Project Summary:
Cancer is a major health burden in Wisconsin and beyond, putting a significant strain on our society and healthcare system. There are approximately 34,000 new cases of cancer in Wisconsin each year alone. These numbers are continuing to increase, especially in younger populations under the age of 50. Despite significant progress in developing effective therapies, the dynamic nature of the disease poses ongoing challenges. This includes new causes of cancers along with emerging resistance to approved drugs. Identifying novel compounds as well as effective combinations of compounds will help address these challenges.
With over 200 FDA-approved cancer drugs available, there are a staggering 21,300-plus potential pairwise combinations to test across various cancers. In addition, with over 100,000 cancer trials currently registered, experimentally testing all these therapies and combinations is not financially or physically viable per cancer type. Therefore, developing data-driven predictive computational tools based on known mechanisms in combination with in vitro experimental data will increase the speed and reduce the costs associated with developing new strategies.
Regardless of the cause, cancer ultimately involves dysregulation of the cell cycle resulting in uncontrolled proliferation and genomic instability. Cell cycle is intricately regulated by changes such as protein concentrations, protein-protein interactions, and enzyme activities to ensure proper cell division and prevent aberrant proliferation. A comprehensive mechanistic model of the human cell cycle remains elusive.
Recent peer-reviewed published advancements by this project team have shown the feasibility of constructing data-driven mechanistic computational models for understanding complex biological functions and mechanisms including studies in CART biology and cancer therapy, viral infection efficiency and tropism, mitosis and viral protein functions, mitochondrial metabolism, and, most recently, liver physiology.
The researchers have come to realize that applying these strategies in studying the full human cell cycle will be a cost-effective approach for in silico testing of new compounds and combinations of compounds when trained on a given target cell. Others have successfully applied similar approaches to interrogate complex relationship in cancer.
In this pilot project, the researchers will construct, experimentally validate, and apply to specific tumor lines a mechanistic computational model of the human cell cycle. They will train the model using a glioma cell line and the CDK4/6 inhibitor, abemaciclib, as a means of demonstrating their strategy. Further, this model will be constructed using recently published web-based software tool BioModME20, developed specifically for investigators without modeling backgrounds to develop, simulate, and experimentally inform existing and new mechanistic computational models. The project will provide to other teams the framework and tools, including in silico and in vitro approaches, for efficient and cost-effective testing of anticancer drugs on any given cultured tumor cell.