Identification of tumor-associated plasma biomarkers using proteomic techniques: from mouse to human

In an effort to identify tumor-associated proteins from plasma of tumor-bearing mice that may be used as diagnostic biomarkers, we developed a strategy that combines a tumor xenotransplantation model in nude mice with comparative proteomic technology. Five human cancer cell lines (SC-M1, HONE-1, CC-M1, OECM1, GBM 8401) derived from stomach, nasopharyngeal, colon, oral and brain cancers were subcutaneously inoculated into nude mice and compared to control nude mice injected with phosphate-buffered saline. One month later, plasma from mice inoculated with cancer cells was collected for proteomic analysis using two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS). Comparison of plasma 2-DE maps from tumor-bearing mice with those produced from control mice revealed the overexpression of several mouse acute phase proteins (APPs) such as haptoglobin. Another APP, serum amyloid A (SAA), was found only in mice bearing tumors induced by the stomach cancer cell line SC-M1, which has not previously been demonstrated in xenotransplatation experiment. Furthermore, by using immunohistochemistry, SAA and haptoglobin were found to originate from the mouse hosts and not from the human cancer cell line donors. The protein alterations were further confirmed on patients with stomach cancers where up-regulated levels of SAA were also observed. These results indicate that APPs may be used as nonspecific tumor-associated serum markers. SAA in particular may serve as a potential marker for detecting stomach cancer. Taken together, the combination of the xenotransplatation model in nude mice and proteomics analysis provided a valuable impact for clinical applications in cancer diagnostics. In addition, our findings demonstrate that a panel of APPs might serve as screening biomarkers for early cancer detection.     

Clinical cancer proteomics: promises and pitfalls

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

Rarity gives a charm: evaluation of trace proteins in plasma and serum

Since plasma potentially contacts every cell as it circulates through the body, it may carry clues both to diagnosis and treatment of disease. It is commonly expected that the growing ability to detect and characterize trace proteins will result in discovery of novel therapeutics and biomarkers; however, the familiar, super-abundant plasma proteins remain a fundamental stumbling block. Furthermore, robust validation of proteomic data is a sometimes overlooked but always necessary component for the eventual development of clinical reagents. This review surveys some of the uses of typical and atypical low-abundance proteins, current analytical methods, existing impediments to discovery, and some innovations that are overcoming the challenges to evaluation of trace proteins in plasma and serum.     

Rarity gives a charm: evaluation of trace proteins in plasma and serum

Since plasma potentially contacts every cell as it circulates through the body, it may carry clues both to diagnosis and treatment of disease. It is commonly expected that the growing ability to detect and characterize trace proteins will result in discovery of novel therapeutics and biomarkers; however, the familiar, super-abundant plasma proteins remain a fundamental stumbling block. Furthermore, robust validation of proteomic data is a sometimes overlooked but always necessary component for the eventual development of clinical reagents. This review surveys some of the uses of typical and atypical low-abundance proteins, current analytical methods, existing impediments to discovery, and some innovations that are overcoming the challenges to evaluation of trace proteins in plasma and serum.     

Pilot study identifying myosin heavy chain 7, desmin,insulin‐like growth factor 7, and annexin A2 as circulating biomarkers of human heart failure

In‐depth proteomic analyses offer a systematic way to investigate protein alterations in disease and, as such, can be a powerful tool for the identification of novel biomarkers. Here, we analyzed proteomic data from a transgenic mouse model with cardiac‐specific overexpression of activated calcineurin (CnA), which results in severe cardiac hypertrophy. We applied statistically filtering and false discovery rate correction methods to identify 52 proteins that were significantly different in the CnA hearts compared to controls. Subsequent informatic analysis consisted of comparison of these 52 CnA proteins to another proteomic dataset of heart failure, three available independent microarray datasets, and correlation of their expression with the human plasma and urine proteome. Following this filtering strategy, four proteins passed these selection criteria, including myosin heavy chain 7, insulin‐like growth factor‐binding protein 7, annexin A2, and desmin. We assessed expression levels of these proteins in mouse plasma by immunoblotting, and observed significantly different levels of expression between healthy and failing mice for all four proteins. We verified antibody cross‐reactivity by examining human cardiac explant tissue by immunoblotting. Finally, we assessed protein levels in plasma samples obtained from four unaffected and four heart failure patients and demonstrated that all four proteins increased between twofold and 150‐fold in heart failure. We conclude that MYH7, IGFBP7, ANXA2, and DESM are all excellent candidate plasma biomarkers of heart failure in mouse and human.     

A proteomic approach for plasma biomarker discovery with 8-plex iTRAQ labeling and SCX-LC-MS/MS

Plasma is recognized as a promising source of disease-related biomarkers, and proteomic approaches for identifying novel plasma biomarkers are in great demand. However, the complexity and dynamic protein concentration range of plasma remain the main obstacles for current research in this field. In this study, plasma proteins were prefractioned by immunodepletion and Protein Equalizer Technology to remove high abundant proteins, then labeled with an 8-plex isobaric tags for relative and absolute quantitation (iTRAQ) to improve the peptide ionization, and analyzed by strong-cation-exchange(SCX) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Our results showed that both prefraction methods were complementary, with regard to the number of identified proteins. Good chromatographic technique is important to further fractionate the iTRAQ labeling peptides, which allowed 320 and 248 different proteins to be characterized from two prefraction methods, respectively, encompassing a wide array of biological functions and a broad dynamic range of 10⁷. Furthermore, the accuracy of iTRAQ relative quantitation for differentially expressed proteins is associated with the number of peptides hits per protein.     

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.     

A new method for isolation of interstitial fluid from human solid tumors applied to proteomic analysis of ovarian carcinoma tissue

Major efforts have been invested in the identification of cancer biomarkers in plasma, but the extraordinary dynamic range in protein composition, and the dilution of disease specific proteins make discovery in plasma challenging. Focus is shifting towards using proximal fluids for biomarker discovery, but methods to verify the isolated sample's origin are missing. We therefore aimed to develop a technique to search for potential candidate proteins in the proximal proteome, i.e. in the tumor interstitial fluid, since the biomarkers are likely to be excreted or derive from the tumor microenvironment. Since tumor interstitial fluid is not readily accessible, we applied a centrifugation method developed in experimental animals and asked whether interstitial fluid from human tissue could be isolated, using ovarian carcinoma as a model. Exposure of extirpated tissue to 106 g enabled tumor fluid isolation. The fluid was verified as interstitial by an isolated fluid:plasma ratio not significantly different from 1.0 for both creatinine and Na(+), two substances predominantly present in interstitial fluid. The isolated fluid had a colloid osmotic pressure 79% of that in plasma, suggesting that there was some sieving of proteins at the capillary wall. Using a proteomic approach we detected 769 proteins in the isolated interstitial fluid, sixfold higher than in patient plasma. We conclude that the isolated fluid represents undiluted interstitial fluid and thus a subproteome with high concentration of locally secreted proteins that may be detected in plasma for diagnostic, therapeutic and prognostic monitoring by targeted methods.     

Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.     

Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma

The plasma proteome has a wide dynamic range of protein concentrations and is dominated by a few highly abundant proteins. Discovery of novel cancer biomarkers using proteomics is particularly challenging because specific biomarkers are expected to be low abundance proteins with normal blood concentrations of low nanograms per milliliter or less. Conventional, one- and two-dimensional proteomic methods including 2D PAGE, 2D DIGE, LC-MS/MS, and LC/LC-MS/MS do not have the capacity to consistently detect many proteins in this range. In contrast, new higher dimensional (Hi-D) separation strategies, utilizing more than two dimensions of fractionation, can profile the low abundance proteome.     

Proteome analysis of bronchoalveolar lavage in lung diseases

The proteomic approach is complementary to genomics and enables protein composition to be investigated under various clinical conditions. Its application to the study of bronchoalveolar lavage (BAL) is extremely promising. BAL proteomic studies were initially based on two-dimensional electrophoretic separation of complex protein samples and subsequent identification of proteins by different methods. With the techniques available today it is possible to attain many different research objectives. BAL proteomics can contribute to the identification of proteins in alveolar spaces with possible insights into pathogenesis and clinical application for diagnosis, prognosis and therapy. Many proteins with different functions have already been identified in BAL. Some could be biomarkers that need to be individually confirmed by correlation with clinical parameters and validation by other methods on larger cohorts of patients. The standardization of BAL sample preparation and processing for proteomic studies is an important goal that would promote and facilitate clinical applications. Here, we review the principal literature on BAL proteomic analysis applied to the study of lung diseases.     

Urinary proteomic profiling for diagnostic bladder cancer biomarkers

The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.     

Investigating pathogen biology at the level of the proteome

Bacterial infections are a major cause of morbidity and mortality throughout the world. By extending our understanding of the process of bacterial pathogenesis at the molecular level new strategies for their treatment and prevention can be developed. Proteomic technologies, along with other methods for global gene expression analysis, play an important role in understanding the mechanism(s) of bacterial pathogenesis. This review highlights the use of proteomics to identify protein biomarkers for virulent bacterial isolates and how these biomarkers can be correlated with the outcome of bacterial infection. Biomarker identification typically looks at the proteomes of bacteria grown under laboratory conditions. It is, however, the characterisation of the bacterial proteome during in vivo infection of its host that will eventually provide the most significant insights into bacterial pathogenesis. Although this area of research has significant technical challenges, a number of complementary proteome analytical approaches are being developed to identify and characterise the bacterial genes specifically expressed in vivo. Ultimately, the development of newly targeted therapies and vaccines using specific protein targets identified through proteomic analyses will be one of the major practical benefits arising from the proteomic analysis of bacterial pathogens.     

Proteomic analysis of cancer tissues: shedding light on carcinogenesis and possible biomarkers

Lung, gastric, colorectal, pancreatic, and esophageal cancers, as well as hepatocellular carcinoma (HCC), were the six most common and highly fatal cancers for Japanese men in Japan in 2003, while for women uterine cervical cancer could also be added to this list. To identify diagnostic or therapeutic biomarkers for these cancers, investigators are nowadays performing proteomic analyses of cancer tissues and cells, and revealing a large number of molecules which are diagnostic, prognostic and informative of carcinogenesis. From reports of proteomic analyses of cancerous tissues and noncancerous tissues sampled from HCC, and pancreatic, esophageal, gastric, colorectal, lung and uterine cervical cancers, we classified the proteins into digestive enzymes, growth factors, cell adhesion molecules, calcium-binding proteins, proteases, protease inhibitors, transporter proteins, structural molecules, apoptosis inhibitor, molecular chaperone, as well as proteins related to cell growth, cell differentiation, cell transformation, tumor invasion, carcinogen metabolism, and others. The aim of this study was to understand carcinogenesis of major cancers from a proteomics perspective using samples from cancer patients, and to elucidate their tumor biomarkers.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Plasma and cerebrospinal fluid-based protein biomarkers for motor neuron disease

Motor neuron diseases (MNDs) and, in particular, amyotrophic lateral sclerosis (ALS), are a heterogeneous group of neurologic disorders characterized by the progressive loss of motor function. In ALS, a selective and relentless degeneration of both upper and lower motor neurons occurs, culminating in mortality typically within 5 years of symptom onset. However, survival rates vary among individual patients and can be from a few months to >10 years from diagnosis. Inadequacies in disease detection and treatment, along with a lack of diagnostic and prognostic tools, have prompted many to turn to proteomics-based biomarker discovery efforts. Proteomics refers to the study of the proteins expressed by a genome at a particular time, and the proteome can respond to and reflect the status of an organism, including health and disease states. Although an emerging field, proteomic applications promise to uncover biomarkers critical for differentiating patients with ALS and other MNDs from healthy individuals and from patients affected by other diseases. Ideally, these studies will also provide mechanistic information to facilitate identification of new drug targets for subsequent therapeutic development. In addition to proper experimental design, standard operating procedures for sample acquisition, preprocessing, and storage must be developed. Biological samples typically analyzed in proteomic studies of neurologic diseases include both plasma and cerebrospinal fluid (CSF). Recent studies have identified individual proteins and/or protein panels from blood plasma and CSF that represent putative biomarkers for ALS, although many of these proteins are not unique to this disease. Continued investigations are required to validate these initial findings and to further pursue the role of these proteins as diagnostic biomarkers or surrogate markers of disease progression. Protein biomarkers specific to ALS will additionally function to evaluate drug efficacy in clinical trials and to identify novel targets for drug design. It is hoped that proteomic technologies will soon integrate the basic biology of ALS with mechanistic disease information to achieve success in the clinical setting.     

A technically detailed and pragmatic protocol for quantitative serum proteomics using iTRAQ

Blood is recognised as a highly important source of disease-related biomarkers, and proteomic approaches for identifying novel blood-borne biomarkers are in demand. The complexity and dynamic protein concentration range of plasma/serum however complicates the analysis process. A number of strategies for simplification of blood prior to proteomic analysis have been developed. In addition, methods for quantifying the levels of proteins in samples, such as isobaric tags for relative and absolute quantification (iTRAQ) are emerging. However, the successful application of these procedures is not always straightforward and technical hurdles must be overcome. Here we provide a technically detailed working protocol for iTRAQ-based quantification of serum proteins following immunodepletion of high abundance proteins. To improve the number of proteins identified and quantified we have introduced several modifications to the standard iTRAQ protocol. We report identifications of 217 proteins (5773 peptides) with a false discovery rate of 1% or 254 proteins with 95% confidence, respectively. Relative quantification data were obtained for 234 (95% confidence) serum proteins, including species present in the concentration range of tissue leakage factors. The samples described here relate to pancreatic cancer; however the protocol can be applied to serum from other control or disease types.     

18O Labeling for a Quantitative Proteomic Analysis of Glycoproteins in Hepatocellular Carcinoma

Introduction

Quantitative proteomics using tandem mass spectrometry is an attractive approach for identification of potential cancer biomarkers. Fractionation of complex tissue samples into subproteomes prior to mass spectrometric analyses increases the likelihood of identifying cancer-specific proteins that might be present in low abundance. In this regard, glycosylated proteins are an interesting class of proteins that are already established as biomarkers for several cancers.

Materials and Methods

In this study, we carried out proteomic profiling of tumor and adjacent non-cancer liver tissues from hepatocellular carcinoma (HCC) patients. Glycoprotein enrichment from liver samples using lectin affinity chromatography and subsequent ¹⁸O/¹⁶O labeling of peptides allowed us to obtain relative abundance levels of lectin-bound proteins. As a complementary approach, we also examined the relative expression of proteins in HCC without glycoprotein enrichment. Lectin affinity enrichment was found to be advantageous to quantitate several interesting proteins, which were not detected in the whole proteome screening approach. We identified and quantitated over 200 proteins from the lectin-based approach. Interesting among these were fetuin, cysteine-rich protein 1, serpin peptidase inhibitor, leucine-rich alpha-2-glycoprotein 1, melanoma cell adhesion molecule, and heparan sulfate proteoglycan-2. Using lectin affinity followed by PNGase F digestion coupled to ¹⁸O labeling, we identified 34 glycosylation sites with consensus sequence N-X-T/S. Western blotting and immunohistochemical staining were carried out for several proteins to confirm mass spectrometry results.

Conclusion

This study indicates that quantitative proteomic profiling of tumor tissue versus non-cancerous tissue is a promising approach for the identification of potential biomarkers for HCC.     

Diverse proteomic alterations in gastric adenocarcinoma

Gastric adenocarcinoma is one of the most common cancers in Asian countries including China. Although its incidence rates in the West are lower than that in Asia, gastric cancer is still a major health problem worldwide, being second only to lung cancers in the number of deaths it causes. Helicobacter pylori infection has been identified as the major pathogen, but the detailed pathogenesis of gastric carcinoma remains elusive. Due to the lack of suitable and specific biomarkers for early detection, most cases of the disease are diagnosed at late stages and the survival rate is low. In this study, we used a proteomic approach to globally analyze the protein profiles of paired surgical specimens of primary gastric adenocarcinoma and nontumor mucosa aiming at identifying specific disease-associated proteins as potential clinical biomarkers and for carcinogenetic study. Compared to nontumor tissues, multiple protein alterations were found in tumor tissues. Some of these alterations involve variations in the expression of cytoskeleton proteins, including an increase in cytokeratin 8 and tropomyosin isoform and a decrease in cytokeratin 20. Co-up-regulations of heat-shock proteins and glycolytic enzymes were observed in tumor tissues, indicating self-protective efforts of cells and the growing energy requirement during malignant transformation. Diverse regulations also occurred with proteins involved in cell proliferation and differentiation, such as GMP reductase 2 and creatine kinase B, and proteins bearing potential tumor suppressor activities, including prohibitin and selenium binding protein 1. More interestingly, a human stomach-specific protein, 18 kDa antrum mucosa protein, was found to be dramatically under-expressed in cancer tissues, implicating a possible special pathological role for this protein in gastric carcinogenesis. Further comprehensive evaluation by globally considering the altered factors may result in the discovery of a biomarker index for effective assessment of the disease and may provide in-depth information for better understanding the pathogenesis of gastric cancer.