The Role of Fucosylation in a Three-Dimensional Multicellular Tumor Spheroid Model of Prostate Cancer
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Authors
Kalaydina, Nicka
Date
Type
thesis
Language
eng
Keyword
Prostate Cancer , Multicellular Tumor Spheroids , Machine Learning , Image Object Detection , YOLO , Fucosylation , Three-dimensional culture
Alternative Title
Abstract
Multicellular tumor spheroids are a three-dimensional model of cancer with a variety of applications. Spheroids are the preferred preclinical model in drug screening because they recapitulate the tumor microenvironment. Gaining a better understanding of how spheroids form can provide insights into tumor formation in patients, which can ultimately lead to uncovering additional therapeutic targets. However, the analysis of spheroids can be challenging. Developing a more precise definition of a spheroid and automating analysis can reduce human error, save time, and improve the accuracy of results. Previously, we have shown that spheroids measuring at least 60µm can be generated from DU145 prostate cancer cells using cyclic Arg-Gly-Asp-D-Phe-Lys peptide modified with 4-carboxybutyl-triphenylphosphonium bromide, or cRGDfK(TPP). We showed that sialylation, a post-translational modification involving the attachment of sialic acids to glycoproteins, facilitates the formation of spheroids generated with cRGDfK(TPP). Here, the role of a closely related glycosylation event called fucosylation was investigated in spheroid formation. It was found that DU145 cells expressed higher cell-sruface alpha-1,6- compared to alpha-1,2-fucose. Only blockade of the alpha-1,6-fucose linkage with Aspergillus oryzae lectin (AOL) prior to the addition of cRGDfK(TPP) to monolayer prostate cancer cells resulted in a statistically significant reduction in prostasphere volume on day five following treatment. Moreover, two improvements to the spheroid model were implemented: a statistically-supported minimum spheroid diameter of 40µm and an automated image object detection system. Using 40µm as the diameter threshold confirmed the role of alpha-1,6 fucosylation in reducing spheroid volume on day five following treatment with AOL. Finally, a state-of-the-art, image object detection system called You Only Look Once version 2 (YOLOv2) was used to automate the analysis of 156 prostaspheres. Preliminary results showed that YOLOv2 corroborated manual MCTS detection and volume estimations with high precision and acceptable accuracy. Refining the definition of a spheroid and implementing YOLOv2 are important improvements to our spheroid model, helping in the study of the role of fucosylation in spheroid formation. The optimization of the MCTS model can ultimately expedite the drug discovery process.
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ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.