The adaptive state determines the impact of mutations on evolving populations

• 成果类型: Article
• 署名作者: Weh, Malgorzata Tyczynska; Kumar, Pragya; Marusyk, Viktoriya; Marusyk, Andriy; Basanta, David
• 署名单位: H Lee Moffitt Cancer Center & Research Institute; State University System of Florida; University of South Florida; H Lee Moffitt Cancer Center & Research Institute
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10333
• DOI: 10.1073/pnas.2427070122
• 发表日期: 2025-07-01
• 关键词: evolution; cancer; fitness; selection; DYNAMICS; Heterogeneity; mutagenesis; adaptation; resistance; mechanisms

摘要

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 model's 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.