Serum glycan signatures of gastric cancer.

Glycomics, a comprehensive study of glycans expressed in biologic systems, is emerging as a simple yet highly sensitive diagnostic tool for disease onset and progression. This study aimed to use glycomics to investigate glycan markers that would differentiate patients with gastric cancer from those with nonatrophic gastritis. Patients with duodenal ulcer were also included because they are thought to represent a biologically different response to infection with Helicobacter pylori, a bacterial infection that can cause either gastric cancer or duodenal ulcer. We collected 72 serum samples from patients in Mexico City that presented with nonatrophic gastritis, duodenal ulcer, or gastric cancer. N-glycans were released from serum samples using the generic method with PNGase F and were analyzed by matrix-assisted laser desorption/ionization Fourier transform-ion cyclotron resonance mass spectrometry. The corresponding glycan compositions were calculated based on accurate mass. ANOVA-based statistical analysis was performed to identify potential markers for each subgroup. Nineteen glycans were significantly different among the diagnostic groups. Generally, decreased levels of high-mannose-type glycans, glycans with one complex type antenna, bigalactosylated biantennary glycans, and increased levels of nongalactosylated biantennary glycans were observed in gastric cancer cases. Altered levels of serum glycans were also observed in duodenal ulcer, but differences were generally in the same direction as gastric cancer. Serum glycan profiles may provide biomarkers to differentiate gastric cancer cases from controls with nonatrophic gastritis. Further studies will be needed to validate these findings as biomarkers and identify the role of protein glycosylation in gastric cancer pathology.

Differentiation of cancer cell origin and molecular subtype by plasma membrane N-glycan profiling.

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, traditional biopsy results are either inconclusive or do not provide full subtype differentiation, possibly leading to inefficient or ineffective treatment. Glycomic profiling of the cell membrane offers an alternate route toward cancer diagnosis. In this study, isomer-sensitive nano-LC/MS was used to directly obtain detailed profiles of the different N-glycan structures present on cancer cell membranes. Membrane N-glycans were extracted from cells representing various subtypes of breast, lung, cervical, ovarian, and lymphatic cancer. Chip-based porous graphitized carbon nano-LC/MS was used to separate, identify, and quantify the native N-glycans. Structure-sensitive N-glycan profiling identified hundreds of glycan peaks per cell line, including multiple isomers for most compositions. Hierarchical clusterings based on Pearson correlation coefficients were used to quickly compare and separate each cell line according to originating organ and disease subtype. 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. More closely related cell lines were differentiated based on several-fold differences in the abundances of individual glycans. Based on characteristic N-glycan profiles, primary cancer origins and molecular subtypes could be distinguished. These results demonstrate that stark differences in cancer cell membrane glycosylation can be exploited to create an MS-based biopsy, with potential applications toward cancer diagnosis and direction of treatment.

Isomer-specific LC/MS and LC/MS/MS profiling of the mouse serum N-glycome revealing a number of novel sialylated N-glycans.

Mice are the premier mammalian models for studies of human physiology and disease, bearing extensive biological similarity to humans with far fewer ethical, economic, or logistic complications. To facilitate glycomic studies based on the mouse model, we comprehensively profiled the mouse serum N-glycome using isomer-specific nano-LC/MS and -LC/MS/MS. N-Glycans were identified by accurate mass MS and structurally elucidated by MS/MS. Porous graphitized carbon nano-LC was able to separate out nearly 300 N-linked glycan compounds (including isomers) from just over 100 distinct N-linked glycan compositions. Additional MS/MS structural analysis was performed on a number of novel N-glycans, revealing the structural characteristics of modifications such as dehydration, O-acetylation, and lactylation. Experimental findings were combined with known glycobiology to generate a theoretical library of all biologically possible mouse serum N-glycan compositions. The library may be used for automated identification of complex mixtures of mouse N-glycans, with possible applications to a wide range of mouse-related research endeavors, including pharmaceutical drug development and biomarker discovery.

Isomer-specific chromatographic profiling yields highly sensitive and specific potential N-glycan biomarkers for epithelial ovarian cancer.

Aberrant glycosylation has been observed for decades in essentially all types of cancer, and is now well established as an indicator of carcinogenesis. Mining the glycome for biomarkers, however, requires analytical methods that can rapidly separate, identify, and quantify isomeric glycans. We have developed a rapid-throughput method for chromatographic glycan profiling using microfluidic chip-based nanoflow liquid chromatography (nano-LC)/mass spectrometry. To demonstrate the utility of this method, we analyzed and compared serum samples from epithelial ovarian cancer cases (n=46) and healthy control individuals (n=48). Over 250 N-linked glycan compound peaks with over 100 distinct N-linked glycan compositions were identified. Statistical testing identified 26 potential glycan biomarkers based on both compositional and structure-specific analyses. Using these results, an optimized model was created incorporating the combined abundances of seven potential glycan biomarkers. The receiver operating characteristic (ROC) curve of this optimized model had an area under the curve (AUC) of 0.96, indicating robust discrimination between cancer cases and healthy controls. Rapid-throughput chromatographic glycan profiling was found to be an effective platform for structure-specific biomarker discovery.

The glycolyzer: automated glycan annotation software for high performance mass spectrometry and its application to ovarian cancer glycan biomarker discovery.

Human serum glycomics is a promising method for finding cancer biomarkers but often lacks the tools for streamlined data analysis. The Glycolyzer software incorporates a suite of analytic tools capable of identifying informative glycan peaks out of raw mass spectrometry data. As a demonstration of its utility, the program was used to identify putative biomarkers for epithelial ovarian cancer from a human serum sample set. A randomized, blocked, and blinded experimental design was used on a discovery set consisting of 46 cases and 48 controls. Retrosynthetic glycan libraries were used for data analysis and several significant candidate glycan biomarkers were discovered via hypothesis testing. The significant glycans were attributed to a glycan family based on glycan composition relationships and incorporated into a linear classifier motif test. The motif test was then applied to the discovery set to evaluate the disease state discrimination performance. The test provided strongly predictive results based on receiver operator characteristic curve analysis. The area under the receiver operator characteristic curve was 0.93. Using the Glycolyzer software, we were able to identify a set of glycan biomarkers that highly discriminate between cases and controls, and are ready to be formally validated in subsequent studies.

Lacto-N-tetraose, fucosylation, and secretor status are highly variable in human milk oligosaccharides from women delivering preterm.

Breast milk is the ideal nutrition for term infants but must be supplemented to provide adequate growth for most premature infants. Human milk oligosaccharides (HMOs) are remarkably abundant and diverse in breast milk and yet provide no nutritive value to the infant. HMOs appear to have at least two major functions: prebiotic activity (stimulation of the growth of commensal bacteria in the gut) and protection against pathogens. Investigations of HMOs in milk from women delivering preterm have been limited. We present the first detailed mass spectrometric analysis of the fucosylation and sialylation in HMOs in serial specimens of milk from 15 women delivering preterm and 7 women delivering at term using nanohigh performance liquid chromatography chip/time-of-flight mass spectrometry. A mixed-effects model with Levene's test was used for the statistical analyses. We find that lacto-N-tetraose, a core HMO, is both more abundant and more highly variable in the milk of women delivering preterm. Furthermore, fucosylation in preterm milk is not as well regulated as in term milk, resulting in higher within and between mother variation in women delivering preterm vs term. Of particular clinical interest, the α1,2-linked fucosylated oligosaccharide 2'-fucosyllactose, an indicator of secretor status, is not consistently present across lactation of several mothers that delivered preterm. The immaturity of HMO production does not appear to resolve over the time of lactation and may have relevance to the susceptibility of premature infants to necrotizing enterocolitis, late onset sepsis, and related neurodevelopmental impairments.