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Updated: May 12, 2026
Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria
Published on: July 6, 2013
In vivo imaging-based mathematical modeling techniques that enhance the understanding of oncogene addiction in
Chinyere Nwabugwu1, Kavya Rakhra, Dean Felsher
1Department of Electrical Engineering, Stanford University School of Medicine, Stanford, CA 94305, USA.
Abstract:
The dependence on the overexpression of a single oncogene constitutes an exploitable weakness for molecular targeted therapy. These drugs can produce dramatic tumor regression by targeting the driving oncogene, but relapse often follows. Understanding the complex interactions of the tumor's multifaceted response to oncogene inactivation is key to tumor regression. It has become clear that a collection of cellular responses lead to regression and that immune-mediated steps are vital to preventing relapse. Our integrative mathematical model includes a variety of cellular response mechanisms of tumors to oncogene inactivation. It allows for correct predictions of the time course of events following oncogene inactivation and their impact on tumor burden. A number of aspects of our mathematical model have proven to be necessary for recapitulating our experimental results. These include a number of heterogeneous tumor cell states since cells following different cellular programs have vastly different fates. Stochastic transitions between these states are necessary to capture the effect of escape from oncogene addiction (i.e., resistance). Finally, delay differential equations were used to accurately model the tumor growth kinetics that we have observed. We use this to model oncogene addiction in MYC-induced lymphoma, osteosarcoma, and hepatocellular carcinoma.
Insights
Targeting oncogenes offers a therapeutic window, but tumor relapse is common. This study develops a mathematical model to understand tumor responses to oncogene inactivation, crucial for preventing relapse.
Area of Science:
- Oncology
- Mathematical Biology
- Immunology
Background:
- Targeted cancer therapies exploit oncogene overexpression but often lead to relapse.
- Tumor regression involves complex cellular responses and immune-mediated mechanisms crucial for preventing relapse.
Purpose of the Study:
- To develop an integrative mathematical model predicting tumor response to oncogene inactivation.
- To understand the multifaceted cellular responses and immune interactions vital for sustained tumor regression.
Main Methods:
- Developed an integrative mathematical model incorporating heterogeneous tumor cell states and stochastic transitions.
- Utilized delay differential equations to accurately model tumor growth kinetics.
- Applied the model to study oncogene addiction in MYC-induced lymphoma, osteosarcoma, and hepatocellular carcinoma.
Main Results:
- The model accurately predicts the time course of events and tumor burden following oncogene inactivation.
- Heterogeneous tumor cell states and stochastic transitions are essential for recapitulating experimental results and modeling resistance.
- The model successfully captures tumor growth kinetics observed in experimental settings.
Conclusions:
- Understanding tumor cell heterogeneity and dynamic state transitions is key to overcoming resistance to targeted therapies.
- Mathematical modeling provides critical insights into the complex interplay of cellular responses and immune mechanisms in tumor regression.
- This approach aids in developing strategies to prevent relapse in cancers driven by oncogene addiction.
