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Updated: Aug 11, 2026

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
Published on: February 6, 2015
Evolution of resistance to cancer therapy
Franziska Michor1, Martin A Nowak, Yoh Iwasa
1Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Mathematics, Harvard University, Cambridge, MA 02138, USA. michor@fas.harvard.edu
Abstract:
Acquired drug resistance is a major limitation for successful treatment of cancer. Resistance emerges due to drug exclusion, drug metabolism and alteration of the drug target by mutation or overexpression. Depending on therapy, the type of cancer and its stage, one or several genetic or epigenetic alterations are necessary to confer resistance to treatment. The fundamental question is the following: if a genetically diverse population of replicating cancer cells is subjected to chemotherapy that has the potential to eradicate it, what is the probability of emergence of resistance? Here, we review a general mathematical framework based on multi-type branching processes designed to study the dynamics of escape of replicating organisms from selection pressures. We apply the general model to evolution of resistance of cancer cells and discuss examples for diverse mechanisms of resistance. Our theory shows how to estimate the probability of success for any treatment regimen.
Insights
Mathematical models predict the probability of cancer cells developing drug resistance. Understanding resistance mechanisms like drug exclusion and target alteration is key to improving cancer treatment success rates.
Area of Science:
- Oncology
- Mathematical Biology
- Evolutionary Biology
Background:
- Acquired drug resistance is a significant obstacle in effective cancer treatment.
- Resistance mechanisms include drug exclusion, metabolism, and target alteration (mutation or overexpression).
- Cancer resistance often involves multiple genetic or epigenetic alterations, varying by cancer type, stage, and therapy.
Purpose of the Study:
- To present a general mathematical framework for studying the emergence of resistance in replicating populations under selection pressure.
- To apply this framework to model the evolution of drug resistance in cancer cells.
- To provide a method for estimating the probability of treatment success based on resistance dynamics.
Main Methods:
- Utilizing a general mathematical framework based on multi-type branching processes.
- Analyzing the dynamics of escape from selection pressures in replicating organisms.
- Applying the model to diverse mechanisms of cancer drug resistance.
Main Results:
- The study reviews a mathematical framework applicable to cancer resistance.
- The framework allows for the study of resistance evolution across various mechanisms.
- The developed theory provides a means to estimate treatment success probabilities.
Conclusions:
- A mathematical framework using multi-type branching processes can model cancer drug resistance.
- This approach aids in understanding the probability of resistance emergence under chemotherapy.
- The findings offer insights into predicting the success of cancer treatment regimens.
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08:46Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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