Evolutionary dynamics in cancer therapy

Abstract

Disseminated cancer remains a largely fatal disease. While systemic therapy can have some initial success, it is rarely durable. Typically, populations of cancer cells resistant to therapy emerge quickly requiring progressively less effective second, third, and fourth line therapies until the patient succumbs. Cancer cells possess a large repertoire of heritable phenotypic strategies that can be used to confer resistance to one or more therapeutic drugs. In addition, environmental factors such as ischemia and hypoxia can reduce therapeutic effects by limiting drug delivery or toxicity. Here, we use a fitness generating function (G-function) approach to model tumor response with respect to evolutionary adaptation and microenvironmental conditions in response to various therapeutic strategies. We examine tumor cell death and the evolution of resistance in single and two drug therapies as well as alternative "evolutionary" approaches. We demonstrate that even monotherapy would be highly successful in the absence of tumor evolution or environmentally mediated resistance. However, environmental and evolutionary factors dramatically reduce the effectiveness of therapy. Two-drug therapy in which adaptation requires two different phenotypic changes will maximally reduce tumor size and delay onset of resistance, but actual eradication of the tumor population is rare. We demonstrate that multiagent therapies in which the first drug both achieves tumor cell toxicity and drives phenotypic adaptation that renders the cell more vulnerable to a second therapy can be highly successful in maintaining durable tumor control. Examples of clinical trials that exploit these results are presented. We conclude that the development of more lethal (cytotoxic) drugs is not likely to fundamentally change the outcome of therapy. Instead, new approaches that incorporate evolutionary strategies into target and drug selection are needed.

Figure 1

Cost of resistance to therapy. As the tumor population evolves phenotypic resistance, the cost of that resistance is reflected in a decreasing carrying capacity. The magnitude of this cost is reflected by the parameter ${\mathrm{\sigma }}_{\mathrm{K}}^2$.

Figure 2

Without the ability of phenotypic evolution, shown as dotted lines, these treatments would be more than sufficient to eliminate the tumor. Unfortunately, with evolution of phenotypic resistance, treatments are unable to eradicate the tumor population.

Figure 3

The top axis shows the fractional resistance, v_i, to the one or two treatments given over time. The bottom axis shows the population density of the tumor. Periods of treatment (50 time units) alternate with periods of no treatment (50 time units).

Figure 4

The top axis shows the fractional resistance, v_i, to the two treatments given over time. The bottom axis shows the population density of the tumor.

Figure 5

The top axis shows the fractional resistance, v_i, to the two treatments given over time. The bottom axis shows the population density of the tumor.

Figure 6

The consequences of de-novo environmental resistance can be seen immediately. Even without the ability of phenotypic evolution, shown as the dotted lines, the monotherapy cannot eliminate the tumor population. The de-novo environmental resistance allows faster evolution of phenotypic resistance and less overall effect on tumor population.

Figure 7

The top axis shows the fractional resistance, v_i, to the two treatments given over time. The bottom axis shows the population density of the tumor.