Proteomics in clinical trials and practice: present uses and future promise

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Targeted proteomics using parallel reaction monitoring confirms salivary proteins indicative of metastatic triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype due to the absence of hormonal receptors. Our study aimed to identify and determine the effectiveness of salivary proteins as candidate markers for metastatic TNBC subtype using parallel reaction monitoring mass spectrometry (PRM-MS). Three salivary proteins (lipocalin-1, SMR3B, and plastin-2) that showed significant differential expression in label-free quantitation (LFQ) between TNBC (N = 6) and health subjects (HS; N = 6) were selected for further validation. The developed PRM assay was used to quantify peptides GLST and NNLE (lipocalin-1), VYAL and MINL (Plastin-2) and GPYP, and IPPP (SMR3B) on a different cohort of TNBC patients (N = 20) and HS (N = 20) for evaluating their discriminating performances. Quantitative validation using PRM correlated well with the LFQ results, and 5 peptides from three proteins showed a similar up-or down-regulation. Subsequently, these proteins were validated by Western blot analysis. Compared to one protein's performance as an individual marker, the five-signature panel with salivary GLST, VYAL, MINL, GPYP, and IPPP achieved better performance in differentiating aggressive TNBC and HS with sensitivity (80%) and specificity (95%). Targeted proteomic analysis of the prioritized proteins highlights a peptide-based signature in saliva as the potential predictor to distinguish between TNBC and HS. SIGNIFICANCE OF THE STUDY: This study was designed to identify and quantify potential markers in saliva from the triple-negative breast cancer (TNBC) patients using parallel reaction monitoring assay. Three salivary proteins, Lipocalin-1 (LCN-1), Submaxillary androgen-regulated protein 3B (SMR3B), and Plastin-2 (LCP-1) selected in the discovery-phase were further quantified by targeted proteomics and Western blots. The salivary proteins successfully differentiated TNBC patients from healthy subjects with a sensitivity (80%) and specificity (95%).

Proteomic identification of ruminal epithelial protein expression profiles in response to starter feed supplementation in pre-weaned lambs

The present study aimed to comparatively characterize the ruminal epithelial protein expression profiles in lambs fed ewe milk or milk plus starter diet using proteome analysis. Twenty new-born lambs were randomly divided into a group receiving ewe milk (M, n = 10) and a group receiving milk plus starter diet (M + S, n = 10). From 10 d old, M group lambs remained with the ewe and suckled ewe milk without receiving the starter diet. The lambs in the M + S group were separated from the ewe and received starter feed. All lambs were slaughtered at 56 d old. Eight rumen epithelia samples (4 per group) were collected to characterize their protein expression profiles using proteomic technology. Proteome analysis showed that 31 upregulated proteins and 40 downregulated proteins were identified in the rumen epithelium of lambs in response to starter diet supplementation. The results showed that starter feeding regulates a variety of biological processes in the epithelium, especially blood vessel development and extracellular matrix protein expression. Meanwhile, the expression of proteins associated with synthesis and degradation of ketone bodies, butanoate metabolism, and citrate cycle signaling transduction pathway were upregulated in the group with starter diet supplementation, including 3-hydroxy-3-methylglutaryl coenzyme A synthase (HMGCS2, fold change [FC] = 1.93), 3-hydroxybutyrate dehydrogenase 1 (BDH1, FC = 1.91), and isocitrate dehydrogenase 1 (IDH1, FC = 8.12). The metabolic processes associated with ammonia detoxification and antioxidant stress were also affected by starter diet supplementation, with proteins, microsomal glutathione S-transferase 3 (MGST3, FC = 2.37) and IDH1, linked to the biosynthesis of glutamate and glutathione metabolism pathway being upregulated in the group with starter diet supplementation. In addition, starter feeding decreased the expression of Ras-related protein rap-1A (RAP1A, FC = 0.48) enriched in Rap1 signaling pathway, Ras signaling pathway, cyclic adenosine monophosphate (cAMP) signaling pathway, and mitogen-activated protein kinase (MAPK) signaling pathway. In summary, starter feed supplementation changed the expression of proteins related to energy production, ammonia detoxification, antioxidant stress, and signaling pathways related to proliferation and apoptosis, which facilitates the rumen epithelia development in lambs. The results provide new insights into the molecular adaptation of rumen epithelia in response to starter diet supplementation at the protein level in lambs.

Top-down proteomics: challenges, innovations, and applications in basic and clinical research

Introduction- A better understanding of the underlying molecular mechanism of diseases is critical for developing more effective diagnostic tools and therapeutics toward precision medicine. However, many challenges remain to unravel the complex nature of diseases. Areas covered- Changes in protein isoform expression and post-translation modifications (PTMs) have gained recognition for their role in underlying disease mechanisms. Top-down mass spectrometry (MS)-based proteomics is increasingly recognized as an important method for the comprehensive characterization of proteoforms that arise from alternative splicing events and/or PTMs for basic and clinical research. Here, we review the challenges, technological innovations, and recent studies that utilize top-down proteomics to elucidate changes in the proteome with an emphasis on its use to study heart diseases. Expert opinion- Proteoform-resolved information can substantially contribute to the understanding of the molecular mechanisms underlying various diseases and for the identification of novel proteoform targets for better therapeutic development . Despite the challenges of sequencing intact proteins, top-down proteomics has enabled a wealth of information regarding protein isoform switching and changes in PTMs. Continuous developments in sample preparation, intact protein separation, and instrumentation for top-down MS have broadened its capabilities to characterize proteoforms from a range of samples on an increasingly global scale.

Comparative proteomic analysis of melanosis coli with colon cancer

The present study aimed to investigate the proteomic difference between melanosis coli (MC) alone and melanosis coli with colon cancer (MCCC). Protein expression in patients with different diseases was analyzed using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF/TOF-MS). A total of 14 protein differences with a confidence level of >95% were found. There were six differences between MC and normal tissues, in which two proteins exhibited upregulated expression levels and four proteins exhibited downregulated expression levels in MC. Furthermore, one protein was expressed only in MC (P<0.05). In addition, there were differences in the expression of eight proteins between MC and MCCC tissues, in which one protein had an upregulated expression in MC tissues and seven proteins had an upregulated expression in MCCC tissues. Furthermore, two proteins were only expressed in MCCC tissues (P<0.05). Eight proteins were identified using mass spectrometry and database search. In conclusion, comparative proteomics accurately displayed the expression differences in eight proteins between MC, MCCC and normal colon tissues.

Identification of serum proteome signatures of locally advanced and metastatic gastric cancer: a pilot study

BACKGROUND

The gastric cancer is one of the most common and mortal cancer worldwide. The initial asymptomatic development and further nonspecific symptoms result in diagnosis at the advanced stage with poor prognosis. Yet, no clinically useful biomarkers are available for this malignancy, and invasive gastrointestinal endoscopy remains the only reliable option at the moment. Hence, there is a need for discovery of clinically useful noninvasive diagnostic and/or prognostic tool as an alternative (or complement) for current diagnostic tools. Here we aimed to search for serum proteins characteristic for local and invasive gastric cancer.

METHODS

Pre-treatment blood samples were collected from patients with diagnosed gastric adenocarcinoma at the different stage of disease: 35 patients with locally advanced cancer and 18 patients with metastatic cancer; 50 healthy donors were also included as a control group. The low-molecular-weight fraction of serum proteome (i.e., endogenous peptidome) was profiled by the MALDI-ToF mass spectrometry, and the whole proteome components were identified and quantified by the LC-MS/MS shotgun approach.

RESULTS

Multicomponent peptidome signatures were revealed that allowed good discrimination between healthy controls and cancer patients, as well as between patients with locally advanced and metastatic cancer. Moreover, a LC-MS/MS approach revealed 49 serum proteins with different abundances between healthy donors and cancer patients (predominantly proteins associated with inflammation and acute phase response). Furthermore, 19 serum proteins with different abundances between patients with locally advanced and metastatic cancer were identified (including proteins associated with cytokine/chemokine response and metabolism of nucleic acids). However, neither peptidome profiling nor shotgun proteomics approach allowed detecting serum components discriminating between two subgroups of patients with local disease who either developed or did not develop metastases during follow-up.

CONCLUSIONS

The molecular differences between locally advanced and metastatic gastric cancer, as well as more obvious differences between healthy individuals and cancer patients, have marked reflection at the level of serum proteome. However, we have no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease. Nevertheless, presented data confirmed potential applicability of a serum proteome signature-based biomarker in diagnostics of gastric cancer.

Application of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Imaging Mass Spectrometry (MALDI-TOF IMS) for Premalignant Gastrointestinal Lesions

Imaging mass spectrometry (IMS) is currently receiving large attention from the mass spectrometric community, although its use is not yet well known in the clinic. As matrix-assisted laser desorption/ionization time-of-flight (MALDI)-IMS can show the biomolecular changes in cells as well as tissues, it can be an ideal tool for biomedical diagnostics as well as the molecular diagnosis of clinical specimens, especially aimed at the prompt detection of premalignant lesions much earlier before overt mass formation, or for obtaining histologic clues from endoscopic biopsy. Besides its use for pathologic diagnosis, MALDI-IMS is also a powerful tool for the detection and localization of drugs, proteins, and lipids in tissue. Measurement of parameters that define and control the implications, challenges, and opportunities associated with the application of IMS to biomedical tissue studies might be feasible through a deep understanding of mass spectrometry. In this focused review series, new insights into the molecular processes relevant to IMS as well as other field applications are introduced.

Identifying serum biomarkers for ovarian cancer by screening with surface-enhanced laser desorption/ionization mass spectrometry and the artificial neural network

OBJECTIVE

The purpose of this study was to screen potential serum tumor biomarkers for the diagnosis of ovarian cancer.

METHODS

The study includes 3 sets. The first set of patients included 37 ovarian cancers and 31 healthy women (healthy controls). The second set included 42 ovarian cancers, 33 patients with benign ovarian tumor, and 29 healthy women (noncancer controls). The third set included 39 ovarian cancers and 35 patients with benign ovarian tumor (benign controls). Serum samples from ovarian cancers, healthy controls, noncancer controls, and benign controls were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

A 3-peak model (peaks of mass-to-charge ratio values at 5766.379 d, 5912.586 d, and 11695.56 d) was established in the training set that discriminated cancer from noncancer with high sensitivity (10/11, 90.90%) and specificity (19/20, 95.00%). The peaks corresponding to 3 potential biomarkers increased significantly with the degree of malignancy.

CONCLUSIONS

The proteins represented by these 3 peaks are biomarker candidates for ovarian cancer diagnosis and/or monitoring treatment response.

Proteomics analysis of tumor microenvironment: Implications of metabolic and oxidative stresses in tumorigenesis

Tumorigenesis is always concomitant with microenvironmental alterations. The tumor microenvironment is a heterogeneous and complex milieu, which exerts a variety of stresses on tumor cells for proliferation, survival, or death. Recently, accumulated evidence revealed that metabolic and oxidative stresses both play significant roles in tumor development and progression that converge on a common autophagic pathway. Tumor cells display increased metabolic autonomy, and the hallmark is the exploitation of aerobic glycolysis (termed Warburg effect), which increased glucose consumption and decreased oxidative phosphorylation to support growth and proliferation. This characteristic renders cancer cells more aggressive; they devour tremendous amounts of nutrients from microenvironment to result in an ever-growing appetite for new tumor vessel formation and the release of more "waste," including key determinants of cell fate like lactate and reactive oxygen species (ROS). The intracellular ROS level of cancer cells can also be modulated by a variety of stimuli in the tumor microenvironment, such as pro-growth and pro-inflammatory factors. The intracellular redox state serves as a double-edged sword in tumor development and progression: ROS overproduction results in cytotoxic effects and might lead to apoptotic cell death, whereas certain level of ROS can act as a second-messenger for regulation of such cellular processes as cell survival, proliferation, and metastasis. The molecular mechanisms for cancer cell responses to metabolic and oxidative stresses are complex and are likely to involve multiple molecules or signaling pathways. In addition, the expression and modification of these proteins after metabolic or oxidative stress challenge are diverse in different cancer cells and endow them with different functions. Therefore, MS-based high-throughput platforms, such as proteomics, are indispensable in the global analysis of cancer cell responses to metabolic and oxidative stress. Herein, we highlight recent advances in the understanding of the metabolic and oxidative stresses associated with tumor progression with proteomics-based systems biology approaches.

High electric field strength two-dimensional peptide separations using a microfluidic device

New instrumentation has been developed to improve the resolution, efficiency, and speed of microfluidic 2D separations using MEKC coupled to high field strength CE. Previously published 2D separation instrumentation [Ramsey, J. D. et al., Anal. Chem. 2003, 75, 3758-3764] from our group was limited to a maximum potential difference of 8.4 kV, resulting in an electric field strength of only approximately 200 V/cm in the first dimension. The circuit described in this report has been designed to couple a higher voltage supply with a rapidly switching, lower voltage supply to utilize the best features of each. Voltages applied in excess of 20 kV lead to high electric field strength separations in both dimensions, increasing the separation resolution, efficiency, and peak capacity while reducing the required analysis time. Detection rates as high as six peptides per second (based on total analysis time) were observed for a model protein tryptic digest separation. Additionally, higher applied voltages used in conjunction with microfluidic chips with longer length channels maintained higher electric field strengths and produced peak capacities of over 4000 for some separations. Total separation time in these longer channel devices was comparable to that obtained in short channels at low field strength; however, resolving power improved approximately threefold.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and classification of cancer. Several published studies have shown that multipeptide signatures selected in numerical tests have potential values for diagnostics of different types of cancer. However due to apparent problems with standardization of methodological details, both experimental and computational, none of the proposed peptide signatures analyzed directly by MALDI/SELDI-ToF spectrometry has been approved for routine diagnostics. Noteworthy, several components of proposed cancer signatures, especially those characteristic for advanced cancer, were identified as fragments of blood proteins involved in the acute phase and inflammatory response. This indicated that among cancer biomarker candidates to be possibly identified by serum proteome profiling were rather those reflecting overall influence of a disease (and the therapy) upon the human organism, than products of cancer-specific genes. Current paper focuses on changes in serum proteome that are related to response of patient's organism to progressing malignancy and toxicity of anticancer treatment. In addition, several methodological issues that affect robustness and interlaboratory reproducibility of MS-based serum proteome profiling are discussed.

Expression pattern of osteopontin and αvβ3 integrin during the implantation window in infertile patients with early stages of endometriosis

BACKGROUND

To study endometrial receptivity in terms of osteopontin (OPN) and αvβ3 integrin expression and co-expression in infertile women with early stages of endometriosis.

METHODS

We investigated the immunohistochemical expression and co-expression of OPN and αvβ3 integrin in the endometrium of 20 infertile patients with Stage I or II endometriosis as the only detectable cause of infertility, 20 infertile patients with unexplained infertility and 20 fertile women undergoing tubal sterilization. Two endometrial biopsies were performed during a single menstrual cycle (postovulatory Day +7 to +8 and 4 days later) in each subject.

RESULTS

No statistically significant differences regarding OPN and αvβ3 integrin expression were found between infertile patients with endometriosis and the two control groups. There was no significant correlation between OPN and αvβ3 integrin staining intensity in the mid-luteal phase biopsies in any of the groups studied.

CONCLUSIONS

Endometrial OPN and αvβ3 integrin expression or co-expression is not impaired during the window of implantation in patients with Stage I-II endometriosis. Further studies are needed to determine whether these results imply normal endometrial receptivity in such patients or add to the increasing uncertainty about the clinical value of assessing the endometrium with these markers of implantation.

Urinary exosomes and proteomics

A number of highly abundant proteins in urine have been identified through proteomics approaches, and some have been considered as disease-biomarker candidates. These molecules might be clinically useful in diagnosis of various diseases. However, none has proven to be specifically indicative of perturbations of cellular processes in cells associated with urogenital diseases. Exosomes could be released into urine which flows through the kidney, ureter, bladder and urethra, with a process of filtration and reabsorption. Urinary exosomes have been recently suggested as alternative materials that offer new opportunities to identify useful biomarkers, because these exosomes secreted from epithelial cells lining the urinary track might reflect the cellular processes associated with the pathogenesis of diseases in their donor cells. Proteomic analysis of such urinary exosomes assists the search of urinary biomarkers reflecting pathogenesis of various diseases and also helps understanding the function of urinary exosomes in urinary systems. Thus, it has been recently suggested that urinary exosomes are one of the most valuable targets for biomarker development and to understand pathophysiology of relevant diseases.

Radiation-related changes in serum proteome profiles detected by mass spectrometry in blood of patients treated with radiotherapy due to larynx cancer

The study aimed to detect features of human serum proteome that were associated with exposure to ionizing radiation. The analyzed group consisted of 46 patients treated with radical radiotherapy for larynx cancer; patients were irradiated with total doses in a range from 51 to 72 Gy. Three consecutive blood samples were collected from each patient: before the start, 2 weeks after the start, and 4-6 weeks after the end of radiotherapy. The low-molecular-weight fraction of the serum proteome (2,000-13,000 Da) was analyzed by the MALDI-ToF mass spectrometry. Proteome profiles of serum samples collected before the start of radiotherapy and during the early stage of the treatment were similar. In marked contrast, mass profiles of serum samples collected several weeks after the end of the treatment revealed clear changes. We found that 41 out of 312 registered peptide ions changed their abundance significantly when serum samples collected after the final irradiation were compared with samples collected at the two earlier time points. We also found that abundances of certain serum peptides were associated with total doses of radiation received by patients. The results of this pilot study indicate that features of serum proteome analyzed by mass spectrometry have potential applicability as a retrospective marker of exposure to ionizing radiation.

Intersectional innovation in biomarker development for patient-centric medicine

The pharmaceutical and healthcare industries are being revolutionized by the use of genomics, proteomics, metabolomics, bioinformatics and molecular imaging. Patient friendly diagnosis, treatment and disease management options that utilize the combination of these technologies are currently in development. New innovations in pharmaceutical advancement are taking place at the intersection of these technologies, and will be coupled with societal changes as we move to a fully networked and individual-centric consumer base. Numerous examples of the combinations of molecular characterization technologies aimed at better preclinical and clinical disease understanding, diagnosis and treatment are highlighted that are ideally situated to generate the intersectional innovation that drives healthcare advancement. The true value in patient-centric medicine will only be realized as the improved molecular characterization of disease provided by these technologies is integrated across platforms that operate directly in the patient and care provider space to provide a comprehensive view of health. Molecular profiling and imaging technologies must become fully integrated and amenable for patient and physician use in a networked environment that can provide a personal health avatar approach to medicine.

Clinical proteomics: current status, challenges, and future perspectives

This account will give an overview and evaluation of the current advances in mass spectrometry (MS)-based proteomics platforms and technology. A general review of some background information concerning the application of these methods in the characterization of molecular sizes and related protein expression profiles associated with different types of cells under varied experimental conditions will be presented. It is intended to provide a concise and succinct overview to those clinical researchers first exposed to this foremost powerful methodology in modern life sciences of postgenomic era. Proteomic characterization using highly sophisticated and expensive instrumentation of MS has been used to characterize biological samples of complex protein mixtures with vastly different protein structure and composition. These systems are then used to highlight the versatility and potential of the MS-based proteomic strategies for facilitating protein expression analysis of various disease-related organisms or tissues of interest. Major MS-based strategies reviewed herein include (1) matrix-assisted laser desorption ionization-MS and electron-spray ionization proteomics; (2) one-dimensional or two-dimensional gel-based proteomics; (3) gel-free shotgun proteomics in conjunction with liquid chromatography/tandem MS; (4) Multiple reaction monitoring coupled tandem MS quantitative proteomics and; (5) Phosphoproteomics based on immobilized metal affinity chromatography and liquid chromatography-MS/MS.

Non-invasive proteomics-thinking about personalized breast cancer screening and treatment

The early diagnosis of breast cancer in potentially curable stages improves prognosis and consecutively reduces mortality of breast cancer patients. Established screening programs have an unfavorable connotation due to significant rates of false negative as well as false positive results leading to overdiagnosis and overtherapy. The combination of a non-invasive breast-cancer-suspectability-biomarker with established clinical diagnostics could help to increase the acceptance of population based breast cancer screening programs by creating an individual risk profile, which is irrespective of mammography quality and interpretation. Recently, non-invasive proteomic biomarkers obtained from blood, saliva or nipple aspiration fluid have been extensively investigated and might play a future role in the personalized management of breast cancer screening. A simple, robust and inexpensive, non-invasive test for screening and diagnosis could easily be performed in every medical practice leading to an affordable, high-throughput instrument. This review describes recently investigated proteomic screening biomarkers that could improve the early diagnosis of breast cancer in the following years.

Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

Background

The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.

Methods

Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.

Results

We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.

Conclusions

The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.

Investigation of the ovarian and prostate cancer peptidome for candidate early detection markers using a novel nanoparticle biomarker capture technology

Current efforts to identify protein biomarkers of disease use mainly mass spectrometry (MS) to analyze tissue and blood specimens. The low-molecular-weight "peptidome" is an attractive information archive because of the facile nature by which the low-molecular-weight information freely crosses the endothelial cell barrier of the vasculature, which provides opportunity to measure disease microenvironment-associated protein analytes secreted or shed into the extracellular interstitium and from there into the circulation. However, identifying useful protein biomarkers (peptidomic or not) which could be useful to detect early detection/monitoring of disease, toxicity, doping, or drug abuse has been severely hampered because even the most sophisticated, high-resolution MS technologies have lower sensitivities than those of the immunoassays technologies now routinely used in clinical practice. Identification of novel low abundance biomarkers that are indicative of early-stage events that likely exist in the sub-nanogram per milliliter concentration range of known markers, such as prostate-specific antigen, cannot be readily detected by current MS technologies. We have developed a new nanoparticle technology that can, in one step, capture, concentrate, and separate the peptidome from high-abundance blood proteins. Herein, we describe an initial pilot study whereby the peptidome content of ovarian and prostate cancer patients is investigated with this method. Differentially abundant candidate peptidome biomarkers that appear to be specific for early-stage ovarian and prostate cancer have been identified and reveal the potential utility for this new methodology.

Fast proteomic protocol for biomarker fingerprinting in cancerous cells

The advance of novel technologies that will enable the detection of large sets of biomarker proteins, to greatly improve the sensitivity and specificity of an assay, represents a major objective in biomedical research. To demonstrate the power of mass spectrometry (MS) detection for large-scale biomarker screening in cancer research, a simple, one-step approach for fast biomarker fingerprinting in complex cellular extracts is described. MCF-7 breast cancer cells were used as a model system. Fast proteomic profiling of whole cellular extracts was achieved on a linear trap quadrupole (LTQ) mass spectrometer by one of the following techniques: (a) data-dependent liquid chromatography (LC)-MS/MS of un-labeled cell extracts, (b) data-dependent LC-MS/MS with pulsed Q dissociation (PQD) detection of iTRAQ labeled samples, and (c) multiple reaction monitoring (MRM)-MS of low abundant proteins that could not be detected with data-dependent MS/MS. The data-dependent LC-MS/MS analysis of MCF-7 cells enabled the identification of 796 proteins (p<0.001) and the simultaneous detection of 156 previously reported putative cancer biomarkers. PQD detection of iTRAQ labeled cells resulted in the detection of 389 proteins and 64 putative biomarkers. MRM-MS analysis enabled the successful monitoring of a panel of low-abundance proteins in one single experiment, highlighting the utility of this technique for targeted analysis in cancer investigations. These results demonstrate that MS-based technologies relying on a one-step separation protocol have the potential to revolutionize biomarker research and screening applications by enabling fast, sensitive and reliable detection of large panels of putative biomarkers. To further stimulate the exploration of proteins that have been previously reported in the literature to be differentially expressed in a variety of cancers, an extensive list of approximately 1100 candidate biomarkers has been compiled and included in the manuscript.

Proteomics role in the search for improved diagnosis, prognosis and treatment of osteoarthritis

OBJECTIVE

Osteoarthritis (OA) is the most common rheumatic pathology. It is related to aging and is characterized primarily by cartilage degradation. Despite its high prevalence, the diagnostic methods currently available are limited and lack sensitivity. The focus of this review is the application of proteomic technologies in the search of new biomarkers for improved diagnosis, prognosis and treatment of OA.

METHODS

This review focuses on the utilization of proteomics in OA biomarker research to enable early diagnosis, improved prognosis and the application of tailored treatments.

RESULTS

New diagnostic tests for OA are urgently needed and would also promote the development of alternative therapeutic strategies. Considering that OA involves different tissues and complex biological processes, the most promising diagnostic approach would be the study of combinations of biomarkers. New experimental approaches for the identification and validation of OA biomarkers have recently emerged and include proteomic technologies. These techniques allow the simultaneous analysis of multiple markers and become a very powerful tool for both biomarker discovery and validation.

CONCLUSIONS

Improvements in proteomics technology will undoubtedly lead to advances in characterizing new OA biomarkers and developing alternative therapies. Even so, further work is required to enhance the performance and reproducibility of proteomics tools before they can be routinely used in clinical trials and practice.

Steps for successful implementation of proteomic research in the OR

Proteomic studies (ie, the investigation and identification of proteins found in biological samples such as blood and tissue) are at the forefront of the identification of disease biomarkers and the understanding of proteins. These studies promise to enhance diagnostic and prognostic analysis across all disciplines of clinical practice. As the practice of nursing and medicine becomes more preventative in nature and predictive in terms of patient care, successfully integrating and implementing proteomic research will become increasingly important, especially in the OR. It is imperative that perioperative nurses and researchers establish a collaborative process for specimen collection. Steps in establishing and maintaining a successful specimen collection program include implementing and evaluating a protocol, developing good communication, and keeping all participants up to date on the progress of the study.

Clinical application of proteomics in ovarian cancer prevention and treatment

As recent scientific findings using whole-genome mutational scanning technologies have concluded, cancer is a protein pathway disease, which is often diagnosed too late, when the success of therapeutic modalities is very limited. Proteomics has been proposed as the field that can help overcome this limitation and usher in a new era of molecular investigation for early diagnosis and classification of tumors. Proteomics applications in cancer research encompass two general aspects: (i) the study and characterization of protein production; and (ii) the definition of protein function. The first aims to identify qualitative or quantitative differences in the proteome that can help differentiate between healthy and diseased states or achieve a better clinical classification of diseases. The second studies the complexity of protein interactions and their activation states, mapping the network of signaling pathways within and outside the cells. The challenges in translating the findings of proteomics research into clinical practice are numerous. Lack of reproducibility, variable availability of samples and the bias associated with their selection and handling, the need for large, prospective validation trials, and finally the strict requirement for a very high level of clinical sensitivity and specificity are some of the hurdles that need to be overcome to achieve early detection and treatment. Nevertheless, proteomics is a field in rapid progression that has already developed beyond initial criticism and is making its way toward important applications and discoveries. Specifically, there has been an increasing number of reports on the potential clinical application of proteomics for early detection as well as risk assessment and management of ovarian cancer. This disease is the leading cause of death from gynecologic malignancies in the US, with poor prognosis resulting from the lack of reliable, sensitive screening tests and the limited understanding of the mechanisms of chemoresistance and relapse. In the future, serum proteomics applications in the gynecologic oncology field could identify blood-based biomarkers that are predictors of disease presence or progression, and tissue proteomics could help define the optimal targeted agent and effective dose for each patient's disease. These advances will allow improved monitoring of therapy response and disease relapse, and aid in the engineering of new drugs and strategies to circumvent resistance mechanisms while avoiding the adverse effects of traditional chemotherapy.

Proteomic profiling in ovarian cancer

OBJECTIVE

To describe the role of proteomic profiling in the diagnosis and treatment of ovarian cancer.

METHODS

We report a thorough review of the literature, ongoing trials, and our group's experience with proteomic profiling for early detection, recurrence, and treatment of ovarian cancer.

RESULTS/CONCLUSIONS

Ovarian cancer remains the deadliest gynecologic malignancy in the western world and is most often diagnosed at a rarely curable late stage. Novel applications of proteomic techniques, such as mass spectrometry, show promise in the quest for reliable multimodality screening programs for the early detection of ovarian cancer. Proteomic analysis of tissue samples has underscored the heterogeneity of this disease process. Development of validated assays that survey the genetic and/or proteomic makeup of an individual tumor will add greatly to the histological classification of the tumor and may lead to different treatment approaches tailored to the unique expression pattern of each individual patient. As novel agents that disrupt signal propagation develop, proteomic profiling by reverse-phase protein arrays can characterize the in-tumor efficacy of the agent by quantification of the changes in expression levels of activated proteins. Together, better understanding of the potential diagnostic and therapeutic targets followed with proof-of-target effect will lead to rational combinations of novel therapy and improve individual ovarian cancer patient outcome.

Protein profile of the luteal phase endometrium by tissue microarray assessment

To investigate the luteal phase endometrial expression of leukemia inhibitor factor (LIF), insulin-like growth factor 1 (IGF-1), progesterone receptor (PR), claudin 4 (CLDN4), vascular-endothelial growth factor receptor 3 (VEGFR-3), bone morphogenetic protein 4 (BMP-4) and citokeratin 7 (CK-7), we obtained luteal phase endometrial samples from 52 women. Samples were dated and integrated using a tissue microarray (TMA). Samples were immunostained for LIF, IGF-1, PR, CLDN4, VEGFR-3, BMP-4 and CK-7. Frequencies of positive expressions at the early, mid and late luteal phases were compared by two proportions test. Concomitant expression of these proteins was assessed with Chi-square or Fischer's test. The frequency of LIF was positively correlated to the frequency of IGF-1 (r = 0.99; p < 0.05) and PR (r = 0.99; p < 0.05), and the correlation between IGF-1 and PR tended to be significant (r = 0.98; p < 0.1). The expression of PR was associated with the absence of CLDN4 (p < 0.001). Thus, expression of LIF, IGF-1 and PR are correlated during the luteal phase, and immunohistochemistry for these proteins might be used to assist in the assessment of endometrial maturation. In addition, the expression of CLDN4 and PR was not concomitant, warranting further investigation on the relationship of their endometrial expression.

Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

Background

Mass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry.

Methods

Blood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves.

Results

We have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity).

Conclusion

MALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Comparative analysis of differential serum low molecular weight proteins between colorectal cancer and other malignant tumors

AIM: To evaluate the diagnostic specificity of differentially expressed serum low molecular proteins for colorectal cancer by comparative analysis between the patients with colorectal cancer and with other malignant tumors.

METHODS: A total of 218 serum samples from patients with colorectal cancer (n = 68) and other malignant tumors (breast, gastric, esophagus, liver, lung and kidney, 25 cases each) were collected. Their serum protein fingerprinting was read by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). Biomarker Wizard software was used to analyze the distinct proteins between colorectal cancer and other malignant tumors and to create receiver operating characteristic (ROC) curves. Then Fisher's discriminant model was established using the distinct proteins with the area under the curve (AUC) > 0.8.

RESULTS: Relatively 12 proteins were highly expressed and 102 proteins were lowly expressed in the sera of colorectal cancer compared with that of the other malignant tumors. Of all the 75 proteins with AUC > 0.5 by ROC analysis, six proteins (AUC > 0.8) were selected to establish the Fisher's discriminant model. The accuracy of distinguishing colorectal cancer from other malignant tumors was 91.7%, and the sensitivity and specificity were 88.2% and 93.3%, respectively.

CONCLUSION: The significantly differentially expressed serum low molecular proteins have a high diagnostic specificity for distinguishing colorectal cancer from other malignant tumors, which deserves further research.

The current state of proteomics in GI oncology

Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy.

Isoflavones and the prevention of breast and prostate cancer: new perspectives opened by nutrigenomics

Epidemiological evidence together with preclinical data from animal and in vitro studies strongly support a correlation between soy isoflavone consumption and protection towards breast and prostate cancers. The biological processes modulated by isoflavones, and especially by genistein, have been extensively studied, yet without leading to a clear understanding of the cellular and molecular mechanisms of action involved. This review discusses the existing gaps in our knowledge and evaluates the potential of the new nutrigenomic approaches to improve the study of the molecular effects of isoflavones. Several issues need to be taken into account for the proper interpretation of the results already published for isoflavones. Too often knowledge on isoflavone bioavailability is not taken into account; supra-physiological doses are frequently used. Characterization of the individual variability as defined by the gut microflora composition and gene polymorphisms may also help to explain the discrepancies observed so far in the clinical studies. Finally, the complex inter-relations existing between tissues and cell types as well as cross-talks between metabolic and signalling pathways have been insufficiently considered. By appraising critically the abundant literature with these considerations in mind, the mechanisms of action that are the more likely to play a role in the preventive effects of isoflavones towards breast and prostate cancers are reviewed. Furthermore, the new perspectives opened by the use of genetic, transcriptomic, proteomic and metabolomic approaches are highlighted.

Use of genome-wide high-throughput technologies in biomarker development

In recent years, the usage of high-throughput technologies in the fields of genomics, transcriptomics, proteomics and metabolomics for biomarker discovery has expanded enormously. Biomarkers can be applied for many purposes, including diagnosis, prognosis, staging and selecting appropriate patient therapy. In addition, biomarkers can provide information on disease mechanism or progression. Biomarker development for clinical application encompasses phases for their discovery and characterization, assay development and, finally, implementation using automated platforms employed in clinical laboratories. However, translation from bench to bedside outside a research-oriented environment has proven to be more difficult. This is reflected by only few new biomarkers being integrated into clinical application in the last years. This article reviews currently used high-throughput technologies for the identification of biomarkers, as well as present approaches to increase the percentage of biomarkers that pass the barriers for clinical application.

Chemical and pathway proteomics: powerful tools for oncology drug discovery and personalized health care

In recent years mass spectrometry-based proteomics has moved beyond a mere quantitative description of protein expression levels and their possible correlation with disease or drug action. Impressive progress in LC-MS instrumentation together with the availability of new enabling tools and methods for quantitative proteome analysis and for identification of posttranslational modifications has triggered a surge of chemical and functional proteomics studies dissecting mechanisms of action of cancer drugs and molecular mechanisms that modulate signal transduction pathways. Despite the tremendous progress that has been made in the field, major challenges, relating to sensitivity, dynamic range, and throughput of the described methods, remain. In this review we summarize recent advances in LC-MS-based approaches and their application to cancer drug discovery and to studies of cancer-related pathways in cell culture models with particular emphasis on mechanistic studies of drug action in these systems. Moreover we highlight the emerging utility of pathway and chemical proteomics techniques for translational research in patient tissue.

Proteomic strategies in bladder cancer: From tissue to fluid and back

We have applied protein expression profiling technologies in combination with immunohistochemistry, using fresh tissue and urine samples, to assess bladder cancer heterogeneity and prognosis as well as to generate protein markers for tumor progression and early diagnosis of the disease. Here, we review some selected lines of investigation and approaches undertaken by our laboratory, drawing on more than 15 years of experience in bladder cancer proteomics, to highlight a number of issues that may be useful for researchers entering the field. In particular, we address the identification of markers for bladder cancer progression and exemplify the potential of gel-based proteomic profiling of urine samples for the early detection of urothelial carcinomas. In addition, we provide a brief description of a novel and highly promising source of biomarkers, the tumor interstitial fluid (TIF) that perfuses the tumor microenvironment.

Advances in tumor-related molecular phenotypes and personalized therapy

Malignant tumor impairs human's health severely. Although progresses have been made in tumor's etiology, clinical management of tumor is still confronted by challenges. The standard treatments against tumor, including surgery, chemotherapy and radiotherapy, are not effective for all patients. The clinicians often feel confused while selecting appropriate therapeutic pattern and judging patients' prognosis. The clinical outcome of target-protein-based cancer therapy revealed that personalized molecular signature determined individualized curative effect. Effective management of tumor needs patient-tailored design. The so-called "omic" researches accelerate the studies in specific molecule phenotype of tumor cells, and the personalized therapy against such individualized malignant phenotype will represent the future direction in tumor treatment.