Limits...
Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations.

Gatenby RA, Cunningham JJ, Brown JS - Nat Commun (2014)

Bottom Line: Drug development strategies target driver mutations, but inter- and intratumoral heterogeneity usually results in emergence of resistance.Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumour burden but almost inevitably fail due to population heterogeneity.An alternative strategy targets gene mutations that are never observed.

View Article: PubMed Central - PubMed

Affiliation: Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, Florida 33612, USA.

ABSTRACT
Genetic and epigenetic changes in cancer cells are typically divided into 'drivers' and 'passengers'. Drug development strategies target driver mutations, but inter- and intratumoral heterogeneity usually results in emergence of resistance. Here we model intratumoral evolution in the context of a fecundity/survivorship trade-off. Simulations demonstrate that the fitness value of any genetic change is not fixed but dependent on evolutionary triage governed by initial cell properties, current selection forces and prior genotypic/phenotypic trajectories. We demonstrate that spatial variations in molecular properties of tumour cells are the result of changes in environmental selection forces such as blood flow. Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumour burden but almost inevitably fail due to population heterogeneity. An alternative strategy targets gene mutations that are never observed. Because up or downregulation of these genes unconditionally reduces cellular fitness, they are eliminated by evolutionary triage but can be exploited for targeted therapy.

Show MeSH

Related in: MedlinePlus

Simulation Setupa) Each of the 16 mutations confers a unique change to fecundity and survivorship. For example, mutation 16 increases both fecundity and survivorship a large and equal amount while mutation 9 greatly decreases survivorship but has no effect on fecundity. Mutations 17–20 true passenger mutations, conferring no change in survivorship or fecundity. b) Normal cells are found on the solid line, with the three specific normal populations used in the simulations highlighted (triangles). When carcinogenesis is allowed cells evolve from their original phenotype toward the dotted lines which represents the trade-off between survivorship and fecundity above which cells require too many resources, and are unable to survive. The point at which the fitness of an evolving cell within the extant environment is maximized is highlighted (stars). The path from a starting point to the maximization point corresponds to somatic evolution during carcinogenesis and represents acquisition of the hallmarks of cancer outlined in the text. c) Our fitness formulation assumes three distinct cell outcomes for each generation. 1) A cell can divide, allowing mutations in both mother and daughter, 2) a cell can survive first and then may divide (death precedes cell division), or divide first and then the progenitor cell may die (cell division precedes death), and 3) a cell may continue to the next generation. The mutation dynamics outline the process by which a mutation event is determined.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4260773&req=5

Figure 7: Simulation Setupa) Each of the 16 mutations confers a unique change to fecundity and survivorship. For example, mutation 16 increases both fecundity and survivorship a large and equal amount while mutation 9 greatly decreases survivorship but has no effect on fecundity. Mutations 17–20 true passenger mutations, conferring no change in survivorship or fecundity. b) Normal cells are found on the solid line, with the three specific normal populations used in the simulations highlighted (triangles). When carcinogenesis is allowed cells evolve from their original phenotype toward the dotted lines which represents the trade-off between survivorship and fecundity above which cells require too many resources, and are unable to survive. The point at which the fitness of an evolving cell within the extant environment is maximized is highlighted (stars). The path from a starting point to the maximization point corresponds to somatic evolution during carcinogenesis and represents acquisition of the hallmarks of cancer outlined in the text. c) Our fitness formulation assumes three distinct cell outcomes for each generation. 1) A cell can divide, allowing mutations in both mother and daughter, 2) a cell can survive first and then may divide (death precedes cell division), or divide first and then the progenitor cell may die (cell division precedes death), and 3) a cell may continue to the next generation. The mutation dynamics outline the process by which a mutation event is determined.

Mentions: (2)1xδxδt=ln(λ) where x is the population size of tumor cells. The formulation of this fitness function is described in Figure 7c. Note S and p combine several cancer “hallmarks” (5) as p is governed by self-sufficiency from growth signals and insensitivity to anti-growth signals and S represents evasion from apoptosis and replicative immortality.


Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations.

Gatenby RA, Cunningham JJ, Brown JS - Nat Commun (2014)

Simulation Setupa) Each of the 16 mutations confers a unique change to fecundity and survivorship. For example, mutation 16 increases both fecundity and survivorship a large and equal amount while mutation 9 greatly decreases survivorship but has no effect on fecundity. Mutations 17–20 true passenger mutations, conferring no change in survivorship or fecundity. b) Normal cells are found on the solid line, with the three specific normal populations used in the simulations highlighted (triangles). When carcinogenesis is allowed cells evolve from their original phenotype toward the dotted lines which represents the trade-off between survivorship and fecundity above which cells require too many resources, and are unable to survive. The point at which the fitness of an evolving cell within the extant environment is maximized is highlighted (stars). The path from a starting point to the maximization point corresponds to somatic evolution during carcinogenesis and represents acquisition of the hallmarks of cancer outlined in the text. c) Our fitness formulation assumes three distinct cell outcomes for each generation. 1) A cell can divide, allowing mutations in both mother and daughter, 2) a cell can survive first and then may divide (death precedes cell division), or divide first and then the progenitor cell may die (cell division precedes death), and 3) a cell may continue to the next generation. The mutation dynamics outline the process by which a mutation event is determined.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4260773&req=5

Figure 7: Simulation Setupa) Each of the 16 mutations confers a unique change to fecundity and survivorship. For example, mutation 16 increases both fecundity and survivorship a large and equal amount while mutation 9 greatly decreases survivorship but has no effect on fecundity. Mutations 17–20 true passenger mutations, conferring no change in survivorship or fecundity. b) Normal cells are found on the solid line, with the three specific normal populations used in the simulations highlighted (triangles). When carcinogenesis is allowed cells evolve from their original phenotype toward the dotted lines which represents the trade-off between survivorship and fecundity above which cells require too many resources, and are unable to survive. The point at which the fitness of an evolving cell within the extant environment is maximized is highlighted (stars). The path from a starting point to the maximization point corresponds to somatic evolution during carcinogenesis and represents acquisition of the hallmarks of cancer outlined in the text. c) Our fitness formulation assumes three distinct cell outcomes for each generation. 1) A cell can divide, allowing mutations in both mother and daughter, 2) a cell can survive first and then may divide (death precedes cell division), or divide first and then the progenitor cell may die (cell division precedes death), and 3) a cell may continue to the next generation. The mutation dynamics outline the process by which a mutation event is determined.
Mentions: (2)1xδxδt=ln(λ) where x is the population size of tumor cells. The formulation of this fitness function is described in Figure 7c. Note S and p combine several cancer “hallmarks” (5) as p is governed by self-sufficiency from growth signals and insensitivity to anti-growth signals and S represents evasion from apoptosis and replicative immortality.

Bottom Line: Drug development strategies target driver mutations, but inter- and intratumoral heterogeneity usually results in emergence of resistance.Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumour burden but almost inevitably fail due to population heterogeneity.An alternative strategy targets gene mutations that are never observed.

View Article: PubMed Central - PubMed

Affiliation: Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, Florida 33612, USA.

ABSTRACT
Genetic and epigenetic changes in cancer cells are typically divided into 'drivers' and 'passengers'. Drug development strategies target driver mutations, but inter- and intratumoral heterogeneity usually results in emergence of resistance. Here we model intratumoral evolution in the context of a fecundity/survivorship trade-off. Simulations demonstrate that the fitness value of any genetic change is not fixed but dependent on evolutionary triage governed by initial cell properties, current selection forces and prior genotypic/phenotypic trajectories. We demonstrate that spatial variations in molecular properties of tumour cells are the result of changes in environmental selection forces such as blood flow. Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumour burden but almost inevitably fail due to population heterogeneity. An alternative strategy targets gene mutations that are never observed. Because up or downregulation of these genes unconditionally reduces cellular fitness, they are eliminated by evolutionary triage but can be exploited for targeted therapy.

Show MeSH
Related in: MedlinePlus