Mononen, T. | bioRxiv Science

bioRxiv2020

Strong Selective Agents Determine Resistance Evolution in a Multidrug Therapeutic Regime

The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time-dependent selection in so-called fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question utilising a comprehensive set of regimes are lacking. Here we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time-invariant and -dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time-invariant environments caused stronger directional selection for resistances compared to time-dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross-resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time-invariant environments and as a function of exposure epoch in time-dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution.

evolutionary biology↗

bioRxiv2020

Drug-induced resistance evolution necessitates less aggressive treatment

Evolution of drug resistance to anticancer, antimicrobial and antiviral therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but also affects the rate of adaptation via altered mutational processes. Here we analyze a model with drug-induced increase in mutation rate and explore its consequences for treatment optimization. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realizes even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Factoring in such costs (or collateral damage) of control is at the core of successful therapy design and can unify different evolution-based approaches to therapy optimization. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimizes the probability of evolutionary rescue. Our solution exploits a trade-off, where increasing the drug concentration has two opposing effects. On the one hand, it reduces de novo mutations by decreasing the size of the target cell population faster; on the other hand, a higher dosage generates a surplus of treatment-induced mutations. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold change[≤] 10) to the baseline mutation rate. Our findings highlight the importance of dose dependencies in resistance evolution and motivate further investigation of the mutagenicity and other hidden collateral costs of therapies that promote resistance. Author summaryThe evolution of drug resistance is a particularly problematic and frequent outcome of cancer and antimicrobial therapies. Recent research suggests that these treatments may enhance the evolvability of the target population not only via inducing intense selection pressures but also via altering the underlying mutational processes. Here we investigate the consequences of such drug-induced evolution by considering a mathematical model with explicitly dose-dependent mutation rate. We identify, characterize and exploit a trade-off between decreasing the target population size as fast as possible and generating a surplus of treatment-induced de novo mutations. By formulating the treatment as an optimal control problem over the evolution of the target population, we find the optimal treatment strategy, which minimizes the probability of evolutionary rescue. We show that this probability changes non-monotonically with the cumulative drug concentration and is minimized at an intermediate dosage. Our results are immediately amenable to experimental investigation and motivate further study of the various mutagenic and other hidden collateral costs of treatment. Taken together, our results add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and stimulate further clinical trials on alternative treatment strategies.

evolutionary biology↗