Modeling of protein signaling networks in clinical proteomics

Molecular interactions that underlie pathophysiological states are being elucidated using techniques that profile proteomicend points in cellular systems. Within the field of cancer research, protein interaction networks play pivotal roles in the establishment and maintenance of the hallmarks of malignancy, including cell division, invasion, and migration. Multiple complementary tools enable a multifaceted view of how signal protein pathway alterations contribute to pathophysiological states.One pivotal technique is signal pathway profiling of patient tissue specimens. This microanalysis technology provides a proteomic snapshot at one point in time of cells directly procured from the native context of a tumor micro environment. To study the adaptive patterns of signal pathway events over time, before and after experimental therapy, it is necessary to obtain biopsies from patients before, during, and after therapy. A complementary approach is the profiling of cultured cell lines with and without treatment. Cultured cell models provide the opportunity to study short-term signal changes occurring over minutes to hours. Through this type of system, the effects of particular pharmacological agents may be used to test the effects of signal pathway inhibition or activation on multiple endpoints within a pathway. The complexity of the data generated has necessitated the development of mathematical models for optimal interpretation of interrelated signaling pathways. In combination,clinical proteomic biopsy profiling, tissue culture proteomic profiling, and mathematical modeling synergistically enable a deeper understanding of how protein associations lead to disease states and present new insights into the design of therapeutic regimens.

Clinical Proteomics of the Neglected Human Malarial Parasite Plasmodium vivax

Recent reports highlight the severity and the morbidity of disease caused by the long neglected malaria parasite Plasmodium vivax. Due to inherent difficulties in the laboratory-propagation of P. vivax, the biology of this parasite has not been adequately explored. While the proteome of P. falciparum, the causative agent of cerebral malaria, has been extensively explored from several sources, there is limited information on the proteome of P. vivax. We have, for the first time, examined the proteome of P. vivax isolated directly from patients without adaptation to laboratory conditions. We have identified 153 proteins from clinical P. vivax, majority of which do not show homology to any previously known gene products. We also report 29 new proteins that were found to be expressed in P. vivax for the first time. In addition, several proteins previously implicated as anti-malarial targets, were also found in our analysis. Most importantly, we found several unique proteins expressed by P. vivax. This study is an important step in providing insight into physiology of the parasite under clinical settings.

Mass spectrometry in clinical proteomics - from the present to the future

MS is an important analytical tool in clinical proteomics, primarily in the disease-specific discovery identification and characterisation of proteomic biomarkers and patterns. MS-based proteomics is increasingly used in clinical validation and diagnostic method development. The latter departs from the typical application of MS-based proteomics by exchanging some of the high performance of analysis for the throughput, robustness and simplicity required for clinical diagnostics. Although conventional MS-based proteomics has become an important field in clinical applications, some of the most recent MS technologies have not yet been extensively applied in clinical proteomics. in this review, we will describe the current state of MS in clinical proteomics and look to the future of this field.

MS-based clinical proteomics: biomarker discovery in men’s cancer

Importance of biomarker discovery in men’s cancer diagnosis and prognosis Each year around 10,000 men in the UK die as a result of prostate cancer (PCa) making it the 3rd most common cancer behind lung and breast cancer; worldwide more than 670,000 men are diagnosed every year with the disease [1]. Current methods of diagnosis of PCa mainly rely on the detection of elevated prostate-specific antigen (PSA) levels in serum and/or physical examination by a doctor for the detection of an abnormal prostate. PSA is a glycoprotein produced almost exclusively by the epithelial cells of the prostate gland [2]. Its role is not fully understood, although it is known that it forms part of the ejaculate and its function is to solubilise the sperm to give them the mobility to swim. Raised PSA levels in serum are thought to be due to both an increased production of PSA from the proliferated prostate cells, and a diminished architecture of affected cells, allowing an easier distribution of PSA into the wider circulatory system.

The use of proteomics for the assessment of clinical samples in research

Proteomics, the analysis of expressed proteins, has been an important developing area of research for the past two decades [Anderson, NG, Anderson, NL. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis 1996;17:443-53]. Advances in technology have led to a rapid increase in applications to a wide range of samples; from initial experiments using cell lines, more complex tissues and biological fluids are now being assessed to establish changes in protein expression. A primary aim of clinical proteomics is the identification of biomarkers for diagnosis and therapeutic intervention of disease, by comparing the proteomic profiles of control and disease, and differing physiological states. This expansion into clinical samples has not been without difficulties owing to the complexity and dynamic range in plasma and human tissues including tissue biopsies. The most widely used techniques for analysis of clinical samples are surface-enhanced laser desorption/ionisation mass spectrometry (SELDI-MS) and 2-dimensional gel electrophoresis (2-DE) coupled to matrix-assisted laser desorption ionisation [Person, MD, Monks, TJ, Lau, SS. An integrated approach to identifying chemically induced posttranslational modifications using comparative MALDI-MS and targeted HPLC-ESI-MS/MS. Chem. Res. Toxicol. 2003;16:598-608]-mass spectroscopy (MALDI-MS). This review aims to summarise the findings of studies that have used proteomic research methods to analyse samples from clinical studies and to assess the impact that proteomic techniques have had in assessing clinical samples. © 2004 The Canadian Society of Clinical Chemists. All rights reserved.

Comprehensive human urine standards for comparability and standardization in clinical proteome analysis

Urine proteomics is emerging as a powerful tool for biomarker discovery. The purpose of this study is the development of a well-characterized "real life" sample that can be used as reference standard in urine clinical proteomics studies.

Saliva proteome research: current status and future outlook

Human saliva harbours proteins of clinical relevance and about 30% of blood proteins are also present in saliva. This highlights that saliva can be used for clinical applications just as urine or blood. However, the translation of salivary biomarker discoveries into clinical settings is hampered by the dynamics and complexity of the salivary proteome. This review focuses on the current status of technological developments and achievements relating to approaches for unravelling the human salivary proteome. We discuss the dynamics of the salivary proteome, as well as the importance of sample preparation and processing techniques and their influence on downstream protein applications; post-translational modifications of salivary proteome and protein: protein interactions. In addition, we describe possible enrichment strategies for discerning post-translational modifications of salivary proteins, the potential utility of selected-reaction-monitoring techniques for biomarker discovery and validation, limitations to proteomics and the biomarker challenge and future perspectives. In summary, we provide recommendations for practical saliva sampling, processing and storage conditions to increase the quality of future studies in an emerging field of saliva clinical proteomics. We propose that the advent of technologies allowing sensitive and high throughput proteome-wide analyses, coupled to well-controlled study design, will allow saliva to enter clinical practice as an alternative to blood-based methods due to its simplistic nature of sampling, non-invasiveness, easy of collection and multiple collections by untrained professionals and cost-effective advantages.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Proteomics science & society : the role of knowledge translation in moving towards clinical applications

Background: Outside the mass-spectrometer, proteomics research does not take place in a vacuum. It is affected by policies on funding and research infrastructure. Proteomics research both impacts and is impacted by potential clinical applications. It provides new techniques & clinically relevant findings, but the possibilities for such innovations (and thus the perception of the potential for the field by funders) are also impacted by regulatory practices and the readiness of the health sector to incorporate proteomics-related tools & findings. Key to this process is how knowledge is translated. Methods: We present preliminary results from a multi-year social science project, funded by the Canadian Institutes of Health Research, on the processes and motivations for knowledge translation in the health sciences. The proteomics case within this wider study uses qualitative methods to examine the interplay between proteomics science and regulatory and policy makers regarding clinical applications of proteomics. Results: Adopting an interactive format to encourage conference attendees’ feedback, our poster focuses on deficits in effective knowledge translation strategies from the laboratory to policy, clinical, & regulatory arenas. An analysis of the interviews conducted to date suggests five significant choke points: the changing priorities of funding agencies; the complexity of proteomics research; the organisation of proteomics research; the relationship of proteomics to genomics and other omics sciences; and conflict over the appropriate role of standardisation. Conclusion: We suggest that engagement with aspects of knowledge translation, such as those mentioned above, is crucially important for the eventual clinical application ofproteomics science on any meaningful scale.

Proteomics science and society :the role of knowledge translation in moving towards clinical applications

Gaining support for proteomics science requires effective knowledge translation. Knowledge translation (KT) processes turn the evidence generated by scientific discovery into recommendations for clinical applications, funding priorities, and policy/regulatory reforms. Clinicians, regulators, and funders need to understand why emerging proteomics knowledge is relevant, and what are the potential applications of that knowledge. A lack of clarity remains about what KT means.

Single cell proteomics microchip to profile immune function, with applications in stem cell biology, translational disease mechanism study and clinical therapeutics monitoring

In response to infection or tissue dysfunction, immune cells develop into highly heterogeneous repertoires with diverse functions. Capturing the full spectrum of these functions requires analysis of large numbers of effector molecules from single cells. However, currently only 3-5 functional proteins can be measured from single cells. We developed a single cell functional proteomics approach that integrates a microchip platform with multiplex cell purification. This approach can quantitate 20 proteins from >5,000 phenotypically pure single cells simultaneously. With a 1-million fold miniaturization, the system can detect down to ~100 molecules and requires only ~104 cells. Single cell functional proteomic analysis finds broad applications in basic, translational and clinical studies. In the three studies conducted, it yielded critical insights for understanding clinical cancer immunotherapy, inflammatory bowel disease (IBD) mechanism and hematopoietic stem cell (HSC) biology.

To study phenotypically defined cell populations, single cell barcode microchips were coupled with upstream multiplex cell purification based on up to 11 parameters. Statistical algorithms were developed to process and model the high dimensional readouts. This analysis evaluates rare cells and is versatile for various cells and proteins. (1) We conducted an immune monitoring study of a phase 2 cancer cellular immunotherapy clinical trial that used T-cell receptor (TCR) transgenic T cells as major therapeutics to treat metastatic melanoma. We evaluated the functional proteome of 4 antigen-specific, phenotypically defined T cell populations from peripheral blood of 3 patients across 8 time points. (2) Natural killer (NK) cells can play a protective role in chronic inflammation and their surface receptor – killer immunoglobulin-like receptor (KIR) – has been identified as a risk factor of IBD. We compared the functional behavior of NK cells that had differential KIR expressions. These NK cells were retrieved from the blood of 12 patients with different genetic backgrounds. (3) HSCs are the progenitors of immune cells and are thought to have no immediate functional capacity against pathogen. However, recent studies identified expression of Toll-like receptors (TLRs) on HSCs. We studied the functional capacity of HSCs upon TLR activation. The comparison of HSCs from wild-type mice against those from genetics knock-out mouse models elucidates the responding signaling pathway.

In all three cases, we observed profound functional heterogeneity within phenotypically defined cells. Polyfunctional cells that conduct multiple functions also produce those proteins in large amounts. They dominate the immune response. In the cancer immunotherapy, the strong cytotoxic and antitumor functions from transgenic TCR T cells contributed to a ~30% tumor reduction immediately after the therapy. However, this infused immune response disappeared within 2-3 weeks. Later on, some patients gained a second antitumor response, consisted of the emergence of endogenous antitumor cytotoxic T cells and their production of multiple antitumor functions. These patients showed more effective long-term tumor control. In the IBD mechanism study, we noticed that, compared with others, NK cells expressing KIR2DL3 receptor secreted a large array of effector proteins, such as TNF-α, CCLs and CXCLs. The functions from these cells regulated disease-contributing cells and protected host tissues. Their existence correlated with IBD disease susceptibility. In the HSC study, the HSCs exhibited functional capacity by producing TNF-α, IL-6 and GM-CSF. TLR stimulation activated the NF-κB signaling in HSCs. Single cell functional proteome contains rich information that is independent from the genome and transcriptome. In all three cases, functional proteomic evaluation uncovered critical biological insights that would not be resolved otherwise. The integrated single cell functional proteomic analysis constructed a detail kinetic picture of the immune response that took place during the clinical cancer immunotherapy. It revealed concrete functional evidence that connected genetics to IBD disease susceptibility. Further, it provided predictors that correlated with clinical responses and pathogenic outcomes.

Cardiovascular proteomics: implications for clinical applications

Proteomics is fulfilling its potential and beginning to impact the diagnosis and therapy of cardiovascular disease. As de novo proteomics analysis gets more streamlined, and robust high-throughput methods are developed, more and more attention is being directed toward the field of cardiovascular serum and plasma biomarker discovery. To take cardiovascular proteomics from bench to bedside, great care must be taken to achieve reproducible results. Despite technical advances, however, the absolute number of clinical biomarkers thus far discovered by a proteomics approach is small. Although several factors contribute to this lack, one step is to build "translation teams" involving a close collaboration between researchers and clinicians.

Clinical implications of recent findings in schistosome proteomics

Schistosomiasis is a neglected tropical disease of clinical significance that, despite years of research, still requires an effective vaccine and improved diagnostics for surveillance, control and potential elimination. Furthermore, the causes of host pathology during schistosomiasis are still not completely understood. The recent sequencing of the genomes of the three key schistosome species has enabled the discovery of many new possible vaccine and drug targets, as well as diagnostic biomarkers, using high-throughput and sensitive proteomics methods. This review focuses on the literature of the last 5 years that has reported on the use of proteomics to both better understand the biology of the schistosome parasites and the disease they cause in definitive mammalian hosts.

Interview with Dr. Young-Ki Paik, President of the Human Proteome Organization (HUPO): Pharmacoproteomics and the approaching wave of “Proteomics Diagnostics”

Dr. Young-Ki Paik directs the Yonsei Proteome Research Center in Seoul, Korea and was elected as the President of the Human Proteome Organization (HUPO) in 2009. In the December 2009 issue of the Current Pharmacogenomics and Personalized Medicine (CPPM), Dr. Paik explains the new field of pharmacoproteomics and the approaching wave of “proteomics diagnostics” in relation to personalized medicine, HUPO’s role in advancing proteomics technology applications, the HUPO Proteomics Standards Initiative, and the future impact of proteomics on medicine, science, and society. Additionally, he comments that (1) there is a need for launching a Gene-Centric Human Proteome Project (GCHPP) through which all representative proteins encoded by the genes can be identified and quantified in a specific cell and tissue and, (2) that the innovation frameworks within the diagnostics industry hitherto borrowed from the genetics age may require reevaluation in the case of proteomics, in order to facilitate the uptake of pharmacoproteomics innovations. He stresses the importance of biological/clinical plausibility driving the evolution of biotechnologies such as proteomics,instead of an isolated singular focus on the technology per se. Dr. Paik earned his Ph.D. in biochemistry from the University of Missouri-Columbia and carried out postdoctoral work at the Gladstone Foundation Laboratories of Cardiovascular Disease, University of California at San Francisco. In 2005, his research team at Yonsei University first identified and characterized the chemical structure of C. elegans dauer pheromone (daumone) which controls the aging process of this nematode. He is interviewed by a multidisciplinary team specializing in knowledge translation, technology regulation, health systems governance, and innovation analysis.