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Updated: Sep 12, 2025
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
A statistical framework for detecting therapy-induced resistance from drug screens
Chenyu Wu1, Einar Bjarki Gunnarsson2, Jasmine Foo3
1Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN, USA. wu000766@umn.edu.
Abstract:
Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we present a robust statistical framework leveraging multi-type branching process models to characterize the evolutionary dynamics of tumor cell populations. This approach enables the detection and quantification of therapy-induced resistance using high-throughput drug screening data involving total cell counts, without requiring information on subpopulation counts. The framework is validated using both simulated (in silico) and recent experimental (in vitro) datasets, demonstrating its ability to generate meaningful predictions.
Insights
This study introduces a new statistical model to detect and quantify therapy-induced cancer cell resistance. The framework uses branching processes and drug screening data to predict tumor evolution and improve cancer treatment strategies.
Area of Science:
- Cancer Research
- Mathematical Biology
- Evolutionary Dynamics
Background:
- Therapy resistance in cancer is a major challenge, often driven by stem-like cells and induced plasticity.
- Anticancer therapies can inadvertently promote resistance through epigenetic changes and de-differentiation.
- Understanding the interplay between anti-tumor effects and induced resistance is crucial for effective treatment strategies.
Purpose of the Study:
- To develop a statistical framework for characterizing tumor cell population dynamics under therapy.
- To enable the detection and quantification of therapy-induced resistance using multi-type branching process models.
- To analyze high-throughput drug screening data without needing subpopulation counts.
Main Methods:
- Utilized multi-type branching process models to simulate tumor cell population evolution.
- Developed a statistical framework to analyze total cell count data from drug screening.
- Validated the model using both in silico simulations and in vitro experimental data.
Main Results:
- The framework successfully detected and quantified therapy-induced resistance in simulated and experimental data.
- Demonstrated the ability to predict evolutionary dynamics of tumor cell populations.
- Provided a robust method for analyzing drug screening results.
Conclusions:
- The developed statistical framework offers a powerful tool for understanding and combating cancer therapy resistance.
- This approach can guide the development of more effective cancer treatment strategies by accounting for induced resistance.
- The model's ability to use total cell counts simplifies resistance analysis in drug screening.
