Evolutionary Dynamics in Cancer Therapy

Abstract

Disseminated cancer remains a largely fatal disease. While systemic therapy can have some initial success, it is rarely durable. Typically, populations of cancer cells resistant to therapy emerge quickly requiring progressively less effective 2nd, 3rd, and 4th line therapies until the patient succumbs. Cancer cells possess a large repertoire of heritable phenotypic strategies that can be used to confer resistance to one or more therapeutic drugs. In addition, environmental factors such as ischemia and hypoxia can reduce therapeutic effects by limiting drug delivery or toxicity. Here, we use a fitness generating function (G-function) approach to model tumor response with respect to evolutionary adaptation and micro-environmental conditions in response to various therapeutic strategies. We examine tumor cell death and evolution of resistance in single and two drug therapies as well as alternative “evolutionary” approaches. We demonstrate that even monotherapy would be highly successful in the absence of tumor evolution or environmentally-mediated resistance. However, environmental and evolutionary factors dramatically reduce the effectiveness of therapy. Two drug therapy in which adaptation requires two different phenotypic changes will maximally reduce tumor size and delay onset of resistance but actual eradication of the tumor population is rare. We demonstrate that multi-agent therapies in which the first drug both achieves tumor cell toxicity and drives phenotypic adaptation that renders the cell more vulnerable to a second therapy can be highly successful in maintaining durable tumor control. Examples of clinical trials that exploit these results are presented. We conclude that development of more lethal (cytotoxic) drugs is not likely to fundamentally change the outcome of therapy. Instead, new approaches that incorporate evolutionary strategies into target and drug selection are needed.

A mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment

“…Mathematical modeling studies in particular have been used to explore both broad and detailed aspects of cancer drug resistance, as reviewed in [43,7,25]. The fundamental question of how the presence of drug resistant cells influences tumor dynamics and treatment outcomes has been thoroughly explored in mathematical models under a wide variety of assumptions [14,37,39,47,58,40,23,24,70,16,45,60,63,4,22,33,44,52,65,3,32,80,13,26,29,46,49,51,57,59,77,18,68,1,9,10,20]. Cancer models have also been utilized to assess how various underlying mechanisms contribute to the resistant phenotype [80,13,59,18,20], and to calculate the probability that drug resistance emerges within a specified time frame, be it before or during cancer treatment …”

“…The question of how frequently, and at what dose, a single cancer drug should be administered given the presence of (or risk of developing) resistant cells has also been explored through mathematical modeling [37,23,24,33,46,49,68,1,9]. When multiple drugs are available, mathematical models have been used to determine the drug schedule (number of drugs to use, dose, sequence, timing) that best controls tumor progression in spite of drug resistance [39,47,70,16,60,63,4,51,10].Resistance to cancer drugs can be classified as either pre-existing or acquired [34]. Preexisting (intrinsic) drug resistance describes the case in which a tumor contains a subpopulation of drug resistant cells at the initiation of treatment, making the therapy (eventually) ineffective due to resistant cell selection [34].…”

“…As examples, pre-existing BCR-ABL kinase domain mutations confer resistance to the tyrosine kinase inhibitor imatinib in chronic myeloid leukemia patients [66,36], and pre-existing MEK1 mutations confer resistance to BRAF inhibitors in melanoma patients [8]. Many mathematical models have considered how the presence of preexisting resistant cells impact cancer progression and treatment [37,39,58,40,23,24,70,16,60,63,4,22,44,3,32,80,29,49,57,59,77,68,9,10].On the other hand, acquired drug resistance broadly describes the case in which drug resistance develops during the course of therapy from a population of cells that were initially drug sensitive [34]. The term "acquired resistance" is really an umbrella term for two distinct phenomena, which complicates the study of acquired resistance.…”

Why Darwin would have loved evolutionary game theory

“…Adaptive therapy [107] aims to use eco-evolutionary dynamics and Darwinian principles to create therapies that anticipate the evolutionary responses of the tumour cells. Such therapies might create evolutionary double-binds [108] where different therapies elicit resistance strategies that drive the cancer cells into the arms of the other therapy [109] and that treat the tumour as a community of coexisting cancer species rather than as a single entity. When 'treating to kill' cannot work, it may be possible to use adaptive therapies to 'treat to contain'.…”

Using Systems Biology to Understand Cancer as an Evolutionary Process

“…A very general limitation on this approach, however, is the acquired drug resistance that results from cellular evolution [54]. This makes resistant relapse a routine sequel to initial tumor shrinkage during therapy [15,54], and most patients who die of cancer are killed by a cancer that has evolved drug resistance. The problem of acquired resistance may prove to be even more limiting for a long-term chemoprevention than it is for therapy.…”