer epidemiology

Profiling of differentially expressed proteins is perhaps the most important and useful approach in developing tools for risk assessment in a population, diagnostic screening, and therapeutics. Proteomic markers have potential for identifying individuals at high risk of developing cancer; however, these markers have not been extensively used in cancer epidemiologic studies. Several markers have to be clinically validated. In this chapter, methods used in proteomic analysis of clinical samples, challenges in the proteomics and cancer epidemiology, and their potential solutions are discussed.

Seromic analysis of antibody responses in non-small cell lung cancer patients and healthy donors using conformational protein arrays

Analysis of antibody responses to self-antigens has driven the development of the field of tumor immunology, with the identification of many protein targets found in cancer but with limited expression in normal tissues. Protein microarray technologies offer an unprecedented platform to assay the serological response of cancer patients to tumor antigens in a comprehensive fashion, against many proteins simultaneously. We developed an array containing 329 full-length proteins, originally identified as antigenic in various cancer patients by serological expression cloning (SEREX), that were immobilized as folded, functional products accessible for antibody binding. To validate the use of these microarrays, we selected 31 sera from non-small cell lung cancer patients previously known to react to the following antigens by ELISA: LAGE-1/CTAG2, MAGEA4, TP53, SSX and SOX2. These sera were compared with 22 sera from healthy donors for reactivity against a series of antigens present on microarrays. The sensitivity and specificity of the arrays compared favorably with standard ELISA techniques (94% concordance). We present here a stringent strategy for data analysis and normalization that is applicable to protein arrays in general, and describe findings suggesting that this approach is suitable for defining potential antigenic targets for cancer vaccine development, serum antibody signatures with clinical value, characterization of predictive serum markers for experimental therapeutics, and eventually for the serological definition of the cancer proteome (seromics).

Autoantibody approach for serum-based detection of head and neck cancer

Currently, no effective tool exists for screening or early diagnosis of head and neck squamous cell carcinoma (HNSCC). Here, we describe an approach for cancer detection based on analysis of patterns of serum immunoreactivity against a panel of biomarkers selected using microarray-based serologic profiling and specialized bioinformatics. We biopanned phage display libraries derived from three different HNSCC tissues to generate 5,133 selectively cloned tumor antigens. Based on their differential immunoreactivity on protein microarrays against serum immunoglobulins from 39 cancer and 41 control patients, we reduced the number of clones to 1,021. The performance of a neural network model (Multilayer Perceptron) for cancer classification on a data set of 80 HNSCC and 78 control samples was assessed using 10-fold cross-validation repeated 100 times. A panel of 130 clones was found to be adequate for building a classifier with sufficient sensitivity and specificity. Using these 130 markers on a completely new and independent set of 80 samples, an accuracy of 84.9% with sensitivity of 79.8% and specificity of 90.1% was achieved. Similar performance was achieved by reshuffling of the data set and by using other classification models. The performance of this classification approach represents a significant improvement over current diagnostic accuracy (sensitivity of 37% to 46% and specificity of 24%) in the primary care setting. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay with sufficient sensitivity and specificity suitable for detection of early-stage HNSCC in high-risk populations.

DNA repair of oxidative DNA damage in human carcinogenesis: potential application for cancer risk assessment and prevention

Efficient DNA repair mechanisms comprise a critical component in the protection against human cancer, as indicated by the high predisposition to cancer of individuals with germ-line mutations in DNA repair genes. This includes biallelic germ-line mutations in the MUTYH gene, encoding a DNA glycosylase that is involved in the repair of oxidative DNA damage, which strongly predispose humans to a rare hereditary form of colorectal cancer. Extensive research efforts including biochemical, enzymological and genetic studies in model organisms established that the oxidative DNA lesion 8-oxoguanine is mutagenic, and that several DNA repair mechanisms operate to prevent its potentially mutagenic and carcinogenic outcome. Epidemiological studies on the association with sporadic cancers of single nucleotide polymorphisms in genes such as OGG1, involved in the repair of 8-oxoguanine yielded conflicting results, and suggest a minor effect at best. A new approach based on the functional analysis of DNA repair enzymatic activity showed that reduced activity of 8-oxoguanine DNA glycosylase (OGG) is a risk factor in lung and head and neck cancer. Moreover, the combination of smoking and low OGG activity was associated with a higher risk, suggesting a potential strategy for risk assessment and prevention of lung cancer, as well as other types of cancer.