Lessons from Applied Ecology: Cancer Control Using an Evolutionary Double Bind

Gatenby, Robert A.; Brown, Joel S.; Vincent, Thomas L.

doi:10.1158/0008-5472.can-09-1354

Cited by 148 publications

(166 citation statements)

References 41 publications

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“…Other authors have suggested that under conditions of tumor genetic instability, heterogeneous tumors contain populations of one or more resistant clones (44,45). These resistant subpopulations can be residual compared with other clones in a pretreated tumor due to the phenotypic cost associated with chemoresistance, but could get favored when the anticancer therapy is applied (46). This hypothesis is supported by our results.…”

Section: Discussionsupporting

confidence: 87%

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

et al. 2013

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Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell proliferation, apoptosis. and metastasis through an intricate regulatory network, which includes other transcription factors like p73 and cancer-related microRNAs like miR-205. To investigate the emergence of drug resistance, we developed a methodology that integrates experimental data with a network biology and kinetic modeling. Using a regulatory map developed to summarize knowledge on E2F1 and its interplay with p73/ DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represents the network response to certain genotoxic and cytostatic anticancer drugs. By perturbing the model parameters, we simulated heterogeneous cell configurations referred to as in silico cell lines. These were used to detect genetic signatures characteristic for single or double drug resistance. We identified a signature composed of high E2F1 and low miR-205 expression that promotes resistance to genotoxic drugs. In this signature, downregulation of miR-205, can be mediated by an imbalance in the p73/DNp73 ratio or by dysregulation of other cancer-related regulators of miR-205 expression such as TGFb-1 or TWIST1. In addition, we found that a genetic signature composed of high E2F1, low miR-205, and high ERBB3 can render tumor cells insensitive to both cytostatic and genotoxic drugs. Our model simulations also suggested that conventional genotoxic drug treatment favors selection of chemoresistant cells in genetically heterogeneous tumors, in a manner requiring dysregulation of incoherent feedforward loops that involve E2F1, p73/DNp73, and miR-205. Cancer Res; 73(12); 3511-24. Ó2013 AACR.

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Section: Discussionsupporting

confidence: 87%

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

et al. 2013

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“…Should treatments thus be tailored to allow survival of "nicer" cancer cells, maintaining on the long run such situations by"adaptive therapy", as proposed by Robert Gatenby [96,97,98]? These are some of the questions control scientists are confronted with.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

Chisholm

Lorenzi

Clairambault

2016

Biochimica et Biophysica Acta (BBA) - General Subjects

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BACKGROUND. Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. SCOPE OF REVIEW.We focus on intra-tumour heterogeneity, namely betweencell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies.MAJOR CONCLUSIONS. Plasticity in cancer cells, i.e., partial reversal to a stemlike status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies.GENERAL SIGNIFICANCE. Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics.

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“…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'.…”

Section: Cancer As An Evolutionary Gamementioning

confidence: 99%

Why Darwin would have loved evolutionary game theory

Brown¹

2016

Proc. R. Soc. B.

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Humans have marvelled at the fit of form and function, the way organisms' traits seem remarkably suited to their lifestyles and ecologies. While natural selection provides the scientific basis for the fit of form and function, Darwin found certain adaptations vexing or particularly intriguing: sex ratios, sexual selection and altruism. The logic behind these adaptations resides in frequency-dependent selection where the value of a given heritable phenotype (i.e. strategy) to an individual depends upon the strategies of others. Game theory is a branch of mathematics that is uniquely suited to solving such puzzles. While game theoretic thinking enters into Darwin's arguments and those of evolutionists through much of the twentieth century, the tools of evolutionary game theory were not available to Darwin or most evolutionists until the 1970s, and its full scope has only unfolded in the last three decades. As a consequence, game theory is applied and appreciated rather spottily. Game theory not only applies to matrix games and social games, it also applies to speciation, macroevolution and perhaps even to cancer. I assert that life and natural selection are a game, and that game theory is the appropriate logic for framing and understanding adaptations. Its scope can include behaviours within species, state-dependent strategies (such as male, female and so much more), speciation and coevolution, and expands beyond microevolution to macroevolution. Game theory clarifies aspects of ecological and evolutionary stability in ways useful to understanding eco-evolutionary dynamics, niche construction and ecosystem engineering. In short, I would like to think that Darwin would have found game theory uniquely useful for his theory of natural selection. Let us see why this is so.

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Lessons from Applied Ecology: Cancer Control Using an Evolutionary Double Bind

Abstract: Because the metastatic cascade

Cited by 148 publications

References 41 publications

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

Why Darwin would have loved evolutionary game theory

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