Detection of pancreatic cancer using antibody microarray-based serum protein profiling

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Identification of a serum biomarker signature associated with metastatic prostate cancer

PURPOSE

Improved early diagnosis and determination of aggressiveness of prostate cancer (PC) is important to select suitable treatment options and to decrease over-treatment. The conventional marker is total prostate specific antigen (PSA) levels in blood, but lacks specificity and ability to accurately discriminate indolent from aggressive disease.

EXPERIMENTAL DESIGN

In this study, we sought to identify a serum biomarker signature associated with metastatic PC. We measured 157 analytes in 363 serum samples from healthy subjects, patients with non-metastatic PC and patients with metastatic PC, using a recombinant antibody microarray.

RESULTS

A signature consisting of 69 proteins differentiating metastatic PC patients from healthy controls was identified.

CONCLUSIONS AND CLINICAL RELEVANCE

The clinical value of this biomarker signature requires validation in larger independent patient cohorts before providing a new prospect for detection of metastatic PC.

Immune-related protein signature in serum stratify relapsed mantle cell lymphoma patients based on risk

Background

Response to modern treatment strategies, which combine cytotoxic compounds with immune stimulatory agents and targeted treatment is highly variable among MCL patients. Thus, providing prognostic and predictive markers for risk adapted therapy is warranted and molecular information that can help in patient stratification is a necessity. In relapsed MCL, biopsies are rarely available and molecular information from tumor tissue is often lacking. Today, the main tool to access risk is the MCL international prognostic index (MIPI), which does not include detailed biological information of relevance for different treatment options. To enable continuous monitoring of patients, non-invasive companion diagnostic tools are needed which can further reduce cost and patient distress and enable efficient measurements of biological markers.

Methods

We have assessed if serum-based protein profiling can identify immune related proteins that stratify relapsed MCL patients based on risk. Overall, 371 scFv targeting 158 proteins were assessed using an antibody microarray platform. We profiled patients (n = 44) who had been treated within the MCL6-Philemon trial combining targeted and immune-modulatory treatment.

Results

The downstream processing led to the identification of the relapsed immune signature (RIS) consisting of 11 proteins with potential to stratify patients with long and short overall survival (OS). Moreover, in this population, MIPI alone failed to separate high, intermediate and low risk patients, but a combined index based on MIPI together with RIS, MIPI_ris, showed improved performance and significantly stratified all three risk groups based on OS.

Conclusions

Our results show that addition of biological parameters to previous prognostic indices improves patient stratification among patients treated with BTK inhibitor triplet combination, particularly, in the identification of an extreme high risk group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07678-4.

Serum Immunoglobulin G Is Associated With Decreased Risk of Pancreatic Cancer in the Swedish AMORIS Study

Background: Emerging evidence points to potential roles of the humoral immune responses in the development of pancreatic cancer. Epidemiological studies have suggested involvement of viral and bacterial infections in pancreatic carcinogenesis. Experimental studies have reported high expression levels of antigens in pancreatic cancer cells. Therefore, we aimed to investigate the role of different components of humoral immunity in the context of pancreatic cancer. We evaluated associations between pre-diagnostic serum markers of the overall humoral immune system [immunoglobulin A (IgA), immunoglobulin G (IgG) and immunoglobulin M (IgM)], and the risk of pancreatic cancer in the Swedish Apolipoprotein-related MORtality RISk (AMORIS) study.

Methods: We selected all participants (≥20 years old) with baseline measurements of IgA, IgG or IgM (n = 41,900, 136,221, and 29,919, respectively). Participants were excluded if they had a history of chronic pancreatitis and individuals were free from pancreatic cancer at baseline. Multivariate Cox proportional hazards regression was used to estimate risk of pancreatic cancer for medical cut-offs of IgA, IgG, and IgM.

Results: Compared to the reference level of 6.10–14.99 g/L, risk of pancreatic cancer was elevated among those with IgG levels <6.10 g/L [HR: 1.69 (95% CI 0.99–2.87)], and an inverse association was observed among those with IgG levels ≥15.00 g/L [0.82 (95% CI 0.64–1.05); Ptrend = 0.027]. The association appeared to be stronger for women than men [HR: 0.64 (95% CI 0.43–0.97) and 0.95 (95% CI 0.69–1.29), respectively]. No associations were observed with IgA or IgM.

Conclusion: An inverse association was observed between pre-diagnostic serum levels of IgG and risk of pancreatic cancer. Our findings highlight the need to further investigate the role of immune response in pancreatic cancer etiology.

Proteomic insights on the metabolism in inflammatory bowel disease

Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory conditions of the gut that include Crohn's disease and ulcerative colitis. The pathogenesis of IBD is not completely unraveled, IBD are multi-factorial diseases with reported alterations in the gut microbiota, activation of different immune cell types, changes in the vascular endothelium, and alterations in the tight junctions’ structure of the colonic epithelial cells. Proteomics represents a useful tool to enhance our biological understanding and to discover biomarkers in blood and intestinal specimens. It is expected to provide reproducible and quantitative data that can support clinical assessments and help clinicians in the diagnosis and treatment of IBD. Sometimes a differential diagnosis of Crohn's disease and ulcerative colitis and the prediction of treatment response can be deducted by finding meaningful biomarkers. Although some non-invasive biomarkers have been described, none can be considered as the “gold standard” for IBD diagnosis, disease activity and therapy outcome. For these reason new studies have proposed an “IBD signature”, which consists in a panel of biomarkers used to assess IBD. The above described approach characterizes “omics” and in this review we will focus on proteomics.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Lectin biosensors in cancer glycan biomarker detection

Cancer has high incidence and it will continue to increase over the next decades. Detection and quantification of cancer-associated biomarkers is frequently carried out for diagnosis, prognosis and treatment monitoring at various disease stages. It is well-known that glycosylation profiles change significantly during oncogenesis. Aberrant glycans produced during tumorigenesis are, therefore, valuable molecules for detection and characterization of cancer, and for therapeutic design and monitoring. Although glycoproteomics has benefited from the development of analytical tools such as high performance liquid chromatography, two-dimensional gel and capillary electrophoresis and mass spectrometry, these approaches are not well suited for rapid point-of-care (POC) testing easily performed by medical staff. Lectins are biomolecules found in nature with specific affinities toward particular glycan structures and bind them thus forming a relatively strong complex. Because of this characteristic, lectins have been used in analytical techniques for the selective capture or separation of certain glycans in complex samples, namely, in lectin affinity chromatography, or to characterize glycosylation profiles in diverse clinical situations, using lectin microarrays. Lectin-based biosensors have been developed for the detection of specific aberrant and cancer-associated glycostructures to aid diagnosis, prognosis and treatment assessment of these patients. The attractive features of biosensors, such as portability and simple use make them highly suitable for POC testing. Recent developments in lectin biosensors, as well as their potential and pitfalls in cancer glycan biomarker detection, are presented in this chapter.

Environment, endocrinology, and biochemistry influence expression of stress proteins in bottlenose dolphins

Natural and anthropogenic stressors have been reported to impact the health of marine mammals. Therefore, investigation of quantifiable biomarkers in response to stressors is required. We hypothesized that stress protein expression would be associated with biological and health variables in wild and managed-care bottlenose dolphins (Tursiops truncatus). To test this hypothesis, our study objectives were to (1) determine if stress proteins in skin, white blood cells (WBCs), and plasma could be measured with an antibody-based microarray, (2) measure stress-protein expression relative to biological data (location, sex, age, environment), and (3) determine if stress-protein expression was associated with endocrine, hematological, biochemical and serological variables and gene expression in bottlenose dolphins. Samples were collected from two wild groups (n = 28) and two managed-care groups (n = 17). Proteins involved in the HPA axis, apoptosis, proteotoxicity, and inflammation were identified as stress proteins. The expression of 3 out of 33 proteins was significantly (P < 0.05) greater in skin than plasma and WBCs. Male dolphins had significantly greater expression levels for 10 proteins in skin compared to females. The greatest number of stress-associated proteins varied by the dolphins' environment; nine were greater in managed-care dolphins and 15 were greater in wild dolphins, which may be related to wild dolphin disease status. Protein expression in skin and WBCs showed many positive relationships with measures of plasma endocrinology and biochemistry. This study provides further understanding of the underlying mechanisms of the stress response in bottlenose dolphins and application of a combination of novel methods to measure stress in wildlife.

A review of lifestyle, metabolic risk factors, and blood-based biomarkers for early diagnosis of pancreatic ductal adenocarcinoma

We aimed to review the epidemiologic literature examining lifestyle and metabolic risk factors, and blood-based biomarkers including multi-omics (genomics, proteomics, and metabolomics) and to discuss how these predictive markers can inform early diagnosis of pancreatic ductal adenocarcinoma (PDAC). A search of the PubMed database was conducted in June 2018 to review epidemiologic studies of (i) lifestyle and metabolic risk factors for PDAC, genome-wide association studies, and risk prediction models incorporating these factors and (ii) blood-based biomarkers for PDAC (conventional diagnostic markers, metabolomics, and proteomics). Prospective cohort studies have reported at least 20 possible risk factors for PDAC, including smoking, heavy alcohol drinking, adiposity, diabetes, and pancreatitis, but the relative risks and population attributable fractions of individual risk factors are small (mostly < 10%). High-throughput technologies have continued to yield promising genetic, metabolic, and protein biomarkers in addition to conventional biomarkers such as carbohydrate antigen 19-9. Nonetheless, most studies have utilized a hospital-based case-control design, and the diagnostic accuracy is low in studies that collected pre-diagnostic samples. Risk prediction models incorporating lifestyle and metabolic factors as well as other clinical parameters have shown good discrimination and calibration. Combination of traditional risk factors, genomics, and blood-based biomarkers can help identify high-risk populations and inform clinical decisions. Multi-omics investigations can provide valuable insights into disease etiology, but prospective cohort studies that collect pre-diagnostic samples and validation in independent studies are warranted.

Lectin-based biosensors as analytical tools for clinical oncology

The review focus on the use of lectin-based biosensors in the oncology field, and ponders the potentialities of using these devices as analytical tools to monitor the levels of cancer glycobiomarkers in biological fluids, helping in the diagnosis, prognosis and treatment assessment. Several examples of lectin-based biosensors directed for cancer biomarkers are described and discussed, and their potential application in the clinic is considered, taking into account their analytical features, advantages and performance in sample analysis. Technical and practical aspects in the construction process, which are specific for lectin biosensors, are debated, as well as the requirements in sample collection and processing, and biosensor validation. Today's challenges for real implementation of these devices in the clinic are presented, along with the future trends in the field.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.

Tumor tissue protein signatures reflect histological grade of breast cancer

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Protein Array-based Approaches for Biomarker Discovery in Cancer

Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, protein array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of protein arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.

Diagnostic value of selected markers and apoptotic pathways for pancreatic cancer

Pancreatic cancer occupies the fourth place as a cause of death from cancer, and the mortality rate is similar to the number of newly detected cases. Due to the late diagnosis, only 5-6% of patients with pancreatic cancer survive for five years. Given that early diagnosis is critical for improving patients' survival rates, there is an urgent need for the discovery and validation of new biomarkers with sufficient sensitivity and specificity to help diagnose pancreatic cancer early. Detection of serum tumor markers (CA19-9, CEA, CA125 and CA242) is conducive to the early diagnosis of pancreatic cancer. The combination of miR-16, miR-196a and CA19-9 plasma level was more effective, especially in early tumor screening. Furthermore, recent studies reported that mainly miR-21, miR-155 and miR-196 were dysregulated in IPMN (intraductal papillary mucinous neoplasms) and PanIN (pancreatic intraepithelial neoplasia) lesions, suggesting their usefulness as early biomarkers of these diseases. The reduced rate of apoptosis plays a crucial role in carcinogenesis, and it is one of the most important characteristics acquired by pancreatic cancer cells, which protects them from attack by the immune system and reduces the effectiveness of pharmacological treatment. This review summarizes the data concerning the clinical utility of selected biomarkers in pancreatic cancer patients. The review mainly focuses on the genetic aspects of signaling pathway disorders associated with apoptosis in the pathogenesis and diagnosis of pancreatic cancer.

Deciphering systemic lupus erythematosus-associated serum biomarkers reflecting apoptosis and disease activity

Systemic lupus erythematosus (SLE) is a severe chronic inflammatory autoimmune connective tissue disease. Despite major efforts, SLE remains a poorly understood disease with unpredictable course, unknown etiology and complex pathogenesis. Apoptosis combined with deficiency in clearing apoptotic cells is an important etiopathogenic event in SLE, which could contribute to the increased load of potential autoantigen(s); however, the lack of disease-specific protein signatures deciphering SLE and the underlying biological processes is striking and represents a key limitation. In this retrospective pilot study, we explored the immune system as a specific sensor for disease, in order to advance our understanding of SLE. To this end, we determined multiplexed serum protein expression profiles of crude SLE serum samples, using antibody microarrays. The aim was to identify differential immunoprofiles, or snapshots of the immune response modulated by the disease, reflecting apoptosis, a key process in the etiology of SLE and disease activity. The results showed that multiplexed panels of SLE-associated serum biomarkers could be decoded, in particular reflecting disease activity, but potentially the apoptosis process as well. While the former biomarkers could display a potential future use for prognosis, the latter biomarkers might help shed further light on the apoptosis process taking place in SLE.

Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Plasma protein profiling in a stage defined pancreatic cancer cohort - Implications for early diagnosis

Pancreatic ductal adenocarcinoma (PDAC) is a disease where detection preceding clinical symptoms significantly increases the life expectancy of patients. In this study, a recombinant antibody microarray platform was used to analyze 213 Chinese plasma samples from PDAC patients and normal control (NC) individuals. The cohort was stratified according to disease stage, i.e. resectable disease (stage I/II), locally advanced (stage III) and metastatic disease (stage IV). Support vector machine analysis showed that all PDAC stages could be discriminated from controls and that the accuracy increased with disease progression, from stage I to IV. Patients with stage I/II PDAC could be discriminated from NC with high accuracy based on a plasma protein signature, indicating a possibility for early diagnosis and increased detection rate of surgically resectable tumors.

Protein Profiling Gastric Cancer and Neighboring Control Tissues Using High-Content Antibody Microarrays

In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative gastric adenocarcinomas and adjacent non-cancerous control tissues were analyzed on microarrays that contain 813 antibodies targeting 724 proteins. Only 17 proteins were found to be differentially regulated, with much fewer molecules than the numbers usually identified in studies comparing tumor to healthy control tissues. Insulin-like growth factor-binding protein 7 (IGFBP7), S100 calcium binding protein A9 (S100A9), interleukin-10 (IL‐10) and mucin 6 (MUC6) exhibited the most profound variations. For an evaluation of the proteins’ capacity for discriminating gastric cancer, a Receiver Operating Characteristic curve analysis was performed, yielding an accuracy (area under the curve) value of 89.2% for distinguishing tumor from non-tumorous tissue. For confirmation, immunohistological analyses were done on tissue slices prepared from another cohort of patients with gastric cancer. The utility of the 17 marker proteins, and particularly the four molecules with the highest specificity for gastric adenocarcinoma, is discussed for them to act as candidates for diagnosis, even in serum, and targets for therapeutic approaches.

Evaluation of Solid Supports for Slide- and Well-Based Recombinant Antibody Microarrays

Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the antibodies and the solid surfaces plays a central role. In this study, we have taken on the first comprehensive view and evaluated the overall impact of solid surfaces on the recombinant antibody microarray design. The results clearly demonstrated the importance of the surface-antibody interaction and showed the effect of the solid supports on the printing process, the array format of planar arrays (slide- and well-based), the assay performance (spot features, reproducibility, specificity and sensitivity) and assay processing (degree of automation). In the end, two high-end recombinant antibody microarray technology platforms were designed, based on slide-based (black polymer) and well-based (clear polymer) arrays, paving the way for future large-scale protein expression profiling efforts.

A Multicenter Trial Defining a Serum Protein Signature Associated with Pancreatic Ductal Adenocarcinoma

Background. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with rapid tumor progression and poor prognosis. This study was motivated by the lack of sensitive and specific PDAC biomarkers and aimed to identify a diagnostic, serum protein signature for PDAC. Methods. To mimic a real life test situation, a multicenter trial comprising a serum sample cohort, including 338 patients with either PDAC or other pancreatic diseases (OPD) and controls with nonpancreatic conditions (NPC), was analyzed on 293-plex recombinant antibody microarrays targeting immunoregulatory and cancer-associated antigens. Results. Serum samples collected from different hospitals were analyzed and showed that (i) sampling from five different hospitals could not be identified as a preanalytical variable and (ii) a multiplexed biomarker signature could be identified, utilizing up to 10 serum markers that could discriminate PDAC from controls, with sensitivities and specificities in the 91–100% range. The first protein profiles associated with the location of the primary tumor in the pancreas could also be identified. Conclusions. The results demonstrate that robust enough serum signatures could be identified in a multicenter trial, potentially contributing to the development of a multiplexed biomarker immunoassay for improved PDAC diagnosis.

Biomarkers of Inflammatory Bowel Disease: From Classical Laboratory Tools to Personalized Medicine

Diagnostics of inflammatory bowel diseases (IBDs) currently relies on a combination of biological and morphological tests. The current method of diagnostic remains a critical challenge for physicians in part due to their invasiveness and also for their limitations in term of diagnosis, prognosis, disease activity and severity assessment, and therapeutic outcomes. Laboratory biomarkers can be used in the diagnosis and management of IBD, but none of them has been proven to be ideal. Increasing efforts are being made to discover new biomarkers that can discriminate between the types of IBD, predict future responses to treatment, and aid in differential diagnosis, treatment planning, and prognosis prediction. This review addresses the potential for current biomarkers and the emergence of the concept of biomarker signatures in IBD diagnostic and personalized medicine.

Nanobodies and recombinant binders in cell biology

Antibodies are key reagents to investigate cellular processes. The development of recombinant antibodies and binders derived from natural protein scaffolds has expanded traditional applications, such as immunofluorescence, binding arrays, and immunoprecipitation. In addition, their small size and high stability in ectopic environments have enabled their use in all areas of cell research, including structural biology, advanced microscopy, and intracellular expression. Understanding these novel reagents as genetic modules that can be integrated into cellular pathways opens up a broad experimental spectrum to monitor and manipulate cellular processes.

Cancer serum biomarkers based on aberrant post-translational modifications of glycoproteins: Clinical value and discovery strategies

Due to the increase in life expectancy in the last decades, as well as changes in lifestyle, cancer has become one of the most common diseases both in developed and developing countries. Early detection remains the most promising approach to improve long-term survival of cancer patients and this may be achieved by efficient screening of biomarkers in biological fluids. Great efforts have been made to identify specific alterations during oncogenesis. Changes at the cellular glycosylation profiles are among such alterations. The "glycosylation machinery" of cells is affected by malignant transformation due to the altered expression of glycogens, leading to changes in glycan biosynthesis and diversity. Alterations in the post-translational modifications of proteins that occur in cancer result in the expression of antigenically distinct glycoproteins. Therefore, these aberrant and cancer-specific glycoproteins and the autoantibodies that are produced in response to their presence constitute targets for cancer biomarkers' search. Different strategies have been implemented for the discovery of cancer glycobiomarkers and are herein reviewed, along with their potentialities and limitations. Practical issues related with serum analysis are also addressed, as well as the challenges that this area faces in the near future.

Antibody microarrays utilizing site-specific antibody-oligonucleotide conjugates

Protein arrays are typically made by random absorption of proteins to the array surface, potentially limiting the amount of properly oriented and functional molecules. We report the development of a DNA encoded antibody microarray utilizing site-specific antibody-oligonucleotide conjugates that can be used for cell immobilization as well as the detection of genes and proteins. This technology allows for the facile generation of antibody microarrays while circumventing many of the drawbacks of conventionally produced antibody arrays. We demonstrate that this method can be used to capture and detect SK-BR-3 cells (Her2+ breast cancer cells) at concentrations as low as 10(2) cells/mL (which is equivalent to 10 cells per 100 μL array) without the use of microfluidics, which is 100- to 10(5)-fold more sensitive than comparable techniques. Additionally, the method was shown to be able to detect cells in a complex mixture, effectively immobilizing and specifically detecting Her2+ cells at a concentration of 10(2) SK-BR-3 cells/mL in 4 × 10(6) white blood cells/mL. Patients with a variety of cancers can have circulating tumor cell counts of between 1 and 10(3) cells/mL in whole blood, well within the range of this technology.

Miniaturization of multiplexed planar recombinant antibody arrays for serum protein profiling

BACKGROUND

Antibody-based microarrays are a developing tool for high-throughput proteomics in health and disease. However, in order to enable global proteome profiling, novel miniaturized high-density antibody array formats must be developed.

RESULTS

In this proof-of-concept study, we have designed a miniaturized planar recombinant (single-chain Fragment variable). antibody array technology platform for multiplexed profiling of non-fractionated, directly labelled serum samples. The size of the individual spot features was reduced 225-times (78.5 μm(2)/spot) and the array density was increased 19-times (38,000 spots/cm(2)). These miniaturized, multiplexed arrays were produced, using a desktop nanofabrication system based on dip-pen nanolithography technology, and interfaced with a high-resolution fluorescent-based scanner. The reproducibility, sensitivity, specificity, and applicability of the set-up were demonstrated by profiling a set of well-characterized serum samples.

CONCLUSION

The designed antibody array platform opens up new possibilities for large-scale, multiplex profiling of crude proteomes in a miniaturized fashion.

Identification of plasma protein profiles associated with risk groups of prostate cancer patients

PURPOSE

Early detection of prostate cancer (PC) using prostate-specific antigen (PSA) in blood reduces PC-death among unscreened men. However, due to modest specificity of PSA at commonly used cut-offs, there are urgent needs for additional biomarkers contributing enhanced risk classification among men with modestly elevated PSA.

EXPERIMENTAL DESIGN

Recombinant antibody microarrays were applied for protein expression profiling of 80 plasma samples from routine PSA-measurements, a priori divided into four risk groups, based on levels of total and %free PSA.

RESULTS

The results demonstrated that plasma protein profiles could be identified that pin-pointed PC (a malignant biomarker signature) and most importantly that showed moderate to high correlation with biochemically defined PC risk groups. Notably, the data also implied that the risk group with midrange PSA and low %free PSA, a priori known to be heterogeneous, could be further stratified into two subgroups, more resembling the lowest and highest risk groups, respectively.

CONCLUSIONS AND CLINICAL RELEVANCE

In this pilot study, we have shown that plasma protein biomarker signatures, associated with risk groups of PC, could be identified from crude plasma samples using affinity proteomics. This approach could in the longer perspective provide novel opportunities for improved risk classification of PC patients.

Multiplexing of miniaturized planar antibody arrays for serum protein profiling--a biomarker discovery in SLE nephritis

In the quest to decipher disease-associated biomarkers, miniaturized and multiplexed antibody arrays may play a central role in generating protein expression profiles, or protein maps, of crude serum samples. In this conceptual study, we explored a novel, 4-times larger pen design, enabling us to, in a unique manner, simultaneously print 48 different reagents (antibodies) as individual 78.5 μm(2) (10 μm in diameter) sized spots at a density of 38,000 spots cm(-2) using dip-pen nanolithography technology. The antibody array set-up was interfaced with a high-resolution fluorescent-based scanner for sensitive sensing. The performance and applicability of this novel 48-plex recombinant antibody array platform design was demonstrated in a first clinical application targeting SLE nephritis, a severe chronic autoimmune connective tissue disorder, as the model disease. To this end, crude, directly biotinylated serum samples were targeted. The results showed that the miniaturized and multiplexed array platform displayed adequate performance, and that SLE-associated serum biomarker panels reflecting the disease process could be deciphered, outlining the use of miniaturized antibody arrays for disease proteomics and biomarker discovery.

The Use of Antibodies in Small-Molecule Drug Discovery

Antibodies are powerful research tools that can be used in many areas of biology to probe, measure, and perturb various biological structures. Successful drug discovery is dependent on the correct identification of a target implicated in disease, coupled with the successful selection, optimization, and development of a candidate drug. Because of their specific binding characteristics, with regard to specificity, affinity, and avidity, coupled with their amenability to protein engineering, antibodies have become a key tool in drug discovery, enabling the quantification, localization, and modulation of proteins of interest. This review summarizes the application of antibodies and other protein affinity reagents as specific research tools within the drug discovery process.

Identification of B-cell lymphoma subsets by plasma protein profiling using recombinant antibody microarrays

B-cell lymphoma (BCL) heterogeneity represents a key issue, often making the classification and clinical management of these patients challenging. In this pilot study, we outlined the first resolved view of BCL disease heterogeneity on the protein level by deciphering disease-associated plasma biomarkers, specific for chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and mantle cell lymphoma, using recombinant antibody microarrays targeting mainly immunoregulatory proteins. The results showed the BCLs to be heterogeneous, and revealed potential novel subgroups of each BCL. In the case of diffuse large B-cell lymphoma, we also indicated a link between the novel subgroups and survival.

Modelling the benefits of early diagnosis of pancreatic cancer using a biomarker signature

Pancreatic cancer (PC) has a poor prognosis, with a 5-year survival of 3-4%. This is mainly due to late diagnosis because of diffuse symptoms, where 80-85% of the patients are inoperable. Consequently, early diagnosis would be of significant benefit, resulting in a potential 5-year survival of 30-40%. However, new technologies must be carefully evaluated concerning effectiveness and healthcare costs. We have developed a framework for modelling cost and health effects from early detection of PC, which for the first time allowed us to analyse its cost-effectiveness. A probabilistic cohort model for estimating costs and quality adjusted life-years (QALY) arising from screening for PC, compared to a "wait-and-see"-approach, was designed. The test accuracy, Swedish survival and costs by tumour stage, expected life gain from early detection and pretest probabilities in risk groups, were retrieved from previous investigations. In a cohort of newly diagnosed diabetic patient (incidence 0.71%) the incremental cost per QALY gained (ICER) was €13,500, which is considered cost-effective in Europe. Results were mainly sensitive to the incidence with the ICER ranging from €315 to €204,000 (familial PC 35% and general population 0.046%, respectively). This is the first study focusing on clinical implementation of advanced testing and what is required for novel technologies in cancer care to be cost-effective. The model clearly demonstrated the potential of multiplexed proteomic-testing of PC and also identified the requirements for test accuracy. Consequently, it can serve as a model for assessing the possibilities to introduce advanced test platforms also for other cancer indications.

Development and optimization of an antibody array method for potential cancer biomarker detection

Biomarkers play an important role in the detection at an early stage of pancreatic cancer. The aim of the present study was to optimize the conditions of antibody arrays for detecting Hippocalcin-like 1 (HPCAL1), phosphatidylethanolamine binding protein 1 (PEBP1), lectin galactoside-binding soluble 7 (LGALS7), and serpin peptidase inhibitor clade E member 2 (SERPINE2) as biomarkers for pancreatic cancer detection in a single assay and to investigate antibodies' specificity and cross-reactivity. Capture antibodies against HPCAL1, PEBP1, LGALS7 and SERPINE2 were printed on nitrocellulose coated glass slides. HPCAL1, PEBP1, LGALS7 and SERPINE2 proteins with different concentrations were incubated with the capture antibodies at different temperatures for different time periods. Biotinylated detection antibodies recognizing a different epitope on the captured proteins and a secondary detection molecule (Streptavidin-PE) were used to detect fluorescent signals. The arrays showed the strongest signals when the concentration of the capture antibodies was at 500 µg/mL in PBST0.05 (PBS with 0.05% Tween-20), and the slides were incubated overnight at 4°C. The lowest protein concentration for detection was 2 ng/mL. Each antibody demonstrated high specificity to the corresponding antigen in detecting a mixture of 4 proteins without significant cross-reactivity. The fluorescence and biomarker concentration displayed a linear correlation. The antibody microarray system could be a useful tool for potential biomarker detection for pancreatic cancer.

Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures

PURPOSE

Pancreatitis is an inflammatory state of the pancreas, for which high-performing serological biomarkers are lacking. The aim of the present study was to evaluate the use of affinity proteomics for identifying potential markers of disease and stratifying pancreatitis subtypes.

EXPERIMENTAL DESIGN

High-content, recombinant antibody microarrays were applied for serum protein expression profiling of 113 serum samples from patients with chronic, acute, and autoimmune pancreatitis, as well as healthy controls. The sample groups were compared using supervised classification based on support vector machine analysis.

RESULTS

This discovery study showed that pancreatitis subtypes could be discriminated with high accuracy. Using unfiltered data, the individual subtypes, as well as the combined pancreatitis cohort, were distinguished from healthy controls with high AUC values (0.96-1.00). Moreover, characteristic protein patterns and AUC values in the range of 0.69-0.95 were observed for the individual pancreatitis entities when compared to each other, and to all other samples combined.

CONCLUSIONS AND CLINICAL RELEVANCE

This study demonstrated the potential of the antibody microarray approach for stratification of pancreatitis. Distinct candidate multiplex serum biomarker signatures for chronic, acute, and autoimmune pancreatitis were defined, which could enhance our fundamental knowledge of the underlying molecular mechanisms, and potentially lead to improved diagnosis.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

Protein analytical assays for diagnosing, monitoring, and choosing treatment for cancer patients

Cancer treatment is often hindered by inadequate methods for diagnosing the disease or insufficient predictive capacity regarding therapeutic efficacy. Targeted cancer treatments, including Bcr-Abl and EGFR kinase inhibitors, have increased survival for some cancer patients but are ineffective in other patients. In addition, many patients who initially respond to targeted inhibitor therapy develop resistance during the course of treatment. Molecular analysis of cancer cells has emerged as a means to tailor treatment to particular patients. While DNA analysis can provide important diagnostic information, protein analysis is particularly valuable because proteins are more direct mediators of normal and diseased cellular processes. In this review article, we discuss current and emerging protein assays for improving cancer treatment, including trends toward assay miniaturization and measurement of protein activity.

Early diagnosis of pancreatic cancer: challenges and new developments

Pancreatic cancer is a lethal malignancy with its incidence almost equivalent to mortality. The complex pathophysiology, absence of early diagnostic and prognostic markers and unresponsiveness to radiation and chemotherapies are major barriers against successful therapy. Poor performance of therapeutic agents, even in the initial stage of invasive cases, emphasizes the importance of early detection for improved survival. The present review discusses the challenges and advances in biomarkers including serological signatures, circulating tumor cells, autoantibodies, epigenetic markers and miRNAs that are being explored to detect this cancer at early stages. Considering the long time gap between the development of malignant lesions and full-blown primary and metastatic pancreatic cancer, unique opportunities are being contemplated for the development of potential diagnostic and prognostic markers.

Differentiation of cancer cell type and phenotype using quantum dot-gold nanoparticle sensor arrays

We demonstrate rapid and efficient sensing of mammalian cell types and states using nanoparticle-based sensor arrays. These arrays are comprised of cationic quantum dots (QDs) and gold nanoparticles (AuNPs) that interact with cell surfaces to generate distinguishable fluorescence responses based on cell surface signatures. The use of QDs as the recognition elements as well as the signal transducers presents the potential for direct visualization of selective cell surface interactions. Notably, this sensor is unbiased, precluding the requirement of pre-knowledge of cell state biomarkers and thus providing a general approach for phenotypic profiling of cell states, with additional potential for imaging applications.

Porous silicon antibody microarrays for quantitative analysis: measurement of free and total PSA in clinical plasma samples

The antibody microarrays have become widespread, but their use for quantitative analyses in clinical samples has not yet been established. We investigated an immunoassay based on nanoporous silicon antibody microarrays for quantification of total prostate-specific-antigen (PSA) in 80 clinical plasma samples, and provide quantitative data from a duplex microarray assay that simultaneously quantifies free and total PSA in plasma. To further develop the assay the porous silicon chips was placed into a standard 96-well microtiter plate for higher throughput analysis. The samples analyzed by this quantitative microarray were 80 plasma samples obtained from men undergoing clinical PSA testing (dynamic range: 0.14-44 ng/ml, LOD: 0.14 ng/ml). The second dataset, measuring free PSA (dynamic range: 0.40-74.9 ng/ml, LOD: 0.47 ng/ml) and total PSA (dynamic range: 0.87-295 ng/ml, LOD: 0.76 ng/ml), was also obtained from the clinical routine. The reference for the quantification was a commercially available assay, the ProStatus PSA Free/Total DELFIA. In an analysis of 80 plasma samples the microarray platform performs well across the range of total PSA levels. This assay might have the potential to substitute for the large-scale microtiter plate format in diagnostic applications. The duplex assay paves the way for a future quantitative multiplex assay, which analyzes several prostate cancer biomarkers simultaneously.

Photonic crystal enhanced fluorescence for early breast cancer biomarker detection

Photonic crystal surfaces offer a compelling platform for improving the sensitivity of surface-based fluorescent assays used in disease diagnostics. Through the complementary processes of photonic crystal enhanced excitation and enhanced extraction, a periodic dielectric-based nanostructured surface can simultaneously increase the electric field intensity experienced by surface-bound fluorophores and increase the collection efficiency of emitted fluorescent photons. Through the ability to inexpensively fabricate photonic crystal surfaces over substantial surface areas, they are amenable to single-use applications in biological sensing, such as disease biomarker detection in serum. In this review, we will describe the motivation for implementing high-sensitivity, multiplexed biomarker detection in the context of breast cancer diagnosis. We will summarize recent efforts to improve the detection limits of such assays though the use of photonic crystal surfaces. Reduction of detection limits is driven by low autofluorescent substrates for photonic crystal fabrication, and detection instruments that take advantage of their unique features.

Identification of serum biomarker signatures associated with pancreatic cancer

Pancreatic cancer is an aggressive disease with poor prognosis, due, in part, to the lack of disease-specific biomarkers that could afford early and accurate diagnosis. With a recombinant antibody microarray platform, targeting mainly immunoregulatory proteins, we screened sera from 148 patients with pancreatic cancer, chronic pancreatitis, autoimmune pancreatitis (AIP), and healthy controls (N). Serum biomarker signatures were derived from training cohorts and the predictive power was evaluated using independent test cohorts. The results identified serum portraits distinguishing pancreatic cancer from N [receiver operating characteristics area under the curve (AUC) of 0.95], chronic pancreatitis (0.86), and AIP (0.99). Importantly, a 25-serum biomarker signature discriminating pancreatic cancer from the combined group of N, chronic pancreatitis, and AIP was determined. This signature exhibited a high diagnostic potential (AUC of 0.88). In summary, we present the first prevalidated, multiplexed serum biomarker signature for diagnosis of pancreatic cancer that may improve diagnosis and prevention in premalignant diseases and in screening of high-risk individuals.

Immunoassay-based proteome profiling of 24 pancreatic cancer cell lines

Pancreatic ductal adenocarcinoma is one of the most deadly forms of cancers, with a mortality that is almost identical to incidence. The inability to predict, detect or diagnose the disease early and its resistance to all current treatment modalities but surgery are the prime challenges to changing the devastating prognosis. Also, relatively little is known about pancreatic carcinogenesis. In order to better understand relevant aspects of pathophysiology, differentiation, and transformation, we analysed the cellular proteomes of 24 pancreatic cancer cell lines and two controls using an antibody microarray that targets 741 cancer-related proteins. In this analysis, 72 distinct disease marker proteins were identified that had not been described before. Additionally, categorizing cancer cells in accordance to their original location (primary tumour, liver metastases, or ascites) was made possible. A comparison of the cells' degree of differentiation (well, moderately, or poorly differentiated) resulted in unique marker sets of high relevance. Last, 187 proteins were differentially expressed in primary versus metastatic cancer cells, of which the majority is functionally related to cellular movement.

Normalization using a tagged-internal standard assay for analysis of antibody arrays and the evaluation of serological biomarkers for liver disease

For minimizing systemic experimental variation in the analysis of antibody array data, we developed a novel median-centered/IgM-tagged-internal standard (TIS) assay normalization using median-centering and TIS assay-based determination of serum IgM concentrations. We evaluated five normalization methods by analyzing correlation coefficients and coefficients of variation for six serum proteins using human serum samples from normal controls (n=25) and patients with liver cirrhosis (n=25) or hepatocellular carcinoma (HCC; n=29). Median-centered normalization improved correlation coefficients, while IgM-based normalizations improved coefficients of variation. The TIS assay was more efficient, economical, and reproducible for determining IgM concentrations than enzyme-linked immunosorbent assay. Additionally, we normalized antibody array data for six serum proteins using the median-centered/IgM-TIS assay, and evaluated serum biomarkers through distribution analysis of normalized fluorescence intensities and receiver operating characteristic analyses for the diagnosis of liver cirrhosis and HCC. Apolipoprotein A-1 and a combination of alpha-fetoprotein and C-reactive protein were determined to be potential serological biomarkers for liver cirrhosis and HCC, respectively. Thus, median-centered/IgM-TIS assay normalization is a useful approach for analyzing antibody array data and evaluating serological biomarkers for the diagnosis of liver disease or cancers.