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. 2014 Nov 19:5:5499.
doi: 10.1038/ncomms6499.

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

R A Gatenby  1 , J J Cunningham  1 , J S Brown  2
Affiliations

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

R A Gatenby et al. Nat Commun. .

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.

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Conflict of interest statement

Competing Financial Interests

The authors declare no competing financial interests

Figures

Figure 1
Figure 1
Evolutionary Trajectories and Gene Prevalence. a) During evolution to ESS1 (Evolutionary Stable State 1), the genes highlighted in green conferred increased fitness depending on starting initial phenotype. The genes circled in red, such as gene 1, reduce both fecundity and survivorship and were never observed in the final simulated cancer populations. b) Multiple evolutionary trajectories exist to ESS1 depending on initial phenotype. This functional equivalence results in genetic heterogeneity within and between patients as seen in c. c) The mutation prevalence varies greatly depending on initial phenotype. The orange line represents the neutral mutation prevalence.
Figure 2
Figure 2. Effect of Different Mutation Rates
(a) The y-axis shows the ratio between the mean fitness reached by the population after 2000 generations and the fitness achievable at the ESS (See Figure 2b). For this example we used ESS 1 with a maximum fitness = 0.3). The x-axis varies the mutation rate with units of mutation/cell/division with the lowest value slightly higher than that of normal cells (24). With a low mutation rate the evolution is too slow to reach the ESS. As the mutation rate increases (between 10−3 and 10−1), the tumor population evolves to the ESS. However, at very high mutation rates, the population is unstable and fitness decreases due to a mutation/selection balance – a phenomenon predicted by Haldane (23). In (b) we demonstrate that the diversity of tumor populations increases with mutation rate. For the remaining simulations we chose a mutation rate of 10−2 (star) as the midpoint between these trade-offs.
Figure 3
Figure 3. Simpson’s Index of Diversity
We demonstrate that heterogeneity of intratumoral phenotypes will vary as tumor populations evolve along the adaptive landscape. In Panel (a) the Simpson’s Index of Diversity is shown for stable or spatially varying landscapes. The solid line marked corresponds to the trajectory in Panel (b) in which there is a single, stable ESS. Initial somatic evolution results in a rapid increase in heterogeneity as the early tumor moves toward the trade-off boundary. However, during later evolution, the intratumoral populations decrease in heterogeneity as the tumor moves closer and closer to the maximal fitness point, ESS1. This predicts that tumor cells in a stable environment will exhibit on limited diversity. In contrast, for spatially complicated landscapes (Panels (c) and (d)) due, for example, to temporal and spatial variations in blood flow, heterogeneity remains high (Panel c). The results suggest that the observed heterogeneity in tumor largely reflects variations in environmental selection forces (i.e. vascular density and blood flow). Similar temporal variations during tumor development were predicted in Reference (2).
Figure 4
Figure 4. Identifying Driver Mutations
a) The gene mutation prevalence for one patient (initial phenotype 1 to ESS1) is shown. The “biopsy” occurs at the end of simulation, at 1000 generations. We assume that if at least 10% of the cells in the whole tumor exhibit a mutation in a particular gene at the time of biopsy, it will be detected. In this particular biopsy, mutations in genes 7, 10, and 16 would be identified. b–d) This biopsy detection scheme is conducted for all patient samples and each detection is tallied. If a mutation in a particular gene is detected in at least 12 of the patients, it is highlighted in red.
Figure 5
Figure 5. Mutation Prevalence and Population Dynamics of Tumors from Initial Phenotype 1 During Targeted Therapy Simulation
The underlying mutational prevalence for four representative tumors are shown in panel a while the corresponding population dynamics are shown in b. At the time of treatment (generation 500) the prevalence of mutation 16 drops to zero. After treatment, if the tumor survives, the evolutionary strategies used to reach maximal fitness can be observed. The top example shows the dynamics in 9.3% of patients where a full response to therapy is observed. The second shows a representative moderate response with eventual proliferation of resistant populations. The third shows a delay in progression though no significant response. The last example shows how in 28% of patients, the therapy had no effect.
Figure 6
Figure 6. “Never” Therapy Outcomes
a) On the left, the evolutionary trajectory to ESS1 from initial phenotype 1 is shown in black. After the “never” therapy is administered the trajectory falls below the sustainability line and results in tumor extinction, shown in orange. More commonly the “never” therapy does not cause tumor eradication but because cells with mutated gene 16 survive and remain sufficiently fit to proliferate and repopulate the tumor b). This represents a classical treatment “double bind’ (41) and renders the tumor exceedingly susceptible to the traditional targeted therapy directed against driver gene 16. In 80% of simulations, this strategy produced complete tumor eradication.
Figure 7
Figure 7. Simulation Setup
a) 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.
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