Proteome analysis for the identification of tumor-associated biomarkers in gastrointestinal cancer

Proteomic Strategies and their Application in Cancer Research

The understanding of carcinogenesis and tumor progression on a molecular basis needs a detailed study of proteins as effector molecules and as critical components of the multiple interconnected signaling pathways that drive the neoplastic phenotype. Thus, the proteomic approach represents a powerful tool for the challenge of the post-genomic era. The term “cancer proteome” refers to the collection of proteins expressed by a given cancer cell and should be considered as a highly dynamic entity within the cell, which affects a variety of cellular activities. The emerging proteomic analysis platforms including 2D-PAGE, mass spectrometry technologies, and protein microarrays represent powerful tools to study and understand cancer. These systems aim to not only identify, catalogue, and characterize cancer proteins, but also to unveil how they interact to affect overall tumor progression. Moreover, recent studies on various cancers have reported promising results concerning the detection of novel molecular biomarkers useful in the early diagnosis of cancer and in drug discovery. Thus, a new subdiscipline named clinical proteomics, concomitant with new molecular technologies that are developed, demonstrates promise to discover new cancer biomarkers. The early diagnosis of cancer, even in a premalignant state, is crucial for the successful treatment of this disease. For these reasons, it is clear that the identification of biomarkers for the early diagnosis of cancer should represent one of the main goals of this emerging field of study.

The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

The advent of functional genomics has been greatly broadening our view and accelerating our way in numerous medical research fields. The complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowed rapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential to make considerable contribution to biomarker identification and to revolutionize both the development of new therapies and drug development process.

Proteomics of pancreatic cancer

Pancreatic cancer is a devastating disease, with a mortality rate almost identical with its incidence. Late diagnosis and limited therapeutic options make early detection of pancreatic cancer a pressing clinical problem. In this context, the investigation of the pancreatic cancer proteome has recently gained considerable attention because profiles of proteins may be able to more accurately identify disease states, such as cancer. Recent pancreatic cancer proteome studies may be categorized into basic studies cataloguing the pancreatic proteome, studies investigating differential protein expression patterns, and studies searching for proteome-based biomarkers for early cancer detection and differentiation. Although these studies clearly demonstrate that a range of biological samples are suitable for proteomic analyses, comparison of different studies is problematic due to the diversity of methodologies, sample sources, and characterization of patient populations. Reproducibility between studies has rarely been investigated, and no investigation has compared the different methods of proteomic research. The results of this review have shown that more stringent requirements concerning the design and the analysis of future studies should be implemented. These include an adequate patient number, obligatory histological examination of tissues, appropriate control groups, identification of proteins and peaks, validation of differential expression using independent cohorts and/or a second methodology, and, finally, demonstration of result reproducibility. This will hopefully lead to the discovery of prognostic and predictive biomarkers that help to improve prognosis of pancreatic cancer patients.

Comparative proteomic analysis of low stage and high stage endometrioid ovarian adenocarcinomas

Ovarian cancer, the second most common gynecological malignancy, accounts for 3% of all cancers among women in the United States, and has a high mortality rate, largely because existing therapies for widespread disease are rarely curative. Ovarian endometrioid adenocarcinoma (OEA) accounts for about 20% of the overall incidence of all ovarian cancer. We have used proteomics profiling to characterize low stage (FIGO stage 1 or 2) versus high stage (FIGO stage 3 or 4) human OEAs. In general, the low stage tumors lacked p53 mutations and had frequent CTNNB1, PTEN, and/or PIK3CA mutations. The high stage tumors had mutant p53, were usually high grade, and lacked mutations predicted to deregulate Wnt/β-catenin and PI3K/Pten/Akt signaling. We utilized 2-D liquid-based separation/mass mapping techniques to elucidate molecular weight and pI measurements of the differentially expressed intact proteins. We generated 2-D protein mass maps to facilitate the analysis of protein expression between both the low stage and high stage tumors. These mass maps (over a pI range of 5.6-4.6) revealed that the low stage OEAs demonstrated protein over-expression at the lower pI ranges (pI 4.8-4.6) in comparison to the high stage tumors, which demonstrated protein over-expression in the higher pI ranges (pI 5.4-5.2). These data suggest that both low and high stage OEAs have characteristic pI signatures of abundant protein expression probably reflecting, at least in part, the different signaling pathway defects that characterize each group. In this study, the low stage OEAs were distinguishable from high stage tumors based upon the proteomic profiles. Interestingly, when only high-grade (grade 2 or 3) OEAs were included in the analysis, the tumors still tended to cluster according to stage, suggesting that the altered protein expression was not solely dependent upon tumor cell differentiation. Further, these protein profiles clearly distinguish OEA from other types of ovarian cancer at the protein level.

Application of proteomics to ecology and population biology

Proteomics is a relatively new scientific discipline that merges protein biochemistry, genome biology and bioinformatics to determine the spatial and temporal expression of proteins in cells, tissues and whole organisms. There has been very little application of proteomics to the fields of behavioral genetics, evolution, ecology and population dynamics, and has only recently been effectively applied to the closely allied fields of molecular evolution and genetics. However, there exists considerable potential for proteomics to impact in areas related to functional ecology; this review will introduce the general concepts and methodologies that define the field of proteomics and compare and contrast the advantages and disadvantages with other methods. Examples of how proteomics can aid, complement and indeed extend the study of functional ecology will be discussed including the main tool of ecological studies, population genetics with an emphasis on metapopulation structure analysis. Because proteomic analyses provide a direct measure of gene expression, it obviates some of the limitations associated with other genomic approaches, such as microarray and EST analyses. Likewise, in conjunction with associated bioinformatics and molecular evolutionary tools, proteomics can provide the foundation of a systems-level integration approach that can enhance ecological studies. It can be envisioned that proteomics will provide important new information on issues specific to metapopulation biology and adaptive processes in nature. A specific example of the application of proteomics to sperm ageing is provided to illustrate the potential utility of the approach.

Proteomics in nutrition research: principles, technologies and applications

The global profiling of the whole protein complement of the genome expressed in a particular cell or organ, or in plasma or serum, makes it possible to identify biomarkers that respond to alterations in diet or to treatment, and that may have predictive value for the modelling of biological processes. Proteomics has not yet been applied on a large scale in nutritional studies, yet it has advantages over transcriptome profiling techniques in that it directly assesses the entities that carry out the biological functions. The present review summarizes the different approaches in proteomics research, with special emphasis on the current technical ‘workhorses’: two-dimensional (2D)-PAGE with immobilized pH gradients and protein identification by MS. Using a work-flow approach, we provide information and advice on sample handling and preparation, protein solubilization and pre-fractionation, protein separation by 2D-PAGE, detection and quantification via computer-assisted analysis of gels, and protein identification and characterization techniques by means of MS. Examples from nutritional studies employing proteomics are provided to demonstrate not only the advantages but also the limitations of current proteome analysis platforms.

The application of genomic and proteomic technologies in predictive, preventive and personalized medicine

The long asymptomatic period before the onset of chronic diseases offers good opportunities for disease prevention. Indeed, many chronic diseases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction is vital for disease prevention so that therapy can be given to those individuals who are most likely to develop the disease. The utility of predictive markers is dependent on three parameters, which must be carefully assessed: sensitivity, specificity and positive predictive value. Specificity is important if a biomarker is to be used to identify individuals either for counseling or for preventive therapy. However, a reciprocal relationship exists between sensitivity and specificity. Thus, successful biomarkers will be highly specific without sacrificing sensitivity. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find for many diseases. One potential solution is to use the combinatorial power of a large number of biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer a great opportunity for biomarker discovery. The newly identified biomarkers have the potential to bring increased accuracy in disease diagnosis and classification, as well as therapeutic monitoring. In this review, we will use type 1 diabetes (T1D) as an example, when appropriate, to discuss pertinent issues related to high throughput biomarker discovery.

Following the Differentiation of Human Pluripotent Stem Cells by Proteomic Identification of Biomarkers

Following the differentiation of cultured stem cells is often reliant on the expression of genes and proteins that provide information on the developmental status of the cell or culture system. There are few molecules, however, that show definitive expression exclusively in a specific cell type. Moreover, the reliance on a small number of molecules that are not entirely accurate biomarkers of particular tissues can lead to misinterpretation in the characterization of the direction of cell differentiation. Here we describe the use of technology that examines the mass spectrum of proteins expressed in cultured cells as a means to identify the developmental status of stem cells and their derivatives in vitro. This approach is rapid and reproducible and it examines the expression of several different biomarkers simultaneously, providing a profile of protein expression that more accurately corresponds to a particular type of cell differentiation.

The role of proteomics in the diagnosis and treatment of ovarian cancer

Ovarian cancer is the leading cause of gynecologic cancer death in the Western world and more than 70% of patients are diagnosed with advanced stage disease. The high mortality rate is due to the difficulty in the early detection of ovarian cancer. Current screening strategies lack the necessary sensitivity and specificity to reliably and accurately diagnose affected women, prompting investigators to seek alternative means of analysis found in protein pathways and networks. Proteomics seeks to advance the understanding of how proteins interact in cancer and may provide a mechanism for early stage diagnosis. The proteomic techniques of laser capture microdissection, mass spectrometry and tissue lysate arrays have led to the discovery of new biomarkers and the identification, development and approval of a number of targeted therapeutic agents. Following validation through clinical trials, the application of these techniques will contribute to the changing paradigm of cancer detection and treatment toward personalized medicine.

Preliminary study on proteomics of gastric carcinoma and its clinical significance

AIM: To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer.

METHODS: A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks’ height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples.

RESULTS: Sixteen mass peaks were found to be potential biomarkers with a significant level of P<0.01. Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30, and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4. Stage I/II gastric cancer samples of the test group were all judged correctly.

CONCLUSION: The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.

Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma

In order to identify new molecular markers for pancreatic intra-epithelial neoplasias (PanINs), the precursor lesions of pancreatic ductal adenocarcinoma, we established a proteomics approach analysing microdissected PanIN cells. Due to the limited amount of proteins available from microdissection, we developed a procedure including fluorescence dye saturation labelling in combination with high resolution two-dimensional gel electrophoresis. With this procedure we were able to analyse proteins extracted from 1000 microdissected cells with a high resolution of up to 2500 protein spots. Using protein lysates from the pancreatic carcinoma tissue as a reference proteome, we were able to successfully identify the proteins. Thus, we could match approximately 2200 protein spots (92%) of the microdissected sample proteome to the reference proteome for protein identification using matrix-assisted laser desorption/ionisation-time of flight mass spectrometry and nanoliquid chromatography-electrospray ionisation tandem mass spectrometry after in-gel digestion. The first proteome analysis of microdissected PanIN-2 grades revealed eight differentially expressed proteins. The differential expression of the three actin filament-associated proteins--transgelin, vimentin and MRCL3 as well as actin itself--indicates a relevant role of the actin cytoskeleton during pancreatic tumour progression. Additionally, two members of the annexin family (annexin A2 and A4) implicate a functional contribution of exocytotic and endocytotic pathways at that stage.

Modeling of SEB-induced host gene expression to correlate in vitro to in vivo responses

Detection of exposure to biological threat agents has relied on ever more sensitive methods for pathogen identification, but that usually requires pathogen proliferation to dangerous, near untreatable levels. Recent events have demonstrated that assessing exposure to a biological threat agent well in advance of onset of illness or at various stages post-exposure is invaluable among the diagnostic options. There is an urgent need for better diagnostic tools that will be sensitive, rapid, and unambiguous. Since human clinical cases of illness induced by biothreat agents are, fortunately, rare, use of animal models that closely mimic the human illness is the only in vivo option. Such studies can be very difficult and expensive; therefore, maximizing the information obtained from in vitro exposures to peripheral blood mononuclear cells (PBMCs) provide an opportunity to investigate dose/time variability in host responses. In our quest to study staphylococcal enterotoxin B (SEB) induced host gene expression patterns, we addressed two core issues using microarray analysis and predictive modeling. Our first objective was to determine gene expression patterns in human PBMCs exposed to SEB in vitro. Second, we compared the in vitro data with host responses gene expression patterns in vivo using PBMCs from an animal model of SEB intoxication that closely replicates the progression of illness in humans. We used cDNA microarrays to study global gene expression patterns in piglets intoxicated with SEB. We applied a supervised learning method for class prediction based on the k-nearest neighbor algorithm for the data obtained in piglets exposed to SEB in vivo against a training data set. This data set included gene expression profiles derived from in vitro exposures to eight different pathogens (Bacillus anthracis, Yersinia pestis, Brucella melitensis, SEB, cholera toxin, Clostridium botulinum toxin A, Venezuelan equine encephalitis, and Dengue-2) in PBMCs. We found that despite differences in gene expression profiles between in vitro and in vivo systems, there exists a subset of genes that show correlations between in vitro and in vivo exposures, which can be used as a predictor of exposure to SEB in vivo.

Proteomic patterns for early cancer detection

The advent of proteomics has brought with it the hope of discovering novel biomarkers that can be used to diagnose diseases, predict susceptibility, and monitor progression. Much of this effort has focused on the mass spectral identification of the thousands of proteins that populate complex biosystems such as serum and tissues. A revolutionary approach in proteomic pattern analysis has emerged as an effective method for the early diagnosis of diseases such as ovarian, breast, and prostate cancer. This technology is capable of analyzing hundreds of clinical samples per day and has the potential to be a novel, highly sensitive diagnostic tool for the early detection of diseases, or as a predictor of response to therapy.