Optimizing a 3D brain microvascular model to study radiation damage

Ivanova, Yoanna

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https://hdl.handle.net/2142/121276 Copy

Description

Title

Optimizing a 3D brain microvascular model to study radiation damage

Author(s)

Ivanova, Yoanna

Issue Date

2023-07-19

Director of Research (if dissertation) or Advisor (if thesis)

Harley, Brendan AC

Department of Study

Bioengineering

Discipline

Bioengineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Glioblastoma
• Tumor Microenvironment, Radiation
• Vasculature
• Hydrogel
• Perivascular Niche

Abstract

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by diffuse metastasis at the tumor margins leading to high rates of recurrence. Radiation therapy, a standard component of current treatment, offers potential for improved patient outcomes. However, radiation therapy has opposing effects, capable of eradicating disease while also damaging normal tissue; this damage can lead to decreased quality of life and can foster a tumor-supportive microenvironment. The perivascular niche (PVN) is a microenvironment of particular interest. The PVN plays a vital role in regulating the glioblastoma stem cell population, treatment resistance, and serves as a site for cancer recurrence and migration. Understanding the impact of radiation on the PVN can better inform radiation schemes and improve our understanding of GBM recurrence. Here we optimize a previously developed three-dimensional in vitro model of the brain perivascular niche to investigate the impact of radiation dosage and delivery. We evaluated our hydrogel formulation to allow for stability, vessel growth, and network visualization. Importantly, we demonstrate that radiation effects on vessel structure can be measured in our model. This suggests our platform can provide useful insights on the effects of radiation on a short time scale relative to in vivo experiments.

Graduation Semester

2023-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/121276

Copyright and License Information

Copyright 2023 Yoanna Ivanova

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