Early detection of cancer and prediction of treatment response continue to face significant challenges. Recently, liquid biopsies have been further extended to analyze Glycoproteins. Glycoproteins are products of post-translational modifications of proteins and play a key role in normal and pathological processes, including cancer. Glycoproteomic analysis has emerged as a promising tool for the discovery and development of biomarkers for the early detection of cancer and the prediction of therapeutic efficacy, including response to immunotherapy.
Recently, Carolyn Bertozzi's team at Stanford University published a review entitled “Decoding the glycoproteome: a new frontier for biomarker discovery in cancer” in the Journal of Hematology & Oncology. The authors summarize significant advances in cancer glycoproteomic biomarker research and the prospects and challenges of integrating them into clinical practice to improve cancer patient care.
Protein Glycosylation is a common post-translational modification that attaches glycans to proteins primarily through N- or O-bonds. It affects a variety of physiological events, including protein folding, stability and trafficking, cell-cell interactions, differentiation, and immune responses. Aberrant protein glycosylation is a hallmark of cancer and is critical in malignant transformation, tumor development, invasive and metastatic disease. Unique tumor-specific glycosylation patterns typically exhibit increased N-glycan branching, increased O-glycan density, incomplete glycan synthesis, neosynthesis, increased sialylation, and increased fucosylation, which are promising targets for liquid biopsies to differentiate between benign and cancerous cells.
Liquid biopsies are analyses of tumor-derived biomarkers in body fluids, most of which focus on ctDNA, CTCs, or exosomes isolated from blood. Despite their great potential, these types of liquid biopsies have limited utility in early cancer detection. One of the most common forms of glycosylation is N-linked glycosylation, a key process that facilitates the proper folding and trafficking of proteins in the secretory pathway and also contributes to a wide range of biological processes, such as intracellular and intercellular signaling and interactions with immune cell receptors.
In recent years, many studies have focused on changes in Hp’s N-glycosylation patterns associated with different diseases such as inflammatory diseases and malignancies. In cancer, alterations in haptoglobin (Hp) glycosylation are manifested at different levels, such as fucosylation, Sialylation, branching, and Lewis antigen. For example, patients with hepatocellular carcinoma (HCC) exhibit changes in α1-6 fucosylation and α2-6 sialylation of this abundant serum glycoprotein.
Tumorigenesis and cancer progression in human organs and tissues significantly affects hepatocyte synthesis and protein release, leading to “tumor-induced liver reprogramming”. Therefore, comprehensive analysis of this “tumor-induced liver reprogramming” by hepatocyte-derived proteomes is a promising liquid biopsy method to assess host responses during tumorigenesis and enable early cancer detection. For example, acute phase proteins, including C-reactive protein (CRP), serum amyloid A (SAA), Hp, and α-1-antitrypsin, are significantly upregulated in response to inflammatory insult. These hepatocyte-secreted proteins enter the bloodstream directly and serve as systemic biomarkers of underlying localized lesions.
Comprehensive analysis of the glycoproteome requires the identification of glycan structures as well as the proteins and sites to which they are attached. Mass spectrometry (MS) technology offers a wide range of additional capabilities and is a key technique for identifying disease-related changes in protein glycosylation. MS is a robust technology commonly used for cancer biomarker discovery that offers high sensitivity, compatibility with a variety of biological matrices, scalability potential to provide structural information on very small amounts of biological samples, and a large instrumental dynamic range. In the last few years, MS-based methods have contributed significantly to the study of glycans and glycoproteins through Glycomics (comprehensive characterization of glycan profiles of biological samples) and Glycoproteomics (comprehensive analysis of glycopeptides, providing information on glycans and proteins) analysis.
The development of novel biomarkers with clinical applicability is necessary to achieve breakthroughs in diagnostic performance. Specific forms of glycosylation associated with cancer progression show higher performance in the clinical setting or in the research laboratory than at the protein level alone.
Recent advances in MS-based technologies provide opportunities to discover and validate a set of glycoproteomic biomarkers for disease detection and monitoring. Among the differently targetable methods, multiple reaction monitoring (MRM) is the standard MS method that offers the highest sensitivity, high throughput, and simplicity in sample preparation and data interpretation. MRM methods for glycopeptides were initially developed for a few protein targets (e.g., immunoglobulins and apolipoproteins), and these methods have been applied to various cancer studies.
Fig.1 Biomarker discovery and validation workflow supported by LC-MS and AI technologies. (He, et al., 2024)
Recent studies have highlighted specific biological changes during the transition from adenoma to carcinoma, particularly specific immune responses in the colonic crypts. Abnormal protein glycosylation is a key driver of these responses and is evident in both colonic tissues and circulating glycoproteins. Studies of N-glycome in CRC patients revealed associations with branched and poly-LacNAc elongated N-glycans, which normalized after treatment. Further studies of N-glycan profiles revealed that enhanced fucosylation and sialylation on specific proteins may aid in the detection of CRC. In addition, differences in IgG N glycome profiles were observed between CRC and non-CRC patients, which provide potential diagnostic insights.
Fig.2 Aberrant glycosylation affects the process of colorectal adenoma-to-carcinoma transformation. (He, et al., 2024)
Several studies have investigated the role of glycoproteomic signatures in the diagnosis of lung cancer. A similar glycoproteomics platform used in CRC combines MS with an artificial intelligence-based data processing engine and are applied to the glycoproteome in lung cancer to generate a glycoproteomic classifier that shows high sensitivity and specificity in lung cancer and normal tissues.
In a recent study, a novel blood-based MS combined with machine learning was used to evaluate a glycopeptide classifier for the diagnosis of ovarian cancer, identifying specific glycopeptide biomarkers that could effectively differentiate between patients with benign pelvic masses and those with malignant ovarian cancer.
The field of cancer glycoproteomic biomarker research is rapidly evolving, with significant discoveries in cancer screening/early diagnosis, treatment prediction and monitoring. This review summarizes the importance of current research in glycobiology and incorporates glycoproteomics into cancer liquid biopsies for biomarker discovery, while also clarifying the importance of glycoproteomics in clinical oncology.
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