Journal article
Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives
MATHEMATICAL BIOSCIENCES AND ENGINEERING, v 18(5), pp 6305-6327
2021
PMID: 34517535
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
When eradication is impossible, cancer treatment aims to delay the emergence of resistance while minimizing cancer burden and treatment. Adaptive therapies may achieve these aims, with suc-cess based on three assumptions: resistance is costly, sensitive cells compete with resistant cells, and therapy reduces the population of sensitive cells. We use a range of mathematical models and treatment strategies to investigate the tradeoff between controlling cell populations and delaying the emergence of resistance. These models extend game theoretic and competition models with four additional com-ponents: 1) an Allee effect where cell populations grow more slowly at low population sizes, 2) healthy cells that compete with cancer cells, 3) immune cells that suppress cancer cells, and 4) resource compe-tition for a growth factor like androgen. In comparing maximum tolerable dose, intermittent treatment, and adaptive therapy strategies, no therapeutic choice robustly breaks the three-way tradeoff among the three therapeutic aims. Almost all models show a tight tradeoff between time to emergence of resis-tant cells and cancer cell burden, with intermittent and adaptive therapies following identical curves. For most models, some adaptive therapies delay overall tumor growth more than intermittent thera-pies, but at the cost of higher cell populations. The Allee effect breaks these relationships, with some adaptive therapies performing poorly due to their failure to treat sufficiently to drive populations below the threshold. When eradication is impossible, no treatment can simultaneously delay emergence of resistance, limit total cancer cell numbers, and minimize treatment. Simple mathematical models can play a role in designing the next generation of therapies that balance these competing objectives.
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Details
- Title
- Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives
- Publication Details
- MATHEMATICAL BIOSCIENCES AND ENGINEERING, v 18(5), pp 6305-6327
- Publisher
- AMER INST MATHEMATICAL SCIENCES-AIMS; SPRINGFIELD
- Number of pages
- 22
- Grant note
- The authors thank the members of sLaM, Jason Griffiths, Andrea Bild, and Sandy Anderson for useful discussion. FRA acknowledges support from NIH-CSBC: U54: Combating subclonal evolution of resistant cancer phenotypes (U54 CA209978). CKB received support from the Modeling the Dynamics of Life Fund at the University of Utah and Research Experiences for Undergraduates from the Department of Mathematics at the University of Utah. RST received support from the Department of Mathematics at the University of Utah. We thank two anonymous reviewers for perceptive comments that greatly improved the manuscript.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000688412300002
- Scopus ID
- 2-s2.0-85112721131
- Other Identifier
- 991021860751604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology