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Cancer phenotype as the outcome of an evolutionary game between normal and malignant cells
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Resumo(s)
BACKGROUND: There is variability in the cancer phenotype across individuals: two patients with the same tumour may experience different disease life histories, resulting from genetic variation within the tumour and from the interaction between tumour and host. Until now, phenotypic variability has precluded a clear-cut identification of the fundamental characteristics of a given tumour type. METHODS: Using multiple myeloma as an example, we apply the principles of evolutionary game theory to determine the fundamental characteristics that define the phenotypic variability of a tumour. RESULTS: Tumour dynamics is determined by the frequency-dependent fitness of different cell populations, resulting from the benefits and costs accrued by each cell type in the presence of others. Our study shows how the phenotypic variability in multiple myeloma bone disease can be understood through the theoretical approach of a game that allows the identification of key genotypic features in a tumour and provides a natural explanation for phenotypic variability. This analysis also illustrates how complex biochemical signals can be translated into cell fitness that determines disease dynamics. CONCLUSION: The present paradigm is general and extends well beyond multiple myeloma, and even to non-neoplastic disorders. Furthermore, it provides a new perspective in dealing with cancer eradication. Instead of trying to kill all cancer cells, therapies should aim at reducing the fitness of malignant cells compared with normal cells, allowing natural selection to eradicate the tumour. British Journal of Cancer ( 2009) 101, 1130-1136. doi:10.1038/sj.bjc.6605288 www.bjcancer.com Published online 1 September 2009 (C) 2009 Cancer Research UK
Descrição
This work was supported by Mayo Foundation ( DD), FCT Portugal (FACCC and JMP), FNRS Belgium (FCS) and FWO Belgium (SVS).
Palavras-chave
in-vivo multiple-myeloma game inflammatory cell dynamics ecology myeloma of protein tumor-cells somatic bone-disease staging 1-alpha multiple inhibition evolution system transplantation replicator cancer growth evolutionary osteoprotegerin theory Cancer dynamics Cancer ecology Evolutionary game theory of cancer Multiple myeloma Replicator cell dynamics Somatic evolution of cancer SDG 3 - Good Health and Well-being