Session

The cancer ecosystem: optimizing treatment based on evolution

10 Jul 2018, 10:30
New Law School/--105 (University of Sydney)

New Law School/--105

University of Sydney

60

Conveners

The cancer ecosystem: optimizing treatment based on evolution

  • Jill Gallaher (H. Lee Moffitt Cancer Center)
  • Alexander R. A. Anderson (Moffitt Cancer Center)

Description

Tumours are not simply collections of mutated cells that grow in isolation of the environment in which they live. They interact with and modify both the physical microenvironment and a variety of nontumor cells that make up the organ in which the cancer originated. In nature, ecology and evolution are intimately linked since one provides the players and the other provides the field. The idea of viewing cancer from an ecological perspective fundamentally means that we cannot just consider cancer as a collection of mutated cells but rather a dynamic system of many interacting cellular and microenvironmental elements. Heterogeneity is both a cause and consequence of the interaction between the tumour and its environment and has been observed across genotypic, phenotypic, and environmental scales and has now been recognized as a key driver in cancer drug resistance and treatment failure.
Importantly this complex dialogue is governed by Darwinian dynamics, i.e. local microenvironmental conditions select phenotypic tumour clones that are best adapted to locally survive and proliferate. Conversely, the phenotypic properties of the cells (through release of growth factors or metabolites) affect the environmental properties. While these complex interactions have enormous clinical implications because they promote resistance to therapy, they rarely incorporated into clinical design. In fact, many treatment approaches only focus on specific molecular targets without considering the context or consequence.
In this minisymposium, we present ecological and evolutionary perspectives on cancer progression and treatment. By combining models with biological or clinical data, progression can be better understood and novel treatment strategies can be designed based on ecological and evolutionary principles.

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