Engineered extracellular matrices for modeling patient tumors
Abstract/Contents
- Abstract
- The extracellular matrix (ECM) is a dynamic network of proteins, polysaccharides, glycosaminoglycans, and other polymers that provide structural support for tissues and instruct neighboring cells through biochemical and mechanical cues. In healthy tissues, ECM remodeling is a tightly orchestrated process with constant turnover and reorganization of the matrix architecture in response to tissue growth and development, among other stimuli. In disease states, such as cancer, this ECM homeostasis is often misregulated, resulting in tissue disorganization and altered cell-ECM mechanosignaling that can drive disease progression and inhibit treatment strategies. In this thesis, I explore the in vitro culture of patient-derived tumors (termed cancer organoids) within a 3D engineered ECM as a method to understand how this misregulated ECM can influence cancer progression and drug sensitivity. Cancer organoids can be defined as 3D self-organized assemblies of neoplastic cells derived from patient-specific tissue samples that mimic key histopathological, genetic, and phenotypic features of the parent tumor. These organoid models are revolutionizing our understanding of cancer heterogeneity and its implications for personalized medicine, in part, due to their relatively facile genomic and environmental manipulation compared to traditional cancer models (i.e. 2D culture of cancer cell lines, in vivo mouse models). Additionally, as cancer organoids are often grown within 3D matrices that mimic the native ECM, they serve as an ideal model system to explore mechanistic roles of cell-ECM interactions in a dish. Specifically, this thesis contains a review chapter highlighting the current limitations of cancer organoid techniques and future directions to ensure reproducibility of cancer organoid protocols. This thesis also contains primary research focusing on the development of a 3D engineered ECM with tunable biochemical and mechanical properties that models key features of the pancreatic cancer ECM. In particular, I use this engineered matrix to test causal relationships between several ECM properties and pancreatic cancer chemoresistance, with the goal of identifying novel therapeutic strategies for treating this characteristically lethal disease.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | LeSavage, Bauer Lawrence |
|---|---|
| Degree supervisor | Heilshorn, Sarah |
| Thesis advisor | Heilshorn, Sarah |
| Thesis advisor | Chaudhuri, Ovijit |
| Thesis advisor | Rankin, Erinn |
| Degree committee member | Chaudhuri, Ovijit |
| Degree committee member | Rankin, Erinn |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Bauer Lawrence LeSavage. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/sp321nb7655 |
Access conditions
- Copyright
- © 2022 by Bauer Lawrence LeSavage
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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