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Glycans as cancer biomarkers
Biochimica et Biophysica Acta 1820 (2012) 1347–1353
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Review
Glycans as cancer biomarkers☆ Barbara Adamczyk, Tharmala Tharmalingam, Pauline M. Rudd ⁎ Dublin-Oxford Glycobiology Laboratory, NIBRT—The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland
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Article history: Received 27 September 2011 Received in revised form 11 November 2011 Accepted 1 December 2011 Available online 9 December 2011 Keywords: Glycosylation Biomarker Cancer HPLC Glycan analysis High-throughput
a b s t r a c t Background: Non-invasive biomarkers, such as those from serum, are ideal for disease prognosis, staging and monitoring. In the past decade, our understanding of the importance of glycosylation changes with disease has evolved. Scope of review: We describe potential biomarkers derived from serum glycoproteins for liver, pancreatic, prostate, ovarian, breast, lung and stomach cancers. Methods for glycan analysis have progressed and newly developed high-throughput platform technologies have enabled the analysis of large cohorts of samples in an efficient manner. We also describe this evolution and trends to follow in the future. Major conclusions: Many convincing examples of aberrant glycans associated with cancer have come about from glycosylation analyses. Most studies have been carried out to identify changes in serum glycan profiles or through the isolation and identification of glycoproteins that contain these irregular glycan structures. In a majority of cancers the fucosylation and sialylation expression are found to be significantly modified. Therefore, these aberrations in glycan structures can be utilized as targets to improve existing cancer biomarkers. General significance: The ability to distinguish differences in the glycosylation of proteins between cancer and control patients emphasizes glycobiology as a promising field for potential biomarker identification. Furthermore, the high-throughput and reproducible nature of the chromatography platform have highlighted extensive applications in biomarker discovery and allowed integration of glycomics with other -omics fields, such as proteomics and genomics, making systems glycobiology a reality. This article is part of a Special Issue entitled Glycoproteomics. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Cancer is a leading cause of death, with estimations of mortality at 7.6 million worldwide in 2008 [1]. The highest mortality rates occurring in patients diagnosed with lung, stomach, liver, colon and breast cancers. Breast cancer is the most diagnosed cancer and leading cause of cancer deaths among women, accounting for 23% of total cancer cases and 14% of cancer deaths. Lung cancer is the leading cause of death in men, comprising 17% of total new cancer cases and 23% of total cancer deaths. With the number of deaths increasing over time, there is an urgent need for clinical markers. Biomarkers can determine the risk of developing a disease, serve as tools for initial diagnosis and staging diseases, as well as monitor disease progression and the effect of medication. An ideal tumor biomarker would allow a simple blood test to aid clinical decisions. In the past few years, glycomics has been at the forefront of revolutionizing biological and medical sciences, holding out the promise of both fully understanding and effectively treating human diseases.
☆ This article is part of a Special Issue entitled Glycoproteomics. ⁎ Corresponding author. Tel.: + 353 12158142; fax: + 353 12158116. E-mail address: [email protected] (P.M. Rudd). 0304-4165/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.bbagen.2011.12.001
Recent research in the glycomics field gave insight into the biological significance of the plasma N-glycome in human health and disease. Special emphasis was placed on exploring the connection between altered N-glycosylation of plasma glycoproteins and different diseases, particularly in the study of cancer. It is estimated that over 50% of all human proteins are glycosylated [2]. Glycosylation is found on cell surfaces and in extracellular matrices creating the initial point of contact in cellular interactions [3]. Therefore, the effects of disease states on glycan biosynthesis can be more evident than disease related changes to proteins. It is now well established that altered glycosylation varies significantly for cancer cells compared to normal cells [4–6]. The recognition of glycans as mediators of important biological processes has stimulated growing interests into glycobiology research. The characterization of glycosylation in serum glycoproteins is a challenge due to the heterogeneity of glycoforms. The main methods of glycosylation analysis involve the separation of released glycans by HILIC (hydrophilic-interaction chromatography) HPLC (high performance liquid chromatography), CE (capillary electrophoresis), lectin affinity and MS (mass spectrometry). Typically HPLC based glycan analysis involves the removal of glycans from glycoproteins by enzymatic digestion by PNGaseF (peptide N-glycosidase F), labeling with a fluorescent tag (2-aminobenzamine) and subsequent separation
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using chromatography methods. Resulting peaks are correlated to a dextran ladder and the elution times of glycans are expressed in glucose units (GU). Based on GU values given to each oligosaccharide, structures can be assigned by comparison to previously identified structures in a database, such as Glycobase (http://glycobase.nibrt. ie/) or EurocarbDB (http://www.ebi.ac.uk/eurocarb/home.action) [7–9] [Fig. 1]. These structures can then be confirmed by exoglycosidase digestions and other orthogonal methods. HPLC is robust, reproducible, has a high dynamic range and is quantitative, which are fundamental requirements in the discovery and validation of clinical markers.
Recent advances in the development of high-throughput HPLC technologies have enabled the glycan analysis of large sample sets. Knezevic et al. [10] reported the plasma glycosylation analysis from 1008 individuals and correlated variability, heritability and environmental determinates with variations in their profiles. A more recent publication compared the difference in glycosylation in plasma of 2705 patients and correlated changes to genome-wide association study (GWAS), which found links between single-nucleotide polymorphisms (SNPs) and the levels of specific glycans [11]. These findings suggest that glycosylation status may be a reason why individuals have different risk factors for disease. The potential of
Fig. 1. Technology platform for multidimensional glycan structural analysis and high-throughput profiling of potential biomarkers [7]. Discovery of biomarkers starts with whole plasma or individual glycoproteins immobilized in gel or 96-well plates. Glycans are then released enzymatically, labeled with 2AB and profiled using HILIC separation by HPLC or UPLC before and after digestion with exoglycosidase arrays. Results are interpreted using computer-assisted data handling [8].
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glycomics can be enhanced with further advancements in technology, including decreased separation time and increased resolution. The focus of this review is to give an insight into the biological significance of glycans, highlight examples of potential glyco-biomarkers in cancer, and discuss the exciting possibility of linking three main areas of research including genomics, proteomics and glycomics. 2. Glycan biomarkers in the detection of cancer Alterations in protein glycosylation are common features of tumor cells and may affect any type of cell glycoconjugate such as N-glycans and O-glycans on glycoproteins, glycolipids or glycosaminoglycans [12]. The expression levels of glycosyltransferases, sugar nucleotide donors, as well as disruption of the Golgi may contribute to significant changes between normal and diseased states. These findings can give insights into cancer development and its progression. 3. The successful story of glyco-biomarker in the detection of liver diseases The majority of serum glycoproteins are of hepatic origin. Therefore, the close relationship between liver and serum glycoproteins suggests that liver diseases associated with aberrant glycosylation can be reflected by the changes in serum glycoproteins. So far, the most popular methods to detect liver disorders in clinical practice are hepatic ultrasonography or liver biopsy. Liver biopsy is not an easy procedure, and most importantly not comfortable to the patients with possible complications. Therefore, there is an urgent need for non-invasive markers that can routinely assess the state of the liver. It has been broadly proven that different etiologies of liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC) may be characterized by altered protein glycosylation [13–16]. Among many potential protein candidates, a broadly validated protein was alpha-fetoprotein (AFP) in the diagnosis of HCC. The increase of AFP levels is correlated with increased tumor size; thus AFP levels remain unchanged in early cases of HCC [17]. In addition, the serum concentration of AFP alone is of little use in the differential diagnosis of HCC and benign liver diseases [18]. As the performance of AFP is not sufficient for early detection, an additional tumor marker was proposed—glycosylated AFP-L3 fraction. Using lectin-affinity techniques, a highly significant increase in the fucosylation index was observed in HCC patients in comparison to chronic liver diseases [19,20]. In 2006 the FDA approved AFP-L3 for the early detection of primary HCC [21]. These findings highlight the improvement in diagnostic efficiency of using a serum glycoprotein for disease diagnosis. 4. Fucosylated haptoglobin as a marker for pancreatic cancer Pancreatic cancer is the fourth leading cause of cancer deaths in the United States with the worst prognosis among all cancers [22]. This poor survival rate is mostly due to the lack of a reliable early detection method [23]. As a result, the diagnosis is often made after metastasis. Currently, the most widely used serum-based marker is CA 19-9, whose diagnostic value is limited because of a high false positive rate. In addition, it does not allow early detection and cannot readily discriminate between chronic pancreatitis and pancreatic cancer [24]. Alterations in the degree of fucosylation in N-glycans have been reported as a consequence of pancreatic cancer. It was found that concentration of fucosylated haptoglobin has been increased significantly in the serum of patients with pancreatic cancer compared to those of other types of cancer and healthy controls [25,26]. Increased fucosylation is a promising marker for pancreatic cancer even though the real mechanism still remains unknown [27].
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5. Altered glycosylation of prostate-specific antigen and whole serum glycoproteins as a specific biomarker for prostate cancer Prostate cancer is the second most frequently diagnosed cancer and the sixth leading cause of cancer death in males, and is currently recognized as one of the major medical problems in the male population. Current clinical practice involves three main factors that predict the likelihood of tumor progression including Gleason score (scoring based on microscopic evaluation), clinical stage and prostate-specific antigen (PSA) serum levels [28]. PSA is a 28 kDa glycoprotein that is present in small quantities in the serum of men with healthy prostates, but is often elevated in the presence of prostate cancer and in other prostate disorders. PSA detection was the first test approved by the FDA for early detection of prostate cancer [29]. Normal levels of PSA in seminal plasma are 0.5–3.0 ng/ml; with prostate disease, PSA is released into the bloodstream, and levels can reach 4 ng/ml or higher [30]. However, PSA levels can be varied from normal, due to a number of factors such as obesity, prostatitis, irritation, or benign prostatic hyperplasia (BPH) [31]. Therefore PSA detection can be unspecific and unreliable resulting in false positives. There are many ongoing studies to improve PSA specificity, mainly based on measurement of different subforms of PSA but also on altered posttranslational modifications such as glycosylation. A number of studies have compared the glycosylation of PSA in prostate cancer with control or benign specimens. These studies demonstrated that the combination of aberrant glycoform detection and PSA levels provides a more reliable diagnostic. Saldova et al. [32] reported increased core fucosylation and increased expression of α2-3 linked sialic acid in prostate cancer serum glycomes compared to patients with BPH. The results of the study were correlated with the Gleason score, where Gleason score of 7 indicates patients with more aggressive cancer and a higher chance of relapse compared to Gleason 5. Triantennary trigalactosylated glycans (A3G3) and tetraantennary tetrasialylated glycans with outer arm fucose (A4FS4) were significantly decreased while tetraantennary tetrasialylated glycans (A4S4) were increased in patients with Gleason 7 compared to Gleason 5. Previous reports also showed altered fucosylation and sialylation in PSA and other proteins isolated from serum [33,34]. Additional studies have been performed on PSA altered glycosylation aiming to distinguish prostate cancer from BPH. Sarrats et al. [35] utilized a two-dimensional electrophoresis (2-DE) method to evaluate PSA 2-DE subforms according to their molecular weight and isoelectric point (pI) from prostate cancer sera, BPH and control seminal plasma. Five PSA subforms (F1, F2, F3, F4 and F5) of different pI were obtained. F1, F2, and F3 subforms showed the same N-glycan pattern, containing higher levels of sialic acid than the F4 subform, whereas the F5 subform was unglycosylated [35,36]. When comparing PSA subforms from seminal plasma from prostate cancer to control patients, a decrease in sialylation was observed [35]. The same decreasing trend was reported in sialylation in prostate cancer compared to BPH, as relative percentages of the F3 subform containing mono and disialylated glycans decreased and relative percentages of F4 subform containing monosialylated glycans increased in cancer patients [35,36]. Furthermore, the analysis of glycans from F3, the more abundant PSA subform, showed a higher proportion of α2–3 sialic acid and a decrease in core fucosylated glycans in the prostate cancer [35]. These N-glycan changes in PSA subforms highlight the importance of glycosylation as an indicator of prostate cancer. 6. Differential glycosylation of acute-phase proteins and IgG: potential new glycobiomarkers for ovarian cancer Current methods for detection of ovarian cancer involve ultrasonography and monitoring levels of the serum glycoprotein CA125; however, the use of CA125 as a biomarker for ovarian cancer is inadequate as it is non-specific for ovarian cancer and thus unreliable for
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diagnosing early stage for this disease [37]. Several potential markers are currently being investigated including OVX1, M-CSF, inhibin, kallikreins, TPS, and lysophosphatidic acid [37,38]. Reported glycan alterations in whole serum glycome include the increased expression of sLe X, along with increased core-fucosylation of agalactosylated biantennary glycans (FA2) [39]. In addition, further studies involving glycoproteomics revealed serum glycoproteins that contain altered levels of glycans. Using sensitive techniques, Saldova et al. [39] identified altered glycosylation on acute phase proteins such as haptoglobin, α1-acid glycoprotein, α1-antichymotrypsin and immunoglobulin G (IgG) molecules. The acute phase proteins, mentioned above, contained elevated levels of glycoforms containing sLe X. Analysis of IgG isolated from serum showed reduced galactosylation and sialylation levels in samples from advanced ovarian cancer patients when compared with controls [39]. 7. Glycosylation changes as an alternative option to CA15-3 for breast cancer detection Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females [1]. The diagnosis can be complex and typically involves monitoring of serum glycoprotein CA15-3 (cancer antigen 15-3) and carcinoembryonic antigen levels (CEA). However, both biomarkers lack specificity and sensitivity. Extensive research is being carried out to discover glycan-based biomarkers. Serum N-glycome analysis of whole serum from breast cancer patients has revealed alterations of glycans including increased sialylation, higher levels of sLe X and significant changes in fucosylation [40,41]. A comparison between sera from patients with early breast cancer (21 with lymph node-negative and 20 with lymph nodepositive disease) and 134 women with benign breast disease indicated increased levels of agalactosyl biantennary glycans (FA2) and glycans containing the sLe X epitope (A3F1G1 and A2F1G1) in lymph nodepositive disease [42]. The combined levels of specific glycans were significantly higher in patients with lymph node metastases compared to women without these metastases [42]. Metastatic breast cancer (MBC) is a complex multi-step process involving the expansion of cancerous cells from the breast to other areas of the body. It is currently an incurable condition that is primarily treated with palliative measures. Circulating tumor cells (CTCs) are cells that have separated from the primary tumor and can migrate in the bloodstream. Patients with CTC counts above a certain threshold have a poorer prognosis than those below the threshold. Saldova et al [43] reported significantly higher levels of bi-, tri- and tetraantennary glycans containing sLe X epitopes (A2F1G1, A3F1G1, A4F1G1 and A4F2G2) from sera of advanced breast cancer with CTCs ≥5/7.5 ml compared to the sera of advanced breast cancer patients with CTCs b5/7.5 ml and healthy women. Glycans containing sLe X epitopes were found to be associated with CTCs, and targeting these epitopes as biomarkers provides a new non-invasive approach for determining prognosis in women with MBC. 8. Serum N-glycans as potential biomarkers for lung cancer Among the three major types of cancers, lung cancer shows the highest rate of mortality because of failure to diagnose the disease in its early state. Potential serum protein biomarkers for lung cancer diagnosis include carcinoembryonic antigen, cytokeratin 19 fragment, tissue polypeptide antigen, plasma kallikrein B1 (KLKB1), progastrinreleasing peptide, neuron-specific enolase and tumor M2 pyruvate kinase [44–47]. Despite extensive investigations in biomarker research, only a few of those mentioned above are useful in the clinic but lack sensitivity and specificity. Among several distinct types of biomarkers from various research areas such as genetics, epigenetics and proteomics, extensive studies
in glycomics were carried out. In Arnold et al. [48], the analysis of the N-glycome of serum samples from 100 lung cancer patients (20 from each stage—I, II, IIIA, IIIB and IV) and from 84 control samples yielded lung cancer-related glycan alterations. Significant increases in peaks containing sLe X, glycans with GU > 10.6, monoantennary glycans and significant decreases in peaks containing mostly biantennary corefucosylated glycans were found in the lung cancer patient serum samples. In addition, there were significant alterations in sialylation with increases in trisialylated glycans and decreases in disialylated glycans in the serum glycome of lung cancer patients. The N-linked glycan profile of haptoglobin demonstrated similar alterations to those elucidated in the total serum glycome [48]. Using receiver operator characteristic (ROC) curves, Arnold et al. [48] were able to generate the predicted specificities and sensitivities of distinguishing lung cancer patients from controls. For the individual peaks, the AUC (area under the curve) ranged from 0.640 to 0.811 but the total glycan data, generated a sensitivity and specificity of 85% and 86%, respectively, combined with an AUC of 0.938 (27). These data confirm that analyzing combinations of glycan epitopes may further enhance clinical markers for lung cancer. 9. Glycomic and proteomic approaches to improve biomarkers for stomach cancer In 2008, there were 989,600 new stomach cancer cases and 738,000 deaths due to stomach cancer estimated [1]. However, these numbers are slowly declining over time, especially in the developed world. Stomach cancer is often asymptomatic or causes only nonspecific symptoms in its early stages, which leads to late diagnosis. Although specific causes have not been identified, You et al. [49] reported a high incidence of gastric cancer associated with Helicobacter pylori infection and cigarette smoking. Moreover, there are reports of gastric cancer being linked to diet [50]. Current methods of diagnosis involve endoscopy, tests for detection of carbohydrate antigen 199 (CA19-9) and elevated levels of carcinoembryonic antigen (CEA); however, the assays are generally less sensitive [51]. Together, glycomic and proteomic analyses of serum from cancer patients hold the potential to discover improved biomarkers. A discovery study performed by Bones et al. [52] showed an increase in levels of sLeX epitopes present on triantennary glycans (A3) accompanied from total serum glycoproteins. Furthermore, immunoaffinity depleted serum revealed statistically significant increases in core fucosylated agalactosyl biantennary glycans (FA2) present on IgG (referred to as the IgG G0 glycoform) with increasing disease pathogenesis. Further analysis involving 2-DE returned a number of differentially expressed protein candidates (clusterin, leucine-rich-R2-glycoprotein, and kininogen-1) in the depleted serum, many of which were shown to carry triantennary sLeX during subsequent glycomic investigations. The evaluation of glycans and association with stomach cancer was investigated further by technologies, such as UPLC [53]. A reduction in the levels of asialo- and monosialylated core fucosylated N-glycans was evident with stomach cancer progression. The contribution of the glycosylation present on four highly abundant serum glycoproteins namely, IgG, haptoglobin, transferrin, and α1-acid glycoprotein was also evaluated. As with serum, an increase in sialylation was observed on haptoglobin, transferrin, and α1-acid glycoprotein with progressing cancer state. 10. The challenging pathway from discovery to clinical diagnostics After more than a decade of biomarker discovery using advanced proteomic and genomic approaches, very few biomarkers have been translated from research to clinical diagnostics. Most candidate biomarkers have emphasis placed on the polypeptide component. It is very likely that targeting glycans in combination with the protein
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backbone will provide greater diagnostic performance in regards to sensitivity and specificity. One issue that hampered glycobiology as a target for biomarker discovery is the ability to analyse large cohorts of samples. Today, detailed analyses of hundreds of samples are feasible and serve as the foundation stone for identification of potential glyco-biomarkers. However, there are still many concerns that must be overcome in order to move from discovery phase to clinical use. The main problems that we are currently facing are 1) the validation of the preliminary findings on an unbiased larger sample population; 2) multicenter validation studies to assure that selected biomarkers are not sensitive to variations in sample collection, storage conditions and processing; 3) clinical targets that would be sensitive and specific for a particular cancer; 4) assays that would be feasible to be performed in a clinical environment. Currently used clinical cancer diagnostic tests use existing glycoprotein cancer markers (CA15-3, CA19-9, CA125, CEA, MUC1) but are not specific to a particular cancer. The best known glyco-biomarker and so far the most successful is fucosylated alpha-fetoprotein (AFP-L3) that is significantly elevated in HCC. Recently it has been approved by the FDA as a tumor marker in clinical use. With this successful story in mind, in order to progress the biomarker candidates from the discovery to clinical stages enhanced efforts must be placed on the 4 points mentioned above. With advancements in new glycomic technologies that enable simple and rapid screening methods, we are getting closer to finding glyco-biomarkers that could help to improve clinical diagnosis. However, a combination of markers and techniques will be necessary to achieve this rewarding goal. 11. Update on technologies Technologies for the analysis of glycans involve the use of HPLC, CE, MS and lectin affinity assays. For a detailed review of these techniques see Mariño et al [54]. Over the past decade methods for glycosylation analysis have evolved. An approach that has made a major stride in carbohydrate separation is high-performance liquid chromatography providing an ideal platform for structural determination and quantitation of glycans. Initial papers from the 1990s described the release of glycans from individual gel plugs or in solution releases, and subsequent labeling by 2-aminobenzamide. The separation was based on chromatography columns with 5 μm amide-derivitized silica particles using 3-h separation methods [55]. Optimization of the method yielded a high-throughput N-glycan release method, where glycan release and labeling was done subsequently in a 96-plate format, significantly enhancing the efficiency for sample preparation [7]. Conventional 3 h runs were further shortened to a 2 h run on the Waters 2695 HPLC and further to a 1 h run on the Waters 2795 HPLC using a TSKgel Tosoh Amide-80 5 μm (250 × 4.6 mm) column. Another improvement involved the reduction of separation times to 1 h (or 30 min) by using a TSKgel Amide-80 with matrix particles of 3 μm or 5 μm size column in order to efficiently separate glycans. This methodology coupled with the use of Glycobase and AutoGU allowed for identification of glycans in a limited time frame [8]. Another step forward was achieved by moving from 3 μm (HPLC) to 1.7 μm (UPLC) matrix particles. In a recent publication, Bones et al. [53] described the analysis of glycans from serum stomach cancer patients by a newly developed UPLC method. The efficiency of separation was further enhanced by the use of recently introduced 10 cm Waters BEH glycan column consisting of 1.7 μm beads hydrophilic interaction (HILIC) based stationary phase [56]. This allowed for significant time reduction, from 1 h to 20 min, but also increasing resolution of the obtained chromatography profile. An important implication of the development of high-throughput methodology and enhanced UPLC separation is the ability to study large cohorts of samples in a limited time
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frame; which was successfully achieved in the analysis of the human IgG N-glycome from three isolated populations, total of 2298 individuals [57]. 12. Future directions: combining high-throughput glycomics with GWAS High-throughput technologies allow for large scale sample analysis. In parallel to developments in high-throughput genome analysis, there has been emphasis on the development of a robust and reproducible method for glycan analysis. The study by Knezevic et al. [10] is an example of large scale sample analysis; where the study analysed plasma glycans from 1008 individuals to evaluate the effects of variability, heritability and environmental factors on plasma glycans. The results showed a high level of biological variability with the median ratio of minimal to maximal values of 6.17. In particular, age and gender were found to significantly affect plasma glycan distributions, although their overall effect was less than 10%. The heritability of glycans varied widely across individual glycan species, suggesting that the relative levels of some N-glycans in plasma may be more dependent on genetic factors rather than environmental factors. The ability to analyze thousands of samples in a limited period of time has allowed for a comprehensive analysis of common genetic polymorphisms that affect protein glycosylation. Lauc et al. [11] reported on the first comprehensive GWAS study in 2705 individuals in 3 population cohorts from Croatia and Scotland. The analysis revealed an influence of Hepatocyte Nuclear Factor 1α (HNF1α) and fucosyltransferase genes FUT6 and FUT8 on the N-glycome from human plasma. Further analysis of the functionality of the HNF1α by gene knockdowns showed that HNF1α is an upstream regulator of several key genes involved in different stages of the fucosylation pathway. Fucosylation is a rate-limiting step in plasma protein glycosylation, the de novo synthesis and salvage pathway synthesis of GDP-fucose, leads to up-regulation of antennary fucosyltransferases and down-regulation of core-fucosylation. The changes of fucosylation with cancer progression lead us to question whether there is a genetic regulation of fucosyltransferases with cancer disease state. Further studies involving GWAS and plasma glycoform analysis is required to understand the effects of fucosylation in cancer patients. Combining GWAS and high-throughput glycomic analysis is important for paving the way for a better understanding of human health and disease. 13. Conclusions Glycosylation of proteins has a significant impact in the pathogenesis of numerous diseases, and specific changes can provide insights into disease states and progression. Non-invasive glycan biomarkers, such as those from serum, are promising for the course of disease, including those mentioned in this review. Furthermore, with improved technology and thus the increased efficiency of sample analysis, the field of glycomics is highlighting its underlying potential for biomarker discovery. It is now possible to overlay data from genomics, proteomics, metabolomics and glycomics with pathway analysis. The era of systems glycobiology and a further understanding of disease have dawned now. Abbreviations 2-DE Two Dimensional Electrophoresis BEH Ethylene Bridged Hybrid BPH Benign Prostatic Hyperplasia CE Capillary Electrophoresis CEA Carcinoembryonic Antigen CTCs Circulating Tumor Cells FDA US Food and Drug Administration
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FUT6 and FUT8 Fucosyltransferases Genes GU Glucose Units GWAS Genome-Wide Association Study HCC Hepatocellular Carcinoma HILIC Hydrophilic-Interaction Liquid Chromatography HNF1α Hepatocyte Nuclear Factor 1α HPLC High Performance Liquid Chromatography MBC Metastatic Breast Cancer MS Mass Spectrometry PNGaseF Peptide N-glycosidase F PSA Prostate Specific Antigen sLe X Sialyl Lewis X antigen SNPs Single Nucleotide Polymorphisms TF Thomsen–Friedenreich antigen UPLC Ultra Performance Liquid Chromatography
Funding This work was supported by the Science Foundation Ireland (Reproductive Biology Research Cluster (RBRC) [grant number 07/SRC/ B1156] and Alimentary Glycoscience Research Cluster (AGRC) [grant number 08/SRC/B1393]). European Commission under the Seventh Framework Programme (FP7) EuroGlycoArrays [grant number 215536], SchizDX [grant number 223427], GlycoHIT [grant number 260600], GlycoBioM [grant number 259869], European Commission under the Sixth Framework Programme (FP6) (Glyfdis [No 37661] and EuroCarb DB [No 11952]), Enterprise Ireland [grant number PC/ 2008/0022]. References [1] A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman, Global cancer statistics, CA, Cancer J. Clin. (2011) 20107 (caac). [2] R. Apweiler, H. Hermjakob, N. Sharon, On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database, Biochimica et Biophysica Acta (BBA) Gen. Subj. 1473 (1999) 4–8. [3] K. Ohtsubo, J.D. Marth, Glycosylation in cellular mechanisms of health and disease, Cell 126 (2006) 855–867. [4] A. Cazet, S. Julien, M. Bobowski, M.-A. Krzewinski-Recchi, A. Harduin-Lepers, S. Groux-Degroote, P. Delannoy, Consequences of the expression of sialylated antigens in breast cancer, Carbohydr. Res. 345 (2010) 1377–1383. [5] S. Rachagani, M.P. Torres, N. Moniaux, S.K. Batra, Current status of mucins in the diagnosis and therapy of cancer, Biofactors 35 (2009) 509–527. [6] D.H. Dube, C.R. Bertozzi, Glycans in cancer and inflammation [mdash] potential for therapeutics and diagnostics, Nat. Rev. Drug Discov. 4 (2005) 477–488. [7] L. Royle, M.P. Campbell, C.M. Radcliffe, D.M. White, D.J. Harvey, J.L. Abrahams, Y.-G. Kim, G.W. Henry, N.A. Shadick, M.E. Weinblatt, D.M. Lee, P.M. Rudd, R.A. Dwek, HPLC-based analysis of serum N-glycans on a 96-well plate platform with dedicated database software, Anal. Biochem. 376 (2008) 1–12. [8] M.P. Campbell, L. Royle, C.M. Radcliffe, R.A. Dwek, P.M. Rudd, GlycoBase and autoGU: tools for HPLC-based glycan analysis, Bioinformatics 24 (2008) 1214–1216. [9] C.W. von der Lieth, A. Arda Freire, D. Blank, M.P. Campbell, A. Ceroni, D.R. Damerell, A. Dell, R.A. Dwek, B. Ernst, R. Fogh, M. Frank, H. Geyer, R. Geyer, M.J. Harrison, K. Henrick, S. Herget, W.E. Hull, J. Ionides, H.J. Joshi, J.P. Kamerling, B.R. Leeflang, T. Lutteke, M. Lundborg, K. Maass, A. Merry, R. Ranzinger, J. Rosen, L. Royle, P.M. Rudd, S. Schloissnig, R. Stenutz, W.F. Vranken, G. Widmalm, S.M. Haslam, EUROCarbDB: an open-access platform for glycoinformatics, Glycobiology 21 (2011) 493–502. [10] A. Knezevic, O. Polasek, O. Gornik, I. Rudan, H. Campbell, C. Hayward, A. Wright, I. Kolcic, N. O'Donoghue, J. Bones, P.M. Rudd, G. Lauc, Variability, heritability and environmental determinants of human plasma N-glycome, J. Proteome Res. 8 (2009) 694–701. [11] G. Lauc, A. Essafi, J.E. Huffman, C. Hayward, A. Knezevic, J.J. Kattla, O. Polasek, O. Gornik, V. Vitart, J.L. Abrahams, M. Pucic, M. Novokmet, I. Redzic, S. Campbell, S.H. Wild, F. Borovecki, W. Wang, I. Kolcic, L. Zgaga, U. Gyllensten, J.F. Wilson, A.F. Wright, N.D. Hastie, H. Campbell, P.M. Rudd, I. Rudan, Genomics meets glycomics: the first GWAS study of human N-glycome identifies HNF1A as a master regulator of plasma protein fucosylation, PLoS Genet. 6 (2010) e1001256. [12] A. Varki, Biological roles of oligosaccharides: all of the theories are correct, Glycobiology 3 (1993) 97–130. [13] B. Blomme, C. Van Steenkiste, N. Callewaert, H. Van Vlierberghe, Alteration of protein glycosylation in liver diseases, J. Hepatol. 50 (2009) 592–603. [14] D. Meany, D. Chan, Aberrant glycosylation associated with enzymes as cancer biomarkers, Clin. Proteomics 8 (2011) 7.
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Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbagen
Review
Glycans as cancer biomarkers☆ Barbara Adamczyk, Tharmala Tharmalingam, Pauline M. Rudd ⁎ Dublin-Oxford Glycobiology Laboratory, NIBRT—The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland
a r t i c l e
i n f o
Article history: Received 27 September 2011 Received in revised form 11 November 2011 Accepted 1 December 2011 Available online 9 December 2011 Keywords: Glycosylation Biomarker Cancer HPLC Glycan analysis High-throughput
a b s t r a c t Background: Non-invasive biomarkers, such as those from serum, are ideal for disease prognosis, staging and monitoring. In the past decade, our understanding of the importance of glycosylation changes with disease has evolved. Scope of review: We describe potential biomarkers derived from serum glycoproteins for liver, pancreatic, prostate, ovarian, breast, lung and stomach cancers. Methods for glycan analysis have progressed and newly developed high-throughput platform technologies have enabled the analysis of large cohorts of samples in an efficient manner. We also describe this evolution and trends to follow in the future. Major conclusions: Many convincing examples of aberrant glycans associated with cancer have come about from glycosylation analyses. Most studies have been carried out to identify changes in serum glycan profiles or through the isolation and identification of glycoproteins that contain these irregular glycan structures. In a majority of cancers the fucosylation and sialylation expression are found to be significantly modified. Therefore, these aberrations in glycan structures can be utilized as targets to improve existing cancer biomarkers. General significance: The ability to distinguish differences in the glycosylation of proteins between cancer and control patients emphasizes glycobiology as a promising field for potential biomarker identification. Furthermore, the high-throughput and reproducible nature of the chromatography platform have highlighted extensive applications in biomarker discovery and allowed integration of glycomics with other -omics fields, such as proteomics and genomics, making systems glycobiology a reality. This article is part of a Special Issue entitled Glycoproteomics. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Cancer is a leading cause of death, with estimations of mortality at 7.6 million worldwide in 2008 [1]. The highest mortality rates occurring in patients diagnosed with lung, stomach, liver, colon and breast cancers. Breast cancer is the most diagnosed cancer and leading cause of cancer deaths among women, accounting for 23% of total cancer cases and 14% of cancer deaths. Lung cancer is the leading cause of death in men, comprising 17% of total new cancer cases and 23% of total cancer deaths. With the number of deaths increasing over time, there is an urgent need for clinical markers. Biomarkers can determine the risk of developing a disease, serve as tools for initial diagnosis and staging diseases, as well as monitor disease progression and the effect of medication. An ideal tumor biomarker would allow a simple blood test to aid clinical decisions. In the past few years, glycomics has been at the forefront of revolutionizing biological and medical sciences, holding out the promise of both fully understanding and effectively treating human diseases.
☆ This article is part of a Special Issue entitled Glycoproteomics. ⁎ Corresponding author. Tel.: + 353 12158142; fax: + 353 12158116. E-mail address: [email protected] (P.M. Rudd). 0304-4165/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.bbagen.2011.12.001
Recent research in the glycomics field gave insight into the biological significance of the plasma N-glycome in human health and disease. Special emphasis was placed on exploring the connection between altered N-glycosylation of plasma glycoproteins and different diseases, particularly in the study of cancer. It is estimated that over 50% of all human proteins are glycosylated [2]. Glycosylation is found on cell surfaces and in extracellular matrices creating the initial point of contact in cellular interactions [3]. Therefore, the effects of disease states on glycan biosynthesis can be more evident than disease related changes to proteins. It is now well established that altered glycosylation varies significantly for cancer cells compared to normal cells [4–6]. The recognition of glycans as mediators of important biological processes has stimulated growing interests into glycobiology research. The characterization of glycosylation in serum glycoproteins is a challenge due to the heterogeneity of glycoforms. The main methods of glycosylation analysis involve the separation of released glycans by HILIC (hydrophilic-interaction chromatography) HPLC (high performance liquid chromatography), CE (capillary electrophoresis), lectin affinity and MS (mass spectrometry). Typically HPLC based glycan analysis involves the removal of glycans from glycoproteins by enzymatic digestion by PNGaseF (peptide N-glycosidase F), labeling with a fluorescent tag (2-aminobenzamine) and subsequent separation
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using chromatography methods. Resulting peaks are correlated to a dextran ladder and the elution times of glycans are expressed in glucose units (GU). Based on GU values given to each oligosaccharide, structures can be assigned by comparison to previously identified structures in a database, such as Glycobase (http://glycobase.nibrt. ie/) or EurocarbDB (http://www.ebi.ac.uk/eurocarb/home.action) [7–9] [Fig. 1]. These structures can then be confirmed by exoglycosidase digestions and other orthogonal methods. HPLC is robust, reproducible, has a high dynamic range and is quantitative, which are fundamental requirements in the discovery and validation of clinical markers.
Recent advances in the development of high-throughput HPLC technologies have enabled the glycan analysis of large sample sets. Knezevic et al. [10] reported the plasma glycosylation analysis from 1008 individuals and correlated variability, heritability and environmental determinates with variations in their profiles. A more recent publication compared the difference in glycosylation in plasma of 2705 patients and correlated changes to genome-wide association study (GWAS), which found links between single-nucleotide polymorphisms (SNPs) and the levels of specific glycans [11]. These findings suggest that glycosylation status may be a reason why individuals have different risk factors for disease. The potential of
Fig. 1. Technology platform for multidimensional glycan structural analysis and high-throughput profiling of potential biomarkers [7]. Discovery of biomarkers starts with whole plasma or individual glycoproteins immobilized in gel or 96-well plates. Glycans are then released enzymatically, labeled with 2AB and profiled using HILIC separation by HPLC or UPLC before and after digestion with exoglycosidase arrays. Results are interpreted using computer-assisted data handling [8].
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glycomics can be enhanced with further advancements in technology, including decreased separation time and increased resolution. The focus of this review is to give an insight into the biological significance of glycans, highlight examples of potential glyco-biomarkers in cancer, and discuss the exciting possibility of linking three main areas of research including genomics, proteomics and glycomics. 2. Glycan biomarkers in the detection of cancer Alterations in protein glycosylation are common features of tumor cells and may affect any type of cell glycoconjugate such as N-glycans and O-glycans on glycoproteins, glycolipids or glycosaminoglycans [12]. The expression levels of glycosyltransferases, sugar nucleotide donors, as well as disruption of the Golgi may contribute to significant changes between normal and diseased states. These findings can give insights into cancer development and its progression. 3. The successful story of glyco-biomarker in the detection of liver diseases The majority of serum glycoproteins are of hepatic origin. Therefore, the close relationship between liver and serum glycoproteins suggests that liver diseases associated with aberrant glycosylation can be reflected by the changes in serum glycoproteins. So far, the most popular methods to detect liver disorders in clinical practice are hepatic ultrasonography or liver biopsy. Liver biopsy is not an easy procedure, and most importantly not comfortable to the patients with possible complications. Therefore, there is an urgent need for non-invasive markers that can routinely assess the state of the liver. It has been broadly proven that different etiologies of liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC) may be characterized by altered protein glycosylation [13–16]. Among many potential protein candidates, a broadly validated protein was alpha-fetoprotein (AFP) in the diagnosis of HCC. The increase of AFP levels is correlated with increased tumor size; thus AFP levels remain unchanged in early cases of HCC [17]. In addition, the serum concentration of AFP alone is of little use in the differential diagnosis of HCC and benign liver diseases [18]. As the performance of AFP is not sufficient for early detection, an additional tumor marker was proposed—glycosylated AFP-L3 fraction. Using lectin-affinity techniques, a highly significant increase in the fucosylation index was observed in HCC patients in comparison to chronic liver diseases [19,20]. In 2006 the FDA approved AFP-L3 for the early detection of primary HCC [21]. These findings highlight the improvement in diagnostic efficiency of using a serum glycoprotein for disease diagnosis. 4. Fucosylated haptoglobin as a marker for pancreatic cancer Pancreatic cancer is the fourth leading cause of cancer deaths in the United States with the worst prognosis among all cancers [22]. This poor survival rate is mostly due to the lack of a reliable early detection method [23]. As a result, the diagnosis is often made after metastasis. Currently, the most widely used serum-based marker is CA 19-9, whose diagnostic value is limited because of a high false positive rate. In addition, it does not allow early detection and cannot readily discriminate between chronic pancreatitis and pancreatic cancer [24]. Alterations in the degree of fucosylation in N-glycans have been reported as a consequence of pancreatic cancer. It was found that concentration of fucosylated haptoglobin has been increased significantly in the serum of patients with pancreatic cancer compared to those of other types of cancer and healthy controls [25,26]. Increased fucosylation is a promising marker for pancreatic cancer even though the real mechanism still remains unknown [27].
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5. Altered glycosylation of prostate-specific antigen and whole serum glycoproteins as a specific biomarker for prostate cancer Prostate cancer is the second most frequently diagnosed cancer and the sixth leading cause of cancer death in males, and is currently recognized as one of the major medical problems in the male population. Current clinical practice involves three main factors that predict the likelihood of tumor progression including Gleason score (scoring based on microscopic evaluation), clinical stage and prostate-specific antigen (PSA) serum levels [28]. PSA is a 28 kDa glycoprotein that is present in small quantities in the serum of men with healthy prostates, but is often elevated in the presence of prostate cancer and in other prostate disorders. PSA detection was the first test approved by the FDA for early detection of prostate cancer [29]. Normal levels of PSA in seminal plasma are 0.5–3.0 ng/ml; with prostate disease, PSA is released into the bloodstream, and levels can reach 4 ng/ml or higher [30]. However, PSA levels can be varied from normal, due to a number of factors such as obesity, prostatitis, irritation, or benign prostatic hyperplasia (BPH) [31]. Therefore PSA detection can be unspecific and unreliable resulting in false positives. There are many ongoing studies to improve PSA specificity, mainly based on measurement of different subforms of PSA but also on altered posttranslational modifications such as glycosylation. A number of studies have compared the glycosylation of PSA in prostate cancer with control or benign specimens. These studies demonstrated that the combination of aberrant glycoform detection and PSA levels provides a more reliable diagnostic. Saldova et al. [32] reported increased core fucosylation and increased expression of α2-3 linked sialic acid in prostate cancer serum glycomes compared to patients with BPH. The results of the study were correlated with the Gleason score, where Gleason score of 7 indicates patients with more aggressive cancer and a higher chance of relapse compared to Gleason 5. Triantennary trigalactosylated glycans (A3G3) and tetraantennary tetrasialylated glycans with outer arm fucose (A4FS4) were significantly decreased while tetraantennary tetrasialylated glycans (A4S4) were increased in patients with Gleason 7 compared to Gleason 5. Previous reports also showed altered fucosylation and sialylation in PSA and other proteins isolated from serum [33,34]. Additional studies have been performed on PSA altered glycosylation aiming to distinguish prostate cancer from BPH. Sarrats et al. [35] utilized a two-dimensional electrophoresis (2-DE) method to evaluate PSA 2-DE subforms according to their molecular weight and isoelectric point (pI) from prostate cancer sera, BPH and control seminal plasma. Five PSA subforms (F1, F2, F3, F4 and F5) of different pI were obtained. F1, F2, and F3 subforms showed the same N-glycan pattern, containing higher levels of sialic acid than the F4 subform, whereas the F5 subform was unglycosylated [35,36]. When comparing PSA subforms from seminal plasma from prostate cancer to control patients, a decrease in sialylation was observed [35]. The same decreasing trend was reported in sialylation in prostate cancer compared to BPH, as relative percentages of the F3 subform containing mono and disialylated glycans decreased and relative percentages of F4 subform containing monosialylated glycans increased in cancer patients [35,36]. Furthermore, the analysis of glycans from F3, the more abundant PSA subform, showed a higher proportion of α2–3 sialic acid and a decrease in core fucosylated glycans in the prostate cancer [35]. These N-glycan changes in PSA subforms highlight the importance of glycosylation as an indicator of prostate cancer. 6. Differential glycosylation of acute-phase proteins and IgG: potential new glycobiomarkers for ovarian cancer Current methods for detection of ovarian cancer involve ultrasonography and monitoring levels of the serum glycoprotein CA125; however, the use of CA125 as a biomarker for ovarian cancer is inadequate as it is non-specific for ovarian cancer and thus unreliable for
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diagnosing early stage for this disease [37]. Several potential markers are currently being investigated including OVX1, M-CSF, inhibin, kallikreins, TPS, and lysophosphatidic acid [37,38]. Reported glycan alterations in whole serum glycome include the increased expression of sLe X, along with increased core-fucosylation of agalactosylated biantennary glycans (FA2) [39]. In addition, further studies involving glycoproteomics revealed serum glycoproteins that contain altered levels of glycans. Using sensitive techniques, Saldova et al. [39] identified altered glycosylation on acute phase proteins such as haptoglobin, α1-acid glycoprotein, α1-antichymotrypsin and immunoglobulin G (IgG) molecules. The acute phase proteins, mentioned above, contained elevated levels of glycoforms containing sLe X. Analysis of IgG isolated from serum showed reduced galactosylation and sialylation levels in samples from advanced ovarian cancer patients when compared with controls [39]. 7. Glycosylation changes as an alternative option to CA15-3 for breast cancer detection Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females [1]. The diagnosis can be complex and typically involves monitoring of serum glycoprotein CA15-3 (cancer antigen 15-3) and carcinoembryonic antigen levels (CEA). However, both biomarkers lack specificity and sensitivity. Extensive research is being carried out to discover glycan-based biomarkers. Serum N-glycome analysis of whole serum from breast cancer patients has revealed alterations of glycans including increased sialylation, higher levels of sLe X and significant changes in fucosylation [40,41]. A comparison between sera from patients with early breast cancer (21 with lymph node-negative and 20 with lymph nodepositive disease) and 134 women with benign breast disease indicated increased levels of agalactosyl biantennary glycans (FA2) and glycans containing the sLe X epitope (A3F1G1 and A2F1G1) in lymph nodepositive disease [42]. The combined levels of specific glycans were significantly higher in patients with lymph node metastases compared to women without these metastases [42]. Metastatic breast cancer (MBC) is a complex multi-step process involving the expansion of cancerous cells from the breast to other areas of the body. It is currently an incurable condition that is primarily treated with palliative measures. Circulating tumor cells (CTCs) are cells that have separated from the primary tumor and can migrate in the bloodstream. Patients with CTC counts above a certain threshold have a poorer prognosis than those below the threshold. Saldova et al [43] reported significantly higher levels of bi-, tri- and tetraantennary glycans containing sLe X epitopes (A2F1G1, A3F1G1, A4F1G1 and A4F2G2) from sera of advanced breast cancer with CTCs ≥5/7.5 ml compared to the sera of advanced breast cancer patients with CTCs b5/7.5 ml and healthy women. Glycans containing sLe X epitopes were found to be associated with CTCs, and targeting these epitopes as biomarkers provides a new non-invasive approach for determining prognosis in women with MBC. 8. Serum N-glycans as potential biomarkers for lung cancer Among the three major types of cancers, lung cancer shows the highest rate of mortality because of failure to diagnose the disease in its early state. Potential serum protein biomarkers for lung cancer diagnosis include carcinoembryonic antigen, cytokeratin 19 fragment, tissue polypeptide antigen, plasma kallikrein B1 (KLKB1), progastrinreleasing peptide, neuron-specific enolase and tumor M2 pyruvate kinase [44–47]. Despite extensive investigations in biomarker research, only a few of those mentioned above are useful in the clinic but lack sensitivity and specificity. Among several distinct types of biomarkers from various research areas such as genetics, epigenetics and proteomics, extensive studies
in glycomics were carried out. In Arnold et al. [48], the analysis of the N-glycome of serum samples from 100 lung cancer patients (20 from each stage—I, II, IIIA, IIIB and IV) and from 84 control samples yielded lung cancer-related glycan alterations. Significant increases in peaks containing sLe X, glycans with GU > 10.6, monoantennary glycans and significant decreases in peaks containing mostly biantennary corefucosylated glycans were found in the lung cancer patient serum samples. In addition, there were significant alterations in sialylation with increases in trisialylated glycans and decreases in disialylated glycans in the serum glycome of lung cancer patients. The N-linked glycan profile of haptoglobin demonstrated similar alterations to those elucidated in the total serum glycome [48]. Using receiver operator characteristic (ROC) curves, Arnold et al. [48] were able to generate the predicted specificities and sensitivities of distinguishing lung cancer patients from controls. For the individual peaks, the AUC (area under the curve) ranged from 0.640 to 0.811 but the total glycan data, generated a sensitivity and specificity of 85% and 86%, respectively, combined with an AUC of 0.938 (27). These data confirm that analyzing combinations of glycan epitopes may further enhance clinical markers for lung cancer. 9. Glycomic and proteomic approaches to improve biomarkers for stomach cancer In 2008, there were 989,600 new stomach cancer cases and 738,000 deaths due to stomach cancer estimated [1]. However, these numbers are slowly declining over time, especially in the developed world. Stomach cancer is often asymptomatic or causes only nonspecific symptoms in its early stages, which leads to late diagnosis. Although specific causes have not been identified, You et al. [49] reported a high incidence of gastric cancer associated with Helicobacter pylori infection and cigarette smoking. Moreover, there are reports of gastric cancer being linked to diet [50]. Current methods of diagnosis involve endoscopy, tests for detection of carbohydrate antigen 199 (CA19-9) and elevated levels of carcinoembryonic antigen (CEA); however, the assays are generally less sensitive [51]. Together, glycomic and proteomic analyses of serum from cancer patients hold the potential to discover improved biomarkers. A discovery study performed by Bones et al. [52] showed an increase in levels of sLeX epitopes present on triantennary glycans (A3) accompanied from total serum glycoproteins. Furthermore, immunoaffinity depleted serum revealed statistically significant increases in core fucosylated agalactosyl biantennary glycans (FA2) present on IgG (referred to as the IgG G0 glycoform) with increasing disease pathogenesis. Further analysis involving 2-DE returned a number of differentially expressed protein candidates (clusterin, leucine-rich-R2-glycoprotein, and kininogen-1) in the depleted serum, many of which were shown to carry triantennary sLeX during subsequent glycomic investigations. The evaluation of glycans and association with stomach cancer was investigated further by technologies, such as UPLC [53]. A reduction in the levels of asialo- and monosialylated core fucosylated N-glycans was evident with stomach cancer progression. The contribution of the glycosylation present on four highly abundant serum glycoproteins namely, IgG, haptoglobin, transferrin, and α1-acid glycoprotein was also evaluated. As with serum, an increase in sialylation was observed on haptoglobin, transferrin, and α1-acid glycoprotein with progressing cancer state. 10. The challenging pathway from discovery to clinical diagnostics After more than a decade of biomarker discovery using advanced proteomic and genomic approaches, very few biomarkers have been translated from research to clinical diagnostics. Most candidate biomarkers have emphasis placed on the polypeptide component. It is very likely that targeting glycans in combination with the protein
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backbone will provide greater diagnostic performance in regards to sensitivity and specificity. One issue that hampered glycobiology as a target for biomarker discovery is the ability to analyse large cohorts of samples. Today, detailed analyses of hundreds of samples are feasible and serve as the foundation stone for identification of potential glyco-biomarkers. However, there are still many concerns that must be overcome in order to move from discovery phase to clinical use. The main problems that we are currently facing are 1) the validation of the preliminary findings on an unbiased larger sample population; 2) multicenter validation studies to assure that selected biomarkers are not sensitive to variations in sample collection, storage conditions and processing; 3) clinical targets that would be sensitive and specific for a particular cancer; 4) assays that would be feasible to be performed in a clinical environment. Currently used clinical cancer diagnostic tests use existing glycoprotein cancer markers (CA15-3, CA19-9, CA125, CEA, MUC1) but are not specific to a particular cancer. The best known glyco-biomarker and so far the most successful is fucosylated alpha-fetoprotein (AFP-L3) that is significantly elevated in HCC. Recently it has been approved by the FDA as a tumor marker in clinical use. With this successful story in mind, in order to progress the biomarker candidates from the discovery to clinical stages enhanced efforts must be placed on the 4 points mentioned above. With advancements in new glycomic technologies that enable simple and rapid screening methods, we are getting closer to finding glyco-biomarkers that could help to improve clinical diagnosis. However, a combination of markers and techniques will be necessary to achieve this rewarding goal. 11. Update on technologies Technologies for the analysis of glycans involve the use of HPLC, CE, MS and lectin affinity assays. For a detailed review of these techniques see Mariño et al [54]. Over the past decade methods for glycosylation analysis have evolved. An approach that has made a major stride in carbohydrate separation is high-performance liquid chromatography providing an ideal platform for structural determination and quantitation of glycans. Initial papers from the 1990s described the release of glycans from individual gel plugs or in solution releases, and subsequent labeling by 2-aminobenzamide. The separation was based on chromatography columns with 5 μm amide-derivitized silica particles using 3-h separation methods [55]. Optimization of the method yielded a high-throughput N-glycan release method, where glycan release and labeling was done subsequently in a 96-plate format, significantly enhancing the efficiency for sample preparation [7]. Conventional 3 h runs were further shortened to a 2 h run on the Waters 2695 HPLC and further to a 1 h run on the Waters 2795 HPLC using a TSKgel Tosoh Amide-80 5 μm (250 × 4.6 mm) column. Another improvement involved the reduction of separation times to 1 h (or 30 min) by using a TSKgel Amide-80 with matrix particles of 3 μm or 5 μm size column in order to efficiently separate glycans. This methodology coupled with the use of Glycobase and AutoGU allowed for identification of glycans in a limited time frame [8]. Another step forward was achieved by moving from 3 μm (HPLC) to 1.7 μm (UPLC) matrix particles. In a recent publication, Bones et al. [53] described the analysis of glycans from serum stomach cancer patients by a newly developed UPLC method. The efficiency of separation was further enhanced by the use of recently introduced 10 cm Waters BEH glycan column consisting of 1.7 μm beads hydrophilic interaction (HILIC) based stationary phase [56]. This allowed for significant time reduction, from 1 h to 20 min, but also increasing resolution of the obtained chromatography profile. An important implication of the development of high-throughput methodology and enhanced UPLC separation is the ability to study large cohorts of samples in a limited time
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frame; which was successfully achieved in the analysis of the human IgG N-glycome from three isolated populations, total of 2298 individuals [57]. 12. Future directions: combining high-throughput glycomics with GWAS High-throughput technologies allow for large scale sample analysis. In parallel to developments in high-throughput genome analysis, there has been emphasis on the development of a robust and reproducible method for glycan analysis. The study by Knezevic et al. [10] is an example of large scale sample analysis; where the study analysed plasma glycans from 1008 individuals to evaluate the effects of variability, heritability and environmental factors on plasma glycans. The results showed a high level of biological variability with the median ratio of minimal to maximal values of 6.17. In particular, age and gender were found to significantly affect plasma glycan distributions, although their overall effect was less than 10%. The heritability of glycans varied widely across individual glycan species, suggesting that the relative levels of some N-glycans in plasma may be more dependent on genetic factors rather than environmental factors. The ability to analyze thousands of samples in a limited period of time has allowed for a comprehensive analysis of common genetic polymorphisms that affect protein glycosylation. Lauc et al. [11] reported on the first comprehensive GWAS study in 2705 individuals in 3 population cohorts from Croatia and Scotland. The analysis revealed an influence of Hepatocyte Nuclear Factor 1α (HNF1α) and fucosyltransferase genes FUT6 and FUT8 on the N-glycome from human plasma. Further analysis of the functionality of the HNF1α by gene knockdowns showed that HNF1α is an upstream regulator of several key genes involved in different stages of the fucosylation pathway. Fucosylation is a rate-limiting step in plasma protein glycosylation, the de novo synthesis and salvage pathway synthesis of GDP-fucose, leads to up-regulation of antennary fucosyltransferases and down-regulation of core-fucosylation. The changes of fucosylation with cancer progression lead us to question whether there is a genetic regulation of fucosyltransferases with cancer disease state. Further studies involving GWAS and plasma glycoform analysis is required to understand the effects of fucosylation in cancer patients. Combining GWAS and high-throughput glycomic analysis is important for paving the way for a better understanding of human health and disease. 13. Conclusions Glycosylation of proteins has a significant impact in the pathogenesis of numerous diseases, and specific changes can provide insights into disease states and progression. Non-invasive glycan biomarkers, such as those from serum, are promising for the course of disease, including those mentioned in this review. Furthermore, with improved technology and thus the increased efficiency of sample analysis, the field of glycomics is highlighting its underlying potential for biomarker discovery. It is now possible to overlay data from genomics, proteomics, metabolomics and glycomics with pathway analysis. The era of systems glycobiology and a further understanding of disease have dawned now. Abbreviations 2-DE Two Dimensional Electrophoresis BEH Ethylene Bridged Hybrid BPH Benign Prostatic Hyperplasia CE Capillary Electrophoresis CEA Carcinoembryonic Antigen CTCs Circulating Tumor Cells FDA US Food and Drug Administration
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FUT6 and FUT8 Fucosyltransferases Genes GU Glucose Units GWAS Genome-Wide Association Study HCC Hepatocellular Carcinoma HILIC Hydrophilic-Interaction Liquid Chromatography HNF1α Hepatocyte Nuclear Factor 1α HPLC High Performance Liquid Chromatography MBC Metastatic Breast Cancer MS Mass Spectrometry PNGaseF Peptide N-glycosidase F PSA Prostate Specific Antigen sLe X Sialyl Lewis X antigen SNPs Single Nucleotide Polymorphisms TF Thomsen–Friedenreich antigen UPLC Ultra Performance Liquid Chromatography
Funding This work was supported by the Science Foundation Ireland (Reproductive Biology Research Cluster (RBRC) [grant number 07/SRC/ B1156] and Alimentary Glycoscience Research Cluster (AGRC) [grant number 08/SRC/B1393]). European Commission under the Seventh Framework Programme (FP7) EuroGlycoArrays [grant number 215536], SchizDX [grant number 223427], GlycoHIT [grant number 260600], GlycoBioM [grant number 259869], European Commission under the Sixth Framework Programme (FP6) (Glyfdis [No 37661] and EuroCarb DB [No 11952]), Enterprise Ireland [grant number PC/ 2008/0022]. References [1] A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman, Global cancer statistics, CA, Cancer J. Clin. (2011) 20107 (caac). [2] R. Apweiler, H. Hermjakob, N. Sharon, On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database, Biochimica et Biophysica Acta (BBA) Gen. Subj. 1473 (1999) 4–8. [3] K. Ohtsubo, J.D. Marth, Glycosylation in cellular mechanisms of health and disease, Cell 126 (2006) 855–867. [4] A. Cazet, S. Julien, M. Bobowski, M.-A. Krzewinski-Recchi, A. Harduin-Lepers, S. Groux-Degroote, P. Delannoy, Consequences of the expression of sialylated antigens in breast cancer, Carbohydr. Res. 345 (2010) 1377–1383. [5] S. Rachagani, M.P. Torres, N. Moniaux, S.K. Batra, Current status of mucins in the diagnosis and therapy of cancer, Biofactors 35 (2009) 509–527. [6] D.H. Dube, C.R. Bertozzi, Glycans in cancer and inflammation [mdash] potential for therapeutics and diagnostics, Nat. Rev. Drug Discov. 4 (2005) 477–488. [7] L. Royle, M.P. Campbell, C.M. Radcliffe, D.M. White, D.J. Harvey, J.L. Abrahams, Y.-G. Kim, G.W. Henry, N.A. Shadick, M.E. Weinblatt, D.M. Lee, P.M. Rudd, R.A. Dwek, HPLC-based analysis of serum N-glycans on a 96-well plate platform with dedicated database software, Anal. Biochem. 376 (2008) 1–12. [8] M.P. Campbell, L. Royle, C.M. Radcliffe, R.A. Dwek, P.M. Rudd, GlycoBase and autoGU: tools for HPLC-based glycan analysis, Bioinformatics 24 (2008) 1214–1216. [9] C.W. von der Lieth, A. Arda Freire, D. Blank, M.P. Campbell, A. Ceroni, D.R. Damerell, A. Dell, R.A. Dwek, B. Ernst, R. Fogh, M. Frank, H. Geyer, R. Geyer, M.J. Harrison, K. Henrick, S. Herget, W.E. Hull, J. Ionides, H.J. Joshi, J.P. Kamerling, B.R. Leeflang, T. Lutteke, M. Lundborg, K. Maass, A. Merry, R. Ranzinger, J. Rosen, L. Royle, P.M. Rudd, S. Schloissnig, R. Stenutz, W.F. Vranken, G. Widmalm, S.M. Haslam, EUROCarbDB: an open-access platform for glycoinformatics, Glycobiology 21 (2011) 493–502. [10] A. Knezevic, O. Polasek, O. Gornik, I. Rudan, H. Campbell, C. Hayward, A. Wright, I. Kolcic, N. O'Donoghue, J. Bones, P.M. Rudd, G. Lauc, Variability, heritability and environmental determinants of human plasma N-glycome, J. Proteome Res. 8 (2009) 694–701. [11] G. Lauc, A. Essafi, J.E. Huffman, C. Hayward, A. Knezevic, J.J. Kattla, O. Polasek, O. Gornik, V. Vitart, J.L. Abrahams, M. Pucic, M. Novokmet, I. Redzic, S. Campbell, S.H. Wild, F. Borovecki, W. Wang, I. Kolcic, L. Zgaga, U. Gyllensten, J.F. Wilson, A.F. Wright, N.D. Hastie, H. Campbell, P.M. Rudd, I. Rudan, Genomics meets glycomics: the first GWAS study of human N-glycome identifies HNF1A as a master regulator of plasma protein fucosylation, PLoS Genet. 6 (2010) e1001256. [12] A. Varki, Biological roles of oligosaccharides: all of the theories are correct, Glycobiology 3 (1993) 97–130. [13] B. Blomme, C. Van Steenkiste, N. Callewaert, H. Van Vlierberghe, Alteration of protein glycosylation in liver diseases, J. Hepatol. 50 (2009) 592–603. [14] D. Meany, D. Chan, Aberrant glycosylation associated with enzymes as cancer biomarkers, Clin. Proteomics 8 (2011) 7.
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