Cancer proteomics by quantitative shotgun proteomics

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Assessing technical and biological variation in SWATH-MS-based proteomic analysis of chronic lymphocytic leukaemia cells

Chronic lymphocytic leukaemia (CLL) exhibits variable clinical course and response to therapy, but the molecular basis of this variability remains incompletely understood. Data independent acquisition (DIA)-MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. Here, a CLL-specific spectral library (7736 proteins) is described alongside an analysis of sample replication and data handling requirements for quantitative SWATH-MS analysis of clinical samples. The analysis was performed on 6 CLL samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins across 54 SWATH-MS acquisitions: the sources of variation and different computational approaches for batch correction were assessed. Functional enrichment analysis of proteins associated with IGHV mutational status showed significant overlap with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was implemented. This study provides a valuable resource for researchers working on the proteomics of CLL. It also establishes a sound framework for the design of sufficiently powered clinical proteomics studies. Indeed, this study shows that it is possible to derive biologically plausible hypotheses from a relatively small dataset.

Evaluation of Tumor Interstitial Fluid-Extraction Methods for Proteome Analysis: Comparison of Biopsy Elution versus Centrifugation

The analysis of tumor interstitial fluid (TIF) composition is a valuable procedure to identify antimetastatic targets, and different laboratories have set up techniques for TIF isolation and proteomic analyses. However, those methods had never been compared in samples from the same tumor and patient. In this work, we compared the two most used methods, elution and centrifugation, in pieces of the same biopsy samples of cutaneous squamous cell carcinoma (cSCC). First, we established that high G-force (10 000g) was required to obtain TIF from cSCC by centrifugation. Second, we compared the centrifugation method with the elution method in pieces of three different cSCC tumors. We found that the mean protein intensities based in the number of peptide spectrum matches was significantly higher in the centrifuged samples than in the eluted samples. Regarding the robustness of the methods, we observed higher overlapping between both methods (77-80%) than among samples (50%). These results suggest that there exists an elevated consistence of TIF composition independently of the method used. However, we observed a 3-fold increase of extracellular proteins in nonoverlapped proteome obtained by centrifugation. We therefore conclude that centrifugation is the method of choice to study the proteome of TIF from cutaneous biopsies.

Use of Lectin-based Affinity Techniques in Breast Cancer Glycoproteomics: A Review

Changes in glycoprotein content, altered glycosylations, and aberrant glycan structures are increasingly recognized as cancer hallmarks. Because breast cancer is one of the most common causes of cancer deaths in the world, it is highly urgent to find other reliable biomarkers for its initial diagnosis and to learn as much as possible about this disease. In this Review, the applications of lectins to a screening of potential breast cancer biomarkers published during recent years are overviewed. These data provide a deeper insight into the use of modern strategies, technologies, and scientific knowledge in glycoproteomic breast cancer research. Particular attention is concentrated on the use of lectin-based affinity techniques, applied independently or most frequently in combination with mass spectrometry, as an effective tool for the targeting, separation, and reliable identification of glycoprotein molecules. Individual procedures and lectins used in published glycoproteomic studies of breast-cancer-related glycoproteins are discussed. The summarized approaches have the potential for use in diagnostic and predictive applications. Finally, the use of lectins is briefly discussed from the view of their future applications in the analysis of glycoproteins in cancer.

Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins

Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of spectral count in the MS data-dependent acquisition, however, inherently introduces the spectral count variation for low-abundance proteins in multiplicative LC-MS/MS analysis, which hampers sensitive proteome quantification. As many low-abundance proteins play important roles in cellular processes, deducing low-abundance proteins in a quantitatively reliable manner greatly expands the depth of biological insights. Here, we implemented the Moment Adjusted Imputation error model in the spectral count refinement as a post PLGEM-STN for improving sensitivity for quantitation of low-abundance proteins by reducing spectral count variability. The statistical framework, automated spectral count refinement by integrating the two statistical tools, was tested with LC-MS/MS datasets of MDA-MB468 breast cancer cells grown under normal and glucose deprivation conditions. We identified about 30% more quantifiable proteins that were found to be low-abundance proteins, which were initially filtered out by the PLGEM-STN analysis. This newly developed statistical framework provides a reliable abundance measurement of low-abundance proteins in the spectral count-based label-free proteome quantification and enabled us to detect low-abundance proteins that could be functionally important in cellular processes.

Rapid method towards proteomic analysis of dried blood spots by MALDI mass spectrometry

Neonatal dried blood spots (DBS) are routinely utilized in the clinical setting as a diagnostic tool for various genetic disorders and infectious diseases. DBS allow for minimally invasive, small volume blood collection and are stored at room temperature. Neonatal whole blood and serum samples can be important in determining genetic risk factors and predicting infantile disease; however, at the present time, limited methods exist for rapidly analyzing DBS samples for their proteomic profile, years after samples have been collected. A novel method is presented for the extraction and analysis of target proteins and peptides from neonatal DBS using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Extraction parameters were optimized to achieve ideal signal intensity and resolution to obtain protein identifications. Samples were extracted from filter paper with 0.1% TFA in H₂O for 72 h. The extract was subjected to enzymatic digestion, spotted on an ITO-coated glass slide, and washed in order to remove salts. Analysis of extracted blood spots from ten newborns was completed. Similarities and differences in the proteomic profile of the washed extracts are presented, herein, to verify the viability of this method for analysis of dated DBS samples. This method allows for analysis of DBS samples years after collection and can be utilized to correlate diseases or disorders manifesting later in life with potential risk factors presenting in the proteomic profile of the DBS collected at time of birth.

Novel predication of protein biomarkers in interferon-gamma-stimulated breast cancer cells

OBJECTIVE

Proteomics is the large-scale study of localization, identification, structure, and function of the proteome. A proteome is the complete set of proteins expressed and modified by an organism under a specific set of environmental conditions. This study was undertaken to investigate the novel protein biomarkers that play a role in breast cancer under inflammatory condition.

METHODS

The two-dimensional gel electrophoresis (2-DE) was applied in the context of the breast cancer model system to investigate the effect of interferon-gamma (IFN-γ) on the differential protein expression in breast cancer-derived cell lines CAMA-1 and 3,4-methylenedioxyamphetamine (MDA)-MB-231. Whole cell lysates were prepared from IFN-γ-stimulated and non-stimulated CAMA-1 and MDA-MB-231 cells for 2-DE to obtain information for potential differential protein expression. Protein spots in the gels were visualized by silver staining and analyzed by Progenesis SameSpot. Gels were then scanned using the Epson image scanner with LabScan 6.0 software. The ExPASy tool was used to identify and quantify breast cancer cell membrane proteins expressed in response to IFN-γ.

RESULTS

In the present proteomics study, a series of differentially expressed proteins were analyzed in IFN-γ-stimulated CAMA-1 and MDA-MB-231 cells. While results obtained from this analysis can be used as preliminary data to identify differences between untreated and IFN-γ-treated samples, they were not used for further mass spectrometry analysis.

CONCLUSION

The data described and discussed here can be utilized for further data validation projects and could assist in the discovery of new breast cancer-related proteins and molecular pathways.

The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments

An idealized version of a label-free discovery mass spectrometry proteomics experiment would provide absolute abundance measurements for a whole proteome, across varying conditions. Unfortunately, this ideal is not realized. Measurements are made on peptides requiring an inferential step to obtain protein level estimates. The inference is complicated by experimental factors that necessitate relative abundance estimation and result in widespread non-ignorable missing data. Relative abundance on the log scale takes the form of parameter contrasts. In a complete-case analysis, contrast estimates may be biased by missing data and a substantial amount of useful information will often go unused. To avoid problems with missing data, many analysts have turned to single imputation solutions. Unfortunately, these methods often create further difficulties by hiding inestimable contrasts, preventing the recovery of interblock information and failing to account for imputation uncertainty. To mitigate many of the problems caused by missing values, we propose the use of a Bayesian selection model. Our model is tested on simulated data, real data with simulated missing values, and on a ground truth dilution experiment where all of the true relative changes are known. The analysis suggests that our model, compared with various imputation strategies and complete-case analyses, can increase accuracy and provide substantial improvements to interval coverage.

Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation

We analyzed the tear film proteome of patients with dry eye (DE), meibomian gland dysfunction (MGD), and normal volunteers (CT). Tear samples were collected from 70 individuals. Of these, 37 samples were analyzed using spectral-counting-based LC-MS/MS label-free quantitation, and 33 samples were evaluated in the validation of candidate biomarkers employing customized antibody microarray assays. Comparative analysis of tear protein profiles revealed differences in the expression levels of 26 proteins, including protein S100A6, annexin A1, cystatin-S, thioredoxin, phospholipase A2, antileukoproteinase, and lactoperoxidase. Antibody microarray validation of CST4, S100A6, and MMP9 confirmed the accuracy of previously reported ELISA assays, with an area under ROC curve (AUC) of 87.5%. Clinical endpoint analysis showed a good correlation between biomarker concentrations and clinical parameters. In conclusion, different sets of proteins differentiate between the groups. Apolipoprotein D, S100A6, S100A8, and ceruloplasmin discriminate best between the DE and CT groups. The differences between antileukoproteinase, phospholipase A2, and lactoperoxidase levels allow the distinction between MGD and DE, and the changes in the levels of annexin A1, clusterin, and alpha-1-acid glycoprotein 1, between MGD and CT groups. The functional network analysis revealed the main biological processes that should be examined to identify new candidate biomarkers and therapeutic targets.

Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation

We analyzed the tear film proteome of patients with dry eye (DE), meibomian gland dysfunction (MGD), and normal volunteers (CT). Tear samples were collected from 70 individuals. Of these, 37 samples were analyzed using spectral-counting-based LC-MS/MS label-free quantitation, and 33 samples were evaluated in the validation of candidate biomarkers employing customized antibody microarray assays. Comparative analysis of tear protein profiles revealed differences in the expression levels of 26 proteins, including protein S100A6, annexin A1, cystatin-S, thioredoxin, phospholipase A2, antileukoproteinase, and lactoperoxidase. Antibody microarray validation of CST4, S100A6, and MMP9 confirmed the accuracy of previously reported ELISA assays, with an area under ROC curve (AUC) of 87.5%. Clinical endpoint analysis showed a good correlation between biomarker concentrations and clinical parameters. In conclusion, different sets of proteins differentiate between the groups. Apolipoprotein D, S100A6, S100A8, and ceruloplasmin discriminate best between the DE and CT groups. The differences between antileukoproteinase, phospholipase A2, and lactoperoxidase levels allow the distinction between MGD and DE, and the changes in the levels of annexin A1, clusterin, and alpha-1-acid glycoprotein 1, between MGD and CT groups. The functional network analysis revealed the main biological processes that should be examined to identify new candidate biomarkers and therapeutic targets.

Mass spectrometry for the evaluation of cardiovascular diseases based on proteomics and lipidomics

The identification and quantification of proteins and lipids is of major importance for the diagnosis, prognosis and understanding of the molecular mechanisms involved in disease development. Owing to its selectivity and sensitivity, mass spectrometry has become a key technique in analytical platforms for proteomic and lipidomic investigations. Using this technique, many strategies have been developed based on unbiased or targeted approaches to highlight or monitor molecules of interest from biomatrices. Although these approaches have largely been employed in cancer research, this type of investigation has been met by a growing interest in the field of cardiovascular disorders, potentially leading to the discovery of novel biomarkers and the development of new therapies. In this paper, we will review the different mass spectrometry- based proteomic and lipidomic strategies applied in cardiovascular diseases, especially atherosclerosis. Particular attention will be given to recent developments and the role of bioinformatics in data treatment. This review will be of broad interest to the medical community by providing a tutorial of how mass spectrometric strategies can support clinical trials.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

Suppression of MTHFD2 in MCF-7 Breast Cancer Cells Increases Glycolysis, Dependency on Exogenous Glycine, and Sensitivity to Folate Depletion

Methylenetetrahydrofolate dehydrogenase (NAD(P)+ dependent) 2, methenyltetrahydrofolate cyclohydrolase (MTHFD2) is a mitochondrial enzyme involved in folate metabolism. A number of recent studies have highlighted this enzyme as being highly expressed in many solid tumors, including breast cancer, and to be correlated with poor survival. However, the metabolic functions of MTHFD2 in cancer cells have not been well-defined. To investigate the function of MTHFD2 in breast cancer cells, we generated and characterized MCF-7 cells with stable suppression of MTHFD2 expression using a combination of cellular assays and metabolic profiling. Loss of MTHFD2 caused MCF7 cells to become glycine auxotrophs, that is, reliant on exogenous glycine, and more sensitive to exogenous folate depletion. Another prominent metabolic alteration observed as a consequence of MTHFD2 suppression was a more glycolytic phenotype, consistent with widespread modifications of cellular metabolism. Collectively, these data suggest that targeting MTHFD2 activity is likely to influence multiple metabolic pathways in breast cancer and could be combined with a range of antimetabolite therapies.

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.

Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

At the molecular level, living cells are enormously complicated complex adaptive systems in which intertwined genomic, transcriptomic, proteomic and metabolic networks all play a crucial role. At the same time, cells are spatially heterogeneous systems in which subcellular compartmentalization of different functions is ubiquitous and requires efficient cross-compartmental communication. Dynamic redistribution of multitudinous proteins to different subcellular locations in response to cellular functional state is increasingly recognized as a crucial characteristic of cellular function that seems to be at least as important as overall changes in protein abundance. Characterization of the subcellular spatial dynamics of protein distribution is a major challenge for proteomics and recent results with MCF7 breast cancer cells suggest that this may be of particular importance for cancer cells.

Proteomics approaches for the studies of bone metabolism

Bone is an active tissue, in which bone formation by osteoblast is followed by bone resorption by osteoclasts, in a repeating cycle. Proteomics approaches may allow the detection of changes in cell signal transduction, and the regulatory mechanism of cell differentiation. LC-MS/MS-based quantitative methods can be used with labeling strategies, such as SILAC, iTRAQ, TMT and enzymatic labeling. When used in combination with specific protein enrichment strategies, quantitative proteomics methods can identify various signaling molecules and modulators, and their interacting proteins in bone metabolism, to elucidate biological functions for the newly identified proteins in the cellular context. In this article, we will briefly review recent major advances in the application of proteomics for bone biology, especially from the aspect of cellular signaling. [BMB Reports 2014; 47(3): 141-148]

Proteomics in paediatric cardiac surgery: is a personalised approach feasible?

The incidence of congenital cardiac abnormalities remains high. Paediatric patients with congenital cardiac defects often require surgery at a young age. The surgeries are often long and complex, rendering this population particularly vulnerable to the deleterious effects of cardiopulmonary bypass and cardiac surgery. The search for cardioprotective strategies is ongoing in an attempt to reduce the morbidity in this population. In the post-genomic era, it is apparent that simply determining the genomic sequences holds little diagnostic potential and means to determine progression of disease and response to treatment. The field of proteomics is expanding and application of proteomic techniques in the clinical setting holds great potential to advance our understanding of the proteomic changes involved in specific disease stages. This review will assess the application of proteomic techniques in the setting of paediatric cardiac surgery and highlight the need to obtain a clear understanding of the role of various proteins in children with cardiac conditions. The success and challenges of the available proteomic technology will be discussed as well as the future potential of proteomic methods for advancing our understanding of protein changes in children requiring cardiac surgery.

Prediction of spontaneous regression of cervical intraepithelial neoplasia lesions grades 2 and 3 by proteomic analysis

Regression of cervical intraepithelial neoplasia (CIN) 2-3 to CIN 1 or less is associated with immune response as demonstrated by immunohistochemistry in formaldehyde-fixed paraffin-embedded (FFPE) biopsies. Proteomic analysis of water-soluble proteins in supernatants of biopsy samples with LC-MS (LTQ-Orbitrap) was used to identify proteins predictive of CIN2-3 lesions regression. CIN2-3 in the biopsies and persistence (CIN2-3) or regression (≤CIN1) in follow-up cone biopsies was validated histologically by two experienced pathologists. In a learning set of 20 CIN2-3 (10 regressions and 10 persistence cases), supernatants were depleted of seven high abundance proteins prior to unidimensional LC-MS/MS protein analysis. Mean protein concentration was 0.81 mg/mL (range: 0.55–1.14). Multivariate statistical methods were used to identify proteins that were able to discriminate between regressive and persistent CIN2-3. The findings were validated in an independent test set of 20 CIN2-3 (10 regressions and 10 persistence cases). Multistep identification criteria identified 165 proteins. In the learning set, zinc finger protein 441 and phospholipase D6 independently discriminated between regressive and persistent CIN2-3 lesions and correctly classified all 20 patients. Nine regression and all persistence cases were correctly classified in the validation set. Zinc finger protein 441 and phospholipase D6 in supernatant samples detected by LTQ-Orbitrap can predict regression of CIN2-3.

Proteomic analysis of effects by x-rays and heavy ion in HeLa cells

Background

Carbon ion therapy may be better against cancer than the effects of a photon beam. To investigate a biological advantage of carbon ion beam over X-rays, the radioresistant cell line HeLa cells were used. Radiation-induced changes in the biological processes were investigated post-irradiation at 1 h by a clinically relevant radiation dose (2 Gy X-ray and 2 Gy carbon beam). The differential expression proteins were collected for analysing biological effects.

Materials and methods.

The radioresistant cell line Hela cells were used. In our study, the stable isotope labelling with amino acids (SILAC) method coupled with 2D-LC-LTQ Orbitrap mass spectrometry was applied to identity and quantify the differentially expressed proteins after irradiation. The Western blotting experiment was used to validate the data.

Results

A total of 123 and 155 significantly changed proteins were evaluated with treatment of 2 Gy carbon and X-rays after radiation 1 h, respectively. These deregulated proteins were found to be mainly involved in several kinds of metabolism processes through Gene Ontology (GO) enrichment analysis. The two groups perform different response to different types of irradiation.

Conclusions

The radioresistance of the cancer cells treated with 2 Gy X-rays irradiation may be largely due to glycolysis enhancement, while the greater killing effect of 2 Gy carbon may be due to unchanged glycolysis and decreased amino acid metabolism.

On-beads digestion in conjunction with data-dependent mass spectrometry: a shortcut to quantitative and dynamic interaction proteomics

With the advent of the "-omics" era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

Background:

Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.

Methods:

Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.

Results:

A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.

Conclusions:

Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.

Systematic nucleo-cytoplasmic trafficking of proteins following exposure of MCF7 breast cancer cells to estradiol

We have used a proteomics subcellular spatial razor approach to look at changes in total protein abundance and in protein distribution between the nucleus and cytoplasm following exposure of MCF7 breast cancer cells to estradiol. The dominant response of MCF7 cells to estrogen stimulation involves dynamic changes in protein subcellular spatial distribution rather than changes in total protein abundance. Of the 3604 quantitatively monitored proteins, only about 2% show substantial changes in total abundance (>2-fold), whereas about 20% of the proteins show substantial changes in local abundance and/or redistribution of their subcellular location, with up to 16-fold changes in their local concentration in the nucleus or the cytoplasm. We propose that dynamic redistribution of the subcellular location of multiple proteins in response to stimuli is a fundamental characteristic of cells and suggest that perturbation of cellular spatial control may be an important feature of cancer.

Proteomic analysis of energy metabolism and signal transduction in irradiated melanoma cells

AIM

To analyze proteomic and signal transduction alterations in irradiated melanoma cells.

METHODS

We combined stable isotope labeling with amino acids in cell culture (SILAC) with highly sensitive shotgun tandem mass spectrometry (MS) to create an efficient approach for protein quantification. Protein-protein interaction was used to analyze relationships among proteins.

RESULTS

Energy metabolism protein levels were significantly different in glycolysis and not significantly different in oxidative phosphorylation after irradiation. Conversely, tumor suppressor proteins related to cell growth and development were downregulated, and those related to cell death and cell cycle were upregulated in irradiated cells.

CONCLUSION

Our results indicate that irradiation induces differential expression of the 29 identified proteins closely related to cell survival, cell cycle arrest, and growth inhibition. The data may provide new insights into the pathogenesis of uveal melanoma and guide appropriate radiotherapy.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine

Cancer biomarkers for the early detection of malignancies and selection of therapeutic strategies have been requested in the clinical field. Accurate and informative cancer biomarkers hold significant promise for improvements in the early detection of disease and in the selection of the most effective therapeutic strategies. Recently, significant progress in the comprehensive analysis of the human genome, epigenome, transcriptome, proteome and metabolome has led to revolutionary changes in the discovery of cancer biomarkers. The Human Proteome Organization has launched a global Human Proteome Project to map the entire human protein set. The Human Proteome Project research group has focused on three working proteomic pillars-mass spectrometry-based, antibody-based and knowledge-based proteomics-and each of these technologies is advancing rapidly. In this review, we introduce the proteomic platforms that are currently being used for cancer biomarker discovery, and describe examples of novel cancer biomarkers that were identified with each proteomic technology.

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

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

An integrated workflow for extraction and solubilization of intermediate filaments from colorectal biopsies for proteomic analysis

We report a technique for isolation and solubilization of intermediate filament (IF) proteins from colonic biopsies compatible with both gel electrophoresis and liquid chromatography "shotgun" proteomics using mass spectrometry (MS). This is important because changes in the IF proteome, particularly in keratin expression and modification, are noted in colonic mucosa of patients with colorectal cancer. Though keratins have traditionally been dissolved in high concentration of urea, the latter solvent precludes efficient proteolytic digestion by trypsin prior to gel-free LC-MS/MS approaches. The extraction of cytoskeletal proteins was initially evaluated using MCF-7 cancer cell lines using a published, differential detergent solubilization protocol. IF proteins were extracted from colonic biopsies using a combination of homogenization and sonication. Since comparable efficiency of solubilization was noted on the extracted IF from cell lines between urea and guanidine hydrochloride (GuHCl) in triethylammonium bicarbonate buffer, isolated proteins from endoscopic biopsies were solubilized in GuHCl. Using immunoblotting techniques, we successfully demonstrated isolation of keratins and preservation of posttranslational modifications (phosphorylation, acetylation). Dissolved proteins were tryptically digested and peptides analyzed by MS, showing the functionality of the workflow in shotgun proteomic applications, specifically compatibility of the workflow for isobaric tagging relative and absolute quantification based quantitation approaches.

Benchmarking stable isotope labeling based quantitative proteomics

Several quantitative mass spectrometry based technologies have recently evolved to interrogate the complexity, interconnectivity and dynamic nature of proteomes. Currently, the most popular methods use either metabolic or chemical isotope labeling with MS based quantification or chemical labeling using isobaric tags with MS/MS based quantification. Here, we assess the performance of three of the most popular approaches through systematic independent large scale quantitative proteomics experiments, comparing SILAC, dimethyl and TMT labeling strategies. Although all three methods have their strengths and weaknesses, our data indicate that all three can reach a similar depth in number of identified proteins using a classical (MS2 based) shotgun approach. TMT quantification using only MS2 is heavily affected by co-isolation leading to compromised precision and accuracy. This issue may be partly resolved by using an MS3 based acquisition; however, at the cost of a significant reduction in number of proteins quantified. Interestingly, SILAC and chemical labeling with MS based quantification produce almost indistinguishable results, independent of which database search algorithm used.

Proteomic profiling of the epithelial-mesenchymal transition using 2D DIGE

Metastasis remains the primary cause of cancer patient death. Although the precise molecular mechanisms at play remain largely unknown, tumor progression is currently hypothesized to follow a series of sequential steps known as the metastatic cascade. An important component, thought to be involved early in this cascade, is the process known as epithelial-mesenchymal transition (EMT), whereby epithelial cells undergo morphogenetic alterations and acquire properties typical of mesenchymal cells. EMT confers a metastatic advantage to the cancer cells through the loss of cell-cell adhesion, enhanced proteolytic activity, and increased cell migration and invasiveness. This chapter describes the experimental workflow for the secretome analysis of MDCK cells undergoing oncogenic Ras, and Ras/TGF-β-mediated EMT. To enable this comparison, serum-free cell culture conditions were optimized, and a secretome purification methodology established. Secretome samples were then subjected to DIGE analysis to reveal and quantify proteins that are differentially expressed during EMT. The proteomic strategy detailed within successfully identified several EMT modulators and broadens our understanding of the extracellular facets of the EMT process.

Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering

Comprehensive comparisons of quantitative proteomics techniques are rare in the literature, yet they are crucially important for optimal selection of approaches and methodologies that are ideal for a given proteomics initiative. In this study, two LC-based quantitative proteomics approaches--iTRAQ and label-free--were implemented using the LTQ-Orbitrap Velos platform. For this comparison, the model used was the total protein content from two Chlamydomonas reinhardtii strains in the context of alternative biofuels production. The strain comparison includes sta6 (a starch-less mutant of cw15) that produces twice as many lipid bodies (LB) containing triacylglycerols (TAGs) as its parental strain cw15 (a cell wall-deficient C. reinhardtii strain) under nitrogen starvation. Internal standard addition was used to rigorously assess the quantitation accuracy and precision of each method. Results from iTRAQ-4plex labeling using HCD (higher energy collision-induced dissociation) fragmentation were compared to those obtained using a label-free approach based on the peak area of intact peptides and collision-induced dissociation. The accuracy and precision, number of identified/quantified proteins and statistically significant protein differences detected, as well as efficiency of these two quantitative proteomics methods were evaluated and compared. Four technical and three biological replicates of each strain were performed to assess both the technical and biological variation of both approaches. A total of 896 and 639 proteins were identified with high confidence, and 329 and 124 proteins were quantified significantly with label-free and iTRAQ, respectively, using biological replicates. The results showed that both iTRAQ labeling and label-free methods provide high quality quantitative and qualitative data using nano-LC coupled with the LTQ-Orbitrap Velos mass spectrometer, but the selection of the optimal approach is dependent on experimental design and the biological question to be addressed. The functional categorization of the differential proteins between cw15 and sta6 reveals already known but also new mechanisms likely responsible for the production of lipids in sta6 and sets the baseline for future studies aimed at engineering these strains for high oil production.

Discrimination of grade 2 and 3 cervical intraepithelial neoplasia by means of analysis of water soluble proteins recovered from cervical biopsies

Background

Cervical intraepithelial neoplasia (CIN) grades 2 and 3 are usually grouped and treated in the same way as "high grade", in spite of their different risk to cancer progression and spontaneous regression rates. CIN2-3 is usually diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. This procedure virtually eliminates the availability of water-soluble proteins which could have diagnostic and prognostic value.

Aim

To investigate whether a water-soluble protein-saving biopsy processing method followed by a proteomic analysis of supernatant samples using LC-MS/MS (LTQ Orbitrap) can be used to distinguish between CIN2 and CIN3.

Methods

Fresh cervical punch biopsies from 20 women were incubated in RPMI1640 medium for 24 hours at 4°C for protein extraction and subsequently subjected to standard FFPE processing. P16 and Ki67-supported histologic consensus review CIN grade (CIN2, n = 10, CIN3, n = 10) was assessed by independent gynecological pathologists. The biopsy supernatants were depleted of 7 high abundance proteins prior to uni-dimensional LC-MS/MS analysis for protein identifications.

Results

The age of the patients ranged from 25-40 years (median 29.7), and mean protein concentration was 0.81 mg/ml (range 0.55 - 1.14). After application of multistep identification criteria, 114 proteins were identified, including proteins like vimentin, actin, transthyretin, apolipoprotein A-1, Heat Shock protein beta 1, vitamin D binding protein and different cytokeratins. The identified proteins are annotated to metabolic processes (36%), signal transduction (27%), cell cycle processes (15%) and trafficking/transport (9%). Using binary logistic regression, Cytokeratin 2 was found to have the strongest independent discriminatory power resulting in 90% overall correct classification.

Conclusions

114 proteins were identified in supernatants from fresh cervical biopsies and many differed between CIN2 and 3. Cytokeratin 2 is the strongest discriminator with 90% overall correct classifications.

State of the art in tumor antigen and biomarker discovery

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology.

A practical technique for measuring human biofluid conductivity using high gain-frequency characteristics

Currently, the study of ion composition and performance in human biofluids plays an important role in biomedical engineering research and technology. This field may become universal for human diagnostics; it allows early detection of different diseases in humans by measuring changes in ion behaviour in human biofluids. Practical experiments were conducted to analyse the liquid composite electrolyte conductivity in an alternating electric current field. These experiments allow the contribution of separate types of ions to the overall conductivity to be estimated. The method of estimating the concentration of active ions contained in biofluids is also introduced; it illustrates the possibility of performing qualitative and quantitative analysis over a wide range of concentrations and compositions. The authors present a procedure to determine the concentration of active liquid ions based on conductivity gain-frequency characteristic curve tracing. The experimental results validate the practical use of the proposed method. The results of this research are promising, and further investigation is required to further improve the method.

Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification

BACKGROUND

Cancers of the urinary bladder are the fifth most commonly diagnosed malignancy in the United States. Early clinical diagnosis of bladder cancer remains a major challenge, and the development of noninvasive methods for detection and surveillance is desirable for both patients and health care providers.

APPROACH

To identify urinary proteins with potential clinical utility, we enriched and profiled the glycoprotein component of urine samples by using a dual-lectin affinity chromatography and liquid chromatography/tandem mass spectrometry platform.

RESULTS

From a primary sample set obtained from 54 cancer patients and 46 controls, a total of 265 distinct glycoproteins were identified with high confidence, and changes in glycoprotein abundance between groups were quantified by a label-free spectral counting method. Validation of candidate biomarker alpha-1-antitrypsin (A1AT) for disease association was done on an independent set of 70 samples (35 cancer cases) by using an ELISA. Increased levels of urinary A1AT glycoprotein were indicative of the presence of bladder cancer (P < 0.0001) and augmented voided urine cytology results. A1AT detection classified bladder cancer patients with a sensitivity of 74% and specificity of 80%.

SUMMARY

The described strategy can enable higher resolution profiling of the proteome in biological fluids by reducing complexity. Application of glycoprotein enrichment provided novel candidates for further investigation as biomarkers for the noninvasive detection of bladder cancer.

Impact of peptide modifications on the isobaric tags for relative and absolute quantitation method accuracy

In this study, the impact of amino acid modifications on the accuracy of the iTRAQ (isobaric tags for relative and absolute quantitation) method was evaluated. MCF-7 breast cancer cells, cultured in the presence of 17β-estradiol and tamoxifen, were used as a model system. The cells were labeled and analyzed by reversed-phase liquid chromatography and pulsed Q dissociation ion trap tandem mass spectrometry detection. Database searching was performed by using various combinations of amino acid modification allowances, i.e, Lys/Tyr/Cys and amino terminal iTRAQ labeling, Lys methylation, acetylation and carbamylation, and Cys/Met oxidation. Other than the intended Lys/amino terminal iTRAQ labeling, such modifications occur as a result of either enzymatic or sample preparation related reactions and are typically ignored in quantitation analysis to minimize the rate of false-positive peptide identifications. The study revealed that the modifications with the greatest impact on protein identification and quantitation pertain to Lys and Tyr amino acid residues, that by enabling such modifications the number and type of identified proteins will change (by up to 10%), and that the rate of false-positive protein identifications can be maintained below an upper threshold of 5% if appropriate data filtering conditions are used. In addition, the interference of possible posttranslational modifications (i.e., phosphorylation) with iTRAQ quantitation was examined.

Hippocampal gene profiling: toward a systems biology of the hippocampus

Transcriptomics and proteomics approaches give a unique perspective for understanding brain and hippocampal functions but also pose unique challenges because of the singular complexity of the nervous system. The proliferation of genome-wide expression studies during the last decade has provided important insight into the molecular underpinnings of brain anatomy, neural plasticity, and neurological diseases. Microarray technology has dominated transcriptomics research, but this situation is rapidly changing with the recent technological advances in high-throughput sequencing. The full potential of transcriptomics in the neurosciences will be achieved as a result of its integration with other "-omics" disciplines as well as the development of novel analytical bioinformatics and systems biology tools for meta-analysis. Here, we review some of the most relevant advances in the gene profiling of the hippocampus, its relationship with proteomics approaches, and the promising perspectives for the future.

Phosphoproteomics in cancer

Reversible protein phosphorylation serves as a basis for regulating a number of cellular processes. Aberrant activation of kinase signaling pathways is commonly associated with several cancers. Recent developments in phosphoprotein/phosphopeptide enrichment strategies and quantitative mass spectrometry have resulted in robust pipelines for high-throughput characterization of phosphorylation in a global fashion. Today, it is possible to profile site-specific phosphorylation events on thousands of proteins in a single experiment. The potential of this approach is already being realized to characterize signaling pathways that govern oncogenesis. In addition, chemical proteomic strategies have been used to unravel targets of kinase inhibitors, which are otherwise difficult to characterize. This review summarizes various approaches used for analysis of the phosphoproteome in general, and protein kinases in particular, highlighting key cancer phosphoproteomic studies.

MALDI imaging mass spectrometry--painting molecular pictures

MALDI Imaging Mass Spectrometry is a molecular analytical technology capable of simultaneously measuring multiple analytes directly from intact tissue sections. Histological features within the sample can be correlated with molecular species without the need for target-specific reagents such as antibodies. Several studies have demonstrated the strength of the technology for uncovering new markers that correlate with disease severity as well as prognosis and therapeutic response. This review describes technological aspects of imaging mass spectrometry together with applications in cancer research.

Plasma membrane proteomics and its application in clinical cancer biomarker discovery

Plasma membrane proteins that are exposed on the cell surface have important biological functions, such as signaling into and out of the cells, ion transport, and cell-cell and cell-matrix interactions. The expression level of many of the plasma membrane proteins involved in these key functions is altered on cancer cells, and these proteins may also be subject to post-translational modification, such as altered phosphorylation and glycosylation. Additional protein alterations on cancer cells confer metastatic capacities, and some of these cell surface proteins have already been successfully targeted by protein drugs, such as human antibodies, that have enhanced survival of several groups of cancer patients. The combination of novel analytical approaches and subcellular fractionation procedures has made it possible to study the plasma membrane proteome in more detail, which will elucidate cancer biology, particularly metastasis, and guide future development of novel drug targets. The technical advances in plasma membrane proteomics and the consequent biological revelations will be discussed herein. Many of the advances have been made using cancer cell lines, but because the main goal of this research is to improve individualized treatment and increase cancer patient survival, further development is crucial to direct analysis of clinically relevant patient samples. These efforts include optimized specimen handling and preparation as well as improved proteomics platforms. Identification of potentially useful proteomics-based biomarkers must be validated in larger, well defined retrospective and prospective clinical studies, and these combined efforts should result in identification of biomarkers that will greatly improve early detection, prognosis, and prediction of treatment response.