Literature DB >> 21815657

Evolutionary dynamics in cancer therapy.

Jessica J Cunningham1, Robert A Gatenby, Joel S Brown.   

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 second, third, and fourth 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 microenvironmental conditions in response to various therapeutic strategies. We examine tumor cell death and the 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 multiagent 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 the 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.

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Year:  2011        PMID: 21815657      PMCID: PMC3250072          DOI: 10.1021/mp2002279

Source DB:  PubMed          Journal:  Mol Pharm        ISSN: 1543-8384            Impact factor:   4.939


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