Development of the GlycoHCCTyper for the early detection of HCC

NIH RePORTER · NIH · R41 · $428,443 · view on reporter.nih.gov ↗

Abstract

Alterations in glycosylation have long been associated with the development and progression of many types of chronic and acute diseases. Our group (Mehta) was one of the first to perform glycan analysis in serum and perform proteomics on specific glycoforms (glycoproteomics). Using such methods, we identified a number of serum glycoproteins with altered glycosylation in hepatocellular carcinoma, a primary cancer of the liver. However, because of limitations in technology, we were forced to either examine each protein one at a time or examine pools of proteins without the ability to link a particular glycan to a given protein. In some situations, we performed structural glycan analysis on purified proteins which provided the best “biomarker” information, but took days to weeks for analysis. Alternatively, we could forgo true structural information and use lectins to determine if only one specific sugar moiety was present, and perform analysis in a more rapid manner. But again, this was generally done only on one protein at a time. In all of these situations, the biomarker potential of these glycoproteins was diminished because of the technology used. To address this limitation, GlycoPath has recently developed a streamlined antibody capture slide array approach to directly profile N-linked glycans on captured serum glycoproteins. This process requires only a few microliters of sample and utilizes simple methods that require no protein purification or sugar modifications prior to analysis. This method is referred to as the GlycoTyper. In this method, N-linked glycans are released from antibody captured glycoproteins and are directly analyzed by MALDI-TOF mass spectrometry. We hypothesize that this method can be used to identify glycan biomarkers reflective of the changes that occur during the development of hepatocellular carcinoma. In this Phase I STTR application we anticipate developing a reproducible and translatable workflow using MALDI-MS of captured proteins that can accurately detect the presence of hepatocellular carcinoma.

Key facts

NIH application ID
10382624
Project number
1R41CA268268-01
Recipient
GLYCOPATH, INC
Principal Investigator
Stephen Castellino
Activity code
R41
Funding institute
NIH
Fiscal year
2022
Award amount
$428,443
Award type
1
Project period
2022-03-01 → 2024-02-29