Adaptive Therapy
By Robert A. Gatenby, A. Silva, Robert James Gillies, B. Frieden. Cancer Research, 2009
758 citations recorded by the source corpus.
A number of successful systemic therapies are available for treatment of disseminated cancers. However, tumor response is often transient, and therapy frequently fails due to emergence of resistant populations. The latter reflects the temporal and spatial heterogeneity of the tumor microenvironment as well as the evolutionary capacity of cancer phenotypes to adapt to therapeutic perturbations. Although cancers are highly dynamic systems, cancer therapy is typically administered according to a fixed, linear protocol. Here we examine an adaptive therapeutic approach that evolves in response to the temporal and spatial variability of tumor microenvironment and cellular phenotype as well as therapy-induced perturbations. Initial mathematical models find that when resistant phenotypes arise in the untreated tumor, they are typically present in small numbers because they are less fit than the sensitive population. This reflects the “cost” of phenotypic resistance such as additional substrate and energy used to up-regulate xenobiotic metabolism, and therefore not available for proliferation, or the growth inhibitory nature of environments (i.e., ischemia or hypoxia) that confer resistance on phenotypically sensitive cells. Thus, in the Darwinian environment of a cancer, the fitter chemosensitive cells will ordinarily proliferate at the expense of the less fit chemoresistant cells. The models show that, if resistant populations are present before administration of therapy, treatments designed to kill maximum numbers of cancer cells remove this inhibitory effect and actually promote more rapid growth of the resistant populations. We present an alternative approach in which treatment is continuously modulated to achieve a fixed tumor population. The goal of adaptive therapy is to enforce a stable tumor burden by permitting a significant population of chemosensitive cells to survive so that they, in turn, suppress proliferation of the less fit but chemoresistant subpopulations. Computer simulations show that this strategy can result in prolonged survival that is substantially greater than that of high dose density or metronomic therapies. The feasibility of adaptive therapy is supported by in vivo experiments.
Subfields: Cancer Research, Cell Biology, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/7739728158b2b7d38855621ad6ff3d9101f94134
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Citations in the corpus
- Tumor heterogeneity: causes and consequences (2009, background): “Gatenby and co-authors have recently proposed another strategy, termed adaptive therapy, which both accounts for the heterogeneity of tumor cells and takes advantage of it [153].”
- Spreading speeds for a two-species competition-diffusion system (2018, methodology): “Therapy optimization in the case of heterogeneous tumours, for example, has been studied among others with the use of cellular automata [24, 15], with the construction of complex systems and their numerical studies [1], and also with the construction of simple models and their analytical studies, us”
- Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome (2014, background): “Consequently, tumor subregions develop, each with spatially distinct patterns of blood flow (7, 8), vessel permeability (9), cell proliferation (10), cell death (11), and other features.”