Fine-needle aspiration of thyroid nodules: proteomic analysis to identify cancer biomarkers

At present, the clinical and pathological analysis used in the diagnosis of papillary thyroid cancer (PTC) are insufficient to discern tumor behavior, and new diagnostic and prognostic markers need to be identified. In this study, we performed a comparative proteome analysis to examine the global changes of fine needle aspiration fluid (FNA) protein patterns of two variants of malignant PTC (classical variant PTC (cPTC) and tall cell variant PTC (TCV)) with respect to the controls. Changes in protein expression were identified using two-dimensional electrophoresis (2DE) and peptide mass fingerprinting via MALDI-TOF mass spectrometry (MS), as well as Western blot analysis. A statistical significant up-regulation of 17 protein spots in cPTC and/or TCV with respect to controls was demonstrated. These proteins included transthyretin precursor (TTR), ferritin light chain (FLC), proteasome activator complex subunit 1 and 2, alpha-1-antitrypsin precursor, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase chain B (LDH-B), apolipoprotein A1 precursor (Apo-A1), annexin A1, DJ-1 protein and cofilin-1. In addition, 12 protein spots were found exclusively in cPTC and three exclusively in TCV. These latter proteins (ferritin heavy chain (FHC), peroxiredoxin 1 (PRX1) and 6-phosphogluconate dehydrogenase (6-PDGH)) correspond to stress response proteins and, until now, had not been described in thyroid tumors. These findings illustrate the potential use of FNA proteomics to identify protein changes associated with thyroid cancer and to advance potential protein biomarkers in the diagnostic classification of the disease.     

Recent technical strategies to identify diagnostic biomarkers for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer deaths among North American women. Regrettably, there is currently no reliable circulating biomarker that can detect ovarian cancer in its early stages. The CA125 biomarker is very useful for treatment response monitoring, but its sensitivity is very low for early detection. Thus, there is an urgent need for the identification of new circulating biomarkers/panel of biomarkers that could be used to diagnose ovarian cancer before it becomes clinically detectable and advanced. Unfortunately, the strategies used in the past years to identify such biomarkers have not led to any outstanding candidate. This review summarizes the different approaches used in the last decade and suggests which strategies should be adopted in the near future in order to lead to the successful identification of new ovarian cancer diagnostic biomarkers.     

In-depth proteomic analyses of ovarian cancer cell line exosomes reveals differential enrichment of functional categories compared to the NCI 60 proteome

Molecular communication between cancer cells and its stromal microenvironment is a key factor for cancer progression. Alongside classic secretory pathways, it has recently been proposed that small membranous vesicles are alternative mediators of intercellular communication. Exosomes carry an effector-rich proteome with the ability to modulate various functional properties of the recipient cell. In this study, exosomes isolated from four epithelial ovarian cancer cell lines (OVCAR3, OVCAR433, OVCAR5 and SKOV3) were characterized using mass spectrometry-based proteomics. Using an optimized workflow consisting of efficient exosome solubilization and the latest generation of proteomic instrumentation, we demonstrate improved detection depth. Systematic comparison of our cancer cell line exosome proteome against public data (Exocarta) and the recently published NCI 60 proteome revealed enrichment of functional categories related to signaling biology and biomarker discovery.     

Genomic and proteomic biomarkers for cancer: A multitude of opportunities

Biomarkers are molecular indicators of a biological status, and as biochemical species can be assayed to evaluate the presence of cancer and therapeutic interventions. Through a variety of mechanisms cancer cells provide the biomarker material for their own detection. Biomarkers may be detectable in the blood, other body fluids, or tissues. The expectation is that the level of an informative biomarker is related to the specific type of disease present in the body. Biomarkers have potential both as diagnostic indicators and monitors of the effectiveness of clinical interventions. Biomarkers are also able to stratify cancer patients to the most appropriate treatment. Effective biomarkers for the early detection of cancer should provide a patient with a better outcome which in turn will translate into more efficient delivery of healthcare. Technologies for the early detection of cancer have resulted in reductions in disease-associated mortalities from cancers that are otherwise deadly if allowed to progress. Such screening technologies have proven that early detection will decrease the morbidity and mortality from cancer. An emerging theme in biomarker research is the expectation that panels of biomarker analytes rather than single markers will be needed to have sufficient sensitivity and specificity for the presymptomatic detection of cancer. Biomarkers may provide prognostic information of disease enabling interventions using targeted therapeutic agents as well as course-corrections in cancer treatment. Novel genomic, proteomic and metabolomic technologies are being used to discover and validate tumor biomarkers individually and in panels.     

Effect of ovarian cancer ascites on SKOV-3 cells proteome: new proteins associated with aggressive phenotype in epithelial ovarian cancer

Background

Epithelial ovarian cancer is the second most lethal gynecological cancer worldwide. Ascites can be found in all clinical stages, however in advanced disease stages IIIC and IV it is more frequent and could be massive, associated with worse prognosis. Due to the above, it was our interest to understanding how the ascites of ovarian cancer patients induces the mechanisms by which the cells present in it acquire a more aggressive phenotype and to know new proteins associated to this process.

Methods

A proteomic analysis of SKOV-3 cells treated with five different EOC ascites was performed by two-dimensional electrophoresis coupled to MALDI-TOF. The level of expression of the proteins of interest was validated by RT-PCR because several of these proteins have only been reported at the messenger level.

Results

Among the proteins identified that increased their expression in ascites-treated SKOV-3 cells, were Ran GTPase, ZNF268, and Synaptotagmin like-3. On the other hand, proteins that were negatively regulated by ascites were HLA-I, HSPB1, ARF1, Synaptotagmin 1, and hnRNPH1, among others. Furthermore, an interactome for every one of these proteins was done in order to identify biological processes, molecular actions, and cellular components in which they may participate.

Conclusions

Identified proteins participate in cellular processes highly relevant to the aggressive phenotype such as nuclear transport, regulation of gene expression, vesicular trafficking, evasion of the immune response, invasion, metastasis, and in resistance to chemotherapy. These proteins may represent a source of information which has the potential to be evaluated for the design of therapies directed against these malignant cells that reside on ovarian cancer ascites.

     

Application of proteomic technologies to discover and identify biomarkers for excessive alcohol consumption: a review

Since currently available markers of alcohol abuse are not satisfactory, searches for novel markers are warranted. Proteomic analyses are promising tools to discover and identify novel biomarkers. Using two different proteomic technologies, surface enhanced laser desorption/ionization time-of-flight mass spectrometry and agarose fluorescent two-dimensional difference gel electrophoresis, we could detect and identify a total of 11 potential biomarkers of excessive alcohol consumption. It was noteworthy that the down regulation of the 5.9 kDa protein fragment was consistently seen in habitual drinkers and the diagnostic efficiency was greater than those of conventional markers such as gamma glutamyl transferase and carbohydrate deficient transferrin.     

Urine proteomic profiling to identify biomarkers of steroid resistance in pediatric nephrotic syndrome

Long-term prognosis for children with nephrotic syndrome (NS) is directly related to steroid responsiveness. There are currently no diagnostic tests that accurately predict steroid responsiveness in pediatric NS. The initial prolonged course of daily, high-dose corticosteroid therapy thus serves both as a diagnostic and therapeutic maneuver. Urine proteomics is emerging as a potentially rich source of noninvasive biomarkers of drug responsiveness in NS. In this article, we discuss some of the initial studies of the urinary proteome in NS as well as ongoing and future challenges, define the normal urinary proteome and address the overwhelming abundance of urinary albumin and its impact on biomarker discovery.     

Urine proteomic profiling to identify biomarkers of steroid resistance in pediatric nephrotic syndrome

Long-term prognosis for children with nephrotic syndrome (NS) is directly related to steroid responsiveness. There are currently no diagnostic tests that accurately predict steroid responsiveness in pediatric NS. The initial prolonged course of daily, high-dose corticosteroid therapy thus serves both as a diagnostic and therapeutic maneuver. Urine proteomics is emerging as a potentially rich source of noninvasive biomarkers of drug responsiveness in NS. In this article, we discuss some of the initial studies of the urinary proteome in NS as well as ongoing and future challenges, define the normal urinary proteome and address the overwhelming abundance of urinary albumin and its impact on biomarker discovery.     

A proteomic approach to paclitaxel chemoresistance in ovarian cancer cell lines

Ovarian cancer is the leading cause of gynaecological cancer mortality. Paclitaxel is used in the first line treatment of ovarian cancer, but acquired resistance represents the most important clinical problem and a major obstacle to a successful therapy. Several mechanisms have been implicated in paclitaxel resistance, however this process has not yet been fully explained. To better understand molecular resistance mechanisms, a comparative proteomic approach was undertaken on the human epithelial ovarian cancer cell lines A2780 (paclitaxel sensitive), A2780TC1 and OVCAR3 (acquired and inherently resistant). Proteins associated with chemoresistance process were identified by DIGE coupled with mass spectrometry (MALDI-TOF and LC-MS/MS). Out of the 172 differentially expressed proteins in pairwise comparisons among the three cell lines, 151 were identified and grouped into ten main functional classes. Most of the proteins were related to the category of stress response (24%), metabolism (22%), protein biosynthesis (15%) and cell cycle and apoptosis (11%), suggesting that alterations of those processes might be involved in paclitaxel resistance mechanisms. This is the first direct proteomic comparison of paclitaxel sensitive and resistant ovarian cancer cells and may be useful for further studies of resistance mechanisms and screening of resistance biomarkers for the development of tailored therapeutic strategies.     

Glycoproteomics of paclitaxel resistance in human epithelial ovarian cancer cell lines: Towards the identification of putative biomarkers

Glycosylation, one of the most common post translational modifications (PTMs) of proteins, is often associated with carcinogenesis and tumor malignancy. Ovarian cancer is the sixth cause of cancer-related death in Western countries. Currently, it is treated by debulking surgery followed by chemotherapy based on paclitaxel, alone or in combination with other drugs. However, chemoresistance represents a major obstacle to positive clinical outcome. We used two approaches, Multiplexed Proteomics (MP) technology and Multilectin Affinity Chromatography (MAC) to characterize the glycoproteome of the human ovarian cancer cell line A2780 and its paclitaxel resistant counterpart A2780TC1. Furthermore proteins were separated by traditional 2DE or DIGE and identified by MS (MALDI TOF or LC MS/MS). Seventy glycoproteins were successfully identified in ovarian cancer cells and 10 were found to be differentially expressed between sensitive and resistant cell lines. We focused on four glycoproteins (tumor rejection antigen (gp96) 1, triose phosphate isomerase, palmitoyl-protein thioesterase 1 precursor and ER-associated DNAJ) which were remarkably upregulated in A2780TC1 compared to A2780 cell line and which may represent biomarkers for paclitaxel resistance in ovarian cancer.     

A data review and re-assessment of ovarian cancer serum proteomic profiling

Background

The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research.

Results

The mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10^-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity.

Conclusion

The ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.

     

Linking proteome and genome: how to identify parasite proteins

Parasite genome projects are generating an avalanche of sequence data. If this resource is to be exploited effectively for drug and vaccine design, there is an urgent need to make the link between these DNA sequences and the functional proteins of the parasite, which they encode. Here, we seek to demystify the revolutionary advances in protein identification based on mass spectrometry.     

A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer

Proteomics discovery of novel cancer serum biomarkers is hindered by the great complexity of serum, patient-to-patient variability, and triggering by the tumor of an acute-phase inflammatory reaction. This host response alters many serum protein levels in cancer patients, but these changes have low specificity as they can be triggered by diverse causes. We addressed these hurdles by utilizing a xenograft mouse model coupled with an in-depth 4-D protein profiling method to identify human proteins in the mouse serum. This strategy ensures that identified putative biomarkers are shed by the tumor, and detection of low-abundance proteins shed by the tumor is enhanced because the mouse blood volume is more than a thousand times smaller than that of a human. Using TOV-112D ovarian tumors, more than 200 human proteins were identified in the mouse serum, including novel candidate biomarkers and proteins previously reported to be elevated in either ovarian tumors or the blood of ovarian cancer patients. Subsequent quantitation of selected putative biomarkers in human sera using label-free multiple reaction monitoring (MRM) mass spectrometry (MS) showed that chloride intracellular channel 1, the mature form of cathepsin D, and peroxiredoxin 6 were elevated significantly in sera from ovarian carcinoma patients.     

O-Linked glycome and proteome of high-molecular-mass proteins in human ovarian cancer ascites: Identification of sulfation, disialic acid and O-linked fucose

The O-linked glycosylation of the main acidic high-molecular-weight glycoprotein from ascites fluid from patients with ovarian cancer were analyzed. The O-linked oligosaccharides were shown to consist of mainly highly sialylated core 1 and 2 structures with a smaller amount of sulfated core 2 structures. These structures were shown to be able to be further extended into small keratan sulfate (KS)-type oligosaccharides with up to four N-acetyllactosamine units. Proteomic studies of the acidic fraction of ascites fluid from patients with ovarian cancer showed that this fraction was enriched in proteoglycans. Among them, lumican, agrin, versican and dystroglycans were potential candidates, with threonine- and serine-rich domains that could carry a significant amount of O-linked glycosylation, including also the O-linked KS. Glycomic analysis using liquid chromatography (LC)-tandem mass spectrometry (MS/MS) also showed that the disialic acid NeuAc-NeuAc- was frequently found as the terminating structure on the O-linked core 1 and 2 oligosaccharides from one ascites sample. Also, a small amount of the epidermal growth factor (EGF)-associated O-linked fucose structure Gal-GlcNAc-Fucitol was detected with and without sialic acid in the LC-MS/MS analysis. Candidate proteins containing O-linked fucose were suggested to be proteoglycan-type molecules containing the O-linked fucose EGF consensus domain.     

Gaining insights into cancer biology through exploration of the cancer secretome using proteomic and bioinformatic tools

Introduction: Tumor-associated proteins released by cancer cells and by tumor stroma cells, referred as ‘cancer secretome’, represent a valuable resource for discovery of potential cancer biomarkers. The last decade was marked by a great increase in number of studies focused on various aspects of cancer secretome including, composition and identification of components externalized by malignant cells and by the components of tumor microenvironment.

Areas covered: Here, we provide an overview of achievements in the proteomic analysis of the cancer secretome, elicited through the tumor-associated interstitial fluid recovered from malignant tissues ex vivo or the protein component of conditioned media obtained from cultured cancer cells in vitro. We summarize various bioinformatic tools and approaches and critically appraise their outcomes, focusing on problems and challenges that arise when applied for the analysis of cancer secretomic databases.

Expert commentary: Recent achievements in the omics- analysis of structural and metabolic aspects of altered cancer secretome contribute greatly to the various hallmarks of cancer including the identification of clinically significant biomarkers and potential targets for therapeutic intervention.     

ESEfinder: A web resource to identify exonic splicing enhancers

Point mutations frequently cause genetic diseases by disrupting the correct pattern of pre-mRNA splicing. The effect of a point mutation within a coding sequence is traditionally attributed to the deduced change in the corresponding amino acid. However, some point mutations can have much more severe effects on the structure of the encoded protein, for example when they inactivate an exonic splicing enhancer (ESE), thereby resulting in exon skipping. ESEs also appear to be especially important in exons that normally undergo alternative splicing. Different classes of ESE consensus motifs have been described, but they are not always easily identified. ESEfinder (http://exon.cshl.edu/ESE/) is a web-based resource that facilitates rapid analysis of exon sequences to identify putative ESEs responsive to the human SR proteins SF2/ASF, SC35, SRp40 and SRp55, and to predict whether exonic mutations disrupt such elements.