Recent Advances in the Mass Spectrometry Methods for Glycomics

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Review

Recent Advances in the Mass Spectrometry Methods for Glycomics and Cancer Muchena J. Kailemia, Gege Xu, Maurice Y. Wong, Qiongyu Li, Elisha Goonatilleke, Frank Leon, and Carlito B. Lebrilla Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04202 • Publication Date (Web): 19 Oct 2017 Downloaded from http://pubs.acs.org on October 20, 2017

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Analytical Chemistry

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Recent Advances in the Mass Spectrometry Methods for

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Glycomics and Cancer

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Muchena J. Kailemia1,4, Gege Xu1,4, Maurice Wong1, Qiongyu Li1, Elisha Goonatilleke1, Frank

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Leon1, and Carlito B. Lebrilla1,2,3

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United States

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USA

Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, CA 95616,

Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616,

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Foods for Health Institute, University of California, Davis, CA 95616, USA

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These authors contributed equally to this work

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To whom correspondence should be addressed.

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Carlito B. Lebrilla

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University of California, Davis

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Department of Chemistry

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One Shields Avenue

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Davis, CA, 95616, USA

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E-mail: [email protected]

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Invited Submission to Analytical Chemistry

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ACS Paragon Plus Environment

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1. Introduction

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The review focuses on recent aspects (last three years) of glycosylation analyses that

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provide relevant information about cancer. It includes recent development in glycan and

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protein enrichment methods for discovery of cancer markers. It will however focus on the

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recent technological developments in mass spectrometry (MS), bioinformatics and separation

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methods as they apply toward identifying cancer markers. More specifically, it will cover

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advances in matrix-assisted laser desorption/ionization (MALDI), electrospray ionization (ESI),

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capillary electrophoresis (CE), and liquid chromatography (LC) coupled to mass spectrometry.

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The discussions will include glycans, recently identified as potential markers for cancer that

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have been discovered using the highlighted technologies. We will also discuss emerging

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glycoproteomic techniques and site-specific methods, and how these methods are being

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utilized for cancer biomarker discovery. The large amount of data and the complexity of

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glycoproteomic analysis have been the impetus for developing bioinformatic methods for

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assigning glycosylation sites and characterizing the potentially very large site- or

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microheterogeneity.

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This review will cover the most recent advancements in biomarker discovery of N- and

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O-glycosylation of proteins as well as the glycolipids. This group collectively constitutes

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glycosylation on the cell membrane or the glycocalyx. The review will also highlight methods

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that are highly reproducible, with low coefficient of variation (CV), and scalable for large sample

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sets. The reader is also referred to other notable earlier reviews on glycomic biomarkers for

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cancer. Mereiter et al. describe the recent glycomic effort in gastrointestinal cancer.1 A review

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focused on N-glycomic analysis of colorectal cancer has been published by Sethi and Fanayan.2


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N-Glycan, O-glycan, and glycolipid characteristics of colorectal cancer were reviewed by Holst et

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al.3 Muchena et al. have provided a more general review of glycan biomarkers covering up to

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the current review period.4

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The field of glycoscience also covers a broad area of structures and may include highly

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anionic (glycosaminoglycans) and monosaccharide (e.g. O-GlcNAc) modifications that require

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their specific and unique sets of analytical tools. The latter topics are not covered in this review.

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1.1 Background of Glycosylation and Cancer

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There is nearly 50 years of research illustrating that changes in glycosylation accompany

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cancer.5 Glycosylation is a dynamic process intimately involved in key processes in cells,

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including cell-cell and cell-extracellular communication as well as cell-cell adhesion, and cellular

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metabolism. Glycans expressed in several types of glycoconjugates are known to change during

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cancer genesis and progression.6 These changes increase the structural heterogeneity and alter

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the functions of cells.7 Glycosylation has been found to enable tumor-induced

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immunomodulation and metastasis.8-10 The cell-surface structures allow the immune cells to

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differentiate self/normal cells from non-self/abnormal cells.11 For example, terminal residues

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on N-glycans, such as sialic acids, are involved in immunity and cell-cell communication.12

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Changes in glycosylation of adhesion proteins can largely influence their binding properties,

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leading to altered cell-cell or cell-matrix contacts.13 Other types of glycans are also involved in

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cancer. Gangliosides and sphingolipids are involved in transmembrane communication vital in

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tumor cell growth and invasion.14 Glycosaminoglycans are involved in tumor cell migration15

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and motility.16-18


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The search for effective markers is aided by the understanding of how glycans are

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synthesized. The glycan biosynthetic process is a non-template process involving multiple

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enzymes, some performing competing activities. It is estimated that more than 300 metabolic

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enzymes, composed of glycosyltransferases and glycosidases, are involved in the biosynthesis

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and processing of glycans.19,20 The best-known series of pathways belongs to the production of

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N-glycans (Figure 1). They illustrate the large degree of structural heterogeneity in

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glycosylation. N-Glycans are produced in a step-wise process beginning with the production of

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high mannose structures on a lipid, which are transferred to the nascent polypeptide chain to

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guide protein folding. Once folded, the glycans are then trimmed back and extended to form

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complex and hybrid structures. The folded protein can be secreted with glycans that range from

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early in the process to yield high mannose structures to later in the process corresponding to

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complex or hybrid structures. The number of structures for one glycosylation site can vary by a

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large degree, from a handful for transferrin21 to over 70 structures for IgG, the most abundant

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serum glycoprotein.22,23

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The glycan types and the extent of glycosylation differ between cells from the same

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tissue and between organs. Glycosylation may further depend on the physiological and/or

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pathological condition of the body.24,25 The aberrant changes of glycosylation may be due to

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over-expression, under-expression, or the localization of relevant glycosyltransferases,

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abnormal glycosidase activity, and the tertiary conformation of the protein or the peptide

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affecting the accessibility of the glycosylation site. Not surprisingly, most of the FDA approved

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markers for diseases are glycosylated proteins. Furthermore, antibody based therapies have

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strong glycomic component for treatment of cancers such as breast, lung, gastrointestinal


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system, and melanoma and lymphomas.26 While glycan-focused therapeutics has not been

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extensively pursued, the discovery of robust and verifiable markers could lead to more

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glycosylation-based cancer therapies.

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1.2 Glycomic and glycoproteomic methods for cancer

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Rapid and reproducible glycomic analyses have been made possible by new

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technologies in separation, ionization, and mass analyzers. For glycosylation, two main

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strategies are commonly used (Figure 2). The glycans can be released from a protein mixture,

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globally from a fluid, or from a specific protein. N-Glycans are released from enriched or non-

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enriched biological samples like serum/plasma, urine, or saliva using PNGase F. O-Glycans are

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typically released chemically as there is no equivalent to PNGase F. Chemical approaches can

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also be used to globally release different types of glycans. For example, Song et. al. recently

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developed a method to simultaneously release free reducing N- and O-glycans from

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glycoproteins, and glycan nitriles from glycosphingolipids using household bleach.27 (Figure 3)

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The free glycans are generally analyzed using either MALDI-MS or LC-MS. Alternatively,

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glycoproteins can be analyzed as glycopeptides through protease digestion of the protein,

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thereby keeping a segment of the peptide backbone intact.

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The first global glycomic analysis of serum was reported over 10 years ago for ovarian

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cancer.28 This glycomic profiling method involved MALDI-MS. There are intrinsic properties of

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glycans that need to be addressed in the global glycan analysis. The diversity of glycan species

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and similarities in structures pose unique challenges to mass spectrometry. The similarities in

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structures make it difficult to distinguish isomers, while the presence of both neutral residues


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such as galactose, mannose, fucose, N-acetylglucosamine, and N-acetylgalactosamine and

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anionic residues such as sialic acids and sulfated monosaccharides create ionization biases.

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Anionic oligosaccharides tend to give better signals in the negative mode due to deprotonation,

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while neutral oligosaccharides tend to be cationized with sodium in the positive mode. MALDI

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has two major limitations toward oligosaccharide analysis: it yields no isomer separation, and

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the energetic conditions of MALDI may cause the loss of labile groups such as sialic acids and

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fucoses. These issues have been largely resolved by stabilizing the glycans using derivatization29

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or by decreasing the energy of the MALDI process with post-ionization cooling.30

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Electrospray ionization (ESI or more specifically nanoESI) coupled with liquid

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chromatography overcomes many of the limitations in MALDI profiling. With ESI, singly and

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multiply protonated species are produced, in contrast to MALDI which produces sodium-

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coordinated species. Nanospray increases the sensitivity over MALDI and produces cooler ions,

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minimizing in-source fragmentation. When coupled with liquid chromatography (or nanoLC) it

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extends the number of identified glycans significantly. To further elucidate the exact glycan

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structures,

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permethylation followed by acid hydrolysis and acetylation33,34 need to be used in combination

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with mass spectrometry techniques.

however,

other

approaches

such

as

exoglycosidase

digestion31,32

and

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The next development of glycomic analysis will lie in the elucidation of glycopeptides

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and glycoproteins. In glycoproteomics, issues in proteomics are compounded with the

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difficulties in glycomics, and both will have to be addressed simultaneously. This approach

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requires new methods that can overcome the challenges associated with their specific analysis,

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including incomplete glycosite/glycopeptide identification, site occupancy, difficulties in


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elucidating all the structural details of glycan and its respective polypeptide, and limited glycan

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data interpretation algorithm/software. Despite these challenges, the results obtained from

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these analyses add in-depth knowledge regarding the identities of the glycans at their site of

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attachment within the proteins. This provides the glycan and the protein information that are

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essential in understanding the cancer biology. These types of analyses may provide more

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specific cancer markers and aid in the efforts to identify drug targets.

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2. Methods for profiling glycans and glycoprotein cancer markers

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Mass spectrometry has generally been at the core of the large glycomic effort. MS is

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ideal because of its sensitivity, and large peak capacity. It is also open to broad non-targeted

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and targeted approaches. However, the improvements in mass analyzers have not significantly

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advanced glycomic methods. There are several types of analyzers, but they all appear to be

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equally effective for glycomic analysis. Conversely, ionization and separation methods have

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significantly advanced glycomic and glycoproteomic analysis. The large number of structures in

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the glycome, as well as the large number of glycopeptides, require effective separation

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methods that increase the peak capacity and the information content of the analysis. The

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coupling of separation methods to mass spectrometry yielded an effective glycomic and

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glycoproteomic tool, which has also increased the information content even further.

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2.1. Matrix-assisted laser desorption/ionization (MALDI – MS)

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Several studies have already illustrated the use of MALDI as a tool to monitor alterations

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in glycosylation in cancer and diseases in various tissues including blood, serum, and muscle.4 In


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addition to discovering biomarkers for clinical diagnosis of cancer, characterization of protein

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glycosylation by MALDI-MS further helped elucidate the heterogeneity present in complicated

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biological samples.4,35 MALDI-MS was the first method employed for global glycan profiling of

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serum. It is used primarily for N-glycans, and remains the most common method for glycomic

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profiling.36

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MALDI-MS provides primarily a compositional profile yielding the combination of hexose

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(Hex, typically D-mannose and D-galactose), deoxyhexose (typically L-fucose, or Fuc), N-

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acetylhexosamine (HexNAc, typically N-acetyl-D-glucosamine and N-acetyl-D-galactosamine)

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and sialic acid (typically N-acetylneuraminic acid, or NeuAc).37 Because the biology of glycan

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formation is relatively well known, the glycan composition can be used to deduce the glycan

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type. For disease biomarker discovery, general changes in glycan types such as high mannose,

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complex, hybrid, bisecting GlcNAc, sialylation, and fucosylation are of direct biological

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significance.38 Coarse alterations in glycosylation have been shown to influence various cellular

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processes in many different cancers.39

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MALDI imparts higher energy during ionization causing instability for analyte ions

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sometimes inducing unwanted fragmentation. This may further result in limited representation

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of anionic and sialylated glycan species.40 Permethylation is one of the most commonly used

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derivatization method for stabilizing glycans during ionization. Automated methods are

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emerging to make this process more suitable for glycan analysis. Shubhakar et al. developed a

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high-throughput format to monitor glycosylation in biologics, however the same method can be

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applied to cancer tissues.41 Derivatization of sialic acid to yield linkage information has been

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used by Holst et al. to examine formalin-fixed tissue for MALDI imaging.42 Specific stabilization


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and characterization of sialylated glycans was achieved with linkage-specific sialic acid

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derivatization employing ethyl esterification and dimethylamidation for differential analysis of

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α(2,3)- and α(2,6)-linked sialic acids in plasma and cancer tissues.42,43

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A more recent innovation in MALDI-MS is the application of MALDI imaging on cellular

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glycosylation. Advancements in the characterization of N-linked glycans in cancer tissues using

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MALDI imaging was discussed in a recent review by Drake et al.44 Glycan profiles using tissue-

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based analysis have been used to determine expression in different cancers. Human formalin-

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fixed paraffin-embedded (FFPE) tissues have been used to characterize the spatial profile and

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localization of released N-glycans using MALDI-Imaging Mass Spectrometry (MALDI-IMS).45,46

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FFPE tissues collected from human hepatocellular carcinomas, prostate, and pancreas have

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been found to have distinct glycan profiles when compared to adjacent non-tumor tissue

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slices.45,47 For validation of alterations in glycan expression, tissue microarrays incorporating

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multiple FFPE tumor and normal tissue cores have been used in combination with MALDI-IMS.45

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(Figure 4) A tetraantennary N-glycan Hex7HexNAc6 was found to be elevated, while a high

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mannose N-glycan Hex8HexNAc2 was decreased in hepatocellular carcinoma tissues. A similar

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approach using MALDI with ion mobility MS found high-mannose N-glycans expressed in FFPE

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ovarian cancer tissues.48 A new method for MALDI imaging combining IR MALDI with ESI (IR

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MALDESI) has been used by Nazari and Muddiman to image cancerous chicken ovarian tissues,

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although glycans were not directly examined.49

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2.2. Capillary Electrophoresis - MS


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Capillary electrophoresis has long been used for separating oligonucleotides generally

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based on size with samples containing DNA.50 Minor modifications to instruments for genome

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sequencing have adapted these instruments to profiling oligosaccharides using photometric

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detectors.51 The high separation efficiency and resolution of CE opens the possibility of high

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throughput if the method can be robustly coupled to MS.

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The coupling of CE to MS is difficult and complicated by several issues including the

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presence of buffers in CE that are not compatible with MS, and the low flow rates of CE. CE-MS

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for glycomic analysis has been previously reviewed by Mechref.52 CE performs generally better

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with anionic oligosaccharides. Sun et al. have shown that the coupling of CE to MS using an

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electrokinetic sheath pump offers better modes of analyses for heparin oligosaccharides and

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low molecular weight heparin.53 To couple the microfluidic CE device with MS, Kathri et al

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developed a removable microfluidic chip integrating the separation capillary and a nanoESI

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source as interface to analyze monosaccharides, glycans and glycopeptides.54 This method has

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been applied to standard glycoproteins, and the applicability and durability of the method for

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more complicated biological samples remain to be shown.

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Capillary microchip electrophoresis was used along with MALDI-TOF-MS to profile serum

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glycans from patients with colorectal cancer.55 CE compliments the rapid compositional

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capabilities of MALDI-MS. The microchip electrophoresis can separate structural isomers, which

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could be more specific toward individual diseases. This study identified tri- and tetra-antennary

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fucosylated glycans that were significantly elevated in cancer samples when compared to the

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controls. 55


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2.3. Liquid chromatography - MS

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Liquid chromatography remains the ideal couple for mass spectrometry (LC-MS) in

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glycomic and glycoproteomic analysis. Liquid chromatography alone (with chromophoric

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labeling) has been used extensively to profile N-glycans for disease biomarkers and will not be

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discussed further here given the limited scope of this review.56 The coupling of LC with tandem

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MS (LC-MSn) allows confirmation of the monosaccharide composition and further yields

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structural information of both native and derivatized glycans.40 LC-MS further increases the

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depth of oligosaccharide profiling and is currently the most robust and reliable method for

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isomer separation and MS detection.57

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There are several methods for separating oligosaccharides by LC depending on the types

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of stationary and mobile phases. Although C18 remains the most commonly used stationary

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phase for organic compounds, the hydrophilic nature of oligosaccharides prevents their

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retention on C18 column. Derivatization (typically permethylation) is required for retaining

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glycans on C18, however the separation of permethylated isomeric species is generally poor.58

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A recent review by Vreeker and Wuhrer on the reverse phase chromatographic separation of

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oligosaccharides covers this topic.58

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Hydrophilic interaction liquid chromatography, or HILIC, have been used to separate

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both native and labeled glycans. Ahn et al. employed 1.7-μm amide sorbent as the stationary

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phase for enhanced separation of 2-aminobenzamide labeled glycans including high mannose

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and multiply sialylated species.59 They have developed HILIC separation into a robust and


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repeatable platform for biomarker discovery with fluorescence detection. Although MS is not

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typically involved in these studies, the method is notable in the ability to perform rapid

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throughput analysis for large sample sizes.60-62 Native oligosaccharides can also be efficiently

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separated by HILIC. The hydrophilic nature of oligosaccharides dictate their elution properties

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allowing HILIC columns to separate certain oligosaccharide isomers.63

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An alternative to HILIC for separating native oligosaccharides, is the porous graphitized

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carbon (PGC) stationary phase. Profiling of native oligosaccharides by LC-MS typically employs

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PGC as stationary phase. Shown in Figure 5 is an LC-MS profile of N-glycans released from cell

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membrane proteins combined from three cell lines.25 The profile shows more than 800

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compounds over approximately four orders of magnitude in dynamic range.

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Isomeric separation of native (unlabeled, underivatized) compounds with PGC is often

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so effective that separation of anomers (at the reducing carbon) occurs and produces a splitting

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of the peaks. The splitting occurs greatest for smaller compounds and eventually vanishes for

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large multiantennary N-glycan structures. To address this issue, the glycans are often reduced

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to the alditol form. With the separation of the compounds, the retention time and accurate

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mass are unique for each structure and can be used to identify the compounds. In this way,

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glycans can be ordered according to their abundances based on peak areas (or volumes). PGC

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stationary phase in LC-MS was used by Sethi et al. to determine the reduced N-glycans on cell

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membrane from cell lines of colorectal cancer.64 In the study, increase of high mannose glycans

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and α(2,6)-sialylation was observed for the cancer. Song et al has used this method to

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determine the relative abundances of N-glycans released from serum (Figure 6) as well as

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biological variations between individuals.32


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2.4. Quantitation of glycans for biomarkers

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The ability to quantitate is key to the development of effective disease markers.

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Methods to quantitate glycans continue to be developed, however quantitation of glycans is

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generally easier than proteins but more difficult than metabolites. The major issue with

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quantitation of glycans is the lack of chemically pure and readily available standards.

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There are several levels of quantitation that all have their utility in biomarker discovery.

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Relative quantitation is the simplest therefore the most commonly used. It essentially takes the

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absolute ion abundances and normalizes it to the total abundances. The most desirable method

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is for absolute quantitation where the absolute concentration is determined. This method has

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only been performed on oligosaccharides found in human milk.65 An attempt to obtain absolute

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quantitation of N-glycans has been performed by combining glycan types.66

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Absent absolute quantitation methods, the measurement of absolute fold changes is

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the most accurate and convenient approach used in both MALDI-MS and LC-MS for biomarker

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discovery. Both methods can be used to measure fold changes of individual components with

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high repeatability.67,68 The limitations stem from the assumption that the same compound

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ionizes with the same efficiency in different samples, however this efficiency can vary

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depending on the ionization method. In MALDI where the glycans are all analyzed

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simultaneously, the presence of each glycan can affect the ionization of others. In LC-MS, ion

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suppression is greatly minimized as the compounds are separated, however it is not completely


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eliminated. Relative abundances are also distorted by the variation in ionization, although this

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has been shown to be minimal in the same group of glycans.69

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An alternative approach to ion abundances has been to use isotopic labeling through

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derivatization. A recent review by Etxebarria and Reichardt70 on glycan quantitation addresses

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many of the issues in detail. Oligosaccharides in one sample is permethylated with 12CH3 while

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the other sample is derivatized with

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robust, but it has limitations. Permethylation is never complete, and even 99% conversion can

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still produce partially methylated species that can severely decrease the dynamic range of the

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method. It also limits the comparison to two samples, unless a standard is added to each

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sample. It further adds another chemical reaction to a sample, which should be limited in

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biomarker discovery.

13

CH3 (see for example71). The approach is simple and

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There are other issues that can complicate quantitation, but important in large sample

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set analysis. One is the enzyme PNGase F may have different reactivities depending on the

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supplier and the batch. In large sample sets, the same PNGase F batch has to be used to limit

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this variability. Similarly, other factors need to be concerned to maintain homogeneity. The

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other reagents and for native oligosaccharides, the solid phase extraction media such as porous

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graphitized carbon all need to come from the same manufacturing batch. These issues can

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affect quantitation in more profound ways than the quantitative method employed.

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3. Sample preparation and methods to partition the glycoproteome


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Sample preparation is an important consideration in marker discovery. The quality of

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the clinical sample and the precision of the work-up enhances the sensitivity and reproducibility

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of the overall method. Prolonged sample manipulation can alter the glycoform by the chemical

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release of labile groups such as sialic acids. Therefore, it is desirable to minimize sample

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preparation in steps and in time. Label-free in-situ analysis of N-glycans from hepatocellular

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carcinoma tissues was conducted using MALDI-IMS.47 N-glycans such as Hex8HexNAc2 and

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Hex7HexNAc6 were found to be differentially expressed in tumor and matched normal tissue

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samples. However, loss of sialic acid was observed when the results of native glycans were

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compared with the ethyl esterified glycans from the same samples.

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A method that requires minimal sample preparation for targeted analysis is multiple

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reaction monitoring (MRM). Through the selection of targeted proteins, it is a method for

312

partitioning the glycoproteome by focusing on relatively abundant species. The method is

313

rapidly developing for label-free analysis with quantitation of glycans, peptides, and

314

glycopeptides. It has already been applied to rapid-throughput analysis of serum glycopeptides

315

from several cancers.22,72-74

316

Nevertheless, enrichment methods are often required in cancer studies for extracting

317

interesting glycoproteins or for untargeted biomarker discovery. Methods to enrich specific

318

glycoproteins commonly involve the use of antibodies. Mass spectrometry readily couples with

319

this method providing higher sensitivity for protein-specific glycosylation. Borchers and co-

320

workers have recently reviewed the use of immunoaffinity in combination with mass

321

spectrometry for quantitating proteins in biological fluids.75 A study by Gbormittah et al. using

322

HPLC columns packed with anti-clusterin ligand immobilized agarose media for clusterin


15


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Page 16 of 55

323

enrichment showed site-specific and glycan-specific changes in clear cell renal cell carcinoma.76

324

Immunoaffinity capture was performed on plasma samples from pre- and post-nephrectomy

325

patients. The enriched clusterin was treated with trypsin to analyze the glycopeptides.

326

Increased levels of biantennary, disialylated N-glycans with and without core fucose were

327

observed in post-nephrectomy patients. The results were further confirmed using lectin

328

blotting. Takahashi et al. isolated haptoglobin from the sera of a small number of cancer

329

patients individually with esophageal, gastric, colon, gallbladder, pancreatic, and prostate

330

cancer to determine the site-specific glycosylation associated with the cancer. In all cancers,

331

monofucosylated N-glycans were increased significantly in all glycosylation sites.77

332

Immunoaffinity is also commonly used to purify proteins for intact protein analysis. Transferrin

333

purified from plasma using anti-transferrin beads was employed to examine congenital

334

disorders of glycosylation.78 Though not specifically cancer, the method shows the utility of

335

native MS method to observe changes in protein glycosylation for diseases. Kim et al.

336

performed intact haptoglobin analysis to monitor N-glycan alteration in gastric cancer patients

337

and identified multiple markers with high diagnostic efficacy.79

338

Immunoaffinity enrichment is indeed an attractive method as it is highly specific for

339

targeted enrichment and readily scalable and automated for reproducible rapid throughput

340

analysis. It has been adapted to MALDI, MRM and other MS-based methods.80-81

341

A complimentary enrichment method employs lectins. Antibody enrichment typically

342

targets protein, while lectins enrich through affinity for specific glycan structure. Lectins are

343

non-antibody glycan binding proteins derived typically from microbes or plants. They generally

344

have weaker affinity with only moderate specificity. They are used alone or with other lectins to


16

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345

capture groups of proteins with corresponding glycan motifs. Lectin microarrays and lectin

346

magnetic bead arrays have also been used in combination with glycan release or glycopeptide

347

production coupled with mass spectrometry.

348

A single lectin enrichment approach was applied to protease digest of serum to examine

349

glycopeptides in pancreatic cancer. Maackia amerensis lectin II (MAL II) and Sambucus nigra

350

lectin (SNA) were used to enrich for sialylated N-glycopeptides obtained from proteins of

351

pancreatic cancer patients. Subsequent UPLC-MS analysis revealed 38 and 13 significantly

352

changed glycopeptides from acute pancreatis and pancreatic cancer, respectively.82 There are

353

limitations to this approach as it covers a subset of the glycome. Changes in non-sialylated

354

glycopeptides of some proteins such as IgG have been shown to accompany cancers,72 which

355

could be missed by this method.

356

Multi-lectin affinity chromatography (M-LAC) can be used to partition the

357

glycoproteome while enriching for several glycan motifs simultaneously. Combined with

358

depletion of abundant proteins, this method has been employed to simultaneously examine the

359

depleted proteome, enriched glycoproteome, and N-glycome.83-84 A similar approach was

360

employed for clear cell renal cell carcinoma, where a number of highly sialylated glycans, high

361

mannose glycans, and low-level proteins were upregulated in the M-LAC fractions of plasma

362

from cancer patients.85 Tandem affinity enrichment was used by Zhou et al. to identify

363

glycoproteins through the MS/MS of glycopeptides.86 In the differences between prostate

364

cancer cell types, they find increased fucosylated glycopeptides in the aggressive cell.

365

Lectin arrays have been previously used as a high-throughput stand-alone method for

366

observing, albeit indirectly, changes in glycosylation in cancer.87 Coupled with mass


17


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Page 18 of 55

367

spectrometry via in-situ proteolysis, it provides more specific determination of proteins.88

368

Lectin magnetic beads present an alternative form of the spatial arrays. Beads with lectins were

369

used to pull out specific glycoproteins that can be probed using standard proteomic and

370

glycoproteomic approaches.89 Glycoproteome of tissues from patients with colon cancer has

371

been examined with this method.90 The method was also used to obtain markers in esaphagel

372

adenocarcinoma patients.91 Using a semi-automated pipeline employing 20 unique lectins, Shah

373

et al. obtained 246 lectin-protein candidates, with 45 showing significant differences between

374

the groups. A panel consisting of eight glycoforms produced an area under receiver operating

375

characteristic curve (AUROC) of 0.9425 corresponding to 95% specificity and 80% sensitivity.

376

The compliment method to lectins are glycan arrays.92 While these methods have not

377

yet been used for cancer biomarkers, they may have future applications in detecting aberrant

378

glycosylation in cancer.91

379 380

4. Glycoproteomic analysis for cancer

381

Glycoproteomic analysis of cancer combines both the glycan changes associated with

382

cancer and the protein expression yielding potentially higher disease specificity. Glycoprotein

383

cancer biomarkers are not new, with nearly all protein markers for cancer including CA-125 for

384

ovarian, PSA for prostate, and HER2 for breast being just the most notable examples. While

385

some of these markers are widely used, they may also be associated with inflammation and do

386

not provide the necessary specificity for the cancer. However, glycosylation on proteins such as

387

CA-12593 and PSA94 exhibit glycan differentiation in a cancer-specific manner for the respective

388

diseases.


18

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389

Glycoproteomic analysis of cancer is challenging in much of the same way as biomarker

390

studies using only proteomic methods, but compounded further by the added complexity

391

associated with glycosylation. The complexity of protein glycosylation due to the varying

392

number of sites and the glycan heterogeneity associated with each site complicate the

393

analytical determination but provide opportunity for observing small and unique biological

394

changes. There are at least two potential methods for incorporating comprehensive glycan and

395

protein information simultaneously into cancer biomarkers. A non-targeted approach employs

396

proteomic methods adapted for glycosylation. Glycoproteomic methods generally provide

397

broad coverage of the different sites but limited depth in the structural heterogeneity of the

398

glycan. A more targeted approach involves a smaller number of proteins with more extensive

399

site-specific glycan heterogeneity. Characterizing the protein and glycan heterogeneity

400

simultaneously creates a considerable challenge that is slowly being addressed, however there

401

have been notable advancements.

402 403

4.1. Non-targeted glycoproteomic method

404

Non-targeted glycoproteomic approach to cancer is relatively new but is recently

405

enabled by the availability of bioinformatic tools that directly address glycosylation (See below).

406

Biomarker discovery is limited to mainly cancer cell lines and small number of tissues. Shah et al

407

compared two prostate cancer cells, an androgen-dependent strain LNCaP and an androgen-

408

independent strain PC3.95 To quantitate the glycopeptides, isobaric tags for relative and

409

absolute quantitation (iTRAQ) was used in conjunction with 2D-LC-MS to discover glycopeptides

410

that differed for the two cell lines. Increased fucosylation was reported for PC3 relative to


19


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Page 20 of 55

411

LNCaP. A subsequent cell line study comparing two ovarian cancer cell lines that are sensitive

412

and resistant to doxorubicin treatment, OVCAR8 and NCI/ADR-RES, respectively yielded several

413

N-glycosites that differed between the two cell lines.96 Glycosite occupancy was used to

414

determine changes in glycosylation between three benign lesions and three ovarian cancer

415

tissues97 and identified several glycoproteins that differed at specific glycosylation sites. The

416

glycoproteomic analysis of urine was performed to detect aggressive prostate cancer.98 Unique

417

glycosite containing peptides were found to correlate with Gleason scores of the cancer.

418 419

4.2. Targeted glycoproteomic method

420

Targeted glycoproteomic approaches typically involve the isolation of a single protein or

421

a small group of proteins. These studies are more prevalent because they can be applied to

422

large sample sets more readily. They typically involve a pre-enrichment step using either

423

antibody or lectin affinity methods such as those described above.

424

An alternative approach is to monitor several proteins simultaneously without prior

425

enrichment using multiple reaction monitoring (MRM) methods. MRM can target the

426

glycopeptides of specific proteins after digestion of complicated protein mixtures such as

427

serum. The ease of use and the high repeatability will increase the utility of this method in the

428

future.

429

The key to effective MRM is knowing the glycosite and the glycans associated with it, or

430

the resulting heterogeneous group of glycopeptides for the protein or group of proteins. A

431

procedure for extensive glycosite mapping of serum proteins has been reported by Hong et al.99


20

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432

Glycopeptide MRM requires knowing the fragmentation behavior of glycopeptides.

433

Glycopeptide fragmentation is complicated by the variation in the fragmentation aptitude of

434

the glycan and the peptide backbone. Studies have shown that fragmentation of the

435

glycopeptide under standard CID conditions (1-100 eV) generally produce glycan fragments as

436

the major products.100 The most common fragments with high intensities during CID of multiply

437

charged glycopeptides correspond to the glycan oxonium ions such as m/z 204.1 (HexNAc) and

438

366.1 (Hex1HexNAc1)99 (Figure 7). These fragments are present in nearly all glycopeptide CID

439

spectra regardless of glycan type. To obtain greater specificity, a key feature of glycopeptides in

440

reversed phase liquid chromatography is employed. The glycan part interacts poorly with the

441

C18 stationary phase so that the hydrophobicity of the peptide backbone is the determining

442

factor of the glycopeptide retention time. Shown in Figure 8 are the peptides and glycopeptides

443

for IgG, IgA, and IgM from serum.99 Note that all glycoforms of a specific peptide elute at nearly

444

the same retention times. This behavior means all the glycoforms of the glycopeptide can be

445

searched at a specific retention time range using dynamic MRM (dMRM). This method

446

increases the specificity of the glycopeptide assignments while significantly increasing the peak

447

capacity of the method.

448

Glycan abundances can be measured by directly monitoring the glycopeptides.

449

However, the glycopeptide abundance alone provides no indication of whether the protein

450

concentration or the abundance of a specific glycan at the site is changed. To elucidate the two

451

possibilities, Hong et al. normalized the glycopeptide abundances to the protein abundances,

452

thereby alleviating the lack of glycopeptide standards and removing the variation due to


21


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Page 22 of 55

453

changes in protein expression.99 This method effectively decouples glycan changes from

454

variation of protein abundances.

455

Protein glycosylation not only occurs with glycan site heterogeneity but with the

456

degree of site occupancy. LC-MS based techniques have also improved methodologies for

457

quantification of site occupancy, where both labeling and label-free methods can be used.101

458

MRM is also used for absolute quantification of site occupancy. In this method, the

459

glycopeptide is deglycosylated with PNGase F converting the asparagine to aspartic acid. The

460

intensities of standard peptides containing asparagine or aspartic acid are used for absolute

461

quantification, while it needs to be kept in mind that aspartic acid can occur naturally in

462

potential glycosylation sites.23

463 464

5. Bioinformatics in glycomic and glycoproteomic analysis

465

Glycans are synthesized through non-template bioprocesses making the structures of

466

glycans and glycan-modified proteins both highly heterogeneous and diverse. Analyzing both

467

glycans and proteins in biological samples with mass spectrometry generates even more data

468

than standard proteomic methods. The combination of annotating glycan structures and

469

assigning glycosylation sites on proteins is significantly more challenging than the analysis of

470

unmodified peptides or peptides with only site-specific modifications such as oxidation and

471

phosphorylation.

472

There have been significant attempts to develop software that can identify the proteins

473

as well as the site heterogeneity and site occupancy. Bioinformatics software including a

474

commercial product Byonic102, and those developed in various laboratories including


22

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475

GlycoPeptide Finder (GP Finder)103, pGlyco104,105, and Integrated GlycoProteomeAnalyzer (I-

476

GPA)106 automate the annotation of glycopeptide structures from tandem MS data. Because

477

glycosylation is known to be differentially expressed between normal and cancer disease cells,

478

the proper software can provide comprehensive site-specific information for cancer biomarker

479

studies.107 Glycan annotation tools have also been developed for assigning oligosaccharide

480

structures from tandem MS data. They include GlycoMaid for automatic annotation of N-

481

glycans,108 a de novo sequencing software for N- and O-glycans by Kumozaki et al.,109 and GAG-

482

ID for glycosaminoglycans.110 These tools when automated and included in the workflow

483

increase the sample flow and make it possible to study large sample sizes.

484

Glycan databases are needed to work with informatic software to assign glycan

485

structures and site-specific heterogeneity. Databases for glycomic and glycoproteomic searches

486

have become more available. For glycoproteomic analyses, databases such as Unipep and

487

dbOGAP provide glycosylation sites with corresponding glycans.111 Available glycan databases

488

include Carbohydrate Structure Database,112 UniCorn,113 and GlyTouCan.114 Most glycan

489

databases however contain mainly putative glycan structures.

490 491

6. Glycosphingolipids as cancer markers

492

Glycosphingolipids (GSLs) are a class of glycolipid where the glycan moiety is bonded to

493

a ceramide, a lipid composed of a sphingoid base and a fatty acid. GSLs are found in the cell

494

membrane of all animals, and their compositions are known to be tissue- and species-specific.

495

Aberrant glycosylation in these lipid-bound oligosaccharides has long been recognized as a

496

hallmark of cancer. GSLs have shown changes in their composition in human and animal


23


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Page 24 of 55

497

tumors. These changes arise from dysregulation of their synthetic and catabolic pathways,

498

which are mediated by glycosyltransferases and glycosidases. They can exhibit either

499

incomplete synthesis, which may lead to precursor accumulation, or neosynthesis of GSLs that

500

are not expressed in healthy cells.115 The altered glycosphingolipidome can affect cellular

501

processes such as signaling, growth, adhesion, and differentiation, among others.116 For a more

502

comprehensive and recent perspective on the roles of glycosylation in cancer, the reader is

503

referred to the review by Pinho and Reis.117 Figure 9 shows the schematic representations of

504

root structures of GSLs found in mammalian systems, and the structures of some important

505

GSLs that will be mentioned in the following paragraphs.

506

GSL glycan epitopes already have clinical applications. A widely used marker is the sialyl-

507

Lewis A antigen (also known as CA 19-9), which is present in O-glycans and glycolipds in the sera

508

of gastrointestinal cancer patients.118 However, this field will benefit from further studies using

509

the sensitivity of mass spectrometry-based methods. Candidates for biomarker studies include

510

the GSL globotriaosylceramide (Gb3Cer), which has been correlated with increased cell

511

invasiveness in colorectal cancer.119 Small cell lung cancer also showed increased levels of

512

disialylated gangliosides GD2 and GD1b, and tri-sialylated GT1b.120 Breast cancer cells were

513

found to have increased levels of gangliosides overall. They also expressed GSLs not usually

514

found in healthy tissue, such as O-acetyl-GD3 and O-acetyl-GT3, as well as N-glycolyl-GM3,

515

which is rarely found in human tissue.121 Increased levels of GM3 and GD3 were also found in

516

brain tumors.122,123

517

Tumor onset and progression are also influenced by aberrant expression of gangliosides.

518

Breast cancer stem cells have been found to express stage-specific embryonic antigen-3 (SSEA-


24

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519

3, also known as Gb5Cer), as well as two other closely related globo-series epitopes, SSEA-4 and

520

globo-H.124 In melanoma, increased levels of disialylated gangliosides, GD3 and GD2, enhance

521

the malignancy of cancer cells.125

522

The varied roles that GSLs play are reflected in the structural diversity of both their

523

glycan headgroups and their ceramide tails. Of the glycan headgroups alone, about 450

524

different structures are possible based on what is currently known about GSL biosynthesis, and

525

about half of these have already been reported.126 These numbers are compounded further by

526

dozens of possible ceramide structures which can vary by lipid chain length, degree of

527

unsaturation, and number of hydroxyl groups, leading to tens of thousands of possible GSL

528

structures. As such, analysis and profiling of GSLs will often require multiple approaches for

529

more complete structural elucidation and quantitation. Early methods employed thin layer

530

chromatography (TLC) to separate the GSLs, followed by an immunoassay with lectins and

531

antibodies that bind to specific glycan epitopes for detection and quantitation.127 However, this

532

method is limited to identification of glycan structures for which there are known lectins.

533

Limited sensitivity and specificity of immunoassay techniques can also lead to difficulty in

534

detecting low-level compounds. Furthermore, the signal can be affected by accessibility of the

535

glycan epitope to the antibody.

536

Mass spectrometry-based methods provide comprehensive detection of GSLs and

537

significantly increased sensitivity, both of which are advantageous for discovering new

538

biomarkers. Tandem MS methods can further provide structural information, such as the

539

sequence and connectivity of the glycan and lipid characteristics of the ceramide. However, MS-

540

based analysis can be complicated by the presence of isomeric and isobaric GSLs, and tandem


25


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Page 26 of 55

541

MS data cannot reveal the monosaccharide epimer nor the type of linkages between them.

542

Furthermore, one must be aware of possible glycan rearrangements when interpreting tandem

543

MS data.128 Complete structural analysis of intact GSLs would require complementary methods

544

such as enzymatic degradation.129 Often, prior studies on similar samples and knowledge on

545

GSL biosynthetic pathways can supplement MS results and allow researchers to make

546

reasonable inferences about GSL structures. Broader trends, such as changes in the level of

547

certain GSLs or the degree of fucosylation and sialylation, can also be effectively monitored

548

with MS-based methods without the need for more laborious structural elucidation.

549

GSLs are usually isolated from biological samples through Folch extraction.130 Efforts to

550

avoid chloroform during the lipid extraction process have led to the development of less

551

hazardous solvent systems such as the BuMe method.131 The extracted GSLs can then be

552

subjected to further preparation steps depending on the information desired. When data on

553

both the glycan and the lipid are required, intact GSLs can be analyzed through a variety of

554

methods; the most common ones include reversed phase HPLC-ESI-MS and MALDI-MS. One

555

quick and convenient method is to use TLC in conjunction with MALDI-MS.132 This has the

556

advantage of requiring less sample preparation and cleanup, yet still having the high sensitivity

557

of MALDI-MS. More recently, MALDI coupled with Orbitrap MS has opened new possibilities in

558

localized profiling and imaging of normal and tumor tissues. With this method, Jirasko et al.

559

have reported on novel sulfo-GSL structures in human renal cell cancer.133 In a study performed

560

by Zamfir et al, a microfluidic device coupled to an ESI-Orbitrap MS was used to profile

561

gangliosides in human astrocytoma from a single male patient.134 GSL structures were


26

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562

determined through multistage CID fragmentation; 37, 40, and 56 gangliosides were found in

563

the astrocytes, its surrounding tissue, and normal brain tissue respectively.

564

An approach to studying the glycan heterogeneity is to cleave off the glycan from the

565

ceramide with endoglycoceramidases (EGCase). This allows improved chromatographic

566

separation of the glycans on a HILIC or PGC column with deeper structural analysis, albeit at the

567

expense of information about the lipid. Holst et al. used this approach to analyze the GSL-

568

derived glycans from colorectal cancer tissues of 13 patients, and reported on correlations

569

between tumor progression and increased fucosylation, decreased sulfation and acetylation,

570

and reduced expression of globo-GSLs and disialylated gangliosides.135 Albrecht et al. have

571

recently outlined a method that uses the novel EGCase I from Rhodococcus triatomea.136 This

572

broad-specificity EGCase can effectively cleave most GSLs, including globo-series GSLs and

573

galactosylceramides that other known EGCases are unable to digest.

574

Researchers have been probing protein-carbohydrate interactions for biomarker

575

discovery with novel MS methods. Complexes formed with gangliosides and the B subunit of

576

cholera toxin (CTB) were studied with nano-ESI multistage MS by Capitan et al., and their

577

specific noncovalent interactions were revealed by multistage CID and ETD fragmentation.137

578

An intriguing new technique for analyzing protein-GSL interactions is catch-and-release

579

electrospray ionization-mass spectrometry (CaR-ESI-MS). The method was developed by Li et al.

580

and involves the assembly of picodiscs of proteins, lipids, and GSLs in vitro. It further allows

581

GSLs to be solubilized effectively in an aqueous environment.138 Proteins can then be

582

introduced, and those that interact with the GSLs are captured by the picodiscs. The picodisc-

583

bound protein complexes are then analyzed with a quadrupole-ion mobility separation-time-of-


27


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Page 28 of 55

584

flight (Q-IMS-TOF) MS equipped with ESI. Mild CID conditions in the quadrupole can cause the

585

detachment of the protein-GSL complex.

586

Advances in gene editing such as CRISPR-Cas9 have also provided new avenues for

587

investigating the role of specific glycosyltransferases and glycosidases in tumor progression. A

588

recent study by Alam et al. explored the effect of knocking out a key glycosyltransferase in the

589

biosynthesis of lacto- and neolacto- series GSLs in several ovarian cancer cell lines, and found

590

that it affected not only the biosynthesis of GSLs but also the α(2-6) sialylation on N-

591

glycoproteins.139

592

An interesting avenue to explore is the role of exosomes in cancer progression.

593

Exosomes are small vesicles released by cells to their extracellular milieu.140 Their composition

594

is dependent on the cell type from which they are released, and they can contain proteins and

595

other biomolecules, including GSLs. Exosomes can be released by most tissues, but some

596

tumors are known to release more exosomes and may exhibit changes in their composition,

597

and these changes can have immunomodulatory effects that prevent the destruction of the

598

tumors by the immune system.141 In a study by Llorente and Sandvig, lipid structures in

599

exosomes released by PC-3 prostate cancer cells were analyzed by hybrid triple quadrupole-

600

linear ion trap MS.142 Exosomes were found to be enriched in certain classes of lipids, including

601

GSLs, compared to their parent cells. Exosomal proteins and peptides have already been viewed

602

as potential biomarkers for cancer and other diseases; more studies are currently underway to

603

explore how exosomal lipid analysis can assist in forming a more conclusive set of data for

604

detecting diseases.143

605


28

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606


7. Emerging glycan markers

607

The methods and techniques for glycomic and glycoproteomic analyses of complicated

608

biological samples continue to be improved, however the performance in at least glycomic

609

profiling appears robust and sufficient for performing large samples sets. High-throughput

610

methods for released N- or O-glycan analysis employing MALDI-MS or LC-MS techniques in

611

combination with derivatization and enrichment protocols have been developed and applied to

612

different types of biological samples. With the robustness of these methods, glycan markers

613

have been discovered in various types of cancers, such as high mannose in breast cancer,144

614

truncated serum glycoforms in multiple cancer types.145

615

There are some general trends emerging from the current set of studies. Table 1 is a

616

summary of cancer biomarker discovery studies involving glycans and glycoproteins in the

617

review period. Increased levels of fucosylation, especially core fucosylation, appear to correlate

618

with many types of cancers including esophageal, gastric, colon, gallbladder, pancreatic, liver,

619

ovarian and breast cancer. Similar observations were reported for multi-fucosylated N-glycans

620

with antenna fucosylation. Another type of glycan biomarker for cancer is the truncated N-

621

glycans. Increased levels of truncated non-galactosylated N-glycans existing mainly on IgG in

622

serum have been reported for lung, gastric, and ovarian cancer. Truncated bisecting glycans in

623

tissues were also elevated for ovarian cancer along with amplified expression of the

624

glycosyltransferase GnT-III for bisecting GlcNAc.

625

Other possible glycan markers include high-mannose, hybrid-type N-glycans, sialylated

626

N- or O-glycans, and O-glycans with different core structures. High-mannose and hybrid-type N-

627

glycans are less-processed compared to complex-type. Although high mannose glycans have


29


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Page 30 of 55

628

long been recognized as potential markers for cancer, upregulation appears to be dependent

629

on the cancer type. In recent studies, high mannose has been shown to significantly increase in

630

colorectal cancer tissues and clear cell renal cell carcinoma plasma, while decrease in ovarian

631

cancer serum in several independent studies. Alteration of sialylation is less consistent among

632

studies because it is often structure- and protein-specific. For example, a study by Kaprio et al.

633

showed that the total abundance of sialylated N-glycans was increased from rectal adenoma to

634

carcinoma.146 But detailed glycan profiling showed that the increase was only significant for six

635

glycans, while certain sialylated glycans were actually decreased. O-Glycan cancer markers are

636

less studied compared to N-glycans. In a gastric cancer study, He et al. showed that the

637

expression levels of several extended core2 O-glycans including Hex5HexNAc3 and Hex3HexNAc3

638

were significantly increased in the patients.147

639

For identifying protein specific glycan markers, the most widely used strategy is to

640

isolate glycoprotein of interest using antibody and analyze the purified protein using glycomic

641

or glycoproteomic approaches. This strategy has recently been applied to identify protein

642

specific markers for several cancers, such as α(1-3) fucosylated glycans on alpha-1-acid

643

glycoprotein in pancreatic cancer,148 monofucosylated N-glycans at all sites and difucosylated

644

N-glycans at sites N184, N207 and N241 of haptoglobin in five types of operable

645

gastroenterological cancer.149 Although immunoaffinity enrichment can efficiently isolate

646

specific glycoproteins, the limited number of available antibodies has generally restricted the

647

types of proteins that can be targeted. For more global analysis of protein specific markers,

648

glycoproteomic approaches are needed. A common approach involving quantitative analysis of

649

deglycosylated glycopeptides enriched by lectins have been used to study aberrant changes of


30

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650

core-fucosylation in hepatocellular carcinoma150 and pancreatic cancer.151 Upregulation of core-

651

fucosylated glycopeptides from certain glycoproteins were observed, such as fibronectin for

652

hepatocellular carcinoma and macrophage mannose receptor 1 for pancreatic cancer.

653

Site-specific glycan marker discovery for cancer is more challenging due to the lack of

654

high-throughput sample preparation methods and data processing software for simultaneous

655

identification and quantitation of intact glycopeptides in complicated biological samples. MRM

656

has been regarded as the gold standard methodology for this purpose and applied to the

657

quantitation of immunoglobulin glycopeptides in cancer with high speed, sensitivity and

658

repeatability. Decrease in galactosylation on several subclasses of IgG, and biantennary glycans

659

with bisecting GlcNAc on site N205 of IgA2, as well as increase in biantennary mono- or

660

disialylated glycans on site N209 of IgM have been identified and validated for ovarian cancer.

661

Untargeted approaches have also been used for glycopeptide biomarker studies, but either

662

with very small number of samples or concentrating only on a few proteins. A study by Tanabe

663

et al. profiled lectin-enriched fucosylated glycoproteins in hepatocellular carcinoma serum.152

664

They analyzed the data using an in-house software and found elevated levels of alpha-1-acid

665

glycoprotein with multifucosylated tetraantennary N-glycans in patients.

666

Methods for the analysis of intact glycoproteins are also emerging for discovery of

667

specific cancer biomarkers involving proteins with aberrant glycosylation. A recent example is a

668

gastric cancer study on intact haptoglobin, where novel glycosylated intensity and frequency

669

markers

670

Hex22HexNAc18Fuc2NeuAc10 exhibited AUC of 0.93.

were

discovered.79

The

best

predictive

marker

corresponding

to

671


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Page 32 of 55

8. Future outlook

673

Glycan biomarkers are emerging and will soon fulfill the initial promise for sensitive and

674

specific diagnosis of cancer. The consistency of distinct compounds and classes of compounds

675

between separate and independent studies point to the robustness of the methods, even when

676

different methods are used. Research in this area will continue to grow toward higher

677

throughput glycomic profiles that can be obtained consistently with high reproducibility. Future

678

studies should further focus on large clinical studies to obtain precise markers with high

679

accuracy. The glycomic studies using known chemical structures could further identify the

680

enzymes that alter glycosylation providing potential targets for therapeutic intervention.

681

Glycoproteins and site-specific glycan markers will continue to develop. Glycoproteomic

682

analysis will improve in accuracy as the software tools are further developed for simultaneous

683

quantitation of protein and site-specific glycosylation. Further refinements in this area will

684

include the utility of data independent acquisition (DIA) of glycopeptides, which is now just

685

being explored to analyze glycoproteins.153,154 Included in this effort will be the area of native

686

glycoprotein analysis.

687


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688

Biographies

689

Muchena J. Kailemia completed his bachelor of science in education (B.Sc.Ed) degree at

690

Maseno University, Kenya. After receiving his M.S. degree in analytical chemistry from East

691

Tennessee State University in 2009, he entered the graduate program in Chemistry at the

692

University of Georgia, where he completed his Ph.D. research in the laboratory of Dr. Jonathan

693

Amster. His research focuses on developing mass spectrometry methods for the

694

characterization of glycosaminoglycans and glycoproteins.

695

Gege Xu studied chemistry at Peking University in China, where she received her B.S. degree in

696

2013. She is currently completing her Ph.D research at the University of California, Davis under

697

the supervision of Dr. Carlito B. Lebrilla. Her studies focus on developing qualitative and

698

quantitative methods for the analysis of dietary saccharides, and cell surface glycans and

699

glycoproteins using LC-MS/MS.

700

Maurice Y. Wong obtained his B.S. degree in Chemistry and Materials Science and Engineering

701

from Ateneo de Manila University, Philippines. He worked for the National Reference

702

Laboratory of the Department of Health in the Philippines before entering the graduate

703

program of the University of California, Davis. He is currently performing his Ph.D. research

704

under the supervision of Dr. Carlito B. Lebrilla, with a focus on glycan analysis using mass

705

spectrometry.

706

Qiongyu Li graduated from Hong Kong Polytechnic University in 2014 with a B.S. in chemical

707

technology. Currently, she is a Ph.D. candidate at the University of California, Davis, where her

708

research focuses on the analysis of glycoproteins for biomarker discovery and the method


33


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Page 34 of 55

709

development of click chemistry involved mass spectrometry for studying the role of membrane

710

glycosylation in cell-cell interaction.

711

Elisha Goonatilleke received her B.S. in 2013 and continued with her Ph.D. at the University of

712

California, Davis. Currently, she is a graduate student in the group of Dr. Carlito B. Lebrilla. Her

713

field of study is in analytical chemistry with an emphasis on mass spectrometry. She focuses on

714

the identification, characterization, and quantification of glycoproteins and oligosaccharides in

715

human milk.

716

Frank Leon was born in San Clemente, California. He received his B.S. in molecular and cellular

717

biology from the University of California, Santa Cruz in 2014. After graduating, he worked for

718

biotech industry and clinical diagnostic companies in the San Francisco bay area, and southern

719

California. He entered the Biochemistry, Molecular, Cellular and Developmental Biology

720

graduate program at the University of California, Davis in 2016. His research interests include

721

surveying the biological roles of glycosylation in disease and cancer.

722

Carlito B. Lebrilla completed his B.S. from University of California, Irvine, and his Ph.D. from

723

University of California, Berkeley. He was a NATO-NSF and an Alexander von Humboldt

724

postdoctoral fellow with Prof. Helmut Schwarz at the Technical University in Berlin. He was also

725

a UC President’s postdoctoral fellow at the University of California, Irvine. He has been on the

726

faculty at UC Davis since 1987 and is currently a Distinguished Professor in the Chemistry

727

Department and in Biochemistry and Molecular Medicine. He is on the editorial board of

728

several mass spectrometry journals. His research focus is on bioanalytical chemistry with

729

emphasis on oligosaccharides and glycoconjugates.

730


34

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731

ACKNOWLEDGMENTS

732 733

Funding provided by the National Institutes of Health (grant R01GM049077) is gratefully acknowledged.

734 735


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1042


Table 1. Summary of recent glycan biomarker studies for cancer Cancer

Specimen

No. of * samples

Enrichment method

Analytical method

Kidney

Plasma

20/20 pooled

G, P, GP

12P depletion, M-LAC

LC-MS

85

Ovarian

Serum

100/147

G

PGC SPE

LC-MS

155

Pancreatic

Serum

16/10

GP (sialylated)

Albumin depletion, LAC

LC-MS

82

Liver

Plasma

40/41

P

N/A

LC-MRM

156

↓ Vitronectin and AGP

Pancreatic

Serum

30/37 pooled

De-GP

14P depletion LAC

LC-MS

157

(fucosylated)

• 19 proteins differentially expressed in cancer.

Liver

Tissue

16/16

G

N/A

MALDIIMS

Ovarian

Tissue

3/3

Lectin

LC-MS

145

Pancreatic

Serum

4/6

Immunoaffinity

LC-MS

158

Liver

Tissue

3

G

N/A

MALDIIMS

Liver

Serum

26/26

De-GP

Lectin

LC-MS

150

Ovarian

Plasma

82/82

G

PGC SPE

LC-MS

159

Colorectal

Tissue

5/18

G

C18, PGC SPE

MALDIMS

Pancreatic

Serum

13/20

Immunoaffinity

LC-MS

160

Pancreatic

Serum

13/13

De-GP

Lectin

LC-MS

151

Colon

Tissue

2

G

N/A

MALDIIMS

Ovarian

Cell

1

P

Biotin affinity

LC-MS

Colorectal

Serum

20/42

G

N/A

MALDIMS

G, de-GP

Immunoaffinity Sepharose 4B

Gastric Colon Prostate etc.

Serum

5/5-26

Targets

**

G and P (bisecting)

G (human AGP)

G, P (sialyl-LeX)

(haptoglobin)

LC-MS


Ref

45

47

146

42

161

55

149

Outcome ↑ Highly sialylated and high mannose glycans ↓ High mannose, hybrid, galactosylated biantennary glycans ↑ Fucosylation of fetuin A, AGP, and transferrin ↓ Sialylation

↑Tetra-antennary ↓ Man8 ↑ GnT-III and truncated bisecting glycans ↑ Fucosylated glycans ↑ Core-fucosylation, Hex7HexNAc6 ↑ Core-fucosylation of fibronectin • Seven N-glycans correlated with ovarian cancer stage ↑ Monoantennary, sialylated&fucosylated and small high-mannose N-glycan ↑ Sialyl-Lewis X epitopes on ceruloplasmin • Eight core-fucosylated peptides exhibited significant difference ↑ High mannose in various tumor regions, sialylated glycans in different regions • 589 differentially expressed glycoproteins upon GALNT3 knockdown ↑ Fucosylated tri- and tetraantennary glycans ↑ Monofucosylated Nglycans at all sites and difucosylated at sites N184, N207 and N241

43


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Prostate

Urine, serum

Urine: 40 Serum: 48/119 pooled

Liver

Serum

27/42

Ovarian

Ascites fluid

5

Breast

Serum

43/91

Page 44 of 55

Hydrazide beads, HPLC fractionation

LC-MS

98

LAC

LC-MS

152

G, P, GP

PGC SPE, lectins affinity

LC-MS

162

G

PGC SPE

LC-MS

163

De-GP

GP (fucosylated)

↑ Aggressive prostate cancer associated glycoproteins in patients’ urine than serum samples ↑ Multifucosylated tetraantennary glycans on AGP • Large, highly fucosylated and sialylated complex and hybrid glycans, and unusual glycopeptides identified ↑ Difucosylated N-glycans ↑ Biantennary mono- or disialylated glycans on IgM site N209 ↓ Galactosylation on IgG, and biantennary glycans with bisecting GlcNAc on IgA2 site N205

Ovarian

Serum

84/84

GP (Igs)

N/A

LC-MRM

72

Gastric

Serum

144/157

O-glycome

PGC SPE

LC-MS

147

↑ Large Core2 O-glycans

Pancreatic

Serum

6/19

G, protein (AGP)

Immunoaffinity

LC-MS CZE-UV

148

↑ α(1-3) fucosylation of AGP

Liver, colorectal

Serum

10/10

Gastric

Serum

44/44

GP

Protein (haptoglobin)

Albumin depletion

LC-MS MRM

Immunoaffinity

LC-MS

MALDIMS

164

79

Gastric

Serum

40/163

G

Sepharose beads

Liver

Saliva

50/60

G

Sepharose 4B

MALDIMS

Prostate

Tissue

8/8

P (sialylated)

Biotin affinity

LC-MS

167

Liver

Serum

40/40 pooled

LC-MS

168

Ovarian

Serum, ascetic fluid

20/18

De-GP (β1,6-GlcNAc)

G

LAC

C18, PGC SPE

MALDIMS

165

166

169

↑ Fucosylated N- and disialylated O-glycopeptides of hemopexin in HCC patients previously diagnosed with HCV • N-glycan variation of serum haptoglobin associated with patients with gastric cancer ↑ Hybrid and tri-, tetraantennnary glycans; ↓ Monoantennary, bisecting type and core fucose ↑ Fucosylated ↓ Sialylated • Detection of 21 unique proteins in cancer tissues • Altered N-glycosite occupancy for 11 proteins with β(1,6) GlcNAc branching N-glycans ↑ Branching, sialylation and antennary fucosylation ↓ High mannose, bisecting GlcNAc in ascetic fluid than serum

1043 1044


44

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FIGURE CAPTIONS Figure 1. Representation of the glycosylation pathway of proteins. The pathway illustrates the complexity and heterogeneity of structures. The proteins may exit the pathway with various levels of glycosylation. Figure 2. Workflow scheme of glycan analysis in protein: Released glycan analysis by LC-MS and glycopeptide analysis by LC-MS/MS Figure 3. Strategies for glycan release from glycoproteins and glycosphingolipids: oxidative release of natural glycans (ORNG) vs. traditional glycomics methods. Reprinted by permission from Macmillan Publishers Ltd: Nature Methods, Song, X.; Ju, H.; Lasanajak, Y.; Kudelka, M. R.; Smith, D. F.; Cummings, R. D., Nat. Meth. 2016, 13 (6), 528-534 (ref #27). Copyright 2016. Figure 4. MALDI-MS imaging of glycans on a liver tissue microarray with tumor (T) and normal (N) tissue cores from 16 hepatocellular carcinoma patients. The map corresponds to putative glycan structures (inset). Reprinted from Powers, T.; Holst, S.; Wuhrer, M.; Mehta, A.; Drake, R. Biomolecules 2015, 5 (4), 2554 (ref 42). Under Creative Commons license (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0/). Figure 5. N-Glycan profile with LC-MS from a sample containing cell membranes combined from three cell lines including Caco-2, HT-29, HCC1954. Over 800 compounds are observed in a single the LC-MS run. Figure 6. N-Glycan profile of serum glycans with LC- QTOF MS. Peaks are numbered according to their abundances and correspond to individual annotated structures. Reprinted from Song, T.; Aldredge, D.; Lebrilla, C. B. Anal. Chem. 2015, 87, 7754-7762 (ref 32). Copyright 2015 American Chemical Society. Figure 7. Tandem MS of glycopeptides: (a) neutral glycopeptides, (b) sialylated glycopeptides, and (c) high mannose glycopeptides. Reprinted from Hong, Q.; Ruhaak, L. R.; Stroble, C.; Parker, E.; Huang, J.; Maverakis, E.; Lebrilla, C. B. J Proteome Res. 2015, 14 (12), 5179-92 (ref 99). Copyright 2015 American Chemical Society. Figure 8. MRM of peptides and glycopeptides of immunoglobulins in serum. The spectra were extracted from a single LC-MS run with a total time of 10 minutes. Reprinted from Hong, Q.; Ruhaak, L. R.; Stroble, C.; Parker, E.; Huang, J.; Maverakis, E.; Lebrilla, C. B. J Proteome Res. 2015, 14 (12), 5179-92 (ref 99). Copyright 2015 American Chemical Society. Figure 9. Schematic representation of glycosphingolipid (GSL) root structures found in mammalian systems, and structures of some GSLs of interest discussed in the text.


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Recent Advances in the Mass Spectrometry Methods for Glycomics

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