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Esophageal cancer (EC) is a life-threatening disease, demanding the discovery of new biomarkers and molecular targets for precision oncology. Aberrantly glycosylated proteins hold tremendous potential towards this objective. In the current study, a series of esophageal squamous cell carcinomas (ESCC) and EC-derived circulating tumor cells (CTCs) were screened by immunoassays for the sialyl-Tn (STn) antigen, a glycan rarely expressed in healthy tissues and widely observed in aggressive gastrointestinal cancers. An ESCC cell model was glycoengineered to express STn and characterized in relation to cell proliferation and invasion in vitro. STn was found to be widely present in ESCC (70% of tumors) and in CTCs in 20% of patients, being associated with general recurrence and reduced survival. Furthermore, STn expression in ESCC cells increased invasion in vitro, while reducing cancer cells proliferation. In parallel, an ESCC mass spectrometry-based proteomics dataset, obtained from the PRIDE database, was comprehensively interrogated for abnormally glycosylated proteins. Data integration with the Target Score, an algorithm developed in-house, pinpointed the glucose transporter type 1 (GLUT1) as a biomarker of poor prognosis. GLUT1-STn glycoproteoforms were latter identified in tumor tissues in patients facing worst prognosis. Furthermore, healthy human tissues analysis suggested that STn glycosylation provided cancer specificity to GLUT1. In conclusion, STn is a biomarker of worst prognosis in EC and GLUT1-STn glycoforms may be used to increase its specificity on the stratification and targeting of aggressive ESCC forms.

Background: The glycan moieties sialyl-Lewis-X and/or -A (sLe X/A ) are the primary ligands for E-selectin, regulating subsequent tumor cell extravasation into distant organs. However, the nature of the glycoprotein scaffolds displaying these glycans in breast cancer remains unclear and constitutes the focus of the present investigation., Methods: We isolated glycoproteins that bind E-selectin from the CF1_T breast cancer cell line, derived from a patient with ductal carcinoma. Proteins were identified using bottom-up proteomics approach by nanoLC-orbitrap LTQ-MS/MS. Data were curated using bioinformatics tools to highlight clinically relevant glycoproteins, which were validated by flow cytometry, Western blot, immunohistochemistry and in-situ proximity ligation assays in clinical samples., Results: We observed that the CF1_T cell line expressed sLe X , but not sLe A and the E-selectin reactivity was mainly on N-glycans. MS and bioinformatics analysis of the targeted glycoproteins, when narrowed down to the most clinically relevant species in breast cancer, identified CD44 glycoprotein (HCELL) and CD13 as key E-selectin ligands. Additionally, the co-expression of sLe X -CD44 and sLe X -CD13 was confirmed in clinical breast cancer tissue samples., Conclusions: Both CD44 and CD13 glycoforms display sLe X in breast cancer and bind E-selectin, suggesting a key role in metastasis development. Such observations provide a novel molecular rationale for developing targeted therapeutics., General Significance: While HCELL expression in breast cancer has been previously reported, this is the first study indicating that CD13 functions as an E-selectin ligand in breast cancer. This observation supports previous associations of CD13 with metastasis and draws attention to this glycoprotein as an anti-cancer target., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Glycosylation is one of the most common and dynamic post-translational modification of cell surface and secreted proteins. Cancer cells display unique glycosylation patterns that decisively contribute to drive oncogenic behavior, including disease progression and dissemination. Moreover, alterations in glycosylation are often responsible for the creation of protein signatures holding significant biomarker value and potential for targeted therapeutics. Accordingly, many analytical protocols have been outlined for the identification of abnormally glycosylated proteins by mass spectrometry. Nevertheless, very few studies undergo a comprehensive mining of the generated data. Herein, we build on bladder cancer O-glycoproteomics datasets resulting from a hyphenated technique comprising enrichment by Vicia villosa agglutinin (VVA) lectin and nanoLC-ESI-MS/MS to propose an in silico step-by-step tutorial (Panther, UniProtKB, NetOGlyc, NetNGlyc, Oncomine, Cytoscape) for biomarker discovery in cancer. We envisage that this approach may be generalized to other mass spectrometry-based analytical approaches, including N-glycoproteomics studies, and different types of cancers., Significance: The glycoproteome is an important source of cancer biomarkers holding tremendous potential for targeted therapeutics. We now present an in silico roadmap for comprehensive interpretation of big data generated by mass spectrometry-based glycoproteomics envisaging the identification of clinically relevant glycobiomarkers., (Copyright © 2017 Elsevier B.V. All rights reserved.)