Multiplexed surrogate analysis of glycotransferase activity in whole biospecimens

Dysregulated glycotransferase enzymes in cancer cells produce aberrant glycans - some of which can help facilitate metastases. Within a cell, individual glycotransferases promiscuously help to construct dozens of unique glycan structures, making it difficult to comprehensively track their activity in biospecimens - especially where they are absent or inactive. Here, we describe an approach to deconstruct glycans in whole biospecimens then analytically pool together resulting monosaccharide-and-linkage-specific degradation products ("glycan nodes") that directly represent the activities of specific glycotransferases. To implement this concept, a reproducible, relative quantitation-based glycan methylation analysis methodology was developed that simultaneously captures information from N-, O-, and lipid linked glycans and is compatible with whole biofluids and homogenized tissues; in total, over 30 different glycan nodes are detectable per gas chromatography-mass spectrometry (GC-MS) run. Numerous nonliver organ cancers are known to induce the production of abnormally glycosylated serum proteins. Thus, following analytical validation, in blood plasma, the technique was applied to a group of 59 lung cancer patient plasma samples and age/gender/smoking-status-matched non-neoplastic controls from the Lung Cancer in Central and Eastern Europe (CEE) study to gauge the clinical utility of the approach toward the detection of lung cancer. Ten smoking-independent glycan node ratios were found that detect lung cancer with individual receiver operating characteristic (ROC) c-statistics ranging from 0.76 to 0.88. Two glycan nodes provided novel evidence for altered ST6Gal-I and GnT-IV glycotransferase activities in lung cancer patients. In summary, a conceptually novel approach to the analysis of glycans in unfractionated human biospecimens has been developed that, upon clinical validation for specific applications, may provide diagnostic and/or predictive information in glycan-altering diseases.