Adaptive therapy

Abstract

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.

Figure 1

Simulation of treatment dynamics in a tumor with a dominant subpopulation (p) that is sensitive to some treatment and a small subpopulation (q) that is less fit (resulting in slower proliferation) but completely resistant to the treatment. Therapy is applied of time t = t₁ and is sufficient to entirely eradicate subpopulation p. The tumor size initially decreases. However, the resistant phenotype is now also the fittest extant population and rapidly regrows, resulting in tumor regrowth and resistance to any further therapy.

Figure 2

A, algorithm used for adjusting the environmental sensitivity. Once a tumor increases in size, the environmental sensitivity is decreased by the inverse of the growth rate. A delay of δ is then added to the original function so that the environmental sensitivity still grows asymptotically toward 2 but will require more time. B, dynamics of tumor regrowth and adaptive therapy treatment. Before the therapy is applied, the tumor is measured, and its size compared with the value at the prior time step. Should the tumor be bigger, the therapy intensity is increased; if the tumor is smaller, the dose density is decreased.

Figure 3

Simulations for application of five therapy strategies: (a) MTD; (b) adaptive therapy; (c) metronomic therapy (continuous infusion); (d) metronomic therapy (high-frequency administration); and (e) metronomic therapy (low-frequency administration). Four combinations of mixed cell populations that include FR with high free-field fitness and high sensitivity to therapy, and R with lower fitness and low sensitivity to therapy, S with low fitness and high sensitivity, and ER with high intrinsic sensitivity and fitness but in an environment that restricts proliferation and response. Combinations shown in (a) “FS and R,” (b) “S and FR,” (c) “FS and R and ER,” and (d) “FS and ER.” The results show that at day 1,500 of tumor growth (1,100 d after initiation of therapy), the tumor treated using the MTD strategy was largest whereas those treated with metronomic therapy were smallest. When the simulations were run until the tumor burden achieved the lethal threshold, all patients in the MTD and metronomic therapies succumbed to their disease. However, the tumors treated with adaptive therapy remained stable even after a period exceeding 10,000 d.

Figure 4

Progression of tumor properties with time in initial response to different therapeutic strategies. All graphs use two populations, FS and ER, the first being fit and therapy sensitive, the second environmentally resistant. The top left graph (Total tumor size) shows that MTD therapy results in a better initial result but the tumor promptly acquires resistance and recurs. Metronomic therapy (METC, METH, and METL) also reduces the tumor and is able to keep it stable for longer, but eventually, the resistant populations emerge once sensitive population is depleted (top right, Phenotypically sensitive). As shown in Fig. 3, these resistant populations result in tumor regrowth and patient death. Tumors treated with adaptive therapy (ADP) decrease in volume much less than with the other therapies but then maintain a stable tumor size for a prolonged period of time. Control (CTRL) corresponds to untreated tumors.

Figure 5

Two different experiments as described in the text. The y-axis is the mean tumor volume for the four animals in each experimental group, and the x-axis is the time from s.c. inoculation of 10⁷ tumor cells. Each experiment included four animals in three experimental arms: (a) control (vehicle only); (b) “standard” high dose therapy consisting of 60 mg/kg q4 days for 3 doses; (c) adaptive therapy which begins with a dose of 50 mg/kg and then adjusts the dose to maintain a stable tumor volume. The arrows on the x-axis represent days in which therapy was given in the adaptive group. In the top experiment, the doses are (from left to right) 50, 40, 40, 30, 30, 20, 20, 10, 10, 10, 10, 10, 10, 10, 10, 10 mg/kg. In the lower experiment, the doses are 50, 50, 40, 40, 30, 20, 20, 10, 10, 10, 10, 10, 10 mg/kg.