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PubMed2002

Clinical applications of proteomics: proteomic pattern diagnostics.

Clinical proteomics is an exciting new subdiscipline of proteomics that involves bedside application of proteomic technologies. A new and potentially revolutionary technology and approach for early disease detection, surveillance, and monitoring is proteomic pattern diagnostics. Using this approach, high throughput mass spectrometry generates a proteomic fingerprint of a given body fluid, such as serum or nipple fluid aspirants (NAF), in less than 30 s. This information archive is then used by new types of bioinformatic pattern recognition algorithms to identify patterns of protein changes that can discriminate cancer from healthy and unaffected individuals. This entire process can take place in less than a minute and requires only a droplet of blood, NAF, or ductal lavage washings. The new concept that is introduced by this platform is that the underlying identities of the proteins that comprise the patterns are not known and do not need to be known; the pattern itself becomes the diagnostic.

Algorithms↗
PubMed2006

Better understanding of organ dysfunction requires proteomic involvement.

Organ dysfunction is defined as a systemic consequence of acute and chronic diseases, a critical and important phase of disease development. The mortality of patients with severe illness is highly correlated with the number and duration of dysfunctional organs. There is still not an efficient and specific therapy to improve the prognosis of patients with organ dysfunction, due to the complexity and severity of the disease. There is a great need to understand molecular mechanisms of the disease, identify disease-related biomarkers, and validate therapeutic effects. Thus, it is important to have a special attention from proteomic scientists to explore the combination between advanced proteomic biotechnology, clinical proteomics, tissue imaging and profiling, and organ dysfunction score systems, to improve the clinical outcomes of these patients.

Biomarkers↗
PubMed2005

Population proteomics: addressing protein diversity in humans.

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

Adult↗
PubMedDate not supplied

Genomic messaging system and DNA mark-up language for information-based personalized medicine with clinical and proteome research applications.

The convergence of clinical medicine and the Life Sciences, commencing with opportunities in clinical trials and clinically linked medical research, presents many novel challenges. The Genomic Messaging System (GMS) described here was originally developed as a tool for assembling clinical genomic records of individual and collective patients, and was then generalized to become a flexible workflow component that will link clinical records to a variety of computational biology research tools, for research and ultimately for a more personalized, focused, and preventative healthcare system. Prominent among the applications linked are protein science applications, including the rapid automated modeling of patient proteins with their individual structural polymorphisms. In an initial study, GMS formed the basis of a fully automated system for modeling patient proteins with structural polymorphisms as a basis for drug selection and ultimately design on an individual patient basis.

Clinical Medicine↗
PubMed2005

Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools software.

Recently, applications of mass spectrometry in the field of clinical proteomics have gained tremendous visibility in the scientific and clinical community. One major objective is the search for potential biomarkers in complex body fluids like serum, plasma, urine, saliva, or cerebral spinal fluid. For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. We introduce the new ClinProTools bioinformatics software, which performs all major steps of profiling, screening, and monitoring applications in clinical proteomics. ClinProTools is the data interpretation software of the mass spectrometry-based ClinProt solutions for biomarker analysis. ClinProTools performs data pretreatment, visualization, statistics, pattern determination, pattern evaluation, and classification of spectra. This article will focus on ClinProTool's powerful and intuitive visualization options for clinical proteomics applications.

Algorithms↗
PubMed2002

[Proteomics in clinical research: new approach of mass spectrometry].

A proteome has been defined as the protein complement expressed by the genome of an organism, tissue, or differentiated cell. Knowledge of complete genome sequences has led to considerable effort being increasingly devoted to the large-scale study of proteomes, that is, 'proteomics'. Commonly, two proteomes are compared by a substructive analysis in which differences due to drug treatment, culture conditions, genetic variations, or diseases can be observed. Two-dimensional gel electrophoresis and mass spectrometry are commonly used for this purpose. We applied this approach to the analysis of vitreous humor(VH) proteins. Fifty-two different proteins were identified on silver-stained 2D-gel patterns with VH proteins obtained from diabetic retinopathy and macular hole. Thirty-five proteins, which have not reported in plasma, were found in VH. Pigment epithelium-derived factor, which was reported to be a potent inhibitor of angiogenesis in cornea and vitreous was at a higher concentration in VH with diabetes than in that with macular hole. It is impressive that the inhibitor increases in the vitreous with proliferative angiogenesis. Unique applications in proteomics promise a bright future for molecular biology and hopefully for clinical chemistry.

Aqueous Humor↗
PubMed2005

Applying proteomics in clinical trials: assessing the potential and practical limitations in ovarian cancer.

Ovarian cancer is the leading cause of death from gynecologic malignancies among American women and the fourth most frequent cause of death from cancer in women in Europe and the US. Despite appropriate surgical and chemotherapeutic intervention, the 5-year survival in patients with metastatic cancer remains poor. Currently available screening methods, including CA125, additional biomarkers, and transvaginal ultrasound lack the necessary sensitivity and specificity to provide accurate and cost-efficient screening for the general population or the ability to assess who will benefit most from each treatment. These limitations have prompted the study of proteomic technology and its application in ovarian cancer diagnostics. Proteomics is the study of molecules in the functional protein pathways of normal or diseased states. Clinical trials are currently being conducted to assess the sensitivity and specificity of serum proteomic patterns and additional clinical trials are designed to evaluate the effects of molecularly targeted agents on protein signaling pathways in human subjects. Overcoming both scientific and practical limitations will lead to increased knowledge of deranged protein networks in cancer cells. Clinical trials in proteomics may result in improved early detection, better monitoring, new drugs and molecularly targeted therapeutics, and individualized therapies.

Biomarkers, Tumor↗
PubMed2005

Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery.

For most cancers, survival rates depend on the early detection of the disease. So far, no biomarkers exist to cope with this difficult task. New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples and/or of the setting up of new clinically relevant test systems. Novel mass spectrometry-(MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), have shown promising results in the recent literature. Here, proteomic profiles of control and disease states are compared to find biomarkers for diagnosis. This paper aims to address the authors' own work and that of other groups in clinical cancer proteomics based on SELDI-ToF-MS. Shortcomings and hopes for the future are discussed.

Biomarkers, Tumor↗
PubMed2006

Proteomics in clinical interventions: achievements and limitations in biomarker development.

Development of toxicological and clinical biomarkers for disease diagnosis, quantification of toxicant/drug responses and rapid patient care are major concerns in modern biology. Even after human genome sequencing, identification of specific molecular signatures for unambiguous correlation with toxicity and clinical interventions is a challenging task. Differential protein expression patterns and protein-protein interaction studies have started unraveling rigorous molecular explanation of multi-factorial and toxicant borne diseases. Proteome profiling is extensively used to investigate etiology of diseases, develop predictive biomarkers for toxicity and therapeutic interventions and potential strategies for treatment of complex and toxicant mediated diseases. In this review, achievements and limitations of proteomics in developing predictive biomarkers for toxicological and clinical interventions have been discussed.

Animals↗
PubMed2004

Proteomic analysis of human breast cancer tissue with laser-capture microdissection and reverse-phase protein microarrays.

Despite recent advances in breast cancer therapy, women with similar types of breast cancers may respond very differently to standard treatments. The emerging field of clinical proteomics has the potential to revolutionize breast cancer therapy. The ultimate goal of clinical proteomics is to characterize information flow through protein cascades for individual patients. After the protein networks have been elucidated, drug therapies may be specially designed for each patient. The following review describes the proteomic technologies of laser-capture microdissection (LCM) and reverse-phase protein arrays (RPPAs). These technologies allow scientists to analyze relative abundances of key cellular signaling proteins from pure cell populations. Cell survival and apoptotic protein pathways are currently being monitored with LCM and RPPAs at the National Institutes of Health, in phase II clinical trials of metastatic breast and ovarian cancers. Ultimately, proteomics will become an integral component of tracking and managing individualized breast cancer therapy.

Breast↗
PubMedDate not supplied

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.

Biomarkers, Tumor↗
PubMed2006

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications.

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Biomarkers↗
PubMed2007

Mass spectrometry: uncovering the cancer proteome for diagnostics.

Despite impressive scientific achievements over the past few decades, cancer is still a leading cause of death. One of the major reasons is that most cancer patients are diagnosed with advanced disease. This is clearly illustrated with ovarian cancer in which the overall 5-year survival rates are only 20-30%. Conversely, when ovarian cancer is detected early (stage 1), the 5-year survival rate increases to 95%. Biomarkers, as tools for preclinical detection of cancer, have the potential to revolutionize the field of clinical diagnostics. The emerging field of clinical proteomics has found applications across a wide spectrum of cancer research. This chapter will focus on mass spectrometry as a proteomic technology implemented in three areas of cancer: diagnostics, tissue imaging, and biomarker discovery. Despite its power, it is also important to realize the preanalytical, analytical, and postanalytical limitations currently associated with this methodology. The ultimate endpoint of clinical proteomics is individualized therapy. It is essential that research groups, the industry, and physicians collaborate to conduct large prospective, multicenter clinical trials to validate and standardize this technology, for it to have real clinical impact.

Animals↗
PubMed2005

Proteomics and clinical surgery.

This article concludes the Journal's "Scientific Surgery" series of leaders. The series, published throughout 2005, has highlighted areas of bioscience that may soon be transferred from the laboratory into the clinical sphere. In this final paper Roblick and Auer consider the future role of the surgeon in the era of the "diagnostic chip".

DNA↗
PubMed2024

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

OBJECTIVE: To sought comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. BACKGROUND: Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intrapancreatic microbial dysbiosis may drive malignant transformation. METHODS: Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n = 20; IPMN, n = 20; PDAC, n = 19) and pancreatic cyst fluid (IPMN, n = 30; serous cystadenomas, n = 10; mucinous cystic neoplasm, n = 10). Assessment of bacterial DNA by quantitative polymerase chain reaction and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium were analyzed to validate findings. RESULTS: Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade and high-grade dysplasia IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in high-grade dysplasia cyst fluid. Decontamination analysis using the Clinical Proteomic Tumor Analysis Consortium database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. CONCLUSIONS: Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Humans↗
PubMed2005

Characterisation of protein microheterogeneity and protein complexes using on-chip immunoaffinity purification-mass spectrometry.

Post-translational modification of proteins may influence their interactions with other plasma proteins, as well as having an effect on many aspects of the metabolism of the protein, such as receptor binding, tissue uptake, degradation and excretion. Many post-translational modifications occur in a physiological context, while others are specific for certain diseases, which is why they are of diagnostic importance in clinical proteomics. Analytical approaches to the study of post-translational modifications and protein complexes through the combined use of on-chip immunological affinity purification on a surface-enhanced laser desorption/ionisation platform and subsequent mass spectrometry are illustrated in the author's own work relating to plasma transthyretin (TTR) and retinol-binding protein (RBP). In those studies, both the aspects of post-translational modifications of TTR and the formation of a protein complex between TTR and RBP have been discussed. Such aspects are of diagnostic interest in clinical proteomics, especially with regard to the modification of TTR in relation to the occurrence of amyloidotic diseases.

Amyloid↗