Cancer remains one of the deadliest diseases in the world because we still haven’t fully comprehended why and how it occurs. What we do know is that the proteins on the cell surface are at the forefront. Normally these proteins carry out essential cellular functions, such as division, migration, and transport of material. When they undergo unusual modifications, cells can awry, dividing uncontrollably and consuming the resources of the body rapidly.  

Glycosylation, the addition of complex sugars to these proteins, is perhaps the most influential modification. It is particularly challenging to explore because of the mind-blowing structural and functional diversity of these sugars. However, innovation in glycobiology research can allow us to reach the lesser-explored territories of cancer. With advanced glycan arrays, synthetic chemistry, and high-throughput screening, we can improve the accuracy and speed of glycobiology research to drive a deeper understanding of glycosylation in cancer. This article highlights some areas within cancer research utilizing glycobiology. If you’re interested in learning more about glycobiology and cancer, be sure to check out our eBook, Examining Altered Glycobiology in Cancer.  

Glycobiology in biomarker discovery 

Biomarkers indicate how protein expression, function, and structure are altered in cancer. The vast majority of FDA-approved cancer biomarkers are glycoproteins, cell surface proteins covalently attached to sugar chains called glycans. Therefore, looking at changes in glycosylation in cancer can reveal what led to the formation of aberrant glycoproteins. This creates opportunities to discover novel biomarker candidates. With a concrete knowledge of these biomarkers, researchers can design more targeted anti-cancer therapeutics to mitigate the effects of aberrant glycosylation. 

One of the primary indicators of cancer is changes in the glycome. These changes can manifest in various ways. Certain glycan epitopes might be present, while others may be unusually absent in cancer cells. The relative abundance of specific glycans might vary from healthy to cancerous tissue. Glycan chains can undergo further modifications, such as the addition of fucose or sialic acid at the end of the sugar chain. 

For example, the mucin peptide is found on the surface of epithelial cells in the lungs, stomach, intestines, and various other tissues. Studies have discovered an abundance of mucin with shorter glycans in breast cancer where the glycans were truncated with sialic acid, therefore demonstrating the detection of sialylated mucin structures is an indicator of tumor growth (1). 

Another site of interest is the group of enzymes catalyzing the formation and dissociation of glycoconjugates. Going back to our mucin example, the overexpression of sialyltransferases (e.g., ST6GalNAc-I and II), which add sialic acid to the terminal sugar, can be attributed to the increased levels of shorter glycan chains prevalent in breast cancer (2). 

Biomarker studies have already started to benefit from altered glycan structure discoveries. FDA-approved glycoprotein antigens as cancer biomarkers are on the rise, including CA 15-3 derived from mucin-1 as a serum marker for breast cancer, CA-125 (mucin16) for ovarian cancer, and CA 19-9 (sialyl-LewisA) for pancreatic cancer (3–5). 

Glycobiology in high-throughput drug screening (HTS) 

Precision is key to successful cancer treatment. Unfortunately, conventional radiation, chemotherapy, and surgery approaches are inadequate. These treatments often result in severe adverse effects and recurrence. This clearly indicates a dire need for state-of-the-art strategies employing a more targeted approach.  

Targeted approaches are often hampered due to therapy resistance, and success is achieved in only a subset of patients. High-throughput drug screening can overcome these challenges by screening large compound libraries to discover a broader range of potential hits. Screening methods are accelerated further through automated HTS workflows. However, conventional HTS-derived candidates have failed to reach clinical trials due to their lack of targeted inhibition. 

The target preference for HTS assays is mainly glycosyltransferases. Because one enzyme can modify multiple proteins, the most suitable HTS target is aberrant glycosyltransferase activity. Over the past few years, promising studies have emerged with regard to targeted HTS. The first use of a cell-based HTS assay was in 2017 when researchers discovered a potential inhibitor for the transferase ppGalNAc-T3 (6). Since then, various cell-based and biochemical assays have been developed to target O-GlcNAc transferase (OGT), Galactosyltransferase B4GALT1, and ST6Gal I, among many others. Recent advances in glycosyltransferase-specific HTS assays are summarized in a review article published in Cell (7).   

Glycobiology in combination therapeutics 

Besides initiating tumor-associated behavior in cells, aberrant glycosylation also affects the body’s response to existing anti-cancer therapeutics.  

For example, recent studies revealed the role of glycosylation in metastasis, a characteristic of aggressive cancers. Altered glycosylation was shown to mediate cell migration in hypoxic tumor microenvironments, whereby tumor cells started to relocate due to the low oxygen concentration in the original tumor site (8). This makes it harder for therapeutics to eliminate rapidly-metastasizing tumors.  

Immunotherapeutic antibody treatment is also negatively impacted by glycosylation. In particular, human T cells undergo aberrant glycosylation that disrupts their interaction with tumor cells. This allows the tumor to evade immune checkpoints (9). 

Glycobiology in cancer vaccines 

Vaccines are becoming increasingly compelling in cancer prevention and treatment. There are already vaccines preventing cancer, namely the Human Papillomavirus (HPV) vaccine. Since HPV can cause cervical cancer in the long term, the HPV vaccine is commonly used as a precaution against cervical cancer. However, cancer vaccine development is still in its infancy. 

These vaccines work by introducing tumor-associated carbohydrate antigens (TACAs—the collective name referring to the carbohydrate structures found on the glycoproteins in cancer cells) to the body to trigger an immune response from the body. However, these antigens have proved insufficient in causing immunogenicity. In other words, the body did not necessarily recognize these carbohydrates as threats, even if they are abundant in cancer.  

One of the approaches to overcome this weakness is to develop multivalent vaccines comprising multiple TACAs. Introducing multiple TACAs to the body might elicit a stronger immune response. Glycan arrays have shown promising results by elaborately mapping out immune responses against a myriad of glycan epitopes. The pancreatic cancer vaccine GVAX was recently studied with glycan microarrays. Researchers found a large set of glycan-antigens, including N-linked glycans, O-linked glycans, and blood group antigens that prompted an immune response in pancreatic cancer patients. The findings of this study could guide vaccine manufacturers to improve vaccine efficacy (10).  

Another approach is to couple TACAs with protein carriers to drive immune response. This stems from the idea that even though the body might tolerate a standalone carbohydrate antigen, it will recognize the whole structure (carbohydrate and protein) as a much bigger threat. Preliminary studies demonstrate this hypothesis through improvements in T-cell mediated immune response upon conjugating TACAs to carrier proteins, such as KLH, MUC1, and BSA (11).   

Glycobiology in the future of cancer research 

With cutting-edge imaging and quantification methods, scientists can design and run more complex glycobiology experiments. The results of these experiments will continue to shed light on the complicated relationship between glycosylation and cancer. Targeting the deviations in glycosylation patterns will bring cancer research one step closer to conquering the current bottlenecks in inadequate treatments. 

If you’re interested in learning more about glycobiology and how you can use it to unlock deeper insights in your research, be sure to check out our Glycobiology Resources page and stay tuned to the blog as well. 

References 

1. Burchell JM, et al. 2018. O-linked Mucin-Type Glycosylation in Breast Cancer. Biochemical Society Transactions. 
2. Venturi G, et al. 2019. Impact of Sialyltransferase ST6GAL1 Overexpression on Different Colon Cancer Cell Types. Glycobiology. 
3. Duffy MJ. 1999. CA 15-3 and Related Mucins as Circulating Markers in Breast Cancer. Annals of Clinical Biochemistry: International Journal of Laboratory Medicine. 
4. Sok D, et al. 2009. Novel Fluoroimmunoassay for Ovarian Cancer Biomarker CA-125. Analytical and Bioanalytical Chemistry. 
5. Nie S, et al. 2014. Glycoprotein Biomarker Panel for Pancreatic Cancer Discovered by Quantitative Proteomics Analysis. Journal of Proteome Research. 
6. Song L, et al. 2017. Inhibitor of ppGalNAc-T3-Mediated O-Glycosylation Blocks Cancer Cell Invasiveness and Lowers FGF23 Levels. eLife. 
7. Costa AF, et al. 2020. Targeting Glycosylation: A New Road for Cancer Drug Discovery. Trends in Cancer. 
8. Arriagada C, et al. 2018. Role of Glycosylation in Hypoxia-Driven Cell Migration and Invasion. Cell Adhesion & Migration. 
9. De Bousser E, et al. 2020. Human T cell Glycosylation and Implications on Immune Therapy for Cancer. Human Vaccines & Immunotherapeutics. 
10. Xia L, et al. 2016. Whole-Cell Cancer Vaccines Induce Large Antibody Responses to Carbohydrates and Glycoproteins. Cell Chemical Biology. 
11. Mettu R, et al. 2020. Synthetic Carbohydrate-Based Vaccines: Challenges and Opportunities. Journal of Biomedical Science. 

Scientific insights from studying the genetic code have revealed significant information regarding the cellular building blocks of proteins and lipids. While extensive, the information is not sufficient to explain the diverse structures and functions of these cellular components. As it turns out, these molecules are decorated with covalently attached complex layers of sugars, giving rise to millions of possible structures. The resulting glycoconjugates are the main actors behind protein folding and stability, facilitating essential cellular processes and communication. In essence, glycobiology studies the structure and function of these sugar chains, their conjugates, and their impact on health, disease, and evolution. 

Thanks to the thriving field of glycobiology, we are learning more about the importance of glycans as they relate to biological processes in our bodies. More importantly, we are learning how we can leverage glycans for various biotechnology applications to design cutting-edge therapeutics. 

Keep reading for insights in glycobiology’s role in diagnostics, therapeutics, and stem cell research, and if you’re interested in knowing more about the growing field of glycobiology, check out our Glycobiology Resources page. 

Glycobiology and diagnostics 

Aberrant glycosylation, which is abnormalities in the conjugation between sugars and proteins or lipids, is a clear indicator of disease. Researchers can leverage assays such as chromatography, lectin arrays, and flow cytometry to identify and quantify different glycan structures in healthy and diseased tissues. Furthermore, immunohistochemistry and immunofluorescence methods allow us to monitor the effect of aberrant glycosylation on cell morphology and movement. The combination of these glycan detection tools have provided invaluable insights into the nature of several diseases, including congenital disorders, diabetes, cancer, and COVID-19.  

Today, over 100 congenital diseases have been attributed to altered glycosylation networks, mainly the overexpression of certain N-glycans over others (1). While some of these diseases might be inherited from parents carrying the genes, others might develop due to random mutations in the glycosylation networks. Examples include hypotonia (poor muscle tone), ataxia (impaired balance and coordination), cardiomyopathy (insufficient blood pumped from the heart), dysarthria (slurred speech), liver dysfunction, excessive blood clotting, and scoliosis, all of which severely affect the individual’s quality of life from the moment they are born. 

Dysregulated glycosylation has also been explored in cancer and is now a widely accepted cancer biomarker. The post-translational modifications in proteins help identify cancerous tissues and indicate aggression, malignancy, and metastatic behavior. Today, many FDA-approved cancer biomarkers are glycoproteins for cancers such as ovarian (2), pancreatic (3), and breast (4). While some studies have identified single glycoproteins, others have discovered a panel of glycoproteins affecting cancer progression, such as in head and neck cancers (5).  

Epidemiology is another field where the role of glycosylation is being investigated. Researchers have observed that pathogens attack and enter our cells due to their diverse surface glycans. There are many examples of this, but perhaps the most relevant is how SARS-CoV-2 uses its spike glycoprotein to attach and enter host cells (6). 

Glycobiology and glycan therapeutics 

Many small molecule drugs have glycans in their natural core structures. With the expanding knowledge about glycosylation mechanisms, investigators have started to modify these glycans to improve the efficacy of protein-based therapeutics.  

Zanamivir (Relenza) or Oseltamivir (Tamiflu) for influenza treatment are among the most well-known examples. Adding glycosylated side chains to the original drug molecules increased their inhibition of virus binding without cytotoxicity to the host cell (7). In one of the pioneering studies, IgG antibodies with fucosylated N-glycans bound natural killer cells 50 times tighter than standard IgGs, making them more effective in activating natural killer cells (8).  

In recent years, glycobiology has become increasingly significant in creating engineered microorganisms that mass produce therapeutic glycoproteins (9). Through recombinant DNA technology, researchers can insert glycosyltransferase genes into widely-studied organisms like E. coli to express glycoproteins.  

Glycoprotein vaccines 

Another emerging application of glycobiology is the synthesis of glycoprotein vaccines. Although glycan chains were previously used as vaccine candidates, they exhibited poor immunogenicity and failed to activate T-cells. As a result, researchers shifted gears to synthesize vaccines in glycoconjugate forms to increase long-term B-cell memory. Such vaccines comprise a glycan antigen (usually O-antigen polysaccharides), a conjugate (protein), and an adjuvant molecule to enhance immunogenicity.

Since 1980, several antibacterial glycoconjugate vaccines have received approval from the FDA. These vaccines have been shown to protect infants against various bacterial infections, including Hemophilus influenzae type B, Streptococcus pneumoniae, and Neisseria meningitis (10). 

Current research emphasizes the rapid and cost-effective production of glycoconjugates through engineered microorganisms, mainly E. coli. While the cellular machinery of E. coli strains lacks the post-translational modification mechanism, recombinant DNA technologies can be used to grant them the ability to do so. Preliminary studies have transferred glycosyltransferase genes from eukaryotes into E. coli to produce glycoconjugates against several pathogenic strains (11). 

More recently, glycoconjugate vaccines have started to attract attention in cancer research. Attempts to develop anticancer vaccines primarily focus on training the immune system to attack tumor-associated carbohydrate antigens (TACAs). Extensive research is being performed particularly in vaccines against gangliosides and mucin-type glycoproteins, both identified as biomarkers for several cancer types, including prostate, colon, lung, and ovarian (12).  

Glycobiology and nanomedicine 

Functional nanomaterials, such as iron oxide, quantum dots, and carbon nanotubes, are on the rise due to their potential for targeted drug delivery and their ability to infiltrate otherwise inaccessible tissue parts. These nanomaterials are optimized through chemical processes, such as conjugation, to perform specific tasks (e.g., fluorescence imaging and targeted drug delivery) while remaining compatible with the human body. Combining these materials with glycan-based therapeutics can bring glycoconjugates smart features that they would not harbor in their natural states (13).  

For example, researchers have used the self-assembly capacity of nanomaterials to synthesize self-assembling glycopeptides that formed nanofibers. Preliminary research demonstrated the effectiveness of these nanofibers as galectin inhibitors, indicating their potential against cancer, inflammation, and viral diseases (14). Other preliminary studies have involved the development of self-adjuvant MUC1 vaccines which elicit a more effective immune response against MUC1-associated tumors (15).  

Glycobiology and stem cell research 

A significant portion of embryonic development involves the differentiation of stem cells into different cell types which form our neurons, skin, and organs. Research shows that surface glycoproteins and glycolipids, especially their terminal sialic acids, are the key triggers of stem cell differentiation. Furthermore, the glycan profile has been found to undergo modifications several times throughout the differentiation. In other words, the glycan profile of stem cells could predict their state and type of differentiation. There appears to be a bidirectional relationship between stem cell differentiation and glycosylation that might shed light on the mysteries of developmental biology (16).  

Stem cell research can give rise to groundbreaking cell replacement therapies, enabling repair or replacement of damaged tissues and organs. From this point of view, a robust understanding of glycosylation during stem cell differentiation can greatly benefit regenerative medicine.  

Today, immense effort is put into developing stem cell therapies for many neurological diseases, such as Parkinson’s disease, as glycobiology research has massive potential for generating differentiated neural cell lines (17). There is also potential for glycobiology research to improve our portfolio for cancer stem cell biomarkers. It is now known that cancer stem cells are pivotal in tumor initiation and metastasis (18). Exploring the glycan profiles of this subset of cells can help scientists develop more robust therapeutics against cancer stem cells and reduce the risk of anticancer drug resistance. 

Final word  

The sugar chains on the cell surface are highly diverse with millions of possible structures, so it is an everlasting mission to discover the vast ocean of carbohydrates. Nevertheless, substantial progress is being made in many research fields to understand our origins and improve our quality of life.  

Could glycobiological tools help you unravel your scientific questions? If this article left you curious, head on over to our Lectins & Glycobiology Reagents page or download our new eBook, Exploring the World of Glycobiology, to learn how Vector Laboratories can support your own research on these super sugars.   

References

1. Lyons JJ, et al. 2015. Glycans Instructing Immunity: The Emerging Role of Altered Glycosylation in Clinical Immunology. Frontiers in Pediatrics. 
2. Yin BWT, et al. 2001. Molecular Cloning of the CA125 Ovarian Cancer Antigen. Journal of Biological Chemistry. 
3. Gao CF, et al. 2021. Detection of Chemotherapy-Resistant Pancreatic Cancer Using a Glycan Biomarker, sTRA. Clinical Cancer Research. 
4. Duffy MJ, et al. 2000. CA 15–3: A Prognostic Marker in Breast Cancer. The International Journal of Biological Markers. 
5. Ralhan R, et al. 2008. Discovery and Verification of Head-and-Neck Cancer Biomarkers by Differential Protein Expression Analysis Using iTRAQ Labeling, Multidimensional Liquid Chromatography, and Tandem Mass Spectrometry. Molecular & Cellular Proteomics. 
6. Sanda M, et al. 2021. N-and O-Glycosylation of the SARS-CoV-2 Spike Protein. Analytical Chemistry. 
7. Mishin VP, et al. 2005. Effect of Hemagglutinin Glycosylation on Influenza Virus Susceptibility to Neuraminidase Inhibitors. Journal of Virology. 
8. Shields RL, et al. 2002. Lack of Fucose on Human IgG1 N-Linked Oligosaccharide Improves Binding to Human Fcgamma RIII and Antibody-Dependent Cellular Toxicity. Journal of Biological Chemistry. 
9. Huang CJ, et al. 2012. Industrial Production of Recombinant Therapeutics in Escherichia coli and Its Recent Advancements. Journal of Industrial Microbiology and Biotechnology. 
10. Adamo R, et al. 2013. Synthetically Defined Glycoprotein Vaccines: Current Status and Future Directions. Chemical Science. 
11. Jaffé SRP, et al. 2014. Escherichia coli as a Glycoprotein Production Host: Recent Developments and Challenges. Current Opinion in Biotechnology. 
12. Buskas T, et al. 2009. Immunotherapy for Cancer: Synthetic Carbohydrate-Based Vaccines. Chemical Communications. 
13. Cecioni S, et al. 2015. Glycomimetics Versus Multivalent Glycoconjugates for the Design of High Affinity Lectin Ligands. Chemical Reviews. 
14. Restuccia A, et al. 2015. Self-Assembled Glycopeptide Nanofibers as Modulators of Galectin-1 Bioactivity. Cellular and Molecular Bioengineering. 
15. Huang ZH, et al. 2012. A Totally Synthetic, Self-Assembling, Adjuvant-Free MUC1 Glycopeptide Vaccine for Cancer Therapy. Journal of the American Chemical Society. 
16. Kawasaki T, et al. 2017. Special Issue: Glycobiology on stem cells —editorial. Glycoconjugate Journal. 
17. Yale AR, et al. 2018. Cell Surface N-Glycans Influence Electrophysiological Properties and Fate Potential of Neural Stem Cells. Stem Cell Reports. 
18. Terao N, et al. 2015. Fucosylation is a Common Glycosylation Type in Pancreatic Cancer Stem Cell-Like Phenotypes. World Journal of Gastroenterology. 

This mechanism can be leveraged to produce antibodies which are otherwise challenging to produce in conventional immunization methods. Anti-glycan antibody production is one of the fields that can reap tremendous benefits from sea lamprey research.  

Why do we even need sea lampreys if there is already substantial research on lectins and monoclonal antibodies for detecting carbohydrate expression? As invaluable as they are in binding to general glycan determinants, lectins have broader specificities for N-glycans and O-glycans (1). Monosaccharides are not unique to either N-glycans or O-glycans, and lectins have weak affinities for monosaccharides. This could cause cross-reactivity when only N-glycans or O-glycans need to be detected. Then we have monoclonal antibodies (mAbs) produced from rodents through immunization with human glycans. However, because humans and rodents have very similar glycomes, immunization often produces weak antibodies bearing non-specific glycan recognition patterns (2).  

In contrast, lampreys and mammals have an evolutionary distance of approximately 550 million years. Thus, the VLRBs generated from their immunization could give rise to antibodies that can detect distinct carbohydrate human antigens with higher accuracy, as proven in several studies (3–6).  

Researchers from Harvard Medical School, Emory University School of Medicine, and the University of Copenhagen collaborated to exploit the anti-glycan responses of sea lamprey (7). They first immunized lampreys with a set of cell lines comprising distinct glycomes and screened the lamprey plasma against glycan libraries. Then they used yeast surface display (YSD) libraries to clone and enrich VLRB complementary DNA (cDNA) libraries for anti-glycan antibodies, which they termed smart anti-glycan reagents. These reagents could identify distinct glycan epitopes and their cellular locations. Compared to conventional mouse mAbs, the VLRBs produced in sea lamprey displayed superior specificity and affinity. 

Immunizing Sea Lamprey with Mammalian Cell Lines 

The VLRB production began with immunization with two wild-type cells, two mutant CHO cell lines, pig lung tissue, and human Tn4 cell lines. Then sea lamprey plasma samples were screened on a glycan microarray of 600 unique structures. In every plasma sample, the anti-glycan VLRB profile (i.e., the glycan epitopes they recognized) corresponded to the unique glycan expression profiles of the respective cell lines. 

Comparing Lamprey and Mouse Antibodies Response to Antigens 

While mAb generation from mice is the most widely accepted method in antibody development, it has several limitations. In many cases, the mice cell lines can tolerate the immunogens from human cells because of glycome similarities, so they fail to generate the desired antibodies.  

To demonstrate the superior antibody development capacity of the sea lamprey, the researchers immunized lamprey and mice with human AB blood cells and simian immunodeficiency virus.  Although mice did produce anti-glycan antibodies, many of them were already present in mice serum by default; it was difficult to deduce which antibodies were the result of immunization. In contrast, lamprey-derived anti-glycan VLRBs bound a much larger variety of distinct glycan moieties with unique configurations, which the antibodies in mouse serum failed to recognize. 

Advantages of Lamprey: Specific, Time and Cost-effective 

There are significant advantages of sea lamprey over rodents as anti-glycan antibody resources. The genetic distance to humans is only one example. 

The study discusses how VLRBs can improve experimental design feasibility. For example, VLRBs contain a single peptide chain, unlike mouse mAbs with large polypeptide chains.  

How does this benefit the use of VLRBs for glycan detection? The main advantage occurs during the construction of cDNA libraries for antibodies. For mouse mAbs, the techniques to produce antibody libraries have several limitations, such as the difficulty of enrichment and loss of specificity. These limitations stem from the dimeric nature of mouse immunoglobulin proteins and the random heavy and light chain pairing. Ultimately, amplifying these proteins requires multiplexing with at least 50 primers, which is costly and time-consuming (8).  

In contrast, being single gene products, lamprey VLRBs can be PCR-amplified with a single set of primers. Because of their small modular domains, VLRBs can be incorporated into other proteins without losing their specificity. Lastly, because of their crescent-shaped β-sheet-type binding surfaces, VLRBs recognize binding epitopes that would go unnoticed by Ig-based antibodies with flat binding sites (9). 

The Use of YSD Libraries: Discerning Between H-Antigen Presentations

The advantages listed in the previous section enabled the researchers to clone VLRB cDNA libraries onto yeast strains. Pairing the resulting YSD libraries to glycan microarrays, they managed to enrich these libraries for antibodies recognizing a set of glycans with a conserved motif. The 12 VLRB mAbs from 4 immune libraries were designed to detect the type-2 H-trisaccharide motif found in various blood glycans.  

Then the researchers compared the specificity of VLRB’s from YSD libraries to that of the plant lectin Ulex europaeus agglutinin (UEA-I), conventionally used for H-antigen detection in blood. The binding profiles of the 12 VLRBs revealed that they were each bound to a different type-2 H-antigen presentation. Although all H-antigen structures contained the same binding motif, each VLRB recognized a distinct antigen presentation depending on the glycan structures they carried (e.g., linear vs. branched, N-glycans, O-glycans). Their recognition profiles were similar to 3 VLRBs with previously reported glycan specificity (10). In addition, they did not cross-react with other motifs, such as type 1 and type 3. 

The performance of UEA-I resulted in broad specificity, as the researchers reported that it was bound to different H-antigen structures with similar affinity. It was interesting to note non-blood group H antigens among these structures. These results proved how VLRBs could distinguish between minor differences in glycan presentations. Furthermore, the use of YSD libraries enhance the potential of sea lamprey, since YSD libraries are renewable antibody development platforms compatible with several enrichment assays. 

VLRBs and Sialic Acid Linkage 

The sialylation of glycans is a significant cell biomarker to detect healthy or diseased states. That’s why the researchers also wanted to explore the capability of VLRBs to differentiate between sialylated glycans.  

From the H-antigen detection experiments described in the previous section, they identified 3 VLRBs bound to glycans with different sialic acid linkages. The subject of comparison was another plant lectin, this time Sambucus nigra agglutinin (SNA) from Vector Laboratories, a commonly used sialic acid detection reagent.  

The binding profiles displayed a clear picture of the affinity of different VLRBs to sialic acid linkages. The 3 VLRBs showed clear peaks for certain N-glycans and O-glycans, with clear preferences towards different sialylated core structures. On the other hand, SNA was broader in its binding profile, lacking any substantial peaks, meaning it bound several sialylated glycans with similar affinity.  

For further investigation of distinct binding profiles, the researchers probed 4 types of human tissue arrays (colon, thyroid, lung, and prostate) with the 3 VLRBs and SNA and looked at the staining patterns. While SNA showed similar staining patterns in different tissue arrays, the VLRBs exhibited different stains across the 4 tissue arrays with different sialylated glycan profiles.  

Taken together, VLRBs had advantages against the plant lectin SNA by demonstrating preferential binding to certain sialic acid linkages. This also translated successfully to immunohistochemistry applications since they exhibited distinct staining patterns for different tissues. 

Challenges for Future Studies 

The immunization of sea lampreys gives rise to antibodies that have advantages to mouse mAbs and commercial plant lectins in detecting distinct glycan profiles. These antibodies also prove advantageous in cost and experimental design, such as with the ease of cloning in YSD libraries.  

That said, this is not where the story ends, and there is plenty of room for further research. First of all, we still need to find out whether sea lamprey could show self-tolerance to some human antigens, which would lead to weak binding reagents. To elicit self-tolerance trends in sea lamprey, one needs to screen the immunized lamprey plasma against as many glycans as possible.  

While large glycan microarrays are available, they only cover a fraction of the vast glycan diversity in the human glycome. In addition, they mostly contain synthetic glycans, meaning some glycan presentations might not even be biologically relevant. In other words, just because a VLRB detects a distinct glycan structure in a microarray does not necessarily mean it will show the same behavior in cell culture or tissue samples. To eliminate the false positives and translate the success of VLRBs into cell biology, one needs to derive glycan microarrays from natural cellular glycomes by releasing glycans from biological samples (11). This advancement will make the results of glycan array screenings more applicable to biomedical research.  

References 

1. Manimala JC, et al. 2006. High‐Throughput Carbohydrate Microarray Analysis of 24 Lectins. Angewandte Chemie. https://pubmed.ncbi.nlm.nih.gov/16639753/
2. Manimala JC, et al. 2007. High-Throughput Carbohydrate Microarray Profiling of 27 Antibodies Demonstrates Widespread Specificity Problems. Glycobiology. https://pubmed.ncbi.nlm.nih.gov/17483136/
3. Herrin BR, et al. 2008. Structure and Specificity of Lamprey Monoclonal Antibodies. Proceedings of the National Academy of Sciences of the United States of America. https://pubmed.ncbi.nlm.nih.gov/18238899/
4. Luo M, et al. 2013. Recognition of the Thomsen-Friedenreich Pancarcinoma Carbohydrate Antigen by a Lamprey Variable Lymphocyte Receptor. Journal of Biological Chemistry. https://pubmed.ncbi.nlm.nih.gov/23782692/
5. Nakahara H, et al. 2013. Chronic Lymphocytic Leukemia Monitoring with a Lamprey Idiotope-Specific Antibody. Cancer Immunology Research. https://pubmed.ncbi.nlm.nih.gov/24432304/
6. Velásquez AC, et al. 2017. Leucine-Rich-Repeat-Containing Variable Lymphocyte Receptors as Modules to Target Plant-Expressed Proteins. Plant Methods. https://pubmed.ncbi.nlm.nih.gov/28428809/
7. McKitrick TR, et al. 2020. Development of Smart Anti-Glycan Reagents Using Immunized Lampreys. Communications Biology. https://www.nature.com/articles/s42003-020-0819-2
8. Chao G, et al. 2006. Isolating and Engineering Human Antibodies Using Yeast Surface Display. Nature Protocols. https://pubmed.ncbi.nlm.nih.gov/17406305/
9. Boehm T, et al. 2018. Evolution of Alternative Adaptive Immune Systems in Vertebrates. Annual Review of Immunology. https://pubmed.ncbi.nlm.nih.gov/29144837/
10. Collins BC, et al. 2017. Structural Insights into VLR Fine Specificity for Blood Group Carbohydrates. Structure. https://pubmed.ncbi.nlm.nih.gov/28988747/
11. Song X, et al. 2016. Oxidative Release of Natural Glycans for Functional Glycomics. Nature Methods. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887297/ 

The problem with these biomarkers is that without extensive knowledge of their glycan epitopes, antibody attachment sites in PDAC cells, becomes very challenging to delineate PDAC from other cancers. CA19-9 is associated with not only other abdominal cancer types but also various other diseases (2). In contrast, sTRA glycan epitope varies between tumor subpopulations (3). All in all, there is a need to correlate specific glycan structures with antigens and generate more multi-layered detection methods.

As is the case in various cancers and malignancies, aberrant glycosylation is the definitive indicator of abnormalities. Preliminary studies found abnormalities in truncated O-glycans in pancreatic cancer (4), but N-glycan distribution and composition were overlooked until recently.

A recent study conducted at the Medical University of South Carolina addressed this issue by mapping out N-glycan profiles of normal pancreas and PDAC in great detail using imaging mass spectrometry (IMS) and immunohistochemistry (IHC) (5). They also correlated different aberrant N-glycan profiles with CA19-9 and sTRA expression to determine the antibody-based tests for these antigens.

Normal Pancreas: High Mannose and Sulfation

The first goal was to elucidate the spatial N-glycan distribution in normal pancreatic tissue via mass spectrometry. According to IMS analysis, one of the main features of pancreatic tissue is the abundance of high-mannose N-glycans in healthy pancreatic tissue and, in patient samples, the healthy tissue adjacent to the tumor. This pattern is the opposite of what has been observed in other cancers, including prostate, ovarian, breast, and colorectal, where the tumor cells are richer in high-mannose structures than the healthy surroundings.

The normal pancreatic tissue also exhibited sulfated and terminally galactosylated biantennary N-glycan structures, contributing to the immune response and insulin-glycogen balance in pancreatic tissue (6,7).

PDAC: Modifications in N-glycans

This study is the first of its kind, as it revealed not only N-glycan modifications in pancreatic tumors, but also the localization of the modified glycans to different tumor regions. In particular, the antennae or branching varied across these regions. For example, the necrotic tissue was heterogeneous: comprising biantennary, triantennary, and tetra-antennary structures with no fucosylation or sialylation. In contrast, adenocarcinoma regions mostly have triantennary and tetra-antennary structures containing fucosylated cores, bisecting GlcNAc, and terminal GalNAc residues. In adjacent normal tissue, biantennary structures were the most prevalent.

The sialylation patterns also differed across tumor regions, but they were both correlated with the metastatic behavior of the tumor. While normal tissue had few sialic acid residues, α2,3 sialylation was observed mainly in the tumor core. The less common α2,6 sialylation was more localized to primary adenocarcinoma clusters. The ratio of the two sialylation types varied within the tissue or even within the region, which can be explored further to identify pancreatic cancer subtypes.

Correlating N-glycan Structures to FDA-approved PDAC Biomarkers

Uncovering the relationship between the biomarkers (CA19-9 and sTRA) and N-glycan structures could open new avenues for the accurate evaluation of PDAC tumor tissue.

With that in mind, the study combined IMS with IHC to reveal N-glycan structures co-localizing with the biomarkers.

The researchers first classified the tumor core into four groups: those with high expression of both biomarkers, CA-19-9 only, sTRA only, and those with low expressions of both.

The heat maps they generated from tumor cores revealed significant differences between the four groups. The N-glycans colocalizing with CA19-9 had a variety of antennary structures, all with bisecting GlcNAc and fucosylated residues. However, the tumor cores abundant in sTRA mostly lacked biantennary structures and outer arm fucosylation.

Does N-Glycan Analysis Improve Detection Accuracy?

To address this question, the researchers combined existing carbohydrate antigen data with the glycan data they generated. They were able to form clusters of healthy and cancerous cores with distinct glycan profiles, where different glycan masses indicated tumor-specific glycan properties.

When they compared the predictive values of current biomarkers and N-glycan mass analysis, the predictive value of the combined method was significantly higher than that of the biomarkers alone and moderately higher than the N-glycan masses alone.

How did this translate to tumor detection accuracy? With the addition of glycan masses to biomarkers, the researchers were able to identify tumor cores that biomarkers alone failed to detect. They could also correctly identify healthy cores, which the biomarkers falsely identified as tumors.

That said, there were instances where they falsely identified tumor cores that were, in fact, normal. This discrepancy suggests that there is more to glycan mass and tumor differentiation due to the complexity of N-glycan structures in biomarkers.

Whole-Tissue N-glycan Imaging of Tumors: Analysis of Glycan Motifs

Regarding the false positives, the researchers hypothesized that there were mass isomers between normal pancreas and PDAC cores. To discern between these isomers, they used lectin immunofluorescence (IF) staining. In particular, the mass abundances of bisecting GlcNAc and terminal GalNAc would improve the accuracy in PDAC tumor classification, so the researchers used lectins PHA-E (Phaseolus Vulgaris Erythroagglutinin) and GSL-II (Griffonia (Bandeiraea) Simpllicifolia Lectin II) to detect these two structures, respectively.

The IF analysis of the tissues from the previous section revealed a perplexing relationship between lectin binding of the two glycan motifs, glycan mass abundance, and biomarker detection.

In false-positive tissues with high glycan mass, lectin binding was weak, meaning that bisecting GlcNAc and terminal GalNAc levels were low. On the other hand, some tumor cores correctly identified with glycan mass analysis and biomarkers actually had, low biomarker expression. Some tumor regions had small tumor-associated glycan masses but high lectin staining, indicating high bisecting GlcNAc and terminal GalNAc expression.

The Complexity of N-glycan Profiles in Pancreatic Cancer and Challenges

The findings of lectin staining could elucidate poor detection accuracy in pancreatic cancer. If a tumor tissue has a low expression of FDA-approved biomarkers, the antibodies for those biomarkers are less likely to detect tumors, despite the tumor-associated N-glycan motifs. That’s why incorporating IMS glycan analysis into biomarker-based studies could improve not only the discernment between normal vs. tumor tissue but also tumor classification. IHC and IF could provide an even more comprehensive picture of the correlation between N-glycan distribution and available biomarkers.

That said, there is still room for exploration since there is an immense level of variety in N-glycome across PDAC tumors and tumor regions. For example, the results from the study included tissue samples with high glycan mass but low lectin staining. In other words, these tumor tissues probably contain N-glycan mass isomers with modified binding motifs which could not be detected in the initial lectin staining attempts.

With the improvements in IMS, IHC, and IF instruments, the library of known N-glycans (especially those with high masses) and their mass isomers can be broadened. These improvements could result in the use of other lectins to detect structures that do not contain the common binding motifs. Eventually, this will allow a higher confidence level when classifying PDAC tumors and assessing their progression.

References

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