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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Related Experiment Video

Updated: Mar 3, 2026

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

Modeling Tumor Clonal Evolution for Drug Combinations Design.

Boyang Zhao^1,2, Michael T Hemann^2,3, Douglas A Lauffenburger^2,3,4

¹Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA 02139.

Trends in Cancer

|April 25, 2017

Summary

This summary is machine-generated.

Cancer evolves like a population, leading to drug resistance. Mathematical modeling offers insights into tumor progression and designing better cancer treatments.

Keywords:

Intratumoral heterogeneity drug combinations drug resistance mathematical/computational modeling tumor clonal evolution

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Area of Science:

Oncology
Evolutionary Biology
Mathematical Modeling

Background:

Cancer progresses as a clonal evolutionary process.
Tumor evolution drives therapeutic challenges, particularly drug resistance.
Intratumoral heterogeneity complicates treatment strategies.

Purpose of the Study:

To review the application of mathematical modeling in cancer research.
To explore inter-disciplinary approaches for understanding cancer progression and resistance.
To highlight opportunities for improving cancer treatment design.

Main Methods:

Review of mathematical modeling techniques (population genetics, evolutionary dynamics, engineering).
Integration of modeling with experimental technologies.
Analysis of tumor progression, heterogeneity, and drug resistance dynamics.

Main Results:

Mathematical modeling provides quantitative insights into cancer evolution.
Inter-disciplinary approaches enhance understanding of intratumoral heterogeneity.
Modeling aids in designing rational drug scheduling and combination therapies.

Conclusions:

Quantitative modeling is crucial for advancing cancer biology.
Integrating modeling with experimental data improves therapeutic strategy design.
Mathematical perspectives offer enhanced capabilities for personalized cancer treatment regimens.