The Evolution and Ecology of Resistance in Cancer Therapy

Abstract

Despite continuous deployment of new treatment strategies and agents over many decades, most disseminated cancers remain fatal. Cancer cells, through their access to the vast information of human genome, have a remarkable capacity to deploy adaptive strategies for even the most effective treatments. We note there are two critical steps in the clinical manifestation of treatment resistance. The first, which is widely investigated, requires deployment of a mechanism of resistance that usually involves increased expression of molecular machinery necessary to eliminate the cytotoxic effect of treatment. However, the emergence of a resistant phenotype is not in itself clinically significant. That is, resistant cells affect patient outcomes only when they form a sufficiently large population to allow tumor progression and treatment failure. Importantly, proliferation of the resistant phenotype is by no means certain and, in fact, depends on complex Darwinian dynamics governed by the costs and benefits of the resistance mechanisms in the context of the local environment and competing populations. Attempts to target molecular machinery of resistance have had little clinical success largely because of the diversity within the human genome-therapeutic interruption of one mechanism simply results in its replacement by an alternative. We explore an alternative strategy for overcoming treatment resistance that seeks to understand and exploit the critical evolutionary dynamics that govern proliferation of the resistant phenotypes. In general, this approach has shown that, although emergence of resistance mechanisms in cancer cells to every current therapy is inevitable, proliferation of the resistant phenotypes is not and can be delayed and even prevented with sufficient understanding of the underlying ecoevolutionary dynamics.

Figure 1.

Illustrating the evolutionary consequence of the cost of resistance. To mimic growth dynamics of resistant and sensitive cell populations, labeled MCF7 (sensitive) and MCF7/Dox (resistant) cells were cocultured in the absence or doxorubicin with physiological levels of glucose. The phenotypic cost of resistance decreased fitness of the resistant cells and allowed the sensitive population to proliferate at the expense of the resistant phenotype.

Figure 2.

Conventional, high dose-density treatment compared to an evolution-based strategy. (Top row) Conventional high dose-density therapy explicitly aims to eliminate all cancer cells that are sensitive to therapy. However, this maximally selects for resistant phenotypes and eliminates competitors permitting rapid progression—an evolutionary dynamic termed “competitive release.” (Bottom row) Adaptive therapy (AT) explicitly aims to maintain a small population of cells that are sensitive to treatment. Although the resistant cells survive, the metabolic cost of the molecular machinery of resistance (Fig. 1) renders them less fit in the absence of therapy. Thus, when therapy is withdrawn, the tumor will regrow but the fitness advantage of the sensitive cells allows them to proliferate at the expense of the resistant population. At the end of each cycle, the tumor remains sensitive to therapy.

Figure 3.

Current “personalized medicine” paradigms almost exclusive focus on defining predictive biomarkers that can identify effective treatments. This approach, however, fails to recognize that even highly effective therapies are almost always defeated by evolution of resistance. Here, we propose that “precision medicine” in cancer care requires both identification of optimal treatment modality and understanding of the Darwinian dynamics that govern response and resistance to therapy and, thus, ultimate patient outcomes.