Adaptive therapy.

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Cancer Res. 2009 Jun 1;69(11):4894-903.

Adaptive therapy.
 
Gatenby RA, Silva AS, Gillies RJ, Frieden BR.
Department of Integrative Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida 33612, USA.

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. [Cancer Res 2009;69(11):4894-903]
Major FindingsWe present mathematical analysis of the evolutionary
dynamics of tumor populations with and without therapy. Analytic
solutions and numerical simulations show that, with pretreatment,
therapy-resistant cancer subpopulations are present due to phenotypic
or microenvironmental factors; maximum dose density chemotherapy
hastens rapid expansion of resistant populations. The models predict
that host survival can be maximized if "treatment-for-cure strategy" is
replaced by "treatment-for-stability." Specifically, the models predict
that an optimal treatment strategy will modulate therapy to maintain a
stable population of chemosensitive cells that can, in turn, suppress
the growth of resistant populations under normal tumor conditions
(i.e., when therapy-induced toxicity is absent). In vivo experiments
using OVCAR xenografts treated with carboplatin show that adaptive
therapy is feasible and, in this system, can produce long-term survival.