Multiplexed Surrogate
Analysis of Glycotransferase
Activity in Whole Biospecimens
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Abstract
Dysregulated glycotransferase enzymes in cancer cells
produce aberrant
glycanssome 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 biospecimensespecially 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