Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein–partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein–protein- and protein–partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.     

Profiling proteoforms: promising follow-up of proteomics for biomarker discovery

Today, proteomics usually compares clinical samples by use of bottom-up profiling with high resolution mass spectrometry, where all protein products of a single gene are considered as an integral whole. At the same time, proteomics of proteoforms, which considers the variety of protein species, offers the potential to discover valuable biomarkers. Proteoforms are protein species that arise as a consequence of genetic polymorphisms, alternative splicing, post-translational modifications and other less-explored molecular events. The comprehensive observation of proteoforms has been an exclusive privilege of top-down proteomics. Here, we review the possibilities of a bottom-up approach to address the microheterogeneity of the human proteome. Special focus is given to shotgun proteomics and structure-based bioinformatics as a source of hypothetical proteoforms, which can potentially be verified by targeted mass spectrometry to determine the relevance of proteoforms to diseases.     

Enrichment of low-molecular-weight proteins from biofluids for biomarker discovery

The dramatic progress in mass spectrometry-based methods of protein identification has triggered a new quest for disease-associated biomarkers. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and its variant surface-enhanced laser desorption/ionization mass spectrometry, provide effective means to explore the less studied information slice of the human serum proteome – low-molecular-weight proteins and peptides. These low-molecular-weight proteins and peptides are promising for the detection of important biomarkers. Due to the significant experimental problems imposed by high-abundance and high-molecular-weight proteins, it is important to effectively remove these species prior to mass spectrometry analysis of the low-molecular-weight serum and plasma proteomes. In this review, the advantages afforded by recently introduced methods for prefractionation of serum, as they pertain to the detection and identification of biomarkers, will be discussed.     

IGFBP-4: A promising biomarker for lung cancer

Background: Insulin-like growth factor binding protein-4 (IGFBP-4), a member of the insulin-like growth factor (IGF) family, transports, and regulates the activity of IGFs. The pregnancy-associated plasma protein-A (PAPP-A) has proteolytic activity towards IGFBP-4, and both proteins have been associated with a variety of cancers, including lung cancer. Thus, we aimed to evaluate the use of IGFBP-4 and PAPP-A as potential biomarkers for lung cancer. Methods: Eighty-three volunteers, including 60 patients with lung cancer and 23 healthy individuals, were included in this study. The patients with lung cancer were selected based on their treatment status, histological subgroup, and stage of the disease. Enzyme-linked immunosorbent assays were used to assess the serum levels of IGFBP-4 and PAPPA, whereas the IGF-1 levels were measured using a chemiluminescent immunometric assay. Results: The serum IGFBP-4 levels in all patient groups, regardless of the treatment status and histological differences, were significantly higher than those in the control group (p<0.005). However, the serum PAPP-A levels in the untreated patient group were found to be higher than those in the control group, but this difference was not statistically significant (p=0.086). Conclusions: The serum PAPP-A and IGFBP-4 levels are elevated in lung cancer. However, IGFBP-4 may have better potential than PAPP-A as a lung cancer biomarker.     

Extracellular vesicles in ovarian cancer: applications to tumor biology,immunotherapy and biomarker discovery

In recent years there has been tremendous interest in both the basic biology and applications of extracellular vesicles (EVs) in translational cancer research. This includes a better understanding of their biogenesis and mechanisms of selective cargo packaging, their precise roles in horizontal communication, and their application as non-invasive biomarkers. The rapid advances in next-generation omics technologies are the driving forces for these discoveries. In this review, the authors focus on recent results of EV research in ovarian cancer. A deeper understanding of ovarian cancer-derived EVs, the types of cargo molecules and their biological roles in cancer growth, metastases and drug resistance, could have significant impact on the discovery of novel biomarkers and innovative therapeutics. Insights into the role of EVs in immune regulation could lead to novel approaches built on EV-based immunotherapy.     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

The purpose of the present work is to identify protein profiles that could be used to discover specific biomarkers in serum and discriminate lung cancer. Thirty serum samples from patients with lung cancer (15 cases of primary brochogenic carcinoma, 9 cases of metastasis lung cancer and 6 cases of lung cancer after chemotherapy) and twelve from healthy individuals were analyzed by SELDI (Surfaced Enhanced Laser Desorption/Ionization) technology. Anion-exchange columns were used to fractionate the sera with 6 designated pH washing solutions. Two types of protein chip arrays, IMAC-Cu and WCX2, were employed. Protein chips were examined in PBSII ProteinChip Reader (Ciphergen Biosystems Inc.) and the resulting profiles between cancer and normal were analyzed with Biomarker Wizard System. In total, 15 potential lung cancer biomarkers, of which 6 were up-regulated and 9 were down-regulated, were discovered in the serum samples from patients with lung cancer. 5 of 15 these biomarkers were able to be detected on both WCX2 and IMAC-Cu protein chips. The sensitivities provided by the individual markers range from 44.8% to 93.1% and the specificities were 85.0%–94.4%. Our results suggest that serum is a capable resource for detection of lung cancer with specific biomarkers. Moreover, protein chip array system was shown to be a useful tool for identification, as well as detection of disease biomarkers in sera.     

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery

Bladder cancer (BC) and kidney cancer (KC) are the first two commonly occurring genitourinary cancers in China. In this study, a comprehensive LC-MS-based method, which utilizes both reversed phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of BC, KC, and noncancer controls. An independent test set consisting of different patients has been used to objectively evaluate the predictive ability of the analysis platform. Excellent sensitivity and specificity have been achieved in detection of KC and BC. The results suggest that serum metabolic profiling could be used for different types of genitourinary cancer diagnosis. Furthermore, cancer type-specific biomarkers were found through a critical selection criterion. As a result, eicosatrienol, azaprostanoic acid, docosatrienol, retinol, and 14'-apo-beta-carotenal were found as specific biomarkers for BC; and PE(P-16:0e/0:0), glycerophosphorylcholine, ganglioside GM3 (d18:1/22:1), C17 sphinganine, and SM(d18:0/16:1(9Z)) were found as specific biomarkers for KC. Receiver operating characteristic (ROC) analysis was used for the preliminary evaluation of the biomarkers. These biomarkers have great potential to be used in the clinical diagnosis after further rigorous assessment.     

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.     

Nanoparticles in cancer immunotherapies: An innovative strategy

Cancer has been one of the most significant causes of mortality, worldwide. Cancer immunotherapy has recently emerged as a competent, cancer-fighting clinical strategy. Nevertheless, due to the difficulty of such treatments, costs, and off-target adverse effects, the implementation of cancer immunotherapy described by the antigen-presenting cell (APC) vaccine and chimeric antigen receptor T cell therapy ex vivo in large clinical trials have been limited. Nowadays, the nanoparticles theranostic system as a promising target-based modality provides new opportunities to improve cancer immunotherapy difficulties and reduce their adverse effects. Meanwhile, the appropriate engineering of nanoparticles taking into consideration nanoparticle characteristics, such as, size, shape, and surface features, as well as the use of these physicochemical properties for suitable biological interactions, provides new possibilities for the application of nanoparticles in cancer immunotherapy. In this review article, we focus on the latest state-of-the-art nanoparticle-based antigen/adjuvant delivery vehicle strategies to professional APCs and engineering specific T lymphocyte required for improving the efficiency of tumor-specific immunotherapy.     

Proteomics approaches in cervical cancer: focus on the discovery of biomarkers for diagnosis and drug treatment monitoring

Introduction: The HPV virus accounts for the majority of cervical cancer cases. Although a diagnostic tool (Pap Test) is widely available, cervical cancer incidence still remains high worldwide, and especially in developing countries, attributed to a large extent to suboptimal sensitivities of the Pap test and unavailability of the test in developing countries.

Areas covered: Proteomics approaches have been used in order to understand the HPV virus correlation to cervical cancer pathology, as well as to discover putative biomarkers for early cervical cancer diagnosis and drug mode of action.

Expert commentary: The present review summarizes the latest in vitro and in vivo proteomic studies for the discovery of putative cervical cancer biomarkers and the evaluation of available drugs and treatments.     

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.     

Proteomic methods for biomarker discovery and validation. Are we there yet?

Noninvasive molecular biomarkers are becoming attractive tools to monitor disease progression, aid drug development programs and use as surrogate outcome measures in clinical trials. Cutting edge proteomic methods to assay biomarkers in body fluids have been developed in the past few years, but transitioning them to clinical practice has been slow and depends on the qualification of both the method and the biomarker.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Proteomics and metabolomics in biomarker discovery for cardiovascular diseases: progress and potential

Introduction: The process of discovering novel biomarkers and potential therapeutic targets may be shortened using proteomic and metabolomic approaches.

Areas covered: Several complementary strategies, each one presenting different advantages and limitations, may be used with these novel approaches. In vitro studies show how cells involved in cardiovascular disease react, although the phenotype of cultured cells differs to that occurring in vivo. Tissue analysis either in human specimens or animal models may show the proteins that are expressed in the pathological process, although the presence of structural proteins may be confounding. To identify circulating biomarkers, analyzing the secretome of cultured atherosclerotic tissue, analysis of blood cells and/or plasma may be more straightforward. However, in the latter approach, high-abundant proteins may mask small molecules that could be potential biomarkers. The study of sub-proteomes such as high-density lipoproteins may be useful to circumvent this limitation. Regarding metabolomics, most studies have been performed in small populations, and we need to perform studies in large populations in order to discover robust biomarkers.

Expert commentary: It is necessary to involve the clinicians in these areas to improve the design of clinical studies, including larger populations, in order to obtain consistent novel biomarkers.     

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.