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A hitchhiker's guide to cancer models
Karim I Budhwani1, Zeelu H Patel2, Rachael E Guenter2
1CerFlux, Inc., Birmingham, AL, USA; Department of Radiation Oncology, Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA; Department of Physics, Coe College, Cedar Rapids, IA, USA.
Trends in Biotechnology
|May 9, 2022
Summary
This review explores four categories of cancer models to improve cancer therapeutics and personalized treatment. Understanding these models is crucial for advancing cancer research from the lab to the clinic.
Area of Science:
- Oncology
- Translational Cancer Research
- Biomedical Modeling
Background:
- Cancer affects over 1.7 million individuals annually in the U.S., highlighting the urgent need for advanced treatments.
- Developing effective therapeutics and predictive tools for personalized cancer care is paramount.
- Robust cancer models are essential for bridging the gap between fundamental research and clinical application.
Purpose of the Study:
- To categorize and analyze diverse cancer models.
- To evaluate the strengths, weaknesses, opportunities, and threats of each model type.
- To inform strategies for improving cancer understanding and treatment.
Main Methods:
- A thought experiment approach was employed.
- Four broad categories of cancer models were identified and defined.
- SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was conducted for each category.
Main Results:
- The review delineates four distinct classes of cancer models.
- Each model category presents unique advantages and limitations for cancer research.
- The analysis provides a framework for selecting appropriate models for specific research questions.
Conclusions:
- Cancer models are critical for advancing personalized medicine and therapeutic development.
- A comprehensive understanding of model systems is necessary for effective translation of research findings.
- Future research should focus on optimizing existing models and developing novel systems to address current challenges in oncology.
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