Cancer proteomics: from signaling networks to tumor markers

Along with the great strides that have been made towards understanding cancer, has come a realization of the complexity of molecular events that lead to malignancy. Proteomics-based approaches, which enable the quantitative investigation of both cellular protein expression levels and protein–protein interactions involved in signaling networks, promise to define the molecules controlling the processes involved in cancer.     

Applying proteomics to signaling networks

The information from genome sequencing provides a new framework for a systems-wide understanding of protein networks and cellular function. Whereas microarray technologies provide information about global gene expression within cells, complementary proteomic strategies monitor expression of proteins and their posttranslational modifications. Improved technologies that have emerged for comprehensive and high-throughput protein analysis yield novel insights into cell regulation.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics

To study the global dynamics of phosphotyrosine-based signaling events in early growth factor stimulation, we developed a mass spectrometric method that converts temporal changes to differences in peptide isotopic abundance. The proteomes of three cell populations were metabolically encoded with different stable isotopic forms of arginine. Each population was stimulated by epidermal growth factor for a different length of time, and tyrosine-phosphorylated proteins and closely associated binders were affinity purified. Arginine-containing peptides occurred in three forms, which were quantified; we then combined two experiments to generate five-point dynamic profiles. We identified 81 signaling proteins, including virtually all known epidermal growth factor receptor substrates, 31 novel effectors and the time course of their activation upon epidermal growth factor stimulation. Global activation profiles provide an informative perspective on cell signaling and will be crucial to modeling signaling networks in a systems biology approach.     

Mass spectrometry-based functional proteomics: from molecular machines to protein networks

The study of protein-protein interactions by mass spectrometry is an increasingly important part of post-genomics strategies to understand protein function. A variety of mass spectrometry-based approaches allow characterization of cellular protein assemblies under near-physiological conditions and subsequent assignment of individual proteins to specific molecular machines, pathways and networks, according to an increasing level of organizational complexity. An appropriate analytical strategy can be individually tailored--from an in-depth analysis of single complexes to a large-scale characterization of entire molecular pathways or even an analysis of the molecular organization of entire expressed proteomes. Here we review different options regarding protein-complex purification strategies, mass spectrometry analysis and bioinformatic methods according to the specific question that is being addressed.     

Quantitative proteomics to decipher ubiquitin signaling

Ubiquitin signaling plays an essential role in controlling cellular processes in eukaryotes, and the impairment of ubiquitin regulation contributes to the pathogenesis of a wide range of human diseases. During the last decade, mass spectrometry-based proteomics has emerged as an indispensable approach for identifying the ubiquitinated proteome (ubiquitinome), ubiquitin modification sites, the linkages of complex ubiquitin chains, as well as the interactome of ubiquitin enzymes. In particular, implementation of quantitative strategies allows the detection of dynamic changes in the ubiquitinome, enhancing the ability to differentiate between function-relevant protein targets and false positives arising from biological and experimental variations. The profiling of total cell lysate and the ubiquitinated proteome in the same sets of samples has become a powerful tool, revealing a subset of substrates that are modulated by specific physiological and pathological conditions, such as gene mutations in ubiquitin signaling. This strategy is equally useful for dissecting the pathways of ubiquitin-like proteins.     

Cancer proteomics

Conclusion The future of cancer diagnostics will be based on a panel of proteomic biomarkers. They could be used to detect cancer at an early stage, to predict and to direct therapies. Enzymes and related proteins are important biological molecules, which could serve as cancer biomarkers. These biomarkers could be intact or fragments of proteins. The challenge is to be able to find and validate these potential biomarkers as clinical diagnostics. With the advances in proteomic technologies, we are closer than ever to find these “new” enzyme molecules or fragments. The translation of newly discovered biomarkers could provide an opportunity to revolutionize the era of personalized medicine.     

Effector-triggered immunity signaling: from gene-for-gene pathways to protein-protein interaction networks

In its simplicity and testability, Flor's gene-for-gene hypothesis has been a powerful driver in plant immunity research for decades. Once the molecular underpinnings of gene-for-gene resistance had come into sharper focus, there was a reassessment of Flor's hypothesis and a name change to effector-triggered immunity. As implied by the name change and exemplified by pioneering studies, plant immunity is increasingly described in terms of protein rather than genetic interactions. This progress leads to a reinterpretation of old concepts of pathogen recognition and resistance signaling and, of course, opens up new questions. Here, we provide a brief historical overview of resistance gene function and how a new focus on protein interactions can lead to a deeper understanding of the logic of plant innate immunity signaling.     

Purinergic signaling and tumor microenvironment in cervical Cancer

Purinergic Signalling - Cervical cancer is the fourth most common type of cancer incidence in the world female population, and it has become a public health problem worldwide. Several factors are...     

蛋白质组学新技术及其在肿瘤标志物探索性研究中的应用

作为基因组研究的延伸,蛋白质组学已成为世界生命科学领域的一个极其活跃的部分,是功能基因组时代或后基因组时代的核心。近年来发展起来的几种蛋白质组学新技术克服了传统蛋白质组学技术的不足,有力推进了肿瘤标志物的探索性研究。尽管如此,我们仍应该强调对实验结果的验证,这一点在探索性研究中尤其重要,力求使结果更精确、可靠和有效。     

Identification of dominant signaling pathways from proteomics expression data

The availability of the results of high-throughput analyses coming from ‘omic’ technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a ‘pathway search engine’, where the input is the ‘omic’ data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF_β, stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.     

Qualitative networks: a symbolic approach to analyze biological signaling networks

Background  

A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico.     

Platelet proteomics: from discovery to diagnosis

Introduction: Platelets are the smallest cells within the circulating blood with key roles in physiological hemostasis and pathological thrombosis regulated by the onset of activating/inhibiting processes via receptor responses and signaling cascades.

Areas covered: Proteomics as well as genomic approaches have been fundamental in identifying and quantifying potential targets for future diagnostic strategies in the prevention of bleeding and thrombosis, and uncovering the complexity of platelet functions in health and disease. In this article, we provide a critical overview on current functional tests used in diagnostics and the future perspectives for platelet proteomics in clinical applications.

Expert commentary: Proteomics represents a valuable tool for the identification of patients with diverse platelet associated defects. In-depth validation of identified biomarkers, e.g. receptors, signaling proteins, post-translational modifications, in large cohorts is decisive for translation into routine clinical diagnostics.     

Signaling pathway networks mined from human pituitary adenoma proteomics data

Background

We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins), comparative proteomic data (56 differentially expressed proteins), and nitroproteomic data (17 nitroproteins). There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system.

Methods

The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses.

Results

For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a pituitary control related to gene expression and cellular development, and no canonical toxicity pathways were identified.

Conclusions

This pathway network analysis demonstrated that mitochondrial dysfunction, oxidative stress, cell-cycle dysregulation, and the MAPK-signaling abnormality are significantly associated with a pituitary adenoma. These pathway-network data provide new insights into the molecular mechanisms of human pituitary adenoma pathogenesis, and new clues for an in-depth investigation of pituitary adenoma and biomarker discovery.

     

Quantitative phosphoproteomics to characterize signaling networks

Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify and quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments in phosphoproteomics technology that have facilitated the application of phosphoproteomics to signaling networks and introduce examples of recent system-wide applications of quantitative phosphoproteomics. Despite the great advances in phosphoproteomics technology there are still several outstanding issues and we provide here our outlook on the current limitations and challenges in the field.     

Arabidopsis thaliana proteomics: from proteome to genome

Proteomics has become an important approach for investigating cellular processes and network functions. Significant improvements have been made during the last few years in technologies for high-throughput proteomics, both at the level of data analysis software and mass spectrometry hardware. As proteomics technologies advance and become more widely accessible, efforts of cataloguing and quantifying full proteomes are underway to complement other genomics approaches, such as RNA and metabolite profiling. Of particular interest is the application of proteome data to improve genome annotation and to include information on post-translational protein modifications with the annotation of the corresponding gene. This type of analysis requires a paradigm shift because amino acid sequences must be assigned to peptides without relying on existing protein databases. In this review, advances and current limitations of full proteome analysis are briefly highlighted using the model plant Arabidopsis thaliana as an example. Strategies to identify peptides are also discussed on the basis of MS/MS data in a protein database-independent approach.     

Cancer proteomics: many technologies, one goal

A major goal of the National Cancer Institute is to alleviate patient pain, suffering and death associated with cancer by the year 2015. This goal does not insinuate a cure for cancer, but rather the development of diagnostics and therapeutics that will eventually decrease cancer morbidity and mortality. A part of meeting this goal is to leverage the enormous data-gathering capabilities of proteomic technologies to discover disease-specific biomarkers in serum, plasma, urine, tissues and other biologic samples. The rapid advance in available technologies that have been spurred by the -omics era, has enabled biologic samples to be surveyed for biomarkers in ways never before possible. However, it is not yet clear which specific technologies will be the most successful. Therefore, proteomic laboratories within the National Cancer Institute are taking a multipronged approach to identify disease-specific biomarkers. This review discusses some of these approaches in their context of meeting the National Cancer Institute's 2015 goal.