Identification of gel-separated tumor marker proteins by mass spectrometry

The relationship between postmortem interval and protein changes in mice

Postmortem interval (PMI) estimation is important for forensic pathological autopsy. It has been reported that there is a correlation between certain protein changes in cadavers and PMI. However, no specific protein(s) has been used to determine the PMI so far. In this study, the total protein contents of mouse liver and spleen at different time of death were measured. The data showed that they were negatively correlated with the PMI. The degradation of β-actin was found to be positively correlated with the PMI in the liver. Additionally, proteomic technique was used to study the changes of protein expression related to PMI in the liver of mice. By using Two-dimensional electrophoresis, the expressions of four proteins were found to be significantly decreased and those of other three proteins were unchanged with the increase of PMI. Among the seven proteins, six were identified with peptide mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The three altered proteins were SBP2, ENOA, ALDH2 and three unchanged ones were 3HAO, TPIS, CATA, respectively. In the future, those unchanged proteins could be used as internal references to more accurately and reliably infer the time of death by assessing the level of changed proteins.

Using Antibody Arrays for Biomarker Discovery

Biomarkers for diseases are important for the development of clinical diagnostic tests and can provide early intervention for cancer or cardiovascular patients. Over the past decade, antibody array technology has achieved significant technological improvement in the quantitative measurement of more than a thousand proteins simultaneously and has been utilized to screen and identify unique proteins as disease biomarkers. However, few biomarkers have been translated into clinical application. This chapter will discuss the protocol for the screening and validation of unique proteins that create a new avenue for biomarker discovery.

Gemfibrozil reduces lipid accumulation in SMMC-7721 cells via the involvement of PPARα and SREBP1

Gemfibrozil (GEM) is a member of the fibrate class of lipid-lowering pharmaceuticals and has been widely used in the therapy of different forms of hyperlipidemia and hypercholesterolemia. Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease and is becoming an important public health concern worldwide. However, there is little knowledge about the effects of GEM on NAFLD. In the present study, oleate-treated human hepatoma SMMC-7721 cells were utilized to investigate the role of GEM in regulating hepatic lipid metabolism. The present results demonstrated that GEM attenuated excessive intracellular triglyceride content in the steatosis model. Upregulation of peroxisome proliferator-activated receptor α (PPARα) protein and sterol regulatory element-binding protein 1 (SREBP1) was detected following treatment with GEM. Additionally, reverse transcription-polymerase chain reaction analysis demonstrated that GEM increased the downstream genes related to PPARα and SREBP1, including carnitine palmitoyltransferase 2, acyl-coA oxidase 1, hydroxyacyl-CoA dehydrogenase, LIPIN1 and diacylglycerol O-acyltransferase 1. These findings demonstrated that GEM alleviated hepatic steatosis via the involvement of the PPARα and SREBP1 signaling pathways, which enhances lipid oxidation and interferes with lipid synthesis and secretion. Taken together, the data provide direct evidence that GEM may lower lipid accumulation in hepatocellular steatosis cells in vitro and that it may have a potential therapeutic use for NAFLD.

Antitumor effects of calgranulin B internalized in human colon cancer cells

Calgranulin B is a small, calcium-binding protein expressed in neutrophils that is secreted into the tumor microenvironment in cancer cases. We previously showed that calgranulin B levels are increased in the stools of colorectal cancer patients. In patient tumor tissues, calgranulin B protein levels correlated with the presence of stromal inflammatory cells surrounding tumor cells, and calgranulin B promoter methylation was observed in both paired human tissues and colon cancer cell lines. Cell lines did not express calgranulin B, but in vitro studies showed that colon cancer cells internalized extracellular calgranulin B, while other types of cancer cells did not. Calgranulin B internalization led to reduced cell proliferation and increased apoptotic cell death. AKT and ERK signals were also increased after calgranulin B treatment, as were p53, β-catenin, E-cadherin and cleaved caspase-3 levels. Additionally, a human protein microarray identified aurora A kinase as a calgranulin B binding partner, and binding inhibited aurora A kinase activity in a dose-dependent manner. Our findings demonstrate the antitumor effects of calgranulin B in the inflammatory microenvironment and suggest that calgranulin B could be potentially efficacious in the treatment of colon cancer.

Inhibition of type I insulin-like growth factor receptor tyrosine kinase by picropodophyllin induces apoptosis and cell cycle arrest in T lymphoblastic leukemia/lymphoma

It has been recently shown that the type I insulin-like growth factor receptor (IGF-IR) contributes significantly to the survival of T lymphoblastic leukemia/lymphoma (T-LBL) cells, and it was therefore suggested that IGF-IR could represent a legitimate therapeutic target in this aggressive disease. Picropodophyllin (PPP) is a potent, selective inhibitor of IGF-IR that is currently used with notable success in clinical trials that include patients with aggressive types of epithelial tumors. In the present study, we tested the effects of PPP on Jurkat and Molt-3 cells; two prototype T-LBL cell lines. Our results demonstrate that PPP efficiently induced apoptotic cell death and cell cycle arrest of these two cells. These effects were attributable to alterations of downstream target proteins. By using proteomic analysis, seven different proteins were found to be affected by PPP treatment of Jurkat cells. These proteins are involved in various aspects of cellular metabolism, cytoskeleton organization and signal transduction pathways. The results suggest that PPP affects multiple signaling molecules and inhibits fundamental pathways that control cell growth and survival. Our study also provides novel evidence that PPP could be potentially utilized for the treatment of aggressive T-LBL.

Tropomyosin-4 correlates with higher SBR grades and tubular differentiation in infiltrating ductal breast carcinomas: an immunohistochemical and proteomics-based study

The aim of this study is to evaluate tropomyosin-4 (TM4) expression in infiltrating ductal breast carcinomas (IDCAs), as well as its prognostic significance. Using a 2-DE/MALDI-TOF mass spectrometry investigation coupled with an immunohistochemical approach, we have assessed the expression of TM4 in IDCAs, as well as in other types of breast tumors. Proteomic analyses revealed an increased expression of tropomyosin-4 in IDCA tumors. Using immunohistochemistry, overexpression of tropomyosin-4 was confirmed in 51 additional tumor specimens. Statistical analyses revealed, however, no significant correlations between tropomyosin-4 expression and clinicopathological parameters of the disease including tumor stage, patient age, estrogen and progesterone receptor status, and lymph node metastasis occurrence. A significant association was found, however, with a high Scarf-Bloom-Richardson (SBR) grade, a known marker of tumor severity. Additionally, the SBR component showing a correlation with TM4 expression was the tubular differentiation status. This study demonstrates the upregulation of tropomyosin-4 in IDCA tissues, which may highlight its involvement in breast cancer development. Our findings also support a link between tropomyosin-4 expression and aggressiveness of IDCA tumors.

Mass spectrometry-based proteomics: the road to lung cancer biomarker discovery

Lung cancer is the leading cause of cancer death in men and women in Western nations, and is among the deadliest cancers with a 5-year survival rate of 15%. The high mortality caused by lung cancer is attributable to a late-stage diagnosis and the lack of effective treatments. So, it is crucial to identify new biomarkers that could function not only to detect lung cancer at an early stage but also to shed light on the molecular mechanisms that underlie cancer development and serve as the basis for the development of novel therapeutic strategies. Considering that DNA-based biomarkers for lung cancer showed inadequate sensitivity, specificity, and reproducibility, proteomics could represent a better tool for the identification of useful biomarkers and therapeutic targets for this cancer type. Among the proteomics technologies, the most powerful tool is mass spectrometry. In this review, we describe studies that use mass spectrometry-based proteomics technologies to analyze tumor proteins and peptides, which might represent new diagnostic, prognostic, and predictive markers for lung cancer. We focus in particular on those findings that hold promise to impact significantly on the clinical management of this disease.

Calreticulin expression in infiltrating ductal breast carcinomas: relationships with disease progression and humoral immune responses

The aim of this study was to evaluate calreticulin expression in infiltrating ductal breast carcinomas (IDCAs), as well as its relationships with clinicopathological parameters of the disease. Using a two-dimensional gel electrophoresis/matrix-assisted laser desorption ionization time of flight mass spectrometry investigation coupled to an immunohistochemical approach, we have assessed the expression of calreticulin in IDCAs, as well as in other types of breast tumors. The humoral immune response against calreticulin was estimated using a serological proteomics-based strategy. Proteomic analyses revealed an increased expression of calreticulin in IDCA tumors. Using immunohistochemistry, overexpression of calreticulin was confirmed in 51 additional tumor specimens. Statistical analyses revealed, however, no significant correlations between calreticulin expression and clinicopathological parameters of the disease including tumor stage, patient age, SBR grade, and lymph node metastasis occurrence. A significant association was found, however, with estrogen receptor status. This study demonstrates the upregulation of calreticulin in IDCA tissues which may highlight its involvement in breast cancer development. Our findings also support a link between calreticulin expression and estrogen transduction pathways. Our results do not, however, support the involvement of calreticulin in the development of a humoral immune response in IDCAs.

Proteomic Changes Induced by Podophyllotoxin in Human Cervical Carcinoma HeLa Cells

Podophyllotoxin, a kind of lignan extracted from the Podophyllum plant, has been shown to inhibit the growth of various carcinoma cells. However, the molecular mechanism remains unclear. In this study, the inhibition of cell growth and changes in protein expression induced by podophyllotoxin were investigated in human cervical carcinoma HeLa cells. Our results demonstrate that Podophyllotoxin inhibits HeLa cell growth and induces apoptosis. By using proteomic techniques, seven proteins were found to be significantly regulated by podophyllotoxin compared to the untreated control; among them, four were down-regulated and three were up-regulated. All of the seven proteins were identified with peptide mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) after in-gel trypsin digestion. Five of these proteins are involved in protein metabolism, and the other two play roles in cell communication and signaling transduction pathways. It is suggested that the effect of podophyllotoxin on the growth of tumor cells is significantly related to the metabolism-associated proteins.

Contribution of proteomics to the study of the role of cytokeratins in disease and physiopathology

Cytokeratins (CKs), the most abundant group of cytoskeletal intermediate filaments, and proteomics are strongly connected. On the one hand, proteomics has been extremely useful to uncover new features and functions of CKs, on the other, the highly abundant CKs serve as an exceptional tool to test new technological developments in proteomics. As a result, proteomics has contributed to finding valuable associations of CKs with diseases as diverse as cancer, cystic fibrosis, steatohepatitis, viral and bacterial infection, keratoconus, vitreoretinopathy, preeclampsia or the chronic fatigue syndrome, as well as to characterizing their participation in a number of physiopathological processes, including drug resistance, response to toxicants, inflammation, stem cell differentiation, embryo development, and tissue repair. In some cases, like in cystic fibrosis, CKs have been described as potential therapeutic targets. The development of a specific field of proteomics where CKs become the main subject of research aims and hypotheses is suggested.

In-depth proteomic analysis of nonsmall cell lung cancer to discover molecular targets and candidate biomarkers

Advances in proteomic analysis of human samples are driving critical aspects of biomarker discovery and the identification of molecular pathways involved in disease etiology. Toward that end, in this report we are the first to use a standardized shotgun proteomic analysis method for in-depth tissue protein profiling of the two major subtypes of nonsmall cell lung cancer and normal lung tissues. We identified 3621 proteins from the analysis of pooled human samples of squamous cell carcinoma, adenocarcinoma, and control specimens. In addition to proteins previously shown to be implicated in lung cancer, we have identified new pathways and multiple new differentially expressed proteins of potential interest as therapeutic targets or diagnostic biomarkers, including some that were not identified by transcriptome profiling. Up-regulation of these proteins was confirmed by multiple reaction monitoring mass spectrometry. A subset of these proteins was found to be detectable and differentially present in the peripheral blood of cases and matched controls. Label-free shotgun proteomic analysis allows definition of lung tumor proteomes, identification of biomarker candidates, and potential targets for therapy.

Carbon nanotubes-assisted polyacrylamide gel electrophoresis for enhanced separation of human serum proteins and application in liverish diagnosis

The application of pore-gradient polyacrylamide gel electrophoresis (PG-PAGE) incorporated with carbon nanotube modified by Triton X-100 and carboxylation so as to improve the separation of human serum proteins is reported. The novel PG-PAGE was made by adding water-soluble single-walled carbon nanotubes (CNTs) when preparing the polyacrylamide gel. Significant improvements in separation of complement C3 protein and haptoglobin (Hp) in human serum were achieved. It was estimated that the interactions between the hydrophilic groups on the proteins and the surface of the CNTs result in different adsorption kinetics of complement C3 and Hp subtype on the nanoparticles incorporated in the gel, thus enhancing the separation of the two proteins in serum. This new CNT matrix-assisted PG-PAGE method for enhanced separation of complement C3 and Hp in human serum was successfully applied to distinguish the samples from liverish patients and healthy people.

[The therapeutic contribution of proteomic approaches in cancers]

Up to now, there are no protein tumor markers with a specificity and sensitivity sufficient to have a utility in prognosis and early diagnosis of cancer. Recent advances in proteomics approaches have led to the identification of novel tumor markers of cancer that may have a utility in screening strategies and treatment. The purpose of the current review is to describe the major advances in cancer proteomics, especially those related to the study of serum biomarkers, immune-related responses (autoantibodies) and alterations in cellular proteins.

Utilization of cytokeratin-based biomarkers for pharmacodynamic studies

Cytokeratin (CK)18 is a useful serum biomarker for the determination of cell death of epithelial-derived tumors (carcinomas). ELISAs are available for caspase-cleaved CK18 (M30) released from apoptotic cells, or total CK18 (M65) released by cells undergoing cell death by any cause. These assays have been demonstrated to have prognostic or predictive utility in various types of carcinomas. Encouraging data have been reported by different investigators with regard to the potential use of CK18 as a serum efficacy biomarker for monitoring therapy efficiency in carcinoma patients. The ratio of caspase-cleaved to total CK18 can be determined conveniently in serum or plasma using commercially available ELISA kits (M30-Apoptosense and M65 ELISA, Peviva AB, Bromma, Sweden). M30:M65 ratios potentially provide information as to whether tumor cells undergo apoptosis or necrosis. However, as discussed in this review, M30:M65 ratios should be interpreted with caution and, preferably, only be applied to samples that contain significant levels of CK18. We conclude that M30 and M65 biomarkers provide both quantitative and qualitative information on carcinoma cell death.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

During the last decade, high-throughput technologies including genomic, epigenomic, transcriptomic and proteomic have been applied to further our understanding of the molecular pathogenesis of this heterogeneous disease, and to develop strategies that aim to improve the management of patients with lung cancer. Ultimately, these approaches should lead to sensitive, specific and noninvasive methods for early diagnosis, and facilitate the prediction of response to therapy and outcome, as well as the identification of potential novel therapeutic targets. Genomic studies were the first to move this field forward by providing novel insights into the molecular biology of lung cancer and by generating candidate biomarkers of disease progression. Lung carcinogenesis is driven by genetic and epigenetic alterations that cause aberrant gene function; however, the challenge remains to pinpoint the key regulatory control mechanisms and to distinguish driver from passenger alterations that may have a small but additive effect on cancer development. Epigenetic regulation by DNA methylation and histone modifications modulate chromatin structure and, in turn, either activate or silence gene expression. Proteomic approaches critically complement these molecular studies, as the phenotype of a cancer cell is determined by proteins and cannot be predicted by genomics or transcriptomics alone. The present article focuses on the technological platforms available and some proposed clinical applications. We illustrate herein how the "-omics" have revolutionised our approach to lung cancer biology and hold promise for personalised management of lung cancer.

Mass spectrometry-based proteomic profiling of lung cancer

In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and high-throughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.

Clinical proteomics and breast cancer: strategies for diagnostic and therapeutic biomarker discovery

A major challenge of breast cancer research is the identification of accurate biomarkers that improve screening, early diagnosis, prediction of aggressiveness, and prediction of therapeutic response or toxicity, as well as the identification of new molecular therapeutic targets. The new proteomic techniques promise to be valuable for identifying such tissue and serum markers. The different techniques currently applied to clinical samples of breast cancer and the most important results obtained are summarized in this review.

Proteomics in cancer

Proteomic studies have generated numerous datasets of potential diagnostic, prognostic, and therapeutic significance in human cancer. Two key technologies underpinning these studies in cancer tissue are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Although surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-MS is the mainstay for serum or plasma analysis, other methods including isotope-coded affinity tag technology, reverse-phase protein arrays, and antibody microarrays are emerging as alternative proteomic technologies. Because there is little overlap between studies conducted with these approaches, confirmation of these advanced technologies remains an elusive goal. This problem is further exacerbated by lack of uniform patient inclusion and exclusion criteria, low patient numbers, poor supporting clinical data, absence of standardized sample preparation, and limited analytical reproducibility (in particular of 2D-PAGE). Despite these problems, there is little doubt that the proteomic approach has the potential to identify novel diagnostic biomarkers in cancer. In therapeutic proteomics, the challenge is significant due to the complexity systems under investigation (i.e., cells generate over 10(5) different polypeptides). However, the most significant contribution of therapeutic proteomics research is expected to derive not from single experiments, but from the synthesis and comparison of large datasets obtained under different conditions (e.g., normal, inflammation, cancer) and in different tissues and organs. Thus, standardized processes for storing and retrieving data obtained with different technologies by different research groups will have to be developed. Shifting the emphasis of cancer proteomics from technology development and data generation to careful study design, data organization, formatting, and mining is crucial to answer clinical questions in cancer research.

Proteomics analysis in lung cancer: challenges and opportunities

Recent technological developments in proteomic analysis are bringing us new insights into the molecular classification of tumours. Although proteomic analysis in cancer profiling is still under development both in terms of the instruments used and the data analytical tools, this method has great potential advantages for the analysis of biospecimens of many types. Direct measurement of abnormally expressed or modified proteins in the tumour tissue and/or patient blood may be an effective approach for discovering new biomarkers. Proteomics has the significant advantage of being able to discern not only changes in expression levels but also in post-translational modifications. Thus, the proteomics approach to protein profiling and biomarker discovery uncovers biomarkers from a different viewpoint than microarray analysis. This review summarizes the range of proteomics technologies employed for cancer profiling, and how they have been used to derive new classification models for human lung cancer.

Classifications of ovarian cancer tissues by proteomic patterns

Ovarian cancer is a morphologically and biologically heterogeneous disease. The identification of type-specific protein markers for ovarian cancer would provide the basis for more tailored treatments, as well as clues for understanding the molecular mechanisms governing cancer progression. In the present study, we used a novel approach to classify 24 ovarian cancer tissue samples based on the proteomic pattern of each sample. The method involved fractionation according to pI using chromatofocusing with analytical columns in the first dimension followed by separation of the proteins in each pI fraction using nonporous RP HPLC, which was coupled to an ESI-TOF mass analyzer for molecular weight (MW) analysis. A 2-D mass map of the protein content of each type of ovarian cancer tissue samples based upon pI versus intact protein MW was generated. Using this method, the clear cell and serous ovarian carcinoma samples were histologically distinguished by principal component analysis and clustering analysis based on their protein expression profiles and subtype-specific biomarker candidates of ovarian cancers were identified, which could be further investigated for future clinical study.

Biomarkers in cancer screening, research and detection: present and future: a review

Biomarkers provide a powerful and dynamic approach to understanding the spectrum of malignancies with applications in observational and analytic epidemiology, randomized clinical trials, screening, diagnosis and prognosis. Defined as alterations in the constituents of tissues or body fluids, these markers offer a means for homogeneous classification of a disease and risk factor, and they can extend one's basic information about the underlying pathogenesis of disease. The goals in cancer research include finding biomarkers that can be used for the early detection of cancers, design individual therapies, and to identify underlying processes involved in the disease. Because so many myriad processes are involved in the diseased states, the goal is similar to 'finding a needle in a haystack'. However, the development of many -omic technologies, such as genomics and proteomics, has allowed us to monitor a large number of key cellular pathways simultaneously. This has enabled the identification of biomarkers and signalling molecules associated with cell growth, cell death and cellular metabolism. These are also facilitating in monitoring the functional disturbance, molecular and cellular damage, and damage response. This brief review describes the development of biomarkers in cancer research and detection with emphasis on different proteomic tools for the identification and discovery of new biomarkers, different clinical assays to detect various biomarkers in different specimens, role of biomarkers in cancer screening and last but not the least, the challenges in this direction of cancer research.

Comparative proteomic analysis of cisplatin sensitive IGROV1 ovarian carcinoma cell line and its resistant counterpart IGROV1-R10

Ovarian cancer is one of the leading causes of mortality due to gynaecological cancer. Despite a good response to surgery and initial chemotherapy essentially based on cisplatin (cis-diamino-dichloro-platinum(II) (CDDP)) compounds, late tumour detection and frequent recurrences with chemoresistance acquisition are responsible for poor prognosis. Several mechanisms have been implicated in CDDP resistance but they are not sufficient to exhaustively explain this resistance emergence. We applied a proteomic approach based on 2-DE coupled with MS to identify proteins associated with the chemoresistance process. We first established a proteomic pattern of the CDDP sensitive ovarian cell line IGROV1 using MALDI-TOF-MS and PMF. We then compared this 2-D pattern with that of the CDDP-resistant counterpart IGROV1-R10. Among the 40 proteins identified, cytokeratins 8 and 18 and aldehyde dehydrogenase 1 were overexpressed in IGROV1-R10, whereas annexin IV was down-regulated. These observations have been confirmed by Western blotting. The characterization of such variations could lead to the development of new protein markers or to the establishment of new therapeutic strategies. Moreover, the identification of proteins involved in CDDP resistance in ovarian tumours would be useful in completing our understanding on this complex mechanism.

Proteomics of breast cancer: principles and potential clinical applications

Progresses in screening, early diagnosis, prediction of aggressiveness and of therapeutic response or toxicity, and identification of new targets for therapeutic will improve survival of breast cancer. These progresses will likely be accelerated by the new proteomic techniques. In this review, we describe the different techniques currently applied to clinical samples of breast cancer and the most important results obtained with the two most popular proteomic approaches in translational research (tissue microarrays and SELDI-TOF).

Proteomic analysis of primary esophageal squamous cell carcinoma reveals downregulation of a cell adhesion protein, periplakin

Recent advances in two-dimensional electrophoresis (2-DE) such as fluorescent 2-D differential gel electrophoresis (2-D DIGE) has made it possible to detect and quantitate the critical changes involved in disease pathogenesis. We have previously identified novel proteins with altered expression in primary colorectal cancer using agarose 2-DE that has a higher loading capacity than immobilized pH gradient gel. The aim of this study is to identify novel proteins with altered expression in primary esophageal cancer using the powerful method of agarose 2-DE and agarose 2-D DIGE. Excised tissues from 12 patients of primary esophageal cancer were obtained. Proteins with altered expression between cancer and adjacent non-cancer tissues were analyzed by agarose 2-D DIGE and identified by mass spectrometry. Thirty-three proteins out of 74 spots with altered expression in tumors were identified. Among them, a 195-kDa protein, periplakin, was significantly downregulated in esophageal cancer, which was confirmed by immunoblotting. Immunohistochemistry showed that periplakin was mainly localized at cell-cell boundaries in normal epithelium and dysplastic lesions, while it disappeared from cell boundaries, shifted to cytoplasm, in early cancers and scarcely expressed in advanced cancers. These results suggest that periplakin could be a useful marker for detection of early esophageal cancer and evaluation of tumor progression.

Proteome profiling of human epithelial ovarian cancer cell line TOV-112D

A proteome profiling of the epithelial ovarian cancer cell line TOV-112D was initiated as a protein expression reference in the study of ovarian cancer. Two complementary proteomic approaches were used in order to maximise protein identification: two-dimensional gel electrophoresis (2DE) protein separation coupled to matrix assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and one-dimensional gel electrophoresis (1DE) coupled to liquid-chromatography tandem mass spectrometry (LC MS/MS). One hundred and seventy-two proteins have been identified among 288 spots selected on two-dimensional gels and a total of 579 proteins were identified with the 1DE LC MS/MS approach. This proteome profiling covers a wide range of protein expression and identifies several proteins known for their oncogenic properties. Bioinformatics tools were used to mine databases in order to determine whether the identified proteins have previously been implicated in pathways associated with carcinogenesis or cell proliferation. Indeed, several of the proteins have been reported to be specific ovarian cancer markers while others are common to many tumorigenic tissues or proliferating cells. The diversity of proteins found and their association with known oncogenic pathways validate this proteomic approach. The proteome 2D map of the TOV-112D cell line will provide a valuable resource in studies on differential protein expression of human ovarian carcinomas while the 1DE LC MS/MS approach gives a picture of the actual protein profile of the TOV-112D cell line. This work represents one of the most complete ovarian protein expression analysis reports to date and the first comparative study of gene expression profiling and proteomic patterns in ovarian cancer.

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.

Proteomic approaches in the search for disease biomarkers

Significant technological advances in protein chemistry, physics and computer sciences in the last two decades have greatly improved protein separation methodologies, such as electrophoresis and chromatography, and have established mass spectrometry (MS) as an indispensable tool for protein study. The goal of this review is to provide a brief overview of the recent improvements in these methodologies and present examples from their application in proteome analysis and search for disease biomarkers.

Genomic and Proteomic Profiling of Lung Cancers: Lung Cancer Classification in the Age of Targeted Therapy

Both proteomic and genomic methods offer promise for the classification of human lung carcinomas. This review summarizes the range of proteomic methods in development for lung cancer classification, and describes a number of recent analyses of messenger RNA expression in lung cancer. Multiple independent studies of mRNA expression profiles in lung adenocarcinoma have proven highly reproducible. Analyses of the relationship between expression profiles and tumor development and differentiation, the presence or absence of specific pathogenic mutations, patient prognosis and survival after surgical treatment, and specific histopathology all appear to be promising.

Proteomic detection of changes in protein synthesis induced by lanthanum in BGC-823 human gastric cancer cells

There is increasing interest in the use of rare earth elements in medicine. However, the biological mechanism of action of this metal ion remains unclear. In the present study, changes in protein synthesis induced by lanthanum in BGC-823 human gastric cancer cells were investigated. The proteins were separated using two-dimensional polyacrylamide gel electrophoresis and four proteins were significantly affected by lanthanum treatment when compared to an untreated control. Among them, one was down-regulated and three were up-regulated. Of these, three were successfully identified as RHOJ, CSP6 and MPI2 with peptide mass fingerprinting using matrix-assisted laser desorption/ionization time of flight mass spectrometer (MALDI-TOF-MS) after in-gel trypsin digestion. Among them, RHOJ was down-regulated and CSP6 and MPI2 were up-regulated. The three proteins are involved in various cellular functions, which are correlated with the regulation of cell morphology, gene transcription and cell cycle, respectively. It is suggested that the possible involvement of rare earth elements in the growth arrest of tumor cells is significantly associated with the differential protein expression induced by rare earth ions.

Proteomic patterns: their potential for disease diagnosis

Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and as such are often useful diagnostic and prognostic biomarkers. The underlying mechanism of diseases such as cancer are, however, quite complicated in that often multiple dysregulated proteins are involved. It is for this reason that recent hypotheses suggest that detection of panels of biomarkers may provide higher sensitivities and specificities for disease diagnosis than is afforded with single markers. Recently, a novel approach based on the analysis of protein patterns has emerged that may provide a more effective means to diagnose diseases, such as ovarian and prostate cancer. The method is based on the use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOF-MS) to detect differentially captured proteins from clinical samples, such as serum and plasma. This analysis results in the detection of "proteomic" patterns that have been shown in recent investigations to distinguish diseased and unaffected subjects to varying degrees. This review will discuss the basics of SELDI protein chip technology and highlight its recent applications in disease biomarker discovery with emphasis on cancer diagnosis.

Data mining techniques for cancer detection using serum proteomic profiling

OBJECTIVE

Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. It is possible that unique serum proteomic patterns could be used to discriminate cancer samples from non-cancer ones. Due to the complexity of proteomic profiling, a higher order analysis such as data mining is needed to uncover the differences in complex proteomic patterns. The objectives of this paper are (1) to briefly review the application of data mining techniques in proteomics for cancer detection/diagnosis; (2) to explore a novel analytic method with different feature selection methods; (3) to compare the results obtained on different datasets and that reported by Petricoin et al. in terms of detection performance and selected proteomic patterns.

METHODS AND MATERIAL

Three serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. A support vector machine-based method is applied in this study, in which statistical testing and genetic algorithm-based methods are used for feature selection respectively. Leave-one-out cross validation with receiver operating characteristic (ROC) curve is used for evaluation and comparison of cancer detection performance.

RESULTS AND CONCLUSIONS

The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the classification using features selected by the genetic algorithm consistently outperformed those selected by statistical testing in terms of accuracy and robustness; (3) the discriminatory features (proteomic patterns) can be very different from one selection method to another. In other words, the pattern selection and its classification efficiency are highly classifier dependent. Therefore, when using data mining techniques, the discrimination of cancer from normal does not depend solely upon the identity and origination of cancer-related proteins.

Protein expression patterns in primary carcinoma of the vagina

Protein patterns in six samples from primary vaginal cancers, in five from normal vaginal tissue and in five primary cervical cancers, were analysed using two-dimensional polyacrylamide gel electrophoresis (2-DE). Protein expression profile was evaluated by computer-assisted image analysis (PDQUEST) and proteins were subsequently identified using matrix-assisted laser desorption/ionisation mass spectrometry. The aim was to analyse the protein expression profiles using the hierarchical clustering method in vaginal carcinoma and to compare them with the protein pattern in cervical carcinoma in order to find a helpful tool for correct classification and for increased biomedical knowledge. Protein expression data of a distinct set of 33 protein spots were differentially expressed. These differences were statistically significant (Mann-Whitney signed-Ranked Test, P<0.05) between normal tissue, vaginal and cervical cancer. Furthermore, protein profiles of pairs of primary vaginal and cervical cancers were found to be very similar. Some of the protein spots that have so far been identified include Tropomyosin 1, cytokeratin 5, 15 and 17, Apolipoprotein A1, Annexin V, Glutathione-S-transferase. Others are the stress-related proteins, calreticulin, HSP 27 and HSP 70. We conclude that cluster analysis of proteomics data allows accurate discrimination between normal vaginal mucosa, primary vaginal and primary cervical cancer. However, vaginal and cervical carcinomas also appear to be relatively homogeneous in their gene expression, indicating similar carcinogenic pathways. There might, further, be a possibility to identify tumour-specific markers among the proteins that are differentially expressed. The results from this study have to be confirmed by more comprehensive studies in the future.

Identification of altered protein expression and post-translational modifications in primary colorectal cancer by using agarose two-dimensional gel electrophoresis

PURPOSE

Although numerous proteome studies have been performed recently to identify cancer-related changes in protein expression, only a limited display of relatively abundant proteins has been identified. The aim of this study is to identify novel proteins as potential tumor markers in primary colorectal cancer tissues using a high-resolution two-dimensional gel electrophoresis (2-DE).

EXPERIMENTAL DESIGN

2-DE using an agarose gel for isoelectric focusing was used to compare protein profiling of 10 colorectal cancer tissues and adjacent normal mucosa. Altered expression and post-translational modification of several proteins were examined using Western blot analysis and immunohistochemistry.

RESULTS

Ninety-seven proteins of 107 spots (90.7%) that were differentially expressed between matched normal and tumor tissues were identified by mass spectrometry. Among them, 42 unique proteins (49 spots) significantly increased or decreased in the tumors. They include eukaryotic translation initiation factor 4H, inorganic pyrophosphatase, anterior gradient 2 homologue, aldolase A, and chloride intracellular channel 1, whose elevated expression in tumor tissues was confirmed by Western blot analysis and immunohistochemistry. Interestingly, only isoform 1 of two transcript variants of eukaryotic translation initiation factor 4H was greatly up-regulated in most of the tumor tissues. Moreover, post-translational modifications of the prolyl-4-hydroxylase beta subunit and annexin A2 also were identified.

CONCLUSIONS

We identified several novel proteins with altered expression in primary colorectal cancer using agarose 2-DE. This method is a powerful technique with which to search for not only quantitative but also qualitative changes in a biological process of interest and may contribute to the deeper understanding of underlying mechanisms of human cancer.

Comparison of proteomic and genomic analyses of the human breast cancer cell line T47D and the antiestrogen-resistant derivative T47D-r

In search of novel mechanisms leading to the development of antiestrogen-resistance in human breast tumors, we analyzed differences in the gene and protein expression pattern of the human breast carcinoma cell line T47D and its derivative T47D-r, which is resistant toward the pure antiestrogen ZM 182780 (Faslodex trade mark, fulvestrant). Affymetrix DNA chip hybridizations on the commercially available HuGeneFL and Hu95A arrays were carried out in parallel to the proteomics analysis where the total cellular protein content of T47D or T47D-r was separated on two-dimensional gels. Thirty-eight proteins were found to be reproducibly up- or down-regulated more than 2-fold in T47D-r versus T47D in the proteomics analysis. Comparison with differential mRNA analysis revealed that 19 of these were up- or down-regulated in parallel with the corresponding mRNA molecules, among which are the protease cathepsin D, the GTPases Rab11a and MxA, and the secreted protein hAG-2. For 11 proteins, the corresponding mRNA was not found to be differentially expressed, and for eight proteins an inverse regulation was found at the mRNA level. In summary, mRNA expression data, when combined with proteomic information, provide a more detailed picture of how breast cancer cells are altered in their antiestrogen-resistant compared with the antiestrogen-sensitive state.

Molecular Fingerprinting in Human Lung Cancer

The behavior and outcome of lung cancers are highly variable, and not only is the molecular basis of this variability unknown, but neither standard histopathology nor currently available molecular markers can predict these characteristics. Accordingly, the identification of novel biomarkers to differentiate tumor from normal cells and predict tumor behavior such as pathologic stage, response to chemotherapy, and site of relapse, is of great importance in clinical practice. None of the hundreds of single markers evaluated to date have demonstrated significant clinical utility, but by surveying thousands of genes at once with use of microarrays or proteomic technologies, it is now possible to read the molecular signature of an individual patient's tumor. When the signature is mathematically analyzed, new classes of cancer can be observed and insight can be gained into prediction, prognosis, and mechanism. Although some success has been achieved with genomic approaches, proteomics-based approaches allow examination of expressed proteins of a tissue or cell type, complement the genome initiatives, and are increasingly being used to address biomedical questions. This review aims to summarize the state of the art of gene and protein expression profiling for non-small-cell lung cancer

Current perspectives in cancer proteomics

Proteome technology has been used widely in cancer research and is a useful tool for the identification of new cancer markers and treatment-related changes in cancer. This article details the use of proteome technology in cancer research, and laboratory-based and clinical cancer research studies are described. New developments in proteome technology that enable higher sample-throughput are evaluated and methods for enhancing conventional proteome analysis (based on two-dimensional electrophoresis) discussed. The need to couple laboratory-based proteomics research with clinically relevant models of the disease is also considered, as this remains the next main challenge of cancer-related proteome research.

A protein molecular weight map of ES2 clear cell ovarian carcinoma cells using a two-dimensional liquid separations/mass mapping technique

A molecular weight map of the protein content of ES2 human clear cell ovarian carcinoma cells has been produced using a two-dimensional (2-D) liquid separations/mass mapping technique. This method uses a 2-D liquid separation of proteins from whole cell lysates coupled on-line to an electrospray ionization-time of flight (ESI-TOF) mass spectrometer to map the accurate intact molecular weight (M(r)) of the protein content of the cells. The two separation dimensions involve the use of liquid isoelectric focusing as the first phase and nonporous silica reversed-phase high-performance liquid chromatography (HPLC) as the second phase of separation. The detection by ESI-TOF-MS provides an image of pI versus M(r) analogous to 2-D gel electrophoresis. Each protein is then identified based upon matrix-assisted laser desorption/ionization (MALDI)-TOF-MS peptide mapping and intact M(r) so that a standard map is produced against which other ovarian carcinoma cell lines can be compared. The accurate intact M(r) together with the pI fraction, and peptide map serve to tag the protein for future interlysate comparisons. An internal standard is also used to provide a means for quantitation for future interlysate studies. In the ES2 cell line under study it is shown that nearly 900 M(r) bands are detected over 17 pI fractions from pH 4 to 12 and a M(r) range up to 85 kDa and that around 290 of these bands can be identified using mass spectrometric based techniques. The protein M(r) is detected within an accuracy of 150 ppm and it is shown that many of the proteins in this human cancer sample are modified compared to the database. The protein M(r) map may serve as a highly reproducible standard Web-based method for comparing proteins from related human cell lines.

Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regression tree (CART), would be effective in discriminating ovarian cancer from benign diseases and healthy controls. Serum protein mass spectrum profiles from 139 patients with either ovarian cancer, benign pelvic diseases, or healthy women were analyzed using the BPS software. A decision tree, using five protein peaks, resulted in an accuracy of 81.5% in the cross-validation analysis and 80% in a blinded set of samples in differentiating the ovarian cancer from the control groups. The potential, advantages, and drawbacks of the BPS system as a bioinformatic tool for the analysis of the SELDI high-dimensional proteomic data are discussed.

Postgenomics: Proteomics and Bioinformatics in Cancer Research

Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease.

Two-dimensional liquid separations-mass mapping of proteins from human cancer cell lysates

A review of two-dimensional (2D) liquid separation methods used in our laboratory to map the protein content of human cancer cells is presented herein. The methods discussed include various means of fractionating proteins according to isoelectric point (pI) in the first dimension. The proteins in each pI fraction are subsequently separated using nonporous (NPS) reversed-phase high-performance liquid chromatography (RP-HPLC). The liquid eluent of the RP-HPLC separation is directed on-line into an electrospray ionization time-of-flight (ESI-TOF) mass spectrometer where an accurate value of the protein intact M(r) can be obtained. The result is a 2D map of pI versus M(r) analogous to 2D gel electrophoresis; however the highly accurate and reproducible M(r) serves as the basis for interlysate comparisons. In addition, the use of liquid separations allows for the collection of hundreds of purified proteins in the liquid phase for further analysis via peptide mass mapping using matrix assisted laser desorption ionization TOF MS. A description of the methodology used and its applications to analysis of several types of human cancer cell lines is described. The potential of the method for differential proteomic analysis for the identification of biomarkers of disease is discussed.

Gamma interferon triggers interaction between ICSBP (IRF-8) and TEL, recruiting the histone deacetylase HDAC3 to the interferon-responsive element

ICSBP (IRF-8) is a transcription factor of the IRF family expressed only in the immune system. It is induced in macrophages by gamma interferon (IFN-gamma) and contributes to macrophage functions. By interacting with Ets family protein PU.1, ICSBP binds to the IRF/Ets composite element and stimulates transcription. ICSBP binds to another DNA element, the IFN-stimulated response element (ISRE), a common target of the IRF family. Limited knowledge as to how ICSBP and other IRF proteins regulate ISRE-dependent transcription in IFN-gamma-activated macrophages is available. By mass-spectrometric analysis of ISRE-bound proteins in macrophages, we identified TEL, another Ets member, as a factor recruited to the element in an IFN-gamma-dependent manner. In vitro analysis with recombinant proteins indicated that this recruitment is due to a direct interaction between ICSBP and TEL, which is enhanced by the presence of ISRE. Significantly, the interaction with TEL in turn resulted in the recruitment of the histone deacetytase HDAC3 to the ISRE, causing increased repression of IFN-gamma-mediated reporter activity through the ISRE. This repression may provide a negative-feedback mechanism operating after the initial transcriptional activation by IFN-gamma. By associating with two different Ets family proteins, ICSBP exerts a dual function in IFN-gamma-dependent gene regulation in an immune system-specific manner.

Proteomic dissection of dome formation in a mammary cell line

The study of the development of the mammary gland at the molecular level in animals is difficult because of the complex tissue organization. This review introduces a proteomic approach to investigate mammary gland development in a cell culture system that we have previously developed as an in vitro model for studying mammary cell differentiation. The model is based on two cell lines, one of which is able to differentiate spontaneously and produce hemispherical blisters, called domes, when confluent. Through proteomic dissection of dome-forming cells, two types of key regulatory genes have been identified: genes inducing cellular structural modifications and genes related to functional modifications. We identified several genes in the pathway leading to dome formation in vitro and showed that the functional and structural changes taking place in dome-forming cells correspond to cellular changes occurring in vivo when tubules and alveoli are developed in the mammary gland at pregnancy.

Proteomics in studies of signal transduction in epithelial cells

An understanding of the complexity of cancer is important for correct diagnostics and efficient treatment of this disease. Recent developments of proteomics technologies allow us to address the complexity of tumorigenesis at a level of global protein profiling. This review discusses recent studies of signaling processes in cells of epithelial origin undertaken with the use of global protein profiling. Tumors of epithelial origin comprise about 90% of human breast cancers, and it is believed that transformation of breast epithelial cells shares common features of transformation with other mammalian cells: destabilization of the genome followed by acquisition of immortalization, unrestricted growth, evasion of death-inducing signals, and acquisition of invasive and tumor promoting characteristics. Functional proteomics of growth-promoting, growth-inhibiting, and pro-apoptotic signaling pathways, in combination with proteomics studies of breast epithelial cell differentiation and profiling of breast tumorigenesis, revealed groups of regulated proteins: structural components, stress-regulated proteins, regulators of transcription, translation and RNA processing, and regulators of posttranslational modifications, e.g., kinases, phosphatases, and proteases. The first lesson of proteomics studies is the discovery of significant number of new targets, as compared to total number of affected proteins. The second lesson is the poor correlation between expressions of proteins and their mRNAs. The third lesson is the low amplitude of protein changes compared to that observed for mRNA. These observations also recommend the analysis of signaling patterns rather than separate signaling pathways.

Proteomics of breast cancer: outcomes and prospects

Breast cancer is a major public health problem. The identification of new markers to differentiate neoplastic from the normal cells, more thorough understanding of different stages of the pathology, as well as the definition of new therapeutic targets, are all of critical importance. With the completion of human genome sequencing and the introduction of mass spectrometry, combined with protein identification via advanced bioinformatics, proteomics has emerged as a valuable tool for the discovery of new molecular markers. New methods in functional proteomics have also been developed to study the intracellular signaling pathways that underline the development of breast cancer. As illustrated with the examples of fibroblast growth factor-2 and H19, an oncogenic, noncoding mRNA, proteomics have become a powerful approach for deciphering the complex signaling circuitry involved in tumor growth. Breast cancer proteomics have already identified proteins of potential clinical interest (such as the molecular chaperone 14-3-3 sigma) and technological innovations in large scale/high throughput analysis are now ushering in new prospects.

Contributions of proteome profiling to the molecular analysis of cancer

The proteome is the most functional compartment encoded for in the genome. Technologies for protein separation and quantitation, coupled with mass spectrometry for protein identification, have provided the means for proteome profiling of tumor cell lines and tissues that complement genomic and transcriptomic profiling. The application of established and novel proteomic technologies to the molecular analysis of cancer is reviewed.