Cover picture: proteomics 7-8'12

Over recent years, advances in proteomic technologies have led to the rapid generation of vast volumes of data regarding many clinical applications, acquired from cells through to patients. High-throughput analysis of protein-protein interactions, quantification of protein abundance, global analysis of posttranslational modifications and other approaches have addressed the complexity of cellular regulation in health and disease, which has opened the way to systems-level clinical research. The dysregulation of cell adhesion plays a key role in many disease states. The image represents a systematic analysis of integrin adhesion complexes, which were isolated from erythroleukemia cells and analyzed bymass spectrometry (see Byron et al., Science Signaling 2011, 4, pt2). To understand how specific adhesion complexes may function, the composition of different complexes was compared using hierarchical clustering, and a protein-protein interaction network model of the core receptor-bound subcomplex was constructed. Data-driven, global investigations such as this could pave the road to new therapeutic targets and personalized medicines. Cover image created by Adam Byron (University of Manchester,Manchester, UK); cover design by SCHULZ Grafik-Design.     

Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) – A promising target for breast cancer

Experimental evidences have observed enhanced expression of protease activated receptor 2 (PAR2) in breast cancer consistently. However, it is not yet recognized as an important therapeutic target for breast cancer as the primary molecular mechanisms of its activation are not yet well-defined. Nevertheless, recent reports on the mechanism of GPCR activation and signaling have given new insights to GPCR functioning. In the light of these details, we attempted to understand PAR2 structure & function using molecular modeling techniques. In this work, we generated averaged representative stable models of PAR2, using protease activated receptor 1 (PAR1) as a template and selected conformation based on their binding affinity with PAR2 specific agonist, GB110. Further, the selected model was used for studying the binding affinity of putative ligands. The selected ligands were based on a recent publication on phylogenetic analysis of Class A rhodopsin family of GPCRs. This study reports putative ligands, their interacting residues, binding affinity and molecular dynamics simulation studies on PAR2-ligand complexes. The results reported from this study would be useful for researchers and academicians to investigate PAR2 function as its physiological role is still hypothetical. Further, this information may provide a novel therapeutic scheme to manage breast cancer.     

A comprehensive comparison of GPU- and FPGA-based acceleration of reflection image reconstruction for 3D ultrasound computer tomography

As today’s standard screening methods frequently fail to diagnose breast cancer before metastases have developed, earlier breast cancer diagnosis is still a major challenge. Three-dimensional ultrasound computer tomography promises high-quality images of the breast, but is currently limited by a time-consuming image reconstruction. In this work, we investigate the acceleration of the image reconstruction by GPUs and FPGAs. We compare the obtained performance results with a recent multi-core CPU. We show that both architectures are able to accelerate processing, whereas the GPU reaches the highest performance. Furthermore, we draw conclusions in terms of applicability of the accelerated reconstructions in future clinical application and highlight general principles for speed-up on GPUs and FPGAs.     

State of the art of 2D DIGE

Difference gel electrophoresis enables the accurate quantification of changes in the proteome including combinations of PTMs and protein isoform expression. Here, we review recent advances in study design, image acquisition, and statistical analysis. We also compare DIGE to established and emerging mass spectrometric analysis technologies. Despite these recent advances in MS and the still unsolved limitations of 2DE to map hydrophobic, high molecular weight proteins with extreme pIs, DIGE remains the most comprehensive top-down method to study changes in abundance of intact proteins.     

Proteomic profiling of 13 paired ductal infiltrating breast carcinomas and non-tumoral adjacent counterparts

According to recent statistics, breast cancer remains one of the leading causes of death among women in Western countries. Breast cancer is a complex and heterogeneous disease, presently classified into several subtypes according to their cellular origin. Among breast cancer histotypes, infiltrating ductal carcinoma represents the most common and potentially aggressive form. Despite the current progress achieved in early cancer detection and treatment, including the new generation of molecular therapies, there is still need for identification of multiparametric biomarkers capable of discriminating between cancer subtypes and predicting cancer progression for personalized therapies. One established step in this direction is the proteomic strategy, expected to provide enough information on breast cancer profiling. To this aim, in the present study we analyzed 13 breast cancer tissues and their matched non-tumoral tissues by 2-DE. Collectively, we identified 51 protein spots, corresponding to 34 differentially expressed proteins, which may represent promising candidate biomarkers for molecular-based diagnosis of breast cancer and for pattern discovery. The relevance of these proteins as factors contributing to breast carcinogenesis is discussed.     

新型内质网应激诱导蛋白 TRIM25 在乳腺癌细胞中的作用研究

内质网应激是当细胞受到缺氧或营养剥夺等外界因素刺激时而产生的一种效应,该效应与肿 瘤细胞的存活息息相关。该研究揭示了 TRIM25 作为一种新型内质网应激诱导蛋白在肿瘤细胞中所发挥 的作用,可为发现新的肿瘤靶点提供重要依据。该文以乳腺癌细胞 MCF7 为对象,先筛选构建了稳定 敲低 TRIM25 的 MCF7 细胞系;然后,检测了 TRIM25 敲低对内质网应激、未折叠蛋白反应信号通路 和内质网应激诱导的细胞凋亡的影响,以及 TRIM25 在不同乳腺细胞中的表达;最后,通过生物信息学 分析 TRIM25 表达量与乳腺癌患者预后的相关性。结果显示,内质网应激会诱导 TRIM25 表达水平的大 幅上升。通过敲低 TRIM25 可诱导内质网应激、激活未折叠蛋白反应信号通路从而显著促进乳腺癌细胞 MCF7 的凋亡。研究还发现,乳腺原发上皮细胞转化为乳腺癌细胞过程中伴随有 TRIM25 蛋白水平的上 调,生物信息学分析也显示 TRIM25 在乳腺癌组织中高表达,并提示乳腺癌患者预后不良。     

Glycoproteomics and clinical applications

Glycosylation is the most structurally complicated and diverse type of protein modifications. Protein glycosylation has long been recognized to play fundamental roles in many biological processes, as well as in disease genesis and progression. Glycoproteomics focuses on characterization of proteins modified by carbohydrates. Glycoproteomic studies normally include strategies to enrich glycoproteins containing particular carbohydrate structures from protein mixtures followed by quantitative proteomic analysis. These glycoproteomic studies determine which proteins are glycosylated, the glycosylation sites, the carbohydrate structures, as well as the abundance and function of the glycoproteins in different biological and pathological processes. Here we review the recent development in methods used in glycoproteomic analysis. These techniques are essential in elucidation of the relationships between protein glycosylation and disease states. We also review the clinical applications of different glycoproteomic methods.     

Proteomic identification of proteins in the human brain: Towards a more comprehensive understanding of neurodegenerative disease

Proteomics has revealed itself as a powerful tool in the identification and determination of proteins and their biological significance. More recently, several groups have taken advantage of the high-throughput nature of proteomics in order to gain a more in-depth understanding of the human brain. In turn, this information has provided researchers with invaluable insight into the potential pathways and mechanisms involved in the pathogenesis of several neurodegenerative disorders, e.g., Alzheimer and Parkinson disease. Furthermore, these findings likely will improve methods to diagnose disease and monitor disease progression as well as generate novel targets for therapeutic intervention. Despite these advances, comprehensive understanding of the human brain proteome remains challenging, and requires development of improved sample enrichment, better instrumentation, and innovative analytic techniques. In this review, we will focus on the most recent progress related to identification of proteins in the human brain under normal as well as pathological conditions, mainly Alzheimer and Parkinson disease, their potential application in biomarker discovery, and discuss current advances in protein identification aimed at providing a more comprehensive understanding of the brain.     

Liver proteins as sensor of human malignancies and inflammation

In this review we would like to highlight the importance of acute-phase proteins as sensor of diseases. Both acute-phase protein levels and glycosylation have been reported to be altered in inflammation and other diseases including cancer. Factors that promote acute-phase protein synthesis and enhance the expression of specific glycosyltransferases, such as sialyltransferases and fucosyltransferases, may be up-regulated in some tumours and would explain the changes in acute-phase protein levels and the specific N-glycosylation modifications of some acute-phase proteins in cancer. However, further studies are required to define the potential clinical application of these acute-phase protein cancer-specific modifications as possible cancer diagnostic or monitoring tools.     

基于乳腺X线摄影的肿块检测综述

早期筛查和及时治疗是控制乳腺癌死亡率最为有效的方法.乳腺X线摄影检查作为医学界公认的最有效的早期乳腺癌筛检工具,可以很好地反映出乳腺存在的异常情况.在临床应用中,乳腺癌的X线摄影直接征象为钙化和肿块,对乳腺X线摄影中钙化点的检测技术已经相当的成熟,但对肿块区域的检测和分类依旧是一项具有挑战性的任务.因此,本文对近几年提...     

Optimal breast cancer classification using Gauss–Newton representation based algorithm

Breast cancer is a decisive disease worldwide. It is one of the most widely spread cancer among women. As per the survey, one out of eight women in the world are at risk of breast cancer at some point of time in her life. One of the methods to reduce breast cancer mortality rate is timely detection and effective treatment. That is why, more accurate classification of a breast cancer tumor has become a challenging problem in the medical field. Many classification techniques are proposed in the literature. Today, expert systems and machine learning techniques are being extensively used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we have proposed a novel Gauss-Newton representation based algorithm (GNRBA) for breast cancer classification. It uses the sparse representation with training sample selection. Until now, sparse representation has been successfully applied in pattern recognition only. The proposed method introduces a novel Gauss-Newton based approach to find the optimal weights for the training samples for classification. In addition, it evaluates the sparsity in a computationally efficient way as compared to the conventional l₁-norm method. The effectiveness of the GNRBA is examined on the Wisconsin Breast Cancer Database (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) database from the UCI Machine Learning repository. Various performance measures like classification accuracy, sensitivity, specificity, confusion matrices, a statistical test and the area under the receiver operating characteristic (AUC) are reported to show the superiority of the proposed method as compared to classical models. The experimental results show that the proposed GNRBA could be a good alternative for breast cancer classification for clinical experts.     

Implementation of Proteomics in Clinical Trials

The application of protein (or peptide) biomarkers in clinical studies is a dynamic, ever‐growing field. The introduction of clinical proteomics/peptidomics, such as mass spectrometry–based assays and multiplexed antibody–based protein arrays, has reshaped the landscape of biomarker identification and validation, allowing the discovery of novel biomarkers at an unprecedented rate and reliability. To reflect the current status with respect to implementation of protein/peptide biomarkers, an investigation of the most recent (last 6 years) clinical studies from clinicaltrials.gov is presented. Forty‐two clinical trials involving the direct use of protein or peptide biomarkers in patient stratification and/or disease diagnosis and prognosis are highlighted. Most of the clinical trials that include proteomics/protein assays are aiming toward implementation of non‐invasive diagnostic tools for early detection, while many of the clinical trials are targeting to correlate the protein abundance with the risk of a disease event. Less in number are the studies in which the protein biomarkers are applied to stratify the patients for intervention. All the above areas of application are considered of great importance for improving disease management, in an era where implementation toward precision medicine is the desired outcome of proteomics biomarker research.     

Glycoproteomics using fluid-based specimens in the discovery of lung cancer protein biomarkers: Promise and challenge

Lung cancer is the leading cancer in the United States and worldwide. In spite of the rapid progression in personalized treatments, the overall survival rate of lung cancer patients is still suboptimal. Over the past decade, tremendous efforts have been focused on the discovery of protein biomarkers to facilitate the early detection and monitoring of lung cancer progression during treatment. In addition to tumor tissues and cancer cell lines, a variety of biological material has been studied. Particularly in recent years, studies using fluid-based specimen or so-called “fluid-biopsy” specimens have progressed rapidly. Fluid specimens are relatively easier to collect than tumor tissue, and they can be repeatedly sampled during the disease progression. Glycoproteins are the major content of fluid specimens and have long been recognized to play fundamental roles in many physiological and pathological processes. In this review, we focus the discussion on recent advances of glycoproteomics, particularly in the identification of potential glyco protein biomarkers using fluid-based specimens in lung cancer. The purpose of this review is to summarize current strategies, achievements, and perspectives in the field. This insight will highlight the discovery of tumor-associated glycoprotein biomarkers in lung cancer and their potential clinical applications.     

Revealing determinant factors for early breast cancer recurrence by decision tree

Early breast cancer recurrence is indicative of poor response to adjuvant therapy and poses threats to patients’ lives. Most existing prediction models for breast cancer recurrence are regression-based models and difficult to interpret. We apply a Decision Tree algorithm to the clinical information of a cohort of non-metastatic invasive breast cancer patients, to establish a classifier that categorizes patients based on whether they develop early recurrence and on similarities of their clinical and pathological diagnoses. The classifier predicts for whether a patient developed early disease recurrence; and is estimated to be about 70% accurate. For an independent validation cohort of 65 patients, the classifier predicts correctly for 55 patients. The classifier also groups patients based on intrinsic properties of their diseases; and for each subgroup lists the disease characteristics in a hierarchal order, according to their relevance to early relapse. Overall, it identifies pathological nodal stage, percentage of intra-tumor stroma and components of TGFβ-Smad signaling pathway as highly relevant factors for early breast cancer recurrence. Since most of the disease characteristics used by this classifier are results of standardized tests, routinely collected during breast cancer diagnosis, the classifier can easily be adopted in various research and clinical settings.     

Cysteine cathepsins and their potential in clinical therapy and biomarker discovery

Since their discovery, cysteine cathepsins were generally considered to be involved mainly in the nonspecific bulk protein degradation that takes place within the lysosomes. However, it has become clear that their proteolytical activity can also influence various specific pathological processes such as cancer, arthritis, and atherosclerosis. Furthermore, their localization was found not to be limited strictly to the lysosomes. In the light of those findings, it is not surprising that cysteine cathepsins are currently considered as highly relevant clinical targets. Moreover, recent development of proteomic-based methods for identification of novel physiological substrates of proteases provides a major opportunity also in the field of cysteine cathepsins. In this review, we will therefore present cysteine cathepsin roles in disease progression and discuss their potential relevance as prognostic and diagnostic biomarkers.     

Breast cancer biomarker measurements and standards

Cancer is a heterogeneous disease characterized by changes in the levels and activities of important cellular proteins, including oncogenes and tumor suppressors. Genetic mutations cause changes in protein activity and protein expression levels that result in the altered metabolism, proliferation, and metastasis seen in cancer cells. The identification of the critical biochemical changes in cancer has led to advances in its detection and treatment. An important example of this is the measurement of human epidermal growth factor receptor 2 (HER2), where increased expression occurs in approximately 20–30% of breast cancer tumors. HER2 is a member of the epidermal growth factor receptor family and is an important biomarker expressed on the cell surface. Measurement of the HER2 levels in tumor cells provides diagnostic, prognostic, and treatment information, because a targeted therapeutic is available. The most common methods to measure HER2 levels are immunohistochemistry and in situ hybridization assays. The accurate and reliable measurements of the specific changes in protein biomarkers for detection and treatment of cancer are important challenges. This review is focused on efforts to improve the quantitation and reliability of cancer biomarkers by using standards and reference materials.     

Colorectal cancer proteomics, molecular characterization and biomarker discovery

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

Cancer classification using gene expression data

The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood.In order to gain a deep insight into the cancer classification problem, it is necessary to take a closer look at the problem, the proposed solutions and the related issues all together. In this survey paper, we present a comprehensive overview of various proposed cancer classification methods and evaluate them based on their computation time, classification accuracy and ability to reveal biologically meaningful gene information. We also introduce and evaluate various proposed gene selection methods which we believe should be an integral preprocessing step for cancer classification. In order to obtain a full picture of cancer classification, we also discuss several issues related to cancer classification, including the biological significance vs. statistical significance of a cancer classifier, the asymmetrical classification errors for cancer classifiers, and the gene contamination problem.     

Tracking humoral responses using self assembling protein microarrays

The humoral immune response is a highly specific and adaptive sensor for changes in the body's protein milieu, which responds to novel structures of both foreign and self antigens. Although Igs represent a major component of human serum and are vital to survival, little is known about the response specificity and determinants that govern the human immunome. Historically, antigen-specific humoral immunity has been investigated using individually produced and purified target proteins, a labor-intensive process that has limited the number of antigens that have been studied. Here, we present the development of methods for applying self-assembling protein microarrays and a related method for producing 96-well formatted macroarrays for monitoring the humoral response at the proteome scale. Using plasmids encoding full-length cDNAs for over 850 human proteins and 1700 pathogen proteins, we demonstrate that these microarrays are highly sensitive, specific, reproducible, and can simultaneously measure immunity to thousands of proteins without a priori protein purification. Using this approach, we demonstrate the detection of humoral immunity to known and novel self-antigens, cancer antigens, autoimmune antigens, as well as pathogen-derived antigens. This represents a powerful and versatile tool for monitoring the immunome in health and disease.     

Characterisation of tumoral markers correlated with ErbB2 (HER2/Neu) overexpression and metastasis in breast cancer

The receptor tyrosine kinase ErbB2 (HER2/neu) is overexpressed in ?30% of breast cancers and is associated with poor prognosis and an increased likelihood of metastasis. Clinical treatments such as trastuzumab are effective in less than 35% of women diagnosed as ErbB2‐positive, highlighting the necessity of searching for novel targets and alternative therapies. Herein, a proteomic screening strategy combining quantitative‐based gel electrophoresis and MS was used to compare the protein expression of 48 normal human breast and tumour tissues differing in ErbB2 expression and lymph node status. The aim was to identify proteins associated with the aggressive phenotype of ErbB2‐positive breast cancer which could be potential biomarkers of the disease as well as therapy targets. In total, 177 protein isoforms (107 gene products) differentially expressed between tissue groups were identified. Immunohistochemical staining of a tissue‐microarray was used for validation of selected protein candidates. We found that expression of HSP90α, laminin and GSTP1 significantly correlated with ErbB2 expression, while others such as AGR2, NM23H1 and Annexin 2 were overexpressed in greater than 40% of tumours. Finally, knocking‐down the expression by RNA interference of three candidates, AGR2, Transgelin2 and NM23H1 resulted in an enhanced invasive capacity of MDA‐MB435 cells. These data support the involvement of these targets in tumour progression and identify them as novel biomarkers of the disease.