Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Peptide set test: a peptide-centric strategy to infer differentially expressed proteins

MOTIVATION

The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.

RESULTS

We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.

AVAILABILITY AND IMPLEMENTATION

Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

Exploring Predictive Biomarkers of Relapse in Ulcerative Colitis: A Proteomics Approach

INTRODUCTION AND AIMS

Risk stratification of subjects with a history of inflammatory bowel disease (IBD) into those likely to relapse and those who will remain quiescent continues to be a significant challenge. The aim of this study was to investigate whether certain proteomic signature profiles or biomarkers during remission are associated with future disease relapse in patients with ulcerative colitis (UC).

METHODS

Endoscopic rectal samples from patients with UC in clinical, endoscopic, and histological remission at index endoscopy were collected, as well as samplers from normal control individuals. The patients were stratified to early relapsers (ERs) if they developed clinical signs of UC flare within 6 months of index endoscopy or nonrelapsers (NRs) if there was no relapse after 36 months of follow-up. The pooled rectal samples from ERs, NRs, and control individuals were subjected to nano-liquid chromatography and tandem mass spectrometry as per standard iTRAQ (isobaric tags for relative and absolute quantitation) workflow methodology. Selected proteomics-yielded candidates were subjected to orthogonal validation via immunoblotting, in a biomarker discovery exercise.

RESULTS

Sixty-one patients were included, of whom 8 had clinical relapse within 6 months from the index endoscopy, and 43 patients had no clinical symptoms of relapse within the 36-month follow-up period. Ten patients who had clinical signs of relapse between 6 and 36 months were excluded. Seventeen control individuals were also included. Soluble proteomics analyses between ERs, NRs, and control individuals revealed a series of upregulated and downregulated proteins. Following orthogonal validation, upregulated TRX (P = .001) and IGHA1 (P = .001) were observed in ERs relative to NRs.

CONCLUSIONS

Several novel candidate tissue biomarkers have been identified in this study, which could discriminate patients with UC at risk of early relapse from those in long-term sustained remission. Our findings may pave the way for pre-emptive UC disease monitoring and therapeutic decision making.

Phosphoproteomics analysis of serum from dogs affected with pulmonary hypertension secondary to degenerative mitral valve disease

Pulmonary hypertension (PH), a common complication in dogs affected by degenerative mitral valve disease (DMVD), is a progressive disorder characterized by increased pulmonary arterial pressure (PAP) and pulmonary vascular remodeling. Phosphorylation of proteins, impacting vascular function and cell proliferation, might play a role in the development and progression of PH. Unlike gene or protein studies, phosphoproteomic focuses on active proteins that function as end-target proteins within signaling cascades. Studying phosphorylated proteins can reveal active contributors to PH development. Early diagnosis of PH is crucial for effective management and improved clinical outcomes. This study aimed to identify potential serum biomarkers for diagnosing PH in dogs affected with DMVD using a phosphoproteomic approach. Serum samples were collected from healthy control dogs (n = 28), dogs with DMVD (n = 24), and dogs with DMVD and PH (n = 29). Phosphoproteins were enriched from the serum samples and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Data analysis was performed to identify uniquely expressed phosphoproteins in each group and differentially expressed phosphoproteins among groups. Phosphoproteomic analysis revealed nine uniquely expressed phosphoproteins in the serum of dogs in the DMVD+PH group and 15 differentially upregulated phosphoproteins in the DMVD+PH group compared to the DMVD group. The phosphoproteins previously implicated in PH and associated with pulmonary arterial remodeling, including small nuclear ribonucleoprotein G (SNRPG), alpha-2-macroglobulin (A2M), zinc finger and BTB domain containing 42 (ZBTB42), hemopexin (HPX), serotransferrin (TRF) and complement C3 (C3), were focused on. Their unique expression and differential upregulation in the serum of DMVD dogs with PH suggest their potential as biomarkers for PH diagnosis. In conclusion, this phosphoproteomic study identified uniquely expressed and differentially upregulated phosphoproteins in the serum of DMVD dogs with PH. Further studies are warranted to validate the diagnostic utility of these phosphoproteins.

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

Salivary proteins offer insights into keratinocyte death during aphthous stomatitis. A case-crossover study

BACKGROUND

The death of oral keratinocytes is a crucial step in the emergence of recurrent aphthous stomatitis (RAS, also known as aphthae or aphthous ulcers). Since there are no experimental models available to research aphthous ulcers, little is understood about this process. We hypothesize that saliva can be a data bank of information that offers insights on epithelial damage.

METHODS

In this case-crossover study, we assessed the salivary proteome of patients with RAS (n = 36) in the presence and absence of ulcers using discovery proteomics and bioinformatics. Additionally, we contrasted these patterns with those of healthy individuals (n = 31) who had no prior aphthous ulceration.

RESULTS

Salivary proteome showed that during the ulcerative phase, controlled cell death was downregulated. Due to its ability to distinguish between individuals with and without ulcers, the ATF6B protein raises the possibility that endoplasmic reticulum (ER) stress is responsible for the damage that leads to the death of oral keratinocytes. The high abundance of TRAP1 and ERN1 matches with this biological discovery. The type of death is immunogenic, according to the functional data found in a cell death database.

CONCLUSION

We identified a cellular process that can lead to the death of oral keratinocytes in the etiopathogenesis process of RAS. Future studies should be conducted to identify what is responsible for the increase in ER stress signaling that would lead to an anti-cell death response.

Translational and post-translational dynamics in a model peptidergic system

The cell bodies of hypothalamic magnocellular neurones are densely packed in the hypothalamic supraoptic nucleus whereas their axons project to the anatomically discrete posterior pituitary gland. We have taken advantage of this unique anatomical structure to establish proteome and phosphoproteome dynamics in neuronal cell bodies and axonal terminals in response to physiological stimulation. We have found that proteome and phosphoproteome responses to neuronal stimulation are very different between somatic and axonal neuronal compartments, indicating the need of each cell domain to differentially adapt. In particular, changes in the phosphoproteome in the cell body are involved in the reorganisation of the cytoskeleton and in axonal terminals the regulation of synaptic and secretory processes. We have identified that prohormone precursors including vasopressin and oxytocin are phosphorylated in axonal terminals and are hyperphosphorylated following stimulation. By multi-omic integration of transcriptome and proteomic data we identify changes to proteins present in afferent inputs to this nucleus.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies-A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.

Multi-Omics Approach to Elucidate Cerebrospinal Fluid Changes in Dogs with Intervertebral Disc Herniation

Herniation of the intervertebral disc (IVDH) is the most common cause of neurological and intervertebral disc degeneration-related diseases. Since the disc starts to degenerate before it can be observed by currently available diagnostic methods, there is an urgent need for novel diagnostic approaches. To identify molecular networks and pathways which may play important roles in intervertebral disc herniation, as well as to reveal the potential features which could be useful for monitoring disease progression and prognosis, multi-omics profiling, including high-resolution liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and tandem mass tag (TMT)-based proteomics was performed. Cerebrospinal fluid of nine dogs with IVDH and six healthy controls were used for the analyses, and an additional five IVDH samples were used for proteomic data validation. Furthermore, multi-omics data were integrated to decipher a complex interaction between individual omics layers, leading to an improved prediction model. Together with metabolic pathways related to amino acids and lipid metabolism and coagulation cascades, our integromics prediction model identified the key features in IVDH, namely the proteins follistatin Like 1 (FSTL1), secretogranin V (SCG5), nucleobindin 1 (NUCB1), calcitonin re-ceptor-stimulating peptide 2 precursor (CRSP2) and the metabolites N-acetyl-D-glucosamine and adenine, involved in neuropathic pain, myelination, and neurotransmission and inflammatory response, respectively. Their clinical application is to be further investigated. The utilization of a novel integrative interdisciplinary approach may provide new opportunities to apply innovative diagnostic and monitoring methods as well as improve treatment strategies and personalized care for patients with degenerative spinal disorders.

Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery

Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer's disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in "amyloidome" (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.

Salivary proteome of aphthous stomatitis reveals the participation of vitamin metabolism, nutrients, and bacteria

There are currently no preventative options for recurrent aphthous stomatitis, and the only available treatments are palliative. This is partly due to a poor understanding of its etiopathogenesis. In this case-control study, we characterized the salivary proteome of patients with recurrent aphthous stomatitis in the presence and absence of lesions. Through mass spectrometry-based proteomics and bioinformatics tools, we identified that the presence of oral ulcers is associated with several specific biological processes, including the metabolic pathways of vitamin B9, B12, nitrogen, selenium, and the bacterium Neisseria meningitidis. These changes occurred only in the presence of clinically visible lesions, and there were no relevant differences between patients in anatomical regions unaffected by ulcers. Additionally, using western blot and ELISA assays, we verified that carbonic anhydrase 1 (CA1) and hemoglobin subunit beta (HBB) proteins are highly expressed during the ulcerative and remission phases of recurrent aphthous stomatitis. Our results cumulatively support saliva as an indicator of the pathophysiological changes, which occur during the clinical course of lesions. From a clinical perspective, we suggest that recurrent aphthous stomatitis is a condition triggered by temporary biological changes in people with lesions.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.

A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward

There is an unmet clinical need for objective biomarkers to monitor disease progression and treatment response in Huntington's disease (HD). The aim of this review is, therefore, to provide practical advice for biomarker discovery and to summarise studies on biofluid markers for HD. A PubMed search was performed to review literature with regard to candidate saliva, urine, blood and cerebrospinal fluid biomarkers for HD. Information has been organised into tables to allow a pragmatic approach to the discussion of the evidence and generation of practical recommendations for future studies. Many of the markers published converge on metabolic and inflammatory pathways, although changes in other analytes representing antioxidant and growth factor pathways have also been found. The most promising markers reflect neuronal and glial degeneration, particularly neurofilament light chain. International collaboration to standardise assays and study protocols, as well as to recruit sufficiently large cohorts, will facilitate future biomarker discovery and development.

Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients

Lung cancer is the most common cause of cancer-related mortality worldwide, characterized by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumors in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumors into pulmonary veins. Forty proteins were identified as having an increased abundance in the vein draining from the tumor compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilized the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.

MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.

Effect of haptoglobin on the treatment of chronic hepatitis B with interferon

There are two main types of drugs that are used to treat chronic hepatitis B (CHB), including interferon (IFN) and nucleotide analogues. IFN inhibits the virus through direct antiviral activity and via immune regulation, and it has been widely applied for the treatment of CHB and other infections. However, the efficiency of IFN therapy is not entirely satisfactory. The aim of the present study was to investigate the factors affecting IFN therapy. The plasma of patients with CHB treated with IFN was collected and divided into the virological response group and non-virological response group according to their virological response. Serum proteins of virologically responsive patients were compared before and after IFN therapy using isobaric tags for relative and absolute quantitation technology. ELISA was used to validate these results in the same sample. In in vitro cell experiments, HepG2.2.15 cells were transfected with haptoglobin (Hp)-targeting small interfering RNA to inhibit expression of the Hp protein, and reverse transcription-quantitative polymerase chain reaction and western blotting were utilized to detect hepatitis B virus (HBV)-DNA, IFN and downstream molecular changes in the cell supernatants. The Hp protein levels were demonstrated to be significantly lower following 48 weeks of IFN therapy, and the levels of Hp in patients in the virological response group were significantly lower than those in the non-virological response group. In in vitro cell experiments, following inhibition of Hp protein expression, significantly decreased levels of HBV-DNA, and elevated levels of IFN and its downstream molecules were observed. These findings suggest that Hp may be able to predict the efficacy of IFN therapy, and it may inhibit HBV clearance. There is an association between Hp and IFN, which requires further clinical and laboratory studies to explore.

Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling

Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.

Discovery of a Potential Plasma Protein Biomarker Panel for Acute-on-Chronic Liver Failure Induced by Hepatitis B Virus

Hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF), characterized by an acute deterioration of liver function in the patients with chronic hepatitis B (CHB), is lack of predicting biomarkers for prognosis. Plasma is an ideal sample for biomarker discovery due to inexpensive and minimally invasive sampling and good reproducibility. In this study, immuno-depletion of high-abundance plasma proteins followed by iTRAQ-based quantitative proteomic approach was employed to analyze plasma samples from 20 healthy control people, 20 CHB patients and 20 HBV-ACLF patients, respectively. As a result, a total of 427 proteins were identified from these samples, and 42 proteins were differentially expressed in HBV-ACLF patients as compared to both CHB patients and healthy controls. According to bioinformatics analysis results, 6 proteins related to immune response (MMR), inflammatory response (OPN, HPX), blood coagulation (ATIII) and lipid metabolism (APO-CII, GP73) were selected as biomarker candidates. Further ELISA analysis confirmed the significant up-regulation of GP73, MMR, OPN and down-regulation of ATIII, HPX, APO-CII in HBV-ACLF plasma samples (p < 0.01). Moreover, receiver operating characteristic (ROC) curve analysis revealed high diagnostic value of these candidates in assessing HBV-ACLF. In conclusion, present quantitative proteomic study identified 6 novel HBV-ACLF biomarker candidates and might provide fundamental information for development of HBV-ACLF biomarker.

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

Molecular mechanism of C-reaction protein in promoting migration and invasion of hepatocellular carcinoma cells in vitro

Hepatocellular carcinoma (HCC) is one of most common malignant cancers and is the second leading cause of cancer related deaths. The prognosis and survival of patients are closely related to the degree of tumor metastasis. The mechanism of HCC metastasis is still unclear. In the present study, we investigated the molecular mechanism of C-reaction protein in promoting migration and invasion of hepatocellular carcinoma cells in vitro. We estimated that CRP is overexpressed in liver cancer tissues and that it promotes invasion and metastasis of HCC in vitro. In the present study, we employed iTRAQ-based mass spectrometry to analyze the HepG2 secretory proteins of CRP siRNA-treated cells and negative control siRNA-treated cells. We identified 109 differentially expressed proteins after silencing CRP, of which 45 were upregulated and 64 were downregulated. Some of the differentially expressed proteins were confirmed by western blot analysis and real-time quantitative PCR. Furthermore, we found that knockdown of CRP substantially abrogates HIF-1α expression levels, the luciferase activity of HIF-1α and ERK and Akt phosphorylation in HepG2 cells. The present study provides a novel mechanism by which CRP promotes the proliferation, migration, invasion and metastasis of hepatocellular carcinoma cells. Inhibition of CRP suppressed migration, invasion and healing of hepatoma carcinoma cells by decreasing HIF-1α activity and CTSD.

Temporal profiles of plasma proteome during childhood development

Human blood plasma proteome reflects physiological changes associated with a child's development as well as development of disease states. While age-specific normative values are available for proteins routinely measured in clinical practice, there is paucity of comprehensive longitudinal data regarding changes in human plasma proteome during childhood. We applied TMT-10plex isobaric labeling-based quantitative proteomics to longitudinally profile the plasma proteome in 10 healthy children during their development, each with 9 serial time points from 9months to 15years of age. In total, 1828 protein groups were identified at peptide and protein level false discovery rate of 1% and with at least two razor and unique peptides. The longitudinal expression profiles of 1747 protein groups were statistically modeled and their temporal changes were categorized into 7 different patterns. The patterns and relative abundance of proteins obtained by LC-MS were also verified with ELISA. To our knowledge, this study represents the most comprehensive longitudinal profiling of human plasma proteome to date. The temporal profiles of plasma proteome obtained in this study provide a comprehensive resource and reference for biomarker studies in childhood diseases. Biological significance: A pediatric plasma proteome database with longitudinal expression patterns of 1747 proteins from neonate to adolescence was provided to the research community. 970 plasma proteins had age-dependent expression trends, which demonstrated the importance of longitudinal profiling study to identify the potential biomarkers specific to childhood diseases, and the requirement of strictly age-matched clinical samples in a cross-sectional study in pediatric population.

Identification of IGFBP2 and IGFBP3 As Compensatory Biomarkers for CA19-9 in Early-Stage Pancreatic Cancer Using a Combination of Antibody-Based and LC-MS/MS-Based Proteomics

Pancreatic cancer is one of the most lethal tumors, and reliable detection of early-stage pancreatic cancer and risk diseases for pancreatic cancer is essential to improve the prognosis. As 260 genes were previously reported to be upregulated in invasive ductal adenocarcinoma of pancreas (IDACP) cells, quantification of the corresponding proteins in plasma might be useful for IDACP diagnosis. Therefore, the purpose of the present study was to identify plasma biomarkers for early detection of IDACP by using two proteomics strategies: antibody-based proteomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. Among the 260 genes, we focused on 130 encoded proteins with known function for which antibodies were available. Twenty-three proteins showed values of the area under the curve (AUC) of more than 0.8 in receiver operating characteristic (ROC) analysis of reverse-phase protein array (RPPA) data of IDACP patients compared with healthy controls, and these proteins were selected as biomarker candidates. We then used our high-throughput selected reaction monitoring or multiple reaction monitoring (SRM/MRM) methodology, together with an automated sample preparation system, micro LC and auto analysis system, to quantify these candidate proteins in plasma from healthy controls and IDACP patients on a large scale. The results revealed that insulin-like growth factor-binding protein (IGFBP)2 and IGFBP3 have the ability to discriminate IDACP patients at an early stage from healthy controls, and IGFBP2 appeared to be increased in risk diseases of pancreatic malignancy, such as intraductal papillary mucinous neoplasms (IPMNs). Furthermore, diagnosis of IDACP using the combination of carbohydrate antigen 19-9 (CA19-9), IGFBP2 and IGFBP3 is significantly more effective than CA19-9 alone. This suggests that IGFBP2 and IGFBP3 may serve as compensatory biomarkers for CA19-9. Early diagnosis with this marker combination may improve the prognosis of IDACP patients.

Plasma-based proteomics reveals immune response, complement and coagulation cascades pathway shifts in heat-stressed lactating dairy cows

Heat stress (HS) has an enormous economic impact on the dairy industry. In recent years, many researchers have investigated changes in the gene expression and metabolomics profiles in dairy cows caused by HS. However, the proteomics profiles of heat-stressed dairy cows have not yet been completely elucidated. We compared plasma proteomics from HS-free and heat-stressed dairy cows using an iTRAQ labeling approach. After the depletion of high abundant proteins in the plasma, 1472 proteins were identified. Of these, 85 proteins were differentially abundant in cows exposed to HS relative to HS-free. Database searches combined with GO and KEGG pathway enrichment analyses revealed that many components of the complement and coagulation cascades were altered in heat-stressed cows compared with HS-free cows. Of these, many factors in the complement system (including complement components C1, C3, C5, C6, C7, C8, and C9, complement factor B, and factor H) were down-regulated by HS, while components of the coagulation system (including coagulation factors, vitamin K-dependent proteins, and fibrinogens) were up-regulated by HS. In conclusion, our results indicate that HS decreases plasma levels of complement system proteins, suggesting that immune function is impaired in dairy cows exposed to HS.

BIOLOGICAL SIGNIFICANCE

Though many aspects of heat stress (HS) have been extensively researched, relatively little is known about the proteomics profile changes that occur during heat exposure. In this work, we employed a proteomics approach to investigate differential abundance of plasma proteins in HS-free and heat-stressed dairy cows. Database searches combined with GO and KEGG pathway enrichment analyses revealed that HS resulted in a decrease in complement components, suggesting that heat-stressed dairy cows have impaired immune function. In addition, through integrative analyses of proteomics and previous metabolomics, we showed enhanced glycolysis, lipid metabolic pathway shifts, and nitrogen repartitioning in dairy cows exposed to HS. Our findings expand our current knowledge on the effects of HS on plasma proteomics in dairy cows and offer a new perspective for future research.

Antigen arrays for profiling autoantibody repertoires

Autoantibodies are a key component for the diagnosis, prognosis and monitoring of various diseases. In order to discover novel autoantibody targets, highly multiplexed assays based on antigen arrays hold a great potential and provide possibilities to analyze hundreds of body fluid samples for their reactivity pattern against thousands of antigens in parallel. Here, we provide an overview of the available technologies for producing antigen arrays, highlight some of the technical and methodological considerations and discuss their applications as discovery tools. Together with recent studies utilizing antigen arrays, we give an overview on how the different types of antigen arrays have and will continue to deliver novel insights into autoimmune diseases among several others.

A quantitative proteomics study on olfactomedin 4 in the development of gastric cancer

Gastric cancer (GC) is now one of the most common malignancies with a relatively high incidence and high mortality rate. The prognosis is closely related to the degree of tumor metastasis. The mechanism of metastasis is still unclear. Proteomics analysis is a powerful tool to study and evaluate protein expression in tumor tissues. In the present study, we collected 15 gastric cancer and adjacent normal gastric tissues and used the isobaric tags for relative and absolute quantitation (iTRAQ) method to identify differentially expressed proteins. A total of 134 proteins were differentially expressed between the cancerous and non-cancerous samples. Azurocidin 1 (AZU1), CPVL, olfactomedin 4 (OLFM4) and Villin 1 (VIL1) were upregulated and confirmed by western blot analysis, real-time quantitative PCR and immunohistochemical analyses. These results were in accordance with iTRAQ. Furthermore, silencing the OLFM4 expression suppressed the migration, invasion and proliferation of the GC cells in vitro. The present study represents a successful application of the iTRAQ method in analyzing the expression levels of thousands of proteins. Overexpression of OLFM4 in gastric cancer may induce the development of gastric cancer. Overall, suppression of OLFM4 expression may be a promising strategy in the development of novel cancer therapeutic drugs.

Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

C-reactive protein is a biomarker of AFP-negative HBV-related hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive cancers worldwide and is associated with the high rates of morbidity and mortality. α-fetoprotein (AFP) is common used in diagnosis of HCC; however, a growing body of research is questioning the diagnostic power of AFP. There is, therefore, an urgent need to develop additional novel non-invasive techniques for the early diagnosis of HCC, particularly for patients with AFP-negative [AFP(-)] HCC. Accordingly, in the present study, we employed iTRAQ-based mass spectro-metry to analyze the plasma proteins of subjects with AFP(-) HBV-related HCC, AFP(+) HBV-related HCC and non-malignant cirrhosis. We identified 14 aberrantly expressed proteins specific to the HCC patients, including 10 upregulated and 4 downregulated proteins. We verified C-reactive protein (CRP) overexpression by ELISA and immunohistochemical staining of clinical samples. Per ROC curve analyses, CRP was positive in 73.3% of patients with HBV-related HCC, and CRP overexpression had significant diagnostic power for AFP(-) HBV-related HCC. Furthermore, we found that silencing CRP caused a >2-fold decease in HBV replication. Additionally, we determined that this reduction in HBV replication involved the interferon-signaling pathway. However, silencing CRP also promoted HCC invasion and migration in vitro. In conclusion, we demonstrated that CRP can serve as a diagnostic biomarker for AFP(-) HBV-related HCC.

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.

Proteomics analysis of bodily fluids in pancreatic cancer

Proteomics study of pancreatic cancer using bodily fluids emphasizes biomarker discovery and clinical application, presenting unique prospect and challenges. Depending on the physiological nature of the bodily fluid and its proximity to pancreatic cancer, the proteomes of bodily fluids, such as pancreatic juice, pancreatic cyst fluid, blood, bile, and urine, can be substantially different in terms of protein constitution and the dynamic range of protein concentration. Thus, a comprehensive discovery and specific detection of cancer-associated proteins within these varied fluids is a complex task, requiring rigorous experiment design and a concerted approach. While major challenges still remain, fluid proteomics studies in pancreatic cancer to date have provided a wealth of information in revealing proteome alterations associated with pancreatic cancer in various bodily fluids.

Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments

In quantitative shotgun proteomic analyses by liquid chromatography and mass spectrometry, a rigid study design is necessary in order to obtain statistically relevant results. Hypothesis testing, sample size calculation and power estimation are fundamental concepts that require consideration upon designing an experiment. For this reason, the reproducibility and variability of the proteomic platform needs to be assessed. In this study, we evaluate the technical (sample preparation), labeling (isobaric labels), and total (biological + technical + labeling + experimental) variability and reproducibility of a workflow that employs a shotgun LC-MS/MS approach in combination with TMT peptide labeling for the quantification of peripheral blood mononuclear cell (PBMC) proteome. We illustrate that the variability induced by TMT labeling is small when compared to the technical variation. The latter is also responsible for a substantial part of the total variation. Prior knowledge about the experimental variability allows for a correct design, a prerequisite for the detection of biologically significant disease-specific differential proteins in clinical proteomics experiments.

Potential predictive plasma biomarkers for cervical cancer by 2D-DIGE proteomics and Ingenuity Pathway Analysis

The current methods available for screening and detecting cervical squamous cell carcinoma (CSCC) have insufficient sensitivity and specificity. As a result, many patients suffered from erroneous and missed diagnosis. Because CSCC is usually asymptomatic at potentially curative stages, identification of biomarkers is an urgent need for the early detection of CSCC. Comparative proteomics based on two-dimensional differential in-gel electrophoresis (2D-DIGE) was employed to quantitatively analyze plasma proteins of healthy Uyghur women and with early stage cervical carcinoma. The 2D-DIGE image were analyzed statistically using DeCyder™ 2D software. The statistical analysis of proteomic data revealed that 43 protein spots showed significantly different expression (ratio > 1.5, P < 0.01). A further identification of these protein spots by MALDI-TOF-MS found out 16 different proteins. Bioinformatic analysis within the framework of Ingenuity Pathway Analysis (IPA(@)) showed that 10 plasma proteins as candidate biomarker were screened, mainly including lipid metabolism-related proteins (APOA4, APOA1, APOE), complement (EPPK1, CFHR1), metabolic enzymes (CP, F2, MASP2), glycoprotein (CLU), and immune function-related proteins (IGK@). Networks involved in lipid metabolism, molecular transport, and small molecule biochemistry were dysfunctional in CSCC. Acute phase response signaling and JAK/Stat signaling and IL-4 signaling, etc., were identified as the canonical pathways that are overrepresented in CSCC. Furthermore, the expression of three proteins (APOA1, APOE, CLU) were validated using ELISA in plasma of patients with different stage cervical lesion. With the combined proteomic and bioinformatic approach, this study was successful in identifying biomarker signatures for cervical cancer and might provide new insights into the mechanism of CSCC progression, potentially leading to the design of novel diagnostic and therapeutic strategies.

Isobaric labeling-based relative quantification in shotgun proteomics

Mass spectrometry plays a key role in relative quantitative comparisons of proteins in order to understand their functional role in biological systems upon perturbation. In this review, we review studies that examine different aspects of isobaric labeling-based relative quantification for shotgun proteomic analysis. In particular, we focus on different types of isobaric reagents and their reaction chemistry (e.g., amine-, carbonyl-, and sulfhydryl-reactive). Various factors, such as ratio compression, reporter ion dynamic range, and others, cause an underestimation of changes in relative abundance of proteins across samples, undermining the ability of the isobaric labeling approach to be truly quantitative. These factors that affect quantification and the suggested combinations of experimental design and optimal data acquisition methods to increase the precision and accuracy of the measurements will be discussed. Finally, the extended application of isobaric labeling-based approach in hyperplexing strategy, targeted quantification, and phosphopeptide analysis are also examined.

The role of von Willebrand factor as a biomarker of tumor development in hepatitis B virus-associated human hepatocellular carcinoma: a quantitative proteomic based study

Chronic hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC), the sixth most common cancer worldwide. To explore potential biomarkers for HCC, iTRAQ coupled with mass spectrometry was used to analyze proteins in plasma from individuals with HBV-associated HCC, nonmalignant cirrhosis, chronic hepatitis B, and healthy individuals. Twenty-one aberrantly expressed proteins were identified from HCC patients as compared with nontumor controls. Overexpression of von Willebrand factor (vWF) was confirmed by Western blotting, and immunohistochemical analysis from liver biopsies and ELISA from plasma samples revealed a correlation between vWF expression and HCC clinicopathologic staging. Furthermore, siRNA-induced vWF silencing reduced HBV replication by over two-fold via the interferon-signaling pathway and impaired the invasion and migration of HCC cells in vitro. These results indicate that vWF can serve as a biomarker, and perhaps an alternative target for therapeutic intervention of HCC progression and HBV viral infection.

BIOLOGICAL SIGNIFICANCE

We report comparative plasma proteome profiles of HBV-associated HCC and nonmalignant chronic liver diseases, including chronic hepatitis B and cirrhosis. The quantification of these datasets showed altered abundance of 21 proteins in HBV-related HCC and provides a reference point for future applied and basic research. In addition, we have demonstrated that the candidate protein vWF is involved in the pathogenesis of HBV infection and replication, and also associated with clinicopathologic staging of HCC patients with HBV infection. Overall these findings provide information on the mechanism of HCC development, which may assist in the development of novel cancer and HBV therapeutic drugs.

Complementary iTRAQ proteomics and RNA-seq transcriptomics reveal multiple levels of regulation in response to nitrogen starvation in Synechocystis sp. PCC 6803

Sequential adaptation to environmental stress needs complex regulation at different cellular levels in cyanobacteria. To uncover the regulatory mechanism in response to nitrogen starvation, we investigated the genome-wide correlation between protein abundance and gene expression in a model cyanobacterium Synechocystis sp. PCC 6803 using complementary quantitative iTRAQ proteomics and RNA-seq transcriptomics. Consistent with the cell growth inhibition, proteomic analysis indicated phase-dependent down-regulation of proteins related to nitrogen metabolism, ribosome complexes, glycolysis pathway and tricarboxylic acid (TCA) cycles by nitrogen starvation. Transcriptomic analysis also showed that genes related to "Photosynthesis", "Protein synthesis" and "Energy metabolism" were significantly down-regulated by nitrogen starvation. Interestingly, the concordance between protein abundances and their corresponding mRNAs exhibited a functional categories-dependent pattern, with some categories, such as "Protein synthesis" and "Energy metabolism", having a relatively high correlation, while others even with numerous discordant changes in protein-mRNA pairs, indicated divergent regulation of transcriptional and post-transcriptional processes. In particular, an increased abundance of proteins related to "Photosynthesis" upon nitrogen starvation was found to be reversely correlated with the down-regulation of their corresponding mRNAs. In addition, two metabolic modules highly correlated with nitrogen starvation were identified by a co-expression network analysis, and were found to contain mostly photosynthetic proteins and hypothetical proteins, respectively. We further confirmed the involvement of the photosynthetic genes in nitrogen starvation tolerance by constructing and analyzing the psbV gene deletion mutant.

Phase II Trial of Cetuximab and Conformal Radiotherapy Only in Locally Advanced Pancreatic Cancer with Concurrent Tissue Sampling Feasibility Study

BACKGROUND

Preclinical data have indicated the anti-epidermal growth factor receptor (EGFR) agent cetuximab (Erbitux) as a radiosensitizer in pancreatic cancer, but this has not been specifically addressed in a clinical study. We report the results of an original study initiated in 2007, where cetuximab was tested with radiotherapy (RT) alone in locally advanced pancreatic cancer in a phase II trial (PACER).

METHODS

Patients (n = 21) received cetuximab loading dose (400 mg/m(2)) and weekly dose (250 mg/m(2)) during RT (50.4 Gy in 28 fractions). Toxicity and disease response end point data were prospectively assessed. A feasibility study of on-trial patient blood and skin sampling was incorporated.

RESULTS

Treatment was well tolerated, and toxicity was low; most patients (71%) experienced acute toxicities of grade 2 or less. Six months posttreatment, stable local disease was achieved in 90% of evaluable patients, but only 33% were free from metastatic progression. Median overall survival was 7.5 months, and actuarial survival was 33% at 1 year and 11% at 3 years, reflecting swift metastatic progression in some patients but good long-term control of localized disease in others. High-grade acneiform rash (P = .0027), posttreatment stable disease (P = .0059), and pretreatment cancer antigen 19.9 (CA19.9) level (P = .0042) associated with extended survival. Patient skin and blood samples yielded sufficient RNA and good quality protein, respectively.

CONCLUSIONS

The results indicate that cetuximab inhibits EGFR-mediated radioresistance to achieve excellent local control with minimal toxicity but does not sufficiently control metastatic progression in all patients. Translational studies of patient tissue samples may yield molecular information that may enable individual treatment response prediction.

Proteome-wide analyses of human hepatocytes during differentiation and dedifferentiation

Failure to predict hepatotoxic drugs in preclinical testing makes it imperative to develop better liver models with a stable phenotype in culture. Stem cell-derived models offer promise, with differentiated hepatocyte-like cells currently considered to be "fetal-like" in their maturity. However, this judgment is based on limited biomarkers or transcripts and lacks the required proteomic datasets that directly compare fetal and adult hepatocytes. Here, we quantitatively compare the proteomes of human fetal liver, adult hepatocytes, and the HepG2 cell line. In addition, we investigate the proteome changes in human fetal and adult hepatocytes when cultured in a new air-liquid interface format compared to conventional submerged extracellular matrix sandwich culture. From albumin and urea secretion, and luciferase-based cytochrome P450 activity, adult hepatocytes were viable in either culture model over 2 weeks. The function of fetal cells was better maintained in the air-liquid interface system. Strikingly, the proteome was qualitatively similar across all samples but hierarchical clustering showed that each sample type had a distinct quantitative profile. HepG2 cells more closely resembled fetal than adult hepatocytes. Furthermore, clustering showed that primary adult hepatocytes cultured at the air-liquid interface retained a proteome that more closely mimicked their fresh counterparts than conventional culture, which acquired myofibroblast features. Principal component analysis extended these findings and identified a simple set of proteins, including cytochrome P450 2A6, glutathione S transferase P, and alcohol dehydrogenases as specialized indicators of hepatocyte differentiation.

CONCLUSION

Our quantitative datasets are the first that directly compare multiple human liver cells, define a model for enhanced maintenance of the hepatocyte proteome in culture, and provide a new protein "toolkit" for determining human hepatocyte maturity in cultured cells.

A caspase-3 'death-switch' in colorectal cancer cells for induced and synchronous tumor apoptosis in vitro and in vivo facilitates the development of minimally invasive cell death biomarkers

Novel anticancer drugs targeting key apoptosis regulators have been developed and are undergoing clinical trials. Pharmacodynamic biomarkers to define the optimum dose of drug that provokes tumor apoptosis are in demand; acquisition of longitudinal tumor biopsies is a significant challenge and minimally invasive biomarkers are required. Considering this, we have developed and validated a preclinical 'death-switch' model for the discovery of secreted biomarkers of tumour apoptosis using in vitro proteomics and in vivo evaluation of the novel imaging probe [(18)F]ML-10 for non-invasive detection of apoptosis using positron emission tomography (PET). The 'death-switch' is a constitutively active mutant caspase-3 that is robustly induced by doxycycline to drive synchronous apoptosis in human colorectal cancer cells in vitro or grown as tumor xenografts. Death-switch induction caused caspase-dependent apoptosis between 3 and 24 hours in vitro and regression of 'death-switched' xenografts occurred within 24 h correlating with the percentage of apoptotic cells in tumor and levels of an established cell death biomarker (cleaved cytokeratin-18) in the blood. We sought to define secreted biomarkers of tumor apoptosis from cultured cells using Discovery Isobaric Tag proteomics, which may provide candidates to validate in blood. Early after caspase-3 activation, levels of normally secreted proteins were decreased (e.g. Gelsolin and Midkine) and proteins including CD44 and High Mobility Group protein B1 (HMGB1) that were released into cell culture media in vitro were also identified in the bloodstream of mice bearing death-switched tumors. We also exemplify the utility of the death-switch model for the validation of apoptotic imaging probes using [(18)F]ML-10, a PET tracer currently in clinical trials. Results showed increased tracer uptake of [(18)F]ML-10 in tumours undergoing apoptosis, compared with matched tumour controls imaged in the same animal. Overall, the death-switch model represents a robust and versatile tool for the discovery and validation of apoptosis biomarkers.

Use of PD biomarkers to drive dose selection and early clinical decision making

A major challenge facing the development of new therapies is the high level of compound attrition in late-stage clinical studies. A key factor in reducing these unsustainable levels of attrition is the successful evaluation of the level of drug effect on its target pathway in early development, otherwise known as testing the compound mechanism. Incorporation of PD biomarkers into Phase I/II trials to demonstrate compound binding to its molecular target and the subsequent modulation of downstream pathways enables early testing of compound mechanism and provides a data-driven framework for decisions on compound progression. This review will discuss the identification and validation of such 'fit-for-purpose' PD biomarkers, and case studies illustrating their use and value in dose selection and accelerating the clinical development of small-molecule drugs will be described.

The shotgun proteomic study of the human ThinPrep cervical smear using iTRAQ mass-tagging and 2D LC-FT-Orbitrap-MS: the detection of the human papillomavirus at the protein level

The ThinPrep cervical smear is widely used in clinical practice for the cytological and molecular screening against abnormal cells and Human Papillomavirus (HPV) infection. Current advancements made to LC-MS proteomics include the use of stable isotope labeling for the in-depth analysis of proteins in complex clinical specimens. Such approaches have yet to be realized for ThinPrep clinical specimens. In this study, an LC-MS method based on isobaric (iTRAQ) labeling and high-resolution FT-Orbitrap mass spectrometry was used for the proteomic analysis of 23 human ThinPrep smear specimens. Tandem mass spectrometry analysis was performed with both nitrogen high collision dissociation (HCD MS/MS) and helium collision induced dissociation (CID MS/MS) peptide fragmentation modes. The analysis of three 8-plex sample sets yielded the identification of over 3200 unique proteins at FDR < 1%, of which over 2300 proteins were quantitatively profiled in at least one of the three experiments. The interindividual variability served to define the required sample size needed to identify significant protein expression differences. The degree of in-depth proteome coverage allowed the detection of 6 HPV-derived proteins including the high-risk HPV16 type in the specimens tested. The presence of the HPV strains of origin was also confirmed with PCR-hybridization molecular methods. This proof-of-principle study constitutes the first ever report on the nontargeted analysis of HPV proteins in human ThinPrep clinical specimens with high-resolution mass spectrometry. A further testament to the sensitivity and selectivity of the proposed study method was the confident detection of a significant number of phosphopeptides in these specimens.

Additions to the Human Plasma Proteome via a Tandem MARS Depletion iTRAQ-Based Workflow

Robust platforms for determining differentially expressed proteins in biomarker and discovery studies using human plasma are of great interest. While increased depth in proteome coverage is desirable, it is associated with costs of experimental time due to necessary sample fractionation. We evaluated a robust quantitative proteomics workflow for its ability (1) to provide increased depth in plasma proteome coverage and (2) to give statistical insight useful for establishing differentially expressed plasma proteins. The workflow involves dual-stage immunodepletion on a multiple affinity removal system (MARS) column, iTRAQ tagging, offline strong-cation exchange chromatography, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Independent workflow experiments were performed in triplicate on four plasma samples tagged with iTRAQ 4-plex reagents. After stringent criteria were applied to database searched results, 689 proteins with at least two spectral counts (SC) were identified. Depth in proteome coverage was assessed by comparison to the 2010 Human Plasma Proteome Reference Database in which our studies reveal 399 additional proteins which have not been previously reported. Additionally, we report on the technical variation of this quantitative workflow which ranges from ±11 to 30%.

Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs

BACKGROUND

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.

RESULTS

We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background.

CONCLUSIONS

We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html.