Pathway-based biomarker search by high-throughput proteomics profiling of secretomes

Hepatokine ITIH3 protects against hepatic steatosis by downregulating mitochondrial bioenergetics and de novo lipogenesis

Recent studies demonstrate that liver secretory proteins, also known as hepatokines, regulate normal development, obesity, and simple steatosis to non-alcoholic steatohepatitis (NASH) progression. Using a panel of ∼100 diverse inbred strains of mice and a cohort of bariatric surgery patients, we found that one such hepatokine, inter-trypsin inhibitor heavy chain 3 (ITIH3), was progressively lower in severe non-alcoholic fatty liver disease (NAFLD) disease states highlighting an inverse relationship between Itih3/ITIH3 expression and NAFLD severity. Follow-up animal and cell culture models demonstrated that hepatic ITIH3 overexpression lowered liver triglyceride and lipid droplet accumulation, respectively. Conversely, ITIH3 knockdown in mice increased the liver triglyceride in two independent NAFLD models. Mechanistically, ITIH3 reduced mitochondrial respiration and this, in turn, reduced liver triglycerides, via downregulated de novo lipogenesis. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD.

Proteomic profiling of interferon-responsive reactive astrocytes in rodent and human

Astrocytes are a heterogeneous population of central nervous system glial cells that respond to pathological insults and injury by undergoing a transformation called "reactivity." Reactive astrocytes exhibit distinct and context-dependent cellular, molecular, and functional state changes that can either support or disturb tissue homeostasis. We recently identified a reactive astrocyte sub-state defined by interferon-responsive genes like Igtp, Ifit3, Mx1, and others, called interferon-responsive reactive astrocytes (IRRAs). To further this transcriptomic definition of IRRAs, we wanted to define the proteomic changes that occur in this reactive sub-state. We induced IRRAs in immunopanned rodent astrocytes and human iPSC-differentiated astrocytes using TNF, IL1α, C1Q, and IFNβ and characterized their proteomic profile (both cellular and secreted) using unbiased quantitative proteomics. We identified 2335 unique cellular proteins, including IFIT2/3, IFITM3, OASL1/2, MX1/2/3, and STAT1. We also report that rodent and human IRRAs secrete PAI1, a serine protease inhibitor which may influence reactive states and functions of nearby cells. Finally, we evaluated how IRRAs are distinct from neurotoxic reactive astrocytes (NRAs). While NRAs are described by expression of the complement protein C3, it was not upregulated in IRRAs. Instead, we found ~90 proteins unique to IRRAs not identified in NRAs, including OAS1A, IFIT3, and MX1. Interferon signaling in astrocytes is critical for the antiviral immune response and for regulating synaptic plasticity and glutamate transport mechanisms. How IRRAs contribute to these functions is unknown. This study provides the basis for future experiments to define the functional roles of IRRAs in the context of neurodegenerative disorders.

Honey proteomic signatures for the identification of honey adulterated with syrup, producing country, and nectar source using SWATH-MS approach

Honey is widely consumed by humans, due to its multiple applications as a food constituent and its therapeutic effects. This study reports on the discrimination of honey products from different geographical and botanical sources, as well as honey products containing distinct forms of syrup used in honey adulteration. Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS)-based proteomic analysis combined with chemometrics was successfully applied in identifying characteristic proteins that can be used as biomarkers of the original source of honey. Honey samples from different producing regions (Tainan, Changhua, and Taichung), countries (Taiwan and Thailand), and distinct botanical sources (longan and litchi) were clearly distinguished by the developed orthogonal projections to latent structures discriminant analysis (OPLS-DA) model with good fitness and prediction ability. Furthermore, we successfully discriminated the adulteration of honey with syrup in different proportions (even with honey content as low as 20%) with this proteomic SWATH-MS platform.

Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics

Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.

Clinical Implications of Extracellular HMGA1 in Breast Cancer

The unconventional secretion of proteins is generally caused by cellular stress. During the tumorigenesis, tumor cells experience high levels of stress, and the secretion of some theoretically intracellular proteins is activated. Once in the extracellular space, these proteins play different paracrine and autocrine roles and could represent a vulnerability of cancer. One of these proteins is the high mobility group A1 (HMGA1), which is frequently overexpressed in tumors and presents a low expression in normal adult tissues. We have recently described that HMGA1 establishes an autocrine loop in invasive triple-negative breast cancer (TNBC) cells. The secretion of HMGA1 and its binding to the receptor for advanced glycation end products (RAGE) mediates the migration, invasion, and metastasis of TNBC cells and predicts the onset of metastasis in these patients. In this review, we summarized different strategies to exploit the novel tumorigenic phenotype mediated by extracellular HMGA1. We envisioned future clinical applications where the association between its change in subcellular localization and breast cancer progression could be used to predict tumor aggressiveness and guide treatment decisions. Furthermore, we proposed that targeting extracellular HMGA1 as monotherapy using monoclonal antibodies, or in combination with chemotherapy and other targeted therapies, could bring new therapeutic options for TNBC patients.

Impact of the secretome of activated pancreatic stellate cells on growth and differentiation of pancreatic tumour cells

Pancreatic ductal adenocarcinoma (PDAC) exists in a complex desmoplastic microenvironment. As part of it, pancreatic stellate cells (PSCs) provide a fibrotic niche, stimulated by a dynamic communication between activated PSCs and tumour cells. Investigating how PSCs contribute to tumour development and for identifying proteins that the cells secrete during cancer progression, we studied by means of complex antibody microarrays the secretome of activated PSCs. A large number of secretome proteins were associated with cancer-related functions, such as cell apoptosis, cellular growth, proliferation and metastasis. Their effect on tumour cells could be confirmed by growing tumour cells in medium conditioned with activated PSC secretome. Analyses of the tumour cells' proteome and mRNA revealed a strong inhibition of tumour cell apoptosis, but promotion of proliferation and migration. Many cellular proteins that exhibited variations were found to be under the regulatory control of eukaryotic translation initiation factor 4E (eIF4E), whose expression was triggered in tumour cells grown in the secretome of activated PSCs. Inhibition by an eIF4E siRNA blocked the effect, inhibiting tumour cell growth in vitro. Our findings show that activated PSCs acquire a pro-inflammatory phenotype and secret proteins that stimulate pancreatic cancer growth in an eIF4E-dependent manner, providing further insight into the role of stromal cells in pancreatic carcinogenesis and cancer progression.

Differential secretome profiling of a swine tracheal cell line infected with mycoplasmas of the swine respiratory tract

Mycoplasma hyopneumoniae and Mycoplasma flocculare are genetically similar. However, M. hyopneumoniae causes porcine enzootic pneumonia, while M. flocculare is a commensal bacterium. M. hyopneumoniae and M. flocculare do not penetrate their host cells, and secreted proteins are important for bacterium-host interplay. Thus, the secretomes of a swine trachea cell line (NPTr) infected with M. hyopneumoniae 7448 (a pathogenic strain), M. hyopneumoniae J (a non-pathogenic strain) and M. flocculare were compared to shed light in bacterium-host interactions. Medium from the cultures was collected, and secreted proteins were identified by a LC-MS/MS. Overall numbers of identified host and bacterial proteins were, respectively, 488 and 58, for NPTr/M. hyopneumoniae 7448; 371 and 67, for NPTr/M. hyopneumoniae J; and 203 and 81, for NPTr/M. flocculare. The swine cells revealed different secretion profiles in response to the infection with each M. hyopneumoniae strain or with M. flocculare. DAMPs and extracellular proteasome proteins, secreted in response to cell injury and death, were secreted by NPTr cells infected with M. hyopneumoniae 7448. All three mycoplasmas secreted virulence factors during NPTr infection, but M. hyopneumoniae 7448 secreted higher number of adhesins and hypothetical proteins, that may be related with pathogenicity. SIGNIFICANCE: The enzootic pneumonia caused by mycoplasmas of swine respiratory tract has economic loss consequences in pig industry due to antibiotic costs and pig weight loss. However, some genetically similar mycoplasmas are pathogenic while others, such as Mycoplasma hyopneumoniae and Mycoplasma flocculare, are non-pathogenic. Here, we conducted an infection assay between swine cells and pathogenic and non-pathogenic mycoplasmas to decipher secreted proteins during host-pathogen interaction. Mycoplasma response to cell infection was also observed. Our study provided new insights on secretion profile of swine cells in response to the infection with pathogenic and non-pathogenic mycoplasmas. It was possible to observe that pathogenic M. hyopneumoniae 7448 secreted known virulence factors and swine cells responded by inducing cell death. Otherwise, M. hyopneumoniae J and M. flocculare, non-pathogenic mycoplasmas, secreted a different profile of virulence factors in response to swine cells. Consequently, swine cells altered their secretome profile, but the changes were not sufficient to cause disease.

High Resolution Proteomic Analysis of the Cervical Cancer Cell Lines Secretome Documents Deregulation of Multiple Proteases

BACKGROUND

Oncogenic infection by HPV, eventually leads to cervical carcinogenesis, associated by deregulation of specific pathways and protein expression at the intracellular and secretome level. Thus, secretome analysis can elucidate the biological mechanisms contributing to cervical cancer. In the present study we systematically analyzed its constitution in four cervical cell lines employing a highly sensitive proteomic technology coupled with bioinformatics analysis.

MATERIALS AND METHODS

LC/MS-MS proteomics and bioinformatics analysis were performed in the secretome of four informative cervical cell lines SiHa (HPV16⁺), HeLa (HPV18⁺), C33A (HPV^-) and HCK1T (normal).

RESULTS

The proteomic pattern of each cancer cell line compared to HCK1T was identified and a detailed bioinformatics analysis disclosed inhibition of matrix metalloproteases in cancer cell lines. This prediction was further confirmed via zymography for MMP-2 and MMP-9, western blot analysis for ADAM10 and by MRM for TIMP1. The differential expression of important secreted proteins such as CATD, FUCA1 and SOD2 was also confirmed by western blot analysis. MRM-targeted proteomics analysis confirmed the differential expression of CATD, CATB, SOD2, QPCT and NEU1.

CONCLUSION

High resolution proteomics analysis of cervical cancer secretome revealed significantly deregulated biological processes and proteins implicated in cervical carcinogenesis.

A Strategy for Discovery of Endocrine Interactions with Application to Whole-Body Metabolism

Inter-tissue communication via secreted proteins has been established as a vital mechanism for proper physiologic homeostasis. Here, we report a bioinformatics framework using a mouse reference population, the Hybrid Mouse Diversity Panel (HMDP), which integrates global multi-tissue expression data and publicly available resources to identify and functionally annotate novel circuits of tissue-tissue communication. We validate this method by showing that we can identify known as well as novel endocrine factors responsible for communication between tissues. We further show the utility of this approach by identification and mechanistic characterization of two new endocrine factors. Adipose-derived Lipocalin-5 is shown to enhance skeletal muscle mitochondrial function, and liver-secreted Notum promotes browning of white adipose tissue, also known as "beiging." We demonstrate the general applicability of the method by providing in vivo evidence for three additional novel molecules mediating tissue-tissue interactions.

GeLC-MS: A Sample Preparation Method for Proteomics Analysis of Minimal Amount of Tissue

Application of various proteomics methodologies have been implemented for the global and targeted proteome analysis of many different types of biological samples such as tissue, urine, plasma, serum, blood, and cell lines. Among the aforementioned biological samples, tissue has an exceptional role into clinical research and practice. Disease initiation and progression is usually located at the tissue level of different organs, making the analysis of this material very important for the understanding of the disease pathophysiology. Despite the significant advances in the mass spectrometry instrumentation, tissue proteomics still faces several challenges mainly due to increased sample complexity and heterogeneity. However, the most prominent challenge is attributed to the invasive procedure of tissue sampling which restricts the availability of fresh frozen tissue to minimal amounts and limited number of samples. Application of GeLC-MS sample preparation protocol for tissue proteomics analysis can greatly facilitate making up for these difficulties. In this chapter, a step by step guide for the proteomics analysis of minute amounts of tissue samples using the GeLC-MS sample preparation protocol, as applied by our group in the analysis of multiple different types of tissues (vessels, kidney, bladder, prostate, heart) is provided.

Identification of Plasma Membrane Glycoproteins Specific to Human Glioblastoma Multiforme Cells Using Lectin Arrays and LC-MS/MS

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant type of brain cancer and has poor prognosis with a median survival of less than one year. While the structural changes of tumor cell surface carbohydrates are known to be associated with invasive behavior of tumor cells, the cell surface glycoproteins to differentiate the low- and high-grade glioma cells can be potential diagnostic markers and therapeutic targets for GBMs. In the present study, lectin arrays consisting of eight lectins were employed to explore cell surface carbohydrate expression patterns on low-grade oligodendroglioma cells (Hs683) and GBM cells (T98G). Griffonia simplicifolia I (GS I) was found to selectively bind to T98G cells and not to Hs683 cells. For identification of the glioblastoma-specific cell surface markers, the glycoproteins from each cell type were captured by a GS I lectin column and analyzed by LC-MS/MS. The identified proteins from the two cell types were quantified using label-free quantitative analysis based on spectral counting. Of cell surface glycoproteins showing significant increases in T98G cells, five proteins were selected for verification of both protein and glycosylation level changes using Western blot and GS I lectin-based immunosorbent assay.

Proteomic profiling of the cancer cell secretome: informing clinical research

INTRODUCTION

Cancer represents one of the major causes of human deaths. Identification of proteins as biomarkers for early detection of cancer and therapeutic targets for cancer treatment are important issues in precision medicine. Secretome of cancer cells represents the collection of proteins secreted or shed from cancer cells. Proteomic profiling of the cancer cell secretome has been proven to be a convenient and efficient way to discover cancer biomarker and/or therapeutic targets. Areas covered: There have been numerous reviews describing the history and application of secretome analysis in cancer biomarker/therapeutic target research. The present review focuses on the technological advancement for profiling low-molecular-mass proteins in secretome, the latest information regarding the new candidate biomarkers and molecular mechanisms discovered on the basis of cancer cell secretome analysis, as well as the previously discovered candidate biomarkers that enter into clinical trials. Expert commentary: Current technologies for protein sample preparation/separation and MS-based protein identification have allowed in-depth analysis of cancer cell secretome. Future efforts should focus on the comprehensiveness of cancer cell secretome, meta-analysis of different secretome datasets and integrated analysis via combining other omics datasets, as well as the incorporation of MS-based biomarker verification pipeline into both preclinical studies and clinical trials.

Galectin-3 Binding Protein Secreted by Breast Cancer Cells Inhibits Monocyte-Derived Fibrocyte Differentiation

To metastasize, tumor cells often need to migrate through a layer of collagen-containing scar tissue which encapsulates the tumor. A key component of scar tissue and fibrosing diseases is the monocyte-derived fibrocyte, a collagen-secreting profibrotic cell. To test the hypothesis that invasive tumor cells may block the formation of the fibrous sheath, we determined whether tumor cells secrete factors that inhibit monocyte-derived fibrocyte differentiation. We found that the human metastatic breast cancer cell line MDA-MB-231 secretes activity that inhibits human monocyte-derived fibrocyte differentiation, whereas less aggressive breast cancer cell lines secrete less of this activity. Purification indicated that Galectin-3 binding protein (LGALS3BP) is the active factor. Recombinant LGALS3BP inhibits monocyte-derived fibrocyte differentiation, and immunodepletion of LGALS3BP from MDA-MB 231 conditioned media removes the monocyte-derived fibrocyte differentiation-inhibiting activity. LGALS3BP inhibits the differentiation of monocyte-derived fibrocytes from wild-type mouse spleen cells, but not from SIGN-R1(-/-) mouse spleen cells, suggesting that CD209/SIGN-R1 is required for the LGALS3BP effect. Galectin-3 and galectin-1, binding partners of LGALS3BP, potentiate monocyte-derived fibrocyte differentiation. In breast cancer biopsies, increased levels of tumor cell-associated LGALS3BP were observed in regions of the tumor that were invading the surrounding stroma. These findings suggest LGALS3BP and galectin-3 as new targets to treat metastatic cancer and fibrosing diseases.

Quantitative Analysis of Differential Proteome Expression in Bladder Cancer vs. Normal Bladder Cells Using SILAC Method

The best way to increase patient survival rate is to identify patients who are likely to progress to muscle-invasive or metastatic disease upfront and treat them more aggressively. The human cell lines HCV29 (normal bladder epithelia), KK47 (low grade nonmuscle invasive bladder cancer, NMIBC), and YTS1 (metastatic bladder cancer) have been widely used in studies of molecular mechanisms and cell signaling during bladder cancer (BC) progression. However, little attention has been paid to global quantitative proteome analysis of these three cell lines. We labeled HCV29, KK47, and YTS1 cells by the SILAC method using three stable isotopes each of arginine and lysine. Labeled proteins were analyzed by 2D ultrahigh-resolution liquid chromatography LTQ Orbitrap mass spectrometry. Among 3721 unique identified and annotated proteins in KK47 and YTS1 cells, 36 were significantly upregulated and 74 were significantly downregulated with >95% confidence. Differential expression of these proteins was confirmed by western blotting, quantitative RT-PCR, and cell staining with specific antibodies. Gene ontology (GO) term and pathway analysis indicated that the differentially regulated proteins were involved in DNA replication and molecular transport, cell growth and proliferation, cellular movement, immune cell trafficking, and cell death and survival. These proteins and the advanced proteome techniques described here will be useful for further elucidation of molecular mechanisms in BC and other types of cancer.

HCSD: the human cancer secretome database

The human cancer secretome database (HCSD) is a comprehensive database for human cancer secretome data. The cancer secretome describes proteins secreted by cancer cells and structuring information about the cancer secretome will enable further analysis of how this is related with tumor biology. The secreted proteins from cancer cells are believed to play a deterministic role in cancer progression and therefore may be the key to find novel therapeutic targets and biomarkers for many cancers. Consequently, huge data on cancer secretome have been generated in recent years and the lack of a coherent database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer types. It has a simple and user friendly query system for basic and advanced search based on gene name, cancer type and data type as the three main query options. The results are visualized in an explicit and interactive manner. An example of a result page includes annotations, cross references, cancer secretome data and secretory features for each identified protein.

Database URL:www.cancersecretome.org.

MTRAQ-based quantitative analysis combined with peptide fractionation based on cysteinyl peptide enrichment

In the present study, the fractionation scheme for cysteinyl peptide enrichment (CPE) was combined with the mass differential tags for relative and absolute quantification (mTRAQ) method to reduce sample complexity and increase proteome coverage. Cysteine residues of the proteins were first alkylated using iodoacetyl PEG2-biotin instead of other conventional alkylating agents such as iodoacetamide. After trypsin digestion, amine groups were labeled with mTRAQ, and these labeled peptides were fractionated according to the presence or absence of cysteine residues using avidin-biotin affinity chromatography. With these approaches, we were able to divide the peptides into the two fractions with more than 90% fractionation efficiency for standard protein and MCF7 cell lysate. When the fractionation strategy was applied to colorectal cancer tissue samples, we were able to obtain quantitative information that was consistent with the previous study based on mTRAQ quantification, implying that the cysteine-based fractionation method does not affect mTRAQ quantification. We expect that the mTRAQ-based quantitative analysis combined with peptide fractionation through the CPE strategy would allow for deep-down analysis of proteome samples and ultimately for increasing proteome coverage with simultaneous quantification for biomarker discovery.

Role of phosphoproteomics in the development of personalized cancer therapies

Cell signalling pathways driven by protein and lipid kinases contribute to the onset and progression of virtually all cancer types. Consequently, several inhibitors against these enzymes have clinical utility for the treatment of different forms of cancer. A problem that hampers further development is that not all patients respond equally well to kinase inhibitors and a significant proportion of those that initially respond eventually develop resistance. This review considers how an integrative analysis of kinase signalling may be used to address this issue. Advances in the biophysics of mass spectrometry, in biochemical procedures for phosphopeptide enrichment, and in computational approaches for label-free quantification have contributed to the development of phosphoproteomics workflows compatible with the analysis of clinical material. These developments, together with new bioinformatics tools to derive information on signalling circuitry from phosphoproteomics data, allow investigating kinase networks with unprecedented depth. Phosphoproteomics technology is starting to be used in translational research and, with further developments, such methods may also be able to measure the circuitry of cancer signalling networks in routine clinical assays. This review reflects on how this information could be used to accurately predict the best kinase inhibitor for each individual cancer patient.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

Background

The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic. Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature of transcriptional and translation regulatory mechanisms and the disparities between genomic and proteomic data from the same samples. In this study, we have examined tumor antigens as potential biomarkers for breast cancer using genomics and proteomics data from previously reported laser capture microdissected ER+ tumor samples.

Results

We applied proteogenomic analyses to study the genetic aberrations of 32 tumor antigens determined in the proteomic data. We found that tumor antigens that are aberrantly expressed at the genetic level and expressed at the protein level, are likely involved in perturbing pathways directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found to be perturbed.

Conclusion

Tumor antigens are a group of proteins recognized by the cells of the immune system. Specifically, they are recognized in tumor cells where they are present in larger than usual amounts, or are physiochemically altered to a degree at which they no longer resemble native human proteins. This proteogenomic analysis of 32 tumor antigens suggests that tumor antigens have the potential to be highly specific biomarkers for different cancers.

Enhancing the Biological Relevance of Secretome-Based Proteomics by Linking Tumor Cell Proliferation and Protein Secretion

Secretome profiling has become a methodology of choice for the identification of tumor biomarkers. We hypothesized that due to the dynamic nature of secretomes cellular perturbations could affect their composition but also change the global amount of protein secreted per cell. We confirmed our hypothesis by measuring the levels of secreted proteins taking into account the amount of proteome produced per cell. Then, we established a correlation between cell proliferation and protein secretion that explained the observed changes in global protein secretion. Next, we implemented a normalization correcting the statistical results of secretome studies by the global protein secretion of cells into a generalized linear model (GLM). The application of the normalization to two biological perturbations on tumor cells resulted in drastic changes in the list of statistically significant proteins. Furthermore, we found that known epithelial-to-mesenchymal transition (EMT) effectors were only statistically significant when the normalization was applied. Therefore, the normalization proposed here increases the sensitivity of statistical tests by increasing the number of true-positives. From an oncology perspective, the correlation between protein secretion and cellular proliferation suggests that slow-growing tumors could have high-protein secretion rates and consequently contribute strongly to tumor paracrine signaling.

Aminopeptidase activities as prospective urinary biomarkers for bladder cancer

PURPOSE

Proteases have been implicated in cancer progression and invasiveness. We have investigated the activities, as opposed to simple protein levels, of selected aminopeptidases in urine specimens to serve as potential novel biomarkers for urothelial cancer.

EXPERIMENTAL DESIGN

The unique urinary proteomes of males and females were profiled to establish the presence of a gender-independent set of aminopeptidases. Samples were also collected from patients with urothelial cancer and matched controls. A SOP for urine processing was developed taking into account hydration variation. Five specific aminopeptidase activity assays, using fluorophoric substrates, were optimized for evaluation of marker potential.

RESULTS

Nineteen exopeptidases and 21 other proteases were identified in urine and the top-five most abundant aminopeptidases, identical in both genders, selected for functional studies. Depending on the enzyme, activities were consistently lower (p ≤ 0.05), higher or unchanged in the cancer samples as compared to controls. Two selected aminopeptidase activities used as a binary classifier resulted in a ROC curve with an AUC = 0.898.

CONCLUSION AND CLINICAL RELEVANCE

We have developed functional assays that characterize aminopeptidase activities in urine specimens with adequate technical and intraindividual reproducibility. With further testing, it could yield a reliable biomarker test for bladder cancer detection or prognostication.

Cancer secretomes and their place in supplementing other hallmarks of cancer

The secretome includes all macromolecules secreted by cells, in particular conditions at defined times, allowing cell-cell communication. Cancer cell secretomes that are altered compared to normal cells have shown significant potential for elucidating cancer biology. Proteins of secretomes are secreted by various secretory pathways and can be studied using different methods. Cancer secretomes seem to play an important role in known hallmarks of cancers such as excessive proliferation, reduced apoptosis, immune invasion, angioneogenesis, alteration in energy metabolism, and development of resistance against anti-cancer therapy [1, 2]. If a significant role of an altered secretome can be identified in cancer cells, using advanced mass spectrometry-based techniques, this may allow researchers to screen and characterize the secretome proteins involved in cancer progression and open up new opportunities to develop new therapies. We aim to elaborate upon recent advances in cancer cell secretome analysis using different proteomics techniques. In this review, we highlight the role of the altered secretome in contributing to already recognized and emerging hallmarks of cancer and we discuss new challenges in the field of secretome analysis.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

Mapping proteolytic processing in the secretome of gastric cancer-associated myofibroblasts reveals activation of MMP-1, MMP-2, and MMP-3

Cancer progression involves changes in extracellular proteolysis, but the contribution of stromal cell secretomes to the cancer degradome remains uncertain. We have now defined the secretome of a specific stromal cell type, the myofibroblast, in gastric cancer and its modification by proteolysis. SILAC labeling and COFRADIC isolation of methionine containing peptides allowed us to quantify differences in gastric cancer-derived myofibroblasts compared with myofibroblasts from adjacent tissue, revealing increased abundance of several proteases in cancer myofibroblasts including matrix metalloproteinases (MMP)-1 and -3. Moreover, N-terminal COFRADIC analysis identified cancer-restricted proteolytic cleavages, including liberation of the active forms of MMP-1, -2, and -3 from their inactive precursors. In vivo imaging confirmed increased MMP activity when gastric cancer cells were xenografted in mice together with gastric cancer myofibroblasts. Western blot and enzyme activity assays confirmed increased MMP-1, -2, and -3 activity in cancer myofibroblasts, and cancer cell migration assays indicated stimulation by MMP-1, -2, and -3 in cancer-associated myofibroblast media. Thus, cancer-derived myofibroblasts differ from their normal counterparts by increased production and activation of MMP-1, -2, and -3, and this may contribute to the remodelling of the cancer cell microenvironment.

Exoproteomics: exploring the world around biological systems

The term 'exoproteome' describes the protein content that can be found in the extracellular proximity of a given biological system. These proteins arise from cellular secretion, other protein export mechanisms or cell lysis, but only the most stable proteins in this environment will remain in abundance. It has been shown that these proteins reflect the physiological state of the cells in a given condition and are indicators of how living systems interact with their environments. High-throughput proteomic approaches based on a shotgun strategy, and high-resolution mass spectrometers, have modified the authors' view of exoproteomes. In the present review, the authors describe how these new approaches should be exploited to obtain the maximum useful information from a sample, whatever its origin. The methodologies used for studying secretion from model cell lines derived from eukaryotic, multicellular organisms, virulence determinants of pathogens and environmental bacteria and their relationships with their habitats are illustrated with several examples. The implication of such data, in terms of proteogenomics and the discovery of novel protein functions, is discussed.

Whole gel processing procedure for GeLC-MS/MS based proteomics

Background

SDS-PAGE followed by in-gel digestion (IGD) is a popular workflow in mass spectrometry-based proteomics. In GeLC-MS/MS, a protein lysate of a biological sample is separated by SDS-PAGE and each gel lane is sliced in 5–20 slices which, after IGD, are analyzed by LC-MS/MS. The database search results for all slices of a biological sample are combined yielding global protein identification and quantification for each sample. In large scale GeLC-MS/MS experiments the manual processing steps including washing, reduction and alkylation become a bottleneck. Here we introduce the whole gel (WG) procedure where, prior to gel slice cutting, the processing steps are carried out on the whole gel.

Results

In two independent experiments human HCT116 cell lysate and mouse tumor tissue lysate were separated by 1D SDS PAGE. In a back to back comparison of the IGD procedure and the WG procedure, both protein identification (>80% overlap) and label-free protein quantitation (R²=0.94) are highly similar between procedures. Triplicate analysis of the WG procedure of both HCT116 cell lysate and formalin-fixed paraffin embedded (FFPE) tumor tissue showed identification reproducibility of >88% with a CV<20% on protein quantitation.

Conclusions

The whole gel procedure allows for reproducible large-scale differential GeLC-MS/MS experiments, without a prohibitive amount of manual processing and with similar performance as conventional in-gel digestion. This procedure will especially enable clinical proteomics for which GeLC-MS/MS is a popular workflow and sample numbers are relatively high.

A novel anti-CCN1 monoclonal antibody suppresses Rac-dependent cytoskeletal reorganization and migratory activities in breast cancer cells

CCN1, a secreted matrix-associated molecule, is involved in multiple cellular processes. Accumulating evidence supports that CCN1 plays an important role in tumorigenesis and progression of breast cancer. In this study, we have developed a novel CCN1 function-blocking monoclonal antibody (mAb), designated YM1B. YM1B binds to human CCN1 with high specificity, recognizing the native CCN1 structure with undisturbed disulfide linkages. Our analyses have mapped the YM1B recognition region to domain IV of CCN1, likely in proximity to the DM site. In breast cancer cells, CCN1 can induce actin reorganization, formation of lamellipodia, and cell migration/invasion through the αV integrins/Rac1/ERK signaling axis; these CCN1-dependent activities can be effectively suppressed by YM1B. Our results also suggest that YM1B may exert its CCN1-blocking effect by perturbing the interaction of CCN1 with vitronectin and fibronectin, which are ligands of αV integrins and instrumental for integrin activation. This CCN1-specific mAb may open a new potential avenue for therapeutic intervention of breast cancer progression.

Constitutive HER2 signaling promotes breast cancer metastasis through cellular senescence

Senescence, a terminal cell proliferation arrest, can be triggered by oncogenes. Oncogene-induced senescence is classically considered a tumor defense barrier. However, several findings show that, under certain circumstances, senescent cells may favor tumor progression because of their secretory phenotype. Here, we show that the expression in different breast epithelial cell lines of p95HER2, a constitutively active fragment of the tyrosine kinase receptor HER2, results in either increased proliferation or senescence. In senescent cells, p95HER2 elicits a secretome enriched in proteases, cytokines, and growth factors. This secretory phenotype is not a mere consequence of the senescence status and requires continuous HER2 signaling to be maintained. Underscoring the functional relevance of the p95HER2-induced senescence secretome, we show that p95HER2-induced senescent cells promote metastasis in vivo in a non-cell-autonomous manner.

Isolation and identification of mannose-binding proteins and estimation of their abundance in sera from hepatocellular carcinoma patients

The interaction of glycan-binding proteins (GBPs) and glycans plays a significant biological role that ranges from cell-cell recognition to cell trafficking, and glycoprotein targeting. The anomalies of GBPs related to the types and/or quantities were not clearly known in cancer incidence. It is imperative to identify and annotate the GBPs related with the canceration. Here the mannose-binding proteins (MBPs) from the clinical sera were isolated and identified by the mannose-magnetic particle conjugates and the high-accuracy MS analysis. Seventy-five MBPs from normal donors' sera and 79 MBPs from hepatocellular carcinoma patients' sera were identified and annotated. By using the stringent criteria of exponentially modified protein abundance index (emPAI) quantification, 12 MBPs were estimated to be significantly upregulated (emPAI ratio > 4) and nine MBPs were estimated to be significantly downregulated (emPAI ratio < 0.25) in the hepatocellular carcinoma sera. Real-time quantitative PCR, Western blotting, and protein microarrays were also used to confirm the altered MBPs expression level and the specific binding between the isolated MBPs and mannose. The sequence recognition motifs and structure preference of the isolated MBPs were characterized. The functional enrichment analysis revealed that over 57% of the isolated MBPs were binding protein and the upregulated MBPs were involved in cell death, tumor progression, and macromolecular complex remodeling.

Secretome analysis using a hollow fiber culture system for cancer biomarker discovery

Secreted proteins, collectively referred to as the secretome, were suggested as valuable biomarkers in disease diagnosis and prognosis. However, some secreted proteins from cell cultures are difficult to detect because of their intrinsically low abundance; they are frequently masked by the released proteins from lysed cells and the substantial amounts of serum proteins used in culture medium. The hollow fiber culture (HFC) system is a commercially available system composed of small fibers sealed in a cartridge shell; cells grow on the outside of the fiber. Recently, because this system can help cells grow at a high density, it has been developed and applied in a novel analytical platform for cell secretome collection in cancer biomarker discovery. This article focuses on the advantages of the HFC system, including the effectiveness of the system for collection of secretomes, and reviews the process of cell secretome collection by the HFC system and proteomic approaches to discover cancer biomarkers. The HFC system not only provides a high-density three-dimensional (3D) cell culture system to mimic tumor growth conditions in vivo but can also accommodate numerous cells in a small volume, allowing secreted proteins to be accumulated and concentrated. In addition, cell lysis rates can be greatly reduced, decreasing the amount of contamination by abundant cytosolic proteins from lysed cells. Therefore, the HFC system is useful for preparing a wide range of proteins from cell secretomes and provides an effective method for collecting higher amounts of secreted proteins from cancer cells. This article is part of a Special Issue entitled: An Updated Secretome.

Proteomic profiling of high glucose primed monocytes identifies cyclophilin A as a potential secretory marker of inflammation in type 2 diabetes

Hyperglycemia is widely recognized to be a potent stimulator of monocyte activity, which is a crucial event in the pathogenesis of atherosclerosis. We analyzed the monocyte proteome for potential markers that would enhance the ability to screen for early inflammatory status in Type 2 diabetes mellitus (T2DM), using proteomic technologies. Monocytic cells (THP-1) were primed with high glucose (HG), their protein profiles were analyzed using 2DE and the downregulated differentially expressed spots were identified using MALDI TOF/MS. We selected five proteins that were secretory in function with the help of bioinformatic programs. A predominantly downregulated protein identified as cyclophilin A (sequence coverage 98%) was further validated by immunoblotting experiments. The cellular mRNA levels of cyclophilin A in various HG-primed cells were studied using qRT-PCR assays and it was observed to decrease in a dose-dependent manner. LC-ESI-MS was used to identify this protein in the conditioned media of HG-primed cells and confirmed by Western blotting as well as ELISA. Cyclophilin A was also detected in the plasma of patients with diabetes. We conclude that cyclophilin A is secreted by monocytes in response to HG. Given the paracrine and autocrine actions of cyclophilin A, the secreted immunophilin could be significant for progression of atherosclerosis in type 2 diabetes. Our study also provides evidence that analysis of monocyte secretome is a viable strategy for identifying candidate plasma markers in diabetes.

Unconventional secretion is a major contributor of cancer cell line secretomes

A challenge in achieving optimal management of cancer is the discovery of secreted biomarkers that represent useful surrogates for the disease and could be measured noninvasively. Because of the problems encountered in the proteomic interrogation of plasma, secretomes have been proposed as an alternative source of tumor markers that might be enriched with secreted proteins relevant to the disease. However, secretome analysis faces analytical challenges that interfere with the search for true secreted tumor biomarkers. Here, we have addressed two of the main challenges of secretome analysis in comparative discovery proteomics. First, we carried out a kinetics experiment whereby secretomes and lysates of tumor cells were analyzed to monitor cellular viability during secretome production. Interestingly, the proteomic signal of a group of secreted proteins correlated well with the apoptosis induced by serum starvation and could be used as an internal cell viability marker. We then addressed a second challenge relating to contamination of serum proteins in secretomes caused by the required use of serum for tumor cell culture. The comparative proteomic analysis between cell lines labeled with SILAC showed a number of false positives coming from serum and that several proteins are both in serum and being secreted from tumor cells. A thorough study of secretome methodology revealed that under optimized experimental conditions there is a substantial fraction of proteins secreted through unconventional secretion in secretomes. Finally, we showed that some of the nuclear proteins detected in secretomes change their cellular localization in breast tumors, explaining their presence in secretomes and suggesting that tumor cells use unconventional secretion during tumorigenesis. The unconventional secretion of proteins into the extracellular space exposes a new layer of genome post-translational regulation and reveals an untapped source of potential tumor biomarkers and drug targets.

Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis

Cancer is currently considered as the end point of numerous genomic and epigenomic mutations and as the result of the interaction of transformed cells within the stromal microenvironment. The present work focuses on breast cancer, one of the most common malignancies affecting the female population in industrialized countries. In this study, we perform a proteomic analysis of bioptic samples from human breast cancer, namely, interstitial fluids and primary cells, normal vs disease tissues, using tandem mass tags (TmT) quantitative mass spectrometry combined with the MudPIT technique. To the best of our knowledge, this work, with over 1700 proteins identified, represents the most comprehensive characterization of the breast cancer interstitial fluid proteome to date. Network analysis was used to identify functionally active networks in the breast cancer associated samples. From the list of differentially expressed genes, we have retrieved the associated functional interaction networks. Many different signaling pathways were found activated, strongly linked to invasion, metastasis development, proliferation, and with a significant cross-talking rate. This pilot study presents evidence that the proposed quantitative proteomic approach can be applied to discriminate between normal and tumoral samples and for the discovery of yet unknown carcinogenesis mechanisms and therapeutic strategies.

Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics

Shotgun proteomics has become the standard proteomics technique for the large-scale measurement of protein abundances in biological samples. Despite quantitative proteomics has been usually performed using label-based approaches, label-free quantitation offers advantages related to the avoidance of labeling steps, no limitation in the number of samples to be compared, and the gain in protein detection sensitivity. However, since samples are analyzed separately, experimental design becomes critical. The exploration of spectral counting quantitation based on LC-MS presented here gathers experimental evidence of the influence of batch effects on comparative proteomics. The batch effects shown with spiking experiments clearly interfere with the biological signal. In order to minimize the interferences from batch effects, a statistical correction is proposed and implemented. Our results show that batch effects can be attenuated statistically when proper experimental design is used. Furthermore, the batch effect correction implemented leads to a substantial increase in the sensitivity of statistical tests. Finally, the applicability of our batch effects correction is shown on two different biomarker discovery projects involving cancer secretomes. We think that our findings will allow designing and executing better comparative proteomics projects and will help to avoid reaching false conclusions in the field of proteomics biomarker discovery.

Investigating the secretome: lessons about the cells that comprise the heart

The cell/environment interface is composed of the proteins of plasma membrane which face the extracellular space and by the proteins secreted directly by the cell of origin or by neighboring cells. The secreted proteins can act as extracellular matrix proteins and/or autocrine/paracrine proteins. This report discusses the technical aspects involved in the identification and characterization of the secreted proteins of specific cell types that comprise the heart. These aspects include the culturing of the cells, cell co-culturing and quantitative labeling, conditioned media collection and dealing with high abundant serum proteins, post-translational modification enrichment, the use of protein separation methods and mass spectrometry, protein identification and validation and the incorporation of pathway analysis to better understand the novel discovery on the background of already known experimental biological systems. The proteomic methods have the solid emplacement in cardiovascular research and the identification of proteins secreted by cardiac cells has been used in various applications such as determination the specificity between secretomes of different cell types, e.g. cardiac stem cells and cardiac myocytes, for the global secretome screening of e.g. human arterial smooth muscle cells, for the mapping of the beneficial effect of conditioned medium of one cell type on the other cell type, e.g. conditioned medium of human mesenchymal stem cells on cardiac myocytes, and for the searching the candidate paracrine factors and potential biomarkers.

Profiling of differentially expressed proteins in stage IV colorectal cancers with good and poor outcomes

Screening patients at high risk of recurrence of cancer would allow for more accurate and personalized treatment. In this study, we tried to identify the prognosis-related protein profile by applying two different quantitative proteomic techniques, difference in-gel electrophoresis and cleavable isotope-coded affinity tag method. Six tumor tissues were obtained from stage IV colorectal cancer (CRC) patients, of which three have survived more than five years (good prognostic group, GPG) and the other three died within 25 months (poor prognostic group, PPG) after palliative surgery and subsequent chemotherapy treatment. From the two independent quantitative analyses, we identified 175 proteins with abundance ratios greater than 2-fold. Gene ontology analysis revealed that proteins related to cellular assembly/organization and movement were generally increased in the PPG. Twenty-two proteins, including caveolin-1, were chosen for confirmatory studies by Western blot and immunohistochemistry. The Western blot data for each individual protein were analyzed with Mann-Whitney tests, and a multi-marker panel was generated by logistic regression analysis. Five proteins, fatty acid binding protein 1, intelectin 1, transitional endoplasmic reticulum ATPase, transgelin and tropomyosin 2, were significantly different between the two prognostic groups within 95% confidence. No single protein could completely distinguish the two groups from each other. However, a combination of the five proteins effectively distinguished PPG from GPG patients (AUC=1). Our study suggests a multi-marker panel composed of proteome signatures that provide accurate predictive information and potentially assist personalized therapy. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Relative quantitation of proteins in expressed prostatic secretion with a stable isotope labeled secretome standard

Expressed prostatic secretion (EPS) is a proximal fluid directly derived from the prostate and, in the case of prostate cancer (PCa), is hypothesized to contain a repertoire of cancer-relevant proteins. Quantitative analysis of the EPS proteome may enable identification of proteins with utility for PCa diagnosis and prognosis. The present investigation demonstrates selective quantitation of proteins in EPS samples from PCa patients using a stable isotope labeled proteome standard (SILAP) generated through the selective harvest of the "secretome" from the PC3 prostate cancer cell line grown in stable isotope labeled cell culture medium. This stable isotope labeled secretome was digested with trypsin and equivalently added to each EPS digest, after which the resultant mixtures were analyzed by liquid chromatography-tandem mass spectrometry for peptide identification and quantification. Relative quantification of endogenous EPS peptides was accomplished by comparison of reconstructed mass chromatograms to those of the chemically identical SILAP peptides. A total of 86 proteins were quantified from 263 peptides in all of the EPS samples, 38 of which were found to be relevant to PCa. This work demonstrates the feasibility of using a SILAP secretome standard to simultaneously quantify many PCa-relevant proteins in EPS samples.

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Analytical constraints for the analysis of human cell line secretomes by shotgun proteomics

Human cell line secretome represents a valuable source of therapeutic targets and candidate biomarkers. Secreted proteins found in biological fluids or culture media are by essence highly diluted. Secretome investigation with proteomic approaches is hardly compatible with the high content of proteins found in complete cell culture media. Therefore, many studies are currently done with media containing few or no protein. Such conditions may perturb cell metabolism and proliferation. Here, we compared seventeen different compositions of culture media for the human bronchial epithelial BEAS-2B cell line. Cell viability, proliferation rate and initial protein charge were systematically compared. We have shown that an important difficulty for the proteomic analysis is due to the presence of detergents such as Pluronic F-68 which hinders peptide mass spectrometry. The high glucose containing DMEM medium which is free of proteins was shown to preserve a good viability and proliferation of cells. With this conditioning medium, we identified 81 extracellular proteins in the secretome of BEAS-2B cells. Moreover, to illustrate this approach, we exposed BEAS-2B cells to a low toxic dose of CoCl(2,) and found 24 extracellular proteins modulated by cobalt. This study highlights the possible contribution of such proteomic approach in the field of toxicology.

Secretome compartment is a valuable source of biomarkers for cancer-relevant pathways

In principle, targeted therapies have optimal activity against a specific subset of tumors that depend upon the targeted molecule or pathway for growth, survival, or metastasis. Consequently, it is important in drug development and clinical practice to have predictive biomarkers that can reliably identify patients who will benefit from a given therapy. We analyzed tumor cell-line secretomes (conditioned cell media) to look for predictive biomarkers; secretomes represent a potential source for potential biomarkers that are expressed in intracellular signaling and therefore may reflect changes induced by targeted therapy. Using Gene Ontology, we classified by function the secretome proteins of 12 tumor cell lines of different histotypes. Representations and hierarchical relationships among the functional groups differed among the cell lines. Using bioinformatics tools, we identified proteins involved in intracellular signaling pathways. For example, we found that secretome proteins related to TGF-beta signaling in thyroid cancer cells, such as vasorin, CD109, and βIG-H3 (TGFBI), were sensitive to RPI-1 and dasatinib treatments, which have been previously demonstrated to be effective in blocking cell proliferation. The secretome may be a valuable source of potential biomarkers for detecting cancer and measuring the effectiveness of cancer therapies.

Phosphoproteomics identifies oncogenic Ras signaling targets and their involvement in lung adenocarcinomas

Background

Ras is frequently mutated in a variety of human cancers, including lung cancer, leading to constitutive activation of MAPK signaling. Despite decades of research focused on the Ras oncogene, Ras-targeted phosphorylation events and signaling pathways have not been described on a proteome-wide scale.

Methodology/Principal Findings

By functional phosphoproteomics, we studied the molecular mechanics of oncogenic Ras signaling using a pathway-based approach. We identified Ras-regulated phosphorylation events (n = 77) using label-free comparative proteomics analysis of immortalized human bronchial epithelial cells with and without the expression of oncogenic Ras. Many were newly identified as potential targets of the Ras signaling pathway. A majority (∼60%) of the Ras-targeted events consisted of a [pSer/Thr]-Pro motif, indicating the involvement of proline-directed kinases. By integrating the phosphorylated signatures into the Pathway Interaction Database, we further inferred Ras-regulated pathways, including MAPK signaling and other novel cascades, in governing diverse functions such as gene expression, apoptosis, cell growth, and RNA processing. Comparisons of Ras-regulated phosphorylation events, pathways, and related kinases in lung cancer-derived cells supported a role of oncogenic Ras signaling in lung adenocarcinoma A549 and H322 cells, but not in large cell carcinoma H1299 cells.

Conclusions/Significance

This study reveals phosphorylation events, signaling networks, and molecular functions that are regulated by oncogenic Ras. The results observed in this study may aid to extend our knowledge on Ras signaling in lung cancer.

Identification of proteins secreted by head and neck cancer cell lines using LC-MS/MS: Strategy for discovery of candidate serological biomarkers

In search of blood-based biomarkers that would enhance the ability to diagnose head and neck/oral squamous cell carcinoma (HNOSCC) in early stages or predict its prognosis, we analyzed the HNOSCC secretome (ensemble of proteins secreted and/or shed from the tumor cells) for potential biomarkers using proteomic technologies. LC-MS/MS was used to identify proteins in the conditioned media of four HNOSCC cell lines (SCC4, HSC2, SCC38, and AMOSIII); 140 unique proteins were identified on the basis of 5% global false discovery rate, 122 of which were secretory proteins, with 29 being previously reported to be overexpressed in HNOSCC in comparison to normal head and neck tissues. Of these, five proteins including α-enolase, peptidyl prolyl isomerase A/cyclophilin A, 14-3-3 ζ, heterogeneous ribonucleoprotein K, and 14-3-3 σ were detected in the sera of HNOSCC patients by Western blot analysis. Our study provides the evidence that analysis of head and neck cancer cells' secretome is a viable strategy for identifying candidate serological biomarkers for HNOSCC. In future, these biomarkers may be useful in predicting the likelihood of transformation of oral pre-malignant lesions, prognosis of HNOSCC patients and evaluate response to therapy using minimally invasive tests.

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.