Date of Award
12-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Chemical and Biomolecular Engineering
Committee Chair/Advisor
Dr. Marc Birtwistle
Committee Member
Dr. F. Alex Feltus
Committee Member
Dr. Jessica Larsen
Committee Member
Dr. Sarah Harcum
Abstract
Cancer is a lethal disease and complex at multiple levels of cell biology. Despite many advances in treatments, many patients do not respond to therapy. This is owing to the complexity of cancer-genetic variability due to mutations, the multi-variate biochemical networks within which drug targets reside and existence and plasticity of multiple cell states. It is generally understood that a combination of drugs is a way to address the multi-faceted drivers of cancer and drug resistance. However, the sheer number of testable combinations and challenges in matching patients to appropriate combination treatments are major issues.
Here, we first present a general method of network inference which can be applied to infer biological networks. We apply this method to infer different kinds of networks in biological levels where cancer complexity resides-a biochemical network, gene expression and cell state transitions. Next, we focus our attention on glioblastoma and with pharmacological and biological considerations, obtain a ranked list of important drug targets in glioblastoms. We perform drug dose response experiments for 22 blood brain barrier penetrant drugs against 3 glioblastoma cell lines. These methods and experimental results inform a construction of a temporal cell state model to predict and experimentally validate combination treatments for certain drugs. We improve an experimental method to perform high throughput western blots and apply the method to discover biochemical interactions among some important proteins involved in temporal cell state transitions. Lastly, we illustrate a method to investigate potential resistance mechanisms in genome scale proteomic data.
We hope that methods and results presented here can be adapted and improved upon to help in the discovery of biochemical interactions, capturing cell state transitions and ultimately help predict effective combination therapies for cancer.
Recommended Citation
Sarmah, Deepraj, "Combining Network Modeling and Experimental Approaches to Predict Drug Combination Responses" (2022). All Dissertations. 3191.
https://tigerprints.clemson.edu/all_dissertations/3191
Author ORCID Identifier
https://orcid.org/0000-0003-0212-7120