Dataset Information

Profiling of Subclass-Specific IgG Glycosylation in Colorectal Cancer

ABSTRACT: Changes in IgG glycosylation have been reported to be associated with colorectal cancer (CRC), but the alteration in specific subclass of IgG is unknown. Herein, we optimized five common IgG glycopeptides enrichment methods to acquire comprehensive profiling of IgG glycoproteomics in CRC. Notably, incomplete tryptic digestion of IgG occurred when using ordinary ratio of protease to protein, which significantly impacted the final statistical analysis of association between IgG glycosylation and CRC. We introduced a method based on two-step enzymatic digestion, enabling the complete digestion of IgG glycopeptides and further improving the detection intensity of the target glycopeptides. By this approach, total 47 IgG glycopeptides can be identified by nanoLC-ESI-MS/MS. Following rapid and automatic data processing through the self-developed program, we observed that IgG1_H3N4F1 and IgG1_H3N4 were substantially increased in CRC, while IgG1_H5N5F1, IgG1_H5N4F1S1and IgG2_H5N4F1 were markedly decreased. Strikingly, these four glycopeptides were continually significantly changed in early and late stages of CRC. Further evaluation of the diagnostic performance showed they all obtained a fair performance in discriminating the patients from the normal. In terms of the glycan features, it demonstrated that CRC progression associates with the increased agalactosylation, and the decreased digalactosylation and galactosylation per antenna on diantennay glycans of IgG1 and IgG2. Concurrently, the decreased sialylation of IgG1 was strongly correlated with CRC. Moreover, tumor-specific glycosylation analysis showed that the alterations of subclass-specific IgG glycosylation were more significant in colon cancer, and no obvious difference between colon and rectal cancer was found. More importantly, all these findings were validated in an independent cohort. This two-step enzymatic approach is crucial to explore the relationship between the disease progression and IgG glycosylation.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Cell, Blood Serum

DISEASE(S): Colon Cancer

SUBMITTER: Si Liu

LAB HEAD: Xin Liu

PROVIDER: PXD016318 | Pride | 2022-10-27

REPOSITORIES: Pride

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Publications

Three Major Gastrointestinal Cancers Could Be Distinguished through Subclass-Specific IgG Glycosylation.

Liu Si S Liu Yuanyuan Y Lin Jiajing J Wang Yi Y Li Dong D Xie Gui-Yan GY Guo An-Yuan AY Liu Bi-Feng BF Cheng Liming L Liu Xin X

Journal of proteome research 20221021 11

Esophageal cancer (EC), gastric cancer (GC), and colorectal cancer (CRC) are three major digestive tract tumors with higher morbidity and mortality due to significant molecular heterogeneity. Altered IgG glycosylation has been observed in inflammatory activities and disease progression, and the IgG glycome profile could be used for disease stratification. However, IgG <i>N</i>-glycome profiles in these three cancers have not been systematically investigated. Herein, subclass-specific IgG glycosy ...[more]

PMID: 36268885

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Project description:The glycosylation of antibody-based proteins is vital in conferring the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. The simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation in complex LC-MS1 data of a glycoproteomic analysis. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 minutes with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures, and it offers valuable insights into the effect of culture media changes on bioprocess and biologic characterisation. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy and scalable data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

Dataset Information

Profiling of Subclass-Specific IgG Glycosylation in Colorectal Cancer

Publications

Three Major Gastrointestinal Cancers Could Be Distinguished through Subclass-Specific IgG Glycosylation.

Similar Datasets

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