Contributed Commentary by Klaus Lindpaintner, InterVenn Biosciences
April 8, 2022 | For many clinicians, glycoproteomics is a new and unfamiliar field. However, this emerging “omic” has demonstrated potential to identify novel biomarkers and advance the field of diagnostics. Formerly confined to the research laboratory, the once daunting study of glycoproteomics (or “when sugar meet protein”) has been transformed by the use of machine learning.
These advances make it possible to develop efficient real-world applications which illuminate how the effects of the addition of sugar molecules on structure and function of proteins can make the difference between disease and health, unlocking earlier detection of cancers and other conditions. With a deeper understanding of the causes of disease, novel tools to assist in clinical decision-making are being created.
At the core of this emerging field are glycans, which have been called the “third alphabet of biology” alongside proteins and nucleic acids. Glycans are a uniquely important class of information-carrying macromolecules made up of complex carbohydrates. They occupy myriad positions along the amino acid backbone which forms all proteins. The addition of glycan molecules to proteins—a process known as glycosylation—is found in many biological pathways. The resulting molecules are called glycoproteins.
The Meaning of Glycosylation Patterns and Glycoforms
The type and location of glycans stuck to our proteins—called glycosylation patterns—plays a crucial role in biological processes and influences how a glycoprotein behaves within the body. As large sugar molecules bind to proteins, they can change in how these proteins are folded upon themselves. The three-dimensional structure of the parent protein changes depending on its glycosylation pattern, creating a family of “glycoforms” with differential functions, e.g., how they interact with receptors on cells and tissues.
Not surprisingly, the original parent protein behaves very differently in the body depending on which of its glycoforms is present. Different glycoforms activate or inhibit different cellular processes. The field of glycoproteomics is unlocking these complex relationships and how they relate to health and disease.
Up to 80% of all proteins in the body undergo glycosylation, and these glycosylation patterns are a focus of intense research. Because these patterns hold clues to how glycoproteins function in various states of health, they are leading to the discovery of new and highly informative biomarkers, which in turn open the door to development of useful new diagnostic blood tests. For example, ovarian cancer is currently quite difficult to identify at a curable early stage. However, glycoforms of specific glycoproteins reliably differentiate between women with and without ovarian cancer. This discovery has led to the development of a blood test that discriminates between benign pelvic tumors and ovarian cancer.
Artificial Intelligence Puts Complex Knowledge Within Reach
Until the advent of artificial intelligence, the technical challenges inherent in characterizing these complex molecules presented an insurmountable barrier to progress in understanding the biological functions of glycoproteins, not to mention the development of useful clinical applications of this knowledge.
Recently, however, the combination of artificial intelligence and advancements in mass spectrometry have finally put this complex information within practical reach. Specifically, these innovative technologies have vastly shortened the time needed to understand and analyze glycosylation patterns.
Astonishingly, a series of assays which formerly required months of fulltime effort in the lab can now be completed in minutes. Because of these advances, I believe that glycoproteomics is now ready for prime time, and scientists seek the most useful applications for this emerging field of science. One potential example of such an application is described below.
A Multi-Omic Approach to Clinical Decision-Making
Only a few decades ago, the field of genomics was emerging much as glycoproteomics is today. Genomics, the study of DNA and its role in creating the proteins of life, promised to unlock secrets of health and disease in an entirely novel way.
Today genomics has fulfilled that promise and the resulting knowledge is now firmly entrenched in the clinical management of a wide variety of diseases, including cancer. However, genomics is a limited tool when it comes to helping us understand dynamic processes that modify inherited genetic material and the proteins they create.
The following example describes a “multi-omic” approach to care of women with an inherited genetic risk for breast and ovarian cancer, namely the BRCA1/2 mutations. Today, if a woman undergoes genetic testing and certain BRCA mutations are detected, she may be advised to undergo the preventive removal of both breasts and both ovaries rather than risk developing advanced and incurable cancer before it can be detected. This woman is faced with the choice between radical surgery versus a high likelihood of a potentially fatal cancer.
Further genome-based testing cannot aid in this woman’s management, since genetic characteristics will not change. Imagine, however, if blood tests were available which could reliably and in real-time detect very early cancer in this woman. If she could be tested periodically to detect changes in protein glycosylation patterns associated with very early stages of these malignancies, it might be safe to defer the radical surgery, perhaps until she has had the chance to have children or fulfill other life goals. This is just one example of how combining glycoproteomic testing with genomic risk assessment for a “multi-omic” approach to clinical management of people at high risk of developing malignancies could dramatically advance the practice of medicine as well as the fate of patients.
Anticipated for a long time, but so far elusive, the complementary strengths of different -omics technology platforms are now beginning to materialize, with the emerging prospect that synergies thus possible may extend beyond the merely additive, and, thus, significantly improve clinical management and care.
Klaus Lindpaintner, MD, MPH; serves as InterVenn’s Distinguished Scientist and focuses as the scientific alliance ambassador to the global glycobiology constituency. His efforts will aim at raising awareness of glycobiology across life science and biomedical communities, fostering cross-disciplinary collaborations across basic and clinical research, supporting working groups for the harmonization of standards, terminologies, and processes in this still evolving field, and attracting young scientist to the domain. After serving on the faculty at Harvard University, he joined industry, working in executive positions at Roche, Pfizer, and Thermo Fisher Scientific, with a focus on the field of personalized health care. Klaus holds degrees in medicine and public health, is a board-certified internist, cardiologist, and medical geneticist, and an elected Fellow of the American Colleges of Physicians, Cardiology, and Medical Genetics. He has co-authored some 250 publications, with basic research interests in population and epidemiological genetics. Klaus is fascinated by the potential of InterVenn’s platform to contribute to a broad spectrum of health care needs, with a current focus on cancer. He can be reached at email@example.com.