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PubMed2004

Proteomic analysis to identify breast cancer biomarkers in nipple aspirate fluid.

PURPOSE: Proteomic analysis of breast nipple aspirate fluid (NAF) holds promise as a noninvasive method to identify markers of breast cancer. The objectives of the study were to: (a) describe the NAF proteome, (b) identify candidate markers of breast cancer in NAF by using proteomic analysis, and (c) validate the markers identified by using a quantitative, high-throughput ELISA analysis. EXPERIMENTAL DESIGN: For proteome analysis, NAF proteins from a single subject without breast cancer were separated by two-dimensional PAGE and were subjected to matrix-assisted laser desorption ionization time-of-flight mass spectometry identification. A total of 41 different proteins were identified, 25 of which were known to be secreted. To identify breast cancer markers, we separated 20 NAF samples (10 normal, 10 cancer) by two-dimensional PAGE. Three protein spots were detected that were up-regulated in three or more cancer samples. These spots were identified to be gross cystic disease fluid protein (GCDFP)-15, apolipoprotein D (apoD), and alpha1-acid glycoprotein (AAG). To validate these three potential biomarkers, 105 samples (53 from benign breasts and 52 from breasts with cancer) were analyzed using ELISA. RESULTS: Among all of the subjects, GCDFP-15 levels were lower (P <0.001) and AAG levels were higher (P=0.001) in breasts with cancer. This was also true in premenopausal (GCDFP-15, P=0.011; AAG, P=0.002) but not in postmenopausal women. GCDFP-15 levels were lowest (P=0.003) and AAG levels highest (P <0.001) in women with ductal carcinoma in situ (DCIS). Menopausal status influenced GCDFP-15 and AAG more in women without breast cancer than in women with breast cancer. apoD levels did not correlate significantly with breast cancer. CONCLUSIONS: Our study revealed that the NAF proteome, as defined by two-dimensional PAGE, consists of a limited number of proteins, and that the expression of AAG and GCDFP-15 correlates with disease presence and stage.

Adolescent↗

PubMed2006

Predicting cancer drug response by proteomic profiling.

PURPOSE: Accurate prediction of an individual patient's drug response is an important prerequisite of personalized medicine. Recent pharmacogenomics research in chemosensitivity prediction has studied the gene-drug correlation based on transcriptional profiling. However, proteomic profiling will more directly solve the current functional and pharmacologic problems. We sought to determine whether proteomic signatures of untreated cells were sufficient for the prediction of drug response. EXPERIMENTAL DESIGN: In this study, a machine learning model system was developed to classify cell line chemosensitivity exclusively based on proteomic profiling. Using reverse-phase protein lysate microarrays, protein expression levels were measured by 52 antibodies in a panel of 60 human cancer cell (NCI-60) lines. The model system combined several well-known algorithms, including random forests, Relief, and the nearest neighbor methods, to construct the protein expression--based chemosensitivity classifiers. The classifiers were designed to be independent of the tissue origin of the cells. RESULTS: A total of 118 classifiers of the complete range of drug responses (sensitive, intermediate, and resistant) were generated for the evaluated anticancer drugs, one for each agent. The accuracy of chemosensitivity prediction of all the evaluated 118 agents was significantly higher (P < 0.02) than that of random prediction. Furthermore, our study found that the proteomic determinants for chemosensitivity of 5-fluorouracil were also potential diagnostic markers of colon cancer. CONCLUSIONS: The results showed that it was feasible to accurately predict chemosensitivity by proteomic approaches. This study provides a basis for the prediction of drug response based on protein markers in the untreated tumors.

Antineoplastic Agents↗

PubMed2003

Identification of novel signaling complexes by functional proteomics.

The rapid development of proteomic technologies, combined with the completion of the Human Genome Map, has enabled the compiling of an unprecedented inventory of cellular proteins. Functional proteomics is an emerging field that aims to utilize the enormous amount of information provided by these proteomic technologies to understand the functions of cellular proteins. The utility of functional proteomics has been recently exploited to elucidate cellular mechanisms in numerous fields, of particular salience in the area of signal transduction. This review presents a functional proteomic approach for the study of cardiac cell signaling. It illustrates the strategies by which the subproteome of a targeted signaling system is characterized in an unbiased fashion, the manner in which the biochemical functions of this subproteome are assessed using established molecular and protein chemistry methods, and the challenges associated with these studies.

Animals↗

PubMed2026

Proteomics-Based Soluble Urokinase Plasminogen Activator Receptor Levels Are Associated With Adverse Cardiovascular Outcomes in the General Population: Insights From the UK Biobank.

BACKGROUND: Elevated soluble urokinase plasminogen activator receptor (suPAR) levels are associated with inflammation, immune activation, and major adverse cardiovascular events in coronary artery disease. Encoded by the PLAUR gene, suPAR levels are influenced by the rs4760 genetic variant. Whether proteomics-based suPAR levels predict adverse outcomes in the general population remains unknown. METHODS: Proteomics-based suPAR levels were measured using the Olink Immunoassay in 33 963 UK Biobank participants without known coronary artery disease. Fine-Gray and Cox proportional hazards models assessed associations between suPAR and major adverse cardiovascular events (primary outcome: cardiovascular mortality, nonfatal myocardial infarction, or stroke), cardiovascular mortality, and all-cause mortality (secondary outcomes), after adjustment for demographic and clinical risk factors, hs-CRP (high-sensitivity C-reactive protein), and the rs4760 variant. Incremental discrimination was evaluated using C-statistics. RESULTS: Participants were aged 56.4 (SD, 8.2) years; 45% were men, and 93.4% were White. Over a median follow-up of 14 years (476 177 person-years), 10.7% experienced major adverse cardiovascular events, 2.6% experienced cardiovascular mortality, and 9.4% experienced all-cause mortality. Each 1-SD increment in proteomics-based suPAR was associated with significantly higher risk of major adverse cardiovascular events (hazard ratio [HR], 3.2 [95% CI, 2.9-4.5]), cardiovascular mortality (HR, 5.9 [95% CI, 5.1-6.9]), and all-cause mortality (HR, 5.0 [95% CI, 4.6-5.5]), independent of clinical risk factors and hs-CRP. Additional adjustment for rs4760 did not attenuate these associations. Proteomics-based suPAR significantly improved discrimination beyond clinical risk factors (C-statistic: 0.719 versus 0.732; P<0.001). CONCLUSIONS: Proteomics-based suPAR independently predicts adverse cardiovascular outcomes in the general population, beyond conventional risk factors, hs-CRP, and genetic predisposition to elevated suPAR levels.

Humans↗

PubMed2006

Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow.

BACKGROUND: In the post-genome era, most research scientists working in the field of proteomics are confronted with difficulties in management of large volumes of data, which they are required to keep in formats suitable for subsequent data mining. Therefore, a well-developed open source laboratory information management system (LIMS) should be available for their proteomics research studies. RESULTS: We developed an open source LIMS appropriately customized for 2-D gel electrophoresis-based proteomics workflow. The main features of its design are compactness, flexibility and connectivity to public databases. It supports the handling of data imported from mass spectrometry software and 2-D gel image analysis software. The LIMS is equipped with the same input interface for 2-D gel information as a clickable map on public 2DPAGE databases. The LIMS allows researchers to follow their own experimental procedures by reviewing the illustrations of 2-D gel maps and well layouts on the digestion plates and MS sample plates. CONCLUSION: Our new open source LIMS is now available as a basic model for proteome informatics, and is accessible for further improvement. We hope that many research scientists working in the field of proteomics will evaluate our LIMS and suggest ways in which it can be improved.

Computational Biology↗

PubMed2002

Proteomic approaches to characterize protein modifications: new tools to study the effects of environmental exposures.

Proteomics is the study of proteomes, which are the collections of proteins expressed in cells. Whereas genomes are essentially invariant in different cells in an organism, proteomes vary from cell to cell, with time and as a function of environmental stimuli and stress. The integration of new mass spectrometry (MS) methods, data analysis algorithms, and information from databases of protein and gene sequences has enabled the characterization of proteomes. Many environmental agents directly or indirectly generate reactive electrophiles that covalently modify proteins. Although considerable evidence supports a key role for protein adducts in adverse effects of chemicals, limitations in analytical technology have slowed progress in this area. New applications of liquid chromatography-tandem mass spectrometry (LC-MS-MS) now offer the potential to identify protein targets of reactive electrophiles and to map adducts at the level of amino acid sequence. Use of the data-analysis tools Sequest and SALSA (Scoring Algorithm for Spectral Analysis) together with LC-MS-MS analyses of protein digests enables the identification of modified forms of proteins in a sample. These approaches can map adducts to specific amino acids in protein targets and are being adapted to searches for protein adducts in complex proteomes. These tools will facilitate the identification of new biomarkers of chemical exposure and studies of mechanisms by which protein modifications contribute to the adverse effects of environmental exposures.

Algorithms↗

PubMed2006

Serum proteomic fingerprints of adult patients with severe acute respiratory syndrome.

BACKGROUND: Severe acute respiratory syndrome (SARS) is an emerging infectious disease caused by a new coronavirus strain, SARS-CoV. Specific proteomic patterns might be present in serum in response to the infection and could be useful for early detection of the disease. METHODS: Using surface-enhanced laser desorption/ionization (SELDI) ProteinChip technology, we profiled and compared serum proteins of 39 patients with early-stage SARS infection and 39 non-SARS patients who were suspected cases during the SARS outbreak period. Proteomic patterns associated with SARS were identified by bioinformatic and biostatistical analyses. Features of interest were then purified and identified by tandem mass spectrometry. RESULTS: Twenty proteomic features were significantly different between the 2 groups. Fifteen were increased in the SARS group, and 5 were decreased. Their concentrations were correlated with 2 or more clinical and/or biochemical variables. Two were correlated with the SARS-CoV viral load. Hierarchical clustering analysis showed that a majority of the SARS patients (95%) had similar serum proteomic profiles and identified 2 subgroups with poor prognosis. ROC curve analysis identified individual features as potential biomarkers for SARS diagnosis (areas under ROC curves, 0.733-0.995). ROC curve areas were largest for an N-terminal fragment of complement C3c alpha chain (m/z 28 119) and an internal fragment of fibrinogen alpha-E chain (m/z 5908). Immunoglobulin kappa light chain (m/z 24 505) positively correlated with viral load. CONCLUSIONS: Specific proteomic fingerprints in the sera of adult SARS patients could be used to identify SARS cases early during onset with high specificity and sensitivity.

Adult↗

PubMed2004

Exploring and exploiting bacterial proteomes.

The plethora of data now available from bacterial genome sequencing has opened a wealth of new research opportunities. Many of these have been reviewed in preceding chapters. Genomics alone, however, cannot capture a biological snapshot from an organism at a given point in time. The genome itself is static, and it is the changes in expression of genes, leading to the production of functional proteins, which allows an organism to survive and adapt to a constantly changing environment. Proteomics is the term used to describe the global analysis of proteins involved in a particular biological process. Such processes may be analyzed via comparative studies that examine bacterial strain differences, both phenotypic and genetic, bacteria grown under nutrient limiting conditions, growth phase, temperature, or in the presence of chemical compounds, such as antibiotics. Proteomics also provides the researcher with a tool to begin characterizing the functions of the vast proportion of "hypothetical" or "unknown" proteins elucidated from genome sequencing and database comparisons. For example, study of protein-protein, protein-ligand, protein-substrate, and protein-nucleic acid interactions for a given target protein may all help to define the functions of previously unknown proteins. Furthermore, genetic manipulation combined with proteomics technologies can provide an understanding of how gene expression is regulated. This chapter examines the technologies used in proteome analysis and the applications of proteomics to microbiological research, with an emphasis on clinically-relevant bacteria.

Bacterial Proteins↗

PubMed2006

Recent developments in proteomics: implications for the study of cardiac hypertrophy and failure.

The key components to the molecular understanding of the pathophysiology of various forms of heart failure involve global and/or large-scale identifications of proteins, their patterns of expression, posttranslational modifications, and functional characterization. Particularly, proteins involved in the induction of cardiac (mal)adaptive hypertrophic growth, interstitial fibrosis, and contractile dysfunction are of interest. In general, with the accumulation of vast amounts of DNA sequences in databases, researchers have become aware that merely having complete sequences of genomes and transcriptional changes for thousands of genes simultaneously will not be sufficient to elucidate, in molecular terms, the etiology and pathophysiology of cardiovascular disease. In the last decade, a new technology called proteomics has become available that allows biological and (patho)physiological questions to be approached exclusively from the protein perspective. Proteomics may enable us to map the entire complement of proteins expressed by the heart at any time and condition. This approach creates the unique possibility to identify, by differential analysis, protein alterations associated with the etiology of heart disease and its progression, outcome, and response to therapy. To illustrate the true power of proteomics, most of the currently available methodologies are first reviewed, including their limitations. This review also deals with the current status and the perspectives of proteomics applications in research on heart failure in general. Furthermore, examples of our recent data on global protein profiling of the pressure-overloaded rat right ventricle and of endothelin-1-stimulated cultures of neonatal rat cardiac myocytes are provided. The last section is devoted to the continuous advances in proteomic technologies, including protein separation methods, mass spectrometric instrumentation, computational analysis, and bioinformatic tools, together with integrative databases.

Cardiomegaly↗

PubMed2005

A distinctive molecular signature of multiple sclerosis derived from MALDI-TOF/MS and serum proteomic pattern analysis: detection of three biomarkers.

Although multiple sclerosis (MS) is an immune-mediated disorder, serological testing to aid in its diagnosis has not been developed. To test the hypothesis that the pathological changes in MS can be detected by analyzing a molecular signature of serum proteomic patterns, we tested sera from 25 newly diagnosed relapsing-remitting MS patients and 25 healthy controls with matrix-assisted laser desorption/ ionization time of flight (MALDI-TOF) mass spectrometry, following albumin depletion and desalting. Analysis of MALDI-TOF/mass spectrometry data, using proteomic spectral pattern recognition software, revealed a distinct proteomic pattern in the MS group determined by 3 biomarkers at 8687, 8773, and 8818 mass-to-charge ratios. Although our data are representative of analysis on a small number of samples and are preliminary, we conclude that MALDI-TOF/mass spectrometry, in combination with serum proteomic pattern analysis, could be useful in the diagnosis of MS, and a larger, masked trial to identify proteomic spectral patterns characteristic of relapsing-remitting, primary progressive and secondary progressive variants of MS is justified.

Biomarkers↗

PubMed2007

A proteomic primer for the clinician.

Proteomics is a rapidly developing field and it opens new horizons in many research areas of life sciences. In the field of medicine, proteomics promises to accelerate the discovery of new drug targets and protein disease markers useful for in vitro diagnosis. In this article, we review the current proteomics technologies for biomarker discovery and validation, which include two-dimensional gel electrophoresis, one- and two-dimensional liquid chromatography, and proteomic microarrays. We will also review proteomic strategies for protein-protein interactions and identification of post-translational modifications. Selection of the more effective technology or combination of technologies is required to maximize the interpretation and utility of the data.

Biomarkers↗

PubMed2004

Proteomics-on-a-chip: the challenge to couple lab-on-a-chip unit operations.

This review describes a vision of a proteomics-on-a-chip device to separate, detect and identify the proteome. It guides the reader towards a development strategy, avoiding some of the pitfalls. It also describes the current state-of-the-art developments in proteomic analysis including available technologies, current market issues, the elements of an envisaged proteomics-on-a-chip device, the required microfabrication processes and the integration of the elements into one device. Address-flow microfluidics is a tool for connecting separation and detection platforms. The final section contains an expert opinion on the recommended development strategies, benefits of proteomics-on-a-chip in the life sciences and the anticipated market.

Drug Design↗

PubMed2005

CSF proteome: a protein repository for potential biomarker identification.

Proteomic analysis is not limited to the analysis of serum or tissues. Synovial, peritoneal, pericardial and cerebrospinal fluid represent unique proteomes for disease diagnosis and prognosis. In particular, cerebrospinal fluid serves as a rich source of putative biomarkers that are not solely limited to neurologic disorders. Peptides, proteolytic fragments and antibodies are capable of crossing the blood-brain barrier, thus providing a repository of pathologic information. Proteomic technologies such as immunoblotting, isoelectric focusing, 2D gel electrophoresis and mass spectrometry have proven useful for deciphering this unique proteome. Cerebrospinal fluid proteins are generally less abundant than their corresponding serum counterparts, necessitating the development and use of sensitive analytical techniques. This review highlights some of the promising areas of cerebrospinal fluid proteomic research and their clinical applications.

Alzheimer Disease↗

PubMed2005

Host-pathogen interactions: a proteomic view.

Host-pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host-pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host-pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host-pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.

Animals↗

PubMed2006

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Computational Biology↗

PubMed2006

Genome annotating proteomics pipelines: available tools.

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.

Animals↗

PubMed2004

High-resolution serum proteomic features for ovarian cancer detection.

Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a well-controlled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity (P<0.00001) throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

Biomarkers, Tumor↗

PubMed2001

Proteomics as a tool in the pharmaceutical drug design process.

Proteomics is a technology platform that is gaining widespread use in drug discovery and drug development programs. Defined as the protein complement of the genome, the proteome is a varied and dynamic repertoire of molecules that in many ways dictates the functional form that is taken by the genome. The importance of proteomics is a direct consequence of the central role that proteins play in establishing the biological phenotype of organisms in healthy and diseased states. Moreover, proteins constitute the vast majority of drug targets against which pharmaceutical drug design processes are initiated. By studying interrelationships between proteins that occur in health and disease and following drug treatment, proteomics contributes important insight that can be used to determine the pathophysiological basis for disease and to study the mechanistic basis for drug action and toxicity. Proteomics is also an effective means to identify biomarkers that have the potential to improve decision making surrounding drug efficacy and safety issues based on data derived from the study of key tissues and the discovery and appropriate utilization of biomarkers.

Chromatography, High Pressure Liquid↗