Drug-induced resistance evolution necessitates less aggressive treatment

Abstract

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.

A theoretical analysis of tumour containment

“…Finally, although we have checked that random genetic mutations from sensitive to resistant occurring after treatment initiation do not substantially affect our results (Supplementary Information, Section 6.3; Extended Data Fig. 5), we have not investigated treatment-induced mutations [33,34], accumulation of driver mutations, nor models involving quiescent cancer stem cells.…”

Turnover shapes evolution of birth and death rates

“…The presented results are also of particular relevance to rapidly evolving cell populations, such as cancer and microbial cell communities, which can exhibit diverse life-history strategies and great turnover differences, ranging from hyperproliferative cells to complete quiescence. Indeed, the role of turnover seems to be evident especially in the context of cancer evolution [51, 52], where detailed understanding of the mechanisms of birth and death can further elucidate patterns of tumor progression and waiting times to cancer as well as guide optimal treatment decisions [53, 54]. However, in order to fully leverage the utility of the presented theory and test its predictions, it is necessary to be able to experimentally access the birth and death rates, and not only their difference.…”

Poloxamer‐linked prodrug of a topoisomerase I inhibitor SN22 shows efficacy in models of high‐risk neuroblastoma with primary and acquired chemoresistance

“…As a result, the non-curatively treated high-risk disease is likely to recur in a multidrug-resistant form, with a major shift in chemosensitivity severely compromising the efficacy of rescue therapy. 53 The goal at this stage is, therefore, to maximize tumor regression and, whenever possible, to eliminate residual disease and to prevent its recurrence. 54 In accordance with this approach, PF108-[SN22] 2 achieved complete elimination of most IMR-32 tumors, in contrast to the transient response to irinotecan observed in this model.…”

“…While often achieving significant early responses in newly diagnosed high‐risk patients, conventional therapies apply strong selection pressure promoting the acquisition of drug resistance by the initially chemosensitive tumors. As a result, the non‐curatively treated high‐risk disease is likely to recur in a multidrug‐resistant form, with a major shift in chemosensitivity severely compromising the efficacy of rescue therapy 53 54 .…”