Evolutionary therapy

Resistance to one drug can lead to unwanted cross-resistance to some other drugs[10] and "collateral" sensitivity to yet other drugs [11][12][13][14][15] This phenomenon can be exploited to create cyclic therapeutic regimes where each subsequent drug would make population of evolving disease agent sensitive to at least one other drug, though this process is difficult secondary to the stochasticity of evolution. [5] Alternative methods include incorporating stochastic control algorithms to direct the evolution to specific states of resistance that encode sensitivity to other drugs.[16]

The standard approach to treating cancer is giving patients the maximum tolerated amount of chemotherapy with the goal of doing the maximum possible damage to the tumor without killing the patient. This method is relatively effective, but it also causes major toxicities.[17] Adaptive therapy is an evolutionary therapy that aims to maintain or reduce tumor volume by employing minimum effective drug doses or timed drug holidays.[18][19] The timing and duration of these holidays, which relies on the ability to modulate resistant vs. sensitive populations of cancer cells through competition, is a subject which has been studied using dynamic programming[20] as well as optimal control[21] in theoretical studies based on Evolutionary game theory based models. The ability to modulate these populations secondary relies on the assumption that there is a both frequency-dependent selection, and an associated fitness cost to that resistance, a form of which, competitive exclusion, has been directly observed in EGFR lung cancer cell lines,[22] and posited in others.

Proof of principle for adaptive therapy has also been established in a recent phase 2 clinical trial[23] [24] as well as in vivo,[17] and more rigorous quantitative studies in vitro.[25]

In the evolutionary double bind, one drug causes increased susceptibility of the evolving cancer to another drug. Some have found that effectiveness might be based on interactions of populations through commensalism.[26] Others imply that population control may be possible if resistance to therapy requires a substantial and costly phenotypic adaptation that reduces the organism's fitness. [27]

Extinction therapy is inspired by mass extinction events from the Anthropocene era. [28] This treatment strategy is also sometimes referred to as first strike-second strike, where the first strike reduces the size and heterogeneity of a population so that the second strike that follows can kill the surviving, often fragmented population below a threshold by stochastic perturbations. [29]

A recent article. [30] introduced a potential therapeutic model that aims to create a tragedy of the commons within the populations of pathogens (bacteria, viruses, or even cancer). It is a well-established evolutionary prediction; cheaters can drive the whole population to go extinct. However, the success of free riders is usually supposed to be limited. Because cheater's patches will go extinct rapidly before they arrange successful migrations to other patches, this might be the fundamental problem of cheaters. However, this problem could be solved if we get a manipulated genetically engineered strain of the same pathogen, adopting a conditional defection strategy wherein free-riders would cooperate only for spread. The actors of this selfish strategy would have a high dispersal rate with the lowest possible cost because they share migration costs. Thus, the exploitation rate of public goods and interactions among defectors and cooperators will increase. In other words, this strain of conditional defectors can exclusively cooperate for all collective behaviors related to migration but defect otherwise. Therefore, these selfish successful migrators can be used as suicidal agents to drive the population of pathogens into the self-destruction process.