# Development of the GlycoHCCTyper for the early detection of HCC

> **NIH NIH R41** · GLYCOPATH, INC · 2022 · $428,443

## 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 organization:** GLYCOPATH, INC
- **Principal Investigator:** Stephen Castellino
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $428,443
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10382624

## Citation

> US National Institutes of Health, RePORTER application 10382624, Development of the GlycoHCCTyper for the early detection of HCC (1R41CA268268-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10382624. Licensed CC0.

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