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Glycoproteomics: A Novel Domain of Clinical Testing
Article

Glycoproteomics: A Novel Domain of Clinical Testing

Glycoproteomics: A Novel Domain of Clinical Testing
Article

Glycoproteomics: A Novel Domain of Clinical Testing

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Genomic and proteomic analysis has driven research on disease pathology for the past several decades, leading to breakthroughs in disease detection and treatment, particularly in cancer care. Despite striking advances, however, outcomes and overall survival have improved only modestly for the majority of cancer patients. This realization motivates physicians and scientists to seek additional mechanisms that influence tumor behavior, with the goals of identifying more informative biomarker signatures for early disease detection, targeting therapies, monitoring treatment outcomes, and discovering new drug targets. The emerging field of glycoproteomics – the study of when sugars meet proteins – has the potential to contribute meaningfully to all these goals. This article describes the background and potential of this emerging field.


From genome to glycoproteome


Glycoproteomics analyzes the myriad variants of proteins generated by glycosylation, adding glycans as a powerfully differentiated alphabet of information-carrying molecules which affect and are affected by biologic processes.


In contrast to the static nature of germline DNA, glycoproteomics has the potential to reflect dynamically changes in response to perturbations of the physicochemical milieu of the body. This new level of submolecular detail promises to offer insights into the mechanisms responsible for public health scourges such as cancer, diabetes, atherosclerosis and a host of other diseases.  


What accounts for the exponential increase in information content as we move from the genome to the glycoproteome? Let’s begin with a simplified review of how proteins are created, starting at the level of the gene. The relatively static alphabet of the DNA double helix first undergoes transcription to messenger RNA (mRNA) molecules, each of which then code for the assembly of amino acids into proteins, a process called translation. The resulting “proteome” contains the enormous alphabet of all proteins, each of which undergoes further processing, acted on by physical and chemical processes known collectively as “post-translational protein modification”.  


Image showing the exponential increase of complexity along the biological cascade from genome to glycome.

 Figure 1: Exponential increase of complexity along the biological cascade.


  

Important among the protein modifications is glycosylation, the addition of carbohydrates, or glycans, at a variety of points on the protein scaffold, differentiating individual proteins into distinct glycoproteins. At each stage in this journey from DNA to modified proteins, the absolute numbers of unique molecules increase exponentially (Figure 1). Glycoproteomics research seeks to understand the richly complex biologic information contained in this “third alphabet of biology”.   


Protein glycosylation is the most common form of post-translational protein modification. Due to its impact on protein structure and function, it has long been implicated in disease biology. Until recently, however, scientists lacked the sophisticated tools needed to decipher the complex structure and relevance of glycoproteins at sufficient scale. As a result, their insights were incomplete; while earlier hypotheses explained the role of glycans as a “protective” coating for proteins, their physiological functions are much more varied and complex. Perhaps the best-known example of glycans’ impact on biology came from work done in the mid-1900s, when it was discovered that the chemical structures of glycans coating red blood cells are responsible for the different ABO blood types (Figure 2).


Image showing the molecular make-up of the A, B and O blood groups

Figure 2: ABO blood-group system, based on the expression of different glycans on the surface of erythrocytes. 


Advances in technology are enabling an increasingly granular understanding of how glycosylation affects important biological processes at the cellular and systemic level. One of the key technologies needed to characterize glycoprotein structure, mass spectrometry, has recently been paired with artificial intelligence and neural networking, accelerating the data processing by several orders of magnitude.


For the first time, we now have the means to identify glycoproteomic relationships that are relevant to human disease at a scale that is useful for actionable clinical research. These tools have opened the door for the new field of clinical glycoproteomics, which aims to profile the entirety of glycans and glycosylated proteins, with exciting potential for expanding clinical diagnostics.

 

Scaling glycoproteomics for cancer care


The addition of glycans to proteins has major impact on the structure and function of the proteins, changing critical interactions among biomolecules. A variety of glycans can be added to a given protein, giving rise to a large and diverse family of “glyco-isoforms” of the parent protein. The relative abundance of individual glyco-isoforms is quite dynamic, responding readily to influences such as hormonal, environmental, metabolic factors and occurrence of disease.


Specifically, glycoprotein “profiles” or “signatures”, which are essentially snapshots of the relative concentrations within a family of glyco-isoforms, are recognized to provide real-time biological information about the development of cancer. Glycosylation patterns are particularly relevant to oncology research because they provide clues about how cells become malignant and respond to therapies.


Among other applications, these findings raise the hope of advancing the effectiveness and safety of cancer immunotherapy. While immune checkpoint-inhibitor therapies have proven to be powerful new tools against certain cancers, they do not work for everyone, achieving response rates of only 30–50% among eligible patients.1,2 Reliable tools to predict which patients are likely to respond to treatment do not exist, however. Since the responses, when they occur, can be curative, these drugs are typically administered to all eligible patients. This means that 50–70% of patients who receive these potentially lifesaving agents do not benefit. Instead, they may experience severe, unnecessary side effects and incur significant health care expenditures. 


The need for better tools to guide the use of this class of cancer medicine is obvious. Enter glycoproteomics: early studies in cancer patients have shown that glycoprotein profiles are uniquely suited to provide this guidance by accurately identifying likely responders and non-responders to immune checkpoint-inhibitors.   


Glycoproteomic biomarker signatures hold the potential to help clinicians determine the appropriate choice of treatment regimen and monitor patients’ progress during treatment. They can offer researchers innovative approaches to drug discovery. Conceivably, these tests could be deployed in combination with existing genomic tests, by signaling when genetic risk factors give rise to actual disease. For example, a woman with a pathogenic BRCA mutation, conferring a very high risk of breast and ovarian cancer, might in the future be monitored closely using a glycoproteomic test for evidence of a developing malignancy. The test would turn positive at very early stages of cancer, enabling the woman to avoid undergoing the traumatic and life-changing prophylactic removal at a young age of her breasts and ovaries. Real world applications of glycoproteomic analyses are currently in their infancy, but this potentially fertility-preserving example illustrates the tremendous promise that many scientists see in this emerging field.  


Early use cases target patient response to cancer immunotherapy


Early studies show some potential use cases for glycoproteomic data that could help guide individual patient treatment:


  • Pre-treatment peripheral blood glycopeptide signatures in a small, retrospective pilot study of patients with metastatic malignant melanoma showed strong correlations, which differentiated patients with long-term response to immunotherapy from those with early treatment failure.3 These findings have subsequently been confirmed in a larger, more comprehensive study.4
  • Serum glycoproteomic signatures also predicted clinical outcomes in patients with inoperable, advanced non-small-cell lung cancer (NSCLC) who received first-line immunotherapy.5 While additional research is needed to validate these findings and their clinical utility, this early work is highly promising.
  • The first glycoproteomic-based blood test recently received certification in the US under Clinical Laboratory Improvement Amendments regulations. It differentiates benign from malignant pelvic tumors with a high level of accuracy.


Much remains unknown about the biology of sugar molecules and their role in cancer and other disease conditions. While glycans are recognized as biologically important, their role and impact, particularly in translational and applied research, has been difficult to decipher due to their structural heterogeneity and complexity, the sheer numbers of different molecular sub-moieties present, and the analytical challenges of characterizing them. High-resolution mass spectrometry remains central to analyzing these structures and continues to experience major technical improvements. Pairing this with artificial intelligence- and neural-network-based data processing engines has provided a critical boost in efficiency, allowing nothing less than an unveiling of a heretofore inaccessible layer of ubiquitous and highly relevant biologic molecules.  

  

References

 

1. Man J, Millican J, Mulvey A, Gebski V, Hui R. Response rate and survival at key timepoints with PD-1 blockade vs chemotherapy in PD-L1 subgroups: meta-analysis of metastatic NSCLC trials. JNCI Cancer Spectr. 2021;5(3). doi: 10.1093/jncics/pkab012.

2. Wolchok JD, Kluger H, Callahan MK, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013;369(2):122-33. doiI: 10.1056/NEJMoa1302369.

3. Xu G, Rice R, Huang H, et al. Abstract 387: Glycoproteomics as a powerful liquid biopsy-based predictor of checkpoint-inhibitor treatment response. Cancer Research. 2021;81(13_Supplement):387-387. doi: 10.1158/1538-7445.Am2021-387.

4. Lindpaintner K, Mitchell A, Pickering C, et al. Glycoproteomics as a powerful liquid biopsy-based predictor of checkpoint inhibitor treatment benefit in metastatic malignant melanoma. J Clin Oncol. 2022;40:suppl 16:abstr 9545. doi: 10.1200/JCO.2022.40.16_suppl.9545.

5. Lindpaintner K, Cheng M, Prendergast J, et al. 30 blood-based glycoprotein signatures in advanced non-small-cell lung carcinoma (NSCLC) receiving first-line immune checkpoint blockade. J Immunother Cancer. 2021;9(Suppl 2):A35-A35. doi: 10.1136/jitc-2021-SITC2021.030.

  


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