The molecular make-up of a tumour: proteomics in cancer research

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Plasma exosomal protein PLG and SERPINA1 in colorectal cancer diagnosis and coagulation abnormalities

PURPOSE

Early diagnosis of colorectal cancer (CRC) is critical to patient prognosis; however, there is lack of non-invasive biomarkers that are extremely sensitive and specific for early screening and diagnosis. Exosomes are a novel tool applied to the diagnosis and treatment of cancer. Changes in plasma exosomal proteins have a certain relationship with the development of various diseases including tumors. Here, we aimed to find exosomal biomarkers for early diagnosis of CRC.

METHODS

Exosomes obtained by ultracentrifugation from CRC patients and healthy donors were characterized by transmission electron microscopy (TEM), qNano and western blotting. Proteomic and functional enrichment analyses confirmed differences in the specific expression of exosomal proteins in plasma between CRC patients and healthy donors. Western blotting with enzyme-linked immunosorbent assay (ELISA) was used to verify the difference proteins. Statistical methods were used to analyze the relationship between protein levels and CRC.

RESULTS

The expression levels of serpin peptidase inhibitor clade A member 1 (SERPINA1) and fibrinogen (PLG) in CRC patients were significantly higher than those in healthy groups. Receptor operating characteristic (ROC) curves analysis was superior to CEA and CA19-9 for the diagnosis of colorectal cancer and early-stage colorectal cancer. The two were related to TNM staging and coagulation, and the difference was statistically significant.

CONCLUSION

The results of this study have potential value in advancing the clinical diagnosis of colorectal cancer.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers

PURPOSE

Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic.

EXPERIMENTAL DESIGN

The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma.

RESULTS

A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects.

CONCLUSIONS AND CLINICAL RELEVANCE

Through the established glycoproteomics workflow, intact O- and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

Clinical Proteomics: Closing the Gap from Discovery to Implementation

Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.

Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

PURPOSE OF REVIEW

Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years.

RECENT FINDINGS

Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, α-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression.

SUMMARY

Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making.

Urinary biomarkers for renal tract malformations

INTRODUCTION

Renal tract malformations (RTMs) are congenital anomalies of the kidneys and urinary tract, which are the major cause of end-stage renal disease in children. Using immunoassay-based approaches (ELISA, western blot), individual urinary proteins including transforming growth factor β, tumor necrosis factor and monocyte attractant proteins 1 were found to be associated to RTMs. However, only mass spectrometry (MS) based methods leading to the identification of panels of protein-based markers composed of fragments of the extracellular matrix allowed the prediction of progression of RTMs and its complications. Areas covered: In this review, we summarized relevant studies identified in "Pubmed" using the keywords "urinary biomarkers" and "proteomics" and "renal tract malformations" or "hydronephrosis" or "ureteropelvic junction obstruction" or "posterior urethral valves" or "vesicoureteral reflux". These publications represent studies on potential protein-based biomarkers, either individually or combined in panels, of RTMs in human and animal models. Expert commentary: Successful use in the clinic of these protein-based biomarkers will need to involve larger scale studies to reach sufficient power. Improved performance will potentially come from combining immunoassay- and MS-based markers.

Body fluid peptide and protein signatures in diabetic kidney diseases

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results.

Discovering cancer biomarkers from clinical samples by protein microarrays

Cancer biomarkers are of potential use in early cancer diagnosis, anticancer therapy development, and monitoring the responses to treatments. Protein-based cancer biomarkers are major forms in use, as they are much easier to be monitored in body fluids or tissues. For cancer biomarker discovery, high-throughput techniques such as protein microarrays hold great promises, because they are capable of global unbiased monitoring but with a miniaturized format. In doing so, novel and cancer type specific biomarkers can be systematically discovered at an affordable cost. In this review, we give a relatively complete picture on protein microarrays applied to clinical samples for cancer biomarker discovery, and conclude this review with the future perspectives.

Advances in urinary proteome analysis and applications in systems biology

The urinary proteome is the focus of many studies due to the ease of urine collection and the relative proteome stability. Systems biology allows the combination of multiple omics studies, forming a link between proteomics, metabolomics, genomics and transcriptomics. In-depth data interpretation is achieved by bioinformatics analysis of -omics data sets. It is expected that the contribution of systems biology to the study of the urinary proteome will offer novel insights. The main focus of this review is on technical aspects of proteomics studies, available tools for systems biology analysis and the application of urinary proteomics in clinical studies and systems biology.

HaloTag technology: a versatile platform for biomedical applications

Exploration of protein function and interaction is critical for discovering links among genomics, proteomics, and disease state; yet, the immense complexity of proteomics found in biological systems currently limits our investigational capacity. Although affinity and autofluorescent tags are widely employed for protein analysis, these methods have been met with limited success because they lack specificity and require multiple fusion tags and genetic constructs. As an alternative approach, the innovative HaloTag protein fusion platform allows protein function and interaction to be comprehensively analyzed using a single genetic construct with multiple capabilities. This is accomplished using a simplified process, in which a variable HaloTag ligand binds rapidly to the HaloTag protein (usually linked to the protein of interest) with high affinity and specificity. In this review, we examine all current applications of the HaloTag technology platform for biomedical applications, such as the study of protein isolation and purification, protein function, protein–protein and protein–DNA interactions, biological assays, in vitro cellular imaging, and in vivo molecular imaging. In addition, novel uses of the HaloTag platform are briefly discussed along with potential future applications.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Proteomic biomarkers in kidney disease: issues in development and implementation

Proteomic biomarkers offer the hope of improving the management of patients with kidney diseases by enabling more accurate and earlier detection of renal pathology than is possible with currently available biomarkers, serum creatinine and urinary albumin. In addition, proteomic biomarkers could also be useful to define the most suitable therapeutic targets in a given patient or disease setting. This Review describes the current status of proteomic and protein biomarkers in the context of kidney diseases. The valuable lessons learned from early clinical studies of potential proteomic biomarkers in kidney disease are presented to give context to the newly identified biomarkers, which have potential for actual clinical implementation. This article also includes an overview of protein-based biomarker candidates that are undergoing development for use in nephrology, focusing on those with the greatest potential for clinical implementation. Relevant issues and problems associated with the discovery, validation and clinical application of proteomic biomarkers are discussed, along with suggestions for solutions that might help to guide the design of future proteomic studies. These improvements might remove some of the current obstacles to the utilization of proteomic biomarkers, with potentially beneficial results.

Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Proteomics in hypertension and other cardiovascular diseases

Hypertension is a major cardiovascular risk factor with a multifactorial pathogenesis, including genetic and environmental factors. In addition to hypothesis-driven strategies, unbiased approaches such as genomics, proteomics, and metabolomics are useful tools to help unravel the pathophysiology of hypertension and associated organ damage. During development of cardiovascular disease the key organs and tissues undergo extensive functional and structural changes that are characterized by alterations in the amount and type of proteins that are expressed. Proteomic approaches study the expression of large numbers of proteins in organs, tissues, cells, and body fluids. A number of different proteomic platforms are available, many of which combine two methods to separate proteins and peptides after an initial digestion step. Identification of these peptides and changes in their expression in parallel with disease processes or medical treatment will help to identify as yet unknown pathophysiological pathways. There is also potential to use proteomic signatures as biomarkers of cardiovascular disease that will contribute to population screening, diagnosis of diseases and their severity, and monitoring of therapeutic interventions.

An overview of esophageal squamous cell carcinoma proteomics

Esophageal squamous cell carcinoma (ESCC) still remains the leading cancer-caused mortality in northern China, in particular in areas nearby Taihang Mountain. Late-stage diagnosis of ESCC increases the mortality and morbidity of ESCC. Therefore, it is imperative to identify biomarkers for early diagnosis, monitoring of tumor progression and identifying potential therapeutic targets of ESCC. Proteomics provides a functional translation of the genome and represents a richer source for the functional description of diseases and biomarkers implicated in cancer. In this review, we discuss the dysregulated proteins associated with ESCC identified by proteomic approaches and aim to enhance our understanding of molecular mechanisms implicated in ESCC development and progression from a proteomics perspective and discuss the potential biomarkers of ESCC as well.

Angiotensin II differentially regulates Morg1 expression in kidney cells

BACKGROUND

The mitogen-activated protein kinase organizer 1 (Morg1) belongs to the WD-40 repeat protein family and is a scaffold molecule for the extracellular regulated kinase signaling pathway. Morg1 also binds to prolyl-hydroxylase 3 (PHD3) and regulates the hypoxia-inducible factor-1α (HIF-1α) expression via PHD3 stabilization. Morg1 has been detected in the kidney as well as in other cell tissues but its expression in renal cells has not been well investigated. It has been widely shown that angiotensin II (ANG II) mediates renal damage. We have previously shown that ANG II downregulates the expression of PHD3 in PC12 cells. The aim of this study was to analyze whether ANG II regulates Morg1 expression in mouse mesangial cells (MMC), mouse proximal tubular cells (MTC) and in differentiated podocytes. The correlation between the expression of Morg1 and PHD3 activity was also addressed.

METHODS

Effect of ANG II on the Morg1 mRNA expression level was assessed by real-time PCR. Morg1 and HIF-1α cellular localization was analyzed by immunohistochemistry. HIF-1α promoter activity was investigated using a reporter gene system. PHD3 hydroxylase activity test was measured with a hydroxylation-coupled decarboxylation assay.

RESULTS

ANG II differentially regulates Morg1 expression in MMC, MTC and differentiated podocytes. We detected a biphasic effect of ANG II on Morg1 mRNA expression which was time dependent. While 9-hour ANG II treatment downregulated Morg1 expression in MMC, it induced Morg1 expression in MTC. Conversely, 24-hour ANG II stimulation upregulated the expression of Morg1 mRNA in MMC, but showed an opposite effect in MTC and differentiated podocytes. In addition, we found that ANG II signals mostly through the AT(1) receptor subtype in MMC and via the AT(2) subtype in MTC. PHD3 activity correlated to Morg1 expression patterns. Our data also demonstrate that HIF-1α transcriptional activity in MTC contrasted to PHD3 activity at 9 and 24 h, whereas in the MMC and in podocytes we did not find any correlation between PHD3 HIF-1α hydroxylation ability and HIF-1α transcriptional activation, suggesting a different mechanism of regulation in these cell types. Interestingly, the reduced expression of Morg1 in mesangial cells isolated from Morg1 (+/-) heterozygous mice correlated with a reduced PHD3 enzymatic activity and an increased HIF-1α transcriptional activity compared with mesangial cells originated from wild-type (Morg1 +/+) mice.

CONCLUSIONS

We show for the first time in various renal cells that ANG II modulates Morg1 expression and HIF-1α transcriptional activity via cell type-specific mechanisms, demonstrating a novel mechanism by which ANG II may contribute to renal disease.

Proteomic strategies and challenges in tumor metastasis research

The rapidly evolving field of proteomics offers new approaches to understanding the pathogenesis of cancer and metastatic disease. Although numerous tumor markers have been identified with different genomic methods in the past, most are either not specific or sensitive enough to be used in routine clinical setting. The rationale for proteomic profiling is based on the fact that proteins represent the dynamic state of the cells, reflecting pathophysiological changes in the disease more accurately than genomic and epigenetic alterations. Emerging proteomic techniques allow simultaneous assessment of a large number of proteins at one time. The study of protein profiles in complex systems, such as plasma, serum or tissues of cancer patients is likely to become valuable for monitoring the response of patients during treatment or for detecting recurrence of the disease.

Comparative proteomical and metalloproteomical analyses of human plasma from patients with laryngeal cancer

Laryngeal cancer is a significant disease worldwide, which presents an increasing incidence. Two contrasting ideas of the immune system role during cancer development are accepted: (1) it fights tumor cells, and (2) it aids tumor progression. Thus, there is no clear understanding about the immune response in laryngeal cancer. Furthermore, since tobacco is the main cause of laryngeal cancer and it contains various carcinogenic components, including metallic elements, these may play a role on cancer development. Plasmas of patients with laryngeal cancer and of healthy smokers were evaluated by 2D gel electrophoresis and mass spectrometry. Proteins were detected on every gel around pH 4.0-10.0 from molecular mass of 10-60 kDa. Few differences were found among cancer and control patients. However, three spots gathered between pI 7.3 and 7.6 with different molecular masses appeared exclusively in cancer profiles. From ten spots identified, six correspond to immune system components, including the three differential ones. The latter were observed only in cancer patients. The presence of several trace elements in the identified proteins was determined by inductively coupled plasma mass spectrometry, where chromium was increased in all proteins analyzed from patients with cancer. This study reinforces the importance of the immune response as target in the understanding and treatment of laryngeal cancer and the possibility that chromium is important in the carcinogenic progress.

Proteomic identification of multitasking proteins in unexpected locations complicates drug targeting

Proteomics has revealed that many proteins are present in unexpected cellular locations. Moreover, it is increasingly recognized that proteins can translocate between intracellular and extracellular compartments in non-conventional ways. This increases gene pleiotrophy as the diverse functions of the protein that the gene encodes are dependent on the cellular location. Given that trafficking drug targets may exist in various forms--often with completely different functions--in multiple cellular compartments, careful interpretation of proteomics data is needed for an accurate understanding of gene function. This Perspective is intended to inspire the investigation of unusual protein localizations, rather than assuming that they are due to mislocalization or artefacts. Given a fair chance, proteomics could reveal novel and unforeseen biology with important ramifications for target validation in drug discovery.

Institutional shared resources and translational cancer research

The development and maintenance of adequate shared infrastructures is considered a major goal for academic centers promoting translational research programs. Among infrastructures favoring translational research, centralized facilities characterized by shared, multidisciplinary use of expensive laboratory instrumentation, or by complex computer hardware and software and/or by high professional skills are necessary to maintain or improve institutional scientific competitiveness. The success or failure of a shared resource program also depends on the choice of appropriate institutional policies and requires an effective institutional governance regarding decisions on staffing, existence and composition of advisory committees, policies and of defined mechanisms of reporting, budgeting and financial support of each resource. Shared Resources represent a widely diffused model to sustain cancer research; in fact, web sites from an impressive number of research Institutes and Universities in the U.S. contain pages dedicated to the SR that have been established in each Center, making a complete view of the situation impossible. However, a nation-wide overview of how Cancer Centers develop SR programs is available on the web site for NCI-designated Cancer Centers in the U.S., while in Europe, information is available for individual Cancer centers. This article will briefly summarize the institutional policies, the organizational needs, the characteristics, scientific aims, and future developments of SRs necessary to develop effective translational research programs in oncology.

In fact, the physical build-up of SRs per se is not sufficient for the successful translation of biomedical research. Appropriate policies to improve the academic culture in collaboration, the availability of educational programs for translational investigators, the existence of administrative facilitations for translational research and an efficient organization supporting clinical trial recruitment and management represent essential tools, providing solutions to overcome existing barriers in the development of translational research in biomedical research centers.

A kernel-based integration of genome-wide data for clinical decision support

Background

Although microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework.

Methods

We propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted.

Results

For the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis.

Conclusions

For both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.

Role of immunogenic fetuin A on L5178Y-R lymphoma tumorigenesis

In the present study, we report the detection of immunogenic fetuin-A on L5178Y-R (LY-R) lymphoma cells. Fetuin-A was recognized by antibodies present in the serum of LY-R tumor-bearing and immunized mice, but not by sera of mice immunized with the non-tumorigenic variant LY-S or by healthy mouse sera. However, according with Western blot analysis with commercial anti-fetuin antibodies, fetuin-A is present in both cell types which suggests that the fetuin recognized by anti-LY-R antibodies is an immunogenic form associated only with the tumorigenic LY-R cells and might be involved in tumor progression in this lymphoma.

Proteomics and cancer epidemiology

Profiling of differentially expressed proteins is perhaps the most important and useful approach in developing tools for risk assessment in a population, diagnostic screening, and therapeutics. Proteomic markers have potential for identifying individuals at high risk of developing cancer; however, these markers have not been extensively used in cancer epidemiologic studies. Several markers have to be clinically validated. In this chapter, methods used in proteomic analysis of clinical samples, challenges in the proteomics and cancer epidemiology, and their potential solutions are discussed.

Human papillomavirus in cervical cancer screening: important role as biomarker

Cervical cytology screening has reduced cervical cancer morbidity and mortality but shows important shortcomings in terms of sensitivity and specificity. Infection with distinct types of human papillomavirus (HPV) is the primary etiologic factor in cervical carcinogenesis. This causal relationship has been exploited for the development of molecular technologies for viral detection to overcome limitations linked to cytologic cervical screening. HPV testing has been suggested for primary screening, triage of equivocal Pap smears or low-grade lesions and follow-up after treatment for cervical intraepithelial neoplasia. Determination of HPV genotype, viral load, integration status and RNA expression could further improve the effectiveness of HPV-based screening and triage strategies. The prospect of prophylactic HPV vaccination stresses the importance of modification of the current cytology-based screening approach.

Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry

With unmatched mass resolution, mass accuracy, and exceptional detection sensitivity, Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) has the potential to be a powerful new technique for high-throughput metabolomic analysis. In this study, we examine the properties of an ultrahigh-field 12-Tesla (12T) FTICR-MS for the identification and absolute quantitation of human plasma metabolites, and for the untargeted metabolic fingerprinting of inbred-strain mouse serum by direct infusion (DI). Using internal mass calibration (mass error ≤1 ppm), we determined the rational elemental compositions (incorporating unlimited C, H, N and O, and a maximum of two S, three P, two Na, and one K per formula) of approximately 250 out of 570 metabolite features detected in a 3-min infusion analysis of aqueous extract of human plasma, and were able to identify more than 100 metabolites. Using isotopically-labeled internal standards, we were able to obtain excellent calibration curves for the absolute quantitation of choline with sub-pmol sensitivity, using 500 times less sample than previous LC/MS analyses. Under optimized serum dilution conditions, chemical compounds spiked into mouse serum as metabolite mimics showed a linear response over a 600-fold concentration range. DI/FTICR-MS analysis of serum from 26 mice from 2 inbred strains, with and without acute trichloroethylene (TCE) treatment, gave a relative standard deviation (RSD) of 4.5%. Finally, we extended this method to the metabolomic fingerprinting of serum samples from 49 mice from 5 inbred strains involved in an acute alcohol toxicity study, using both positive and negative electrospray ionization (ESI). Using these samples, we demonstrated the utility of this method for high-throughput metabolomics, with more than 400 metabolites profiled in only 24 h. Our experiments demonstrate that DI/FTICR-MS is well-suited for high-throughput metabolomic analysis.

From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities

Substantial research efforts into predictive radiation oncology have so far produced very little in terms of clinically applicable assays. This may change with the development of novel high-throughput assays that are of potential interest in a radiation oncology setting. However, it seems that much current research is opportunistic, driven by the available technologies rather than addressing pertinent clinical or biological questions. This review looks at the experience gained from the attempts to develop cellular radiobiology assays. The research process and, in particular, the need for rigorous validation of any promising assay in an independent dataset are stressed. Some common design problems are discussed using examples from radiation oncology. The statistical challenges and some of the key concepts in analyzing dense datasets from high-throughput assays are briefly reviewed. Single nucleotide polymorphisms, immunohistochemical markers, and DNA microarray gene signatures are used as examples of assays that show promise in radiation oncology applications. Some recent studies suggest a differential treatment response between tumor stem cells and other tumor cells. If this is a general pattern, then future predictive assays may have to be performed on stems cells rather than on unselected tumor cells. Advances in radiogenomics or radioproteomics will come from large collaborative research networks, collecting high-quality dosimetric and clinical outcome data and combining state-of-the-art laboratory techniques with appropriate biostatical methods.

In silico modulation of apoptotic Bcl-2 proteins by mistletoe lectin-1: functional consequences of protein modifications

The mistletoe lectin-1 (ML-1) modulates tumor cell apoptosis by triggering signaling cascades through the complex interplay of phosphorylation and O-linked N-acetylglucosamine (O-GlcNAc) modification in pro- and anti-apoptotic proteins. In particular, ML-1 is predicted to induce dephosphorylation of Bcl-2-family proteins and their alternative O-GlcNAc modification at specific, conserved Ser/Thr residues. The sites for phosphorylation and glycosylation were predicted and analyzed using Netphos 2.0 and YinOYang 1.2. The involvement of modified Ser/Thr, and among them the potential Yin Yang sites that may undergo both types of posttranslational modification, is proposed to mediate apoptosis modulation by ML-1.

Proteomic identification of interleukin-2 therapy response in metastatic renal cell cancer

PURPOSE

To detect a predictive protein profile that distinguishes interleukin-2 therapy responders and nonresponders among patients with metastatic renal cell carcinoma we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

MATERIALS AND METHODS

Protein extracts from 56 patients with metastatic clear cell patients renal cell carcinoma obtained from radical nephrectomy specimens before interleukin-2 therapy were applied to protein chip arrays of different chromatographic properties and analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. A class prediction algorithm was applied to identify a subset of protein peaks with expression values associated with interleukin-2 response status. Multivariate analysis was performed to assess the association between the proteomic profile and interleukin-2 response status, controlling for the effect of lymphadenopathy.

RESULTS

From 513 protein peaks we discovered a predictor set of 11 that performed optimally for predicting interleukin-2 response status with 86% accuracy (Fisher's p <0.004, permutation p <0.01). Results were validated in an independent data set with 72% overall accuracy (p <0.05, permutation p <0.01). On multivariate analysis the proteomic profile was significantly associated with the interleukin-2 response when corrected for lymph node status (p <0.04).

CONCLUSIONS

We identified and validated a proteomic pattern that is an independent predictor of the interleukin-2 response. The ability to predict the probability of the interleukin-2 response could permit targeted selection of the patients most likely to respond to interleukin-2, while avoiding unwanted toxicity in patients less likely to respond. This proteomic predictor has the potential to significantly aid clinicians in the decision making of appropriate therapy for patients with metastatic renal cell carcinoma.

Proteomics technologies and challenges

Proteomics is the study of proteins and their interactions in a cell. With the completion of the Human Genome Project, the emphasis is shifting to the protein compliment of the human organism. Because proteome reflects more accurately on the dynamic state of a cell, tissue, or organism, much is expected from proteomics to yield better disease markers for diagnosis and therapy monitoring. The advent of proteomics technologies for global detection and quantitation of proteins creates new opportunities and challenges for those seeking to gain greater understanding of diseases. High-throughput proteomics technologies combining with advanced bioinformatics are extensively used to identify molecular signatures of diseases based on protein pathways and signaling cascades. Mass spectrometry plays a vital role in proteomics and has become an indispensable tool for molecular and cellular biology. While the potential is great, many challenges and issues remain to be solved, such as mining low abundant proteins and integration of proteomics with genomics and metabolomics data. Nevertheless, proteomics is the foundation for constructing and extracting useful knowledge to biomedical research. In this review, a snapshot of contemporary issues in proteomics technologies is discussed.

High-resolution proteome/peptidome analysis of peptides and low-molecular-weight proteins in urine

All organisms contain thousands of proteins and peptides in their body fluids. A deeper insight into the functional relevance of these polypeptides under different physiological and pathophysiological conditions and the discovery of specific peptide biomarkers would greatly enhance diagnosis and therapy of specific diseases. The low-molecular-weight proteome, also termed peptidome, provides a rich source of information. Due to its unique features, the technical challenges differ somewhat from those in "common" proteomics. In this manuscript, we focus on the low-molecular-weight urinary proteome. We review the methodological aspects of sample collection, preparation, analysis, and subsequent data evaluation. In the second part of this review, we summarize the recent progress in the definition and identification of clinically relevant polypeptide markers.

Oncogenic signaling pathways and deregulated target genes

A limited number of somatic mutations are known to trigger malignancy via chronic activation of cellular signaling pathways. High-throughput analysis of gene expression in cancer cells has revealed a plethora of deregulated genes by far exceeding the number of known genetic alterations. Targeted tumor therapy takes advantage of deregulated signaling in cancer. However, cancer cells may evade successful therapy, e.g., targeting oncogenic kinases, due to mutation of the target protein or to resistance mechanisms acting downstream of or parallel to the therapeutic block. To improve therapy and molecular diagnostics, we need detailed information on the wiring of pathway components and targets that ultimately execute the malignant properties of advanced tumors. Here we review work on Ras-mediated signal transduction and Ras pathway-responsive targets. We introduce the concept of signal-regulated transcriptional modules comprising groups of target genes responding to individual branches of the pathway network. Furthermore, we discuss functional approaches based on RNA interference for elucidating critical nodes in oncogenic signaling and the targets essential for malignancy.

Mass spectrometry-based "omics" technologies in cancer diagnostics

Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.