Cancer in light of experimental evolution

Abstract

Cancer initiation, progression, and the emergence of therapeutic resistance are evolutionary phenomena of clonal somatic cell populations. Studies in microbial experimental evolution and the theoretical work inspired by such studies are yielding deep insights into the evolutionary dynamics of clonal populations, yet there has been little explicit consideration of the relevance of this rapidly growing field to cancer biology. Here, we examine how the understanding of mutation, selection, and spatial structure in clonal populations that is emerging from experimental evolution may be applicable to cancer. Along the way, we discuss some significant ways in which cancer differs from the model systems used in experimental evolution. Despite these differences, we argue that enhanced prediction and control of cancer may be possible using ideas developed in the context of experimental evolution, and we point out some prospects for future research at the interface between these traditionally separate areas.

Figure 1

Experimental evolution and cancer. (A) In serial transfer evolution experiments, several replicate microbial populations are seeded from a culture originating from a single ancestral colony (derived from a single cell). Thereafter, a small subsample of each replicate population is regularly transferred into fresh medium. Aliquots of the evolving populations are preserved at regular time intervals for future analysis. (B) In evolution experiments conducted in a chemostat, the number of cells remains essentially constant. As fresh medium is fed into the chemostat, waste medium is removed. Cells can be sampled repeatedly from the waste medium, allowing for analysis of the population over time (adapted from [170]). (C) Somatic cells within individuals may evolve over time to become cancerous. The ancestral genotype for each neoplasm is the germ line genotype of the individual with the neoplasm.

Figure 2

Dynamics of clonal evolution. Several variations on a visualisation of clonal evolution originated by Muller in 1932 [53] and reinterpreted by Crow and Kimura [171]. We consider the fate of new mutations in a finite population of constant size. Time is depicted on the x-axis beginning at an arbitrary instant at which we assume that the population has no competing clones; the population sizes of clones harbouring mutations that have arisen since that instant are depicted on the y-axis. The total number of evolving cells in this neoplasm is constant (as might occur in the early stages of neoplastic progression), but the fraction of cells that have a given genotype varies as mutations arise and then either expand or are lost. The genotypes of clones are depicted to the right; darker colours indicate clones harbouring increasing numbers of beneficial mutations. (A) When new, beneficial mutations (‘+’) are rare, they are likely to sweep to fixation in the population before the next beneficial mutation arises. In this case, all the cells in the final population will have the +₁ and +₂ mutations. (B) Beneficial mutations are thought to be rarer than neutral mutations (‘o’); neutral mutations may hitchhike to fixation with a beneficial mutation. In this case, all of the cells in the population will have the neutral o₁ and the beneficial +₂ mutations, as indicated on the lower right margin of the panel. Additional neutral mutations may arise and expand in the population (e.g., o₃), leading to intrapopulation heterogeneity. It is also possible for neutral mutations to arise and go extinct: two such mutations are illustrated here. (C) When the beneficial mutation supply rate is high, several beneficial mutations may arise in separate clones and compete, as depicted here by the +₁ and +₂ beneficial mutations (and also the +₃ and +₄ beneficial mutations). The competition between the clones may delay the fixation of any one of the beneficial mutations and thus prolong intrapopulation heterogeneity. Here, there are four distinct genotypes in the neoplasm. (D) The time between new beneficial mutations will tend to be longer in small populations than in large populations, given the same beneficial mutation rate.