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Minimally invasive technologies for early diagnosis of epithelial ovarian cancer (EOC) remain an unmet clinical need. CA-125, a tumor marker secreted into the circulation, is utilized to monitor treatment response and disease relapse in EOC, but has limited utility in accurately triaging patients with pelvic masses of unknown histology. To address this unmet need, we applied a novel blood-based glycoproteomic platform that relies on mass spectrometry coupled to machine learning tools, and identified glycopeptide biomarkers that differentiate between patients with benign pelvic masses and malignant EOC. We then used a subset of these markers to generate a classifier that discriminated between benign pelvic tumors and EOC with sensitivity and specificity of 83.5% and 90.1% in the training set and 86.7 and 86.7% in the testing set, respectively. On subgroup analyses, we noticed that patients with malignant EOC had higher levels of fucosylated markers, primarily of hepatic origin. Furthermore, patients with late-stage EOC (FIGO stage III and IV) had markedly higher levels of tri- and tetra-antennary glycopeptide markers containing fucose. We used these markers to build an independent algorithm that can differentiate between early- and late-stage EOC. Lastly, we detected a similar upregulation of fucosylated glycans and gene expression signatures suggestive of multi-antennary glycans in late-stage EOC tissues. We posit that common mechanisms - possibly driven by cytokines - affect both the tumor glycocalyx and liver-derived glycoproteins. In summary, we generated blood glycoproteomic profiles resemblant of distinct tumor states and identified biomarkers that differentiate between benign and malignant pelvic masses, and/or between early- and late-stage EOC. We also provide mechanistic insights suggesting a direct link between the tumor site and the circulating glycoproteome. These data may inform the development of robust clinical tests to diagnose and stage patients with EOC.

Fatty liver disease progresses through stages of fat accumulation and inflammation to nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis, and eventually hepatocellular carcinoma (HCC). Currently available diagnostic tools for HCC lack sensitivity and specificity. In this study, we investigated the use of circulating serum glycoproteins to identify a panel of potential prognostic markers that may be indicative of progression from the healthy state to NASH and further to HCC. Serum samples were processed and analyzed using a novel high-throughput glycoproteomics platform. Our initial dataset contained healthy, NASH, and HCC serum samples. We analyzed 413 glycopeptides, representing 57 abundant serum proteins, and compared among the three phenotypes. We studied the normalized abundance of common glycoforms and found 40 glycopeptides with statistically significant differences in abundances in NASH and HCC compared to controls. Summary level relative abundances of core-fucosylated, sialylated, and branched glycans containing glycopeptides were higher in NASH and HCC as compared to controls. We replicated some of our findings in an independent set of samples of individuals with benign liver conditions and HCC. Our results may be of value in the management of liver diseases. Data generated in this work can be downloaded from MassIVE (https://massive.ucsd.edu) with identifier MSV000088809.

Background: Protein glycosylation is the most common and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography-mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time, highly scalable interrogation of the glycoproteome. Here we report the performance of this platform to predict likely benefit from immune-checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC). Methods: Our platform was utilized to assess 532 glycopeptide (GP) and peptide signatures representing 75 serum proteins in pretreatment blood samples from a cohort of 123 individuals (54 females, 69 males, age range 30 to 88 years). Inclusion criteria were a diagnosis of unresectable stage 3 or 4 NSCLC, treatment with pembrolizumab monotherapy (26 patients), or treatment with combination pembrolizumab-chemotherapy (97 patients). Overall survival (OS) data were available for all patients. Results: An ensemble multivariable-model-based glycoproteomic classifier consisting of 7 GP and non-glycosylated peptide biomarker features selected from a generalized additive model for OS was developed using ≈2/3rds of the full cohort (n=88) and validated in the remainder of patients (n=35). The classifier yielded similar statistical significance in Cox regression analysis for separating patients who are likely to benefit from ICI therapy from those who are not, to accurately predict likely ICI benefit with a sensitivity of >95% while performing at a specificity of 33% to predict those who are unlikely to benefit. Results were further analyzed in patients with either non-squamous or squamous NSCLC with first-line therapy (n=98). The classifier yielded a hazard ratio (HR) for prediction of likely ICI benefit of 3.6 with median OS of 13.9 vs. 4.2 months, and of 3.5 with median OS of 13.5 vs. 4.5 months in the entire cohort and the first-line treated patients, respectively. Conclusions: The glycoproteomic classifier described here predicts with high sensitivity which patients are likely to benefit from ICI therapy. In addition to potentially reducing the use of ICIs in a safe manner in patients who would be unnecessarily subjected to possible adverse drug reactions, our classifier simultaneously has the potential of reducing the burden of health care expenditures. Our results indicate that glycoproteomics holds a strong promise as a predictor for ICI treatment benefit which appears to significantly outperform other currently pursued biomarker approaches. Citation Format: Klaus Lindpaintner, Chad Pickering, Alan Mitchell, Gege Xu, Xin Cong, Daniel Serie. A peripheral blood-based glycoproteomic predictor of checkpoint inhibitor treatment benefit in advanced non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5314.

A cross-linking method is developed to elucidate glycan-mediated interactions between membrane proteins through sialic acids. The method provides information on previously unknown extensive glycomic interactions on cell membranes. The vast majority of membrane proteins are glycosylated with complicated glycan structures attached to the polypeptide backbone. Glycan–protein interactions are fundamental elements in many cellular events. Although significant advances have been made to identify protein–protein interactions in living cells, only modest advances have been made on glycan–protein interactions. Mechanistic elucidation of glycan–protein interactions has thus far remained elusive. Therefore, we developed a cross-linking mass spectrometry (XL-MS) workflow to directly identify glycan–protein interactions on the cell membrane using liquid chromatography-mass spectrometry (LC-MS). This method involved incorporating azido groups on cell surface glycans through biosynthetic pathways, followed by treatment of cell cultures with a synthesized reagent, N-hydroxysuccinimide (NHS)–cyclooctyne, which allowed the cross-linking of the sialic acid azides on glycans with primary amines on polypeptide backbones. The coupled peptide–glycan–peptide pairs after cross-linking were identified using the latest techniques in glycoproteomic and glycomic analyses and bioinformatics software. With this approach, information on the site of glycosylation, the glycoform, the source protein, and the target protein of the cross-linked pair were obtained. Glycoprotein–protein interactions involving unique glycoforms on the PNT2 cell surface were identified using the optimized and validated method. We built the GPX network of the PNT2 cell line and further investigated the biological roles of different glycan structures within protein complexes. Furthermore, we were able to build glycoprotein–protein complex models for previously unexplored interactions. The method will advance our future understanding of the roles of glycans in protein complexes on the cell surface., The cell surface glycocalyx is highly interactive defined by extensive covalent and non-covalent interactions. A method for cross-linking and characterizing glycan–peptide interactions in situ is developed.

Proteins on cell membrane are modified by N- and O-glycans. N-Glycans have been extensively characterized using advanced separation and mass spectrometry techniques. However, O-glycans remain a challenge, because of the lack of universal enzymes to release them and the large background abundances of N-glycans. Here, we report a method for in-depth structural analysis and quantitation of O-glycans derived from human cell membrane. O-Glycans were chemically released from isolated cell membrane glycoproteins following N-glycan and lipid/glycolipid removal by PNGase F digestion and Folch extraction, respectively. Released O-glycans were purified by an optimized protocol to eliminate interference from small molecules and degraded proteins. Cell surface O-glycans were then analyzed using a nanoLC-chip-QTOF mass spectrometer with a porous graphitized carbon (PGC) column, while the N-glycans and glycolipids isolated from the same cell membrane fractions were analyzed in parallel using previously reported methods. The monosaccharide compositions and linkages of the detected O-glycans were identified by exoglycosidase digestion facilitated with tandem mass spectrometry (MS/MS). Using this method, we identified 44 cell membrane O-glycan isomers with MS/MS, and, among them, we unambiguously characterized 25 O-glycan structures with exoglycosidase digestion to create a library with their complete structures, accurate masses, and retention times. In this process, we identified and characterized unexpected mannose oligomers that are α(1-2/3) linked. This library enabled the identification and quantification of unique cell surface O-glycans from different cell lines and the study of specific O-glycan changes during cell differentiation.

Structure and shape engineering has great potential to enable unique graphitic carbon nitride (CN) nanostructures with adjustable electronic properties and enhanced photocatalytic activities. In this study, a novel type of self-assembled melamine aggregate was formed in dimethyl sulfoxide (DMSO) without additional chemicals and templates, and these aggregates were then directly calcined to obtain three-dimensional hierarchically nanoporous CN. This structure-retention from the precursor to CN demonstrated that the structure and shape of CN were free from collapse during melamine thermal polycondensation and thus exposed vast active sites. Besides, the oxygen dopant resulting from structure-retention adjusted the electronic properties of CN derived from melamine aggregates (MACN), facilitating the hydrogen evolution reaction. The Pt-decorated MACN showed the fastest hydrogen production rate of 8.2 μmol h−1 for 3 wt% Pt/MACN under visible light, which is 4.5 times higher than that of the Pt-decorated CN sample obtained from the calcination of commercial melamine precursor. The structure-retention strategy via melamine self-assembly is a facile, rapid, and robust method to access unique nanostructured CN with enhanced photocatalytic performance under visible light, thereby enriching the toolbox for the fabrication of CN materials.

Salmonellainvades and disrupts gut epithelium integrity, creating an infection-generated electric field that can drive directional migration of macrophages, a process called galvanotaxis. Phagocytosis of bacteria reverses the direction of macrophage galvanotaxis, implicating a bioelectrical mechanism to initiate life-threatening disseminations. The force that drives direction reversal of macrophage galvanotaxis is not understood. One hypothesis is thatSalmonellacan alter the electrical properties of the macrophages by modifying host cell surface glycan composition, which is supported by the fact that cleavage of surface-exposed sialic acids with a bacterial neuraminidase severely impairs macrophage galvanotaxis, as well as phagocytosis. Here, we utilize N-glycan profiling by nanoLC-chip QTOF mass cytometry to characterize the bacterial neuraminidase-associated compositional shift of the macrophage glycocalyx, which revealed a decrease in sialylated and an increase in fucosylated and high mannose structures. TheSalmonellananHgene, encoding a putative neuraminidase, is required for invasion and internalization in a human colonic epithelial cell infection model. To determine whether NanH is required for theSalmonellainfection-dependent direction reversal, we constructed and characterized ananHdeletion mutant and found that NanH is partially required forSalmonellainfection in primary murine macrophages. However, compared to wild typeSalmonella, infection with thenanHmutant only marginally reduced the cathode-oriented macrophage galvonotaxis, without canceling direction reversal. Together, these findings strongly suggest that while neuraminidase-mediated N-glycan modification impaired both macrophage phagocytosis and galvanotaxis, yet to be defined mechanisms other than NanH may play a more important role in bioelectrical control of macrophage trafficking, which potentially triggers dissemination.

69 Background: Colorectal cancer (CRC) remains a leading cancer despite current screening modalities. Precancerous lesions, or Advanced Adenomas (AA), commonly precede invasive cancer development by years. Newer technologies use circulating tumor DNA and/or proteins for CRC detection but have not been able to effectively detect AA. Aberrant protein glycosylation is associated with (pre-)malignant lesions. To detect glycoproteome profiles associated with the occurrence of AA, we studied serum glycoproteins in AA/CRC. Methods: A novel platform combining liquid-chromatography/mass-spectrometry (LC-MS) and artificial-intelligence (AI)-powered data processing allowing high resolution, high throughput glycoproteomic profiling was used to identify glycoprotein biomarkers in peripheral blood. Samples were sourced from biorepositories and included patients diagnosed with CRC, AA, ulcerative colitis (UC) and controls. The samples were split into a training (50%) and a hold-out testing set (50%) for the development of a machine learning (ML)-based multivariable predictive model. Statistical analysis was performed on normalized data to identify biomarkers differentiating AAs and different stages of CRC from controls. Results: We studied 563 patient samples: 196 controls (mean age 51.7; 52% female); 32 AA (mean age 68.6; 53% female); 247 CRC (mean age 65.6; 50% female) and 88 UC (mean age 44.1; 47% female). There were 250 differentially abundant (FDR < 0.05) glycopeptides/peptides when comparing CRC and AA samples with healthy and UC controls. A subset was assessed, generating a six (6) biomarker ML classification model. This model was applied to the hold-out test and achieved an overall sensitivity of 91.4% and specificity of 91.8% for predicting AA/CRC versus healthy/UC with an area under the receiver operating characteristic of 0.962. AA and CRC separately were predicted with a sensitivity of 84.4% and 92.8%, respectively, relative to healthy/UC with sensitivities for CRC stage 1/2 and stage 3/4 being 91.2% and 93.2%, respectively). Conclusions: Glycoproteomic serum profiles accurately detect precancerous AA in addition to CRC and offer a new approach to effective CRC screening. We will have completed an interim analysis of a large prospective observational study at the time of the meeting. Clinical trial information: NCT05445570 . [Table: see text]

BackgroundImmune checkpoint blockade is an integral component of first-line therapy for most patients with ad-vanced non-small cell lung cancer (NSCLC), however individual patient outcomes are highly variable and improved biomarkers are needed. Protein glycosylation is an emerging mechanism of immune evasion in cancer. We examined blood-based glycopeptide signatures in a cohort of advanced NSCLC patients treated with first-line immune checkpoint blockade.MethodsPretreatment blood samples were obtained from 46 advanced NSCLC patients treated with first line pembrolizumab or pembrolizumab + carboplatin + pemetrexed. All patients provided written in-formed consent to the institutional review board–approved protocols (#02–180 and 13–367) at the Da-na-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki. Samples were analyzed using an advanced glycoproteomics platform (Inter-Venn Biosciences) that combines ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometry with a proprietary neural-network-based data processing engine. 409 individual glycopeptide (GP) signatures derived from 67 abundant serum proteins were analyzed and correlated with overall survival (OS) and other clinical outcomes.ResultsWe identified 30 GPs with abundance differences using a False Discovery Rate (FDR) threshold of 0.05. Using the 5 most predictive GP markers, we created a multivariable model for OS by generating leave-one-out cross-validation (LOOCV) scores and determining an optimized cutoff value of -0.83 (range: -2.2 - 3.4) for these scores using Harrell’s concordance index. The median overall survival was 2.8 years for patients (n=14) whose GP classifier value was above the cutoff and 0.8 years for patients (n=32) whose GP classifier value was below the cutoff (HR 7.4, 95% CI 1.7–32.1, p=0.007) The model’s perfor-mance was not affected by sex, age, or treatment regimen.ConclusionsBlood-based glycopeptide signatures may represent novel, non-invasive biomarkers of clinical out-come to first-line immune checkpoint blockade in advanced NSCLC. Additional research is needed to validate these findings in larger cohorts and to explore potential applications relevant to clinical decision-making.Ethics ApprovalThe study obtained ethics approval from the institutional review board (approved protocol #02–180 and 13–367) at the Dana-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki.ConsentAll patients provided written informed consent to the institutional review board–approved protocols (#02–180 and 13–367) at the Dana-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki.

Objective To determine whether aldehyde dehydrogenase 1 (ALDH1) immunostaining in axillary lymph node metastases in patients with breast cancer is associated with poor clinical prognosis. Methods This retrospective study reviewed data from the medical records of patients with immunohistochemistry-confirmed invasive ductal carcinoma (IDC) and 1–3 metastatic lymph nodes in the ipsilateral axilla between December 2012 and July 2015. The association between ALDH1 immunostaining in axillary lymph node metastases and clinical parameters and prognosis was analysed using χ2-test, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analyses. Results A total of 229 patients with IDC were enrolled in the study. The median follow-up was 61 months (range, 20–89 months). Patients with ALDH1-positive axillary lymph node metastases had significantly shorter relapse-free survival and overall survival compared with those with ALDH1-negative axillary lymph node metastases. ALDH1 immunostaining in axillary lymph node metastases was a significant predictor of poor prognosis in univariate and multivariate analyses. Conclusion This large study with long-term follow-up suggests that ALDH1 immunostaining in axillary lymph node metastases is an independent predictor of poor prognosis in patients with breast cancer. The clinical relevance of this finding should be confirmed in further well-designed prospective studies.

We have recently introduced multiple reaction monitoring (MRM) mass spectrometry as a novel tool for glycan biomarker research and discovery. Herein, we employ this technique to characterize the site-specific glycan alterations associated with primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC). Glycopeptides associated with disease severity were also identified. Multinomial regression modelling was employed to construct and validate multi-analyte diagnostic models capable of accurately distinguishing PBC, PSC, and healthy controls from one another (AUC=0.93±0.03). Finally, to investigate how disease-relevant environmental factors can influence glycosylation, we characterized the ability of bile acids known to be differentially expressed in PBC to alter glycosylation. We hypothesize that this could be a mechanism by which altered self-antigens are generated and become targets for immune attack. This work demonstrates the utility of the MRM method to identify diagnostic site-specific glycan classifiers capable of distinguishing even related autoimmune diseases from one another.

Polysaccharides make up the largest non-water component of plant-based foods. Their ability to manipulate the gut microbiome and modulate the immune system has increased interest in the rapid elucidation of their structures. A necessary component for the structural characterization of polysaccharides is the determination of their monosaccharide composition. Current methods of monosaccharide analysis are not suitable for analyzing large sample-sets and are limited by their inability to analyze polysaccharides. We have developed a 96-well plate hydrolysis and derivatization procedure followed by a rapid and sensitive 10-min ultra-high performance liquid chromatography triple quadrupole mass spectrometry analysis capable of the absolute quantitation of 14 plant monosaccharides. Four polysaccharide standards, inulin, xyloglucan, arabinogalactan, and rhamnogalacturonan-I, which are commonly found in plants, were used to optimize and validate the method. The optimized conditions were applied to eight foods to show the method’s reproducibility and ability to analyze complicated and insoluble polysaccharide mixtures. This approach will allow researchers to obtain accurate and absolute quantitation of monosaccharides in the large sample-sets that are required for agricultural, food, clinical, and nutrition-based studies.

Analysis of serum protein glycovariants has the potential to identify new biomarkers of human disease. However, the inability to rapidly quantify glycans in a site-specific fashion remains the major barrier to applying such biomarkers clinically. Advancements in sample preparation and glycopeptide quantification are thus needed to better bridge glycoscience with biomarker discovery research. We present here the successful utilization of several sample preparation techniques, including multienzyme digestion and glycopeptide enrichment, to increase the repertoire of glycopeptides that can be generated from serum glycoproteins. These techniques combined with glycopeptide retention time prediction and UHPLC-QqQ conditions optimization were then used to develop a dynamic multiple-reaction monitoring (dMRM)-based strategy to simultaneously monitor over 100 glycosylation sites across 50 serum glycoproteins. In total, the abundances of over 600 glycopeptides were simultaneously monitored, some of which were identified by utilizing theoretically predicted ion products and presumed m/ z values. The dMRM method was found to have good sensitivity. In the targeted dMRM mode, the limit of quantitation (LOQ) of nine standard glycoproteins reached femtomole levels with dynamic ranges spanning 3-4 orders of magnitude. The dMRM-based strategy also showed high reproducibility with regards to both instrument and sample preparation performance. The high coverage of the serum glycoproteins that can be quantitated to the glycopeptide level makes this method especially suitable for the biomarker discovery from large sample sets. We predict that, in the near future, biomarkers, such as these, will be deployed clinically, especially in the fields of cancer and autoimmunity.

Introduction: Nasopharyngeal carcinoma (NPC) is a malignancy characterized by distinct geographic, ethnic, and genetic differences. It presents a major threat to human health, particularly in East Asia and Southeast Asia. NPC commonly invades adjacent tissues and organs, resulting in complex and difficult to treat clinical presentations. NPC commonly remains largely asymptomatic during early disease stages due to its small primary foci. Hence, NPC patients are diagnosed mostly at advanced stage. Currently, no accurate and effective methods are available for the early diagnosis, evaluation, or prognosis for NPC. While Epstein-Barr virus (EBV) DNA and EBV serology tests have been advocated for early diagnosis and screening for NPC, the sensitivities of these two tests vary. due to the fact that only a small proportion of individuals infected with EBV ultimately develop NPC. The ability to detect neoplasms at early stages provides useful information for early intervention. We have recently demonstrated that interrogating the serum glycoproteome, using a proprietary platform that couples artificial intelligence (AI) to targeted liquid chromatography-massspectrometry (LC-MS) yields highly informative biomarkers in a range of neoplastic diseases. Experimental procedures in this proof-of-principle study, a total of 106 plasma samples (1:1 ratio of histologically confirmed cases of NPC and cancer-free controls), which were matched for age (average 49 years) were used. All subjects were male, from a multi-ethnic patient population in Malaysia, including three ethnic minority groups (Bidayuh, Iban and Kadaza). To identify diagnostic glycoprotein profiles for NPC we applied an in-house developed targeted glycoproteomics workflow, whereby plasma proteins were first digested with trypsin/LysC, followed by targeted analysis using LC-MS. The LC-MS generated chromatographic peaks were integrated using an in-house AI-based software for high throughput analysis and further subjected to multivariate statistical analysis. Summary of new data among the 579 glycopeptides interrogated in this study we found 62 glycopeptides statistically significantly abundant in NPC cases compared to healthy controls (at FDR Conclusions: In summary, our results indicate that LC-MS-based glycopeptide profiling provides a highly accurate means of for differentiating NPC cases from healthy controls. Follow-up studies with larger sample size are currently in progress. Citation Format: Klaus Lindpaintner, Thi Thin Aye, Gege Xu, Rachel Rice, Alan Mtchell, Daniel Serie, A Malek, LH Low, Lp Tan, Ct Low, Cy Wong, H Sawali, Yy Yapp, As Khoo. Peripheral blood glycoproteomic biomarkers as a powerful new tool for the detection of nasopharyngeal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2799.

Introduction: While immune checkpoint inhibitor (ICI) therapy has added a powerful new arsenal of highly effective drugs for the subset of cancer patients who respond to these agents, their use remains burdened by the fact that we lack reliable biomarkers to identify likely responders, to avoid the adverse event incidence and cost of treating likely non-responders. Likewise, we currently have no tools which would help identify the optimal choice of agents; current prescribing practice is not guided by any objective criteria. We have recently demonstrated that interrogating the serum glycoproteome, using a proprietary platform that couples artificial intelligence to targeted liquid chromatography-mass spectrometry yields highly informative biomarkers for a range of use cases, including prediction of response to ICI treatment. We recently demonstrated this for metastatic malignant melanoma (MM). In the current study, we examined if we could also predict preferential response to individual ICIs. Experimental Procedures: We carried out glycoproteomic analysis of pretreatment blood samples in advanced MM patients treated with pembrolizumab (P; n=24) or nivolumab-ipilimumab (N; n=11). Individual glycopeptide (GP)signatures derived from 67 serum proteins were analyzed and correlated with treatment, and progression-free survival (PFS). Summary of New Data: Two response groups were defined based on PFS: early failures (EF; PFS event within 6 months) and sustained control (SC; no events for ≥ 12 months). Differential relative abundances for 498 serum GPs were calculated between SC and EF patients to determine GPs more abundant in SC vs. EF by treatment group. A score was developed for each treatment group based on the 20 GPs within each treatment group identified as most statistically significant (one-sided Wilcoxon test). For any patient, the score is the proportion of GPs with relative abundance exceeding their median abundance. A low score is associated with high risk for EF. When examined in all patients in the cohort (regardless of treatment), both scores isolated EF from SC. Only 2 glycopeptides overlapped between the treatment group scores, suggesting that the information is indeed drug specific. Algorithmic assignment was performed by choosing the treatment with the highest treatment-specific score (e.g., if N- score > P-score, then assign to N). PFS was superior for cases where the assigned treatment matched the treatment received. Log-rank p-values comparing PFS by assigned treatment within P- and N -treated cases were 0.009 and 0.0004, respectively. Conclusions: Our results show that a proprietary serum glycoproteomic analytical approach can guide individualized treatment assignment to the most likely successful agent among different ICIs. This could importantly improve the clinical use of immuno-therapy. Citation Format: Klaus Lindpaintner, Gege Xu, Rachel Rice, Alan Mitchell, Dennie Frederick, Genevieve Boland, Daniel Seie. Glycoproteomics-based liquid biopsy informs optimal checkpoint-inhibitor drug choice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1270.

e15529 Background: Excluding skin cancers, colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Colorectal cancer (CRC) affects men and women of all racial and ethnic groups and is most often found in people who are 50 years old or older. To aid diagnosis and improve screening for CRC, this study focuses on identifying glycoprotein biomarkers using blood serum. Methods: Novel methods including liquid-chromatography/mass-spectrometry (LC-MS) with in-house peak integration software PB-Net were used to identify glycoprotein biomarkers by analyzing blood serum. Samples were sourced from different biorepositories including 245 CRC, 38 adenoma and 196 healthy controls. The data were split into 75% training and 25% hold-out test set for multivariable predictions. Statistical analysis was performed on normalized data to identify potential biomarkers differentiating adenoma and different stages of CRC samples from the healthy controls. Results: There were 419 significantly differentially expressed glycopeptides/peptides from comparisons between CRC and adenoma samples against the healthy control samples with an FDR < 0.05. A subset of these biomarkers were assessed, generating a 21-biomarker multivariable classifier model. We observed a test set AUC of 0.926, and the sensitivity for all stages of CRC was 90% (87% early stage, 92% late stage). Notably, sensitivity for adenomas was 79%, a large improvement upon the state of the art in adenoma diagnosis. Conclusions: Identification of these key glycopeptides/peptides in blood serum could prove to be a promising non-invasive diagnostic tool that can help improve screening and aid in early detection of advanced adenomas and CRC.

e17604 Background: Ovarian cancer (OC) is the fifth- leading cause of cancer-related deaths among women, and the most lethal gynecological cancer. Currently available biomarkers, including CA-125 and HE4, show suboptimal diagnostic performance for differentiating among benign and malignant pelvic tumors, and the early recognition of OC. Differentiation of benign and malignant pelvic tumors is required for proper patient triaging and management, yet non-invasive methods remain a largely unmet medical need. Methods: We applied a novel platform for characterizing peripheral blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) for the targeted quantification of serum protein glycosylation at intact glycopeptide level to analyze serum samples from 296 treatment-naïve women with histopathology-confirmed diagnosis of either benign (n = 151) or malignant (n = 145) tumors, and from 55 healthy control subjects, procured from a commercial biobank. Using data-dependent acquisition, a panel of 683 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, was interrogated. Samples were randomly divided into a training and a testing set for multivariable analysis. Data processing was performed using PB-Net, an in-house-developed high-throughput peak integration software. Raw data were normalized, processed by statistical analysis, and applied to machine learning models. Results: Comparison of glycopeptide abundances among patients with OC and benign pelvic tumors yielded 428 statistically significantly differentially expressed glycopeptides/peptides (at FDR < 0.05). A subpanel of these markers used to generate a score for predicting OC yielded areas under the receiver-operating-characteristic of 0.955 and 0.894 in the training and testing sets, respectively. The predicted probability of malignancy increased with cancer stage, and probability distributions were similar across training and test sets. Applying the model to healthy subjects, not utilized in training, resulted in few misclassifications and a spread nearly equivalent to that of the benign tumor cases. This indicates the signature robustly predicts malignancy and severity of disease. Conclusions: Our novel approach exhibited impressive levels of accuracy for the noninvasive differentiation of benign and malignant pelvic masses, compared with existing biomarkers and algorithms, thereby demonstrating the utility of glycoprotein profiles as a powerful, noninvasive new diagnostic modality.

e12545 Background: Breast cancer is the most common cancer among women worldwide.Traditional methods of cancer detection such as tissue biopsy are invasive, costly, time consuming and not amenable for repetition. As a result, minimally invasive liquid biopsies, especially blood-based biomarkers show potential value for breast cancer risk prediction and early detection. In this study, we investigated the use of serum glycoproteins circulating in blood to identify a panel of potential prognostic markers that may aid in predicting breast cancer in women. Methods: We applied a novel platform for characterizing blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) to analyze serum samples from 279 breast cancer patients (median age 56 years, with stage 0-4 N’s: 1 / 83 / 114 / 56 / 25) and 102 healthy control samples (median age 52 years). A panel of 596 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, were analyzed. Age-adjusted differential expression analysis for 596 normalized biomarkers were performed to evaluate statistically significant differential abundances using an FDR q-value of 0.05 as a cutoff. Using the top differentially expressed markers as input, a LASSO penalized logistic regression model with 5-fold repeated cross validation was applied to identify the top biomarkers contributing to the separation between healthy controls and breast cancer patients. Results: We identified 243 out of 596 markers that were differentially expressed (FDR <

e21148 Background: Protein glycosylation is the most abundant and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has so far been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography/mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time, highly scalable interrogation of the glycoproteome. Methods: Using this platform, we interrogated 694 glycopeptide (GP) and non-glycosylated peptide transitions derived from 74 serum proteins in pre-treatment peripheral blood samples from a cohort of 316 individuals with non-small-cell lung cancer (NSCLC) (128 females, 187 males, 1 with unknown sex, median age 66 years, age range 31-89 years, stage 0-4 N’s: 1 / 99 / 80 / 84 / 49, 3 missing) and a comparison cohort of 194 healthy control samples (102 females, 92 males, median age 52 years, age range 30-63 years). Age- and sex-adjusted differential expression analysis for 596 normalized biomarkers were performed to evaluate statistically significant differential abundances using an FDR-adjusted q-value of 0.05 as a cutoff. Repeated five-fold cross-validated LASSO-regularized logistic regression was performed to create a multivariable classifier that predicts whether a serum sample belongs to the healthy or NSCLC cohort. Results: We identified 432 biomarkers with significant abundance differences at FDR ≤ 0.05 between samples with NSCLC and healthy controls. Using 70% of the complete cohort (balanced by case/control membership, NSCLC stage, sex, and age quartile) as a training set, we selected a total of 375 glycopeptide and non-glycosylated peptide biomarker features that remained differentially expressed at FDR-adjusted q-value ≤ 0.05 as input into a LASSO-regularized multivariable classifier. This resulting in a 19-biomarker model exhibiting an accuracy of 94.8% (96.9% sensitivity, 91.2% specificity) and AUC of 0.989. This classifier was validated in an independent test set comprising the remaining 30% of subjects, yielding an accuracy of 94.5% (95.5% sensitivity, 93.0% specificity) and AUC of 0.975. Sensitivity in the test set was 100% / 96% / 99% / 96% / 94% / 10%, in stages 0-4 and missing, respectively. Conclusions: Our results indicate that glycoproteomic biomarkers can be leveraged as a strong liquid biopsy-based screening tool for patients at high risk of NSCLC, as an alternative to imaging modalities.

9545 Background: Protein glycosylation is the most abundant and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has so far been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography/mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time highly scalable interrogation of the glycoproteome. Methods: Using this platform, we interrogated 526 glycopeptide (GP) signatures derived from 75 serum proteins in pretreatment blood samples from a cohort of 205 individuals (66 females, 139 males, age range 24 to 97 years) with metastatic malignant melanoma treated either with nivolumab plus ipilimumab (95 patients) or pembrolizumab (110 patients) immune-checkpoint inhibitor (ICI) therapy. Results: In an optimized assay containing 27 glycopeptides and 20 non-glycosylated peptides, we identified 14 GPs with abundance differences at FDR q≤0.05 with regard to PFS. Using 40% of the cohort as a training set and selecting 12 glycopeptide and non-glycosylated peptide biomarker features of the 47 total by LASSO shrinkage, we created a multivariable-model-based classifier for PFS that yielded a hazard ratio (HR) for prediction of likely ICI benefit of 7.5 at p < 0.0001. This classifier was validated in the test set comprised of the held-out 60% of patients, yielding a HR of 4.7 at a similar p-value for separating patients likely benefiting from ICI therapy and those likely not benefiting from ICI therapy (50% PFS of 18 months vs. 3 months based on classifier score above/below cutoff). This classifier has a sensitivity of > 99% to predict likely ICI benefit, while still performing at a specificity of 26%, thus helping to safely reduce ultimately unnecessary and non-beneficial exposure to these agents of one in four who otherwise would unnecessarily be exposed to them. Conclusions: Our results indicate that glycoproteomics holds a strong promise as a predictor for checkpoint inhibitor treatment benefit that appears to significantly outperform other currently pursued biomarker approaches in this context.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background Bifidobacterium longum subsp. infantis (B. infantis) is a commensal bacterium that colonizes the gastrointestinal tract of breast-fed infants. B. infantis can efficiently utilize the abundant supply of oligosaccharides found in human milk (HMO) to help establish residence. We hypothesized that metabolites from B. infantis grown on HMO produce a beneficial effect on the host. Results In a previous study, we demonstrated that B. infantis routinely dominated the fecal microbiota of a breast fed Bangladeshi infant cohort (1). Characterization of the fecal metabolome of binned samples representing high and low B. infantis populations from this cohort revealed higher amounts of the tryptophan metabolite indole-3-lactic acid (ILA) in feces with high levels of B. infantis. Further in vitro analysis confirmed that B. infantis produced significantly greater quantities of the ILA when grown on HMO versus lactose, suggesting a growth substrate relationship to ILA production. The direct effects of ILA were assessed in a macrophage cell line and intestinal epithelial cell lines. ILA (1-10 mM) significantly attenuated lipopolysaccharide (LPS)-induced activation of NF-kB in macrophages. ILA significantly attenuated TNF-α- and LPS-induced increase in the pro-inflammatory cytokine IL-8 in intestinal epithelial cells. ILA increased mRNA expression of the aryl hydrogen receptor (AhR)-target gene CYP1A1 and nuclear factor erythroid 2–related factor 2 (Nrf2)-targeted genes glutathione reductase 2 (GPX2), superoxide dismutase 2 (SOD2), and NAD(P) H dehydrogenase (NQO1). Pretreatment with either the AhR antagonist or Nrf-2 antagonist inhibited the response of ILA on downstream effectors. Conclusions These findings suggest that ILA, a predominant metabolite from B. infantis grown on HMO and elevated in infant stool high in B. infantis, and protects gut epithelial cells in culture via activation of the AhR and Nrf2 pathway.

Alterations in the human glycome have been associated with cancer and autoimmunity. Thus, constructing a site-specific map of the human glycome for biomarker research and discovery has been a highly sought-after objective. However, due to analytical barriers, comprehensive site-specific glycoprofiling is difficult to perform. To develop a platform to detect easily quantifiable, site-specific, disease-associated glycan alterations for clinical applications, we have adapted the multiple reaction monitoring mass spectrometry method for use in glycan biomarker research. The adaptations allow for highly precise site-specific glycan monitoring with minimum sample prep. Using this technique, we successfully mapped out the relative abundances of the most common 159 glycopeptides in the plasma of 97 healthy volunteers. This plasma glycome map revealed 796 significant (FDR 2 = 0.62 ± 0.12). The human plasma site-specific glycan map described herein has utility in applications ranging from glycan biomarker research and discovery to the development of novel glycan-altering interventions.

Hard antler extract (HAE) is a traditional Chinese medicine and has potent antitumor, antioxidative, anti-inflammatory, and immunomodulatory activities. Previous studies have demonstrated that HAE can inhibit human prostate cancer metastasis and murine breast cancer proliferation. However, the effect of HAE on human breast cancer cells has not been clarified.To investigate the effects and underlying mechanism of HAE on self-renewal of stem-like cells and spontaneous and transforming growth factor (TGF)-β1-enhanced wound healing, invasion and epithelial-mesenchymal transition (EMT) in breast cancer cells.HAE was prepared from sika deer by sequential enzymatic digestions and the active compounds were determined by HPLC. The effects of HAE on the viability, mammosphere formation, wound healing and invasion of MDA-MB-231 and SK-BR3 cells were determined. The impact of HAE treatment on spontaneous and TGF-β1-promoted EMT and the nuclear factor (NF)-κB signaling in breast cancer cells was examined by quantitative RT-PCR and western blotting.Treatment with HAE at varying concentrations did not change the viability of breast cancer cells. However, HAE at 0.25 or 0.5 mg/mL significantly reduced the number and size of formed mammospheres, and inhibited spontaneous and TGF-β1-enhanced wound healing, invasion and EMT in MDA-MB-231 and SK-BR3 cells in a dose-dependent manner. TGF-β1 treatment significantly decreased IκBα expression and increased NF-kBp65 phosphorylation in breast cancer cells, indicating that TGF-β1 enhanced NF-κB signaling. In contrast, HAE treatment attenuated the spontaneous and TGF-β1-enhanced NF-κB signaling in breast cancer cells.Our data indicated that HAE inhibited the self-renewal of stem-like cells and spontaneous and TGF-β1-enhanced wound healing, invasion and EMT in breast cancer cells by attenuating the NF-κB signaling in vitro.

Saccharides in our diet are major sources of carbon for the formation of biomass such as proteins, lipids, nucleic acids and glycans. Among the dietary monosaccharides, glucose occupies a central role in metabolism, but human blood contains regulated levels of other monosaccharides as well. Their influence on metabolism and how they are utilized have not been explored thoroughly. Applying metabolic flux analysis on glycan synthesis can reveal the pathways that supply glycosylation precursors and provide a snapshot of the metabolic state of the cell. In this study, we traced the incorporation of six 13C uniformly labeled monosaccharides in the N-glycans, O-glycans and glycosphingolipids of both pluripotent and neural NTERA-2 cells. We gathered detailed isotopologue data for hundreds of glycoconjugates using mass spectrometry methods. The contributions of de novo synthesis and direct incorporation pathways for glucose, mannose, fructose, galactose, N-acetylglucosamine and fucose were determined based on their isotope incorporation. Co-feeding studies revealed that fructose incorporation is drastically decreased by the presence of glucose, while mannose and galactose were much less affected. Furthermore, increased sialylation slowed down the turnover of glycans, but fucosylation attenuated this effect. Our results demonstrated that exogenous monosaccharide utilization can vary markedly depending on the cell differentiation state and monosaccharide availability, and that the incorporation of carbons can also differ among different glycan structures. We contend that the analysis of metabolic isotope labeling of glycans can yield new insights about cell metabolism.

Membrane-bound oligosaccharides form the interfacial boundary between the cell and its environment, mediating processes such as adhesion and signaling. These structures can undergo dynamic changes in composition and expression based on cell type, external stimuli, and genetic factors. Glycosylation, therefore, is a promising target of therapeutic interventions for presently incurable forms of advanced cancer. Here, we show that cholangiocarcinoma metastasis is characterized by down-regulation of the Golgi α-mannosidase I coding gene MAN1A1, leading to elevation of extended high-mannose glycans with terminating α-1,2-mannose residues. Subsequent reshaping of the glycome by inhibiting α-mannosidase I resulted in significantly higher migratory and invasive capabilities while masking cell surface mannosylation suppressed metastasis-related phenotypes. Exclusive elucidation of differentially expressed membrane glycoproteins and molecular modeling suggested that extended high-mannose glycosylation at the helical domain of transferrin receptor protein 1 promotes conformational changes that improve noncovalent interaction energies and lead to enhancement of cell migration in metastatic cholangiocarcinoma. The results provide support that α-1,2-mannosylated N-glycans present on cancer cell membrane proteins may serve as therapeutic targets for preventing metastasis.

Additional file 1: Supplemental Figure 1. Metabolites produced by growth if B. infantis on lactose or HMO in the differing concentrations of tryptophan. Supplemental Table 1. Composition of milk oligosaccharide. Supplemental Table 2. Primer sequences. Supplemental Table 3. Summary of statistics from TukeyHSD posthoc comparisons of ILA dose responses.

Summary Since hundreds of clinical trials are investigating the use of multipotent stromal cells (MSCs) for therapeutic purposes, effective delivery of the cells to target tissues is critical. We have found an unexplored mechanism, by which basic fibroblast growth factor (FGF2) induces expression of fucosyltransferase 8 (FUT8) to increase core fucosylations of N-linked glycans of membrane-associated proteins, including several integrin subunits. Gain- and loss-of-function experiments show that FUT8 is both necessary and sufficient to induce migration of MSCs. Silencing FUT8 also affects migration of MSCs in zebrafish embryos and a murine bone fracture model. Finally, we use in silico modeling to show that core fucosylations restrict the degrees of freedom of glycans on the integrin’s surface, hence stabilizing glycans on a specific position. Altogether, we show a mechanism whereby FGF2 promotes migration of MSCs by modifying N-glycans. This work may help improve delivery of MSCs in therapeutic settings., Graphical Abstract, Highlights • FGF2 upregulates FUT8 and core fucosylations in MSCs • FUT8 is both necessary and sufficient to promote migration of MSCs • Core fucosylations stabilize the position of N-glycans on integrins, In this article, Fierro and colleagues used RNA-seq and mass spectrometry to find that FGF2 induces migration of MSCs by inducing expression of FUT8, which leads to more core fucosylations of N-linked glycans of membrane-associated proteins, including several integrin subunits. This affects cell migration in zebrafish embryos and toward bone fracture in mice.

A rapid method for the quantitation of sixteen neutral and acidic monosaccharides, from both animal and plant sources was developed using ultra-high performance liquid chromatography triple quadrupole mass spectrometry (UHPLC/QqQ-MS) in dynamic multiple reaction monitoring (dMRM) mode. Monosaccharides including three pentoses (ribose, xylose, arabinose), two deoxyhexoses (rhamnose, fucose), five hexoses (fructose, mannose, allose, glucose, galactose), two hexuronic acids (glucuronic acid, galacturonic acid), and two N-acetyl-hexosamines (GlcNAc, GalNAc), were derivatized with 1-phenyl-3-methyl-5-pyrazolone (PMP), while underivatized sialic acids, N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc), were simultaneously analyzed with a 10-minute run. With the optimized UHPLC conditions, baseline separations of the isomers were achieved. The sensitivity and calibration ranges of this method were determined. The limits of detection were between femtomoles and attomoles with linear ranges spanning four to six orders of magnitude and CVs ≤ 7.2%. Spiking experiments performed on a pooled fecal sample demonstrated the high accuracy of this method even when applied to samples with complicated matrices. The validated method was applied to fecal samples from an infant transitioning from breast milk to weaning foods. Major milk monosaccharides including galactose, fucose, glucose, GlcNAc, and Neu5Ac were found to be the most abundant components in the feces of milk-fed infants. PMP-derivatives of other monosaccharides including apiose, lyxose, altrose, talose, gulose, glucosamine, galactosamine, mannosamine, and N-acetylmannosamine (ManNAc) were also tested and could be added to the quantitation method depending on the need. The speed and sensitivity of the method makes it readily adaptable to rapid throughput analysis of monosaccharides in biological samples.

Fucosyltransferase 8 (FUT8) encodes a Golgi-localized α1,6 fucosyltransferase that is essential for transferring the monosaccharide fucose into N-linked glycoproteins, a process known as "core fucosylation." Here we describe three unrelated individuals, who presented with intrauterine growth retardation, severe developmental and growth delays with shortened limbs, neurological impairments, and respiratory complications. Each underwent whole-exome sequencing and was found to carry pathogenic variants in FUT8. The first individual (consanguineous family) was homozygous for c.715C>T (p.Arg239∗), while the second (non-consanguineous family) was compound heterozygous for c.1009C>G (p.Arg337Gly) and a splice site variant c.1259+5G>T. The third individual (consanguineous family) was homozygous for a c.943C>T (p.Arg315∗). Splicing analysis confirmed the c.1259+5G>T resulted in expression of an abnormal FUT8 transcript lacking exon 9. Functional studies using primary fibroblasts from two affected individuals revealed a complete lack of FUT8 protein expression that ultimately resulted in substantial deficiencies in total core fucosylated N-glycans. Furthermore, serum samples from all three individuals showed a complete loss of core fucosylation. Here, we show that loss of function mutations in FUT8 cause a congenital disorder of glycosylation (FUT8-CDG) characterized by defective core fucosylation that phenotypically parallels some aspects of the Fut8-/- knockout mouse. Importantly, identification of additional affected individuals can be easily achieved through analysis of core fucosylation of N-glycans.

Introduction: Protein glycosylation is the most abundant and most complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has so far been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometry with a proprietary machine learning and neural-network-based data processing engine that allows, for the first time, high-throughput, highly scalable interrogation of the glycoproteome. Experimental Procedures: Using this platform we interrogated 413 individual glycopeptide (GP) signatures derived from 69 abundant serum proteins in pretreatment blood samples from a cohort of 36 individuals (11 females, 25 males, age range 28 to 90 years) with metastatic malignant melanoma treated either with nivolumab plus ipilimumab (12 patients) or pembrolizumab (24 patients). Progression-free survival (PFS) data with follow-up of up to 3.7 years (median: 0.8 years) were used as clinical endpoint phenotype against which the predictive power of differential abundance of GPs was assessed. PFS data were analyzed using Cox Proportional Hazards models, and Kaplan Meier curves were generated for GP markers that showed statistically significant differential abundances using an FDR-adjusted p-value of ≤0.1 as a cutoff. Summary of Results: We identified 27 GPs with abundance differences at FDR p≤0.1, and among them 8 at p≤0.001. Using the latter 8 markers, we created a multivariable model for PFS by generating leave-one-out cross-validation (LOOCV) scores and determining an optimized cutoff value for these scores using Harrel's concordance index. Dichotomizing the LOOCV scores using this cutoff value demonstrated the model to yield a hazard ratio of 9.2 at a p-value of 10-5 for separating treatment responders and non-responders (70% vs. 0% PFS, respectively, at 18 months based on LOOCV score above/below cutoff), as compared to a hazard ratio of 1.5, p=0.5 for PDL1 expression. Conclusions: Our results indicate that glycoproteomics holds a strong promise as a response predictor to checkpoint inhibitor treatment that appears to significantly outperform other currently pursued biomarker approaches in this context. Citation Format: Gege Xu, Rachel Rice, Hector Huang, Klaus Lindpaintner, Jillian M. Prendergast, Karl Normington, Dennie Frederick, Genevieve M. Boland, Daniel Serie. Glycoproteomics as a powerful liquid biopsy-based predictor of checkpoint-inhibitor treatment response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 387.

The review focuses on recent aspects (last three years) of glycosylation analyses that provide relevant information about cancer. It includes recent development in glycan and protein enrichment methods for discovery of cancer markers. It will however focus on the recent technological developments in mass spectrometry (MS), bioinformatics and separation methods as they apply toward identifying cancer markers. More specifically, it will cover advances in matrix-assisted laser desorption/ionization (MALDI), electrospray ionization (ESI), capillary electrophoresis (CE), and liquid chromatography (LC) coupled to mass spectrometry. The discussions will include glycans, recently identified as potential markers for cancer that have been discovered using the highlighted technologies. We will also discuss emerging glycoproteomic techniques and site-specific methods, and how these methods are being utilized for cancer biomarker discovery. The large amount of data and the complexity of glycoproteomic analysis have been the impetus for developing bioinformatic methods for assigning glycosylation sites and characterizing the potentially very large site- or microheterogeneity. This review will cover the most recent advancements in biomarker discovery of N- and O-glycosylation of proteins as well as the glycolipids. This group collectively constitutes glycosylation on the cell membrane or the glycocalyx. The review will also highlight methods that are highly reproducible, with low coefficient of variation (CV), and scalable for large sample sets. The reader is also referred to other notable earlier reviews on glycomic biomarkers for cancer. Mereiter et al. describe the recent glycomic effort in gastrointestinal cancer.1 A review focused on N-glycomic analysis of colorectal cancer has been published by Sethi and Fanayan.2 N-Glycan, O-glycan, and glycolipid characteristics of colorectal cancer were reviewed by Holst et al.3 Muchena et al. have provided a more general review of glycan biomarkers covering up to the current review period.4 The field of glycoscience also covers a broad area of structures and may include highly anionic (glycosaminoglycans) and monosaccharide (e.g. O-GlcNAc) modifications that require their specific and unique sets of analytical tools. The latter topics are not covered in this review. 1.1. Background of Glycosylation and Cancer There is nearly 50 years of research illustrating that changes in glycosylation accompany cancer.5 Glycosylation is a dynamic process intimately involved in key processes in cells, including cell-cell and cell-extracellular communication as well as cell-cell adhesion, and cellular metabolism. Glycans expressed in several types of glycoconjugates are known to change during cancer genesis and progression.6 These changes increase the structural heterogeneity and alter the functions of cells.7 Glycosylation has been found to enable tumor-induced immunomodulation and metastasis.8–10 The cell-surface structures allow the immune cells to differentiate self/normal cells from non-self/abnormal cells.11 For example, terminal residues on N-glycans, such as sialic acids, are involved in immunity and cell-cell communication.12 Changes in glycosylation of adhesion proteins can largely influence their binding properties, leading to altered cell-cell or cell-matrix contacts.13 Other types of glycans are also involved in cancer. Gangliosides and sphingolipids are involved in transmembrane communication vital in tumor cell growth and invasion.14 Glycosaminoglycans are involved in tumor cell migration15 and motility.16–18 The search for effective markers is aided by the understanding of how glycans are synthesized. The glycan biosynthetic process is a non-template process involving multiple enzymes, some performing competing activities. It is estimated that more than 300 metabolic enzymes, composed of glycosyltransferases and glycosidases, are involved in the biosynthesis and processing of glycans.19–20 The best-known series of pathways belongs to the production of N-glycans (Figure 1). They illustrate the large degree of structural heterogeneity in glycosylation. N-Glycans are produced in a step-wise process beginning with the production of high mannose structures on a lipid, which are transferred to the nascent polypeptide chain to guide protein folding. Once folded, the glycans are then trimmed back and extended to form complex and hybrid structures. The folded protein can be secreted with glycans that range from early in the process to yield high mannose structures to later in the process corresponding to complex or hybrid structures. The number of structures for one glycosylation site can vary by a large degree, from a handful for transferrin21 to over 70 structures for IgG, the most abundant serum glycoprotein.22–23 Open in a separate window Figure 1. Representation of the glycosylation pathway of proteins. The pathway illustrates the complexity and heterogeneity of structures. The proteins may exit the pathway with various levels of glycosylation.