The adaptive state determines the impact of mutations on evolving populations

Abstract

Darwinian evolution results from an interplay between stochastic diversification of heritable phenotypes, impacting the chance of survival and reproduction, and fitness-based selection. The ability of populations to evolve and adapt to environmental changes depends on rates of mutational diversification and the distribution of fitness effects of random mutations. In turn, the distribution of fitness effects of stochastic mutations can be expected to depend on the adaptive state of a population. To systematically study the impact of the interplay between the adaptive state of a population on the ability of asexual populations to adapt, we used a spatial agent-based model of a neoplastic population adapting to a selection pressure of continuous exposure to targeted therapy. We found favorable mutations were overrepresented at the extinction bottleneck but depleted at the adaptive peak. The model-based predictions were tested using an experimental cancer model of an evolution of resistance to a targeted therapy. Consistent with the models prediction, we found that enhancement of the mutation rate was highly beneficial under therapy but moderately detrimental under the baseline conditions. Our results highlight the importance of considering population fitness in evaluating the fitness distribution of random mutations and support the potential therapeutic utility of restricting mutational variability. SIGNIFICANCE STATEMENTThe ability of a population to adapt and evolve is heavily influenced by the effects of random mutations on individuals. However, these effects can vary depending on the existing fitness level of the population. Using the development of cancer treatment resistance as an example, our research shows that populations nearing extinction can benefit from an increased rate of mutation. In contrast, mutations have a neutral or harmful effect on well-adapted populations. These findings suggest that new therapeutic strategies that manipulate mutation rates based on a populations current state of adaptation could be effective in preventing cancer and antimicrobial resistance.