Serenus Hua1; Lauren Dimapasoc2; Mary Saunders²; Bum Jin Kim1; Seung Hyup Jeong1; Kit S. Lam², Carlito Lebrilla2Hyun Joo An1
1GRAST, Chungnam National University, Daejeon, SOUTH KOREA; 2University of California, Davis, Davis, CA
NOVEL ASPECT: Stark differences in cancer cell membrane glycosylation can be exploited to create an MS-based biopsy
In clinical settings, biopsies are routinely used to determine cancer type and grade based on tumor cell morphology, as determined via histochemical or immunohistochemical staining. Unfortunately, in a significant number of cases, biopsy results are inconclusive. Moreover, even when primary cancer origin can be identified, phenotypic subtypes are rarely differentiated, often leading to inefficient or ineffective treatment. Glycomic profiling of the cell membrane offers an alternate route towards cancer diagnosis. In this study, isomer-sensitive nano-LC/MS and -LCMS/MS were used to obtain a detailed, structure-specific profile of the different N-glycan structures present on cancer cell membranes. Application of this method to biopsy samples may provide complementary or supplementary information that can be used to aid cancer diagnosis and guide treatment.
Cells were harvested from cell lines representing various subtypes of breast, lung, cervical, ovarian, and lymph node cancer. After gentle lysing, cell membranes were isolated by ultracentrifugation. N-glycans were released enzymatically, and then enriched by graphitized carbon solid phase extraction.
Native glycans were reproducibly profiled and characterized by chip-based nano-LC/MS and -LC/MS/MS. Using established human glycan structure/composition libraries, glycan signals were rapidly identified and sorted into biologically relevant classes and categories, such as high-mannose glycans, hybrid glycans, truncated complex glycans, fucosylated or sialylated complex glycans, etc. Statistical methods including Pearson correlation, hierarchical clustering, and t-tests were used to correlate or differentiate the various cell lines.
Chip-based nano-LC/MS analysis of the cell membrane N-glycomes provided high retention time reproducibility and quantitative precision. Structure-sensitive N-glycan profiling identified hundreds of glycan peaks per cell line, including multiple isomers for most compositions.
Most cancer cell lines exhibited high  levels (~30 to 60% relative abundance) of high-mannose glycosylation, an established hallmark of cancer. However, significant differences between the individual cell lines were easily observable. Hierarchical clustering based on Pearson correlation coefficients was used to quickly compare and separate each cell line according to originating organ and disease subtype. For example, a comparison of four B-cell lymphoma cell lines easily clustered together two cell lines originating from endemic and sporadic Burkitt’s lymphoma patients (from Nigeria and America, respectively) with a correlation coefficient R of 0.9744 while simultaneously differentiating both of them from the other two B-cell lymphoma cell lines.
Similar comparisons were able to differentiate several breast cancer cell lines from a (non-cancerous) fibrocystic breast cell line; and HPV-infected cervical carcinoma cells from non-HPV-infected cervical carcinoma cells.
To demonstrate the diagnostic  possibilities of this method, simple dichotomous keys were created. Based simply on the relative abundances of broad glycan classes (e.g. high mannose, complex/hybrid fucosylated, complex/hybrid sialylated, etc.) most cell lines were readily differentiated and identified. More closely-related cell lines were differentiated based on several-fold differences in the abundances of individual glycans. For example, lung carcinoma cell lines NCI-H358 and A549 were differentiated by parallel six-fold differences between the abundances of biosynthetically-related N-glycans Hex3HexNAc2Fuc, Hex3HexNAc3Fuc, and Hex3HexNAc4Fuc (which each differ from the next by only one HexNAc). In clinical settings, similar keys might allow a diagnostician to quickly and rapidly identify different cancer cell types based on a glycomic profile.