Date of Award

Spring 4-10-2023

Embargo Period

4-12-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Cell and Molecular Pharmacology and Experimental Therapeutics

College

College of Graduate Studies

First Advisor

Anand Mehta

Second Advisor

Peggi Angel

Third Advisor

Nathan Dolloff

Fourth Advisor

Stephen Duncan

Fifth Advisor

Yuri Peterson

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally and is a growing clinical problem with poor survival outcomes beyond early-stage disease. Surveillance for HCC has primarily relied on ultrasound and serum α-fetoprotein (AFP), but combined they only have a sensitivity of 63% for early-stage HCC tumors, suggesting a need for improved diagnostic strategies. Alterations to N-glycan expression are relevant to the progression of cancer, and there a multitude of N-glycan-based cancer biomarkers that have been identified with sensitivity for various cancer types including HCC. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) serves as a new method to evaluate tumor-correlated N-glycosylation and thereby identify potential HCC biomarkers. Previous work has identified significant changes in the N-linked glycosylation of HCC tumors, but has not accounted for the heterogeneous genetic and molecular nature of HCC, which has led to inadequate sensitivity of N-glycan biomarkers. Therefore, we hypothesized that the incorporation of genetic/molecular information into N-glycan-based biomarker development would result in improved sensitivity for HCC. To determine the correlation between HCC-specific N-glycosylation and genetic/molecular tumor features, we profiled HCC tissue samples with MALDI-IMS and correlated the spatial N-glycosylation with a widely used HCC molecular classification that utilizes histological, genetic, and clinical tumor features (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with Subtype 1 demonstrating significantly dysregulated N-glycosylation compared to Subtypes 2 and 3, particularly in regard to fucosylation. In order to further the clinical relevance of subtype-dependent N-glycosylation, we analyzed patient-matching HCC tumor tissue, background liver tissue and serum samples through MALDI-IMS. Results showed a N-glycan based model capable of differentiating tumor tissue from background liver tissue with an AUC of 0.9842. When analyzing the associated serum, 24.7% of detected N-glycans were significantly positively correlated between tumor tissue and serum, suggesting that N-glycosylation trends translate from tissue to serum. Additionally, a serum N-glycan-based model was capable of distinguishing Subtype 1/Subtype 2 tumors from Subtype 3 tumors with an AUC of 0.881. Through the utilization of MALDI-IMS, subtype-dependent N-glycosylation trends were identified in both tissue and serum, which can significantly further the development of HCC biomarkers for clinical application.

Rights

Copyright is held by the author. All rights reserved.

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